Publications from Medical Informatics

Background

Procedures documented by general practitioners in primary care have not been studied in relation to procedure coding systems. We aimed to describe procedures documented by Swedish general practitioners in electronic patient records and to compare them to the Swedish Classification of Health Interventions (KVÅ) and SNOMED CT.

Methods

Procedures in 200 record entries were identified, coded, assessed in relation to two procedure coding systems and analysed.

Results

417 procedures found in the 200 electronic patient record entries were coded with 36 different Classification of Health Interventions categories and 148 different SNOMED CT concepts. 22.8% of the procedures could not be coded with any Classification of Health Interventions category and 4.3% could not be coded with any SNOMED CT concept. 206 procedure-concept/category pairs were assessed as a complete match in SNOMED CT compared to 10 in the Classification of Health Interventions.

Conclusions

Procedures documented by general practitioners were present in nearly all electronic patient record entries. Almost all procedures could be coded using SNOMED CT.

Classification of Health Interventions covered the procedures to a lesser extent and with a much lower degree of concordance. SNOMED CT is a more flexible terminology system that can be used for different purposes for procedure coding in primary care.

Functional magnetic resonance imaging (fMRI) makes it possible to non-invasively measure brain activity with high spatial resolution.There are however a number of issues that have to be addressed. One is the large amount of spatio-temporal data that needsto be processed. In addition to the statistical analysis itself, several preprocessing steps, such as slice timing correction and motioncompensation, are normally applied. The high computational power of modern graphic cards has already successfully been used forMRI and fMRI. Going beyond the first published demonstration of GPU-based analysis of fMRI data, all the preprocessing stepsand two statistical approaches, the general linear model (GLM) and canonical correlation analysis (CCA), have been implementedon a GPU. For an fMRI dataset of typical size (80 volumes with 64 x 64 x 22 voxels), all the preprocessing takes about 0.5 s on theGPU, compared to 5 s with an optimized CPU implementation and 120 s with the commonly used statistical parametric mapping(SPM) software. A random permutation test with 10 000 permutations, with smoothing in each permutation, takes about 50 s ifthree GPUs are used, compared to 0.5 - 2.5 h with an optimized CPU implementation. The presented work will save time forresearchers and clinicians in their daily work and enables the use of more advanced analysis, such as non-parametric statistics, bothfor conventional fMRI and for real-time fMRI.

The use of image denoising techniques is an important part of many medical imaging applications. One common application isto improve the image quality of low-dose, i.e. noisy, computed tomography (CT) data. The medical imaging domain has seen atremendous development during the last decades. It is now possible to collect time resolved volumes, i.e. 4D data, with a number ofmodalities (e.g. ultrasound (US), CT, magnetic resonance imaging (MRI)). While 3D image denoising previously has been appliedto several volumes independently, there has not been much work done on true 4D image denoising, where the algorithm considersseveral volumes at the same time (and not a single volume at a time). By using all the dimensions, it is for example possibleto remove some of the time varying reconstruction artefacts that exist in CT volumes. The problem with 4D image denoising,compared to 2D and 3D denoising, is that the computational complexity increases exponentially.In this paper we describe a novel algorithm for true 4D image denoising, based on local adaptive filtering, and how to implementit on the graphics processing unit (GPU). The algorithm was applied to a 4D CT heart dataset of the resolution 512 x 512 x 445 x 20.The result is that the GPU can complete the denoising in about 25 minutes if spatial filtering is used and in about 8 minutes if FFTbased filtering is used. The CPU implementation requires several days of processing time for spatial filtering and about 50 minutesfor FFT based filtering. Fast spatial filtering makes it possible to apply the denoising algorithm to larger datasets (compared to ifFFT based filtering is used). The short processing time increases the clinical value of true 4D image denoising significantly.

Aim: The influences on the SNR of DENSE MRI of field strength, receiver coil sensitivity and choice of flip angle strategy have been previously investigated individually. In this study, all of these parameters have been investigated in the same setting, and a mutual comparison of their impact on SNR is presented.

Materials and methods: Ten healthy volunteers were imaged in a 1.5T and a 3T MRI system, using standard 5 or 6 channel cardiac coils as well as 32 channel coils, with four different excitation patterns. Variation of spatial coil sensitivity was assessed by regional SNR analysis.

Results: SNR ranging from 2.8 to 30.5 was found depending on the combination of excitation patterns, coil sensitivity and field strength. The SNR at 3T was 53 ± 26% higher than at 1.5T (p<0.001), whereas spatial differences of 59 ± 26% were found in the ventricle (p<0.001). 32 channel coils provided 52 ± 29% higher SNR compared to standard 5 or 6 channel coils (p<0.001). A fixed flip angle strategy provided an excess of 50% higher SNR in half of the imaged cardiac cycle compared to a sweeping flip angle strategy, and a single phase acquisition provided a six-fold increase of SNR compared to a cine acquisition.

Conclusion: The effect of field strength and receiver coil sensitivity influences the SNR with the same order of magnitude, whereas flip angle strategy can have a larger effect on SNR. Thus, careful choice of imaging hardware in combination with adaptation of the acquisition protocol is crucial in order to realize sufficient SNR in DENSE MRI.

Background: In order to satisfy different needs, medical terminology systems musthave richer structures. This study examines whether a Swedish primary health careversion of the mono-hierarchical ICD-10 (KSH97-P) may obtain a richer structureusing category and chapter mappings from KSH97-P to SNOMED CT and SNOMEDCT’s structure. Manually-built mappings from KSH97-P’s categories and chapters toSNOMED CT’s concepts are used as a starting point

Results: The mappings are manually evaluated using computer-producedinformation and a small number of mappings are updated. A new and polyhierarchicalchapter division of KSH97-P’s categories has been created using thecategory and chapter mappings and SNOMED CT’s generic structure. In the newchapter division, most categories are included in their original chapters. Aconsiderable number of concepts are included in other chapters than their originalchapters. Most of these inclusions can be explained by ICD-10’s design. KSH97-P’scategories are also extended with attributes using the category mappings andSNOMED CT’s defining attribute relationships. About three-fourths of all conceptsreceive an attribute of type Finding site and about half of all concepts receive anattribute of type Associated morphology. Other types of attributes are less common.

Conclusions: It is possible to use mappings from KSH97-P to SNOMED CT andSNOMED CT’s structure to enrich KSH97-P’s mono-hierarchical structure with a polyhierarchicalchapter division and attributes of type Finding site and Associatedmorphology. The final mappings are available as additional files for this paper.

A method to acquire multiple displacement encoded slices within asingle breath hold is presented. Efficiency is improved overconventional Displacement ENcoding with Stimulated Echoes (DENSE) without compromising image quality by read-out of multiple slices inthe same cardiac cycle, thus utilizing the position encoded stimulatedecho available in the whole heart. The method was evaluated bycomparing strain values obtained using the proposed method to strainvalues obtained by conventional separate breath hold single-sliceDENSE acquisitions. Good agreement (Lagrangian E₂ strainbias=0.000, 95% limits of agreement ±0.04,root-mean-square-difference 0.02 (9.4% of the mean end-systolic E₂)) was found between the methods, indicating that the proposedmethod can replace a multiple breath hold acquisition. Eliminating theneed for multiple breath holds reduces the risk of changes in breathhold positions or heart rate, results in higher patient comfort andfacilitates inclusion of DENSE in a clinical routine protocol.

Medical image registration is becoming a more and more useful component of a large number of applications. The presented method aims to enrich the ITK library. This method, called Morphon registration algorithm, computes a dense deformation field accepting inputs from different intensity contrasts. This article presents its implementation within the Insight Toolkit. In this paper, we provide a brief description of the algorithm, a presentation of the implementation, the justification of our modified classes and the results given by the algorithm. We demonstrate the algorithm in application of different images intesity constrasts and dimensions.

We prospectively studied the effects of fast food-based hyperalimentation on insulin sensitivity and components of the metabolic syndrome and analyzed this with respect to sex. Twelve nonobese men and 6 nonobese women (26 +/- 6.6 years old), and an age-matched control group were recruited. Subjects in the intervention group aimed for 5% to 15% weight increase by doubling their regular caloric intake based on at least 2 fast food meals a day while also adopting a sedentary lifestyle for 4 weeks (andlt;5000 steps a day). Weight of Subjects in the intervention group increased from 67.6 +/- 9.1 to 74.0 +/- 11 kg (P andlt;.001), with no sex difference with regard to this or with respect to changes of total abdominal fat volumes or waist circumferences. Fasting insulin (men: before, 3.8 +/- 1.7 mu U/mL, after, 7.4 +/- 3.1 mu U/mL; P=.004; women: before, 4.9 +/- 2.3 mu U/mL; after, 5.9 +/- 2.8 mu U/mL; P =.17), systolic blood pressure (men: before, 117 +/- 13 mm Hg; after, 127 +/- 9.1 mm Hg; P =.002; women: before, 102 +/- 5.1 mm Hg; after, 98 +/- 5.4 mm Hg; P =.39), serum low-density lipoprotein cholesterol, and apolipoprotein B increased only in the men of the intervention group. The sex differences in the metabolic responses to the intervention were linked to a considerable difference in the fat accumulation pattern; 41.4% +/- 9.2% of the increase of the fat volume in the abdominal region was accumulated intraabdominally in men and 22.7 +/- 6.5% in women (P andlt;.0001). This Study thus showed that women are protected, compared with men, against developing intraabdominal obesity when adopting a standardized obesity-provoking lifestyle. Our findings suggest that it is not different lifestyles and/or behaviors that underlie the fact that men have a higher cardiovascular risk at the same level of percentage of body fat than women.

An information system supporting pain assessment in palliative home healthcare was implemented at the hospital-based home care clinic of University Hospital in Linkoping, Sweden. Using digital pens and pain diaries, pain assessments were sent from the patients home to the professional caregiver. A total of 12 patients participated in the study. Patients, spouses and professional caregivers were interviewed. Qualitative content analyses were performed on the study material. All patients managed to use the pain assessment method, they experienced an improved contact with the caregivers and had a sense of increased security. After an initial cautious outlook the caregivers experienced positive outcomes for themselves and their patients. The medical records showed that the method had had impact on treatment. In conclusion, the home healthcare solution provided an effortless method for pain assessment with a high degree of user acceptance for palliative patients and had positive influences on the care.

Background

The Archetype formalism and the associated Archetype Definition Language have been proposed as an ISO standard for specifying models of components of electronic healthcare records as a means of achieving interoperability between clinical systems. This paper presents an archetype editor with support for manual or semi-automatic creation of bindings between archetypes and terminology systems.

Methods

Lexical and semantic methods are applied in order to obtain automatic mapping suggestions. Information visualisation methods are also used to assist the user in exploration and selection of mappings.

Results

An integrated tool for archetype authoring, semi-automatic SNOMED CT terminology binding assistance and terminology visualization was created and released as open source.

Conclusion

Finding the right terms to bind is a difficult task but the effort to achieve terminology bindings may be reduced with the help of the described approach. The methods and tools presented are general, but here only bindings between SNOMED CT and archetypes based on the openEHR reference model are presented in detail.

Background

The Archetype formalism and the associated Archetype Definition Language have been proposed as an ISO standard for specifying models of components of electronic healthcare records as a means of achieving interoperability between clinical systems. This paper presents an archetype editor with support for manual or semi-automatic creation of bindings between archetypes and terminology systems.

Methods

Lexical and semantic methods are applied in order to obtain automatic mapping suggestions. Information visualisation methods are also used to assist the user in exploration and selection of mappings.

Results

An integrated tool for archetype authoring, semi-automatic SNOMED CT terminology binding assistance and terminology visualization was created and released as open source.

Conclusion

Finding the right terms to bind is a difficult task but the effort to achieve terminology bindings may be reduced with the help of the described approach. The methods and tools presented are general, but here only bindings between SNOMED CT and archetypes based on the openEHR reference model are presented in detail.

The interest for surgery simulator systems with anatomical models generated from authentic patient data is growing as these systems evolve.With access to volumetric patient data, e.g., from a computer tomography scan, haptic and visual feedback can be created directly from this dataset. This opens the door for patient specific simulations. Hip fracture surgery is one area where simulator systems is useful to train new surgeons and plan operations. To simulate the drilling procedure in this type of surgery, a repositioning of the fractured bone into correct position is first needed. This requires a segmentation process in which the bone segments are identified and the position of the dislocated part is determined. The segmentation must be automatic to cope with the large amount of data from the computer tomography scan. This work presents the first steps in the development of a hip fracture surgery simulation with patient specific models. Visual and haptic feedback is generated from the computer tomography data by simulating fluoroscopic images and the drilling process. We also present an automatic segmentation method to identify the fractured bone and determine the dislocation. This segmentation method is based on nonrigid registration with the Morphon method.

We present a study using a method able to assess tissue oxygenation, taking into account the absorption and the level of scattering in myocardial tissue using a calibrated fiber optic probe. With this method, interindividual comparisons of oxygenation can be made despite varying tissue optical properties during coronary artery bypass grafting (CABG). During CABG, there are needs for methods allowing continuous monitoring and prediction of the metabolism in the myocardial tissue. 14 patients undergoing CABG are investigated for tissue oxygenation during different surgical phases using a handheld fiber optic spectroscopic probe with a source-detector distance of less than 1 mm. The probe is calibrated using a light transport model, relating the absorption and reduced scattering coefficients (mu(a) and mu()(s)) to the measured spectra. By solving the inverse problem, absolute measures of tissue oxygenation are evaluated by the sum of oxygenized hemoglobin and myoglobin. Agreement between the model and measurements is obtained with an average correlation coefficient R-2 of 0.96. Oxygenation is found to be significantly elevated after aorta cross-clamping and cardioplegic infusion, as well as after reperfusion, compared to a baseline (p < 0.05). Tissue oxygenation decreases during cardiac arrest and increases after reperfusion.

Pseudomonas aeruginosa is 1 of the bacteria most adaptive to anti-bacterial treatment. Previous studies have shown nosocomial spread and transmission of clonal strains of P. aeruginosa in European hospitals. In this study we investigated antibiotic susceptibility and clonality in 101 P. aeruginosa isolates from 88 patients admitted to 8 Swedish ICUs during 2002. We also compared phenotypes and genotypes of P. aeruginosa and carried out cluster analysis to determine if phenotypic data can be used for surveillance of clonal spread. All isolates were collected on clinical indication as part of the NPRS II study in Sweden and were subjected to AFLP analysis for genotyping. 68 isolates with unique genotypes were found. Phenotyping was performed using MIC values for 5 anti-pseudomonal agents. Almost 6% of the isolates were multi-drug resistant (MDR), and this figure rose to almost 8% when intermediate isolates were also included. We found probable clonal spread in 9 cases, but none of them was found to be an MDR strain. Phenotypical cluster analysis produced 40 clusters. Comparing partitions did not demonstrate any significant concordance between the typing methods. The conclusion of our study is that cross-transmission and clonal spread of MDR P. aeruginosa does not present a clinical problem in Swedish ICUs, but probable cross-transmission of non-MDR clones indicate a need for improved hygiene routines bedside. The phenotype clusters were not concordant with genotype clusters, and genotyping is still recommended for epidemiological tracking.

Multidimensional imaging resolving both the cardiac and respiratory cycles simultaneously has the potential to describe important physiological interdependences between the heart and pulmonary processes. A fully five-dimensional acquisition with three spatial and two temporal dimensions is hampered, however, by the long acquisition time and low spatial resolution. A technique is proposed to reduce the scan time substantially by extending the k-t BLAST framework to two temporal dimensions. By sampling the k-t space sparsely in a lattice grid, the signal in the transform domain, x-f space, can be densely packed, exploiting the fact that large regions in the field of view have low temporal bandwidth. A volumetric online prospective triggering approach with full cardiac and respiratory cycle coverage was implemented. Retrospective temporal interpolation was used to refine the timing estimates for the center of k-space, which is sampled for all cardiac and respiratory time frames. This resulted in reduced reconstruction error compared with conventional k-t BLAST reconstruction. The k-t² BLAST technique was evaluated by decimating a fully sampled five-dimensional data set, and feasibility was further demonstrated by performing sparsely sampled acquisitions. Compared to the fully sampled data, a fourfold improvement in spatial resolution was accomplished in approximately half the scan time.

Background: Intensive care units (ICUs) are hot zones for emergence and spread of antibiotic resistance because of frequent invasive procedures, antibiotic usage and transmission of bacteria. We report prospective data on antibiotic use and bacterial resistance from 14 academic and non-academic ICUs, participating in the ICU-STRAMA programme 1999-2003. Methods: The quantity of antibiotics delivered to each ICU was calculated as defined daily doses per 1000 occupied bed days (DDD1000). Specimens for culture were taken on clinical indications and only initial isolates were considered. Species-related breakpoints according to the Swedish Reference Group for Antibiotics were used. Antibiotic resistance was defined as the sum of intermediate and resistant strains. Results: Mean antibiotic use increased from 1245 DDD1000 in 1999 to 1510 DDD1000 in 2003 (P = 0.11 for trend). Of Staphylococcus aureus, 0-1.8% were methicillin resistant (MRSA). A presumptive extended spectrum beta-lactamase (ESBL) phenotype was found in <2.4% of Escherichia coli, based on cefotaxime susceptibility, except a peak in 2002 (4.6%). Cefotaxime resistance was found in 2.6-4.9% of Klebsiella spp. Rates of resistance among Enterobacter spp. to cefotaxime (20-33%) and among Pseudomonas aeruginosa to imipenem (22-33%) and ciprofloxacin (5-21%) showed no time trend. Conclusion: MRSA and cefotaxime-resistant E. coli and Klebsiella spp strains were few despite high total antibiotic consumption. This may be the result of a slow introduction of resistant strains into the ICUs, and good infection control. The cause of imipenem and ciprofloxacin resistance in P. aeruginosa could reflect the increased consumption of these agents plus spread of resistant clones. © 2007 The Authors.

Postmastectomy radiotherapy (PMRT) is prescribed in order to reduce the local recurrence of breast cancer and improve overall survival. A guideline supports the trade-off between benefits and adverse effects of PMRT. However, this guideline is not always followed in practice. This study tries to find a method for revealing patterns of non-compliance between the actual treatment and the PMRT guideline.

Data from breast cancer patients admitted to Linköping University Hospital between 1990 and 2000 were analyzed in this study. Cases that were not treated in accordance with the guideline were selected and analyzed by decision tree induction (DTI). Thereafter, four resulting rules, as representations for groups of patients, were compared to the guideline.

Finding patterns of non-compliance with guidelines by means of rules can be an appropriate alternative to manual methods, i.e. a case-by-case comparison when studying very large datasets. The resulting rules can be used in a knowledge base of a guideline-based decision support system to alert when inconsistencies with the guidelines may appear.

Since the prescription of antibiotics in the hospital setting is often empiric, particularly in the critically ill, and therefore fraught with potential error, we analysed the use of antibiotic agents in Swedish intensive care units (ICUs). We examined indications for antibiotic treatment, agents and dosage prescribed among 393 patients admitted to 23 ICUs at 7 tertiary care centres, 11 secondary hospitals and 5 primary hospitals over a 2-week period in November 2000. Antibiotic consumption was higher among ICU patients in tertiary care centres with a median of 84% (range 58-87%) of patients on antibiotics compared to patients in secondary hospitals (67%, range 35-93%) and in primary hospitals (38%, range 24-80%). Altogether 68% of the patients received antibiotics during the ICU stay compared to 65% on admission. Cefuroxime was the most commonly prescribed antibiotic before and during admission (28% and 24% of prescriptions, respectively). A date for decision to continue or discontinue antibiotic therapy was set in 21% (6/29) of patients receiving prophylaxis, in 8% (16/205) receiving empirical treatment and in 3% (3/88) when culture-based therapy was given. No correlation between antibiotic prescription and laboratory parameters such as CRP levels, leukocyte and thrombocyte counts, was found. The treatment was empirical in 64% and prophylactic in 9% of cases. Microbiological data guided prescription more often in severe sepsis (median 50%, range 40-60% of prescriptions) than in other specified forms of infection (median 32%, range 21-50%). The empirically chosen antibiotic was found to be active in vitro against the pathogens found in 55 of 58 patients (95%) with a positive blood culture. This study showed that a high proportion of ICU patients receive antimicrobial agents and, as expected, empirical-based therapy is more common than culture-based therapy. Antibiotics given were usually active in vitro against the pathogen found in blood cultures. We ascribe this to a relatively modest antibiotic resistance problem in Swedish hospitals.

Turbulent flow, characterized by velocity fluctuations, is a contributing factor to the pathogenesis of several cardiovascular diseases. A clinical noninvasive tool for assessing turbulence is lacking, however. It is well known that the occurrence of multiple spin velocities within a voxel during the influence of a magnetic gradient moment causes signal loss in phase-contrast magnetic resonance imaging (PC-MRI). In this paper a mathematical derivation of an expression for computing the standard deviation (SD) of the blood flow velocity distribution within a voxel is presented. The SD is obtained from the magnitude of PC-MRI signals acquired with different first gradient moments. By exploiting the relation between the SD and turbulence intensity (TI), this method allows for quantitative studies of turbulence. For validation, the TI in an in vitro flow phantom was quantified, and the results compared favorably with previously published laser Doppler anemometry (LDA) results. This method has the potential to become an important tool for the noninvasive assessment of turbulence in the arterial tree. © 2006 Wiley-Liss, Inc.

Background: This paper reports on a parallel collection of rubrics from the medical terminology systems ICD-10, ICF, MeSH, NCSP and KSH97-P and its use for semi-automatic creation of an English-Swedish dictionary of medical terminology. The methods presented are relevant for many other West European language pairs than English-Swedish. Methods: The medical terminology systems were collected in electronic format in both English and Swedish and the rubrics were extracted in parallel language pairs. Initially, interactive word alignment was used to create training data from a sample. Then the training data were utilised in automatic word alignment in order to generate candidate term pairs. The last step was manual verification of the term pair candidates. Results: A dictionary of 31,000 verified entries has been created in less than three man weeks, thus with considerably less time and effort needed compared to a manual approach, and without compromising quality. As a side effect of our work we found 40 different translation problems in the terminology systems and these results indicate the power of the method for finding inconsistencies in terminology translations. We also report on some factors that may contribute to making the process of dictionary creation with similar tools even more expedient. Finally, the contribution is discussed in relation to other ongoing efforts in constructing medical lexicons for non-English languages. Conclusion: In three man weeks we were able to produce a medical English-Swedish dictionary consisting of 31,000 entries and also found hidden translation errors in the utilized medical terminology systems. © 2006 Nyström et al, licensee BioMed Central Ltd.

Canonical correlation analysis (CCA) has previously been shown to work well for detecting neural activity in fMRI data. The reason is that CCA enables simultaneous temporal modeling and adaptive spatial filtering of the data. This article introduces a novel method for adaptive anisotropic filtering using the CCA framework and compares it to a previously proposed method. Isotropic adaptive filtering, which is only able to form isotropic filters of different sizes, is also presented and evaluated. It is shown that a new feature of the proposed method is invariance to the orientation of activated regions, and that the detection performance is superior to both that of the previous method and to isotropic filtering.

The Morphon, a non-rigid registration method is presented and applied to a number of registration applications. The algorithm takes a prototype image (or volume) and morphs it into a target image using an iterative, multi-resolution technique. The deformation process is done in three steps: displacement estimation, deformation field accumulation and deformation. The framework could be described in very general terms, but in this paper we focus on a specific implementation of the Morphon framework. The method can be employed in a wide range of registration tasks, which is shown in four very different registration examples, 2D photographs of hands and faces, 3D CT data of the hip region, and 3D MR brain images.

The paper discusses which properties of filter sets used in local structure estimation that are the most important. Answers are provided via the introduction of a number of fundamental invariances. Mathematical formulations corresponding to the required invariances leads up to the introduction of a new class of filter sets termed loglets. Loglets are polar separable and have excellent uncertainty properties. The directional part uses a spherical harmonics basis. Using loglets it is shown how the concepts of quadrature and phase can be defined in n-dimensions. It is also shown how a reliable measure of the certainty of the estimate can be obtained by finding the deviation from the signal model manifold. Local structure analysis algorithms are quite complex and involve a lot more than the filters used. This makes comparisons difficult to interpret from a filter point of view. To reduce the number 'free' parameters and target the filter design aspects a number of simple 2D experiments have been carried out. The evaluation supports the claim that loglets are preferable to other designs. In particular it is demonstrated that the loglet approach outperforms a Gaussian derivative approach in resolution and robustness. (c) 2005 Elsevier B.V. All rights reserved.

Laboratory medicine is a challenge for the metrologically and terminologically inclined scientist. One main reason is the need for a sound theory that can be applied in a systematic way to cover all aspects of examinations, i.e., those procedures whose results are reported on an ordinal scale and those reported on more primitive scales (e.g., classifications and narratives). Validation of procedures for examinations involving properties on a nominal scale is especially difficult to achieve because it is hard to find gold standards, in the conventional sense, against which to validate and which combine performance characteristics and clinically relevant specificity and sensitivity. We present a systematic, unambiguously defined terminology (the C-NPU coding scheme) for metrologically derived terms for expressing properties, and present some examples of how to attain diagnostic goals. If the analytic process in the laboratory can be subsumed into medical contexts in a systematic way, many pitfalls in reporting results can be avoided. © 2005 Elsevier Ltd. All rights reserved.

Assessment of the association between risk factors and outcomes in cardiac surgery is a complex problem. The aim of this study was to explore the relationship between possible risk factors and several clinical outcomes in cardiac surgery by using canonical correlation analysis (CCA). This retrospective study of 2605 consecutive adult patients who underwent cardiac surgery, evaluated 74 potential risk factors and up to 12 outcomes by canonical correlation analysis. For three serious outcomes, sternal wound complications/mediastinitis, cerebral complications, and perioperative myocardial infarctions, CCA was preceded by univariate analyses and backward stepwise multivariate logistic regression analyses. The CCA suggests that the major risk factors for complications in these models are intraoperative and postoperative risk factors. The power of risk prediction models developed with multivariate regression analysis can be enhanced by application of canonical correlation analysis, thereby offering new ways of analyzing and interpreting sets of potential risk factors in relation to sets of clinical outcomes.

This article addresses the impact that colored noise, temporal filtering, and temporal detrending have on the fMRI analysis situation. Specifically, it is shown why the detection of event-related designs benefit more from pre-whitening than blocked designs in a colored noise structure. Both theoretical and empirical results are provided. Furthermore, a novel exploratory method for producing drift models that efficiently capture trends and drifts in the fMRI data is introduced. A comparison to currently employed detrending approaches is presented. It is shown that the novel exploratory model is able to remove a major part of the slowly varying drifts that are abundant in fMRI data. The value of such a model lies in its ability to remove drift components that otherwise would have contributed to a colored noise structure in the voxel time series. © 2004 Elsevier Inc. All rights reserved.

In this paper, we review the results of BIOINFOMED, a study funded by the European Commission (EC) with the purpose to analyse the different issues and challenges in the area where Medical Informatics and Bioinformatics meet. Traditionally, Medical Informatics has been focused on the intersection between computer science and clinical medicine, whereas Bioinformatics have been predominantly centered on the intersection between computer science and biological research. Although researchers from both areas have occasionally collaborated, their training, objectives and interests have been quite different. The results of the Human Genome and related projects have attracted the interest of many professionals, and introduced new challenges that will transform biomedical research and health care. A characteristic of the 'post genomic' era will be to correlate essential genotypic information with expressed phenotypic information. In this context, Biomedical Informatics (BMI) has emerged to describe the technology that brings both disciplines (BI and MI) together to support genomic medicine. In recognition of the dynamic nature of BMI, institutions such as the EC have launched several initiatives in support of a research agenda, including the BIOINFOMED study.

As a way of exploring differences between medical domains regarding management of urinary tract infections, we investigated the MEDLINE® database for differences in indexing patterns. Further, our intention was to assess the MEDLINE® database as a source for studying medical domains. We examined the use of main headings, subheadings and the level of main headings in six medical domains that manage urinary tract infections. Many intuitive but also some counterintuitive results were found indicating that the MEDLINE® database is difficult to use for studying medical domains mainly due to unclear semantics both in the headings and the indexing process, which results in variability in indexing. This variability probably hides sig-nificant results. We also conclude that the differences found indicate that in addition to differences between domains, there are also large variations within domains.

Local infection control measures, antibiotic consumption and patient demographics from 1999-2000 together with bacteriological analyses were investigated in 29 ICUs participating in the ICU-STRAMA programme. The median antibiotic consumption per ICU was 1147 (range 605-2143) daily doses per 1000 occupied bed d (DDD₁₀₀₀). Antibiotics to which >90% of isolates of an organism were susceptible were defined as treatment alternatives (TA₉₀). The mean number of TA₉₀ was low (1-2 per organism) for Enterococcus faecium (vancomycin:VAN), coagulase negative staphylococci (VAN), Pseudomonas aeruginosa (ceftazidime:CTZ, netilmicin: NET) and Stenotrophomonas maltophilia (CTZ, trimethoprim-sulfamethoxazole: TSU), but higher (3-7) for Acinetobacter spp. (imipenem:IMI, NET, TSU), Enterococcus faecalis (ampicillin:AMP, IMI, VAN), Serratia spp. (ciprofloxacin:CIP, IMI, NET), Enterobacter spp. (CIP, IMI, NET, TSU), E. coli (cefuroxime:CXM, cefotaxime/ceftazidime:CTX/CTZ, CIP, IMI, NET, piperacillin-tazobactam:PTZ, TSU), Klebsiella spp. (CTX/CTZ CIP, IMI, NET, PTZ, TSU) and Staphylococcus aureus (clindamycin, fusidic acid, NET, oxacillin, rifampicin, VAN). Of S. aureus isolates 2% were MRSA. Facilities for alcohol hand disinfection at each bed were available in 96% of the ICUs. The numbers of TA₉₀ available were apparently higher than in ICUs in southern Europe and the US, despite a relatively high antibiotic consumption. This may be due to a moderate ecological impact of the used agents and the infection control routines in Swedish ICUs.

The question of which properties of a local structure estimator are important is discussed. Answers are provided via the introduction of a number of fundamental invariances. Mathematical formulations corresponding to the required invariances leads up to the introduction of a new class of filter sets termed loglets. Using loglets it is shown how the concepts of quadrature and phase can be defined in n-dimensions. A number of experiments support the claim that loglets are preferable to other designs. In particular it is demonstrated that the loglet approach outperforms a Gaussian derivative approach in resolution and robustness to variations in object illumination. It is also shown how a measure of the certainty of the estimate can be obtained using the consistency of the generalized phase with respect to orientation.

This paper presents a filtering technique for regularizing tensor fields. We use a nonlinear filtering technique termed normalized convolution [Knutsson and Westin 1993], a general method for filtering missing and uncertain data. In the present work we extend the signal certainty function to depend on locally derived certainty information in addition to the a priory voxel certainty. This results in reduced blurring between regions of different signal characteristics, and increased robustness to outliers. A driving application for this work has been filtering of data from Diffusion Tensor MRI.

Objective: The Glasgow Coma Scale (GCS) is a well-known source of error in outcome prediction models. We compared assessment of cerebral responsiveness with the GCS and the Reaction Level Scale (RLS) in two otherwise similar outcome prediction models. Design and setting: Prospective, observational study in a general intensive care unit. Patients and participants: All admissions of patients with or at risk of developing impaired brain function between 1997 and 1998 (n=534). Measurements and results: Admissions were scored by RLS and APACHE II (includes scoring with the GCS). The RLS scores were transformed to APACHE II central nervous system scores according to a predetermined protocol. APACHE II estimated probability of death was calculated conventionally with the GCS and the RLS. Vital status 90 days after admission was secured from a national database. Bias and precision was 0.5% and 16.6%, respectively. The area under receiver operating characteristic curves was slightly but significantly greater with the RLS-based APACHE 11 model than with the GCS-based model (0.92 vs. 0.90). Discrimination was improved primarily in admissions with low and intermediate probability of death. Conclusions: Scoring of cerebral responsiveness with the RLS instead of the GCS was associated with minimal bias of the APACHE 11 probability of death estimate. Assessment of consciousness in critically ill with the RLS deserves further evaluation.

Background. The use of left ventricular assist devices (LVADs) is established as a bridge to heart transplantation. Methods. All Swedish patients on the waiting list for heart transplantation, treated with LVAD since 1993 were retrospectively collected into a database and analyzed in regards to risk factors for mortality and morbidity. Results. Fifty-nine patients (46 men) with a median age of 49 years (range, 14 to 69 years), Higgins score median of 9 (range, 3 to 15), EuroScore median of 10 (range, 5 to 17) were investigated. Dominating diagnoses were dilated cardiomyopathy in 61% (n = 36) and ischemic cardiomyopathy in 18.6% (n = 11). The patients were supported with LVAD for a median time of 99.5 days (range, 1 to 873 days). Forty-five (76%) patients received transplants, and 3 (5.1%) patients were weaned from the device. Eleven patients (18.6%) died during LVAD treatment. Risk factor analysis for mortality before heart transplantation showed significance for a high total amount of autologous blood transfusions (p < 0.001), days on mechanical ventilation postoperatively (p < 0.001), prolonged postoperative intensive care unit stay (p = 0.007), and high central venous pressure 24 hours postoperatively and at the final measurement (p = 0.03 and 0.01, respectively). Mortality with LVAD treatment was 18.6% (n = 11). High C-reactive protein (p = 0.001), low mean arterial pressure (p = 0.03), and high cardiac index (p = 0.03) preoperatively were risk factors for development of right ventricular failure during LVAD treatment. Conclusions. The Swedish experience with LVAD as a bridge to heart transplantation was retrospectively collected into a database. This included data from transplant and nontransplant centers. Figures of mortality and morbidity in the database were comparable to international experience. Specific risk factors were difficult to define retrospectively as a result of different protocols for follow-up among participating centers. © 2003 by The Society of Thoracic Surgeons.

This paper presents new methods for use of dense motion fields for motion compensation of interlaced video. The motion estimation is based on previously decoded field-images. The motion is then temporally predicted and used for motion compensated prediction of the field-image to be coded. The motion estimation algorithm is phase-based and uses two or three field-images to achieve motion estimates with sub-pixel accuracy. To handle non-constant motion and the specific characteristics of the field-image to be coded, the initially predicted image is refined using forward motion compensation, based on block-matching. Tests show that this approach achieves higher PSNR than forward block-based motion estimation, when coding the residual with the same coder. The subjective performance is also better.

Objective: To investigate necessary conditions for the establishment of a database of diseases and health problems for research and health care planning, based on electronic patient records in everyday clinical use among general practitioners (GPs). Design: Postal questionnaire study. Setting: Primary health care in Sweden. Subjects: Three hundred randomly selected GPs. Main outcome measures: Degree of computerisation of patient records. User frequency and characteristics of diagnosis classification systems and coding tools. Frequency of coding activities and retrieval of codes, and related attitudes. Opinions on a primary health care version of ICD-10. Results: A total of 184 GPs (61% of the 300 GPs) were included in the study. About 92% used an electronic record system, some type of diagnostic classification was used by 93%, and ICD based classifications by 88%. The classification in use was computerised for 74%. Mainly simple tools were used to retrieve diagnostic codes. About 76% of GPs reported classifying at least one symptom or disease per encounter. The codes were retrieved 'once a month' or more by 19%. Classification of diseases was considered important for follow-up by 83%, and for the care of the patient by 75% of the GPs. The primary health care version of ICD-10 with a total of 972 codes was considered too limited in size by 31%. Conclusion: Electronic patient records in everyday clinical use in Swedish general practice provide several fundamentals for a database of diagnostic data. However, there are several barriers to the establishment of such a database that is both valid and reliable. ⌐ 2002 Elsevier Science Ireland Ltd. All rights reserved.

A novel and computationally efficient method for exploratory analysis of functional MRI data is presented. The basic idea is to reveal underlying components in the fMRI data that have maximum autocorrelation. The tool for accomplishing this task is Canonical Correlation Analysis. The relation to Principal Component Analysis and Independent Component Analysis is discussed and the performance of the methods is compared using both simulated and real data.

IT support for home health care is an expanding area within health care IT development. Home health care differs from other in- or outpatient care delivery forms in a number of ways, and thus, the introduction of home health care applications must be based on a rigorous analysis of necessary requirements to secure safe and reliable health care. This article reports early experiences from the development of a home health care application based on emerging technologies. A prototype application for the follow-up of diabetes patients is presented and discussed in relation to a list of general requirements on home health care applications.

Implementation of problem lists and their relation to standardized coding systems have been approached and analysed in different ways. Most evaluations concern quantitative aspects such as content coverage in a specific domain. In order to reveal the qualitative aspects of diagnostic coding, medical record texts from primary health care encounters were compared with terms from a coding system that was used for describing them statistically. The records were coded by six general practitioners, and in some cases, an applied diagnostic term was found within the text, while other record text-coding system relationships were categorized as synonyms, alternative terms, and interpretations. Thus, the categories roughly corresponded to a measure of semantic distance between the terms in the record text and the rubrics of the coding system, and there was a correlation between semantic distance and inter-rater agreement. The subcategories of this scheme corresponded fairly well to recently published desiderata for clinical terminology servers, including functionality such as word normalization and spelling correction. However, not all problems could have been automatically coded by means of lexical methods, which can be partly explained by the fact that diagnostic coding also relies on clinical knowledge. In addition, proper automation relies on context representation within the records.

Objectives: Sternal wound complications often have a late onset and are detected after patients are discharged from the hospital. In an effort to catch all sternal wound complications, different postdischarge surveillance methods have to be used. Together with this long-term follow-up an analysis of risk factors may help to identify patients at risk and can lead to more effective preventive and control measures. Methods: This retrospective study of 3008 adult patients who underwent consecutive cardiac surgery from January 1996 through September 1999 at Link÷ping University Hospital, Sweden, evaluated 42 potential risk factors by univariate analysis followed by backward stepwise multivariate logistic regression analysis. Results: Two-thirds of the 291 (9.7%) sternal wound complications that occurred were identified after discharge. Of the 291 patients, 47 (1.6%) had deep sternal infections, 50 (1.7%) had postoperative mediastinitis, and 194 (6.4%) had superficial sternal wound complications. Twenty-three variables were selected by univariate analysis (P<0.15) and included in a multivariate analysis where eight variables emerged as significant (P<0.05). Preoperative risk factors for deep sternal infections/mediastinitis were obesity, insulin-dependent diabetes, smoking, peripheral vascular disease, and high New York Heart Association score. An intraoperative risk factor was bilateral use of internal mammary arteries, and a postoperative risk factor was prolonged ventilator support. Risk factors for superficial sternal wound complications were obesity, and an age of <75 years. The 30 day mortality was 2.7% for patients without sternal wound complications and 2/291 (0.7%) for all patients with sternal wound complications, 0.5% for superficial sternal wound complications, and 1.0% for deep sternal infections/mediastinitis. The 1 year mortality rate was 4.8% for patients without sternal wound complications and 11/291 (3.8%) for patients with sternal wound complications, 2.1% for superficial sternal wound complications, and 7.2% for deep sternal infections/mediastinitis. Conclusions: The risk factors found in this study have been detected and reported in previous studies. The predictive ability was stronger though for deep sternal infections/mediastinitis (those needing surgical revisions) than for superficial sternal wound complications. Earlier recognition of sternal wound complications and aggressive treatment have probably contributed to the relatively low mortality rate seen in this study. Copyright ⌐ 2001 Elsevier Science B.V.

A novel method for detecting neural activity in functional magnetic resonance imaging (fMRI) data is introduced. It is based on canonical correlation analysis (CCA), which is a multivariate extension of the univariate correlation analysis widely used in fMRI. To detect homogeneous regions of activity, the method combines a subspace modeling of the hemodynamic response and the use of spatial relationships. The spatial correlation that undoubtedly exists in fMR images is completely ignored when univariate methods such as as t-tests, F-tests, and ordinary correlation analysis are used. Such methods are for this reason very sensitive to noise, leading to difficulties in detecting activation and significant contributions of false activations. In addition, the proposed CCA method also makes it possible to detect activated brain regions based not only on thresholding a correlation coefficient, but also on physiological parameters such as temporal shape and delay of the hemodynamic response. Excellent performance on real fMRI data is demonstrated.

The calculation of blood flow in the heart by surface integration of velocity vectors (SIVV) using Doppler ultrasound is independent of the angle. Flow is normally calculated from velocity in a spherical thick shell with its center located at the ultrasound transducer. In a numerical simulation, we have shown that the ratio between minor and major axes of an elliptic flow area substantially influences the accuracy of the estimation of flow in a single scan plane. The accuracy of flow measurements by SIVV can be improved by calculating the mean of the values from more than one scan plane. We have produced an automated computer program that includes an antialiasing procedure. We confirmed an improvement of flow measurements in a pulsatile hydraulic flow model, the 95% confidence interval for single estimations being reduced from 20% to 10% (p < 0.05) using the newly developed software. We think that the SIVV method has important implications for clinical transthoracic echocardiography.

Objectives: To describe the clinical protocol regarding monitoring, pharmacologic interventions, and postoperative care during and after coronary artery bypass grafting (CABG) on the beating heart with an axial flow pump and a short-acting β-blocker.

Design: A retrospective study.

Setting: A university hospital.

Participants: Seventeen patients scheduled for elective CABG.

Interventions: Invasive monitoring was performed with either a standard pulmonary artery catheter (PAC) or a surgically placed PAC. An axial flow pump was inserted through a graft sutured to the ascending aorta. A short-acting β-blocker was administered to decrease the motion of the heart and make conditions for CABG adequate and safe.

Measurements and main results: Compared with baseline measurements, there were significant decreases in mean arterial blood pressure, mixed venous oxygen saturation, and right ventricular ejection fraction during maximal axial flow pump support and β-blockade. No significant change in heart rate was observed at this time. Hemodynamic variables were normalized in the intensive care unit. All patients were separated from the Hemopump without inotropic support, and values of troponin-T, aspartate aminotransferase, and alanine aminotransferase were low postoperatively. All patients survived and were discharged from the hospital.

Conclusion: The anesthetic protocol for patients undergoing surgery with a beating heart and the combined use of an axial flow pump and a short-acting β-blocker is outlined. Multiple-vessel CABG on the beating heart was performed with maintenance of an acceptable hemodynamic situation.

As welfare diseases become more common all over the world the demand for angiography examinations is increasing rapidly. The development of advanced medical signal processing methods has with few exceptions been concentrated towards CT and MR while traditional contrast based radiology depend on methods developed for ancient photography techniques despite the fact that angiography sequences are generally recorded in digital form. This article presents a new approach for processing of angiography sequences based on advanced image processing methods. The developed algorithm automatically processes angiography sequences containing motion artifacts that cannot be processed by conventional methods like digital subtraction angiography (DSA) and pixel shift due to non uniform motions. The algorithm can in simple terms be described as an ideal pixelshift filter carrying out shifts of different directions and magnitude according to the local motions in the image. In difference to conventional methods it is fully automatic, no mask image needs to be defined and the manual pixelshift operations, which are extremely time consuming, are eliminated. The algorithm is efficient and robust and is designed to run on standard hardware of a powerful workstation which excludes the need for expensive dedicated angiography platforms. Since there is no need to make additional recordings if the patient moves, the patient is exposed to less amount of radiation and contrast fluid. The most exciting benefits by this method are, however, that it opens up new areas for contrast based angiography that are not possible to process with conventional methods e.g. nonuniform motions and multiple layers of moving tissue. Advanced image processing methods provide significantly better image quality and noise suppression but do also provide the means to compute flow velocity and visualize the flow dynamics in the arterial trees by e.g. using color. Initial tests have proven that it is possible to discriminate capillary blood flow from angiography data which opens up interesting possibilities for estimating the blood flow in the heart muscle without use of nuclear methods.

Background

In this study, we describe postoperative monitoring, pharmacologic therapy, and hemodynamic responses in patients receiving Hemopump support after postcardiotomy heart failure.

Methods

The Hemopump was used in 24 patients with severe left ventricular dysfunction after coronary artery bypass grafting.

Results

Fourteen patients (58%) were weaned from the Hemopump. Low to moderate doses of a combination of catecholamines, phosphodiesterase inhibitors, vasodilators, and vasoconstrictors were required to optimize Hemopump function and left ventricular unloading. Mean arterial blood pressure, mixed venous oxygen saturation, and urinary output were the most important therapy guidelines.

Conclusions

Together with our clinical protocol, the Hemopump effectively unloaded the failing ventricle while maintaining vital-organ perfusion. Doses of vasoactive drugs could be kept low. This approach to treatment provides good conditions for recovery of the stunned myocardium.

A new form of image estimator, which takes account of linear features, is derived using a signal equivalent formulation. The estimator is shown to be a nonstationary linear combination of three stationary estimators. The relation of the estimator to human visual physiology is discussed. A method for estimating the nonstationary control information is described and shown to be effective when the estimation is made from noisy data. A suboptimal approach which is computationally less demanding is presented and used in the restoration of a variety of images corrupted by additive white noise. The results show that the method can improve the quality of noisy images even when the signal-to-noise ratio is very low.

The mathematical basis is described of a new radiographic method by which an arbitrarily thick layer of the patient may be reconstructed. The reconstruction is performed from at least 60 images of the volume under examination. Each of these images, which have to be in digital form, is subjected to a special filtration process of its spatial frequencies. The combination of all the images will form the resulting image of the layer--the ectomogram. The method has been analysed and tested in experiments simulated with a computer.

In medical imaging, signals acquired in non-Cartesian coordinate systems are common. For instance, CT and MRI often produce significantly higher resolution within scan planes, compared to the distance between two adjacent planes. Even oblique sampling occurs, by the use of gantry tilt. In ultrasound imaging, samples are acquired in a polar coordinate system, which implies a spatially varying metric.

In order to produce a geometrically correct image, signals are generally resampled to a Cartesian coordinate system. This paper concerns estimation of local structure tensors directly from the non-Cartesian coordinate system, thus avoiding deteriorated signal and noise characteristics caused by resampling. In many cases processing directly in the warped coordinate system is also less time-consuming. A geometrically correct tensor must obey certain transformation rules originating from fundamental differential geometry. Subsequently, this fact also affects the tensor estimation. As the local structure tensor is estimated using filters, a change of coordinate system also change the shape of the spatial support of these filters. Implications and limitations brought on by sampling require the filter design criteria to be adapted to the coordinate system.

Image registration is frequently used within the medical image domain and where methods with high performance are required. The need for high accuracy coupled with high speed is especially important for applications such as adaptive radiation therapy and image-guided surgery. During the last years, a number of significant projects have been introduced to make the computational power of GPUs available to a wider audience. The most well known project is the introduction of CUDA (Compute Unified Device Architecture). In this paper, we present a CUDA based GPU implementation of a non-rigid image registration algorithm, known as the Morphon, and compare it with a CPU implementation of the Morphon. The achieved speedup, in the range of 51-54x, is also compared with speedups reported from other non-rigid registration methods mplemented on the GPU. These include the Demons algorithm and a mutual information based algorithm.

The visualization of images with a large dynamic range is a difficult task and this is especially the case for gray-level images. In radiology departments, this will force radiologists to review medical images several times, since the images need to be visualized with several different contrast windows (transfer functions) in order for the full information content of each image to be seen. Previously suggested methods for handling this situation include various approaches using histogram equalization and other methods for processing the image data. However, none of these utilize the underlying human anatomy in the images to control the visualization and the fact that different transfer functions are often only relevant for disjoint anatomical regions. In this paper, we propose a method for using model-based local transfer functions. It allows the reviewing radiologist to apply multiple transfer functions simultaneously to a medical image volume. This provides the radiologist with a tool for making the review process more efficient, by allowing him/her to review more of the information in a medical image volume with a single visualization. The transfer functions are automatically assigned to different anatomically relevant regions, based upon a model registered to the volume to be visualized. The transfer functions can be either pre-defined or interactively changed by the radiologist during the review process. All of this is achieved without adding any unfamiliar aspects to the radiologist’s normal work-flow, when reviewing medical image volumes.

Canonical correlation analysis (CCA) is a statistical methodthat can be preferable to the general linear model (GLM) for analysisof functional magnetic resonance imaging (fMRI) data. There are,however, two problems with CCA based fMRI analysis. First, it is notfeasible to use a parametric approach to calculate an activity thresholdfor a certain signi cance level. Second, two covariance matrices need tobe estimated in each voxel, from a rather small number of time samples.We recently solved the rst problem by doing random permutation testson the graphics processing unit (GPU), such that the null distribution ofany maximum test statistics can be estimated in the order of minutes. Inthis paper we consider the second problem. We extend the idea of variancepooling, that previously has been used for the GLM, to covariancepooling to improve the estimates of the covariance matrices. Our GPUimplementation of random permutation tests is used to calculate signicance thresholds, which are needed to compare the di erent activitymaps in an objective way. The covariance pooling results in more robustestimates of the covariance matrices. The number of signi cantly activevoxels that are detected (thresholded at p = 0.05, corrected for multiplecomparisons) is increased with 40 - 120% (if 8 mm smoothing is appliedto the covariance estimates). Too much covariance pooling can howeverresult in a loss of small activity clusters, 7-10 mm of smoothing givesthe best results. The calculations that were made in order to generatethe results in this paper would have taken a total of about 65 days witha Matlab implementation and about 10 days with a multithreaded Cimplementation, with our multi-GPU implementation they took about 2hours. By using fast random permutation tests, suggested improvementsof existing methods for fMRI analysis can be evaluated in an objective way.

The need of image registration is increasing, especially in the medical image domain. The simplest kind of image registration is to match two images that have similar intensity. More advanced cases include the problem of registering images of different intensity, for which phase based algorithms have proven to be superior. In some cases the phase based registration will fail as well, for instance when the images to be registered do not only differ in intensity but also in local phase. This is the case if a dark circle in the reference image is a bright circle in the source image. While rigid registration algorithms can use other parts of the image to calculate the global transformation, this problem is harder to solve for non-rigid registration. The solution that we propose in this work is to use the local phase of the magnitude of the local structure tensor, instead of the local phase of the image intensity. By doing this, we achieve invariance both to the image intensity and to the local phase and thereby only use the structural information, i.e. the shapes of the objects, for registration.

Parametric statistical methods are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it isassumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. In this work it is shown how the computational power of the Graphics Processing Unit (GPU) can be used to speedup non-parametric tests, such as random permutation tests. With random permutation tests it is possible to calculate significance thresholds for any test statistics. As an example, fMRI activity maps from the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level.

Methods for presentation of disease and health problem distribution in a health care environment rely among other things on the inherent structure of the controlled terminology used for coding. In the present study, this aspect is explored with a focus on ICD-10 and SNOMED CT. The distribution of 2,5 million diagnostic codes from primary health care in the Stockholm region is presented and analyzed through the “lenses” of ICD-10 and SNOMED CT. The patient encounters, originally coded with a reduced set of ICD-10 codes used in primary health care in Sweden, were mapped to SNOMED CT concepts through a mapping table. The method used for utilizing the richer structure of SNOMED CT as compared to ICD-10 is presented, together with examples of produced disease distributions. Implications of the proposed method for enriching a traditional classification such as ICD-10 through mappings to SNOMED CT are discussed.

Many applications in image analysis include the problem of linear structure detection, e.g. segmentation of blood vessels in medical images, roads in satellite images, etc. A simple and efficient solution is to apply linear filters tuned to the structures of interest and extract line and edge positions from the filter output. However, if the filter is not carefully designed, artifacts such as ringing can distort the results and hinder a robust detection. In this paper, we study the ringing effects using a common Gabor filter for linear structure detection, and suggest a method for generating non-ring filters in 2D and 3D. The benefits of the non-ring design are motivated by results on both synthetic and natural images.

Image registration is a crucial task in many applications and applied in a variety of different areas. In addition to the primary task of image alignment, the deformation field is valuable when studying structural/volumetric changes in the brain. In most applications a regularizing term is added to achieve a smoothly varying deformation field. This can sometimes cause conflicts in situations of local complex deformations. In this paper we present a new regularizer, which aims at handling local complex deformations while maintaining an overall smooth deformation field. It is based on an adaptive anisotropic regularizer and its usefulness is demonstrated by two examples, one synthetic and one with real MRI data from a pre- and post-op situation with normal pressure hydrocephalus.

Quantitative magnetic resonance imaging has the major advantage that it handles absolute measurements of physical parameters. Quantitative MRI can for example be used to estimate the amount of different tissue types in the brain, but other applications are possible. Parameters such as relaxation rates R1 and R2 and proton density (PD) are independent of MR scanner settings and imperfections and hence are directly representative of the underlying tissue characteristics. Brain tissue quantification is an important aid for diagnosis of neurological diseases, such as multiple sclerosis (MS) and dementia. It is applied to estimate the volume of each tissue type, such as white tissue, grey tissue, myelin and cerebrospinal fluid (CSF). Tissue that deviates from normal values can be found automatically using computer aided diagnosis. In order for the quantification to have a clinical value, both the time in the MR scanner and the time for the data analysis have to be minimized. A challenge in MR quantification is to keep the scan time within clinically acceptable limits. The quantification method that we have used is based on the work by Warntjes et al.

We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverse pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum both with real activity and imagined activity. The developments here have a potential to aid people with communication disabilities e.g., locked in people. It might also be a tool for stroke patients to be ableto train the damaged brain area and get real-time feedback of when they do it right.

The structure of images has been studied for decades and the use of local structure tensor fields appeared during the eighties [3, 14, 6, 9, 11]. Since then numerous varieties of tensors and estimation schemes have been developed. Tensors have for instance been used to represent orientation [7], velocity, curvature [2] and diffusion [19] with applications to adaptive filtering [8], motion analysis [10] and segmentation [17]. Even though sampling in non-Cartesian coordinate system are common, analysis and processing of local structure tensor fields in such systems is less developed. Previous work on local structure in non-Cartesian coordinate systems include [21, 16, 1, 18].

We present e method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverse pendulum by activating the left or right hand or resting. The brain activity is clasified each second by a neural network and the classification is sent to a pendulum simulator to change the state of the pendulum. The state of the inverse pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverse pendulum during a 7 minute test run.

There is an increasing demand for imaging methods that can be used for automatic, accurate and quantitative determination of the amounts of abdominal fat. Such methods are important as they will allow the evaluation of some of the risk factors underlying the ’metabolic syndrome’. The metabolic syndrome is becoming common in large parts of the world, and it appears that a dominant risk factor for developing this syndrome is abdominal obesity. Subjects that are afflicted with the metabolic syndrome are exposed to a high risk for developing a large range of diseases such as type 2 diabetes, cardiac failure, and stroke. The aim of this work

Tensors have been broadly used in mathematics and physics, since they are a generalization of scalars or vectors and allow to represent more complex properties. In this chapter we present an overview of some tensor applications, especially those focused on the image processing field. From a mathematical point of view, a lot of work has been developed about tensor calculus, which obviously is more complex than scalar or vectorial calculus. Moreover, tensors can represent the metric of a vector space, which is very useful in the field of differential geometry. In physics, tensors have been used to describe several magnitudes, such as the strain or stress of materials. In solid mechanics, tensors are used to define the generalized Hooke’s law, where a fourth order tensor relates the strain and stress tensors. In fluid dynamics, the velocity gradient tensor provides information about the vorticity and the strain of the fluids. Also an electromagnetic tensor is defined, that simplifies the notation of the Maxwell equations. But tensors are not constrained to physics and mathematics. They have been used, for instance, in medical imaging, where we can highlight two applications: the diffusion tensor image, which represents how molecules diffuse inside the tissues and is broadly used for brain imaging; and the tensorial elastography, which computes the strain and vorticity tensor to analyze the tissues properties. Tensors have also been used in computer vision to provide information about the local structure or to define anisotropic image filters.

We present a method for controlling a dynamical system using real-time fMRI. The objective for the subject in the MR scanner is to balance an inverted pendulum by activating the left or right hand or resting. The brain activity is classified each second by a neural network and the classification is sent to a pendulum simulator to change the force applied to the pendulum. The state of the inverted pendulum is shown to the subject in a pair of VR goggles. The subject was able to balance the inverted pendulum during several minutes, both with real activity and imagined activity. In each classification 9000 brain voxels were used and the response time for the system to detect a change of activity was on average 2-4 seconds. The developments here have a potential to aid people with communication disabilities, such as locked in people. Another future potential application can be to serve as a tool for stroke and Parkinson patients to be able to train the damaged brain area and get real-time feedback for more efficient training.

Computerized guidelines can provide decision support and facilitate the use of clinical guidelines. Several computerized guideline representation models (GRMs) exist but the poor interoperability between the guideline systems and the Electronic Health Record (EHR) systems limits their clinical usefulness. In this study we analyzed the clinical use of a published lymphoma chemotherapy guideline. We found that existing GRMs have limitations that can make it difficult to meet the clinical requirements. We hypothesized that guidelines could be represented as data and logic using openEHR archetypes, templates and rules. The design was tested by implementing the lymphoma guideline. We conclude that using the openEHR models and rules to represent chemotherapy guidelines is feasible and confers several advantages both from a clinical and from aninformatics perspective.

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The Direct Weight Optimization (DWO) approach is a nonparametric estimation approach that has appeared in recent years within the field of nonlinear system identification. In previous work, all function classes for which DWO has been studied have included only continuous functions. However, in many applications it would be desirable also to be able to handle discontinuous functions. Inspired by the bilateral filter method from image processing, such an extension of the DWO framework is proposed for the smoothing problem. Examples show that the properties of the new approach regarding the handling of discontinuities are similar to the bilateral filter, while at the same time DWO offers a greater flexibility with respect to different function classes handled.

With the introduction of EHR two-level modelling and archetype methodologies pioneered by openEHR and standardized by CEN/ISO, we are one step closer to semantic interoperability and future-proof adaptive healthcare information systems. Along with the opportunities, there are also challenges. Archetypes provide the full semantics of EHR data explicitly to surrounding systems in a platform-independent way, yet it is up to the receiving system to interpret the semantics and process the data accordingly. In this paper we propose a design of an archetype-based platform-independent testing framework for validating implementations of the openEHR archetype formalism as a means of improving quality and interoperability of EHRs.

This paper introduces a new method for automaticquantification of subcutaneous, visceral and nonvisceralinternal fat from MR-images acquired usingthe two point Dixon technique in the abdominal region.The method includes (1) a three dimensionalphase unwrapping to provide water and fat images, (2)an image intensity inhomogeneity correction, and (3) amorphon based registration and segmentation of thetissue. This is followed by an integration of the correctedfat images within the different fat compartmentsthat avoids the partial volume effects associated withtraditional fat segmentation methods. The method wastested on 18 subjects before and after a period of fastfoodhyper-alimentation showing high stability andperformance in all analysis steps.

Correlation is often used to measure the similarity between signals and is an important tool in signal and image processing. In some applications it is common that signals are corrupted by local bursts of noise. This adversely affects the performance of signal recognition algorithms. This paper presents a novel correlation estimator, which is robust to locally corrupted signals. The estimator is generalized to multivariate correlation analysis (general linear model, GLM, and canonical correlation analysis, CCA). Synthetic functional MRI data is used to demonstrate the estimator, and its robustness is shown to increase the performance of signal detection.

Studies of myocardial motion using magnetic resonance imaging usually require multiple breath holds and several methods have been proposed in order to reduce the scan time. Rapid imaging using k-t BLAST has gained much attention with its high reduction factors and image quality. Temporal smoothing, however, may reduce the accuracy when assessing cardiac function. In the present work, a modified reconstruction filter is proposed, that preserves more of the high temporal frequencies. Artificial decimation of a fully sampled data set was used to evaluate the reconstruction filter. Compared to the conventional k-t BLAST reconstruction, the modified filter produced images with sharper temporal delineation of the myocardial walls. Quantitative analysis by means of regional velocity estimation showed that the modified reconstruction filter produced more accurate velocity estimations.

We present a one-pass framework for filtering vector-valued images and unordered sets of data points in an N-dimensional feature space. It is based on a local Bayesian framework, previously developed for scalar images, where estimates are computed using expectation values and histograms. In this paper we extended this framework to handle N-dimensional data. To avoid the curse of dimensionality, it uses importance sampling instead of histograms to represent probability density functions. In this novel computational framework we are able to efficiently filter both vector-valued images and data, similar to e.g. the well-known bilateral, median and mean shift filters.

This paper presents a novel method for phase unwrapping for phase sensitive reconstruction in MR imaging. The unwrapped phase is obtained by integrating the phase gradient by solving a Poisson equation. An efficient solver, which has been made publicly available, is used to solve the equation. The proposed method is demonstrated on a fat quantification MRI task that is a part of a prospective study of fat accumulation. The method is compared to a phase unwrapping method based on region growing. Results indicate that the proposed method provides more robust unwrapping. Unlike region growing methods, the proposed method is also straight-forward to implement in 3D.