Student theses from Medical Informatics

Transcranial direct current stimulation (tDCS), together with speech therapy, is known to relieve the symptoms of aphasia. Knowledge about amount of current to apply and stimulation location is needed to ensure the best result possible. Segmented tissues are used in a finite element method (FEM) simulation and by creating a mesh, information to guide the stimulation is gained. Thus, correct segmentation is crucial. Manual segmentation is known to produce the most accurate result, although it is not useful in the clinical setting since it currently takes weeks to manually segment one image volume. Automatic segmentation is faster, although both acute stroke lesions and nectrotic stroke lesions are known to cause problems.

Three automatic segmentation routines are evaluated using default settings and two sets of tissue probability maps (TPMs). Two sets of stroke patients are used; one set with acute stroke lesions (which can only be seen as a change in image intensity) and one set with necrotic stroke lesions (which are cleared out and filled with cerebrospinal fluid (CSF)). The original segmentation routine in SPM8 does not produce correct segmentation result having problems with lesion and paralesional areas. Mohamed Seghier’s ALI, an automatic segmentation routine developed to handle lesions as an own tissue class, does not produce satisfactory result. The new segmentation routine in SPM8 produces the best results, especially if Chris Rorden’s (professor at The Georgia Institute of Technology) improved TPMs are used. Unfortunately, the layer of CSF is not continuous. The segmentation result can still be used in a FEM simulation, although the result from the simulatation will not be ideal.

Neither of the automatic segmentation routines evaluated produce an acceptable result (see Figure 5.7) for stroke patients. Necrotic stroke lesions does not affect the segmentation result as much as the acute dito, especially if there is only a small amount of scar tissue present at the lesion site. The new segmentation routine in SPM8 has the brightest future, although changes need to be made to ensure anatomically correct segmentation results. Post-processing algorithms, relying on morphological prior constraints, can improve the segmentation result further.

Image registration is the process of aligning two images such that their mutual features overlap. This is of great importance in several medical applications. In 2008 a novel method for simultaneous T1, T2 and proton density quantification was suggested. The method is in the field of quantitative Magnetic Resonance Imaging or qMRI. In qMRI parameters are quantified by a pixel-to-pixel fit of the image intensity as a function of different MR scanner settings. The quantification depends on several volumes of different intensities to be aligned. If a patient moves during the data aquisition the datasets will not be aligned and the results are degraded due to this. Since the quantification takes several minutes there is a considerable risk of patient movements. In this master thesis three image registration methods are presented and a comparison in robustness and speed was made. The phase based algorithm was suited for this problem and limited to finding rigid motion. The other two registration algorithms, originating from the Statistical Parametrical Mapping, SPM, package, were used as references. The result shows that the pixel-to-pixel fit is greatly improved in the datasets with found motion. In the comparison between the different methods the phase based algorithm turned out to be both the fastest and the most robust method.

The novel method of this thesis work is based on using quadrature filters to estimate an orientation tensor and to use the advantage of tensor information to control 3D adaptive filters. The adaptive filters are applied to enhance the Magnetic Resonance Angiography (MRA) images. The tubular structures are extracted from the volume dataset by using the quadrature filters. The idea of developing adaptive filtering in this thesis work is to enhance the volume dataset and suppress the image noise. Then the output of the adaptive filtering can be a clean dataset for segmentation of blood vessel structures to get appropriate volume visualization.

The local tensors are used to create the control tensor which is used to control adaptive filters. By evaluation of the tensor eigenvalues combination, the local structures like tubular structures and stenosis structures are extracted from the dataset. The method has been evaluated with synthetic objects, which are vessel models (for segmentation), and onion like synthetic object (for enhancement). The experimental results are shown on clinical images to validate the proposed method as well.

AssistMe är ett webbaserat kunskapsutvinningssystem förthoraxkirurgi där kunskapen utvinns från patientuppgifterinsamlade vid thoraxoperationer. Till AssistMe har en databas föratt lagra patientuppgifter och information om patientuppgifterdesignats och implementerats i en databashanterare. För att läsapatientuppgifter från databashanteraren har ettapplikationsprogramgränssnitt designats och implementerats i Java.

Målsättningen har varit att åstadkomma en enkel och flexibelanvändning av patientuppgifter i AssistMe. Det uppnås genom attdet är lätt att förändra både de patientuppgifter som lagrats idatabasen och patientuppgifternas struktur i databasen.

Databasen och applikationsprogramgränssnittet medför att nyafunktioner som behandlar patientuppgifter kan implementeras iAssistMe på ett enklare och snabbare sätt än tidigare. Det beror påatt all databashantering sköts av applikationsprogramgränssnittetpå ett sätt som är anpassat för AssistMe.