Feasibility of diffusion tensor imaging (DTI) with fibre tractography of the normal female pelvic floor

To prospectively determine the feasibility of diffusion tensor imaging (DTI) with fibre tractography as a tool for the three-dimensional (3D) visualisation of normal pelvic floor anatomy. Five young female nulliparous subjects (mean age 28 ± 3 years) underwent DTI at 3.0T. Two-dimensional diffusion-weighted axial spin-echo echo-planar (SP-EPI) pulse sequence of the pelvic floor was performed, with additional T2-TSE multiplanar sequences for anatomical reference. Fibre tractography for visualisation of predefined pelvic floor and pelvic wall muscles was performed offline by two observers, applying a consensus method. Three eigenvalues (λ1, λ2, λ3), fractional anisotropy (FA) and mean diffusivity (MD) were calculated from the fibre trajectories. In all subjects fibre tractography resulted in a satisfactory anatomical representation of the pubovisceral muscle, perineal body, anal - and urethral sphincter complex and internal obturator muscle. Mean FA values ranged from 0.23 ± 0.02 to 0.30 ± 0.04, MD values from 1.30 ± 0.08 to 1.73 ± 0.12 × 10(-)³ mm²/s. Muscular structures in the superficial layer of the pelvic floor could not be satisfactorily identified. This study demonstrates the feasibility of visualising the complex three-dimensional pelvic floor architecture using 3T-DTI with fibre tractography. DTI of the deep female pelvic floor may provide new insights into pelvic floor disorder

Solving The Westervelt Equation With Losses Using First And Second Order Finite Element Method

In this Bachelor thesis we researched the second order finite element method as a method to solve the one-dimensional Westervelt wave equation. The Westervelt equation is a wave equation that describes the propagation of a nonlinear plane wave. The goal was to use a first order finite element method and a second order finite element method to solve the Westervelt equation, research the difference between these methods and known solutions and simulate the effect of certain changes in parameters. First we developed an understanding of the nonlinear wave propagation by analyzing the Burgers equation, which is an approximation to the Westervelt equation without attenuation term in another coordinate system. This analysis explains the influence of nonlinearity on the frequency spectrum of the solution. By looking at the derivation of the Westervelt equation changes in wave form during propagation are explained. We used our own finite element method to make implementations in Matlab to solve the linear wave equation, the Westervelt equation without attenuation term and the complete Westervelt equation. To deduce the implementations, we assumed that our unknown solution was a linear combination of first order basis functions. We then wrote the term of the equations into these linear combination and paraphrased them as matrix vector multiplications. Finally, a backwards differential method was used to solves these matrix vector multiplications iteratively in time.We compared the solutions of the linear wave equation using FEM to the known analytic solution and compared the solutions of the Westervelt equation without attenuation term to the solution of the Burgers equation. The finite element method cannot not be completely accurate because it depends on a mesh size that is not infinitely small. This means we always use an approximation in space. There is also an error in time, because we use backwards difference scheme to solve the equation iteratively in time. To approve accuracy we should put research into an adaptive mesh, where we choose a mesh with a lot more points around the peaks. The difference between the first and second basis functions of the finite element method was for the linear wave equation only caused by the small phase shift of the solutions. For the Westervelt equation with and without attenuation term the difference was larger, because of the effect of the nonlinear term on the Westervelt equation. The nonlinearity caused the slope of the wave to steepen when going from a maximum to a minimum. This effect eventually caused the formation of shock waves. The attenuation term inhibited the effect of the nonlinearity on the form of the wave during propagation and thus made the slope of the wave less steep. As a result, no shock waves were formed. The formation of shock waves was immediately clear, when we looked at the solutions at two times the shock wave distance. We concluded from this that the finite element method is not usable after shock waves are formed. We have also shown the frequency spectrum of the solutions and saw the transfer of energy to higher harmonics for the solutions that depended on nonlinearity. The attenuation term damped the transfer of energy and indeed in the spectra we saw less peaks in the higher harmonics. Lastly, we discussed a sudden change in velocity of the wave and calculated the amplitude of the reflected and transmitted wave. We simulated these waves with our implementation in Matlab and concluded that the theory about reflection and transmission agrees with our simulations

Automatic Segmentation of Low-Field MRI Brain Scans by Integrating Analytic and Deep Learning Techniques

Hydrocephalus is a disease where an excess of cerebrospinal fluid (CSF) is built up in the brain. It affects approximately 180.000 infants per year in sub-Saharan Africa. Magnetic resonance imaging (MRI) is an advantageous imaging method to diagnose hydrocephalus and examine the amount of fluid in the brain for treatment. Unfortunately, in sub-Saharan Africa there is limited access to MRI scanners. That is why an inexpensive, portable, low-field MRI scanner is being built for the treatment of hydrocephalus in Uganda. One main restriction of this scanner is simplicity in use. Therefore, the goal is to make software for the MRI scanner that automatically makes and processes the scans. Part of the processing is the automatic segmentation of the scan into CSF and brain tissue regions. Automatic segmentation is complex due to noise and artifacts present in low-field scans. Also, automation of segmentation processes is complicated. Therefore, in this thesis project we aimed to realize the foundations for a fast, practical and automatic 3D segmentation method for brain scans obtained by the low-field MRI scanner. First, analytic segmentation methods were investigated. Multiple segmentation methods were applied to different high-field and low-field scans. Data analysis showed that Li’s method, where the intensity non-homogeneity artifact was corrected for during segmentation, improved the segmentation results evidently when the scan was affected by a distinct bias field. The major disadvantage of Li’s method was the number of parameters and initialization values that had to be chosen. Therefore, it would be complex to satisfy the automation requirement by using an analytic segmentation method. However, this led to the idea of integrating Li’s method with a neural network. A neural network would solve the problem of automation, while the incorporation of Li’s method, would lead to the fitting energy of the segmentation being minimized, which could improve the segmentation results. A neural network for the segmentation of CSF, white matter and gray matter and the prediction of the bias field was built. Unfortunately, a low-field dataset was not available to train on. Therefore, for the training of the network, artificial high-field data was used, together with its ground truth segmentations. Then, the network was trained by not only comparing the predictions with the ground truth segmentations and bias field, but also by minimizing the fitting energy of the predicted segmentation. To evaluate the segmentation results Dice scores were computed between the ground truth and the predicted segmentations. The Dice scores of the train and test set showed that the segmentation results of the neural network improved when the analytic segmentation loss was added to the network. To further investigate the promising effect of the analytic segmentation loss, the trained network was transferred to a new dataset containing infant brain scans, for which the ground truth segmentations were ignored. The network was therefore trained on this new dataset by only using the analytic segmentation loss function. Unfortunately, the results of segmentation became worse. This occurred due to non-brain tissues wrongly being segmented in the brain tissue clusters, since the signal intensities were close to each other. After the non-brain tissues were removed by brain masking, the results of segmentation for all tissues had improved. When more low-field brain scans are available the neural network should be transferred to these scans, since automatic segmentation of low-field scans is the final objective. It is recommended to first implement an automatic brain masking technique on the scans for optimal results. The promising results of Li’s method applied to the low-field scans and of transferring the neural network to the infant dataset show excellent future perspective for the fast, practical and automatic 3D segmentation of low-field scans.Double degree in Applied Mathematics and BioMedical EngineeringApplied MathematicsBioMedical Engineerin

Advances in MRI of the colon and pelvic floor

Er zijn meerdere beeldvormende technieken beschikbaar om de dikke darm te bekijken. Frank Zijta onderzocht onder meer de diagnostische waarde van de MRI-techniek colografie voor het detecteren van dikkedarmafwijkingen. MR-colografie blijkt een nauwkeurige methode voor het opsporen van grote poliepen. Zijta introduceert daarnaast Diffusion Tensor Imaging (DTI) als een nieuwe techniek voor de beoordeling van de vrouwelijke bekkenbodem. DTI met vezeltractografie maakt de driedimensionale visualisatie mogelijk van de spieren van de vrouwelijke bekkenbodem. Met de techniek kan nu echter nog onvoldoende spierschade aangetoond worden

Magnetic resonance (MR) colonography in the detection of colorectal lesions: a systematic review of prospective studies

To determine the diagnostic accuracy of MR-colonography for the detection of colorectal lesions. A comprehensive literature search was performed for comparative MR-colonography studies, published between May 1997 and February 2009, using the MEDLINE, EMBASE and Cochrane databases. We included studies if MR-colonography findings were prospectively compared with conventional colonoscopy in (a)symptomatic patients. Two reviewers independently extracted study design characteristics and data for summarising sensitivity and specificity. Heterogeneity in findings between studies was tested using I (2) test statistics. Sensitivity and specificity estimates with 95% confidence intervals (CI) were calculated on per patient basis and summary sensitivity on per polyp basis, using bivariate and univariate statistical models. Thirty-seven studies were found to be potentially relevant and 13 fulfilled the inclusion criteria. The study population comprised 1,285 patients with a mean disease prevalence of 44% (range 22-63%). Sensitivity for the detection of CRC was 100%. Significant heterogeneity was found for overall per patient sensitivity and specificity. For polyps with a size of 10 mm or larger, per patient sensitivity and specificity estimates were 88% (95% CI 63-97%; I (2) = 37%) and 99% (95% CI 95-100%; I (2) = 60%). On a per polyp basis, polyps of 10 mm or larger were detected with a sensitivity of 84% (95% CI 66-94%; I (2) = 51%). The data were too heterogeneous for polyps smaller than 6 mm and 6-9 mm. MR-colonography can accurately detect colorectal polyps more than 10 mm in siz

Abdominal Tuberculosis Complicated by Intestinal Perforation

Although relatively rare, there is an increasing incidence of abdominal tuberculosis (TB) in the developed countries, with the peritoneum being the most common site of involvement. Manifestation of abdominal TB should be considered in patients with relevant clinical symptoms and risk factors, including a history of prior TB infection and residence in or travel to an area where tuberculosis is endemic. We report a case of intestinal tuberculosis with a complicated disease course after the completion of treatment. Persisting abdominal symptoms during or after treatment should raise suspicion of subclinical intestinal obstruction. Early clinical recognition and surgical treatment may avoid poor outcome due to intestinal perforation