Quantitative Analysis of Bladder Wall Thickness for Magnetic Resonance Cystoscopy

Objective: To find an effective way for quantitative evaluation on wall thickness variation of human bladder with/without bladder tumor, a novel pipeline of thickness measurement and analysis for magnetic resonance (MR) cystography is proposed. Methods: After the acquisition of volumetric bladder images with a high-resolution T2-weighted 3-D sequence, the inner and outer borders of the bladder wall were segmented simultaneously by a coupled directional level-set method. Then, the bladder wall thickness (BWT) was estimated using the Laplacian method. To reducing the influence of individual variation and urine filling on wall thickness, a thickness normalization using Z-score is performed. Finally, a parametric surface mapping strategy was applied to map thickness distribution onto a unified sphere surface, for quantitative intra- and intersubject comparison between bladders of different shapes. Results: The proposed pipeline was tested with a database composed of MR bladder images acquired from 20 volunteers and 20 patients with bladder cancer. The results indicate that the thickness normalization step using Z-score makes the quantitative comparison of wall thickness quite possible and there is a significant difference on BWT between patients and volunteers. Using the proposed pipeline, we established a thickness template for a normal bladder wall based on dataset of all volunteers. Conclusion: As a first attempt to establish a general pipeline for bladder wall analysis, the presented work provides an effective way to achieve the goal of evaluating the entire bladder wall for detection and diagnosis of abnormality. In addition, it can be easily extended to quantitative analyses of other bladder features, such as, intensity-based or texture features.

[1]  Annette Kuhn,et al.  How should bladder wall thickness be measured? A comparison of vaginal, perineal and abdominal ultrasound , 2010, Neurourology and urodynamics.

[2]  Ron Kikinis,et al.  Tumor detection in the bladder wall with a measurement of abnormal thickness in CT scans , 2003, IEEE Transactions on Biomedical Engineering.

[3]  Marcus Settles,et al.  Reliability of MR imaging-based virtual cystoscopy in the diagnosis of cancer of the urinary bladder. , 2002, AJR. American journal of roentgenology.

[4]  Arnulf Stenzl,et al.  Guidelines on Bladder Cancer Muscle-invasive and Metastatic , 2008 .

[5]  Yang Liu,et al.  Cortical Thinning in Patients with Recent Onset Post-Traumatic Stress Disorder after a Single Prolonged Trauma Exposure , 2012, PloS one.

[6]  Jerry L. Prince,et al.  An Eulerian PDE approach for computing tissue thickness , 2003, IEEE Transactions on Medical Imaging.

[7]  Hessel Wijkstra,et al.  Manual versus automatic bladder wall thickness measurements: a method comparison study , 2009, World Journal of Urology.

[8]  Haissam Haidar,et al.  New Numerical Solution of the Laplace Equation for Tissue Thickness Measurement in Three-Dimensional MRI , 2005, J. Math. Model. Algorithms.

[9]  Zhengrong Liang,et al.  A Coupled Level Set Framework for Bladder Wall Segmentation With Application to MR Cystography , 2010, IEEE Transactions on Medical Imaging.

[10]  Xuelong Li,et al.  Adaptive Shape Prior Constrained Level Sets for Bladder MR Image Segmentation , 2014, IEEE Journal of Biomedical and Health Informatics.

[11]  E. Suleyman,et al.  Bladder tumors: virtual MR cystoscopy , 2006, Abdominal Imaging.

[12]  M. Wirth,et al.  Bladder wall thickness in normal adults and men with mild lower urinary tract symptoms and benign prostatic enlargement , 2000, Neurourology and urodynamics.

[13]  Xuelong Li,et al.  Coupled Directional Level Set for MR Image Segmentation , 2012, 2012 11th International Conference on Machine Learning and Applications.

[14]  Ernst J. Rummeny,et al.  MR cystography for bladder tumor detection , 2004, European Radiology.

[15]  Hongbing Lu,et al.  Parametric mapping model for bladder using free-form deformation , 2013, 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (2013 NSS/MIC).

[16]  Zhengrong Liang,et al.  A unified EM approach to bladder wall segmentation with coupled level-set constraints , 2013, Medical Image Anal..

[17]  Zhengrong Liang,et al.  Bladder wall thickness mapping for magnetic resonance cystography , 2013, Physics in medicine and biology.

[18]  Ron Kikinis,et al.  Tumor detection by virtual cystoscopy with color mapping of bladder wall thickness. , 2002, The Journal of urology.

[19]  Zhengrong Liang,et al.  An EM Approach to MAP Solution of Segmenting Tissue Mixtures: A Numerical Analysis , 2009, IEEE Transactions on Medical Imaging.

[20]  Zhengrong Liang,et al.  Volume-Based Features for Detection of Bladder Wall Abnormal Regions via MR Cystography , 2011, IEEE Transactions on Biomedical Engineering.