Multispectral MRI-based virtual cystoscopy

Bladder cancer is the fifth cause of cancer deaths in the United States. Virtual cystoscopy (VC) can be a screening means for early detection of the cancer using non-invasive imaging and computer graphics technologies. Previous researches have mainly focused on spiral CT (computed tomography), which invasively introduces air into bladder lumen for a contrast against bladder wall via a small catheter. However, the tissue contrast around bladder wall is still limited in CT-based VC. In addition, CT-based technique carries additional radiation. We have investigated a procedure to achieve the screening task by MRI (magnetic resonance imaging). It utilizes the unique features of MRI: (1) the urine has distinct T1 and T2 relaxation times as compared to its surrounding tissues, and (2) MRI has the potential to obtain good tissue contrast around bladder wall. The procedure is fully non-invasive and easy in implementation. In this paper, we proposed a MRI-based VC system for computer aided detection (CAD) of bladder tumors. The proposed VC system is an integration of partial volume-based segmentation containing texture information and fast marching-based CAD employing geometrical features for detecting of bladder tumors. The accuracy and efficiency of the integrated VC system are evaluated by testing the diagnoses against a database of patients.

[1]  Zhengrong Liang,et al.  Reduction of false positives by internal features for polyp detection in CT-based virtual colonoscopy. , 2005, Medical physics.

[2]  Zhengrong Liang,et al.  Volume-based feature analysis of mucosa for automatic initial polyp detection in virtual colonoscopy , 2008, International Journal of Computer Assisted Radiology and Surgery.

[3]  A J Aschoff,et al.  Virtual cystoscopy based on helical CT scan datasets: perspectives and limitations. , 1998, The British journal of radiology.

[4]  E. Wong,et al.  'Routine urinalysis'. Is the dipstick enough? , 1985, JAMA.

[5]  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.

[6]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[7]  T V Bell,et al.  Virtual cystoscopy: early clinical experience. , 1997, Radiology.

[8]  D R Haynor,et al.  Partial volume tissue classification of multichannel magnetic resonance images-a mixel model. , 1991, IEEE transactions on medical imaging.

[9]  Steinberg Gd,et al.  Metastatic bladder cancer. Natural history, clinical course, and consideration for treatment. , 1992 .

[10]  D. Lamm,et al.  Bladder cancer, 1996 , 1996, Ca.

[11]  Jerry L. Prince,et al.  Adaptive fuzzy segmentation of magnetic resonance images , 1999, IEEE Transactions on Medical Imaging.

[12]  S. Cohen,et al.  Epidemiology and etiology of bladder cancer. , 1992, The Urologic clinics of North America.

[13]  R K Babayan,et al.  Thin-section helical computed tomography of the bladder: initial clinical experience with virtual reality imaging. , 1997, Urology.

[14]  Lihong Li,et al.  BLADDER CANCER SCREENING BY MAGNETIC RESONANCE IMAGING , 2008 .

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

[16]  J. Platt,et al.  Bladder tumor detection at virtual cystoscopy. , 2001, Radiology.

[17]  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.

[18]  David R. Stelts,et al.  CT cystoscopy: an innovation in bladder imaging. , 1996, AJR. American journal of roentgenology.

[19]  Bin Li,et al.  Multiscan MRI-based virtual cystoscopy , 2000, Medical Imaging.

[20]  Hiroyuki Yoshida,et al.  Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps , 2001, IEEE Transactions on Medical Imaging.

[21]  Zhengrong Liang,et al.  Segmentation of multispectral bladder MR images with inhomogeneity correction for virtual cystoscopy , 2008, SPIE Medical Imaging.

[22]  Alex M. Andrew,et al.  Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science (2nd edition) , 2000 .

[23]  Zhengrong Liang,et al.  Partial volume segmentation of brain magnetic resonance images based on maximum a posteriori probability. , 2005, Medical physics.