Bladder wall thickness mapping for magnetic resonance cystography

Clinical studies have shown evidence that the bladder wall thickness is an effective biomarker for bladder abnormalities. Clinical optical cystoscopy, the current gold standard, cannot show the wall thickness. The use of ultrasound by experts may generate some local thickness information, but the in...

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Bibliographic Details
Published in:Physics in medicine & biology Vol. 58; no. 15; p. 5173
Main Authors: Zhao, Yang, Liang, Zhengrong, Zhu, Hongbin, Han, Hao, Duan, Chaijie, Yan, Zengmin, Lu, Hongbing, Gu, Xianfeng
Format: Journal Article
Language:English
Published: England 07-08-2013
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Summary:Clinical studies have shown evidence that the bladder wall thickness is an effective biomarker for bladder abnormalities. Clinical optical cystoscopy, the current gold standard, cannot show the wall thickness. The use of ultrasound by experts may generate some local thickness information, but the information is limited in field-of-view and is user dependent. Recent advances in magnetic resonance (MR) imaging technologies lead MR-based virtual cystoscopy or MR cystography toward a potential alternative to map the wall thickness for the entire bladder. From a high-resolution structural MR volumetric image of the abdomen, a reasonable segmentation of the inner and outer borders of the bladder wall can be achievable. Starting from here, this paper reviews the limitation of a previous distance field-based approach of measuring the thickness between the two borders and then provides a solution to overcome the limitation by an electric field-based strategy. In addition, this paper further investigates a surface-fitting strategy to minimize the discretization errors on the voxel-like borders and facilitate the thickness mapping on the three-dimensional patient-specific bladder model. The presented thickness calculation and mapping were tested on both phantom and human subject datasets. The results are preliminary but very promising with a noticeable improvement over the previous distance field-based approach.
ISSN:1361-6560
DOI:10.1088/0031-9155/58/15/5173