Paper
23 February 2010 Bladder wall flattening with conformal mapping for MR cystography
Author Affiliations +
Abstract
Magnetic resonance visual cystoscopy or MR cystography (MRC) is an emerging tool for bladder tumor detection, where three-dimensional (3D) endoscopic views on the inner bladder surface are being investigated by researchers. In this paper, we further investigate an innovative strategy of visualizing the inner surface by flattening the 3D surface into a 2D display, where conformal mapping, a mathematically-proved algorithm with shape preserving, is used. The original morphological, textural and even geometric information can be visualized in the flattened 2D image. Therefore, radiologists do not have to manually control the view point and angle to locate the possible abnormalities like what they do in the 3D endoscopic views. Once an abnormality is detected on the 2D flattened image, its locations in the original MR slice images and in the 3D endoscopic views can be retrieved since the conformal mapping is an invertible transformation. In such a manner, the reading time needed by a radiologist can be expected to be reduced. In addition to the surface information, the bladder wall thickness can be visualized with encoded colors on the flattened image. Both normal volunteer and patient studies were performed to test the reconstruction of 3D surface, the conformal flattening, and the visualization of the color-coded flattened image. A bladder tumor of 3 cm size is so obvious on the 2D flattened image such that it can be perceived only at the first sight. The patient dataset shows a noticeable difference on the wall thickness distribution than that of the volunteer's dataset.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ruirui Jiang, Hongbin Zhu, Wei Zeng, Xiaokang Yu, Yi Fan, Xianfeng Gu, and Zhengrong Liang "Bladder wall flattening with conformal mapping for MR cystography", Proc. SPIE 7625, Medical Imaging 2010: Visualization, Image-Guided Procedures, and Modeling, 76250E (23 February 2010); https://doi.org/10.1117/12.844462
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Cited by 3 scholarly publications.
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KEYWORDS
Bladder

Visualization

Optical spheres

Image segmentation

Image visualization

3D image processing

Associative arrays

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