Bladder cancer is reported to be the fifth leading cause of cancer deaths in the United States. Recent advances in medical
imaging technologies, such as magnetic resonance (MR) imaging, make virtual cystoscopy a potential alternative with
advantages as being a safe and non-invasive method for evaluation of the entire bladder and detection of abnormalities.
To help reducing the interpretation time and reading fatigue of the readers or radiologists, we introduce a computer-aided
detection scheme based on the thickness mapping of the bladder wall since locally-thickened bladder wall often appears
around tumors. In the thickness mapping method, the path used to measure the thickness can be determined without any
ambiguity by tracing the gradient direction of the potential field between the inner and outer borders of the bladder wall.
The thickness mapping of the three-dimensional inner border surface of the bladder is then flattened to a twodimensional
(2D) gray image with conformal mapping method. In the 2D flattened image, a blob detector is applied to
detect the abnormalities, which are actually the thickened bladder wall indicating bladder lesions. Such scheme was
tested on two MR datasets, one from a healthy volunteer and the other from a patient with a tumor. The result is
preliminary, but very promising with 100% detection sensitivity at 7 FPs per case.
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.
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