TY - GEN
T1 - A precise calculation of bladder wall thickness for detection of bladder abnormalities via MR cystography
AU - Zhao, Yang
AU - Zhu, Hongbin
AU - Duan, Chaijie
AU - Gu, Xiangfeng
AU - Liang, Zhengrong
PY - 2011
Y1 - 2011
N2 - 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 to the clinical optical cystoscopy with advantages as being a safe and non-invasive method for evaluation of the entire bladder and detection of abnormalities. To help reduce the interpretation time and reading fatigue of the readers or radiologists using MR virtual cystoscopy, a computer-aided detection scheme is widely employed, where the thickness mapping of the bladder wall is recognized as a biomarker since locally-thickened bladder wall often appears around abnormalities or tumors. So the detection precision and sensitivity are critically determined by the accuracy of the thickness calculation. In our previous thickness mapping method, a distance transform (DT) method was used to approximate the thickness calculation. This DT method ensures that the path used to measure the thickness can be determined without any ambiguity by tracing the gradient direction of the DT. But it has limitations in accuracy because of the approximation. In this work, we presented an accurate and efficient calculation of the wall thickness by tracing the path of electric field lines between the inner and outer borders of the wall. The resulted path therefore represents a precise length for the thickness mapping. This scheme was experimented on both phantoms and patient datasets. The results are preliminary but very promising with an efficient running speed (14.6 minutes for a 130x130x130 data) and a noticeable improvement in accuracy.
AB - 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 to the clinical optical cystoscopy with advantages as being a safe and non-invasive method for evaluation of the entire bladder and detection of abnormalities. To help reduce the interpretation time and reading fatigue of the readers or radiologists using MR virtual cystoscopy, a computer-aided detection scheme is widely employed, where the thickness mapping of the bladder wall is recognized as a biomarker since locally-thickened bladder wall often appears around abnormalities or tumors. So the detection precision and sensitivity are critically determined by the accuracy of the thickness calculation. In our previous thickness mapping method, a distance transform (DT) method was used to approximate the thickness calculation. This DT method ensures that the path used to measure the thickness can be determined without any ambiguity by tracing the gradient direction of the DT. But it has limitations in accuracy because of the approximation. In this work, we presented an accurate and efficient calculation of the wall thickness by tracing the path of electric field lines between the inner and outer borders of the wall. The resulted path therefore represents a precise length for the thickness mapping. This scheme was experimented on both phantoms and patient datasets. The results are preliminary but very promising with an efficient running speed (14.6 minutes for a 130x130x130 data) and a noticeable improvement in accuracy.
UR - https://www.scopus.com/pages/publications/84863341405
U2 - 10.1109/NSSMIC.2011.6152574
DO - 10.1109/NSSMIC.2011.6152574
M3 - Conference contribution
AN - SCOPUS:84863341405
SN - 9781467301183
T3 - IEEE Nuclear Science Symposium Conference Record
SP - 3153
EP - 3157
BT - 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2011 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2011
Y2 - 23 October 2011 through 29 October 2011
ER -