title: Motion correction for MR cystography by an image processing approach.
abstract: Magnetic resonance (MR) cystography or MR-based virtual cystoscopy is a promising new technology to evaluate the entire bladder in a fully noninvasive manner. It requires the anatomical bladder images be acquired at high spatial resolution and with adequate signal-to-noise ratio (SNR). This often leads to a long-time scan (>5 min) and results in image artifacts due to involuntary bladder motion and deformation. In this paper, we investigated an image-processing approach to mitigate the problem of motion and deformation. Instead of a traditional single long-time scan, six repeated short-time scans (each of approximately 1 min) were acquired for the purpose of shifting bladder motion from intrascan into interscans. Then, the interscan motions were addressed by registering the short-time scans to a selected reference and finally forming a single average motion-corrected image. To evaluate the presented approach, three types of images were generated: 1) the motion-corrected image by registration and average of the short-time scans; 2) the directly averaged image of the short-time scans (without motion correction); and 3) the single image of the corresponding long-time scan. Six experts were asked to blindly score these images in terms of two important aspects: 1) the definition of the bladder wall and 2) the overall expression on the image quality. Statistical analysis on the scores suggested that the best result in both the aspects is achieved by the presented motion-corrected average. Furthermore, the superiority of the motion-corrected average over the other two is statistically significant by the measure of a linear mixed-effect model with p -values < 0.05. Our findings may facilitate the detection of bladder abnormality in MR cystography by mitigating the motion challenge. The effectiveness of this approach depends on the noise level of acquired short-time scans and the robustness of image registration, and future effort on these two aspects is needed.
authors.affiliation: College of Electronics and Information Engineering, Sichuan University, Chengdu 610064, China. qinlin@mil.sunysb.edu
authors.forename: Qin
authors.initials: Q
authors.lastname: Lin
authors.forename: Zhengrong
authors.initials: Z
authors.lastname: Liang
authors.forename: Chaijie
authors.initials: C
authors.lastname: Duan
authors.forename: Jianhua
authors.initials: J
authors.lastname: Ma
authors.forename: Haifang
authors.initials: H
authors.lastname: Li
authors.forename: Clement
authors.initials: C
authors.lastname: Roque
authors.forename: Jie
authors.initials: J
authors.lastname: Yang
authors.forename: Guangxiang
authors.initials: G
authors.lastname: Zhang
authors.forename: Hongbing
authors.initials: H
authors.lastname: Lu
authors.forename: Xiaohai
authors.initials: X
authors.lastname: He
mesh_terms: D000328:Adult; D000465:Algorithms; D003558:Cystoscopy / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019370:Observation; D059629:Signal-To-Noise Ratio; D001743:Urinary Bladder / Q000033:anatomy & histology / Q000648:ultrastructure