Estimating the 4D respiratory lung motion by spatiotemporal registration and super‐resolution image reconstruction. Issue 3 (25th February 2013)

• Record Type: Journal Article
• Authors: Wu, Guorong; Wang, Qian; Lian, Jun; Shen, Dinggang
• Is Part Of: Medical physics. Volume 40:Issue 3(2013)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2013-02-25
• Subjects: Errata -- Pneumodyamics, respiration -- Cancer -- Spatial resolution -- Reconstruction -- Computed tomography; cancer -- computerised tomography -- image reconstruction -- image resolution -- iterative methods -- lung -- medical image processing -- motion estimation -- pneumodynamics -- radiation therapy -- spatiotemporal phenomena -- tumours; 4D‐CT -- spatial‐temporal registration -- super‐resolution -- sparse representation -- lung motion estimation; Computerised tomographs -- Analysis of motion; Medical imaging -- Lungs -- Medical image reconstruction -- Motion estimation -- Cancer -- Medical image smoothing -- Medical image quality -- Image registration -- Dictionaries -- Image reconstruction; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4790689
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: : One of the main challenges in lung cancer radiation therapy is how to reduce the treatment margin but accommodate the geometric uncertainty of moving tumor. 4D‐CT is able to provide the full range of motion information for the lung and tumor. However, accurate estimation of lung motion with respect to the respiratory phase is difficult due to various challenges in image registration, e.g., motion artifacts and large interslice thickness in 4D‐CT. Meanwhile, the temporal coherence across respiration phases is usually not guaranteed in the conventional registration methods which consider each phase image in 4D‐CT independently. To address these challenges, the authors present a unified approach to estimate the respiratory lung motion with two iterative steps. Methods: : First, the authors propose a novel spatiotemporal registration algorithm to align all phase images of 4D‐CT (in low‐resolution) to a high‐resolution group‐mean image in the common space. The temporal coherence of registration is maintained by a set of temporal fibers that delineate temporal correspondences across different respiratory phases. Second, a super‐resolution technique is utilized to build the high‐resolution group‐mean image with more anatomical details than any individual phase image, thus largely alleviating the registration uncertainty especially in correspondence detection. In particular, the authors use the concept of sparse representation to keep the group‐mean image as sharp as possible. Results: : The performance of our 4D motion estimation method has been extensively evaluated on both the simulated datasets and real lung 4D‐CT datasets. In all experiments, our method achieves more accurate and consistent results in lung motion estimation than all other state‐of‐the‐art approaches under comparison. Conclusions: : The authors have proposed a novel spatiotemporal registration method to estimate the lung motion in 4D‐CT. Promising results have been obtained, which indicates the high applicability of our method in clinical lung cancer radiation therapy.