Access the full text.
Sign up today, get DeepDyve free for 14 days.
H. Dang, Adam Wang, M. Sussman, J. Siewerdsen, J. Stayman (2014)
dPIRPLE: a joint estimation framework for deformable registration and penalized-likelihood CT image reconstruction using prior imagesPhysics in Medicine & Biology, 59
Yan Liu, Jianhua Ma, Yi Fan, Zhengrong Liang (2012)
Adaptive-weighted total variation minimization for sparse data toward low-dose x-ray computed tomography image reconstructionPhysics in Medicine and Biology, 57
Hengyong Yu (2009)
Compressed sensing based interior tomographyPhysics in Medicine & Biology, 54
T. Eguchi, A. Yoshizawa, S. Kawakami, H. Kumeda, Tetsuya Umesaki, H. Agatsuma, T. Sakaizawa, Yoshiaki Tominaga, M. Toishi, M. Hashizume, T. Shiina, Kazuo Yoshida, S. Asaka, Mina Matsushita, T. Koizumi (2014)
Tumor Size and Computed Tomography Attenuation of Pulmonary Pure Ground-Glass Nodules Are Useful for Predicting Pathological InvasivenessPLoS ONE, 9
H. Davies, C. Wathen, F. Gleeson (2011)
The risks of radiation exposure related to diagnostic imaging and how to minimise themBMJ : British Medical Journal, 342
N. Shamul, Leo Joskowicz (2017)
Radon Space Dose Optimization in Repeat CT ScanningIEEE Transactions on Medical Imaging, 36
Brody (1982)
Digital subtraction angiography, –IEEE Transactions on Nuclear Science, 29
J. Ko, Margrit Betke (2001)
Chest CT: automated nodule detection and assessment of change over time--preliminary experience.Radiology, 218 1
R. Vivanti, Adi Szeskin, N. Lev-Cohain, J. Sosna, Leo Joskowicz (2017)
Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studiesInternational Journal of Computer Assisted Radiology and Surgery, 12
Shiying Hu, E. Hoffman, J. Reinhardt (2001)
Automatic lung segmentation for accurate quantitation of volumetric X-ray CT imagesIEEE Transactions on Medical Imaging, 20
S. Zabic, Qiu Wang, T. Morton, K. Brown (2013)
A low dose simulation tool for CT systems with energy integrating detectors.Medical physics, 40 3
C. Crawford, A. Kak (1979)
ALIASING ARTIFACTS IN COMPUTERIZED TOMOGRAPHYJournal of Computer Assisted Tomography, 4
H. Yoshimura, T. Murakami, Tonsok Kim, Hironobu Nakamura, N. Hirabuki, M. Sakon, K. Wakasa, Y. Inoue (2002)
Angiomyolipoma of the liver with least amount of fat component: imaging features of CT, MR, and angiographyAbdominal Imaging, 27
N. Shamul, Leo Joskowicz (2020)
Automatic Change Detection in Sparse Repeat CT ScanningIEEE Transactions on Medical Imaging, 39
M. Werner-Wasik, Ying Xiao, Edward Pequignot, Walter Curran, Walter Hauck (2001)
Assessment of lung cancer response after nonoperative therapy: tumor diameter, bidimensional product, and volume. A serial CT scan-based study.International journal of radiation oncology, biology, physics, 51 1
Jing Huang, Yunwan Zhang, Jianhua Ma, D. Zeng, Z. Bian, S. Niu, Qianjin Feng, Zhengrong Liang, Wufan Chen (2013)
Iterative Image Reconstruction for Sparse-View CT Using Normal-Dose Image Induced Total Variation PriorPLoS ONE, 8
Xuejun Gu, Dongju Choi, C. Men, Hubert Pan, Amitava Majumdar, Steve Jiang (2009)
GPU-based ultra-fast dose calculation using a finite size pencil beam modelPhysics in Medicine & Biology, 54
K. Cleary, T. Peters (2010)
Image-guided interventions: technology review and clinical applications.Annual review of biomedical engineering, 12
Guang-Hong Chen, Jie Tang, S. Leng (2008)
Prior image constrained compressed sensing (PICCS): a method to accurately reconstruct dynamic CT images from highly undersampled projection data sets.Medical physics, 35 2
W. Brody (2007)
IEEE Transactions on Nuclear Science, Vol. NS-29, No. 3, June 1982 DIGITAL SUBTRACTION ANGIOGRAPHY
G. Medan, N. Shamul, Leo Joskowicz (2017)
Sparse 3D Radon Space Rigid Registration of CT Scans: Method and Validation StudyIEEE Transactions on Medical Imaging, 36
C. McCollough, M. Bruesewitz, J. Kofler (2006)
CT dose reduction and dose management tools: overview of available options.Radiographics : a review publication of the Radiological Society of North America, Inc, 26 2
Sidky (2008)
Image reconstruction in circular cone-beam computed tomography by constrained total-variation minimizationPhysics Medicine and Biology, 53
T. Heimann, B. Ginneken, M. Styner, Y. Arzhaeva, V. Aurich, Christian Bauer, A. Beck, Christoph Becker, R. Beichel, György Bekes, F. Bello, G. Binnig, H. Bischof, A. Bornik, P. Cashman, Ying Chi, A. Cordova, B. Dawant, M. Fidrich, J. Furst, D. Furukawa, L. Grenacher, J. Hornegger, Dagmar Kainmüller, R. Kitney, H. Kobatake, H. Lamecker, T. Lange, Jeongjin Lee, B. Lennon, Rui Li, Senhu Li, H. Meinzer, G. Németh, D. Raicu, Anne-Mareike Rau, E. Rikxoort, Mikaël Rousson, L. Ruskó, K. Saddi, G. Schmidt, D. Seghers, A. Shimizu, P. Slagmolen, E. Sorantin, G. Soza, Ruchaneewan Susomboon, Jonathan Waite, A. Wimmer, I. Wolf (2009)
Comparison and Evaluation of Methods for Liver Segmentation From CT DatasetsIEEE Transactions on Medical Imaging, 28
Jing Wang, X. Gu (2012)
High-quality four-dimensional cone-beam CT by deforming prior imagesPhysics in Medicine and Biology, 58
G. Nunzio, E. Tommasi, A. Agrusti, R. Cataldo, I. Mitri, M. Favetta, S. Maglio, A. Massafra, M. Quarta, M. Torsello, I. Zecca, R. Bellotti, S. Tangaro, P. Calvini, N. Camarlinghi, F. Falaschi, P. Cerello, P. Oliva (2011)
Automatic Lung Segmentation in CT Images with Accurate Handling of the Hilar RegionJournal of Digital Imaging, 24
Zeev Adelman, Leo Joskowicz (2020)
Deformable registration and region-of-interest image reconstruction in sparse repeat CT scanning.Journal of X-ray science and technology
Amir Pourmorteza, H. Dang, J. Siewerdsen, J. Stayman (2016)
Reconstruction of difference in sequential CT studies using penalized likelihood estimationPhysics in Medicine & Biology, 61
Binsheng Zhao, L. James, C. Moskowitz, P. Guo, M. Ginsberg, R. Lefkowitz, Yilin Qin, Gregory Riely, M. Kris, L. Schwartz (2009)
Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer.Radiology, 252 1
R. Zeng, J. Fessler, J. Balter (2007)
Estimating 3-D Respiratory Motion From Orbiting Views by Tomographic Image RegistrationIEEE Transactions on Medical Imaging, 26
BACKGROUND:Detecting and interpreting changes in the images of follow-up CT scans by the clinicians is often time-consuming and error-prone due to changes in patient position and non-rigid anatomy deformations. Thus, reconstructed repeat scan images are required, precluding reduced dose sparse-view repeat scanning.OBJECTIVE:A method to automatically detect changes in a region of interest of sparse-view repeat CT scans in the presence of non-rigid deformations of the patient’s anatomy without reconstructing the original images.METHODS:The proposed method uses the sparse sinogram data of two CT scans to distinguish between genuine changes in the repeat scan and differences due to non-rigid anatomic deformations. First, size and contrast level of the changed regions are estimated from the difference between the scans’ sinogram data. The estimated types of changes in the repeat scan help optimize the method’s parameter values. Two scans are then aligned using Radon space non-rigid registration. Rays which crossed changes in the ROI are detected and back-projected onto image space in a two-phase procedure. These rays form a likelihood map from which the binary changed region map is computed.RESULTS:Experimental studies on four pairs of clinical lung and liver CT scans with simulated changed regions yield a mean changed region recall rate > 86%and a mean precision rate > 83%when detecting large changes with low contrast, and high contrast changes, even when small. The new method outperforms image space methods using prior image constrained compressed sensing (PICCS) reconstruction, particularly for small, low contrast changes (recall = 15.8%, precision = 94.7%).CONCLUSION:Our method for automatic change detection in sparse-view repeat CT scans with non-rigid deformations may assist radiologists by highlighting the changed regions and may obviate the need for a high-quality repeat scan image when no changes are detected.
Journal of X-Ray Science and Technology – IOS Press
Published: Oct 29, 2021
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.