Cine Cone Beam CT Reconstruction Using Low-Rank Matrix Factorization: Algorithm and a Proof-of-Principle Study
Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to o...
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| Veröffentlicht in: | IEEE transactions on medical imaging Jg. 33; H. 8; S. 1581 - 1591 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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United States
IEEE
01.08.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 0278-0062, 1558-254X, 1558-254X |
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| Abstract | Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images. |
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| AbstractList | Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images. Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images.Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over one breathing cycle and the fourth dimension is actually breathing phase instead of time. In many clinical applications, it is desirable to obtain true 4DCBCT with the fourth dimension being time, i.e., each constituent CBCT image corresponds to an instantaneous projection. Theoretically it is impossible to reconstruct a CBCT image from a single projection. However, if all the constituent CBCT images of a 4DCBCT scan share a lot of redundant information, it might be possible to make a good reconstruction of these images by exploring their sparsity and coherence/redundancy. Though these CBCT images are not completely time resolved, they can exploit both local and global temporal coherence of the patient anatomy automatically and contain much more temporal variation information of the patient geometry than the conventional 4DCBCT. We propose in this work a computational model and algorithms for the reconstruction of this type of semi-time-resolved CBCT, called cine-CBCT, based on low rank approximation that can utilize the underlying temporal coherence both locally and globally, such as slow variation, periodicity or repetition, in those cine-CBCT images. |
| Author | Hao Gao Hongkai Zhao Xun Jia Jiang, Steve B. Zuowei Shen Jian-Feng Cai |
| Author_xml | – sequence: 1 surname: Jian-Feng Cai fullname: Jian-Feng Cai organization: Dept. of Math., Univ. of Iowa, Iowa City, IA, USA – sequence: 2 surname: Xun Jia fullname: Xun Jia organization: Dept. of Radiat. Oncology, Univ. of Texas, Dallas, TX, USA – sequence: 3 surname: Hao Gao fullname: Hao Gao organization: Dept. of Math., Shanghai Jiao Tong Univ., Shanghai, China – sequence: 4 givenname: Steve B. surname: Jiang fullname: Jiang, Steve B. organization: Dept. of Radiat. Oncology, Univ. of Texas, Dallas, TX, USA – sequence: 5 surname: Zuowei Shen fullname: Zuowei Shen organization: Dept. of Math., Nat. Univ. of Singapore, Singapore, Singapore – sequence: 6 surname: Hongkai Zhao fullname: Hongkai Zhao organization: Dept. of Math., Univ. of California Irvine, Irvine, CA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/24771574$$D View this record in MEDLINE/PubMed |
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| Snippet | Respiration-correlated CBCT, commonly called 4DCBCT, provides respiratory phase-resolved CBCT images. A typical 4DCBCT represents averaged patient images over... |
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| SubjectTerms | Algorithms Approximation methods Cine cone beam computed tomography (CBCT) Coherence Cone-Beam Computed Tomography - methods Four-Dimensional Computed Tomography - methods Humans Image Processing, Computer-Assisted - methods Image reconstruction low-rank matrix Matrix decomposition Models, Biological Phantoms, Imaging Radiography, Thoracic reconstruction Respiration Sparse matrices Transforms X-ray imaging |
| Title | Cine Cone Beam CT Reconstruction Using Low-Rank Matrix Factorization: Algorithm and a Proof-of-Principle Study |
| URI | https://ieeexplore.ieee.org/document/6803058 https://www.ncbi.nlm.nih.gov/pubmed/24771574 https://www.proquest.com/docview/1551283875 https://www.proquest.com/docview/1551818518 https://www.proquest.com/docview/1560118599 https://pubmed.ncbi.nlm.nih.gov/PMC6022849 |
| Volume | 33 |
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