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...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on medical imaging 2014-08, Vol.33 (8), p.1581-1591
Main Authors: Jian-Feng Cai, Xun Jia, Hao Gao, Jiang, Steve B., Zuowei Shen, Hongkai Zhao
Format: Article
Language:English
Subjects:
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0278-0062
1558-254X
DOI:10.1109/TMI.2014.2319055