Title :
Limited view CT reconstruction via constrained metric labeling
Author :
Singh, Vikas ; Dinu, Petru M. ; Mukherjee, Lopamudra ; Xu, Jinhui ; Hoffmann, Kenneth R.
Author_Institution :
State Univ. of New York at Buffalo, Buffalo
Abstract :
This paper proposes an new optimization framework for tomographic reconstruction of 3D volumes when only a limited number of projection views are available. The problem has several important clinical applications spanning coronary angiographic imaging, breast tomosynthesis and dental imaging. We first show that the limited view reconstruction problem can be formulated as a "constrained" version of the metric labeling problem. This lays the groundwork for a linear programming framework that brings together metric labeling classification and classical algebraic tomographic reconstruction (ART) in a unified model. If the imaged volume is known to be comprised of a finite set of attenuation coefficients, given a regular limited view reconstruction as an input, we can view it as a "denoising" task - where voxels must be reassigned subject to maximally maintaining consistency with the input reconstruction and the objective of ART simultaneously. The approach can reliably reconstruct volumes with several multiple contrast objects as well as the simpler binary contrast case which can be solved near-optimally in practice. We present evaluations on cone bean computed tomography, it can also be readily extended to other tomographic modalities as a viable approach for limited-view tomographic reconstruction.
Keywords :
algebra; computerised tomography; image classification; image denoising; image reconstruction; linear programming; medical image processing; CT reconstruction; algebraic tomographic reconstruction; breast tomosynthesis; constrained metric labeling; dental imaging; image classification; image denoising; linear programming; medical image processing; optimization; spanning coronary angiographic imaging; Attenuation; Breast; Computed tomography; Dentistry; Image reconstruction; Labeling; Linear programming; Maintenance; Noise reduction; Subspace constraints;
Conference_Titel :
Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on
Conference_Location :
Rio de Janeiro
Print_ISBN :
978-1-4244-1630-1
Electronic_ISBN :
1550-5499
DOI :
10.1109/ICCV.2007.4409146