Interior and Sparse-View Image Reconstruction Using a Mixed Region and Voxel-Based ML-EM Algorithm

We propose a new interior region-of-interest (ROI) image reconstruction method for emission tomography. The additional information to make the interior problem uniquely solvable is that a specific region inside the interior ROI is known to have uniform intensity level, but the constant level is unkn...

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Veröffentlicht in:IEEE transactions on nuclear science Jg. 59; H. 5; S. 1997 - 2007
Hauptverfasser: Jingyan Xu, Tsui, B. M. W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2012
Schlagworte:
Attenuation
Collimators
Detectors
Exponential radon transform
Image reconstruction
Image representation
interior problem
ML-EM
molecular imaging
multimodality
multipinhole collimator
Phantoms
Poisson likelihood
ROI image reconstruction
Single photon emission computed tomography
sparse-view reconstruction
stationary SPECT
ISSN:0018-9499, 1558-1578
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Abstract We propose a new interior region-of-interest (ROI) image reconstruction method for emission tomography. The additional information to make the interior problem uniquely solvable is that a specific region inside the interior ROI is known to have uniform intensity level, but the constant level is unknown. The uniqueness of solution in this situation is analyzed by combining two existing approaches, namely (1) when the full knowledge of a small region inside the interior ROI is known, and (2) when the complete interior ROI is piecewise constant. The image reconstruction is provided by a mixed region and voxel based Poisson likelihood ML-EM algorithm that takes care of the photon statistics and the uniform attenuation effect in emission tomography. This algorithm reconstructs the unknown constant (region-model) and the rest of the interior ROI (voxel-model) simultaneously. The uniqueness result assumes that all line integrals through the interior ROI are acquired. When only a finite number of projection views are available, the mixed region and voxel based ML-EM algorithm can also reduce image artifacts from sparse-view and interior data acquisition in stationary multipinhole SPECT.
AbstractList We propose a new interior region-of-interest (ROI) image reconstruction method for emission tomography. The additional information to make the interior problem uniquely solvable is that a specific region inside the interior ROI is known to have uniform intensity level, but the constant level is unknown. The uniqueness of solution in this situation is analyzed by combining two existing approaches, namely (1) when the full knowledge of a small region inside the interior ROI is known, and (2) when the complete interior ROI is piecewise constant. The image reconstruction is provided by a mixed region and voxel based Poisson likelihood ML-EM algorithm that takes care of the photon statistics and the uniform attenuation effect in emission tomography. This algorithm reconstructs the unknown constant (region-model) and the rest of the interior ROI (voxel-model) simultaneously. The uniqueness result assumes that all line integrals through the interior ROI are acquired. When only a finite number of projection views are available, the mixed region and voxel based ML-EM algorithm can also reduce image artifacts from sparse-view and interior data acquisition in stationary multipinhole SPECT.
Author Jingyan Xu
Tsui, B. M. W.
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Snippet We propose a new interior region-of-interest (ROI) image reconstruction method for emission tomography. The additional information to make the interior problem...
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SubjectTerms Attenuation
Collimators
Detectors
Exponential radon transform
Image reconstruction
Image representation
interior problem
ML-EM
molecular imaging
multimodality
multipinhole collimator
Phantoms
Poisson likelihood
ROI image reconstruction
Single photon emission computed tomography
sparse-view reconstruction
stationary SPECT
Title Interior and Sparse-View Image Reconstruction Using a Mixed Region and Voxel-Based ML-EM Algorithm
URI https://ieeexplore.ieee.org/document/6248719
Volume 59
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