Interior and sparse-view image reconstruction using a mixed region and voxel-based ml-em algorithm

Jingyan Xu; Benjamin M.W. Tsui

doi:10.1109/TNS.2012.2202290

Interior and sparse-view image reconstruction using a mixed region and voxel-based ml-em algorithm

Jingyan Xu, Benjamin M.W. Tsui

School of Medicine

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

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.

Original language	English (US)
Article number	6248719
Pages (from-to)	1997-2007
Number of pages	11
Journal	IEEE Transactions on Nuclear Science
Volume	59
Issue number	5 PART 1
DOIs	https://doi.org/10.1109/TNS.2012.2202290
State	Published - 2012

Keywords

Exponential radon transform
Interior problem
ML-EM
Molecular imaging
Multimodality
Multipinhole collimator
Poisson likelihood
ROI image reconstruction
Sparse-view reconstruction
Stationary SPECT

ASJC Scopus subject areas

Nuclear and High Energy Physics
Nuclear Energy and Engineering
Electrical and Electronic Engineering

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10.1109/TNS.2012.2202290

Cite this

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title = "Interior and sparse-view image reconstruction using a mixed region and voxel-based ml-em algorithm",

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.",

keywords = "Exponential radon transform, Interior problem, ML-EM, Molecular imaging, Multimodality, Multipinhole collimator, Poisson likelihood, ROI image reconstruction, Sparse-view reconstruction, Stationary SPECT",

author = "Jingyan Xu and Tsui, {Benjamin M.W.}",

note = "Funding Information: Manuscript received January 03, 2012; revised March 28, 2012; accepted May 23, 2012. Date of publication July 25, 2012; date of current version October 09, 2012. This work was supported in part by the National Institutes of Health Grant R01 EB008730. The authors are with the Department of Radiology, Johns Hopkins University, Baltimore, MD 21287 USA (e-mail: jxu@jhmi.edu; btsui@jhmi.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TNS.2012.2202290",

year = "2012",

doi = "10.1109/TNS.2012.2202290",

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AU - Xu, Jingyan

AU - Tsui, Benjamin M.W.

N1 - Funding Information: Manuscript received January 03, 2012; revised March 28, 2012; accepted May 23, 2012. Date of publication July 25, 2012; date of current version October 09, 2012. This work was supported in part by the National Institutes of Health Grant R01 EB008730. The authors are with the Department of Radiology, Johns Hopkins University, Baltimore, MD 21287 USA (e-mail: jxu@jhmi.edu; btsui@jhmi.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TNS.2012.2202290

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N2 - 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.

AB - 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.

KW - Exponential radon transform

KW - Interior problem

KW - ML-EM

KW - Molecular imaging

KW - Multimodality

KW - Multipinhole collimator

KW - Poisson likelihood

KW - ROI image reconstruction

KW - Sparse-view reconstruction

KW - Stationary SPECT

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SP - 1997

EP - 2007

JO - IEEE Transactions on Nuclear Science

JF - IEEE Transactions on Nuclear Science

IS - 5 PART 1

M1 - 6248719

ER -

Interior and sparse-view image reconstruction using a mixed region and voxel-based ml-em algorithm

Abstract

Keywords

ASJC Scopus subject areas

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