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Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging.

Yu X, Chen S, Hu Z, Liu M, Chen Y, Shi P, Liu H - PLoS ONE (2015)

Bottom Line: In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse.The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods.The effectiveness of the proposed scheme is illustrated on three data sets.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China.

ABSTRACT
In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data acquisition, besides, it is unable to afford the separated information about the structure and the variation of shape and rate of metabolism which play a major role in improving the visualization of contrast for some requirement of the diagnosing in application. This paper presents a novel low rank-based activity map reconstruction scheme from emission sinograms of dynamic PET, termed as SLCR representing Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging. In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse. The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. In this paper, the linearized alternating direction method is applied. The effectiveness of the proposed scheme is illustrated on three data sets.

No MeSH data available.


The reconstruction images for brain phantom for count level 2 and 3.From left to right, the reconstruction images are the results of the ML-EM (first), the sum of two components of SLCR (ST + SP, second), the stationary (ST, third) and time-varying (SP, forth) components of the SLCR for the brain phantom at the #10 frame in count Level 2 to 3.
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pone.0142019.g006: The reconstruction images for brain phantom for count level 2 and 3.From left to right, the reconstruction images are the results of the ML-EM (first), the sum of two components of SLCR (ST + SP, second), the stationary (ST, third) and time-varying (SP, forth) components of the SLCR for the brain phantom at the #10 frame in count Level 2 to 3.

Mentions: To evaluate the robustness of the SLCR in low count data, two extra count levels were simulated and the corresponding proportions of the random and scatter events were recorded. Level 2: the total count was 1.32 × 106, the proportion of the scatter events was 19.7%, and the proportion of the random events was 1.63%); Level 3: the total count was 6 × 105, the proportion of the scatter events was 39.75%, and the proportion of the random events was 3.363%. Data sets in both level 2 and level 3 are considered as the low-count data. The images reconstructed by ML-EM and SLCR methods for these two levels are shown in Fig 6. Though all images reconstructed by ML-EM and SLCR go worse when the data count decrease, the SLCR method provide more accurate and less aliasing artifacts reconstructions than ML-EM in both count levels, especially in the target regions. In addition, it is easy to locate the position of the target regions in the time-varying component of SLCR.


Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging.

Yu X, Chen S, Hu Z, Liu M, Chen Y, Shi P, Liu H - PLoS ONE (2015)

The reconstruction images for brain phantom for count level 2 and 3.From left to right, the reconstruction images are the results of the ML-EM (first), the sum of two components of SLCR (ST + SP, second), the stationary (ST, third) and time-varying (SP, forth) components of the SLCR for the brain phantom at the #10 frame in count Level 2 to 3.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4634927&req=5

pone.0142019.g006: The reconstruction images for brain phantom for count level 2 and 3.From left to right, the reconstruction images are the results of the ML-EM (first), the sum of two components of SLCR (ST + SP, second), the stationary (ST, third) and time-varying (SP, forth) components of the SLCR for the brain phantom at the #10 frame in count Level 2 to 3.
Mentions: To evaluate the robustness of the SLCR in low count data, two extra count levels were simulated and the corresponding proportions of the random and scatter events were recorded. Level 2: the total count was 1.32 × 106, the proportion of the scatter events was 19.7%, and the proportion of the random events was 1.63%); Level 3: the total count was 6 × 105, the proportion of the scatter events was 39.75%, and the proportion of the random events was 3.363%. Data sets in both level 2 and level 3 are considered as the low-count data. The images reconstructed by ML-EM and SLCR methods for these two levels are shown in Fig 6. Though all images reconstructed by ML-EM and SLCR go worse when the data count decrease, the SLCR method provide more accurate and less aliasing artifacts reconstructions than ML-EM in both count levels, especially in the target regions. In addition, it is easy to locate the position of the target regions in the time-varying component of SLCR.

Bottom Line: In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse.The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods.The effectiveness of the proposed scheme is illustrated on three data sets.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China.

ABSTRACT
In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data acquisition, besides, it is unable to afford the separated information about the structure and the variation of shape and rate of metabolism which play a major role in improving the visualization of contrast for some requirement of the diagnosing in application. This paper presents a novel low rank-based activity map reconstruction scheme from emission sinograms of dynamic PET, termed as SLCR representing Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging. In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse. The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. In this paper, the linearized alternating direction method is applied. The effectiveness of the proposed scheme is illustrated on three data sets.

No MeSH data available.