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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 the cardiac phantoms in the count Level 1.From the top to bottom, the first line is the images reconstructed by ML-EM, the second line is the images of the sum of stationary and time-varying components of SLCR (ST + SP), the third line is the images of the stationary component (ST) of SLCR, and the last line is the images of the time-varying component (SP) of SLCR.
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pone.0142019.g008: The reconstruction images for the cardiac phantoms in the count Level 1.From the top to bottom, the first line is the images reconstructed by ML-EM, the second line is the images of the sum of stationary and time-varying components of SLCR (ST + SP), the third line is the images of the stationary component (ST) of SLCR, and the last line is the images of the time-varying component (SP) of SLCR.

Mentions: In the third experiment, a series of the cardiac phantoms with respected to the short axis of cardiac in stress (only five frames were shown in the results due to the limitation of the number of pages) were simulated. And the region selected for quantitative analysis is marked by the black rectangle in the 5th picture. Such a highlight area always indicates a potential lesions or abnormal tissue in clinical situation. These cardiac phantoms were based on a 61-year-old patient with arterial hypertensionand type 2 diabetes mellitus. A distinctive shape deformations (volume variation and myocardial wall motion) were included in these data sets. The main purpose of this experiment was to evaluate the effectiveness of the motion extraction of the SLCR method. The radioactivity tracer was 13N − Ammonia. In addition, to evaluate the robustness of the SLCR method, two count levels were simulated. The proportions of the scatter and random events in these two levels were recorded, level 1: the total count is 5.4 × 106, and it contains 0.1% scatter events and 0.06% random events. level 2: the total count is 1.2 × 105 (low count data), and it contains 19.54% scatter events and 1.2% random events. Similar to the former two experiments, the ML-EM method is implemented as the comparison. The truth images sequences of the cardiac phantom are given in Fig 7. The images reconstructed by the ML-EM and SLCR in different count levels have been shown in Fig 8 (Level 1) and Fig 9 (Level 2), respectively. From these figures, two conclusions could be concluded:


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 the cardiac phantoms in the count Level 1.From the top to bottom, the first line is the images reconstructed by ML-EM, the second line is the images of the sum of stationary and time-varying components of SLCR (ST + SP), the third line is the images of the stationary component (ST) of SLCR, and the last line is the images of the time-varying component (SP) of SLCR.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0142019.g008: The reconstruction images for the cardiac phantoms in the count Level 1.From the top to bottom, the first line is the images reconstructed by ML-EM, the second line is the images of the sum of stationary and time-varying components of SLCR (ST + SP), the third line is the images of the stationary component (ST) of SLCR, and the last line is the images of the time-varying component (SP) of SLCR.
Mentions: In the third experiment, a series of the cardiac phantoms with respected to the short axis of cardiac in stress (only five frames were shown in the results due to the limitation of the number of pages) were simulated. And the region selected for quantitative analysis is marked by the black rectangle in the 5th picture. Such a highlight area always indicates a potential lesions or abnormal tissue in clinical situation. These cardiac phantoms were based on a 61-year-old patient with arterial hypertensionand type 2 diabetes mellitus. A distinctive shape deformations (volume variation and myocardial wall motion) were included in these data sets. The main purpose of this experiment was to evaluate the effectiveness of the motion extraction of the SLCR method. The radioactivity tracer was 13N − Ammonia. In addition, to evaluate the robustness of the SLCR method, two count levels were simulated. The proportions of the scatter and random events in these two levels were recorded, level 1: the total count is 5.4 × 106, and it contains 0.1% scatter events and 0.06% random events. level 2: the total count is 1.2 × 105 (low count data), and it contains 19.54% scatter events and 1.2% random events. Similar to the former two experiments, the ML-EM method is implemented as the comparison. The truth images sequences of the cardiac phantom are given in Fig 7. The images reconstructed by the ML-EM and SLCR in different count levels have been shown in Fig 8 (Level 1) and Fig 9 (Level 2), respectively. From these figures, two conclusions could be concluded:

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.