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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 profile of the reconstruction result.The reconstruction profiles through the marked lines (blue line) in Fig 3.
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pone.0142019.g005: The profile of the reconstruction result.The reconstruction profiles through the marked lines (blue line) in Fig 3.

Mentions: The data set based on Hoffman brain phantom (Fig 3) was simulated by Monte Carlo simulation in the second experiment. This phantom contains complicated physical structural information and eight highlight areas, the areas marked by red and green rectangle are target areas which present two tumors in human brain, and the reconstruction pixel value of these regions are used for quantitative analysis. The blue line marks the lateral displacement profile. The spatial resolution of the simulated scanner was 3.5 mm full width at half maximum (FWHM) in sagittal or coronal plane and 3.2 mm FWHM in axial plane. The radioactivity tracer was fluorodeoxyglucose (FDG), and the concentration was 333–444 MBq (9–12 mCi/cc). The total scanning time was 30 mins. The data set was divided into 20 frames. The total count of the recorded event was 1.92 × 107 (count level 1) in this data set, the proportion of the scatter events was 0.12%, and the proportion of the random events was 0.063%. The images reconstructed by ML-EM and SLCR (the sum of two components of SLCR (ST + SP), the stationary (ST) and time-varying (SP) components) methods for the #10 frames are shown in Fig 4. And Fig 5 shows the profiles of reconstruction results by ML-EM and SLCR compared with the ground truth. For a fair comparison, the sum of stationary and time-varying components was used in this profiles. It is clear that the SLCR gives the closer fit to ground truth. In Fig 4, the stationary component extracts the background of brain phantom data set, and the time-varying component extracts the dynamic information. It lists the bias and variances and CRC of images reconstructed by ML-EM and SLCR in Table 1. The calculated biases and variances shows that the SLCR provides a more accurate reconstruction than ML-EM. The values of CRC shows that the decomposition of SLCR results in improving the contrast in time-varying component.


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 profile of the reconstruction result.The reconstruction profiles through the marked lines (blue line) in Fig 3.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0142019.g005: The profile of the reconstruction result.The reconstruction profiles through the marked lines (blue line) in Fig 3.
Mentions: The data set based on Hoffman brain phantom (Fig 3) was simulated by Monte Carlo simulation in the second experiment. This phantom contains complicated physical structural information and eight highlight areas, the areas marked by red and green rectangle are target areas which present two tumors in human brain, and the reconstruction pixel value of these regions are used for quantitative analysis. The blue line marks the lateral displacement profile. The spatial resolution of the simulated scanner was 3.5 mm full width at half maximum (FWHM) in sagittal or coronal plane and 3.2 mm FWHM in axial plane. The radioactivity tracer was fluorodeoxyglucose (FDG), and the concentration was 333–444 MBq (9–12 mCi/cc). The total scanning time was 30 mins. The data set was divided into 20 frames. The total count of the recorded event was 1.92 × 107 (count level 1) in this data set, the proportion of the scatter events was 0.12%, and the proportion of the random events was 0.063%. The images reconstructed by ML-EM and SLCR (the sum of two components of SLCR (ST + SP), the stationary (ST) and time-varying (SP) components) methods for the #10 frames are shown in Fig 4. And Fig 5 shows the profiles of reconstruction results by ML-EM and SLCR compared with the ground truth. For a fair comparison, the sum of stationary and time-varying components was used in this profiles. It is clear that the SLCR gives the closer fit to ground truth. In Fig 4, the stationary component extracts the background of brain phantom data set, and the time-varying component extracts the dynamic information. It lists the bias and variances and CRC of images reconstructed by ML-EM and SLCR in Table 1. The calculated biases and variances shows that the SLCR provides a more accurate reconstruction than ML-EM. The values of CRC shows that the decomposition of SLCR results in improving the contrast in time-varying component.

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.