TOF capability evaluation on a panel PET for human body imaging

Xiao, Peng; Cao, Xiaopei; Li, Y; Xie, Qingguo

doi:10.1088/1748-0221/8/04/p04001

Cited by 8 publications

(4 citation statements)

References 26 publications

(32 reference statements)

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“…The ratio of the detected events to the total ones was calculated as one entry of the system response matrix. For TOF reconstruction, the entry should be multiplied by a TOF kernel (Xiao et al 2013).…”

Section: Reconstruction Settingmentioning

confidence: 99%

“…LMROS's versatility makes itself adaptive for all kinds of applications. For example, LMROS was implemented in this work for undersampling imaging, which is important in the developments of untraditional systems like partial ring (Surti and Karp 2008, Chen et al 2011, Lee et al 2011 and panel PET (Kao et al 2009, Xiao et al 2013. Four regularization methods were realized for sparse-view and limited-view reconstructions.…”

Section: Introductionmentioning

confidence: 99%

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A regularized relaxed ordered subset list-mode reconstruction algorithm and its preliminary application to undersampling PET imaging

2014

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List mode format is commonly used in modern positron emission tomography (PET) for image reconstruction due to certain special advantages. In this work, we proposed a list mode based regularized relaxed ordered subset (LMROS) algorithm for static PET imaging. LMROS is able to work with regularization terms which can be formulated as twice differentiable convex functions. Such a versatility would make LMROS a convenient and general framework for fulfilling different regularized list mode reconstruction methods. LMROS was applied to two simulated undersampling PET imaging scenarios to verify its effectiveness. Convex quadratic function, total variation constraint, non-local means and dictionary learning based regularization methods were successfully realized for different cases. The results showed that the LMROS algorithm was effective and some regularization methods greatly reduced the distortions and artifacts caused by undersampling.

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Section: Reconstruction Settingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A regularized relaxed ordered subset list-mode reconstruction algorithm and its preliminary application to undersampling PET imaging

2014

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“…For 3D dual-panel scanners, the acquired data violate Orlov's condition, and exact image reconstruction is not possible. However, the TOF information can reduce the artifacts due to incomplete data sampling [58, 63, 36, 60]. The major challenges of stationary dual-panel PET imaging are the limited-angle problem, data truncation and depth-of-interaction (DOI) effects, which can cause artifacts due to incomplete data sampling and anisotropic blurring.…”

Section: Introductionmentioning

confidence: 99%

Fourier rebinning and consistency equations for time-of-flight PET planograms

Defrise

Matej

et al. 2016

Inverse Problems

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Due to the unique geometry, dual-panel PET scanners have many advantages in dedicated breast imaging and on-board imaging applications since the compact scanners can be combined with other imaging and treatment modalities. The major challenges of dual-panel PET imaging are the limited-angle problem and data truncation, which can cause artifacts due to incomplete data sampling. The time-of-flight (TOF) information can be a promising solution to reduce these artifacts. The TOF planogram is the native data format for dual-panel TOF PET scanners, and the non-TOF planogram is the 3D extension of linogram. The TOF planograms is five-dimensional while the objects are three-dimensional, and there are two degrees of redundancy. In this paper, we derive consistency equations and Fourier-based rebinning algorithms to provide a complete understanding of the rich structure of the fully 3D TOF planograms. We first derive two consistency equations and John's equation for 3D TOF planograms. By taking the Fourier transforms, we obtain two Fourier consistency equations and the Fourier-John equation, which are the duals of the consistency equations and John's equation, respectively. We then solve the Fourier consistency equations and Fourier-John equation using the method of characteristics. The two degrees of entangled redundancy of the 3D TOF data can be explicitly elicited and exploited by the solutions along the characteristic curves. As the special cases of the general solutions, we obtain Fourier rebinning and consistency equations (FORCEs), and thus we obtain a complete scheme to convert among different types of PET planograms: 3D TOF, 3D non-TOF, 2D TOF and 2D non-TOF planograms. The FORCEs can be used as Fourier-based rebinning algorithms for TOF-PET data reduction, inverse rebinnings for designing fast projectors, or consistency conditions for estimating missing data. As a byproduct, we show the two consistency equations are necessary and sufficient for 3D TOF planograms. Finally, we give numerical examples of implementation of a fast 2D TOF planogram projector and Fourier-based rebinning for a 2D TOF planograms using the FORCEs to show the efficacy of the Fourier-based solutions.

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“…and systems are becoming increasingly common. It has been well recognized that TOF PET imaging can increase the image signal-to-noise ratio (SNR)[1] [2] and yield other benefits [3]; and the degree of these enhancements is higher as the timing resolution improves. Consequently, PET researchers have been spending significant effort on improving the timing resolution of TOF PET detectors and systems.…”

mentioning

confidence: 99%

Timing optimization for digital PET detector module based on FPGA-only MVT digitizers

Zeng

Liu

et al. 2014

2014 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC)

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Nowadays, time-of-flight (TOF) PET detectors and systems are becoming increasingly common since scintillators, photodetectors and electronics has made significant progress. It has been well recognized that TOF PET imaging can increase the image signal-to-noise ratio (SNR) and yield other benefits; and the degree of these enhancements is higher as the timing resolution improves. Previously we have developed a digital PET detector module using 48-channel FPGA-only MVT digitizer board and measured a coincidence resolving time (CRT) of 684ps (FWHM) and an energy resolution of 15% @511KeV. In this work, we investigate to replace our currently used SiPM with SiPMs that exhibit better timing characteristics. Specifically, we consider SensL FB30035 and Excelitas C30742-33-50C. We set a pair of single channel LYSO/SiPM detectors, first connect their output to an oscilloscope for both of SiPMs under test and measure their performance in different bias voltage. The results indicate that the CRT obtained for the SensL FM30035, SensL FB30035 and Excelitas C30742-33-50C-X are, respectively, 464ps, 310ps and 247ps FWHM. Based on these data sampled by oscilloscope, we also simulate the MVT sampling process and evaluate two common pulse model's suitability for both of SiPMs. Then we reconnect the signal from these SiPMs to our upgraded 72-channel MVT digitizer board. Timing resolution of 357ps and 278ps are achieved respectively by FB30035 and C30742.

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TOF capability evaluation on a panel PET for human body imaging

Cited by 8 publications

References 26 publications

A regularized relaxed ordered subset list-mode reconstruction algorithm and its preliminary application to undersampling PET imaging

A regularized relaxed ordered subset list-mode reconstruction algorithm and its preliminary application to undersampling PET imaging

Fourier rebinning and consistency equations for time-of-flight PET planograms

Timing optimization for digital PET detector module based on FPGA-only MVT digitizers

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