Scatter correction with combined single-scatter simulation and Monte Carlo simulation for 3D PET

Ye, Jinghan; Song, Xiyun; Hu, Zhiqiang

doi:10.1109/nssmic.2014.7431033

Cited by 20 publications

(38 citation statements)

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“…MCS-SSS is a combination of SSS with a technique for scaling the results of SSS using the scaling factor calculated by MCS (12,13,16). Here, SSS estimates the scatter contribution from a source distribution map in combination with an attenuation map.…”

Section: Overview Of Mcs-sssmentioning

confidence: 99%

“…A new scatter-correction method based on Monte Carlo simulation (MCS) was developed in 2014 (12). It combines single-scatter simulation (SSS) for estimating the scatter contribution with MCS (MCS-SSS) for calculating the scaling factor.…”

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confidence: 99%

“…It combines single-scatter simulation (SSS) for estimating the scatter contribution with MCS (MCS-SSS) for calculating the scaling factor. In 2 studies, artifacts were successfully reduced on whole-body 18 F-FDG PET images with MCS-SSS, when the imaged object was large with respect to the FOV of the PET (12,13). Unlike TFS-SSS, MCS-SSS does not require CT images for identification of the boundaries.…”

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confidence: 99%

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Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with ¹⁵O-Gas Inhalation

et al. 2017

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In 3-dimensional PET/CT imaging of the brain with O-gas inhalation, high radioactivity in the face mask creates cold artifacts and affects the quantitative accuracy when scatter is corrected by conventional methods (e.g., single-scatter simulation [SSS] with tail-fitting scaling [TFS-SSS]). Here we examined the validity of a newly developed scatter-correction method that combines SSS with a scaling factor calculated by Monte Carlo simulation (MCS-SSS). We performed phantom experiments and patient studies. In the phantom experiments, a plastic bottle simulating a face mask was attached to a cylindric phantom simulating the brain. The cylindric phantom was filled with F-FDG solution (3.8-7.0 kBq/mL). The bottle was filled with nonradioactive air or various levels ofF-FDG (0-170 kBq/mL). Images were corrected either by TFS-SSS or MCS-SSS using the CT data of the bottle filled with nonradioactive air. We compared the image activity concentration in the cylindric phantom with the true activity concentration. We also performed O-gas brain PET based on the steady-state method on patients with cerebrovascular disease to obtain quantitative images of cerebral blood flow and oxygen metabolism. In the phantom experiments, a cold artifact was observed immediately next to the bottle on TFS-SSS images, where the image activity concentrations in the cylindric phantom were underestimated by 18%, 36%, and 70% at the bottle radioactivity levels of 2.4, 5.1, and 9.7 kBq/mL, respectively. At higher bottle radioactivity, the image activity concentrations in the cylindric phantom were greater than 98% underestimated. For the MCS-SSS, in contrast, the error was within 5% at each bottle radioactivity level, although the image generated slight high-activity artifacts around the bottle when the bottle contained significantly high radioactivity. In the patient imaging with O and CO inhalation, cold artifacts were observed on TFS-SSS images, whereas no artifacts were observed on any of the MCS-SSS images. MCS-SSS accurately corrected the scatters inO-gas brain PET when the 3-dimensional acquisition mode was used, preventing the generation of cold artifacts, which were observed immediately next to a face mask on TFS-SSS images. The MCS-SSS method will contribute to accurate quantitative assessments.

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Section: Overview Of Mcs-sssmentioning

confidence: 99%

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Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with ¹⁵O-Gas Inhalation

et al. 2017

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“…For both cameras, images were recorded in 3D mode with a 576 mm × 576 mm field of view and reconstructed with 2 mm voxels using an ordered subset expectation maximization (OSEM) algorithm [20] associated with (i) attenuation corrections provided by conventional CT-based methods, (ii) a scatter correction based on a Monte Carlo simulation [21], (iii) a random subtraction with smoothed delayed coincidence sinogram [22], (iv) a component-based normalization of the non-uniform response of PET detectors [23], and (v) a regularized version [24] of the Richardson-Lucy algorithm [25,26] for resolution recovery with 1 iteration and 6-mm regularization kernel, with these parameters maintained unchanged for all experiments. No image filtering was currently applied and the relaxation parameter was set to 1.0, the latter controlling the magnitude of the image change induced by each iteration.…”

Section: Pet-ct Systemsmentioning

confidence: 99%

Head-to-head comparison between digital and analog PET of human and phantom images when optimized for maximizing the signal-to-noise ratio from small lesions

et al. 2020

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Background: Routine PET exams are increasingly performed with reduced injected activities, leading to the use of different image reconstruction parameters than the NEMA parameters, in order to prevent from any deleterious decrease in signal-tonoise ratio (SNR) and thus, in lesion detectability. This study aimed to provide a global head-to-head comparison between digital (Vereos, Philips®) and analog (Ingenuity TF, Philips®) PET cameras of the trade-off between SNR and contrast through a wide-ranging number of reconstruction iterations, and with a further reconstruction optimization based on the SNR of small lesions.Methods: Image quality parameters were compared between the two cameras on human and phantom images for a number of OSEM reconstruction iterations ranging from 1 to 10, the number of subsets being fixed at 10, and with the further identification of reconstruction parameters maximizing the SNR of spheres and adenopathies nearing 10 mm in diameter. These reconstructions were additionally obtained with and without time-of-flight (TOF) information (TOF and noTOF images, respectively) for further comparisons.Results: On both human and phantom TOF images, the compromise between SNR and contrast was consistently more advantageous for digital than analog PET, with the difference being particularly pronounced for the lowest numbers of iterations and the smallest spheres. SNR was maximized with 1 and 2 OSEM iterations for the TOF images from digital and analog PET, respectively, whereas 4 OSEM iterations were required for the corresponding noTOF images from both cameras. On the TOF images obtained with this SNR optimization, digital PET exhibited a 37% to 44% higher SNR as compared with analog PET, depending on sphere size. These relative differences were however much lower for the noTOF images optimized for SNR (− 4 to + 18%), as well as for images reconstructed according to NEMA standards (− 4 to + 12%).Conclusion: SNR may be dramatically higher for digital PET than for analog PET, especially when optimized for small lesions. This superiority is mostly attributable to enhanced TOF resolution and is significantly underestimated in NEMA-based analyses.

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“…Other methods of scatter correction involve performing a Monte Carlo simulation to simulate the amount of scatter, but this is much slower than the analytical methods. [118,119] Without a doubt, scatter correction is one of the most difficult PET corrections to implement, due to the difficulty of accurately modeling and determining scatter in the patient, which in theory requires a priori knowledge of both the scattering medium and the emission distribution.…”

Section: Blue Line Is the Recorded Line Of Responsementioning

confidence: 99%

A Study of the Prevalence of Patient Body Motion and Its Subsequent Correction by Projection Consistency Conditions

Hunter¹

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Patient motion is a common problem during dynamic PET scans for quantification of myocardial blood flow (MBF). The purpose of this study was to quantify the prevalence of body motion in a clinical setting, and evaluate with realistic phantoms the effects of motion on blood flow quantification, including CT attenuation correction artifacts that result from PET-CT misalignment. In addition we evaluated a consistency-based motion correction technique using noise added simulations.A cohort of 236 sequential patients was analyzed for patient motion under resting and peak stress conditions by two independent observers. The presence of motion, affected time frames, and direction of motion was recorded. Based on these results, patient body motion effects on MBF quantification were characterized using the digital NURBS-based Cardiac-Torso (NCAT) phantom, with characteristic time activity curves (TAC) assigned to the heart wall (myocardium) and blood regions. Noise was added to sinograms using the analytical simulator ASIM.In the patient cohort mild motion 0.5 ± 0.1 cm occurred in 24%, and severe motion 1.0 ± 0.3 cm occurred in 38% of patients. Motion in the superior/inferior direction accounted for 45% of all detected motion. Anterior/posterior direction motion accounted for 29%, and left/right motion occurred in 24% of cases. Computer simulation studies indicated that errors in MBF can approach 500% for scans with severe patient motion (up to 2 cm). The largest errors occurred when the heart wall was shifted left towards the adjacent lung region. Body motion effects were more detrimental for higher iii resolution PET imaging (2 vs 10 mm FWHM), for the physically smaller phantom, and for motion occurring during the mid-to-late time frames. Motion correction of the reconstructed dynamic image series resulted in significant reduction in MBF errors. MBF bias was reduced further using global partial-volume correction, and using dynamic alignment of the PET projection data to the CT scan for accurate attenuation correction during image reconstruction.To reduce MBF errors, new motion correction algorithms must be effective in identifying motion in the left/right direction, and in the mid-to-late time frames, since these conditions produce the largest errors in MBF. motion effects on the quantification of regional myocardial blood flow with dynamic PET imaging. Patient body motion affects myocardial blood flow quantification with rubidium-82 PET imaging. J Nucl Med, 2013; 54:541 Chad Hunter, Ran Klein, and Robert deKemp, Correction for patient body motion in Rb-82 PET-CT: An NCAT simulation study. J Nucl Med, 2014; 55:2054 Chad Hunter, Adam Alessio, and Robert deKemp, Projection based patient body motion correction in Rb-82 PET-CT using consistency condition. J Nucl Med, 2015; 56:486 vi

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Scatter correction with combined single-scatter simulation and Monte Carlo simulation for 3D PET

Cited by 20 publications

References 4 publications

Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with ¹⁵O-Gas Inhalation

Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with ¹⁵O-Gas Inhalation

Head-to-head comparison between digital and analog PET of human and phantom images when optimized for maximizing the signal-to-noise ratio from small lesions

A Study of the Prevalence of Patient Body Motion and Its Subsequent Correction by Projection Consistency Conditions

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Scatter correction with combined single-scatter simulation and Monte Carlo simulation for 3D PET

Cited by 20 publications

References 4 publications

Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with 15O-Gas Inhalation

Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with 15O-Gas Inhalation

Head-to-head comparison between digital and analog PET of human and phantom images when optimized for maximizing the signal-to-noise ratio from small lesions

A Study of the Prevalence of Patient Body Motion and Its Subsequent Correction by Projection Consistency Conditions

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Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with ¹⁵O-Gas Inhalation

Scatter Correction with Combined Single-Scatter Simulation and Monte Carlo Simulation Scaling Improved the Visual Artifacts and Quantification in 3-Dimensional Brain PET/CT Imaging with ¹⁵O-Gas Inhalation