Assessment of Quantification Accuracy with ML Scatter Scaling for Variable Count Statistics

Bal, Harshali; Panin, Vladimir; Conti, Maurizio

doi:10.1109/nss/mic42101.2019.9059677

Cited by 2 publications

(8 citation statements)

References 11 publications

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“…The MLBS algorithm is detailed by Bal et al 14 . Briefly, the algorithm (a) employs an alternating iterative reconstruction that consists of a nested scatter sinogram scaling factor estimation step and a TOF‐based ML activity image update allowing negative values (NEG‐ML) and (b) utilizes both TOF and 3D sinogram information in the scatter scaling step.…”

Section: Methodsmentioning

confidence: 99%

“…The MLBS algorithm is detailed by Bal et al 14 Briefly, the algorithm (a) employs an alternating iterative reconstruction that consists of a nested scatter sinogram scaling factor estimation step and a TOF-based ML activity image update allowing negative values (NEG-ML) and (b) utilizes both TOF and 3D sinogram information in the scatter scaling step. Both the total NEG-ML reconstruction iterations and the nested updates for scatter factor scales were adjusted based on convergence criteria for the scatter scale factors.…”

Section: B1 Scatter Correctionmentioning

confidence: 99%

“…Prior MLBS approaches have been developed to obtain 2D axial scaling factors for SSS based scatter estimates with rebinned 2D data and 3D time-of-flight (TOF) data. 8,11,12 Recently, an MLBS algorithm was developed 13,14 based on the approach by Panin et al, 8 but adapting the work of Rezaei et al 12 to include three-dimensional and TOF information in the solution. Preliminary phantom and limited patient test datasets suggest that the proposed MLBS algorithm outperforms TFBS and ABS approaches.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of maximum likelihood and conventional PET scatter scaling methods for ¹⁸F‐FDG and ⁶⁸Ga‐DOTATATE PET/CT

et al. 2021

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Purpose We aim to quantify differences between a new maximum likelihood (ML) background scaling (MLBS) algorithm and two conventional scatter scaling methods for clinical PET/CT. A common source of reduced image quantification with conventional scatter corrections is attributed to erroneous scaling of the initial scatter estimate to match acquired scattered events in the sinogram. MLBS may have performance advantages over conventional methods by using all available data intersecting the subject. Methods A retrospective analysis was performed on subjects injected with 18F‐FDG (N = 71) and 68Ga‐DOTATATE (N = 11) and imaged using time‐of‐flight (TOF) PET/CT. The scatter distribution was estimated with single scatter simulation approaches. Conventional scaling algorithms included (a) tail fitted background scaling (TFBS), which scales the scatter to “tails” outside the emission support, and (b) absolute scatter correction (ABS), which utilizes the simulated scatter distribution with no scaling applied. MLBS consisted of an alternating iterative reconstruction with a TOF‐based ML activity image update allowing negative values (NEG‐ML) and nested loop ML scatter scaling estimation. Scatter corrections were compared using reconstructed images as follows: (a) normalized relative difference images were generated and used for voxel‐wise analysis, (b) liver and suspected lesion ROIs were drawn to compute mean SUVs, and (c) a qualitative analysis of overall diagnostic image quality, impact of artifacts, and lesion conspicuity was performed. Absolute quantification and normalized relative differences were also assessed with an 18F‐FDG phantom study. Results For human subjects 18F‐FDG data, Bland‐Altman plots demonstrated that the largest normalized voxel‐wise differences were observed close to the lower limit (SUV = 1.0). MLBS reconstructions trended towards higher scatter fractions compared to TFBS and ABS images, with median voxel differences across all subjects for TFBS‐MLBS measured at 1.7% and 7.6% for 18F‐FDG and 68Ga‐DOTATATE, respectively. For mean SUV analysis, there was a high degree of correlation between the scatter corrections. For 18F‐FDG, ABS scatter correction reconstructions trended towards higher liver mean SUVs relative to MLBS. The qualitative image analysis revealed no significant differences between TFBS and MLBS image reconstructions. For a uniformly filled relatively large 37 cm diameter phantom, MLBS produced the lowest bias in absolute quantification, while normalized voxel‐wise differences showed a trend in scatter correction performance consistent with the human subjects study. Conclusions For 18F‐FDG, MLBS is at least a valid substitute to TFBS, providing reconstructed image performance comparable to TFBS in most subjects but exhibiting quantitative differences in cases where TFBS is typically prone to inaccuracies (e.g., due to patient motion and CT‐based attenuation map truncation). Particularly for low contrast regions, quantification differs for ABS compared to MLBS and TFBS, and caution shoul...

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Section: Methodsmentioning

confidence: 99%

Section: B1 Scatter Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of maximum likelihood and conventional PET scatter scaling methods for ¹⁸F‐FDG and ⁶⁸Ga‐DOTATATE PET/CT

et al. 2021

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“…Accurate scatter correction of PET data is a necessity to obtain image quantification that improves the diagnostic quality of the resulting images [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ]. Widely used scatter-scaling algorithms include tail-fitted scatter scaling (TFSS) and absolute scatter correction (ABS), both of which are based on a scatter distribution estimate calculated from a single scatter simulation (SSS) [ 9 , 10 , 11 , 12 ]. Scatter scaling by the use of TFSS subsequently rescales the scatter estimate and prompt gamma (PG) distribution following the tails of the scatter distributions based on the sinograms.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, TFSS may also lead to the overcorrection of scatter in cases of poor count statistics in the sinogram tail region. This is observed for receptor-based tracers and/or tracers with high specific uptake, such as 68 Ga-labeled prostate-specific membrane ([ 68 Ga]Ga-PSMA-11) [ 10 , 13 ]. Examples of tracers that possess high specificity are 68 Ga-NODAGA-E[c(RGDyK)]2 ([ 68 Ga]Ga-RGD) [ 16 ] and 68 Ga-urokinase-Plasminogen-Activator-Receptor ([ 68 Ga]Ga-uPAR) [ 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

Improved Positron Emission Tomography Quantification: Evaluation of a Maximum-Likelihood Scatter Scaling Algorithm

Overbeck,

Ahangari,

Conti

et al. 2024

Diagnostics

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Incorrect scatter scaling of positron emission tomography (PET) images can lead to halo artifacts, quantitative bias, or reconstruction failure. Tail-fitted scatter scaling (TFSS) possesses performance limitations in multiple cases. This study aims to investigate a novel method for scatter scaling: maximum-likelihood scatter scaling (MLSS) in scenarios where TFSS tends to induce artifacts or are observed to cause reconstruction abortion. [68Ga]Ga-RGD PET scans of nine patients were included in cohort 1 in the scope of investigating the reduction of halo artifacts relative to the scatter estimation method. PET scans of 30 patients administrated with [68Ga]Ga-uPAR were included in cohort 2, used for an evaluation of the robustness of MLSS in cases where TFSS-integrated reconstructions are observed to fail. A visual inspection of MLSS-corrected images scored higher than TFSS-corrected reconstructions of cohort 1. The quantitative investigation near the bladder showed a relative difference in tracer uptake of up to 94.7%. A reconstruction of scans included in cohort 2 resulted in failure in 23 cases when TFSS was used. The lesion uptake values of cohort 2 showed no significant difference. MLSS is suggested as an alternative scatter-scaling method relative to TFSS with the aim of reducing halo artifacts and a robust reconstruction process.

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Assessment of Quantification Accuracy with ML Scatter Scaling for Variable Count Statistics

Cited by 2 publications

References 11 publications

Comparison of maximum likelihood and conventional PET scatter scaling methods for ¹⁸F‐FDG and ⁶⁸Ga‐DOTATATE PET/CT

Comparison of maximum likelihood and conventional PET scatter scaling methods for ¹⁸F‐FDG and ⁶⁸Ga‐DOTATATE PET/CT

Improved Positron Emission Tomography Quantification: Evaluation of a Maximum-Likelihood Scatter Scaling Algorithm

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Assessment of Quantification Accuracy with ML Scatter Scaling for Variable Count Statistics

Cited by 2 publications

References 11 publications

Comparison of maximum likelihood and conventional PET scatter scaling methods for 18F‐FDG and 68Ga‐DOTATATE PET/CT

Comparison of maximum likelihood and conventional PET scatter scaling methods for 18F‐FDG and 68Ga‐DOTATATE PET/CT

Improved Positron Emission Tomography Quantification: Evaluation of a Maximum-Likelihood Scatter Scaling Algorithm

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Comparison of maximum likelihood and conventional PET scatter scaling methods for ¹⁸F‐FDG and ⁶⁸Ga‐DOTATATE PET/CT

Comparison of maximum likelihood and conventional PET scatter scaling methods for ¹⁸F‐FDG and ⁶⁸Ga‐DOTATATE PET/CT