Comparison of maximum likelihood and conventional PET scatter scaling methods for 18F‐FDG and 68Ga‐DOTATATE PET/CT. Issue 8 (28th June 2021)

• Record Type: Journal Article
• Authors: Bal, Harshali; Kiser, Jackson W.; Conti, Maurizio; Bowen, Spencer L.
• Is Part Of: Medical physics. Volume 48:Issue 8(2021)
• Page Start: 4218
• Page End: 4228
• Publication Date: 2021-06-28
• Subjects: image quantification -- iterative reconstruction -- joint estimation -- PET/CT -- scatter correction; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.14988
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : 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 18 F‐FDG ( N = 71) and 68 Ga‐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 18 F‐FDG phantom study. Results: For human subjects 18 F‐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 18 F‐FDG and 68 Ga‐DOTATATE, respectively. For mean SUV analysis, there was a high degree of correlation between the scatter corrections. For 18 F‐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 18 F‐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 should be taken when utilizing ABS for decision‐making based on quantitative metrics.