New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies

Akamatsu, Go; Shimada, Naoki; Matsumoto, Keīichi; Daisaki, Hiromitsu; Suzuki, Kazufumi; Watabe, Hiroshi; Oda, Kazuhiro; Senda, Michio; Terauchi, Takashi; Tateishi, Ukihide

doi:10.1007/s12149-021-01709-1

Cited by 10 publications

(7 citation statements)

References 75 publications

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“…The fit parameter c models the proportionality between axial position and number of events (can be interpreted as the slope of the sensitivity profile). The fit is used to determine at which axial position the CV exceeds the set threshold for clinical acceptable noise of 15% as defined by [21][22][23][24]. The useful aFOV in this manuscript is defined as the axial extend for which this threshold is not exceeded.…”

Section: Image Noisementioning

confidence: 99%

Image quality assessment along the one metre axial field-of-view of the total-body Biograph Vision Quadra PET/CT system for 18F-FDG

et al. 2022

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Aim Recently, total-body PET/CT systems with an extended axial field-of-view (aFOV) became commercially available which allow acquiring physiologic information of multiple organs simultaneously. However, the nominal aFOV may clinically not be used effectively due to the inherently reduced sensitivity at the distal ends of the aFOV. The aim of this study was to assess the extent of the useful aFOV of the Biograph Vision Quadra PET/CT system. Methods A NEMA image quality (IQ) phantom mimicking a standard [18F]FDG examination was used. Image contrast and noise were assessed across the 106 cm aFOV of the Biograph Vision Quadra PET/CT system (Siemens Healthineers). Phantom acquisitions were performed at different axial positions. PET data were rebinned to simulate different acquisition times for a standard injected activity and reconstructed using different filter settings to evaluate the noise and images along the axial direction. Results Image noise and contrast were stable within the central 80 cm of the aFOV. Outside this central area, image contrast variability as well as image noise increased. This degradation of IQ was in particular evident for short acquisition times of less than 30 s. At 10 min acquisition time and in the absence of post-reconstruction filtering, the useful aFOV was 100 cm. For a 2 min acquisition time, a useful aFOV with image noise below 15% was only achievable using Gaussian filtering with axial extents of between 83 and 103 cm when going from 2 to 6 mm full-width-half-maximum, respectively. Conclusion Image noise increases substantially towards the ends of the aFOV. However, good IQ in compliance with generally accepted benchmarks is achievable for an aFOV of > 90 cm. When accepting higher image noise or using dedicated protocol settings such as stronger filtering a useful aFOV of around 1 m can be achieved for a 2 min acquisition time.

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Section: Image Noisementioning

confidence: 99%

Image quality assessment along the one metre axial field-of-view of the total-body Biograph Vision Quadra PET/CT system for 18F-FDG

et al. 2022

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“…For SUV harmonization, the SUVmax of hot spheres must satisfy the specified range (Fig. 3 B) [ 33 , 42 ]. A post-smoothing process was sometimes needed for high-resolution systems to meet this range, just as for the EARL limits.…”

Section: Introductionmentioning

confidence: 99%

“…A post-smoothing process was sometimes needed for high-resolution systems to meet this range, just as for the EARL limits. In 2022, a JSNM working group proposed new upper and lower ranges for SUVmax recovery curves [ 33 ]. This SUV harmonization range was proposed based on NEMA NU-2 image quality phantom data measured with 23 PET/CT scanners.…”

Section: Introductionmentioning

confidence: 99%

A review of harmonization strategies for quantitative PET

et al. 2023

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PET can reveal in vivo biological processes at the molecular level. PET-derived quantitative values have been used as a surrogate marker for clinical decision-making in numerous clinical studies and trials. However, quantitative values in PET are variable depending on technical, biological, and physical factors. The variability may have a significant impact on a study outcome. Appropriate scanner calibration and quality control, standardization of imaging protocols, and any necessary harmonization strategies are essential to make use of PET as a biomarker with low bias and variability. This review summarizes benefits, limitations, and remaining challenges for harmonization of quantitative PET, including whole-body PET in oncology, brain PET in neurology, PET/MR, and non-18F PET imaging. This review is expected to facilitate harmonization of quantitative PET and to promote the contribution of PET-derived biomarkers to research and development in medicine.

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“…Despite all the efforts and initiatives, there are no universally accepted standard guidelines for harmonizing brain PET images in multi-center studies that encompass phantom selection, data acquisition, post-processing steps, and harmonization contrasts with the situation in oncological whole-body PET where harmonization standards and accreditation programs exist for accurate and reproducible PET outcomes [ 20 – 22 ]. Harmonization of oncological PET images is done using either NEMA NU2 anthropomorphic or Deluxe Jaszczak phantoms, with fillable spherical inserts representative of elevated focal uptakes.…”

Section: Introductionmentioning

confidence: 99%

Harmonization of brain PET images in multi-center PET studies using Hoffman phantom scan

Shekari,

Verwer,

Yaqub

et al. 2023

EJNMMI Phys

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Background Image harmonization has been proposed to minimize heterogeneity in brain PET scans acquired in multi-center studies. However, standard validated methods and software tools are lacking. Here, we assessed the performance of a framework for the harmonization of brain PET scans in a multi-center European clinical trial. Method Hoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using “Hoffman 3D brain Analysis tool,” indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method’s robustness against typical dose-calibrator-based errors was assessed. Results The EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040–0.012), and Contrast SD (0.14–0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR. Conclusion Harmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.

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New standards for phantom image quality and SUV harmonization range for multicenter oncology PET studies

Cited by 10 publications

References 75 publications

Image quality assessment along the one metre axial field-of-view of the total-body Biograph Vision Quadra PET/CT system for 18F-FDG

Image quality assessment along the one metre axial field-of-view of the total-body Biograph Vision Quadra PET/CT system for 18F-FDG

A review of harmonization strategies for quantitative PET

Harmonization of brain PET images in multi-center PET studies using Hoffman phantom scan

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