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Given the constant pressure to increase patient throughput while respecting radiation protection, global body PET image quality (IQ) is not satisfactory in all patients. We first studied the association between IQ and other variables, in particular body habitus, on a digital PET/CT. Second, to improve and homogenize IQ, we evaluated a deep learning PET denoising solution (Subtle PETTM) using convolutional neural networks. We analysed retrospectively in 113 patients visual IQ (by a 5-point Likert score in two readers) and semi-quantitative IQ (by the coefficient of variation in the liver, CVliv) as well as lesion detection and quantification in native and denoised PET. In native PET, visual and semi-quantitative IQ were lower in patients with larger body habitus (p < 0.0001 for both) and in men vs. women (p ≤ 0.03 for CVliv). After PET denoising, visual IQ scores increased and became more homogeneous between patients (4.8 ± 0.3 in denoised vs. 3.6 ± 0.6 in native PET; p < 0.0001). CVliv were lower in denoised PET than in native PET, 6.9 ± 0.9% vs. 12.2 ± 1.6%; p < 0.0001. The slope calculated by linear regression of CVliv according to weight was significantly lower in denoised than in native PET (p = 0.0002), demonstrating more uniform CVliv. Lesion concordance rate between both PET series was 369/371 (99.5%), with two lesions exclusively detected in native PET. SUVmax and SUVpeak of up to the five most intense native PET lesions per patient were lower in denoised PET (p < 0.001), with an average relative bias of −7.7% and −2.8%, respectively. DL-based PET denoising by Subtle PETTM allowed [18F]FDG PET global image quality to be improved and homogenized, while maintaining satisfactory lesion detection and quantification. DL-based denoising may render body habitus adaptive PET protocols unnecessary, and pave the way for the improvement and homogenization of PET modalities. [ABSTRACT FROM AUTHOR]

Purpose: We investigated whether artificial intelligence (AI)-based denoising halves PET acquisition time in digital PET/CT. Methods: One hundred ninety-five patients referred for [18F]FDG PET/CT were prospectively included. Body PET acquisitions were performed in list mode. Original "PET90" (90 s/bed position) was compared to reconstructed ½-duration PET (45 s/bed position) with and without AI-denoising, "PET45AI and PET45". Denoising was performed by SubtlePET™ using deep convolutional neural networks. Visual global image quality (IQ) 3-point scores and lesion detectability were evaluated. Lesion maximal and peak standardized uptake values using lean body mass (SULmax and SULpeak), metabolic volumes (MV), and liver SULmean were measured, including both standard and EARL1 (European Association of Nuclear Medicine Research Ltd) compliant SUL. Lesion-to-liver SUL ratios (LLR) and liver coefficients of variation (CVliv) were calculated. Results: PET45 showed mediocre IQ (scored poor in 8% and moderate in 68%) and lesion concordance rate with PET90 (88.7%). In PET45AI, IQ scores were similar to PET90 (P = 0.80), good in 92% and moderate in 8% for both. The lesion concordance rate between PET90 and PET45AI was 836/856 (97.7%), with 7 lesions (0.8%) only detected in PET90 and 13 (1.5%) exclusively in PET45AI. Lesion EARL1 SULpeak was not significantly different between both PET (P = 0.09). Lesion standard SULpeak, standard and EARL1 SULmax, LLR and CVliv were lower in PET45AI than in PET90 (P < 0.0001), while lesion MV and liver SULmean were higher (P < 0.0001). Good to excellent intraclass correlation coefficients (ICC) between PET90 and PET45AI were observed for lesion SUL and MV (ICC ≥ 0.97) and for liver SULmean (ICC ≥ 0.87). Conclusion: AI allows [18F]FDG PET duration in digital PET/CT to be halved, while restoring degraded ½-duration PET image quality. Future multicentric studies, including other PET radiopharmaceuticals, are warranted. [ABSTRACT FROM AUTHOR]

Spirometric gating devices (SGDs) can measure the respiratory signal with high temporal resolution and accuracy. The primary objective of this study was to assess the feasibility and tolerance of a gated lung PET/CT acquisition using an SGD. The secondary objective was to compare the technical quality, accuracy, and interoperability of the SGD with that of a standard respiratory gating device, Real-Time Position Management (RPM), based on measurement of vertical thoracoabdominal displacement. Methods: A prospective phase I monocentric clinical study was performed on patients undergoing 18F-FDG PET/CT for assessment of a solitary lung nodule, staging of lung malignancy, or planning of radiotherapy. After whole-body PET/CT, a centered gated acquisition of both PET and CT was simultaneously obtained with the SGD and RPM during normal breathing. Results: Of the 46 patients who were included, 6 were prematurely excluded (1 because of hyperglycemia and 5 because of distant metastases revealed by whole-body PET/CT, leading to an unjustified extra gated acquisition). No serious adverse events were observed. Of the 40 remaining patients, the gated acquisition was prematurely stopped in 1 patient because of mask discomfort (2.5%; confidence interval [CI], 0.1%-13.2%). This event was considered patient tolerance failure. The SGD generated accurately gated PET/CT images, with more than 95% of the breathing cycle detected and high temporal resolution, in 34 of the 39 patients (87.2%; 95% CI, 60.0%-100.0%) and failed to generate a biologic tumor volume in 1 of 21 patients with increased 18F-FDG uptake (4.8%; 95% CI, 0.1%-26.5%). The quality and accuracy of respiratory signal detection and synchronization were significantly better than those obtained with RPM (P < 0.05). Conclusion: This trial supports the use of an SGD for gated lung PET/CT because of its high patient tolerance and accuracy. Although this technique seems to technically outperform RPM for gated PET/CT, further assessment of its superiority and the clinical benefit is warranted. We believe that this technique could be used as a gold standard to develop innovative approaches to eliminate respiration-induced blurring artifacts. [ABSTRACT FROM AUTHOR]

Purpose: To investigate whether MRI (RECIST 1.1, WHO criteria and the volumetric approach) or F-FDG PET/CT (PERCIST 1.0) are able to predict long-term outcome in nonsurgical patients with giant cell tumour of the tendon sheath or of the diffuse type (GCT-TS/DT). Methods: Fifteen 'nonsurgical' patients with a histological diagnosis of GCT-TS/DT were divided into two groups: symptomatic patients receiving targeted therapy and asymptomatic untreated patients. All 15 patients were evaluated by MRI of whom 10 were treated, and a subgroup of 7 patients were evaluated by PET/CT of whom 4 were treated. Early evolution was assessed according to MRI and PET/CT scans at baseline and during follow-up. Cohen's kappa coefficient was used to evaluate the degree of agreement between PERCIST 1.0, RECIST 1.1, WHO criteria, volumetric approaches and the reference standard (long-term outcome, delay 505 ± 457 days). The response rate in symptomatic patients with GCT-TS/DT receiving targeted therapy was also assessed in a larger population that included additional patients obtained from a review of the literature. Results: The kappa coefficients for agreement between RECIST/WHO/volumetric criteria and outcome (15 patients) were respectively: 0.35 ( p = 0.06), 0.26 ( p = 0.17) and 0.26 ( p = 0.17). In the PET/CT subgroup (7 patients), PERCIST was in perfect agreement with the late symptomatic evolution (kappa = 1, p < 0.05). In the treated symptomatic group including the additional patients from the literature the response rates to targeted therapies according to late symptomatic assessment, and PERCIST and RECIST criteria were: 65 % (22/34), 77 % (10/13) and 26 % (10/39). Conclusion: F-FDG PET/CT with PERCIST is a promising approach to the prediction of the long-term outcome in GCT-TS/DT and may avoid unnecessary treatments, toxicity and costs. On MRI, WHO and volumetric approaches are not more effective than RECIST using the current thresholds. [ABSTRACT FROM AUTHOR]

Simple Summary: Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters. Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters. [ABSTRACT FROM AUTHOR]

Introduction: New radiotherapy machines such as Halcyon are capable of delivering dose-rate of 600 monitor-units per minute, allowing large numbers of patients treated per day. However, patient-specific quality assurance (QA) is still required, which dramatically decrease machine availability. Innovative artificial intelligence (AI) algorithms could predict QA result based on complexity metrics. However, no AI solution exists for Halcyon machines and the complexity metrics to be used have not been definitively determined. The aim of this study was to develop an AI solution capable of firstly determining the complexity indices to be obtained and secondly predicting patient-specific QA in a routine clinical setting., Methods: Three hundred and eighteen beams from 56 patients with breast cancer were used. The seven complexity indices named Modulation-Complexity-Score (MCS), Small-Aperture-Score (SAS10), Beam-Area (BA), Beam-Irregularity (BI), Beam-Modulation (BM), Gantry and Collimator angles were used as input to the AI model. Machine learning (ML) and deep learning (DL) models using tensorflow were set up to predict DreamDose QA conformance., Results: MCS, BI, gantry and collimator angle are not correlated with QA compliance. Therefore, ML and DL models were trained using SAS10, BA and BM complexity indices. ROC analyses enabled to find best predicted probability threshold to increase specificity and sensitivity. ML models did not show satisfactory performance with an area under-the-curve (AUC) of 0.75 and specificity and sensitivity of 0.88 and 0.86. However, optimised DL model showed better performance with an AUC of 0.95 and specificity and sensitivity of 0.98 and 0.97., Conclusion: The DL model demonstrated a high degree of accuracy in its predictions of the quality assurance (QA) results. Our online predictive QA-platform offers significant time savings in terms of accelerator occupancy and working time., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: The yield per elution of a 68 Ge/ 68 Ga generator decreases during its lifespan. This affects the number of patients injected per elution or the injected dose per patient, thereby negatively affecting the cost of examinations and the quality of PET images due to increased image noise. We aimed to investigate whether AI-based PET denoising can offset this decrease in image quality parameters., Methods: All patients addressed to our PET unit for a 68 Ga-DOTATOC PET/CT from April 2020 to February 2021 were enrolled. Forty-four patients underwent their PET scans according to Protocol&#95;FixedDose (150 MBq) and 32 according to Protocol&#95;WeightDose (1.5 MBq/kg). Protocol&#95;WeightDose examinations were processed using the Subtle PET software (Protocol&#95;WeightDose AI ). Liver and vascular SUV mean were recorded as well as SUVmax, SUVmean and metabolic tumour volume (MTV) of the most intense tumoural lesion and its background SUVmean. Liver and vascular coefficients of variation (CV), tumour-to-background and tumour-to-liver ratios were calculated., Results: The mean injected dose of 2.1 (0.4) MBq/kg per patient was significantly higher in the Protocol&#95;FixedDose group as compared to 1.5 (0.1) MBq/kg for the Protocol&#95;WeightDose group. Protocol&#95;WeightDose led to noisier images than Protocol&#95;FixedDose with higher CVs for liver (15.57% ± 4.32 vs. 13.04% ± 3.51, p = 0.018) and blood-pool (28.67% ± 8.65 vs. 22.25% ± 10.37, p = 0.0003). Protocol&#95;WeightDose AI led to less noisy images than Protocol&#95;WeightDose with lower liver CVs (11.42% ± 3.05 vs. 15.57% ± 4.32, p < 0.0001) and vascular CVs (16.62% ± 6.40 vs. 28.67% ± 8.65, p < 0.0001). Tumour-to-background and tumour-to-liver ratios were lower for protocol&#95;WeightDose AI : 6.78 ± 3.49 vs. 7.57 ± 4.73 ( p = 0.01) and 5.96 ± 5.43 vs. 6.77 ± 6.19 ( p < 0.0001), respectively. MTVs were higher after denoising whereas tumour SUVmax were lower: the mean% differences in MTV and SUVmax were + 11.14% (95% CI = 4.84-17.43) and -3.92% (95% CI = -6.25 to -1.59)., Conclusion: The degradation of PET image quality due to a reduction in injected dose at the end of the 68 Ge/ 68 Ga generator lifespan can be effectively counterbalanced by using AI-based PET denoising., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Quak, Weyts, Jaudet, Prigent, Foucras and Lasnon.)

Spirometric gating devices (SGDs) can measure the respiratory signal with high temporal resolution and accuracy. The primary objective of this study was to assess the feasibility and tolerance of a gated lung PET/CT acquisition using an SGD. The secondary objective was to compare the technical quality, accuracy, and interoperability of the SGD with that of a standard respiratory gating device, Real-Time Position Management (RPM), based on measurement of vertical thoracoabdominal displacement. Methods: A prospective phase I monocentric clinical study was performed on patients undergoing 18 F-FDG PET/CT for assessment of a solitary lung nodule, staging of lung malignancy, or planning of radiotherapy. After whole-body PET/CT, a centered gated acquisition of both PET and CT was simultaneously obtained with the SGD and RPM during normal breathing. Results: Of the 46 patients who were included, 6 were prematurely excluded (1 because of hyperglycemia and 5 because of distant metastases revealed by whole-body PET/CT, leading to an unjustified extra gated acquisition). No serious adverse events were observed. Of the 40 remaining patients, the gated acquisition was prematurely stopped in 1 patient because of mask discomfort (2.5%; confidence interval [CI], 0.1%-13.2%). This event was considered patient tolerance failure. The SGD generated accurately gated PET/CT images, with more than 95% of the breathing cycle detected and high temporal resolution, in 34 of the 39 patients (87.2%; 95% CI, 60.0%-100.0%) and failed to generate a biologic tumor volume in 1 of 21 patients with increased 18 F-FDG uptake (4.8%; 95% CI, 0.1%-26.5%). The quality and accuracy of respiratory signal detection and synchronization were significantly better than those obtained with RPM ( P < 0.05). Conclusion: This trial supports the use of an SGD for gated lung PET/CT because of its high patient tolerance and accuracy. Although this technique seems to technically outperform RPM for gated PET/CT, further assessment of its superiority and the clinical benefit is warranted. We believe that this technique could be used as a gold standard to develop innovative approaches to eliminate respiration-induced blurring artifacts., (© 2019 by the Society of Nuclear Medicine and Molecular Imaging.)