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The Brain Tumor Segmentation (BraTS) Challenge has been a main driver of the development of deep learning (DL) algorithms and provides by far the largest publicly available expert-annotated brain tumour dataset but contains solely preoperative examinations. The aim of our study was to facilitate the use of the BraTS dataset for training DL brain tumour segmentation algorithms for a postoperative setting. To this end, we introduced an automatic conversion of the three-label BraTS annotation protocol to a two-label annotation protocol suitable for postoperative brain tumour segmentation. To assess the viability of the label conversion, we trained a DL algorithm using both the three-label and the two-label annotation protocols. We assessed the models pre- and postoperatively and compared the performance with a state-of-the-art DL method. The DL algorithm trained using the BraTS three-label annotation misclassified parts of 10 out of 41 fluid-filled resection cavities in 72 postoperative glioblastoma MRIs, whereas the two-label model showed no such inaccuracies. The tumour segmentation performance of the two-label model both pre- and postoperatively was comparable to that of a state-of-the-art algorithm for tumour volumes larger than 1 cm 3 . Our study enables using the BraTS dataset as a basis for the training of DL algorithms for postoperative tumour segmentation.

Artificial intelligence (AI) may decrease 18 F - FDG PET/CT-based gross tumor volume (GTV) delineation variability and automate tumor-volume-derived image biomarker extraction. Hence, we aimed to identify and evaluate promising state-of-the-art deep learning methods for head and neck cancer (HNC) PET GTV delineation. Methods: We trained and evaluated deep learning methods using retrospectively included scans of HNC patients referred for radiotherapy between January 2014 and December 2019 (ISRCTN16907234). We used 3 test datasets: an internal set to compare methods, another internal set to compare AI-to-expert variability and expert interobserver variability (IOV), and an external set to compare internal and external AI-to-expert variability. Expert PET GTVs were used as the reference standard. Our benchmark IOV was measured using the PET GTV of 6 experts. The primary outcome was the Dice similarity coefficient (DSC). ANOVA was used to compare methods, a paired t test was used to compare AI-to-expert variability and expert IOV, an unpaired t test was used to compare internal and external AI-to-expert variability, and post hoc Bland-Altman analysis was used to evaluate biomarker agreement. Results: In total, 1,220 18 F - FDG PET/CT scans of 1,190 patients (mean age ± SD, 63 ± 10 y; 858 men) were included, and 5 deep learning methods were trained using 5-fold cross-validation ( n = 805). The nnU-Net method achieved the highest similarity (DSC, 0.80 [95% CI, 0.77-0.86]; n = 196). We found no evidence of a difference between expert IOV and AI-to-expert variability (DSC, 0.78 for AI vs. 0.82 for experts; mean difference of 0.04 [95% CI, -0.01 to 0.09]; P = 0.12; n = 64). We found no evidence of a difference between the internal and external AI-to-expert variability (DSC, 0.80 internally vs. 0.81 externally; mean difference of 0.004 [95% CI, -0.05 to 0.04]; P = 0.87; n = 125). PET GTV-derived biomarkers of AI were in good agreement with experts. Conclusion: Deep learning can be used to automate 18 F - FDG PET/CT tumor-volume-derived imaging biomarkers, and the deep-learning-based volumes have the potential to assist clinical tumor volume delineation in radiation oncology., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Recent advancements in PET/CT, including the emergence of long axial field-of-view (LAFOV) PET/CT scanners, have increased PET sensitivity substantially. Consequently, there has been a significant reduction in the required tracer activity, shifting the primary source of patient radiation dose exposure to the attenuation correction (AC) CT scan during PET imaging. This study proposes a parameter-transferred conditional generative adversarial network (PT-cGAN) architecture to generate synthetic CT (sCT) images from non-attenuation corrected (NAC) PET images, with separate networks for [ 18 F]FDG and [ 15 O]H 2 O tracers. The study includes a total of 1018 subjects ( n = 972 [ 18 F]FDG, n = 46 [ 15 O]H 2 O). Testing was performed on the LAFOV scanner for both datasets. Qualitative analysis found no differences in image quality in 30 out of 36 cases in FDG patients, with minor insignificant differences in the remaining 6 cases. Reduced artifacts due to motion between NAC PET and CT were found. For the selected organs, a mean average error of 0.45% was found for the FDG cohort, and that of 3.12% was found for the H 2 O cohort. Simulated low-count images were included in testing, which demonstrated good performance down to 45 s scans. These findings show that the AC of total-body PET is feasible across tracers and in low-count studies and might reduce the artifacts due to motion and metal implants.

Introduction: Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images., Methods: A total of 1309 patients with cognitive impairment scanned with [ 11 C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation., Results: The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader's classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R 2  = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status., Conclusion: The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline., (© 2023. The Author(s).)

Frequent somatostatin receptor PET, for example, 64 Cu-DOTATATE PET, is part of the diagnostic work-up of patients with neuroendocrine neoplasms (NENs), resulting in high accumulated radiation doses. Scan-related radiation exposure should be minimized in accordance with the as-low-as-reasonably achievable principle, for example, by reducing injected radiotracer activity. Previous investigations found that reducing 64 Cu-DOTATATE activity to below 50 MBq results in inadequate image quality and lesion detection. We therefore investigated whether image quality and lesion detection of less than 50 MBq of 64 Cu-DOTATATE PET could be restored using artificial intelligence (AI). Methods: We implemented a parameter-transferred Wasserstein generative adversarial network for patients with NENs on simulated low-dose 64 Cu-DOTATATE PET images corresponding to 25% (PET 25% ), or about 48 MBq, of the injected activity of the reference full dose (PET 100% ), or about 191 MBq, to generate denoised PET images (PET AI ). We included 38 patients in the training sets for network optimization. We analyzed PET intensity correlation, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mean-square error (MSE) of PET AI /PET 100% versus PET 25% /PET 100% Two readers assessed Likert scale-defined image quality (1, very poor; 2, poor; 3, moderate; 4, good; 5, excellent) and identified lesion-suspicious foci on PET AI and PET 100% in a subset of the patients with no more than 20 lesions per organ ( n = 33) to allow comparison of all foci on a 1:1 basis. Detected foci were scored (C 1 , definite lesion; C 0 , lesion-suspicious focus) and matched with PET 100% as the reference. True-positive (TP), false-positive (FP), and false-negative (FN) lesions were assessed. Results: For PET AI /PET 100% versus PET 25% /PET 100% , PET intensity correlation had a goodness-of-fit value of 0.94 versus 0.81, PSNR was 58.1 versus 53.0, SSIM was 0.908 versus 0.899, and MSE was 2.6 versus 4.7. Likert scale-defined image quality was rated good or excellent in 33 of 33 and 32 of 33 patients on PET 100% and PET AI , respectively . Total number of detected lesions was 118 on PET 100% and 115 on PET AI Only 78 PET AI lesions were TP, 40 were FN, and 37 were FP, yielding detection sensitivity (TP/(TP+FN)) and a false discovery rate (FP/(TP+FP)) of 66% (78/118) and 32% (37/115), respectively. In 62% (23/37) of cases, the FP lesion was scored C 1 , suggesting a definite lesion. Conclusion: PET AI improved visual similarity with PET 100% compared with PET 25% , and PET AI and PET 100% had similar Likert scale-defined image quality. However, lesion detection analysis performed by physicians showed high proportions of FP and FN lesions on PET AI , highlighting the need for clinical validation of AI algorithms., (© 2023 by the Society of Nuclear Medicine and Molecular Imaging.)

Introduction: Patients with MS are MRI scanned continuously throughout their disease course resulting in a large manual workload for radiologists which includes lesion detection and size estimation. Though many models for automatic lesion segmentation have been published, few are used broadly in clinic today, as there is a lack of testing on clinical datasets. By collecting a large, heterogeneous training dataset directly from our MS clinic we aim to present a model which is robust to different scanner protocols and artefacts and which only uses MRI modalities present in routine clinical examinations., Methods: We retrospectively included 746 patients from routine examinations at our MS clinic. The inclusion criteria included acquisition at one of seven different scanners and an MRI protocol including 2D or 3D T2-w FLAIR, T2-w and T1-w images. Reference lesion masks on the training ( n = 571) and validation ( n = 70) datasets were generated using a preliminary segmentation model and subsequent manual correction. The test dataset ( n = 100) was manually delineated. Our segmentation model https://github.com/CAAI/AIMS/ was based on the popular nnU-Net, which has won several biomedical segmentation challenges. We tested our model against the published segmentation models HD-MS-Lesions, which is also based on nnU-Net, trained with a more homogenous patient cohort. We furthermore tested model robustness to data from unseen scanners by performing a leave-one-scanner-out experiment., Results: We found that our model was able to segment MS white matter lesions with a performance comparable to literature: DSC = 0.68, precision = 0.90, recall = 0.70, f1 = 0.78. Furthermore, the model outperformed HD-MS-Lesions in all metrics except precision = 0.96. In the leave-one-scanner-out experiment there was no significant change in performance ( p < 0.05) between any of the models which were only trained on part of the dataset and the full segmentation model., Conclusion: In conclusion we have seen, that by including a large, heterogeneous dataset emulating clinical reality, we have trained a segmentation model which maintains a high segmentation performance while being robust to data from unseen scanners. This broadens the applicability of the model in clinic and paves the way for clinical implementation., 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 Hindsholm, Andersen, Cramer, Simonsen, Askløf, Magyari, Madsen, Hansen, Sellebjerg, Larsson, Langkilde, Frederiksen, Højgaard, Ladefoged and Lindberg.)

Purpose: This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards., Methods: Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022. Reported outcomes for image quality, tissue classification performance, regional and global bias were extracted to evaluate ML-AC performance. Methodological quality of included studies and the quality of evidence of analysed outcomes were assessed using QUADAS-2 and GRADE, respectively., Results: A total of 19 studies (2371 participants) met the inclusion criteria. Overall, the global bias of ML methods was 0.76 ± 1.2%. For image quality, the relative mean square error (RMSE) was 0.20 ± 0.4 while for tissues classification, the Dice similarity coefficient (DSC) for bone/soft tissue/air were 0.82 ± 0.1 / 0.95 ± 0.03 / 0.85 ± 0.14., Conclusions: In general, ML-AC performance is within acceptable limits for clinical PET imaging. The sparse information on ML-AC robustness and its limited qualitative clinical evaluation may hinder clinical implementation in neuroimaging, especially for PET/MRI or emerging brain PET systems where standard AC approaches are not readily available., Competing Interests: Declaration of Competing Interest The authors declare that they have no existing conflict of interest., (Copyright © 2023 Elsevier Masson SAS. All rights reserved.)

Purpose: Conventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[ 18 F]fluoroethyl)-L-tyrosine ([ 18 F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [ 18 F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants., Methods: The MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [ 18 F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC., Results: Ninety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method., Conclusion: We evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment., 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 Ladefoged, Andersen, Andersen, Anderberg, Engkebølle, Madsen, Højgaard, Henriksen and Law.)

In the context of brain tumour response assessment, deep learning-based three-dimensional (3D) tumour segmentation has shown potential to enter the routine radiological workflow. The purpose of the present study was to perform an external evaluation of a state-of-the-art deep learning 3D brain tumour segmentation algorithm (HD-GLIO) on an independent cohort of consecutive, post-operative patients. For 66 consecutive magnetic resonance imaging examinations, we compared delineations of contrast-enhancing (CE) tumour lesions and non-enhancing T2/FLAIR hyperintense abnormality (NE) lesions by the HD-GLIO algorithm and radiologists using Dice similarity coefficients (Dice). Volume agreement was assessed using concordance correlation coefficients (CCCs) and Bland-Altman plots. The algorithm performed very well regarding the segmentation of NE volumes (median Dice = 0.79) and CE tumour volumes larger than 1.0 cm 3 (median Dice = 0.86). If considering all cases with CE tumour lesions, the performance dropped significantly (median Dice = 0.40). Volume agreement was excellent with CCCs of 0.997 (CE tumour volumes) and 0.922 (NE volumes). The findings have implications for the application of the HD-GLIO algorithm in the routine radiological workflow where small contrast-enhancing tumours will constitute a considerable share of the follow-up cases. Our study underlines that independent validations on clinical datasets are key to asserting the robustness of deep learning algorithms.

Purpose: Brain 2-Deoxy-2-[ 18 F]fluoroglucose ([ 18 F]FDG-PET) is widely used in the diagnostic workup of Alzheimer's disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [ 18 F]FDG-PET baseline from the patient's own MRI, and showcase its applicability in detecting AD pathology., Methods: We included [ 18 F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities., Results: The model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects., Conclusion: This work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [ 18 F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines., 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 © 2022 Hinge, Henriksen, Lindberg, Hasselbalch, Højgaard, Law, Andersen and Ladefoged.)

Purpose: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [ 18 F]FE-PE2I and [ 11 C]PiB PET/CT scans, simulating low injected activity or scanning time reduction, in combination with AI-assisted denoising., Methods: A total of 162 patients with clinically uncertain Alzheimer's disease underwent amyloid [ 11 C]PiB PET/CT and 509 patients referred for clinically uncertain Parkinson's disease underwent dopamine transporter (DAT) [ 18 F]FE-PE2I PET/CT. Simulated low-activity data were obtained by random sampling of 5% of the events from the list-mode file and a 5% time window extraction in the middle of the scan. A three-dimensional convolutional neural network (CNN) was trained to denoise the resulting PET images for each disease cohort., Results: Noise reduction of low-activity PET images was successful for both cohorts using 5% of the original activity with improvement in visual quality and all similarity metrics with respect to the ground-truth images. Clinically relevant metrics extracted from the low-activity images deviated < 2% compared to ground-truth values, which were not significantly changed when extracting the metrics from the denoised images., Conclusion: The presented models were based on the same network architecture and proved to be a robust tool for denoising brain PET images with two widely different tracer distributions (delocalized, ([ 11 C]PiB, and highly localized, [18F]FE-PE2I). This broad and robust application makes the presented network a good choice for improving the quality of brain images to the level of the standard-activity images without degrading clinical metric extraction. This will allow for reduced dose or scan time in PET/CT to be implemented clinically., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: To implement and validate an existing algorithm for automatic delineation of white matter lesions on magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) on a local single-center dataset., Methods: We implemented a white matter hyperintensity segmentation model, based on a 2D convolutional neural network, using the conventional T2-weighted fluid attenuated inversion recovery (FLAIR) MRI sequence as input. The model was adapted for delineation of MS lesions by further training on a local dataset of 93 MS patients with a total of 3040 lesions. A quantitative evaluation was performed on ten test patients, in which model-generated masks were compared to manually delineated masks from two expert delineators. A subsequent qualitative evaluation of the implemented model was performed by two expert delineators, in which generated delineation masks on a clinical dataset of 53 patients were rated acceptable (< 10% errors) or unacceptable (> 10% errors) based on the total number of true lesions., Results: The quantitative evaluation resulted in an average accuracy score (F1) of 0.71, recall of 0.77 and dice similarity coefficient of 0.62. Our implemented model obtained the highest scores in all three metrics, when compared to three out of the box lesion segmentation models. In the clinical evaluation an average of 94% of our 53 model-generated masks were rated acceptable., Conclusion: After adaptation to our local dataset, the implemented segmentation model was able to delineate MS lesions with a high clinical value as rated by delineation experts while outperforming popular out of the box applications. This serves as a promising step towards implementation of automatic lesion delineation in our MS clinic., (© 2021. The Author(s).)

Introduction: Brain and central nervous system (CNS) tumors are the second most common cancer type in children and adolescents. Positron emission tomography (PET) imaging with radiolabeled amino acids visualizes the amino acid uptake in brain tumor cells compared with the healthy brain tissue, which provides additional information over magnetic resonance imaging (MRI) for differential diagnosis, treatment planning, and the differentiation of tumor relapse from treatment-related changes. However, tumor delineation is a time-consuming task subject to inter-rater variability. We propose a deep learning method for the automatic delineation of O-(2-[ 18 F]fluoroethyl)-l-tyrosine ([ 18 F]FET PET) pediatric CNS tumors., Methods: A total of 109 [ 18 F]FET PET and MRI scans from 66 pediatric patients with manually delineated reference were included. We trained an artificial neural network (ANN) for automatic delineation and compared its performance against the manual reference on delineation accuracy and subsequent clinical metric accuracy. For clinical metrics, we extracted the biological tumor volume (BTV) and tumor-to-background mean and max (TBR mean and TBR max )., Results: The ANN produced high tumor overlap (median dice-similarity coefficient [DSC] of 0.93). The clinical metrics extracted with the manual reference and the ANN were highly correlated ( r ≥ 0.99). The spatial location of TBR max was identical in almost all cases (96%). The ANN and the manual reference produced similar changes in the clinical metrics between baseline and follow-up scans., Conclusion: The proposed ANN achieved high concordance with the manual reference and may be an important tool for decision aid, limiting inter-reader variance and improving longitudinal evaluation in clinical routine, and for future multicenter studies of pediatric CNS tumors., 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 © 2022 Ladefoged, Henriksen, Mathiasen, Schmiegelow, Andersen, Højgaard, Borgwardt, Law and Marner.)

Background: Segmentation of neuroendocrine neoplasms (NENs) in [ 64 Cu]Cu-DOTATATE positron emission tomography makes it possible to extract quantitative measures useable for prognostication of patients. However, manual tumor segmentation is cumbersome and time-consuming. Therefore, we aimed to implement and test an artificial intelligence (AI) network for tumor segmentation. Patients with gastroenteropancreatic or lung NEN with [ 64 Cu]Cu-DOTATATE PET/CT performed were included in our training (n = 117) and test cohort (n = 41). Further, 10 patients with no signs of NEN were included as negative controls. Ground truth segmentations were obtained by a standardized semiautomatic method for tumor segmentation by a physician. The nnU-Net framework was used to set up a deep learning U-net architecture. Dice score, sensitivity and precision were used for selection of the final model. AI segmentations were implemented in a clinical imaging viewer where a physician evaluated performance and performed manual adjustments., Results: Cross-validation training was used to generate models and an ensemble model. The ensemble model performed best overall with a lesion-wise dice of 0.850 and pixel-wise dice, precision and sensitivity of 0.801, 0.786 and 0.872, respectively. Performance of the ensemble model was acceptable with some degree of manual adjustment in 35/41 (85%) patients. Final tumor segmentation could be obtained from the AI model with manual adjustments in 5 min versus 17 min for ground truth method, p < 0.01., Conclusion: We implemented and validated an AI model that achieved a high similarity with ground truth segmentation and resulted in faster tumor segmentation. With AI, total tumor segmentation may become feasible in the clinical routine., (© 2022. The Author(s).)

Background: Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC., Methods: Eleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PET Deep ). (2) Dixon MRI using the vendor-provided atlas-based method (PET Atlas ). (3) CT, serving as reference (PET CT ). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed., Results: The average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PET Deep and -1.3 ± 21.8% for PET Atlas . The error in mean PET uptake in bone/air was much lower for PET Deep (-4%/12%) than for PET Atlas (-15%/84%) and PET Deep also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was -0.6 ± 2.0% for PET Deep and -3.5 ± 4.6% for PET Atlas ., Conclusion: The deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air., (© 2022. The Author(s).)

Quantitative [ 15 O]H 2 O positron emission tomography (PET) is the accepted reference method for regional cerebral blood flow (rCBF) quantification. To perform reliable quantitative [ 15 O]H 2 O-PET studies in PET/MRI scanners, MRI-based attenuation-correction (MRAC) is required. Our aim was to compare two MRAC methods (RESOLUTE and DeepUTE) based on ultrashort echo-time with computed tomography-based reference standard AC (CTAC) in dynamic and static [ 15 O]H 2 O-PET. We compared rCBF from quantitative perfusion maps and activity concentration distribution from static images between AC methods in 25 resting [ 15 O]H 2 O-PET scans from 14 healthy men at whole-brain, regions of interest and voxel-wise levels. Average whole-brain CBF was 39.9 ± 6.0, 39.0 ± 5.8 and 40.0 ± 5.6 ml/100 g/min for CTAC, RESOLUTE and DeepUTE corrected studies respectively. RESOLUTE underestimated whole-brain CBF by 2.1 ± 1.50% and rCBF in all regions of interest (range -2.4%- -1%) compared to CTAC. DeepUTE showed significant rCBF overestimation only in the occipital lobe (0.6 ± 1.1%). Both MRAC methods showed excellent correlation on rCBF and activity concentration with CTAC, with slopes of linear regression lines between 0.97 and 1.01 and R 2 over 0.99. In conclusion, RESOLUTE and DeepUTE provide AC information comparable to CTAC in dynamic [ 15 O]H 2 O-PET but RESOLUTE is associated with a small but systematic underestimation.

Patients with neuroendocrine neoplasms (NENs) have heterogeneous somatostatin receptor expression, with highly differentiated lesions having higher expression. Receptor expression of the total tumor burden may be visualized by somatostatin receptor imaging, such as with 64 Cu-DOTATATE PET/CT. Assessment of maximal lesion uptake is associated with progression-free survival (PFS) but not overall survival (OS). We hypothesized that the lesion with the lowest, rather than the highest, 64 Cu-DOTATATE uptake would be more prognostic, and we developed a semiautomatic method for evaluating this hypothesis. Methods: Patients with NENs underwent 64 Cu-DOTATATE PET/CT. A standardized semiautomatic tumor delineation method was developed and used to identify the lesion with the lowest uptake, that is, with the lowest SUV mean Additionally, we assessed total tumor volume derived from the semiautomatic tumor delineation. Kaplan-Meier and Cox regression analyses were used to determine whether there was any association with OS and PFS. Results: In 116 patients with NENs, median PFS (95% CI) was 23 mo (range, 20-31 mo) and median OS was 85 mo (range, 68-113 mo). Minimum SUV mean and total tumor volume were significantly associated with PFS and OS in univariate Cox regression analyses, whereas SUV max was significant only for PFS. In multivariate Cox analyses, both minimum SUV mean and total tumor volume remained statistically significant. Minimum SUV mean and total tumor volume were then dichotomized by their median, and patients were categorized into 4 groups: high or low total tumor volume and high or low minimum SUV mean Patients with a low total tumor volume and high minimum SUV mean had a hazard ratio of 0.32 (95% CI, 0.20-0.51) for PFS and 0.24 (95% CI, 0.13-0.43) for OS, both with P values of less than 0.001 (reference: high total tumor volume and low minimum SUV mean ). Conclusion: We propose a standardized semiautomatic tumor delineation method to identify the lesion with the lowest 64 Cu-DOTATATE uptake and total tumor volume. Assessment of the lowest, rather than the highest, lesion uptake greatly increases prognostication by 64 Cu-DOTATATE PET/CT. Combining lesion uptake and total tumor volume, we derived a novel prognostic classification system for patients with NENs., (© 2021 by the Society of Nuclear Medicine and Molecular Imaging.)

Purpose: Radiotherapy planning based only on positron emission tomography/magnetic resonance imaging (PET/MRI) lacks computed tomography (CT) information required for dose calculations. In this study, a previously developed deep learning model for creating synthetic CT (sCT) from MRI in patients with head and neck cancer was evaluated in 2 scenarios: (1) using an independent external dataset, and (2) using a local dataset after an update of the model related to scanner software-induced changes to the input MRI., Methods and Materials: Six patients from an external site and 17 patients from a local cohort were analyzed separately. Each patient underwent a CT and a PET/MRI with a Dixon MRI sequence over either one (external) or 2 (local) bed positions. For the external cohort, a previously developed deep learning model for deriving sCT from Dixon MRI was directly applied. For the local cohort, we adapted the model for an upgraded MRI acquisition using transfer learning and evaluated it in a leave-one-out process. The sCT mean absolute error for each patient was assessed. Radiotherapy dose plans based on sCT and CT were compared by assessing relevant absorbed dose differences in target volumes and organs at risk., Results: The MAEs were 78 ± 13 HU and 76 ± 12 HU for the external and local cohort, respectively. For the external cohort, absorbed dose differences in target volumes were within ± 2.3% and within ± 1% in 95% of the cases. Differences in organs at risk were <2%. Similar results were obtained for the local cohort., Conclusions: We have demonstrated a robust performance of a deep learning model for deriving sCT from MRI when applied to an independent external dataset. We updated the model to accommodate a larger axial field of view and software-induced changes to the input MRI. In both scenarios dose calculations based on sCT were similar to those of CT suggesting a robust and reliable method., (© 2021 The Authors.)

Introduction: Cardiac [ 18 F]FDG-PET is widely used for viability testing in patients with chronic ischemic heart disease. Guidelines recommend injection of 200-350 MBq [ 18 F]FDG, however, a reduction of radiation exposure has become increasingly important, but might come at the cost of reduced diagnostic accuracy due to the increased noise in the images. We aimed to explore the use of a common deep learning (DL) network for noise reduction in low-dose PET images, and to validate its accuracy using the clinical quantitative metrics used to determine cardiac viability in patients with ischemic heart disease., Methods: We included 168 patients imaged with cardiac [ 18 F]FDG-PET/CT. We simulated a reduced dose by keeping counts at thresholds 1% and 10%. 3D U-net with five blocks was trained to de-noise full PET volumes (128 × 128 × 111). The low-dose and de-noised images were compared in Corridor4DM to the full-dose PET images. We used the default segmentation of the left ventricle to extract the quantitative metrics end-diastolic volume (EDV), end-systolic volume (ESV), and left ventricular ejection fraction (LVEF) from the gated images, and FDG defect extent from the static images., Results: Our de-noising models were able to recover the PET signal for both the static and gated images in either dose-reduction. For the 1% low-dose images, the error is most pronounced for EDV and ESV, where the average underestimation is 25%. No bias was observed using the proposed DL de-noising method. De-noising minimized the outliers found for the 1% and 10% low-dose measurements of LVEF and extent. Accuracy of differential diagnosis based on LVEF threshold was highly improved after de-noising., Conclusion: A significant dose reduction can be achieved for cardiac [ 18 F]FDG images used for viability testing in patients with ischemic heart disease without significant loss of diagnostic accuracy when using our DL model for noise reduction. Both 1% and 10% dose reductions are possible with clinically quantitative metrics comparable to that obtained with a full dose.

Metal artefacts in PET/CT images hamper diagnostic accuracy in head and neck cancer (HNC). The aim of this study is to characterise the clinical effects of metal artefacts on PET/CT in HNC and to inform decision-making concerning implementation of MAR techniques. We study a combined dual energy CT and inpainting-based metal artefact reduction (DECT-I-MAR) technique for PET/CT in three settings: (A) A dental phantom with a removable amalgam-filled tooth to evaluate the PET error in comparison to a known reference. (B) PET-positive patients with metallic implants to demonstrate the relationship between CT metal artefacts and PET error. (C) Metabolic tumour volumes delineated in PET-positive patients with metal implants to evaluate the clinical impact. In (A) DECT-I-MAR reduced the PET error significantly. In (B) we demonstrate an increasing PET error with increasing CT artefact severity in patients. In (C) it is shown that the presence of artefacts in the same axial slices as the tumour significantly decreases biomarker stability and increase delineation variability. This work shows the practical feasibility of DECT-I-MAR-based PET/CT imaging, and indicates a positive clinical impact of using the technique routinely for HNC patients. The impact of CT artefacts on PET is considerable, especially in workflows where quantitative PET biomarkers and tumour volumes are used. In such cases, and for patients with tumours in proximity of metals, we recommend that a MAR technique for PET/CT is employed., (Creative Commons Attribution license.)

Purpose: Multiparametric positron emission tomography (PET)/magnetic resonance imaging (MRI) as a one-stop shop for radiation therapy (RT) planning has great potential but is technically challenging. We studied the feasibility of performing multiparametric PET/MRI of patients with head and neck cancer (HNC) in RT treatment position. As a step toward planning RT based solely on PET/MRI, a deep learning approach was employed to generate synthetic computed tomography (sCT) from MRI. This was subsequently evaluated for dose calculation and PET attenuation correction (AC)., Methods and Materials: Eleven patients, including 3 pilot patients referred for RT of HNC, underwent PET/MRI in treatment position after a routine fluorodeoxyglucose-PET/CT planning scan. The PET/MRI scan protocol included multiparametric imaging. A convolutional neural network was trained in a leave-one-out process to predict sCT from the Dixon MRI. The clinical CT-based dose plans were recalculated on sCT, and the plans were compared in terms of relative differences in mean, maximum, near-maximum, and near-minimum absorbed doses for different volumes of interest. Comparisons between PET with sCT-based AC and PET with CT-based AC were assessed based on the relative differences in mean and maximum standardized uptake values (SUV mean and SUV max ) from the PET-positive volumes., Results: All 11 patients underwent PET/MRI in RT treatment position. Apart from the 3 pilots, full multiparametric imaging was completed in 45 minutes for 7 out of 8 patients. One patient terminated the examination after 30 minutes. With the exception of 1 patient with an inserted tracheostomy tube, all dosimetric parameters of the sCT-based dose plans were within ±1% of the CT-based dose plans. For PET, the mean difference was 0.4 ± 1.2% for SUV mean and -0.5 ± 1.0% for SUV max ., Conclusions: Performing multiparametric PET/MRI of patients with HNC in RT treatment position was clinically feasible. The sCT generation resulted in AC of PET and dose calculations sufficiently accurate for clinical use. These results are an important step toward using multiparametric PET/MRI as a one-stop shop for personalized RT planning., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: Robust and reliable attenuation correction (AC) is a prerequisite for accurate quantification of activity concentration. In combined PET/MRI, AC is challenged by the lack of bone signal in the MRI from which the AC maps has to be derived. Deep learning-based image-to-image translation networks present itself as an optimal solution for MRI-derived AC (MR-AC). High robustness and generalizability of these networks are expected to be achieved through large training cohorts. In this study, we implemented an MR-AC method based on deep learning, and investigated how training cohort size, transfer learning, and MR input affected robustness, and subsequently evaluated the method in a clinical setup, with the overall aim to explore if this method could be implemented in clinical routine for PET/MRI examinations., Methods: A total cohort of 1037 adult subjects from the Siemens Biograph mMR with two different software versions (VB20P and VE11P) was used. The software upgrade included updates to all MRI sequences. The impact of training group size was investigated by training a convolutional neural network (CNN) on an increasing training group size from 10 to 403. The ability to adapt to changes in the input images between software versions were evaluated using transfer learning from a large cohort to a smaller cohort, by varying training group size from 5 to 91 subjects. The impact of MRI sequence was evaluated by training three networks based on the Dixon VIBE sequence (DeepDixon), T1-weighted MPRAGE (DeepT1), and ultra-short echo time (UTE) sequence (DeepUTE). Blinded clinical evaluation relative to the reference low-dose CT (CT-AC) was performed for DeepDixon in 104 independent 2-[ 18 F]fluoro-2-deoxy-d-glucose ([ 18 F]FDG) PET patient studies performed for suspected neurodegenerative disorder using statistical surface projections., Results: Robustness increased with group size in the training data set: 100 subjects were required to reduce the number of outliers compared to a state-of-the-art segmentation-based method, and a cohort >400 subjects further increased robustness in terms of reduced variation and number of outliers. When using transfer learning to adapt to changes in the MRI input, as few as five subjects were sufficient to minimize outliers. Full robustness was achieved at 20 subjects. Comparable robust and accurate results were obtained using all three types of MRI input with a bias below 1% relative to CT-AC in any brain region. The clinical PET evaluation using DeepDixon showed no clinically relevant differences compared to CT-AC., Conclusion: Deep learning based AC requires a large training cohort to achieve accurate and robust performance. Using transfer learning, only five subjects were needed to fine-tune the method to large changes to the input images. No clinically relevant differences were found compared to CT-AC, indicating that clinical implementation of our deep learning-based MR-AC method will be feasible across MRI system types using transfer learning and a limited number of subjects., (Copyright © 2020. Published by Elsevier Inc.)

Arterial spin labelling (ASL) is a non-invasive magnetic resonance imaging (MRI) technique that may provide fully quantitative regional cerebral blood flow (rCBF) images. However, before its application in clinical routine, ASL needs to be validated against the clinical gold standard, 15 O-H 2 O positron emission tomography (PET). We aimed to compare the two techniques by performing simultaneous quantitative ASL-MRI and 15 O-H 2 O-PET examinations in a hybrid PET/MRI scanner. Duplicate rCBF measurements were performed in healthy young subjects ( n  = 14) in rest, during hyperventilation, and after acetazolamide (post-ACZ), yielding 63 combined PET/MRI datasets in total. Average global CBF by ASL-MRI and 15 O-H 2 O-PET was not significantly different in any state (40.0 ± 6.5 and 40.6 ± 4.1 mL/100 g/min, respectively in rest, 24.5 ± 5.1 and 23.4 ± 4.8 mL/100 g/min, respectively, during hyperventilation, and 59.1 ± 10.4 and 64.7 ± 10.0 mL/100 g/min, respectively, post-ACZ). Overall, strong correlation between the two methods was found across all states (slope = 1.01, R 2  = 0.82), while the correlations within individual states and of reactivity measures were weaker, in particular in rest (R 2  = 0.05, p  = 0.03). Regional distribution was similar, although ASL yielded higher perfusion and absolute reactivity in highly vascularized areas. In conclusion, ASL-MRI and 15 O-H 2 O-PET measurements of rCBF are highly correlated across different perfusion states, but with variable correlation within and between hemodynamic states, and systematic differences in regional distribution.

Artificial intelligence (AI) is a computer-based system, which in diagnostic imaging can improve patient flow, optimise image processing, shorten scan time, reduce radiation dose and be used as decision aid in image interpretation. In this review, we argue that AI algorithms should be based on evidence with initial hypothesis, then a choice of algorithm and development with training on the initial data set; afterwards the algorithms should be tested on a new representative dataset, and finally it should be tested in a prospective study. If the AI is evidence-based and can solve a task better or cheaper than the usual methodology, it can be implemented.

In hybrid positron emission tomography (PET) and MRI systems, attenuation correction for PET image reconstruction is commonly based on processing of dedicated MR images. The image quality of the latter is strongly affected by metallic objects inside the body, such as e.g . dental implants, endoprostheses, or surgical clips which all lead to substantial artifacts that propagate into MRI-based attenuation images. In this work, we review publications about metal artifact correction strategies in MRI-based attenuation correction in PET/MRI. Moreover, we also give an overview about publications investigating the impact of MRI-based attenuation correction metal artifacts on the reconstructed PET image quality and quantification., (© 2019 The Authors. Published by the British Institute of Radiology.)

Background: Positron emission tomography/magnetic resonance imaging (PET/MRI) is a promising diagnostic imaging tool for the diagnosis of dementia, as PET can add complementary information to the routine imaging examination with MRI. The purpose of this study was to evaluate the influence of MRI-based attenuation correction (MRAC) on diagnostic assessment of dementia with [ 18 F]FDG PET. Quantitative differences in both [ 18 F]FDG uptake and z-scores were calculated for three clinically available (DixonNoBone, DixonBone, UTE) and two research MRAC methods (UCL, DeepUTE) compared to CT-based AC (CTAC). Furthermore, diagnoses based on visual evaluations were made by three nuclear medicine physicians and one neuroradiologist (PET CT , PET DeepUTE , PET DixonBone , PET UTE , PET CT + MRI, PET DixonBone + MRI). In addition, pons and cerebellum were compared as reference regions for normalization., Results: The mean absolute difference in z-scores were smallest between MRAC and CTAC with cerebellum as reference region: 0.15 ± 0.11 σ (DeepUTE), 0.15 ± 0.12 σ (UCL), 0.23 ± 0.20 σ (DixonBone), 0.32 ± 0.28 σ (DixonNoBone), and 0.54 ± 0.40 σ (UTE). In the visual evaluation, the diagnoses agreed with PET CT in 74% (PET DeepUTE ), 67% (PET DixonBone ), and 70% (PET UTE ) of the patients, while PET CT + MRI agreed with PET DixonBone + MRI in 89% of the patients., Conclusion: The MRAC research methods performed close to that of CTAC in the quantitative evaluation of [ 18 F]FDG uptake and z-scores. Among the clinically implemented MRAC methods, Dixon Bone should be preferred for diagnostic assessment of dementia with [ 18 F]FDG PET/MRI. However, as artifacts occur in Dixon Bone attenuation maps, they must be visually inspected to assure proper quantification.

In this study, a new hybrid PET/MRI method for quantitative regional cerebral blood flow (rCBF) measurements in healthy newborn infants was assessed and the low values of rCBF in white matter previously obtained by arterial spin labeling (ASL) were tested. Four healthy full-term newborn subjects were scanned in a PET/MRI scanner during natural sleep after median intravenous injection of 14 MBq 15 O-water. Regional CBF was quantified using a one-tissue-compartment model employing an image-derived input function (IDIF) from the left ventricle. PET rCBF showed the highest values in the thalami, mesencephalon and brain stem and the lowest in cortex and unmyelinated white matter. The average global CBF was 17.8 ml/100 g/min. The average frontal and occipital unmyelinated white matter CBF was 10.3 ml/100 g/min and average thalamic CBF 31.3 ml/100 g/min. The average white matter/thalamic ratio CBF was 0.36, significantly higher than previous ASL data. The rCBF ASL measurements were all unsuccessful primarily owing to subject movement. In this study, we demonstrated for the first time, a minimally invasive PET/MRI method using low activity 15 O-water PET for quantitative rCBF assessment in unsedated healthy newborn infants and found a white/grey matter CBF ratio similar to that of the adult human brain.

Aim: Positron emission tomography (PET) imaging is a useful tool for assisting in correct differentiation of tumor progression from reactive changes. O-(2-18F-fluoroethyl)-L-tyrosine (FET)-PET in combination with MRI can add valuable information for clinical decision making. Acquiring FET-PET/MRI simultaneously allows for a one-stop-shop that limits the need for a second sedation or anesthesia as with PET and MRI in sequence. PET/MRI is challenged by lack of a direct measure of photon attenuation. Accepted solutions for attenuation correction (AC) might not be applicable to pediatrics. The aim of this study was to evaluate the use of the subject-specific MR-derived AC method RESOLUTE, modified to a pediatric cohort, against the performance of an MR-AC technique based on deep learning in a pediatric brain tumor cohort. Methods: The modifications to RESOLUTE and the implementation of a deep learning method were performed using 79 pediatric patient examinations. We analyzed the 36 of these with active brain tumor area above 1 mL. We measured background (B), tumor mean and maximal activity (T MEAN , T MAX ), biological tumor volume (BTV), and calculated the clinical metrics T MEAN /B and T MAX /B. Results: Overall, we found both RESOLUTE and our DeepUTE methodologies to accurately reproduce the CT-AC clinical metrics. Regardless of age, both methods were able to obtain AC maps similar to the CT-AC, albeit with DeepUTE producing the most similar based on both quantitative metrics and visual inspection. In the patient-by-patient analysis DeepUTE was the only technique with all patients inside the predefined acceptable clinical limits. It also had a higher precision with relative %-difference to the reference CT-AC (T MAX /B mean: -0.1%, CI: [-0.8%, 0.5%], p = 0.54) compared to RESOLUTE (T MAX /B mean: 0.3%, CI: [-0.6%, 1.2%], p = 0.67) and DIXON-AC (T MAX /B mean: 5.9%, CI: [4.5%, 7.4%], p < 0.0001). Conclusion: Overall, we found DeepUTE to be the AC method that most robustly reproduced the CT-AC clinical metrics per se , closely followed by RESOLUTE modified to pediatric cohorts. The added accuracy due to better noise handling of DeepUTE, ease of use, as well as the improved runtime makes DeepUTE the method of choice for PET/MRI attenuation correction.

Quantitative PET/MRI is dependent on reliable and reproducible MR-based attenuation correction (MR-AC). In this study, we evaluated the quality of current vendor-provided thoracic MR-AC maps and further investigated the reproducibility of their impact on 18 F-FDG PET quantification in patients with non-small cell lung cancer. Methods: Eleven patients with inoperable non-small cell lung cancer underwent 2-5 thoracic PET/MRI scan-rescan examinations within 22 d. 18 F-FDG PET data were acquired along with 2 Dixon MR-AC maps for each examination. Two PET images (PET A and PET B ) were reconstructed using identical PET emission data but with MR-AC from these intrasubject repeated attenuation maps. In total, 90 MR-AC maps were evaluated visually for quality and the occurrence of categorized artifacts by 2 PET/MRI-experienced physicians. Each tumor was outlined by a volume of interest (40% isocontour of maximum) on PET A , which was then projected onto the corresponding PET B SUV mean and SUV max were assessed from the PET images. Within-examination coefficients of variation and Bland-Altman analyses were conducted for the assessment of SUV variations between PET A and PET B Results: Image artifacts were observed in 86% of the MR-AC maps, and 30% of the MR-AC maps were subjectively expected to affect the tumor SUV. SUV mean and SUV max resulted in coefficients of variation of 5.6% and 6.6%, respectively, and scan-rescan SUV variations were within ±20% in 95% of the cases. Substantial SUV variations were seen mainly for scan-rescan examinations affected by respiratory motion. Conclusion: Artifacts occur frequently in standard thoracic MR-AC maps, affecting the reproducibility of PET/MRI. These, in combination with other well-known sources of error associated with PET/MRI examinations, lead to inconsistent SUV measurements in serial studies, which may affect the reliability of therapy response assessment. A thorough visual inspection of the thoracic MR-AC map and Dixon images from which it is derived remains crucial for the detection of MR-AC artifacts that may influence the reliability of SUV., (© 2018 by the Society of Nuclear Medicine and Molecular Imaging.)

The aim of this study was to compare attenuation-correction (AC) approaches for PET/MRI in clinical neurooncology. Methods: Forty-nine PET/MRI brain scans were included: brain tumor studies using 18 F-fluoro-ethyl-tyrosine ( 18 F-FET) ( n = 31) and 68 Ga-DOTANOC ( n = 7) and studies of healthy subjects using 18 F-FDG ( n = 11). For each subject, MR-based AC maps (MR-AC) were acquired using the standard DIXON- and ultrashort echo time (UTE)-based approaches. A third MR-AC was calculated using a model-based, postprocessing approach to account for bone attenuation values (BD, noncommercial prototype software by Siemens Healthcare). As a reference, AC maps were derived from patient-specific CT images (CTref). PET data were reconstructed using standard settings after AC with all 4 AC methods. We report changes in diagnosis for all brain tumor patients and the following relative differences values (RDs [%]), with regards to AC-CTref: for 18 F-FET (A)-SUVs as well as volumes of interest (VOIs) defined by a 70% threshold of all segmented lesions and lesion-to-background ratios; for 68 Ga-DOTANOC (B)-SUVs as well as VOIs defined by a 50% threshold for all lesions and the pituitary gland; and for 18 F-FDG (C)-RD of SUVs of the whole brain and 10 anatomic regions segmented on MR images. Results: For brain tumor imaging (A and B), the standard PET-based diagnosis was not affected by any of the 3 MR-AC methods. For A, the average RDs of SUV mean were -10%, -4%, and -3% and of the VOIs 1%, 2%, and 7% for DIXON, UTE, and BD, respectively. Lesion-to-background ratios for all MR-AC methods were similar to that of CTref. For B, average RDs of SUV mean were -11%, -11%, and -3% and of the VOIs 1%, -4%, and -3%, respectively. In the case of 18 F-FDG PET/MRI (C), RDs for the whole brain were -11%, -8%, and -5% for DIXON, UTE, and BD, respectively. Conclusion: The diagnostic reading of PET/MR patients with brain tumors did not change with the chosen AC method. Quantitative accuracy of SUVs was clinically acceptable for UTE- and BD-AC for group A, whereas for group B BD was in accordance with CTref. Nevertheless, for the quantification of individual lesions large deviations to CTref can be observed independent of the MR-AC method used., (© 2017 by the Society of Nuclear Medicine and Molecular Imaging.)

Aim: Positron emission tomography (PET) imaging is a useful tool for assisting in correct differentiation of tumor progression from reactive changes, and the radiolabeled amino acid analog tracer O-(2- 18 F-fluoroethyl)-L-tyrosine (FET)-PET is amongst the most frequently used. The FET-PET images need to be quantitatively correct in order to be used clinically, which require accurate attenuation correction (AC) in PET/MRI. The aim of this study was to evaluate the use of the subject-specific MR-derived AC method RESOLUTE in post-operative brain tumor patients. Methods: We analyzed 51 post-operative brain tumor patients (68 examinations, 200 MBq [18F]-FET) investigated in a PET/MRI scanner. MR-AC maps were acquired using: (1) the Dixon water fat separation sequence, (2) the ultra short echo time (UTE) sequences, (3) calculated using our new RESOLUTE methodology, and (4) a same day low-dose CT used as reference "gold standard." For each subject and each AC method the tumor was delineated by isocontouring tracer uptake above a tumor(T)-to-brain background (B) activity ratio of 1.6. We measured B, tumor mean and maximal activity (T MEAN , T MAX ), biological tumor volume (BTV), and calculated the clinical metrics T MEAN /B and T MAX /B. Results: When using RESOLUTE 5/68 studies did not meet our predefined acceptance criteria of T MAX /B difference to CT-AC < ±0.1 or 5%, T MEAN /B < ±0.05 or 5%, and BTV < ±2 mL or 10%. In total, 46/68 studies failed our acceptance criteria using Dixon, and 26/68 using UTE. The 95% limits of agreement for T MAX /B was for RESOLUTE (-3%; 4%), Dixon (-9%; 16%), and UTE (-7%; 10%). The absolute error when measuring BTV was 0.7 ± 1.9 mL (N.S) with RESOLUTE, 5.3 ± 10 mL using Dixon, and 1.7 ± 3.7 mL using UTE. RESOLUTE performed best in the identification of the location of peak activity and in brain tumor follow-up monitoring using clinical FET PET metrics. Conclusions: Overall, we found RESOLUTE to be the AC method that most robustly reproduced the CT-AC clinical metrics per se , during follow-up, and when interpreted into defined clinical use cut-off criteria and into the patient history. RESOLUTE is especially suitable for brain tumor patients, as these often present with distorted anatomy where other methods based on atlas/template information might fail.

The purpose of this study was to test the feasibility of migrating a quantitative brain imaging protocol from a positron emission tomography (PET)-only system to an integrated PET/MR system. Potential differences in both absolute radiotracer concentration as well as in the derived kinetic parameters as a function of PET system choice have been investigated. Five healthy volunteers underwent dynamic (R)-[ 11 C]verapamil imaging on the same day using a GE-Advance (PET-only) and a Siemens Biograph mMR system (PET/MR). PET-emission data were reconstructed using a transmission-based attenuation correction (AC) map (PET-only), whereas a standard MR-DIXON as well as a low-dose CT AC map was applied to PET/MR emission data. Kinetic modeling based on arterial blood sampling was performed using a 1-tissue-2-rate constant compartment model, yielding kinetic parameters (K 1 and k 2 ) and distribution volume (V T ). Differences for parametric values obtained in the PET-only and the PET/MR systems were analyzed using a 2-way Analysis of Variance (ANOVA). Comparison of DIXON-based AC (PET/MR) with emission data derived from the PET-only system revealed average inter-system differences of -33 ± 14% ( p < 0.05) for the K 1 parameter and -19 ± 9% ( p < 0.05) for k 2 . Using a CT-based AC for PET/MR resulted in slightly lower systematic differences of -16 ± 18% for K 1 and -9 ± 10% for k 2 . The average differences in V T were -18 ± 10% ( p < 0.05) for DIXON- and -8 ± 13% for CT-based AC. Significant systematic differences were observed for kinetic parameters derived from emission data obtained from PET/MR and PET-only imaging due to different standard AC methods employed. Therefore, a transfer of imaging protocols from PET-only to PET/MR systems is not straightforward without application of proper correction methods. Clinical Trial Registration: www.clinicaltrialsregister.eu, identifier 2013-001724-19.

Aim: To accurately quantify the radioactivity concentration measured by PET, emission data need to be corrected for photon attenuation; however, the MRI signal cannot easily be converted into attenuation values, making attenuation correction (AC) in PET/MRI challenging. In order to further improve the current vendor-implemented MR-AC methods for absolute quantification, a number of prototype methods have been proposed in the literature. These can be categorized into three types: template/atlas-based, segmentation-based, and reconstruction-based. These proposed methods in general demonstrated improvements compared to vendor-implemented AC, and many studies report deviations in PET uptake after AC of only a few percent from a gold standard CT-AC. Using a unified quantitative evaluation with identical metrics, subject cohort, and common CT-based reference, the aims of this study were to evaluate a selection of novel methods proposed in the literature, and identify the ones suitable for clinical use., Methods: In total, 11 AC methods were evaluated: two vendor-implemented (MR-AC DIXON and MR-AC UTE ), five based on template/atlas information (MR-AC SEGBONE (Koesters et al., 2016), MR-AC ONTARIO (Anazodo et al., 2014), MR-AC BOSTON (Izquierdo-Garcia et al., 2014), MR-AC UCL (Burgos et al., 2014), and MR-AC MAXPROB (Merida et al., 2015)), one based on simultaneous reconstruction of attenuation and emission (MR-AC MLAA (Benoit et al., 2015)), and three based on image-segmentation (MR-AC MUNICH (Cabello et al., 2015), MR-AC CAR-RiDR (Juttukonda et al., 2015), and MR-AC RESOLUTE (Ladefoged et al., 2015)). We selected 359 subjects who were scanned using one of the following radiotracers: [ 18 F]FDG (210), [ 11 C]PiB (51), and [ 18 F]florbetapir (98). The comparison to AC with a gold standard CT was performed both globally and regionally, with a special focus on robustness and outlier analysis., Results: The average performance in PET tracer uptake was within ±5% of CT for all of the proposed methods, with the average±SD global percentage bias in PET FDG uptake for each method being: MR-AC DIXON (-11.3±3.5)%, MR-AC UTE (-5.7±2.0)%, MR-AC ONTARIO (-4.3±3.6)%, MR-AC MUNICH (3.7±2.1)%, MR-AC MLAA (-1.9±2.6)%, MR-AC SEGBONE (-1.7±3.6)%, MR-AC UCL (0.8±1.2)%, MR-AC CAR-RiDR (-0.4±1.9)%, MR-AC MAXPROB (-0.4±1.6)%, MR-AC BOSTON (-0.3±1.8)%, and MR-AC RESOLUTE (0.3±1.7)%, ordered by average bias. The overall best performing methods (MR-AC BOSTON , MR-AC MAXPROB , MR-AC RESOLUTE and MR-AC UCL , ordered alphabetically) showed regional average errors within ±3% of PET with CT-AC in all regions of the brain with FDG, and the same four methods, as well as MR-AC CAR-RiDR , showed that for 95% of the patients, 95% of brain voxels had an uptake that deviated by less than 15% from the reference. Comparable performance was obtained with PiB and florbetapir., Conclusions: All of the proposed novel methods have an average global performance within likely acceptable limits (±5% of CT-based reference), and the main difference among the methods was found in the robustness, outlier analysis, and clinical feasibility. Overall, the best performing methods were MR-ACBOSTON, MR-ACMAXPROB, MR-ACRESOLUTE and MR-ACUCL, ordered alphabetically. These methods all minimized the number of outliers, standard deviation, and average global and local error. The methods MR-ACMUNICH and MR-ACCAR-RiDR were both within acceptable quantitative limits, so these methods should be considered if processing time is a factor. The method MR-ACSEGBONE also demonstrates promising results, and performs well within the likely acceptable quantitative limits. For clinical routine scans where processing time can be a key factor, this vendor-provided solution currently outperforms most methods. With the performance of the methods presented here, it may be concluded that the challenge of improving the accuracy of MR-AC in adult brains with normal anatomy has been solved to a quantitatively acceptable degree, which is smaller than the quantification reproducibility in PET imaging., (Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.)

For quantitative tracer distribution in positron emission tomography, attenuation correction is essential. In a hybrid PET/CT system the CT images serve as a basis for generation of the attenuation map, but in PET/MR, the MR images do not have a similarly simple relationship with the attenuation map. Hence attenuation correction in PET/MR systems is more challenging. Typically either of two MR sequences are used: the Dixon or the ultra-short time echo (UTE) techniques. However these sequences have some well-known limitations. In this study, a reconstruction technique based on a modified and optimized non-TOF MLAA is proposed for PET/MR brain imaging. The idea is to tune the parameters of the MLTR applying some information from an attenuation image computed from the UTE sequences and a T1w MR image. In this MLTR algorithm, an [Formula: see text] parameter is introduced and optimized in order to drive the algorithm to a final attenuation map most consistent with the emission data. Because the non-TOF MLAA is used, a technique to reduce the cross-talk effect is proposed. In this study, the proposed algorithm is compared to the common reconstruction methods such as OSEM using a CT attenuation map, considered as the reference, and OSEM using the Dixon and UTE attenuation maps. To show the robustness and the reproducibility of the proposed algorithm, a set of 204 [ 18 F]FDG patients, 35 [ 11 C]PiB patients and 1 [ 18 F]FET patient are used. The results show that by choosing an optimized value of [Formula: see text] in MLTR, the proposed algorithm improves the results compared to the standard MR-based attenuation correction methods (i.e. OSEM using the Dixon or the UTE attenuation maps), and the cross-talk and the scale problem are limited.

Background: In the absence of CT or traditional transmission sources in combined clinical positron emission tomography/magnetic resonance (PET/MR) systems, MR images are used for MR-based attenuation correction (MR-AC). The susceptibility effects due to metal implants challenge MR-AC in the neck region of patients with dental implants. The purpose of this study was to assess the frequency and magnitude of subsequent PET image distortions following MR-AC., Methods: A total of 148 PET/MR patients with clear visual signal voids on the attenuation map in the dental region were included in this study. Patients were injected with [(18)F]-FDG, [(11)C]-PiB, [(18)F]-FET, or [(64)Cu]-DOTATATE. The PET/MR data were acquired over a single-bed position of 25.8 cm covering the head and neck. MR-AC was based on either standard MR-ACDIXON or MR-ACINPAINTED where the susceptibility-induced signal voids were substituted with soft tissue information. Our inpainting algorithm delineates the outer contour of signal voids breaching the anatomical volume using the non-attenuation-corrected PET image and classifies the inner air regions based on an aligned template of likely dental artifact areas. The reconstructed PET images were evaluated visually and quantitatively using regions of interests in reference regions. The volume of the artifacts and the computed relative differences in mean and max standardized uptake value (SUV) between the two PET images are reported., Results: The MR-based volume of the susceptibility-induced signal voids on the MR-AC attenuation maps was between 1.6 and 520.8 mL. The corresponding/resulting bias of the reconstructed tracer distribution was localized mainly in the area of the signal void. The mean and maximum SUVs averaged across all patients increased after inpainting by 52% (± 11%) and 28% (± 11%), respectively, in the corrected region. SUV underestimation decreased with the distance to the signal void and correlated with the volume of the susceptibility artifact on the MR-AC attenuation map., Conclusions: Metallic dental work may cause severe MR signal voids. The resulting PET/MR artifacts may exceed the actual volume of the dental fillings. The subsequent bias in PET is severe in regions in and near the signal voids and may affect the conspicuity of lesions in the mandibular region.

Abnormality in cerebral blood flow (CBF) distribution can lead to hypoxic-ischemic cerebral damage in newborn infants. The aim of the study was to investigate minimally invasive approaches to measure CBF by comparing simultaneous (15)O-water positron emission tomography (PET) and single TI pulsed arterial spin labeling (ASL) magnetic resonance imaging (MR) on a hybrid PET/MR in seven newborn piglets. Positron emission tomography was performed with IV injections of 20 MBq and 100 MBq (15)O-water to confirm CBF reliability at low activity. Cerebral blood flow was quantified using a one-tissue-compartment-model using two input functions: an arterial input function (AIF) or an image-derived input function (IDIF). The mean global CBF (95% CI) PET-AIF, PET-IDIF, and ASL at baseline were 27 (23; 32), 34 (31; 37), and 27 (22; 32) mL/100 g per minute, respectively. At acetazolamide stimulus, PET-AIF, PET-IDIF, and ASL were 64 (55; 74), 76 (70; 83) and 79 (67; 92) mL/100 g per minute, respectively. At baseline, differences between PET-AIF, PET-IDIF, and ASL were 22% (P<0.0001) and -0.7% (P=0.9). At acetazolamide, differences between PET-AIF, PET-IDIF, and ASL were 19% (P=0.001) and 24% (P=0.0003). In conclusion, PET-IDIF overestimated CBF. Injected activity of 20 MBq (15)O-water had acceptable concordance with 100 MBq, without compromising image quality. Single TI ASL was questionable for regional CBF measurements. Global ASL CBF and PET CBF were congruent during baseline but not during hyperperfusion.

The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [(18)F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R(*)2 values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within  ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.

This paper summarises key themes and discussions from the 4th international workshop dedicated to the advancement of the technical, scientific and clinical applications of combined positron emission tomography (PET)/magnetic resonance imaging (MRI) systems that was held in Tübingen, Germany, from February 23 to 27, 2015. Specifically, we summarise the three days of invited presentations from active researchers in this and associated fields augmented by round table discussions and dialogue boards with specific topics. These include the use of PET/MRI in cardiovascular disease, paediatrics, oncology, neurology and multi-parametric imaging, the latter of which was suggested as a key promoting factor for the wider adoption of integrated PET/MRI. Discussions throughout the workshop and a poll taken on the final day demonstrated that attendees felt more strongly that PET/MRI has further advanced in both technical versatility and acceptance by clinical and research-driven users from the status quo of last year. Still, with only minimal evidence of progress made in exploiting the true complementary nature of the PET and MRI-based information, PET/MRI is still yet to achieve its potential. In that regard, the conclusion of last year's meeting "the real work has just started" still holds true.

A challenge when using current magnetic resonance (MR)-based attenuation correction in positron emission tomography/MR imaging (PET/MRI) is that the MRIs can have a signal void around the dental fillings that is segmented as artificial air-regions in the attenuation map. For artifacts connected to the background, we propose an extension to an existing active contour algorithm to delineate the outer contour using the nonattenuation corrected PET image and the original attenuation map. We propose a combination of two different methods for differentiating the artifacts within the body from the anatomical air-regions by first using a template of artifact regions, and second, representing the artifact regions with a combination of active shape models and k-nearest-neighbors. The accuracy of the combined method has been evaluated using 25 [Formula: see text]-fluorodeoxyglucose PET/MR patients. Results showed that the approach was able to correct an average of [Formula: see text] of the artifact areas.

Background: The current MR-based attenuation correction (AC) used in combined PET/MR systems computes a Dixon attenuation map (MR-ACDixon) based on fat and water images derived from in- and opposed-phase MRI. We observed an occasional fat/water inversion in MR-ACDixon. The aim of our study was to estimate the prevalence of this phenomenon in a large patient cohort and assess the possible bias on PET data., Methods: PET/MRI was performed on a Siemens Biograph mMR (Siemens AG, Erlangen, Germany). We visually inspected attenuation maps of 283 brain or head/neck (H/N) patients, classified them as non-inverted or inverted, and calculated the fat/water tissue fraction. We selected ten FDG-PET brain patients with non-inverted attenuation maps for further analysis. Tissue inversion was simulated, and PET images were reconstructed using both original and inverted attenuation maps. The FDG-PET images of the ten brain patients were analyzed using 11 concentric annulus regions of 5 mm width placed over a central transaxial image plane traversing PETDixon., Results: Out of the 283 patients, a fat/water inversion in 23 patients (8.1%) was observed. The average fraction of fat in the correct MR-ACDixon was 13% for brain and 17% for H/N patients. In the inverted cases, we found an average fat fraction of 56% for the brain patients and 41% for the H/N patients. The effect of the simulated tissue inversion in the brain studies was clearly seen on AC-PET images. The percent-difference image revealed a radial error where the largest difference was at the ventricles (30% ± 3%) and smallest at the cortical region (10% ± 2%)., Conclusions: Tissue inversion in Dixon MRI is well known and can occur when there is an error in the off-resonance correction method. Tissue inversion needs to be considered if, based on Dixon-AC, the construction of normal PET databases is performed or any quantitative physiological parameters are fitted. Visual inspection is needed if Dixon-AC is to be used in clinical routine.

Purpose: In combined PET/MRI standard PET attenuation correction (AC) is based on tissue segmentation following dedicated MR sequencing and, typically, bone tissue is not represented. We evaluate PET quantification in whole-body (WB)-PET/MRI following MR-AC without considering bone attenuation and then investigate different strategies to account for bone tissue in clinical PET/MR imaging. To this purpose, bone tissue representation was extracted from separate CT images, and different bone representations were simulated from hypothetically derived MR-based bone classifications., Methods: Twenty oncology patients referred for a PET/CT were injected with either [18F]-FDG or [18F]-NaF and imaged on PET/CT (Biograph TruePoint/mCT, Siemens) and PET/MRI (mMR, Siemens) following a standard single-injection, dual-imaging clinical WB-protocol. Routine MR-AC was based on in-/opposed-phase MR imaging (orgMR-AC). PET(/MRI) images were reconstructed (AW-OSEM, 3 iterations, 21 subsets, 4mm Gaussian) following routine MR-AC and MR-AC based on four modified attenuation maps. These modified attenuation maps were created for each patient by non-linear co-registration of the CT images to the orgMR-AC images, and adding CT bone mask values representing cortical bone: 1200HU (cortCT), spongiosa bone: 350HU (spongCT), average CT value (meanCT) and original CT values (orgCT). Relative difference images of the PET following AC using the modified attenuation maps were compared. SUVmean was calculated in anatomical reference regions and for PET-positive lesions., Results: The relative differences in SUVmean across patients following orgMR-AC and orgCT in soft tissue lesions and in bone lesions were similar (range: 0.0% to -22.5%), with an average underestimation of SUVmean of 7.2% and 10.0%, respectively when using orgMR-AC. In bone lesions, spongCT values were closest to orgCT (median bias of 1.3%, range: -9.0% to 13.5%) while the overestimation of SUVmean with respect to orgCT was highest for cortCT (40.8%, range: 1.5% to 110.8%). For soft tissue lesions the bias was highest using cortCT (13.4%, range: -2.3% to 17.3%) and lowest for spongCT (-2.2%, range: 0.0% to -13.7%)., Conclusions: In PET/MR imaging using standard MR-AC PET uptake values in soft lesions and bone lesions are underestimated by about 10%. In individual patients this bias can be as high as 22%, which is significant during clinical follow-up exams. If bone segmentation is available, then assigning a fixed attenuation value of spongious bone to all bone structures appears reasonable and results in only a minor bias of 5%, or less in uptake values of soft tissue and bone lesions., (Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.)

Aim: Combined PET/MR systems have now become available for clinical use. Given the lack of integrated standard transmission (TX) sources in these systems, attenuation and scatter correction (AC) must be performed using the available MR-images. Since bone tissue cannot easily be accounted for during MR-AC, PET quantification can be biased, in particular, in the vicinity of the skull. Here, we assess PET quantification in PET/MR imaging of patients using phantoms and patient data., Materials and Methods: Nineteen patients referred to our clinic for a PET/CT exam as part of the diagnostic evaluation of suspected dementia were included in our study. The patients were injected with 200MBq [(18)F]FDG and imaged with PET/CT and PET/MR in random sequence within 1h. Both, PET/CT and PET/MR were performed as single-bed acquisitions without contrast administration. PET/CT and PET/MR data were reconstructed following CT-based and MR-based AC, respectively. MR-AC was performed based on: (A) standard Dixon-Water-Fat segmentation (DWFS), (B) DWFS with co-registered and segmented CT bone values superimposed, and (C) with co-registered full CT-based attenuation image. All PET images were reconstructed using AW-OSEM, with neither resolution recovery nor time-of-flight option employed. PET/CT (D) or PET/MR (A-C) images were decay-corrected to the start time of the first examination. PET images following AC were evaluated visually and quantitatively using 10 homeomorphic regions of interest drawn on a transaxial T1w-MR image traversing the central basal ganglia. We report the relative difference (%) of the mean ROI values for (A)-(C) in reference to PET/CT (D). In a separate phantom experiment a 2L plastic bottle was layered with approximately 12mm of Gypsum plaster to mimic skull bone. The phantom was imaged on PET/CT only and standard MR-AC was performed by replacing hyperdense CT attenuation values corresponding to bone (plaster) with attenuation values of water. PET image reconstruction was performed with CT-AC (D) and CT-AC using the modified CT images corresponding to MR-AC using DWFS (A)., Results: PET activity values in patients following MR-AC (A) showed a substantial radial dependency when compared to PET/CT. In all patients cortical PET activity was lower than the activity in the central region of the brain (10-15%). When adding bone attenuation values to standard MR-AC (B and C) the radial gradient of PET activity values was removed. Further evaluation of PET/MR activity following MR-AC (A) relative to MR-AC (C) using the full CT for attenuation correction showed an underestimation of 25% in the cortical regions and 5-10% in the central regions of the brain. Observations in patients were replicated by observations from the phantom study., Conclusion: Our phantom and patient data demonstrate a spatially varying bias of the PET activity in PET/MR images of the brain when bone tissue is not accounted for during attenuation correction. This has immediate implications for PET/MR imaging of the brain. Therefore, refinements to existing MR-AC methods or alternative strategies need to be found prior to adopting PET/MR imaging of the brain in clinical routine and research., (© 2013 Elsevier Inc. All rights reserved.)

Purpose: In combined whole-body PET/MR, attenuation correction (AC) is performed indirectly using the available MR image information and subsequent segmentation. Implant-induced susceptibility artifacts and subsequent signal voids may challenge MR-based AC (MR-AC). We evaluated the accuracy of MR-AC in PET/MR in patients with metallic endoprostheses, and propose a clinically feasible correction method., Methods: We selected patients with uni- or bilateral endoprostheses from 61 consecutive referrals for whole-body PET/MR imaging (mMR; Siemens Healthcare). Simultaneous whole-body PET/MR imaging was performed at 120 min after injection of about 300 MBq [(18)F]FDG. MR-AC was performed using (1) original MR images and subsequent Dixon water-fat segmentation, (2) as method 1 with implant-induced signal voids filled with soft tissue, (3) as method 2 with superimposed coregistered endoprostheses from the CT scan, and (4) as method 1 with implant-induced signal voids filled with metal. Following MR-AC (methods 1-4) PET emission images were reconstructed on 344 × 344 matrices using attenuation-weighted OSEM (three iterations, 21 subsets, 4 mm gaussian). Maximum body-weight normalized standardized uptake values (SUVmax) were obtained for both hips. Mean SUV (SUVmean) in homogeneous reference regions in the gluteal muscle and bladder following MR-AC (methods 1-4) are also reported., Results: In total, four patients presented with endoprostheses, unilateral in two and bilateral in two. The fraction of voxels in MR images affected by the implant was at least twice that of the voxels representing the actual implants. MR-AC using methods 2 and 3 recovered the FDG distribution pattern compared to uncorrected PET images and method 1, while method 4 resulted in severe overestimation of FDG uptake (>460 % SUVmax). When compared to method 1, relative changes in SUVmean in the reference regions from method 2 and 3 were generally small albeit not correlated with the fraction of the attenuation image affected by implant-induced artifacts., Conclusions: Endoprostheses cause PET/MR artifacts that exceed the volume occupied by the implants, and bias PET quantification. Artifacts and bias can be corrected by semiautomated inpainting with soft tissue with a single composition prior to MR-AC, thus restoring quantitative activity distribution.