Since the publication of the European Association of Nuclear Medicine (EANM) procedural guidelines for radionuclide myocardial perfusion imaging (MPI) in 2005, many small and some larger steps of progress have been made, improving MPI procedures. In this paper, the major changes from the updated 2015 procedural guidelines are highlighted, focusing on the important changes related to new instrumentation with improved image information and the possibility to reduce radiation exposure, which is further discussed in relation to the recent developments of new International Commission on Radiological Protection (ICRP) models. Introduction of the selective coronary vasodilator regadenoson and the use of coronary CT-contrast agents for hybrid imaging with SPECT/CT angiography are other important areas for nuclear cardiology that were not included in the previous guidelines. A large number of minor changes have been described in more detail in the fully revised version available at the EANM home page: http://eanm.org/publications/guidelines/2015_07_EANM_FINAL_myocardial_perfusion_guideline.pdf.

The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/ CT-based method for quantifying skeletal tumour burden. Methods: Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18 F-choline-PET/CT and 18 F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method. Results: The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5-6% for the vertebral column and ribs and ≤3% for other bones. Conclusion:The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.

Background: Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image. Results: The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones). Conclusion: The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.

Background Block-sequential regularized expectation maximization (BSREM), commercially Q. Clear (GE Healthcare, Milwaukee, WI, USA), is a reconstruction algorithm that allows for a fully convergent iterative reconstruction leading to higher image contrast compared to conventional reconstruction algorithms, while also limiting noise. The noise penalization factor β controls the trade-off between noise level and resolution and can be adjusted by the user. The aim was to evaluate the influence of different β values for different activity time products (ATs = administered activity × acquisition time) in whole-body 18 F-fluorodeoxyglucose (FDG) positron emission tomography with computed tomography (PET-CT) regarding quantitative data, interpretation, and quality assessment of the images. Twenty-five patients with known or suspected malignancies, referred for clinical 18 F-FDG PET-CT examinations acquired on a silicon photomultiplier PET-CT scanner, were included. The data were reconstructed using BSREM with β values of 100–700 and ATs of 4–16 MBq/kg × min/bed (acquisition times of 1, 1.5, 2, 3, and 4 min/bed). Noise level, lesion SUV max , and lesion SUV peak were calculated. Image quality and lesion detectability were assessed by four nuclear medicine physicians for acquisition times of 1.0 and 1.5 min/bed position. Results The noise level decreased with increasing β values and ATs. Lesion SUV max varied considerably between different β values and ATs, whereas SUV peak was more stable. For an AT of 6 (in our case 1.5 min/bed), the best image quality was obtained with a β of 600 and the best lesion detectability with a β of 500. AT of 4 generated poor-quality images and false positive uptakes due to noise. Conclusions For oncologic whole-body 18 F-FDG examinations on a SiPM-based PET-CT, we propose using an AT of 6 (i.e., 4 MBq/kg and 1.5 min/bed) reconstructed with BSREM using a β value of 500–600 in order to ensure image quality and lesion detection rate as well as a high patient throughput. We do not recommend using AT < 6 since the risk of false positive uptakes due to noise increases.

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Patients with neuroendocrine tumors (NETs) are often treated with somatostatin analogs (SSAs) for control of symptoms and tumor growth. Such therapy could theoretically lead to misinterpretation of somatostatin receptor imaging with 68 Ga-DOTATATE PET/CT by interfering with tracer-receptor binding. Guidelines recommend an interval of 3-4 wk between the last dose and imaging. The aim of this study was to evaluate if long-acting (LA) SSA treatment changes the uptake of 68 Ga-DOTATATE in patients with NETs. Methods: From 2013 to 2016, 296 patients with, or under evaluation for, NETs were included in this prospective observational study. The effect of LA SSA on tracer uptake was evaluated in 2 main patient populations: those undergoing 68 Ga-DOTATATE PET/CT before starting LA SSA treatment and at least once afterward, and those receiving ongoing LA SSA therapy, in whom the effect of the interval between the last dose of LA SSA and the PET/CT exam was analyzed. A third, explorative, analysis was performed to evaluate if clinical disease progression, regression, or stable tumor status changed the uptake of 68 Ga-DOTATATE. In the 3 analyses, measurements of SUV max in normal liver and tumor lesions were compared. Results: The median SUV max in normal liver was significantly higher before treatment (8.6; interquartile range, 7.4-10.2) than after treatment initiation (6.0; 4.7-8.0) (P , 0.001). No significant changes in SUV max were seen in tumor lesions after treatment initiation. No significant differences in SUV max were found in normal liver or tumor lesions dependent on the interval between last dose of LA SSA and PET/CT. Conclusion: Treatment with LA SSA does not change SUV max in tumor lesions, whereas SUV max in normal liver is significantly lower after treatment. The findings have implications for interpretation of 68 Ga-DOTATATE PET/CT for response assessment after SSA therapy and for guidelines on discontinuation of treatment before PET/CT.

Aim To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/ CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-na€ ıve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18 F-choline PET images above a standardized uptake value (SUV) of 2Á65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results The SDI between the automated and the manual volume segmentations was 0Á78 and 0Á79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0Á02), whereas age, PSA, and Gleason score were not. Conclusion Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival.

Prostate-specific membrane antigen (PSMA) radiopharmaceuticals used with positron emission tomography/computed tomography (PET-CT) are a promising tool for managing patients with prostate cancer. This study aimed to determine the accuracy of 18 F-PSMA-1007 PET-CT for detecting tumors in the prostate gland using radical prostatectomy (RP) specimens as a reference method and to determine whether a correlation exists between 18 F-PSMA-1007 uptake and the International Society of Urological Pathology (ISUP) grade and prostate specific antigen (PSA) levels at diagnosis. Methods: Thirty-nine patients referred for 18 F-PSMA-1007 PET-CT for initial staging and who underwent RP within four months were retrospectively included. Uptake of 18 F-PSMA-1007 indicative of cancer was assessed and maximum standardized uptake values (SUVmax) and total lesion uptake (TLU) were calculated for the index tumor. Histopathology was assessed from RP specimens. True positive, false negative, and false positive lesions were calculated. Results: In 94.9% of patients, the index tumor was correctly identified with PET.SUVmax was significantly higher in the tumors vs normal prostate tissue, but no significant differences were found between different ISUP grades and SUVmax. There was a poor correlation between PSA at diagnosis and SUVmax (r=0.23) and moderate agreement between PSA at diagnosis and TLU (r=0.67). When all tumors (also non-index tumors) were considered, many small tumors (approx. 1-2 mm) were not detected with PET. Conclusion: 18 F-PSMA-1007 PET-CT performs well in correctly identifying the index tumor in patients with intermediate to high-risk prostate cancer. Approximately 5% of the index tumors were missed by PET, which agrees with previous studies.