The QIBA Profile for FDG PET/CT as an Imaging Biomarker Measuring Response to Cancer Therapy.

The Quantitative Imaging Biomarkers Alliance (QIBA) Profile for fluorodeoxyglucose (FDG) PET/CT imaging was created by QIBA to both characterize and reduce the variability of standardized uptake values (SUVs). The Profile provides two complementary claims on the precision of SUV measurements. First, tumor glycolytic activity as reflected by the maximum SUV (SUVmax) is measurable from FDG PET/CT with a within-subject coefficient of variation of 10%-12%. Second, a measured increase in SUVmax of 39% or more, or a decrease of 28% or more, indicates that a true change has occurred with 95% confidence. Two applicable use cases are clinical trials and following individual patients in clinical practice. Other components of the Profile address the protocols and conformance standards considered necessary to achieve the performance claim. The Profile is intended for use by a broad audience; applications can range from discovery science through clinical trials to clinical practice. The goal of this report is to provide a rationale and overview of the FDG PET/CT Profile claims as well as its context, and to outline future needs and potential developments.

Preliminary Investigation of Cerebral Blood Flow and Amyloid Burden in Veterans With and Without Combat-Related Traumatic Brain Injury.

This study aimed to examine global and regional cerebral blood flow and amyloid burden in combat veterans with and without traumatic brain injury (TBI). Cerebral blood flow (in milliliters per minute per 100 mL) was measured by quantitative [(15)O]water, and amyloid burden was measured by [(11)C]PIB imaging. Mean global cerebral blood flow was significantly lower in veterans with TBI compared with non-TBI veterans. There were essentially no differences between groups for globally normalized regional cerebral blood flow. Amyloid burden did not differ between TBI and non-TBI veterans. Veterans who have suffered a TBI have significantly lower cerebral blood flow than non-TBI controls but did not manifest increased levels of amyloid, globally or regionally.

An algorithm for automated ROI definition in water or epoxy-filled NEMA NU-2 image quality phantoms.

Drawing regions of interest (ROIs) in positron emission tomography/computed tomography (PET/CT) scans of the National Electrical Manufacturers Association (NEMA) NU-2 Image Quality (IQ) phantom is a time-consuming process that allows for interuser variability in the measurements. In order to reduce operator effort and allow batch processing of IQ phantom images, we propose a fast, robust, automated algorithm for performing IQ phantom sphere localization and analysis. The algorithm is easily altered to accommodate different configurations of the IQ phantom. The proposed algorithm uses information from both the PET and CT image volumes in order to overcome the challenges of detecting the smallest spheres in the PET volume. This algorithm has been released as an open-source plug-in to the Osirix medical image viewing software package. We test the algorithm under various noise conditions, positions within the scanner, air bubbles in the phantom spheres, and scanner misalignment conditions. The proposed algorithm shows run-times between 3 and 4 min and has proven to be robust under all tested conditions, with expected sphere localization deviations of less than 0.2 mm and variations of PET ROI mean and maximum values on the order of 0.5% and 2%, respectively, over multiple PET acquisitions. We conclude that the proposed algorithm is stable when challenged with a variety of physical and imaging anomalies, and that the algorithm can be a valuable tool for those who use the NEMA NU-2 IQ phantom for PET/CT scanner acceptance testing and QA/QC.

Dependency of cardiac rubidium-82 imaging quantitative measures on age, gender, vascular territory, and software in a cardiovascular normal population.

OBJECTIVES: Recent technological improvements to PET imaging equipment combined with the availability of software optimized to calculate regional myocardial blood flow (MBF) and myocardial flow reserve (MFR) create a paradigm shifting opportunity to provide new clinically relevant quantitative information to cardiologists. However, clinical interpretation of the MBF and MFR is entirely dependent upon knowledge of MBF and MFR values in normal populations and subpopulations. This work reports Rb-82-based MBF and MFR measurements for a series of 49 verified cardiovascularly normal subjects as a preliminary baseline for future clinical studies. METHODS: Forty-nine subjects (24F/25M, ages 41-69) with low probability for coronary artery disease and with normal exercise stress test were included. These subjects underwent rest/dipyridamole stress Rb-82 myocardial perfusion imaging using standard clinical techniques (40 mCi injection, 6-minute acquisition) using a Siemens Biograph 40 PET/CT scanner with high count rate detector option. List mode data was rehistogrammed into 26 dynamic frames (12 × 5 seconds, 6 × 10 seconds, 4 × 20 seconds, 4 × 40 seconds). Cardiac images were processed, and MBF and MFR calculated using Siemens syngo MBF, PMOD, and FlowQuant software using a single compartment Rb-82 model. RESULTS: Global myocardial blood flow under pharmacological stress for the 24 females as measured by PMOD, syngo MBF, and FlowQuant were 3.10 ± 0.72, 2.80 ± 0.66, and 2.60 ± 0.63 mL·minute(-1)·g(-1), and for the 25 males was 2.60 ± 0.84, 2.33 ± 0.75, 2.15 ± 0.62 mL·minute(-1)·g(-1), respectively. Rest flows for PMOD, syngo MBF, and FlowQuant averaged 1.32 ± 0.42, 1.20 ± 0.33, and 1.06 ± 0.38 mL·minute(-1)·g(-1) for the female subjects, and 1.12 ± 0.29, 0.90 ± 0.26, and 0.85 ± 0.24 mL·minute(-1)·g(-1) for the males. Myocardial flow reserves for PMOD, syngo MBF, and FlowQuant for the female normals were calculated to be 2.50 ± 0.80, 2.53 ± 0.67, 2.71 ± 0.90, and 2.50 ± 1.19, 2.85 ± 1.19, 2.94 ± 1.31 mL·minute(-1)·g(-1) for males. CONCLUSION: Quantitative normal MBF and MFR values averaged for age and sex have been compiled for three commercial pharmacokinetic software packages. The current collection of data consisting of 49 subjects resulted in several statistically significant conclusions that support the need for a software specific, age, and sex-matched database to aid in interpretation of quantitative clinical myocardial perfusion studies.

Mucociliary Clearance is Impaired in Small Airways of Cystic Fibrosis Pigs.

Rationale: Cystic fibrosis is a genetic disorder characterized by recurrent airway infections, inflammation, and progressive decline in lung function. Autopsy and spirometry data suggest that cystic fibrosis may start in the small airways which, due to the fractal nature of the airways, account for most of the airway tree surface area. However, they are not easily accessible for testing. Objectives: Here, we tested the hypothesis that mucociliary clearance is abnormal in the small airways of newborn cystic fibrosis pigs. Methods: Current mucociliary clearance assays are limited therefore we developed a dynamic positron emission tomography scan assay with high spatial and temporal resolution. Each study was accompanied by a high-resolution computed tomography scan that helped identify the thin outer region of the lung that contained small airways. Measurements and Main Results: Clearance of aerosolized [ 68 Ga]macro aggregated albumin from distal airways occurred within minutes after delivery and followed a two-phase process. In cystic fibrosis pigs, both early and late clearance rates were slower. Stimulation of the cystic fibrosis airways with the purinergic agonist UTP further impaired late clearance. Only 1 cystic fibrosis pig treated with UTP out of 6 cleared more than 20% of the delivered dose. Conclusions: These data indicate that mucociliary transport in the small airways is fast and can easily be missed if the acquisition is not fast enough. The data also indicate that mucociliary transport is impaired in small airways of cystic fibrosis pigs. This defect is exacerbated by stimulation of mucus secretions with purinergic agonists.

Multicenter Evaluation of Frequency and Impact of Activity Infiltration in PET Imaging, Including Microscale Modeling of Skin-Absorbed Dose.

There has been significant recent interest in understanding both the frequency of nuclear medicine injection infiltration and the potential for negative impact, including skin injury. However, no large-scale study has yet correlated visualized injection site activity with actual activity measurement of an infiltrate. Additionally, current skin dosimetry approaches lack sufficient detail to account for critical factors that impact the dose to the radiosensitive epidermis. Methods: From 10 imaging sites, 1,000 PET/CT patient studies were retrospectively collected. At each site, consecutive patients with the injection site in the field of view were used. The radiopharmaceutical, injected activity, time of injection and imaging, injection site, and injection method were recorded. Net injection site activity was calculated from volumes of interest. Monte Carlo image-based absorbed dose calculations were performed using the actual geometry from a patient with a minor infiltration. The simulation model used an activity distribution in the skin microanatomy based on known properties of subcutaneous fat, dermis, and epidermis. Simulations using several subcutaneous fat-to-dermis concentration ratios were performed. Absorbed dose to the epidermis, dermis, and fat were calculated along with relative γ- and ß-contributions, and these findings were extrapolated to a hypothetical worst-case (470 MBq) full-injection infiltration. Results: Only 6 of 1,000 patients had activity at the injection site in excess of 370 kBq (10 µCi), with no activities greater than 1.7 MBq (45 µCi). In 460 of 1,000 patients, activity at the injection site was clearly visualized. However, quantitative assessment of activities averaged only 34 kBq (0.9 µCi), representing 0.008% of the injected activity. Calculations for the extrapolated 470-MBq infiltration resulted in a hypothetical absorbed dose to the epidermis of below 1 Gy, a factor of 2 lower than what is required for deterministic skin reactions. Analysis of the dose distribution demonstrates that the dermis acts as a ß-shield for the radiation-sensitive epidermis. Dermal shielding is highly effective for low-energy 18F positrons but less so with the higher-energy positrons of 68Ga. Conclusion: When quantitative activity measurement criteria are used rather than visual, the frequency of PET infiltration appears substantially below frequencies previously published. Shallow doses to the epidermis from infiltration events are also likely substantially lower than previously reported because of absorption of ß-particles in the dermis.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Deployment and Governance.

The deployment of artificial intelligence (AI) has the potential to make nuclear medicine and medical imaging faster, cheaper, and both more effective and more accessible. This is possible, however, only if clinicians and patients feel that these AI medical devices (AIMDs) are trustworthy. Highlighting the need to ensure health justice by fairly distributing benefits and burdens while respecting individual patients' rights, the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks that arise during the deployment of AIMD: autonomy of patients and clinicians, transparency of clinical performance and limitations, fairness toward marginalized populations, and accountability of physicians and developers. We provide preliminary recommendations for governing these ethical risks to realize the promise of AIMD for patients and populations.

Synthetic PET via Domain Translation of 3-D MRI.

Historically, patient datasets have been used to develop and validate various reconstruction algorithms for PET/MRI and PET/CT. To enable such algorithm development, without the need for acquiring hundreds of patient exams, in this article we demonstrate a deep learning technique to generate synthetic but realistic whole-body PET sinograms from abundantly available whole-body MRI. Specifically, we use a dataset of 56 18F-FDG-PET/MRI exams to train a 3-D residual UNet to predict physiologic PET uptake from whole-body T1-weighted MRI. In training, we implemented a balanced loss function to generate realistic uptake across a large dynamic range and computed losses along tomographic lines of response to mimic the PET acquisition. The predicted PET images are forward projected to produce synthetic PET (sPET) time-of-flight (ToF) sinograms that can be used with vendor-provided PET reconstruction algorithms, including using CT-based attenuation correction (CTAC) and MR-based attenuation correction (MRAC). The resulting synthetic data recapitulates physiologic 18F-FDG uptake, e.g., high uptake localized to the brain and bladder, as well as uptake in liver, kidneys, heart, and muscle. To simulate abnormalities with high uptake, we also insert synthetic lesions. We demonstrate that this sPET data can be used interchangeably with real PET data for the PET quantification task of comparing CTAC and MRAC methods, achieving ≤ 7.6% error in mean-SUV compared to using real data. These results together show that the proposed sPET data pipeline can be reasonably used for development, evaluation, and validation of PET/MRI reconstruction methods.

Ethical Considerations for Artificial Intelligence in Medical Imaging: Data Collection, Development, and Evaluation.

The development of artificial intelligence (AI) within nuclear imaging involves several ethically fraught components at different stages of the machine learning pipeline, including during data collection, model training and validation, and clinical use. Drawing on the traditional principles of medical and research ethics, and highlighting the need to ensure health justice, the AI task force of the Society of Nuclear Medicine and Molecular Imaging has identified 4 major ethical risks: privacy of data subjects, data quality and model efficacy, fairness toward marginalized populations, and transparency of clinical performance. We provide preliminary recommendations to developers of AI-driven medical devices for mitigating the impact of these risks on patients and populations.

Artificial Intelligence in Nuclear Medicine: Opportunities, Challenges, and Responsibilities Toward a Trustworthy Ecosystem.

Trustworthiness is a core tenet of medicine. The patient-physician relationship is evolving from a dyad to a broader ecosystem of health care. With the emergence of artificial intelligence (AI) in medicine, the elements of trust must be revisited. We envision a road map for the establishment of trustworthy AI ecosystems in nuclear medicine. In this report, AI is contextualized in the history of technologic revolutions. Opportunities for AI applications in nuclear medicine related to diagnosis, therapy, and workflow efficiency, as well as emerging challenges and critical responsibilities, are discussed. Establishing and maintaining leadership in AI require a concerted effort to promote the rational and safe deployment of this innovative technology by engaging patients, nuclear medicine physicians, scientists, technologists, and referring providers, among other stakeholders, while protecting our patients and society. This strategic plan was prepared by the AI task force of the Society of Nuclear Medicine and Molecular Imaging.

The RSNA QIBA Profile for Amyloid PET as an Imaging Biomarker for Cerebral Amyloid Quantification.

A standardized approach to acquiring amyloid PET images increases their value as disease and drug response biomarkers. Most 18F PET amyloid brain scans often are assessed only visually (per regulatory labels), with a binary decision indicating the presence or absence of Alzheimer disease amyloid pathology. Minimizing technical variance allows precise, quantitative SUV ratios (SUVRs) for early detection of ß-amyloid plaques and allows the effectiveness of antiamyloid treatments to be assessed with serial studies. Methods: The Quantitative Imaging Biomarkers Alliance amyloid PET biomarker committee developed and validated a profile to characterize and reduce the variability of SUVRs, increasing statistical power for these assessments. Results: On achieving conformance, sites can justify a claim that brain amyloid burden reflected by the SUVR is measurable to a within-subject coefficient of variation of no more than 1.94% when the same radiopharmaceutical, scanner, acquisition, and analysis protocols are used. Conclusion: This overview explains the claim, requirements, barriers, and potential future developments of the profile to achieve precision in clinical and research amyloid PET imaging.

Automated measurement of uptake in cerebellum, liver, and aortic arch in full-body FDG PET/CT scans.

PURPOSE: The purpose of this work was to develop and validate fully automated methods for uptake measurement of cerebellum, liver, and aortic arch in full-body PET/CT scans. Such measurements are of interest in the context of uptake normalization for quantitative assessment of metabolic activity and/or automated image quality control. METHODS: Cerebellum, liver, and aortic arch regions were segmented with different automated approaches. Cerebella were segmented in PET volumes by means of a robust active shape model (ASM) based method. For liver segmentation, a largest possible hyperellipsoid was fitted to the liver in PET scans. The aortic arch was first segmented in CT images of a PET/CT scan by a tubular structure analysis approach, and the segmented result was then mapped to the corresponding PET scan. For each of the segmented structures, the average standardized uptake value (SUV) was calculated. To generate an independent reference standard for method validation, expert image analysts were asked to segment several cross sections of each of the three structures in 134 F-18 fluorodeoxyglucose (FDG) PET/CT scans. For each case, the true average SUV was estimated by utilizing statistical models and served as the independent reference standard. RESULTS: For automated aorta and liver SUV measurements, no statistically significant scale or shift differences were observed between automated results and the independent standard. In the case of the cerebellum, the scale and shift were not significantly different, if measured in the same cross sections that were utilized for generating the reference. In contrast, automated results were scaled 5% lower on average although not shifted, if FDG uptake was calculated from the whole segmented cerebellum volume. The estimated reduction in total SUV measurement error ranged between 54.7% and 99.2%, and the reduction was found to be statistically significant for cerebellum and aortic arch. CONCLUSIONS: With the proposed methods, the authors have demonstrated that automated SUV uptake measurements in cerebellum, liver, and aortic arch agree with expert-defined independent standards. The proposed methods were found to be accurate and showed less intra- and interobserver variability, compared to manual analysis. The approach provides an alternative to manual uptake quantification, which is time-consuming. Such an approach will be important for application of quantitative PET imaging to large scale clinical trials.

Quantification of uptake in pelvis F-18 FLT PET-CT images using a 3D localization and segmentation CNN.

PURPOSE: The purpose of this work was to develop and validate a deep convolutional neural network (CNN) approach for the automated pelvis segmentation in computed tomography (CT) scans to enable the quantification of active pelvic bone marrow by means of Fluorothymidine F-18 (FLT) tracer uptake measurement in positron emission tomography (PET) scans. This quantification is a critical step in calculating bone marrow dose for radiopharmaceutical therapy clinical applications as well as external beam radiation doses. METHODS: An approach for the combined localization and segmentation of the pelvis in CT volumes of varying sizes, ranging from full-body to pelvis CT scans, was developed that utilizes a novel CNN architecture in combination with a random sampling strategy. The method was validated on 34 planning CT scans and 106 full-body FLT PET-CT scans using a cross-validation strategy. Specifically, two different training and CNN application options were studied, quantitatively assessed, and statistically compared. RESULTS: The proposed method was able to successfully locate and segment the pelvis in all test cases. On all data sets, an average Dice coefficient of 0.9396 ± $\pm$ 0.0182 or better was achieved. The relative tracer uptake measurement error ranged between 0.065% and 0.204%. The proposed approach is time-efficient and shows a reduction in runtime of up to 95% compared to a standard U-Net-based approach without a localization component. CONCLUSIONS: The proposed method enables the efficient calculation of FLT uptake in the pelvis. Thus, it represents a valuable tool to facilitate bone marrow preserving adaptive radiation therapy and radiopharmaceutical dose calculation. Furthermore, the method can be adapted to process other bone structures as well as organs.

Repeatability of 68Ga-PSMA-HBED-CC PET/CT-Derived Total Molecular Tumor Volume.

Molecular tumor volume (MTV) is a parameter of interest in prostate cancer for assessing total disease burden on prostate-specific membrane antigen (PSMA) PET. Although software segmentation tools can delineate whole-body MTV, a necessary step toward meaningful monitoring of total tumor burden and treatment response through PET is establishing the repeatability of these metrics. The present study assessed the repeatability of total MTV and related metrics for 68Ga-PSMA-HBED-CC in prostate cancer. Methods: Eighteen patients from a prior repeatability study who underwent 2 test-retest PSMA PET/CT scans within a mean interval of 5 d were reanalyzed. Within-subject coefficient of variation and repeatability coefficients (RCs) were analyzed on a per-lesion and per-patient basis. For the per-lesion analysis, individual lesions were segmented for analysis by a single reader. For the per-patient analysis, subgroups of up to 10 lesions (single reader) and the total tumor volume per patient were segmented (independently by 2 readers). Image parameters were MTV, SUVmax, SUVpeak, SUVmean, total lesion PSMA, and the related metric PSMA quotient (which integrates lesion volume and PSMA avidity). Results: In total, 192 segmentations were analyzed for the per-lesion analysis and 1,662 segmentations for the per-patient analysis (combining the 2 readers and 2 scans). The RC of the MTV of single lesions was 77% (95% CI, 63%-96%). The RC improved to 33% after aggregation of up to 10 manually selected lesions into subgroups assessed per patient (95% CI, 25%-46%). The RC of the semiautomatic MTVtotal (the sum of all voxels in the whole-body total tumor segmentation per patient) was 35% (95% CI, 25%-50%), the Bland-Altman bias was -6.70 (95% CI, -14.32-0.93). Alternating readers between scans led to a comparable RC of 37% (95% CI, 28%-49%) for MTVtotal, meaning that the metric is robust between scanning sessions and between readers. Conclusion:68Ga-PSMA-HBED-CC PET-derived semiautomatic MTVtotal is repeatable and reader-independent, with a change of ±35% representing a true change in tumor volume. Volumetry of single manually selected lesions has considerably lower repeatability, and volumetry based on subgroups of these lesions, although showing acceptable repeatability, is less systematic. The semiautomatic analysis of MTVtotal used in this study offers an efficient and robust means of assessing response to therapy.

Nuclear Medicine and Artificial Intelligence: Best Practices for Evaluation (the RELAINCE Guidelines).

An important need exists for strategies to perform rigorous objective clinical-task-based evaluation of artificial intelligence (AI) algorithms for nuclear medicine. To address this need, we propose a 4-class framework to evaluate AI algorithms for promise, technical task-specific efficacy, clinical decision making, and postdeployment efficacy. We provide best practices to evaluate AI algorithms for each of these classes. Each class of evaluation yields a claim that provides a descriptive performance of the AI algorithm. Key best practices are tabulated as the RELAINCE (Recommendations for EvaLuation of AI for NuClear medicinE) guidelines. The report was prepared by the Society of Nuclear Medicine and Molecular Imaging AI Task Force Evaluation team, which consisted of nuclear-medicine physicians, physicists, computational imaging scientists, and representatives from industry and regulatory agencies.

Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study.

For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a "harmonized" alongside a "standard" dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models.

Nuclear Medicine and Artificial Intelligence: Best Practices for Algorithm Development.

The nuclear medicine field has seen a rapid expansion of academic and commercial interest in developing artificial intelligence (AI) algorithms. Users and developers can avoid some of the pitfalls of AI by recognizing and following best practices in AI algorithm development. In this article, recommendations on technical best practices for developing AI algorithms in nuclear medicine are provided, beginning with general recommendations and then continuing with descriptions of how one might practice these principles for specific topics within nuclear medicine. This report was produced by the AI Task Force of the Society of Nuclear Medicine and Molecular Imaging.

Radiopharmaceutical Delivery for Theranostics: Pharmacokinetics and Pharmacodynamics.

Theranostics is a new and evolving combination diagnostic and/or therapeutic approach that is demonstrating efficacy for treatment of a growing number of cancers. In this approach, a diagnostic radiopharmaceutical is used in concert with positron-emission tomography (PET) or single photon emission computed tomography (SPECT) imaging to identify whether a cancer-specific membrane protein is strongly expressed on a patient's tumors. If the molecular target is detected with sufficient specificity and uptake, a therapeutic radiopharmaceutical, nearly identical to the diagnostic radiopharmaceutical except labeled with a longer-lived alpha or beta-emitting radionuclide, is administered at a therapeutic dose level to treat the cancer. Quantitative imaging methods are being used to elucidate patient-specific pharmacokinetics to select patients for whom the therapeutic radiopharmaceutical would be most beneficial. Similarly, quantitative imaging of the therapeutic radionuclide is being used to image pharmacodynamic response to therapy (cell kill) to guide personalized, patient-specific dosages designed to both reduce radiation toxicities and optimize radiotherapeutic benefit.

Imaging and dosimetric characteristics of 67 Cu.

In recent years the use of beta-emitting radiopharmaceuticals for cancer therapy has expanded rapidly following development of therapeutics for neuroendocrine tumors, prostate cancer, and other oncologic malignancies. One emerging beta-emitting radioisotope of interest for therapy is 67Cu (t1/2: 2.6 d) due to its chemical equivalency with the widely-established positron-emitting isotope 64Cu (t1/2: 12.7 h). In this work we evaluate both the imaging and dosimetric characteristics of 67Cu, as well as producing the first report of SPECT/CT imaging using 67Cu. To this end, 67Cu was produced by photon-induced reactions on isotopically-enriched 68Zn at the Low-Energy Accelerator Facility (LEAF) of Argonne National Laboratory, followed by bulk separation of metallic 68Zn by sublimation and radiochemical purification by column chromatography. Gamma spectrometry was performed by efficiency-calibrated high-purity germanium (HPGe) analysis to verify absolute activity calibration and establish radionuclidic purity. Absolute activity measurements corroborated manufacturer-recommended dose-calibrator settings and no radionuclidic impurities were observed. Using the Clinical Trials Network anthropomorphic chest phantom, SPECT/CT images were acquired. Medium energy (ME) SPECT collimation was found to provide the best image quality from the primary 185 keV gamma emission of 67Cu. Reconstructed images of 67Cu were similar in quality to images acquired using 177Lu. Recovery coefficients were calculated and compared against quantitative images of 99mTc, 177Lu, and 64Cu within the same anthropomorphic chest phantom. Production and clinical imaging of 67Cu appears feasible, and future studies investigating the therapeutic efficacy of 67Cu-based radiopharmaceuticals are warranted.

Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion.

Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About < 3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of - 5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.