We are delighted to present the Rising Stars in PET and SPECT: 2022 article collection. This collection showcases the high-quality work of internationally recognized researchers in the early stages of their independent careers. All Rising Stars researchers were individually nominated by the Frontiers in Nuclear Medicine editors in recognition of their potential to influence the future directions in their respective fields. The work presented here highlights the diversity of research performed across the entire breadth of PET and SPECT and presents advances in theory, experiment, and methodology with applications to compelling problems. New articles will be added to this collection as they are published. Please note that contributions are decided by topic editors only. Please inform topic editors when you wish to submit a manuscript. Early career researchers are defined as within 10 years after completing their Ph.D., M.D., or other equivalent terminal degrees.

Harvard Medical School

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Frontiers in Nuclear Medicine

PET and SPECT

Rising Stars in PET and SPECT: 2022

Editorial on the Research Topic <a href="https://www.frontiersin.org/research-topics/48960/rising-stars-in-pet-and-spect-2022">Rising Stars in PET and SPECT: 2022</a>On this new feature of Frontiers in Nuclear Medicine, we are delighted to present the Research Topic Rising Stars in PET and SPECT: 2022. We would like first to thank all the contributors for their great quality on their submissions, which have undoubtedly enhanced the depth and quality of this compilation. We want to highlight that all these manuscripts have been originated from young talented researchers that are already becoming the stars in the PET and SPECT fields that will shine us the light to guide our future and our path to excellence. It has been a terrific Research Topic with high-quality manuscripts, and for us, as Editors, it has been an immense pleasure to be part of it.In this Research Topic you will find an interesting variety of topics, from reviews on cardiac PET/MRI for sarcoidosis applications and preclinical antibody PET imaging for oncologic applications, to more technical studies such as the use of the background radiation of the Lutetium detectors on the TotalBody scanner or the use of Deep Learning applications for automatic delineation of tumor areas in pediatric gliomas, to a final position paper that challenges the future of reconstruction-free ultra-fast ToF PET scanners.It is with great anticipation that we offer readers a glimpse into the captivating essence of these works, recognizing the profound contributions that each article brings to the broader discourse of PET and SPECT research. We invite our readers to embark on a journey of discovery through these pages, where the boundaries of current knowledge are constantly being pushed and redefined, promising a future enriched by innovation and progress. We hope the reader will forgive us for unveiling some of the really interesting details that these works have put together, but we believe is worth walking the reader briefly through them.In the first place, the work by <a href="https://doi.org/10.3389/fnume.2022.1032444">Munoz et al.</a> touches one of the most challenging areas in cardiac imaging: the diagnosis of sarcoidosis. Sarcoidosis is a multisystemic disorders that mainly affects the lungs, but can affect other organs, including the heart. There is currently no gold-standard for diagnosing sarcoidosis, although PET and MRI, have shown promising results separately in detecting some of the features that allow an accurate diagnosis, but each has its limitations. The use of the integrated PET/MRI scanners has enabled the synergistic fusion of PET and MRI features to provide a better understanding of the underlying disease and hence to provide a more accurate diagnosis. The work of <a href="https://doi.org/10.3389/fnume.2022.1032444">Munoz et al.</a> an outstanding expert in cardiac PET/MRI analysis, especially in MR-based motion correction, is an excellent review that highlights the pros and cons of the different techniques and unveils some of the potential paths to further improve clinical diagnosis using PET/MRI.Secondly, the work presented by <a href="https://doi.org/10.3389/fnume.2022.953202">Brown et al.</a> lead by Dr. Pereira, one of the most talented young PET imaging researchers in the area of cancer biology and radiotheranostics, focuses on one the most promising areas within PET imaging: the use of antibody imaging to improve clinical decisions for cancer therapy. The authors describe the development and evaluation of various radiolabeled antibodies and antibody fragments that can specifically target Programmed cell death protein-1/ligand-1 (PD-1/PD-L1) in different tumor models. However, it's important to note that not all PD-L1-positive tumors respond to immunotherapy, and some patients with PD-L1-negative tumors have shown clinical benefit. This discrepancy is due to the inherent spatial and temporal heterogeneity of PD-L1 tumor expression, which is not accurately captured by biopsy-based diagnostic tests. In particular, the team highlights the first-in-human study of the clinically approved anti-PD-L1 antibody atezolizumab labeled with the positron emitter zirconium-89. They also discuss how PET imaging can reveal the spatial and temporal heterogeneity of PD-L1 expression, which has implications for patient selection and treatment monitoring. The article highlights the potential of molecular imaging of PD-L1 as a complementary tool for clinical oncologists and a powerful method to study cancer biology.Third, <a href="https://doi.org/10.3389/fnume.2022.960820">Ladefoged et al.</a> lead a novel work on the use of Deep Learning applications for the automatic delineation of tumors on pediatric gliomas. The use of Deep Learning (DL) has recently exploded for its applications in non-invasive imaging in the last years. One of the main areas of applications and interests in the community is the use of DL to facilitate or guide the automatic delineation of disease areas. In oncological applications, automatic tumor delineation has been a vast field of study. In this study, Dr. Ladefoged, a world expert in PET/MRI among others an international consortium that review and set the basis for improved brain PET imaging using PET/MRI scanners using accurate MR-based methods, presents their latest results on the application of artificial neural networks (ANN) for pediatric gliomas. The authors use an artificial neural network (ANN) that is pre-trained on a large cohort of adult patients with brain gliomas and then fine-tuned on pediatric patients. The ANN achieves high accuracy and concordance with manual delineation and provides clinically relevant metrics, moreover, performs well in longitudinal studies, showing consistent changes in the metrics over time. It has been well demonstrated that their method has the potential to serve as a complementary tool for clinical oncologists, as well as a powerful means to study cancer biology.Fourth, the work of <a href="https://doi.org/10.3389/fnume.2022.963067">Omidvari et al.</a> shows for the first time the potential of background radiation from the lutetium detectors in a TotalBody scanner. Dr. Omidvari is a young talented PET researcher who has been incredibly active in the field of TotalBody scanners, in our opinion one of the most amazing advances in the last decade in terms of PET technology. TotalBody scanners is a technology that has been made possible by a myriad of researchers at UC Davies, including but not limited to Drs. Badawi, Qi and Cherry, all of whom are co-authors on this incredible work. This novel research re-takes a long-forgotten idea of using the background radiation of the PET detectors as a potential source to provide a transmission scan to be used for attenuation correction, among others. The new technology from TotalBody scanners, mainly the vast number of the detectors and the incredible sensitivity provided by the scanners provides to this technology a second chance to proof its amazing capacity and its utility. The authors conducted a simulation study using Monte Carlo simulations of a 3D whole-body XCAT phantom in the uEXPLORER PET scanner. They focused on ultralow-dose PET scans that are now possible with these scanners and studied the effects of an increased acceptance angle, reduced scan durations, and Compton scattering on PET quantification. The authors compared different methods for reconstructing the attenuation maps from the lutetium background data, and evaluated their effects on PET quantification and image quality. The results showed that quantification and lesion contrast were minimally affected in both long axial field-of-view scanners and in a whole-body 20-min scan. A must-read paper for the lover so PET technical solutions.Finally, the position paper by <a href="https://doi.org/10.3389/fnume.2022.936091">Schramm</a>, a young and very talented scientist who has been working since the rise of combined PET/MRI scanners, challenges for the first time a unique concept and objective towards which many PET researchers have been turned into: the aim to obtain reconstruction-free PET images using ultra-fast time-of-flight (ToF) detectors. For the last years, there has been a lot of traction in the community, including several position papers in which the community is targeting the objective of attaining the limit of 10 ps time-resolution scanners to enable this reconstruction-free PET images. While the challenge is huge and opens the community to track and follow this path, Dr. <a href="https://doi.org/10.3389/fnume.2022.936091">Schramm</a>, raises questions about the enormous technical challenges that are required to make this aim possible, and the long distance that current technology needs to travel in order to reach it. For example, the author argues that quantitative PET imaging still requires the calculation of line integrals to model photon attenuation, the estimation of scattered coincidences, and the suppression of noise, which are not feasible without iterative techniques or advanced modeling. A fascinating manuscript that will not leave anybody indifferent and will generate, without any question, a lot of attention.The study by <a href="https://doi.org/10.3389/fnume.2022.1032444">Munoz et al.</a> explores the potential of hybrid PET-MR imaging for cardiac sarcoidosis. This approach combines the strengths of PET and MRI to provide a more comprehensive view of the disease. However, as you rightly pointed out, more research is needed to fully understand its potential and limitations. <a href="https://doi.org/10.3389/fnume.2022.953202">Brown et al.’s</a> work on preclinical antibody-PET imaging of PD-L1 represents a significant step forward in cancer treatment. The use of antibody imaging to improve clinical decisions for cancer therapy is promising, but further research is needed to fully understand its potential and limitations. The use of deep learning methods for automatic detection and delineation of pediatric gliomas is another significant advancement presented by <a href="https://doi.org/10.3389/fnume.2022.960820">Ladefoged et al.</a> While these methods can automate tumor delineation, further research could be done to optimize the ANN for different types of tumors or clinical settings. <a href="https://doi.org/10.3389/fnume.2022.963067">Omidvari et al.’s</a> work on lutetium background radiation in total-body PET scanners presents a novel approach to PET attenuation correction. This study underscores the potential of using Lutetium background radiation for attenuation correction in total-body PET. However, it also highlights the need for further research to optimize scan parameters and implement appropriate correction methods to improve PET quantification accuracy. Lastly, <a href="https://doi.org/10.3389/fnume.2022.936091">Schramm’s</a> position paper challenges the concept of achieving reconstruction-free PET images using ultra-fast ToF detectors. While this goal has gained significant traction in recent years, <a href="https://doi.org/10.3389/fnume.2022.936091">Schramm</a> raises important questions about the substantial technical hurdles that must be overcome to realize this goal.In summary, the articles presented in this Research Topic highlight the transformative potential of nuclear medicine to improve the diagnosis and treatment of disease. By harnessing the power of cutting-edge imaging techniques, machine learning algorithms, and innovative radiotracers, researchers are redefining the frontiers of medical imaging. However, as with any rapidly evolving field, there are hurdles to overcome. Persistent research efforts and collaborative endeavors will be instrumental in navigating these challenges and realizing the full potential of these groundbreaking approaches. We trust that readers will find the works presented in this Research Topic as compelling as we have. It is our hope that they derive as much pleasure from reading these articles as we have had in curating this Research Topic over the past months. We also encourage our readers to keep an eye on the future contributions of these promising young talents, as they are undoubtedly among the luminaries guiding our journey forward.DI: Writing &#x2013; original draft, Writing &#x2013; review &amp; editing. SK: Writing &#x2013; original draft, Writing &#x2013; review &amp; editing.Author SKK is employed by Brightonix Imaging Inc. However, it is important to clarify that the present research was conducted independently and not as part of his professional duties at Brightonix Imaging Inc. The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The author(s) DIG, SKK declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decisionAll claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Editorial: Rising stars in PET and SPECT: 2022

The diagnosis of cardiac sarcoidosis (CS) remains challenging. While only a small fraction of patients with systemic sarcoidosis present with clinically symptomatic CS, cardiac involvement has been associated with adverse outcomes, such as ventricular arrhythmia, heart block, heart failure and sudden cardiac death. Despite the clinical relevance of having an early and accurate diagnosis of CS, there is no gold-standard technique available for the assessment of CS. Non-invasive PET and MR imaging have shown promise in the detection of different histopathological features of CS. More recently, the introduction of hybrid PET-MR scanners has enabled the acquisition of these hallmarks in a single scan, demonstrating higher sensitivity and specificity for CS detection and risk stratification than with either imaging modality alone. This article describes recent developments in hybrid PET-MR imaging for improving the diagnosis of CS and discusses areas of future development that could make cardiac PET-MRI the preferred diagnostic tool for the comprehensive assessment of CS.

The diagnosis of cardiac sarcoidosis (CS) remains challenging. While only a small fraction of patients with systemic sarcoidosis present with clinically symptomatic CS, cardiac involvement has been associated with adverse outcomes, such as ventricular arrhythmia, heart block, heart failure and sudden cardiac death. Despite the clinical relevance of having an early and accurate diagnosis of CS, there is no gold-standard technique available for the assessment of CS. Non-invasive PET and MR imaging have shown promise in the detection of different histopathological features of CS. More recently, the introduction of hybrid PET-MR scanners has enabled the acquisition of these hallmarks in a single scan, demonstrating higher sensitivity and specificity for CS detection and risk stratification than with either imaging modality alone. This article describes recent developments in hybrid PET-MR imaging for improving the diagnosis of CS and discusses areas of future development that could make cardiac PET-MRI the preferred diagnostic tool for the comprehensive assessment of CS.

Recent advances in PET-MRI for cardiac sarcoidosis

IntroductionBrain 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-[18F]fluoroethyl)-l</sc>-tyrosine ([18F]FET PET) pediatric CNS tumors.</sec>MethodsA total of 109 [18F]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 (TBRmean and TBRmax).</sec>ResultsThe 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 TBRmax 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.</sec>ConclusionThe 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.</sec>

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-[18F]fluoroethyl)-L-tyrosine ([18F]FET PET) pediatric CNS tumors.Methods: A total of 109 [18F]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 (TBRmean and TBRmax).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 TBRmax 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.

Automatic detection and delineation of pediatric gliomas on combined [18F]FET PET and MRI

The current generation of total-body positron emission tomography (PET) scanners offer significant sensitivity increase with an extended axial imaging extent. With the large volume of lutetium-based scintillation crystals that are used as detector elements in these scanners, there is an increased flux of background radiation originating from 176Lu decay in the crystals and higher sensitivity for detecting it. Combined with the ability of scanning the entire body in a single bed position, this allows more effective utilization of the lutetium background as a transmission source for estimating 511 keV attenuation coefficients. In this study, utilization of the lutetium background radiation for attenuation correction in total-body PET was studied using Monte Carlo simulations of a 3D whole-body XCAT phantom in the uEXPLORER PET scanner, with particular focus on ultralow-dose PET scans that are now made possible with these scanners. Effects of an increased acceptance angle, reduced scan durations, and Compton scattering on PET quantification were studied. Furthermore, quantification accuracy of lutetium-based attenuation correction was compared for a 20-min scan of the whole body on the uEXPLORER, a one-meter-long, and a conventional 24-cm-long scanner. Quantification and lesion contrast were minimally affected in both long axial field-of-view scanners and in a whole-body 20-min scan, the mean bias in all analyzed organs of interest were within a ±10% range compared to ground-truth activity maps. Quantification was affected in certain organs, when scan duration was reduced to 5 min or a reduced acceptance angle of 17° was used. Analysis of the Compton scattered events suggests that implementing a scatter correction method for the transmission data will be required, and increasing the energy threshold from 250 keV to 290 keV can reduce the computational costs and data rates, with negligible effects on PET quantification. Finally, the current results can serve as groundwork for transferring lutetium-based attenuation correction into research and clinical practice.

The current generation of total-body positron emission tomography (PET) scanners offer significant sensitivity increase with an extended axial imaging extent. With the large volume of lutetium-based scintillation crystals that are used as detector elements in these scanners, there is an increased flux of background radiation originating from 176Lu decay in the crystals and higher sensitivity for detecting it. Combined with the ability of scanning the entire body in a single bed position, this allows more effective utilization of the lutetium background as a transmission source for estimating 511 keV attenuation coefficients. In this study, utilization of the lutetium background radiation for attenuation correction in total-body PET was studied using Monte Carlo simulations of a 3D whole-body XCAT phantom in the uEXPLORER PET scanner, with particular focus on ultralow-dose PET scans that are now made possible with these scanners. Effects of an increased acceptance angle, reduced scan durations, and Compton scattering on PET quantification were studied. Furthermore, quantification accuracy of lutetium-based attenuation correction was compared for a 20-min scan of the whole body on the uEXPLORER, a one-meter-long, and a conventional 24-cm-long scanner. Quantification and lesion contrast were minimally affected in both long axial field-of-view scanners and in a whole-body 20-min scan, the mean bias in all analyzed organs of interest were within a ±10% range compared to ground-truth activity maps. Quantification was affected in certain organs, when scan duration was reduced to 5 min or a reduced acceptance angle of 17° was used. Analysis of the Compton scattered events suggests that implementing a scatter correction method for the transmission data will be required, and increasing the energy threshold from 250 keV to 290 keV can reduce the computational costs and data rates, with negligible effects on PET quantification. Finally, the current results can serve as groundwork for transferring lutetium-based attenuation correction into research and clinical practice.

Lutetium background radiation in total-body PET—A simulation study on opportunities and challenges in PET attenuation correction

Programmed cell death protein-1/ligand-1 (PD-1/PD-L1) blockade, including antibody therapeutics, has transformed cancer treatment. However, a major challenge in the field relates to selecting patients who are likely to respond to immune checkpoint inhibitors. Indeed, biopsy-based diagnostic tests to determine immune checkpoint protein levels do not accurately capture the inherent spatial and temporal heterogeneity of PD-L1 tumor expression. As a result, not all PD-L1-positive tumors respond to immunotherapies, and some patients with PD-L1-negative tumors have shown clinical benefits. In 2018, a first-in-human study of the clinically-approved anti-PD-L1 antibody Atezolizumab labeled with the positron emitter zirconium-89 validated the ability of positron emission tomography (PET) to visualize PD-L1 expression in vivo and predict tumor response to immunotherapy. These studies have triggered the expansion of PD-L1-targeted immunoPET to assess PD-L1 protein levels and PD-L1 expression heterogeneity in real time and across the whole tumor. First, this mini-review introduces new PD-L1 PET imaging studies of the last 4 years, focusing on the expansion of preclinical tumor models and anti-PD-L1 antibodies/antibody fragments in development. Then, the review discusses how these preclinical models and targeting agents can be utilized to study spatial and temporal heterogeneity of PD-L1 expression.

Programmed cell death protein-1/ligand-1 (PD-1/PD-L1) blockade, including antibody therapeutics, has transformed cancer treatment. However, a major challenge in the field relates to selecting patients who are likely to respond to immune checkpoint inhibitors. Indeed, biopsy-based diagnostic tests to determine immune checkpoint protein levels do not accurately capture the inherent spatial and temporal heterogeneity of PD-L1 tumor expression. As a result, not all PD-L1-positive tumors respond to immunotherapies, and some patients with PD-L1-negative tumors have shown clinical benefits. In 2018, a first-in-human study of the clinically-approved anti-PD-L1 antibody Atezolizumab labeled with the positron emitter zirconium-89 validated the ability of positron emission tomography (PET) to visualize PD-L1 expression in vivo and predict tumor response to immunotherapy. These studies have triggered the expansion of PD-L1-targeted immunoPET to assess PD-L1 protein levels and PD-L1 expression heterogeneity in real time and across the whole tumor. First, this mini-review introduces new PD-L1 PET imaging studies of the last 4 years, focusing on the expansion of preclinical tumor models and anti-PD-L1 antibodies/antibody fragments in development. Then, the review discusses how these preclinical models and targeting agents can be utilized to study spatial and temporal heterogeneity of PD-L1 expression.