Forward to the Past: The Case for Quantitative PET Imaging

PET Imaging and Key Radiotracers for Evaluating Response to Locoregional Therapy in Hepatocellular Carcinoma

Locoregional therapies (LRTs) play a considerable role in the management of hepatocellular carcinoma (HCC), especially for patients who are not suitable for radical resection or transplantation. In clinical practice, assessment of LRTs is mainly based on computed tomography and MR imaging, but functional and metabolic information is less accessible. This article reviews the use of various the standardized uptake value parameters based on PET and multiple radiotracers for managing HCC after treatment with different LRTs, as well as parts of preclinical research. It discusses the current use of PET in more detail, as well as its advantages, disadvantages, and prospects.

Harmonizing Aβ deposition threshold for 18F-florbetaben PET imaging: Addressing discrepancies and calibration between PET/CT and PET/MRI

PURPOSE

Discrepancies between PET/CT and PET/MRI scanners can affect the determination of amyloid beta (Aβ) deposition thresholds in patients with cognitive impairment. This study aimed to identify these differences and propose a calibration method to standardize Aβ quantification across imaging modalities.

METHODS

A total of 133 patients with cognitive impairment underwent Aβ PET imaging and were divided into four groups: a head-to-head PET/CT and PET/MRI cohort (group A, n = 6), an independent PET/CT cohort (group B, n = 48), an independent PET/MRI cohort (group C, n = 79), and another independent PET/MRI cohort (group D, n = 10). Standardized uptake value ratios (SUVR) of global cortical target (CTXsuvr) and centiloid (CL) values were compared within group A and between groups B and C. A whole cerebellum (WC)-referenced SUVR method was used to calibrate CL values in group C, with verification in group D.

RESULTS

CTXsuvr values were significantly higher in PET/MRI than in PET/CT in both group A (P < 0.05) and group C versus group B (P < 0.001). Aβ-negative/positive cases showed mean ± variance of CTXsuvr as 1.023 ± 0.104/1.479 ± 0.203 in group B and 1.146 ± 0.100/1.743 ± 0.254 in group C, with cutoffs of 1.140 (CL = 20) and 1.401 (CL = 60), respectively. WC-referenced calibration adjusted PET/MRI cutoff to 1.132 (CL = 19) in group C, aligning it with PET/CT thresholds and validated in group D.

CONCLUSION

WC-referenced SUVR calibration effectively mitigates differences in Aβ thresholds between PET/CT and PET/MRI, enhancing Aβ quantification standardization in multi-modal imaging.

Metabolic Homeostasis of Immune Cells Modulates Cardiovascular Diseases

Recent investigations into the mechanisms underlying inflammation have highlighted the pivotal role of immune cells in regulating cardiac pathophysiology. Notably, these immune cells modulate cardiac processes through alternations in intracellular metabolism, including glycolysis and oxidative phosphorylation, whereas the extracellular metabolic environment is changed during cardiovascular disease, influencing function of immune cells. This dynamic interaction between immune cells and their metabolic environment has given rise to the novel concept of "immune metabolism". Consequently, both the extracellular and intracellular metabolic environment modulate the equilibrium between anti- and pro-inflammatory responses. This regulatory mechanism subsequently influences the processes of myocardial ischemia, cardiac fibrosis, and cardiac remodeling, ultimately leading to a series of cardiovascular events. This review examines how local microenvironmental and systemic environmental changes induce metabolic reprogramming in immune cells and explores the subsequent effects of aberrant activation or polarization of immune cells in the progression of cardiovascular disease. Finally, we discuss potential therapeutic strategies targeting metabolism to counteract abnormal immune activation.

Comparison of 18F-FDG PET image quality and quantitative parameters between DPR and OSEM reconstruction algorithm in patients with lung cancer

OBJECTIVES

The present study aimed to investigate the influence of the deep progressive learning reconstruction (DPR) algorithm on the 18F-FDG PET image quality and quantitative parameters.

METHODS

In this retrospective study, data were collected from 55 healthy individuals and 184 patients with primary malignant pulmonary tumors who underwent 18F-FDG PET/CT examinations. PET data were reconstructed using the ordered subset expectation maximization (OSEM) and DPR algorithms. The influence of DPR algorithm on quantitative parameters was explored, including the SUVmax, SUVmean, standard deviation of SUV (SUVSD), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and tumor-to-background uptake ratio (TBR). Finally, the differences in image quality parameters, including signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR), between the two reconstruction algorithms were evaluated.

RESULTS

DPR algorithm significantly reduced the SUVmax and SUVSD of background tissues (all, P < 0.001) compared to OSEM algorithm, while no statistical difference was observed in SUVmean between the two algorithms (all, P > 0.05). DPR algorithm notably increased the SUVmax, SUVmean, and TBR of lesions (all, P < 0.001) and reduced MTV (P = 0.005), with minimal differences in TLG noted between the reconstruction algorithms (P < 0.001). The percentage differences in SUVmax (P = 0.001), SUVmean (P = 0.005), and TBR (P = 0.001) between the two algorithms were significantly higher in solid nodules than in pure ground glass nodules (pGGNs). The ΔCNR between solid nodules (P = 0.031) and mixed ground glass nodules (P = 0.020) was greater than that between pGGNs. SNR and CNR obtained using the DPR algorithm were markedly improved compared to those determined using the OSEM algorithm (all, P < 0.001).

CONCLUSION

Under identical acquisition conditions, the DPR algorithm enhanced the accuracy of quantitative parameters in pulmonary lesions and potentially improved lesion detectability. The DPR algorithm increased image SNR and CNR compared to those obtained using the OSEM algorithm, significantly optimizing overall image quality. This advancement facilitated precise clinical diagnosis, underpinning its potential to significantly contribute to the field of medical imaging.

Robust and generalizable artificial intelligence for multi-organ segmentation in ultra-low-dose total-body PET imaging: a multi-center and cross-tracer study

PURPOSE

Positron Emission Tomography (PET) is a powerful molecular imaging tool that visualizes radiotracer distribution to reveal physiological processes. Recent advances in total-body PET have enabled low-dose, CT-free imaging; however, accurate organ segmentation using PET-only data remains challenging. This study develops and validates a deep learning model for multi-organ PET segmentation across varied imaging conditions and tracers, addressing critical needs for fully PET-based quantitative analysis.

MATERIALS AND METHODS

This retrospective study employed a 3D deep learning-based model for automated multi-organ segmentation on PET images acquired under diverse conditions, including low-dose and non-attenuation-corrected scans. Using a dataset of 798 patients from multiple centers with varied tracers, model robustness and generalizability were evaluated via multi-center and cross-tracer tests. Ground-truth labels for 23 organs were generated from CT images, and segmentation accuracy was assessed using the Dice similarity coefficient (DSC).

RESULTS

In the multi-center dataset from four different institutions, our model achieved average DSC values of 0.834, 0.825, 0.819, and 0.816 across varying dose reduction factors and correction conditions for FDG PET images. In the cross-tracer dataset, the model reached average DSC values of 0.737, 0.573, 0.830, 0.661, and 0.708 for DOTATATE, FAPI, FDG, Grazytracer, and PSMA, respectively.

CONCLUSION

The proposed model demonstrated effective, fully PET-based multi-organ segmentation across a range of imaging conditions, centers, and tracers, achieving high robustness and generalizability. These findings underscore the model's potential to enhance clinical diagnostic workflows by supporting ultra-low dose PET imaging.

CLINICAL TRIAL NUMBER

Not applicable. This is a retrospective study based on collected data, which has been approved by the Research Ethics Committee of Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine.

EMATA: a toolbox for the automatic extraction and modeling of arterial inputs for tracer kinetic analysis in [18F]FDG brain studies

PURPOSE

PET imaging is a pivotal tool for biomarker research aimed at personalized medicine. Leveraging the quantitative nature of PET requires knowledge of plasma radiotracer concentration. Typically, the arterial input function (AIF) is obtained through arterial cannulation, an invasive and technically demanding procedure. A less invasive alternative, especially for [18F]FDG, is the image-derived input function (IDIF), which, however, often requires correction for partial volume effect (PVE), usually performed via venous blood samples. The aim of this paper is to present EMATA: Extraction and Modeling of Arterial inputs for Tracer kinetic Analysis, an open-source MATLAB toolbox. EMATA automates IDIF extraction from [18F]FDG brain PET images and additionally includes a PVE correction procedure that does not require any blood sampling.

METHODS

To assess the toolbox generalizability and present example outputs, EMATA was applied to brain [18F]FDG dynamic data of 80 subjects, extracted from two distinct datasets (40 healthy controls, 40 glioma patients). Additionally, to compare with the reference standard, quantification using both IDIF and AIF was carried out on a third open-access dataset of 18 healthy individuals.

RESULTS

EMATA consistently performs IDIF extraction across all datasets, despite differences in scanners and acquisition protocols. Remarkably high agreement is observed when comparing Patlak's Ki between IDIF and AIF (R2: 0.98 ± 0.02).

CONCLUSION

EMATA proved adaptability to different datasets characteristics and the ability to provide arterial input functions that can be used for reliable PET quantitative analysis.

Subclinical atherosclerosis and brain health in midlife: Rationale and design of the PESA-Brain study

RATIONALE

Cognitive decline and dementia have been reportedly linked to atherosclerosis, the main cause of cardiovascular disease. Cohort studies identifying early brain alterations associated with subclinical atherosclerosis are warranted to understand the potential of prevention strategies before cerebral damage becomes symptomatic and irreversible.

METHODS & DESIGN

The Progression of Early Subclinical Atherosclerosis (PESA) study is a longitudinal observational cohort study that recruited 4,184 asymptomatic middle-aged individuals (40-54 years) in 2010 in Madrid (Spain) to thoroughly characterize subclinical atherosclerosis development over time. In this framework, the PESA-Brain study has been designed to identify early structural, functional and vascular brain changes associated with midlife atherosclerosis and cardiovascular risk factors. The PESA-Brain study targets 1,000 participants at the 10-year follow-up PESA visit and consists of thorough neuropsychological testing, advanced multimodal neuroimaging, and quantification of blood-based neuropathological biomarkers.

PRIMARY HYPOTHESIS

We hypothesize that, in middle-age, the presence of cardiovascular risk factors and a high burden of subclinical atherosclerosis will be associated with structural, functional and vascular brain alterations, greater amyloid burden and subtle cognitive impairment. We further hypothesize that the link between subclinical atherosclerosis and poor brain health in midlife will be mediated by cerebrovascular pathology and intracranial atherosclerosis.

ENROLLMENT DATES

The PESA-Brain study started in October 2020 and is estimated to be completed by December 2024.

CONCLUSION

This study is in a unique position to unveil novel relationships between cardiovascular and brain alterations in the health-to-disease transition, which may have important implications for interventional and therapeutic approaches.

CLINICALTRIALS

gov identifier: NCT01410318.

Application of Dynamic [18F]FDG PET/CT Multiparametric Imaging Leads to an Improved Differentiation of Benign and Malignant Lung Lesions

PURPOSE

To evaluate the potential of whole-body dynamic (WBD) 2-deoxy-2-[18F]fluoro-D-glucose positron emission tomography/computed tomography ([18F]FDG PET/CT) multiparametric imaging in the differential diagnosis between benign and malignant lung lesions.

PROCEDURES

We retrospectively analyzed WBD PET/CT scans from patients with lung lesions performed between April 2020 and March 2023. Multiparametric images including standardized uptake value (SUV), metabolic rate (MRFDG) and distribution volume (DVFDG) were visually interpreted and compared. We adopted SUVmax, metabolic tumor volume (MTV) and total lesion glycolysis (TLG) for semi-quantitative analysis, MRmax and DVmax values for quantitative analysis. We also collected the patients' clinical characteristics. The variables above with P-value < 0.05 in the univariate analysis were entered into a multivariate logistic regression. The statistically significant metrics were plotted on receiver-operating characteristic (ROC) curves.

RESULTS

A total of 60 patients were included for data evaluation. We found that most malignant lesions showed high uptake on MRFDG and SUV images, and low or absent uptake on DVFDG images, while benign lesions showed low uptake on MRFDG images and high uptake on DVFDG images. Most malignant lesions showed a characteristic pattern of gradually increasing FDG uptake, whereas benign lesions presented an initial rise with rapid fall, then kept stable at a low level. The AUC values of MRmax and SUVmax are 0.874 (95% CI: 0.763-0.946) and 0.792 (95% CI: 0.667-0.886), respectively. DeLong's test showed the difference between the areas is statistically significant (P < 0.001).

CONCLUSIONS

Our study demonstrated that dynamic [18F]FDG PET/CT imaging based on the Patlak analysis was a more accurate method of distinguishing malignancies from benign lesions than conventional static PET/CT scans.

FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool

Objective.Simulation of positron emission tomography (PET) images is an essential tool in the development and validation of quantitative imaging workflows and advanced image processing pipelines. Existing Monte Carlo or analytical PET simulators often compromise on either efficiency or accuracy. We aim to develop and validate fast analytical simulator of tracer (FAST)-PET, a novel analytical framework, to simulate PET images accurately and efficiently.Approach. FAST-PET simulates PET images by performing precise forward projection, scatter, and random estimation that match the scanner geometry and statistics. Although the same process should be applicable to other scanner models, we focus on the Siemens Biograph Vision-600 in this work. Calibration and validation of FAST-PET were performed through comparison with an experimental scan of a National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Further validation was conducted between FAST-PET and Geant4 Application for Tomographic Emission (GATE) quantitatively in clinical image simulations in terms of intensity-based and texture-based features and task-based tumor segmentation.Main results.According to the NEMA IQ phantom simulation, FAST-PET's simulated images exhibited partial volume effects and noise levels comparable to experimental images, with a relative bias of the recovery coefficient RC within 10% for all spheres and a coefficient of variation for the background region within 6% across various acquisition times. FAST-PET generated clinical PET images exhibit high quantitative accuracy and texture comparable to GATE (correlation coefficients of all features over 0.95) but with ∼100-fold lower computation time. The tumor segmentation masks comparison between both methods exhibited significant overlap and shape similarity with high concordance CCC > 0.97 across measures.Significance.FAST-PET generated PET images with high quantitative accuracy comparable to GATE, making it ideal for applications requiring extensive PET image simulations such as virtual imaging trials, and the development and validation of image processing pipelines.

The development process of 'fit-for-purpose' imaging biomarkers to characterize the tumor microenvironment

Immune-based treatment approaches are successfully used for the treatment of patients with cancer. While such therapies can be highly effective, many patients fail to benefit. To provide optimal therapy choices and to predict treatment responses, reliable biomarkers for the assessment of immune features in patients with cancer are of significant importance. Biomarkers (BM) that enable a comprehensive and repeatable assessment of the tumor microenvironment (TME), the lymphoid system, and the dynamics induced by drug treatment can fill this gap. Medical imaging, notably positron emission tomography (PET) and magnetic resonance imaging (MRI), providing whole-body imaging BMs, might deliver such BMs. However, those imaging BMs must be well characterized as being 'fit for purpose' for the intended use. This review provides an overview of the key steps involved in the development of 'fit-for-purpose' imaging BMs applicable in drug development, with a specific focus on pharmacodynamic biomarkers for assessing the TME and its modulation by immunotherapy. The importance of the qualification of imaging BMs according to their context of use (COU) as defined by the Food and Drug Administration (FDA) and National Institutes of Health Biomarkers, EndpointS, and other Tools (BEST) glossary is highlighted. We elaborate on how an imaging BM qualification for a specific COU can be achieved.

Full kinetic modeling analysis of [18F]fluorocholine Positron Emission Tomography (PET) at initial diagnosis of high-grade glioma

PURPOSE

The main objective was to characterize the tracer uptake kinetics of [18F]fluoromethylcholine ([18F]F-CHO) in high-grade gliomas (HGG) through a full PET kinetic modeling approach. Secondarily, we aimed to explore the relationship between the PET uptake measures and the HGG molecular features.

MATERIALS AND METHODS

Twenty-four patients with a suspected diagnosis of HGG were prospectively included. They underwent a dynamic brain [18F]F-CHO-PET/CT, from which a tumoral time-activity curve was extracted. The plasma input function was obtained through arterial blood sampling with metabolite correction. These data were fitted to 1- and 2-tissue-compartment models, the best of which was selected through the Akaike information criterion. We assessed the correlation between the kinetic parameters and the conventional static PET metrics (SUVmax, SUVmean and tumor-to-background ratio TBR). We explored the association between the [18F]F-CHO-PET quantitative parameters and relevant molecular biomarkers in HGG.

RESULTS

Tumoral time-activity curves in all patients showed a rapid rise of [18F]F-CHO uptake followed by a plateau-like shape. Best fits were obtained with near-irreversible 2-tissue-compartment models. The perfusion-transport constant K1 and the net influx rate Ki showed strong correlation with SUVmax (r = 0.808-0.861), SUVmean (r = 0.794-0.851) and TBR (r = 0.643-0.784), p < 0.002. HGG was confirmed in 21 patients, of which those with methylation of the O-6-methylguanine-DNA methyltransferase (MGMT) gene promoter showed higher mean Ki (p = 0.020), K1 (p = 0.025) and TBR (p = 0.001) than the unmethylated ones.

CONCLUSION

[18F]F-CHO uptake kinetics in HGG is best explained by a 2-tissue-compartment model. The conventional static [18F]F-CHO-PET measures have been validated against the perfusion-transport constant (K1) and the net influx rate (Ki) derived from kinetic modeling. A relationship between [18F]F-CHO uptake rate and MGMT methylation is suggested but needs further confirmation.

PET Imaging of Metabolism, Perfusion, and Hypoxia: FDG and Beyond

ABSTRACT

Imaging glucose metabolism with [18F]fluorodeoxyglucose positron emission tomography has transformed the diagnostic and treatment algorithms of numerous malignancies in clinical practice. The cancer phenotype, though, extends beyond dysregulation of this single pathway. Reprogramming of other pathways of metabolism, as well as altered perfusion and hypoxia, also typifies malignancy. These features provide other opportunities for imaging that have been developed and advanced into humans. In this review, we discuss imaging metabolism, perfusion, and hypoxia in cancer, focusing on the underlying biology to provide context. We conclude by highlighting the ability to image multiple facets of biology to better characterize cancer and guide targeted treatment.

Diagnostic value of [ 68 Ga]Ga-Pentixafor PET/CT in malignant melanoma: a pilot study

OBJECTIVE

To evaluate the diagnostic value of [ 68 Ga] Ga-Pentixafor in malignant melanoma patients.

METHODS

In this prospective study, patients with histology-proven melanoma were included and underwent [ 18 F]fluoro-D-glucose ([ 18 F]FDG) and [ 68 Ga] Ga-Pentixafor PET/computed tomography (CT) within a week. Suspicious lesions were interpreted as benign vs. malignant, and the corresponding semi-quantitative PET/CT parameters were recorded and compared.

RESULTS

Twelve consecutive melanoma patients (mean age: 60 ± 6) were included. Two patients were referred for initial staging, two for detecting recurrence and eight for evaluating the extent of metastases. Overall, [ 18 F]FDG PET/CT showed 236 tumoral lesions, including two primary tumors, two recurrent lesions, 29 locoregional metastases and 203 distant metastases. In [ 68 Ga]Ga-Pentixafor PET/CT, 101 tumoral lesions were detected, including two primary tumors, one recurrence, 16 locoregional metastases and 82 distant metastases. Notably, a documented brain metastasis was only visualized on [ 68 Ga]Ga-Pentixafor PET/CT images. Compared with [ 18 F]FDG, [ 68 Ga]Ga-Pentixafor PET/CT provided a 42% detection rate. Regarding semi-quantitative measures, the intensity of uptake and tumor-to-background ratios were significantly lower on [ 68 Ga]Ga-Pentixafor PET/CT [average maximum standard uptake value (SUV max ) of 2.72 ± 1.33 vs. 11.41 ± 14.79; P value <0.001 and 1.17 ± 0.53 vs. 5.32 ± 7.34; P value <0.001, respectively].

CONCLUSION

When comparing [ 68 Ga]Ga-Pentixafor PET/CT with [ 18 F]FDG PET/CT, not only did [ 68 Ga]Ga-Pentixafor PET/CT detect fewer lesions, but the intensity of uptake and the TBRs were also lower on [ 68 Ga]Ga-Pentixafor PET/CT. Thus, our results may indicate a limited potential of this novel tracer in cutaneous melanoma patients compared to [ 18 F]FDG PET/CT. Given the lower TBRs, applying this radiotracer in radioligand therapies is also questionable.

Advances and challenges in immunoPET methodology

Immuno-positron emission tomography (immunoPET) enables imaging of specific targets that play a role in targeted therapy and immunotherapy, such as antigens on cell membranes, targets in the disease microenvironment, or immune cells. The most common immunoPET applications use a monoclonal antibody labeled with a relatively long-lived positron emitter such as 89Zr (T 1/2 = 78.4 h), but smaller antibody-based constructs labeled with various other positron emitting radionuclides are also being investigated. This molecular imaging technique can thus guide the development of new drugs and may have a pivotal role in selecting patients for a particular therapy. In early phase immunoPET trials, multiple imaging time points are used to examine the time-dependent biodistribution and to determine the optimal imaging time point, which may be several days after tracer injection due to the slow kinetics of larger molecules. Once this has been established, usually only one static scan is performed and semi-quantitative values are reported. However, total PET uptake of a tracer is the sum of specific and nonspecific uptake. In addition, uptake may be affected by other factors such as perfusion, pre-/co-administration of the unlabeled molecule, and the treatment schedule. This article reviews imaging methodologies used in immunoPET studies and is divided into two parts. The first part summarizes the vast majority of clinical immunoPET studies applying semi-quantitative methodologies. The second part focuses on a handful of studies applying pharmacokinetic models and includes preclinical and simulation studies. Finally, the potential and challenges of immunoPET quantification methodologies are discussed within the context of the recent technological advancements provided by long axial field of view PET/CT scanners.

Improving SUVR quantification by correcting for radiotracer clearance in tissue

Standardized Uptake Value Ratio (SUVR) is a widely reported semi-quantitative positron emission tomography (PET) outcome measure, partly because of its ease of measurement from short scan durations. However, in brain, SUVR is often a biased estimator of the gold-standard distribution volume ratio (DVR) due to non-equilibrium conditions, i.e., clearance of the radiotracer in relevant tissues. Factors that affect radiotracer metabolism and clearance such as medication or subject groups could lead to artificial differences in SUVR. This work developed a correction that reduces the bias in SUVR (estimated from a short 15-30 min PET imaging session) by accounting for the effects of tracer clearance observed during the late SUVR time window. The proposed correction takes the form of a one-step non-linear algebraic transform of SUVR that is a function of radiotracer dependent parameters such as clearance rates from the reference and target tissues, and population averaged reference region clearance rate (k 2 , ref ). An important observation was the need for accurate estimation of radiotracer clearance rate in target tissue, which was addressed with a regression based model. Simulations and human data from two different radiotracers (healthy controls for [11C]LSN3172176, healthy controls and Parkinson's disease subjects for [18F]FE-PE2I) were used to validate the correction and evaluate its benefits and limitations. SUVR correction in human data significantly reduced mean SUVR bias across brain regions and subjects (from ∼25% for SUVR to <10% for corrected SUVR). This correction also significantly reduced the variability of this bias across brain regions for both tracers (approximately 50% for [11C]LSN3172176, 20% for [18F]FE-PE2I). Future work should investigate the benefits of using corrected SUVR in other populations and with different tracers.

The prognostic value of F18 Fluorothymidine positron emission tomography for assessing the response of malignant pleural mesothelioma to chemotherapy - A prospective cohort study

INTRODUCTION

Malignant pleural mesothelioma is difficult to prognosticate. F18-Fluorodeoxyglucose positron emission tomography (FDG PET) shows promise for response assessment but is confounded by talc pleurodesis. F18-Fluorothymidine (FLT) PET is an alternative tracer specific for proliferation. We compared the prognostic value of FDG and FLT PET and determined the influence of talc pleurodesis on these parameters.

METHODS

Overall, 29 prospectively recruited patients had FLT PET, FDG PET and CT-scans performed prior to and post one chemotherapy cycle; 10 had prior talc pleurodesis. Patients were followed for overall survival. CT response was assessed using mRECIST. Radiomic features were extracted using the MiM software platform. Changes in maximum SUV (SUVmax), mean SUV (SUVmean), FDG total lesion glycolysis (TLG), FLT total lesion proliferation (TLP) and metabolic tumour volume (MTV) after one chemotherapy cycle.

RESULTS

Cox univariate analysis demonstrated FDG PET radiomics were confounded by talc pleurodesis, and that percentage change in FLT MTV was predictive of overall survival. Cox multivariate analysis showed a 10% increase in FLT tumour volume corresponded with 9.5% worsened odds for overall survival (P = 0.028, HR = 1.095, 95% CI [1.010, 1.187]). No other variables were significant on multivariate analysis.

CONCLUSION

This is the first prospective study showing the statistical significance of FLT PET tumour volumes for measuring mesothelioma treatment response. FLT may be better than FDG for monitoring mesothelioma treatment response, which could help optimise mesothelioma treatment regimes.

Evaluation of Non-Invasive Methods for (R)-[11C]PK11195 PET Image Quantification in Multiple Sclerosis

This study aims to evaluate non-invasive PET quantification methods for (R)-[11C]PK11195 uptake measurement in multiple sclerosis (MS) patients and healthy controls (HC) in comparison with arterial input function (AIF) using dynamic (R)-[11C]PK11195 PET and magnetic resonance images. The total volume of distribution (VT) and distribution volume ratio (DVR) were measured in the gray matter, white matter, caudate nucleus, putamen, pallidum, thalamus, cerebellum, and brainstem using AIF, the image-derived input function (IDIF) from the carotid arteries, and pseudo-reference regions from supervised clustering analysis (SVCA). Uptake differences between MS and HC groups were tested using statistical tests adjusted for age and sex, and correlations between the results from the different quantification methods were also analyzed. Significant DVR differences were observed in the gray matter, white matter, putamen, pallidum, thalamus, and brainstem of MS patients when compared to the HC group. Also, strong correlations were found in DVR values between non-invasive methods and AIF (0.928 for IDIF and 0.975 for SVCA, p < 0.0001). On the other hand, (R)-[11C]PK11195 uptake could not be differentiated between MS patients and HC using VT values, and a weak correlation (0.356, p < 0.0001) was found between VTAIF and VTIDIF. Our study shows that the best alternative for AIF is using SVCA for reference region modeling, in addition to a cautious and appropriate methodology.

The quest for multifunctional and dedicated PET instrumentation with irregular geometries

We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.

Short-axis PET image quality improvement based on a uEXPLORER total-body PET system through deep learning

PURPOSE

The axial field of view (AFOV) of a positron emission tomography (PET) scanner greatly affects the quality of PET images. Although a total-body PET scanner (uEXPLORER) with a large AFOV is more sensitive, it is more expensive and difficult to widely use. Therefore, we attempt to utilize high-quality images generated by uEXPLORER to optimize the quality of images from short-axis PET scanners through deep learning technology while controlling costs.

METHODS

The experiments were conducted using PET images of three anatomical locations (brain, lung, and abdomen) from 335 patients. To simulate PET images from different axes, two protocols were used to obtain PET image pairs (each patient was scanned once). For low-quality PET (LQ-PET) images with a 320-mm AFOV, we applied a 300-mm FOV for brain reconstruction and a 500-mm FOV for lung and abdomen reconstruction. For high-quality PET (HQ-PET) images, we applied a 1940-mm AFOV during the reconstruction process. A 3D Unet was utilized to learn the mapping relationship between LQ-PET and HQ-PET images. In addition, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were employed to evaluate the model performance. Furthermore, two nuclear medicine doctors evaluated the image quality based on clinical readings.

RESULTS

The generated PET images of the brain, lung, and abdomen were quantitatively and qualitatively compatible with the HQ-PET images. In particular, our method achieved PSNR values of 35.41 ± 5.45 dB (p < 0.05), 33.77 ± 6.18 dB (p < 0.05), and 38.58 ± 7.28 dB (p < 0.05) for the three beds. The overall mean SSIM was greater than 0.94 for all patients who underwent testing. Moreover, the total subjective quality levels of the generated PET images for three beds were 3.74 ± 0.74, 3.69 ± 0.81, and 3.42 ± 0.99 (the highest possible score was 5, and the minimum score was 1) from two experienced nuclear medicine experts. Additionally, we evaluated the distribution of quantitative standard uptake values (SUV) in the region of interest (ROI). Both the SUV distribution and the peaks of the profile show that our results are consistent with the HQ-PET images, proving the superiority of our approach.

CONCLUSION

The findings demonstrate the potential of the proposed technique for improving the image quality of a PET scanner with a 320 mm or even shorter AFOV. Furthermore, this study explored the potential of utilizing uEXPLORER to achieve improved short-axis PET image quality at a limited economic cost, and computer-aided diagnosis systems that are related can help patients and radiologists.

Quantitation of dynamic total-body PET imaging: recent developments and future perspectives

BACKGROUND

Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades.

CHALLENGES

The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results.

CONCLUSIONS

In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.

International EANM-SNMMI-ISMRM consensus recommendation for PET/MRI in oncology

PREAMBLE

The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The merged International Society for Magnetic Resonance in Medicine (ISMRM) is an international, nonprofit, scientific association whose purpose is to promote communication, research, development, and applications in the field of magnetic resonance in medicine and biology and other related topics and to develop and provide channels and facilities for continuing education in the field.The ISMRM was founded in 1994 through the merger of the Society of Magnetic Resonance in Medicine and the Society of Magnetic Resonance Imaging. SNMMI, ISMRM, and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine and/or magnetic resonance imaging. The SNMMI, ISMRM, and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and/or magnetic resonance imaging and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each practice guideline, representing a policy statement by the SNMMI/EANM/ISMRM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI, ISMRM, and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging and magnetic resonance imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized. These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI, the ISMRM, and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question. The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines. The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment. Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.

[18F]GE-180-PET and Post Mortem Marker Characteristics of Long-Term High-Fat-Diet-Induced Chronic Neuroinflammation in Mice

Obesity is characterized by immoderate fat accumulation leading to an elevated risk of neurodegenerative disorders, along with a host of metabolic disturbances. Chronic neuroinflammation is a main factor linking obesity and the propensity for neurodegenerative disorders. To determine the cerebrometabolic effects of diet-induced obesity (DIO) in female mice fed a long-term (24 weeks) high-fat diet (HFD, 60% fat) compared to a group on a control diet (CD, 20% fat), we used in vivo PET imaging with the radiotracer [¹⁸F]FDG as a marker for brain glucose metabolism. In addition, we determined the effects of DIO on cerebral neuroinflammation using translocator protein 18 kDa (TSPO)-sensitive PET imaging with [¹⁸F]GE-180. Finally, we performed complementary post mortem histological and biochemical analyses of TSPO and further microglial (Iba1, TMEM119) and astroglial (GFAP) markers as well as cerebral expression analyses of cytokines (e.g., Interleukin (IL)-1β). We showed the development of a peripheral DIO phenotype, characterized by increased body weight, visceral fat, free triglycerides and leptin in plasma, as well as increased fasted blood glucose levels. Furthermore, we found obesity-associated hypermetabolic changes in brain glucose metabolism in the HFD group. Our main findings with respect to neuroinflammation were that neither [¹⁸F]GE-180 PET nor histological analyses of brain samples seem fit to detect the predicted cerebral inflammation response, despite clear evidence of perturbed brain metabolism along with elevated IL-1β expression. These results could be interpreted as a metabolically activated state in brain-resident immune cells due to a long-term HFD.

Neuroendocrine Neoplasms: Total-body PET/Computed Tomography

Total-body PET/computed tomography (CT) (uExplorer) static and dynamic scan using low-dose (48.1 to 73.6 MBq) gallium-68 (⁶⁸Ga) DOTATATE combined with low-dose (1.55 MBq/kg) or ultra-low-dose (0.37 MBq/kg) 18F-fluorodeoxyglucose (18F-FDG) were used as a routine in patients with neuroendocrine neoplasms (NENs). ⁶⁸Ga DOTATATE and 18F-FDG PET/CT static imaging play complementary roles in diagnosis, staging, and therapy-response evaluation in a patient with NENs. Kinetic parameters and time activity curve derived from the dynamic scan is helpful for understanding tumors biological characteristics and differential diagnosis of NENs.

Quantitative assessment of translocator protein (TSPO) in the non-human primate brain and clinical translation of [18F]LW223 as a TSPO-targeted PET radioligand

OBJECTIVES

Translocator protein 18 kDa (TSPO) positron emission tomography (PET) can be harnessed for the non-invasive detection of macrophage-driven inflammation. [18F]LW223, a newly reported TSPO PET tracer which was insensitive to rs6971 polymorphism, showed favorable performance characteristics in a recent imaging study involving a rat myocardial infarction model. To enable quantitative neuroimaging with [18F]LW223, we conducted kinetic analysis in the non-human primate (NHP) brain. Further, we sought to assess the utility of [18F]LW223-based TSPO imaging in a first-in-human study.

METHODS

Radiosynthesis of [18F]LW223 was accomplished on an automated module, whereas molar activities, stability in formulation, lipophilicity and unbound free fraction (fu) of the probe were measured. Brain penetration and target specificity of [18F]LW223 in NHPs were corroborated by PET-MR imaging under baseline and pre-blocking conditions using the validated TSPO inhibitor, (R)-PK11195, at doses ranging from 5 to 10 mg/kg. Kinetic modeling was performed using one-tissue compartment model (1TCM), two-tissue compartment model (2TCM) and Logan graphical analyses, using dynamic PET data acquisition, arterial blood collection and metabolic stability testing. Clinical PET scans were performed in two healthy volunteers (HVs). Regional brain standard uptake value ratio (SUVr) was assessed for different time intervals.

RESULTS

[18F]LW223 was synthesized in non-decay corrected radiochemical yields (n.d.c. RCYs) of 33.3 ± 6.5% with molar activities ranging from 1.8 ± 0.7 Ci/µmol (n = 11). [18F]LW223 was stable in formulation for up to 4 h and LogD7.4 of 2.31 ± 0.13 (n = 6) and fu of 5.80 ± 1.42% (n = 6) were determined. [18F]LW223 exhibited good brain penetration in NHPs, with a peak SUV value of ca. 1.79 in the whole brain. Pre-treatment with (R)-PK11195 substantially accelerated the washout and attenuated the area under the time-activity curve, indicating in vivo specificity of [18F]LW223 towards TSPO. Kinetic modeling demonstrated that 2TCM was the most suitable model for [18F]LW223-based neuroimaging. Global transfer rate constants (K1) and total volumes of distribution (VT) were found to be 0.10 ± 0.01 mL/cm3/min and 2.30 ± 0.17 mL/cm3, respectively. Dynamic PET data analyses across distinct time windows revealed that the VT values were relatively stable after 60 min post-injection. In a preliminary clinical study with two healthy volunteers, [18F]LW223 exhibited good brain uptake and considerable tracer retention across all analyzed brain regions. Of note, an excellent correlation between SUVr with VT was obtained when assessing the time interval from 20 to 40 min post tracer injection (SUVr(20-40 min), R2 = 0.94, p < 0.0001), suggesting this time window may be suitable to estimate specific binding to TSPO in human brain.

CONCLUSION

Our findings indicate that [18F]LW223 is suitable for quantitative TSPO-targeted PET imaging in higher species. Employing state-of-the-art kinetic modeling, we found that [18F]LW223 was effective in mapping TSPO throughout the NHP brain, with best model fits obtained from 2TCM and Logan graphical analyses. Overall, our results indicate that [18F]LW223 exhibits favorable tracer performance characteristics in higher species, and this novel imaging tool may hold promise to provide effective neuroinflammation imaging in patients with neurological disease.

Detecting Early Cognitive Decline in Alzheimer's Disease with Brain Synaptic Structural and Functional Evaluation

Early cognitive decline in patients with Alzheimer's (AD) is associated with quantifiable structural and functional connectivity changes in the brain. AD dysregulation of Aβ and tau metabolism progressively disrupt normal synaptic function, leading to loss of synapses, decreased hippocampal synaptic density and early hippocampal atrophy. Advances in brain imaging techniques in living patients have enabled the transition from clinical signs and symptoms-based AD diagnosis to biomarkers-based diagnosis, with functional brain imaging techniques, quantitative EEG, and body fluids sampling. The hippocampus has a central role in semantic and episodic memory processing. This cognitive function is critically dependent on normal intrahippocampal connections and normal hippocampal functional connectivity with many cortical regions, including the perirhinal and the entorhinal cortex, parahippocampal cortex, association regions in the temporal and parietal lobes, and prefrontal cortex. Therefore, decreased hippocampal synaptic density is reflected in the altered functional connectivity of intrinsic brain networks (aka large-scale networks), including the parietal memory, default mode, and salience networks. This narrative review discusses recent critical issues related to detecting AD-associated early cognitive decline with brain synaptic structural and functional markers in high-risk or neuropsychologically diagnosed patients with subjective cognitive impairment or mild cognitive impairment.

Recent technologies in cardiac imaging

Cardiac imaging allows physicians to view the structure and function of the heart to detect various heart abnormalities, ranging from inefficiencies in contraction, regulation of volumetric input and output of blood, deficits in valve function and structure, accumulation of plaque in arteries, and more. Commonly used cardiovascular imaging techniques include x-ray, computed tomography (CT), magnetic resonance imaging (MRI), echocardiogram, and positron emission tomography (PET)/single-photon emission computed tomography (SPECT). More recently, even more tools are at our disposal for investigating the heart's physiology, performance, structure, and function due to technological advancements. This review study summarizes cardiac imaging techniques with a particular interest in MRI and CT, noting each tool's origin, benefits, downfalls, clinical application, and advancement of cardiac imaging in the near future.

NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data

Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved [Formula: see text] correlation with PPET, with absolute difference [Formula: see text] for linearised Logan and MRTM2 methods, and [Formula: see text] correlation with QModeling, with absolute difference [Formula: see text] for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential ([Formula: see text]), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available ( https://github.com/AMYPAD/NiftyPAD ), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines.

Technical opportunities and challenges in developing total-body PET scanners for mice and rats

Positron emission tomography (PET) is the most sensitive in vivo molecular imaging technique available. Small animal PET has been widely used in studying pharmaceutical biodistribution and disease progression over time by imaging a wide range of biological processes. However, it remains true that almost all small animal PET studies using mouse or rat as preclinical models are either limited by the spatial resolution or the sensitivity (especially for dynamic studies), or both, reducing the quantitative accuracy and quantitative precision of the results. Total-body small animal PET scanners, which have axial lengths longer than the nose-to-anus length of the mouse/rat and can provide high sensitivity across the entire body of mouse/rat, can realize new opportunities for small animal PET. This article aims to discuss the technical opportunities and challenges in developing total-body small animal PET scanners for mice and rats.

Cerebral blood flow, amyloid burden, and cognition in cognitively normal individuals

PURPOSE

The role of cerebral blood flow (CBF) in the early stages of Alzheimer's disease is complex and largely unknown. We investigated cross-sectional and longitudinal associations between CBF, amyloid burden, and cognition, in cognitively normal individuals with subjective cognitive decline (SCD).

METHODS

We included 187 cognitively normal individuals with SCD from the SCIENCe project (65 ± 8 years, 39% F, MMSE 29 ± 1). Each underwent a dynamic (0-70 min) [18F]florbetapir PET and T1-weighted MRI scan, enabling calculation of mean binding potential (BPND; specific amyloid binding) and R1 (measure of relative (r)CBF). Eighty-three individuals underwent a second [18F]florbetapir PET (2.6 ± 0.7 years). Participants annually underwent neuropsychological assessment (follow-up time 3.8 ± 3.1 years; number of observations n = 774).

RESULTS

A low baseline R1 was associated with steeper decline on tests addressing memory, attention, and global cognition (range betas 0.01 to 0.27, p < 0.05). High BPND was associated with steeper decline on tests covering all domains (range betas - 0.004 to - 0.70, p < 0.05). When both predictors were simultaneously added to the model, associations remained essentially unchanged. Additionally, we found longitudinal associations between R1 and BPND. High baseline BPND predicted decline over time in R1 (all regions, range betasBP×time - 0.09 to - 0.14, p < 0.05). Vice versa, low baseline R1 predicted increase in BPND in frontal, temporal, and composite ROIs over time (range betasR1×time - 0.03 to - 0.08, p < 0.05).

CONCLUSION

Our results suggest that amyloid accumulation and decrease in rCBF are two parallel disease processes without a fixed order, both providing unique predictive information for cognitive decline and each process enhancing the other longitudinally.

Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 ¹⁸F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only ¹⁸F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body ¹⁸F-FDG PET/CT images with high accuracy.

Quantitative PET imaging of the CD4 pool in nonhuman primates

PURPOSE

Previous SPECT and PET semi-quantitative in vivo imaging studies in monkeys have demonstrated specific uptake of radiolabeled rhesus recombinant anti-CD4 monoclonal antibody fragment CD4R1-F(ab΄)2 in the spleen and clusters of lymph nodes (LNs) but yielded conflicting results of imaging the gut CD4 + T-cell pool. Here, using PET dynamic imaging with kinetic analysis, we performed a fully quantitative CD4 imaging in rhesus macaques.

METHODS

The biodistributions of [89Zr]Zr-CD4R1-F(ab΄)2 and/or of [89Zr]Zr-ibalizumab were performed with static PET scans up to 144 h (6 days) post-injection in 18 rhesus macaques with peripheral blood CD4 + T cells/μl ranging from ~ 20 to 2400. Fully quantitative analysis with a 4-h dynamic scan, arterial sampling, metabolite evaluation, and model fitting was performed in three immunocompetent monkeys to estimate the binding potential of CD4 receptors in the LNs, spleen, and gut.

RESULTS

The biodistributions of [89Zr]Zr-CD4R1-F(ab΄)2 and [89Zr]Zr-ibalizumab were similar in lymphoid tissues with a clear delineation of the CD4 pool in the LNs and spleen and a significant difference in lymphoid tissue uptake between immunocompetent and immunocompromised macaques. Consistent with our previous SPECT imaging of [99mTc]Tc-CD4R1-F(ab΄)2, the [89Zr]Zr-CD4R1-F(ab΄)2 and [89Zr]Zr-Ibalizumab uptakes in the gut were low and not different between uninfected and SIV-infected CD4-depleted monkeys. Ex vivo studies of large and small intestines confirmed the in vivo images.

CONCLUSION

The majority of specific binding to CD4 + tissue was localized to LNs and spleen with minimal uptake in the gut. Binding potential derived from fully quantitative studies revealed that the contribution of the gut is lower than the spleen's contribution to the total body CD4 pool.

Shortened duration whole body 18F-FDG PET Patlak imaging on the Biograph Vision Quadra PET/CT using a population-averaged input function

Background

Excellent performance characteristics of the Vision Quadra PET/CT, e.g. a substantial increase in sensitivity, allow for precise measurements of image-derived input functions (IDIF) and tissue time activity curves. Previously we have proposed a method for a reduced 30 min (as opposed to 60 min) whole body ¹⁸F-FDG Patlak PET imaging procedure using a previously published population-averaged input function (PIF) scaled to IDIF values at 30–60 min post-injection (p.i.). The aim of the present study was to apply this method using the Vision Quadra PET/CT, including the use of a PIF to allow for shortened scan durations.

Methods

Twelve patients with suspected lung malignancy were included and received a weight-based injection of ¹⁸F-FDG. Patients underwent a 65-min dynamic PET acquisition which were reconstructed using European Association of Nuclear Medicine Research Ltd. (EARL) standards 2 reconstruction settings. A volume of interest (VOI) was placed in the ascending aorta (AA) to obtain the IDIF. An external PIF was scaled to IDIF values at 30–60, 40–60, and 50–60 min p.i., respectively, and parametric ¹⁸F-FDG influx rate constant (K_i) images were generated using a t* of 30, 40 or 50 min, respectively. Herein, tumour lesions as well as healthy tissues, i.e. liver, muscle tissue, spleen and grey matter, were segmented.

Results

Good agreement between the IDIF and corresponding PIF scaled to 30–60 min p.i. and 40–60 min p.i. was obtained with 7.38% deviation in K_i. Bland–Altman plots showed excellent agreement in K_i obtained using the PIF scaled to the IDIF at 30–60 min p.i. and at 40–60 min p.i. as all data points were within the limits of agreement (LOA) (− 0.004–0.002, bias: − 0.001); for the 50–60 min p.i. K_i, all except one data point fell in between the LOA (− 0.021–0.012, bias: − 0.005).

Conclusions

Parametric whole body ¹⁸F-FDG Patlak K_i images can be generated non-invasively on a Vision Quadra PET/CT system. In addition, using a scaled PIF allows for a substantial (factor 2 to 3) reduction in scan time without substantial loss of accuracy (7.38% bias) and precision (image quality and noise interference).

Supplementary Information

The online version contains supplementary material available at 10.1186/s40658-022-00504-9.

Longitudinal change in ATN biomarkers in cognitively normal individuals

BACKGROUND

Biomarkers for amyloid, tau, and neurodegeneration (ATN) have predictive value for clinical progression, but it is not clear how individuals move through these stages. We examined changes in ATN profiles over time, and investigated determinants of change in A status, in a sample of cognitively normal individuals presenting with subjective cognitive decline (SCD).

METHODS

We included 92 individuals with SCD from the SCIENCe project with [¹⁸F]florbetapir PET (A) available at two time points (65 ± 8y, 42% female, MMSE 29 ± 1, follow-up 2.5 ± 0.7y). We additionally used [¹⁸F]flortaucipir PET for T and medial temporal atrophy score on MRI for N. Thirty-nine individuals had complete biomarker data at baseline and follow-up, enabling the construction of ATN profiles at two time points. All underwent extensive neuropsychological assessments (follow-up time 4.9 ± 2.8y, median number of visits n = 4). We investigated changes in biomarker status and ATN profiles over time. We assessed which factors predisposed for a change from A- to A+ using logistic regression. We additionally used linear mixed models to assess change from A- to A+, compared to the group that remained A- at follow-up, as predictor for cognitive decline.

RESULTS

At baseline, 62% had normal AD biomarkers (A-T-N- n = 24), 5% had non-AD pathologic change (A-T-N+ n = 2,) and 33% fell within the Alzheimer's continuum (A+T-N- n = 9, A+T+N- n = 3, A+T+N+ n = 1). Seventeen subjects (44%) changed to another ATN profile over time. Only 6/17 followed the Alzheimer's disease sequence of A → T → N, while 11/17 followed a different order (e.g., reverted back to negative biomarker status). APOE ε4 carriership inferred an increased risk of changing from A- to A+ (OR 5.2 (95% CI 1.2-22.8)). Individuals who changed from A- to A+, showed subtly steeper decline on Stroop I (β - 0.03 (SE 0.01)) and Stroop III (- 0.03 (0.01)), compared to individuals who remained A-.

CONCLUSION

We observed considerable variability in the order of ATN biomarkers becoming abnormal. Individuals who became A+ at follow-up showed subtle decline on tests for attention and executive functioning, confirming clinical relevance of amyloid positivity.

Editorial on Special Issue "Quantitative PET and SPECT"

Since the introduction of personalized (or precision) medicine, where individually tailored treatments are designed to deliver the right treatment to the right patient at the right time, the primary focus of imaging has moved from detection and diagnosis to tissue characterization, determination of prognosis, prediction of treatment efficacy, and measurement of treatment response [...].

Quantification of amyloid PET for future clinical use: a state-of-the-art review

Amyloid-β (Aβ) pathology is one of the earliest detectable brain changes in Alzheimer's disease (AD) pathogenesis. The overall load and spatial distribution of brain Aβ can be determined in vivo using positron emission tomography (PET), for which three fluorine-18 labelled radiotracers have been approved for clinical use. In clinical practice, trained readers will categorise scans as either Aβ positive or negative, based on visual inspection. Diagnostic decisions are often based on these reads and patient selection for clinical trials is increasingly guided by amyloid status. However, tracer deposition in the grey matter as a function of amyloid load is an inherently continuous process, which is not sufficiently appreciated through binary cut-offs alone. State-of-the-art methods for amyloid PET quantification can generate tracer-independent measures of Aβ burden. Recent research has shown the ability of these quantitative measures to highlight pathological changes at the earliest stages of the AD continuum and generate more sensitive thresholds, as well as improving diagnostic confidence around established binary cut-offs. With the recent FDA approval of aducanumab and more candidate drugs on the horizon, early identification of amyloid burden using quantitative measures is critical for enrolling appropriate subjects to help establish the optimal window for therapeutic intervention and secondary prevention. In addition, quantitative amyloid measurements are used for treatment response monitoring in clinical trials. In clinical settings, large multi-centre studies have shown that amyloid PET results change both diagnosis and patient management and that quantification can accurately predict rates of cognitive decline. Whether these changes in management reflect an improvement in clinical outcomes is yet to be determined and further validation work is required to establish the utility of quantification for supporting treatment endpoint decisions. In this state-of-the-art review, several tools and measures available for amyloid PET quantification are summarised and discussed. Use of these methods is growing both clinically and in the research domain. Concurrently, there is a duty of care to the wider dementia community to increase visibility and understanding of these methods.

Clinical Impact of Dual Time Point 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Fusion Imaging in Pancreatic Cancer

We examined the value of preoperative dual time point (DTP) 18F-fluorodeoxyglucose positron emission tomography/computed tomography fusion imaging (FDG PET/CT) as a predictor of early recurrence or the outcomes in patients with pancreatic cancer. Standardized uptake values (SUVs) in DTP FDG PET/CT were performed as preoperative staging. SUVmax1 and SUVmax2 were obtained in 60 min and 120 min, respectively. ΔSUVmax% was defined as (SUVmax2 − SUVmax1)/SUVmax1 × 100. The optimal cut-off values for SUVmax parameters were selected based on tumor relapse within 1 year of surgery. Optimal cut-off values for SUVmax1 and ΔSUVmax% were 7.18 and 24.25, respectively. The combination of SUVmax1 and ΔSUVmax% showed higher specificity and sensitivity, and higher positive and negative predictive values for tumor relapse within 1 year than SUVmax1 alone. Relapse-free survival (RFS) was significantly worse in the subgroups of high SUVmax1 and high ΔSUVmax% (median 7.0 months) than in the other subgroups (p < 0.0001). The multivariate Cox analysis of RFS identified high SUVmax1 and high ΔSUVmax% as independent prognostic factors (p = 0.0060). DTP FDG PET/CT may effectively predict relapse in patients with pancreatic cancer. The combination of SUVmax1 and ΔSUVmax% identified early recurrent patient groups more precisely than SUVmax1 alone.

Modeling venous plasma samples in [18F] FDG PET studies: a nonlinear mixed-effects approach

The gold-standard approach to quantifying dynamic PET images relies on using invasive measures of the arterial plasma tracer concentration. An attractive alternative is to employ an image-derived input function (IDIF), corrected for spillover effects and rescaled with venous plasma samples. However, venous samples are not always available for every participant. In this work, we used the nonlinear mixed-effects modeling approach to develop a model which infers venous tracer kinetics by using venous samples obtained from a population of healthy individuals and integrating subject-specific covariates. Population parameters (fixed effects), their between-subject variability (random effects), and the effects of covariates were estimated. The selected model will allow to reliably infer venous tracer kinetics in subjects with missing measurements. Clinical relevance - The derived model will be relevant for fully noninvasive dynamic FDG PET quantification using image-derived input functions in both healthy and patient populations when hemodynamics is not impaired.

Optimal timing of interictal FDG-PET for epilepsy surgery: a systematic review on time since last seizure

Interictal 18F‐Fluorodeoxyglucose positron emission tomography (FDG‐PET) is used in the workup for epilepsy surgery when MRI and EEG video monitoring are not conclusive. Timing of FDG‐PET is crucial to avoid the metabolically dynamic (post)ictal state that complicates interpretation, but the exact time window is unclear. We performed a systematic review to provide an evidence‐based recommendation for the minimal time interval between last seizure and FDG‐PET acquisition. We searched PubMed and Embase for articles on the effect of time since last seizure on FDG‐PET outcome. Quality assessment was conducted with the Critical Appraisal Skills Programme Cohort Study Checklist. We identified five studies. Three studies were classified as of low to moderate quality, mainly due to undocumented data or insufficient statistical measurements. Two high‐quality studies included only adults with Temporal Lobe Epilepsy (TLE). The metabolic interictal phase is 24 or 48 hours after the last seizure, depending on seizure type. The recommendation is based on the best available evidence from two small study populations for TLE. If clinically possible, interictal FDG‐PET in adults should be performed at least 24 hours after focal aware seizures and 48 hours after focal impaired awareness and focal to bilateral tonic–clonic seizures.

Impact of cerebral blood flow and amyloid load on SUVR bias

Background

Despite its widespread use, the semi-quantitative standardized uptake value ratio (SUVR) may be biased compared with the distribution volume ratio (DVR). This bias may be partially explained by changes in cerebral blood flow (CBF) and is likely to be also dependent on the extent of the underlying amyloid-β (Aβ) burden. This study aimed to compare SUVR with DVR and to evaluate the effects of underlying Aβ burden and CBF on bias in SUVR in mainly cognitively unimpaired participants. Participants were scanned according to a dual-time window protocol, with either [¹⁸F]flutemetamol (N = 90) or [¹⁸F]florbetaben (N = 31). The validated basisfunction-based implementation of the two-step simplified reference tissue model was used to derive DVR and R₁ parametric images, and SUVR was calculated from 90 to 110 min post-injection, all with the cerebellar grey matter as reference tissue. First, linear regression and Bland–Altman analyses were used to compare (regional) SUVR with DVR. Then, generalized linear models were applied to evaluate whether (bias in) SUVR relative to DVR could be explained by R₁ for the global cortical average (GCA), precuneus, posterior cingulate, and orbitofrontal region.

Results

Despite high correlations (GCA: R² ≥ 0.85), large overestimation and proportional bias of SUVR relative to DVR was observed. Negative associations were observed between both SUVR or SUVR_bias and R_1, albeit non-significant.

Conclusion

The present findings demonstrate that bias in SUVR relative to DVR is strongly related to underlying Aβ burden. Furthermore, in a cohort consisting mainly of cognitively unimpaired individuals, the effect of relative CBF on bias in SUVR appears limited. EudraCT Number: 2018-002277-22, registered on: 25-06-2018.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00898-8.

caliPER: A software for blood-free parametric Patlak mapping using PET/MRI input function

Routine clinical use of absolute PET quantification techniques is limited by the need for serial arterial blood sampling for input function and more importantly by the lack of automated pharmacokinetic analysis tools that can be readily implemented in clinic with minimal effort. PET/MRI provides the ability for absolute quantification of PET probes without the need for serial arterial blood sampling using image-derived input functions (IDIFs). Here we introduce caliPER, a modular and scalable software for simplified pharmacokinetic modelling of PET probes with irreversible uptake or binding based on PET/MR IDIFs and Patlak Plot analysis. caliPER generates regional values or parametric maps of net influx rate (K_i) using reconstructed dynamic PET images and anatomical MRI aligned to PET for IDIF vessel delineation. We evaluated the performance of caliPER for blood-free region-based and pixel-wise Patlak analyses of [¹⁸F] FDG by comparing caliPER IDIF to serial arterial blood input functions and its application in imaging brain glucose hypometabolism in Frontotemporal dementia. IDIFs corrected for partial volume errors including spill-out and spill-in effects were similar to arterial blood input functions with a general bias of around 6-8%, even for arteries <5 mm. The K_i and cerebral metabolic rate of glucose estimated using caliPER IDIF were similar to estimates using arterial blood sampling (<2%) and within limits of whole brain values reported in literature. Overall, caliPER is a promising tool for irreversible PET tracer quantification and can simplify the ability to perform parametric analysis in clinical settings without the need for blood sampling.

Imaging Synaptic Density: The Next Holy Grail of Neuroscience?

The brain is the central and most complex organ in the nervous system, comprising billions of neurons that constantly communicate through trillions of connections called synapses. Despite being formed mainly during prenatal and early postnatal development, synapses are continually refined and eliminated throughout life via complicated and hitherto incompletely understood mechanisms. Failure to correctly regulate the numbers and distribution of synapses has been associated with many neurological and psychiatric disorders, including autism, epilepsy, Alzheimer’s disease, and schizophrenia. Therefore, measurements of brain synaptic density, as well as early detection of synaptic dysfunction, are essential for understanding normal and abnormal brain development. To date, multiple synaptic density markers have been proposed and investigated in experimental models of brain disorders. The majority of the gold standard methodologies (e.g., electron microscopy or immunohistochemistry) visualize synapses or measure changes in pre- and postsynaptic proteins ex vivo. However, the invasive nature of these classic methodologies precludes their use in living organisms. The recent development of positron emission tomography (PET) tracers [such as (¹⁸F)UCB-H or (¹¹C)UCB-J] that bind to a putative synaptic density marker, the synaptic vesicle 2A (SV2A) protein, is heralding a likely paradigm shift in detecting synaptic alterations in patients. Despite their limited specificity, novel, non-invasive magnetic resonance (MR)-based methods also show promise in inferring synaptic information by linking to glutamate neurotransmission. Although promising, all these methods entail various advantages and limitations that must be addressed before becoming part of routine clinical practice. In this review, we summarize and discuss current ex vivo and in vivo methods of quantifying synaptic density, including an evaluation of their reliability and experimental utility. We conclude with a critical assessment of challenges that need to be overcome before successfully employing synaptic density biomarkers as diagnostic and/or prognostic tools in the study of neurological and neuropsychiatric disorders.

Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics

Small animal models play a fundamental role in brain research by deepening the understanding of the physiological functions and mechanisms underlying brain disorders and are thus essential in the development of therapeutic and diagnostic imaging tracers targeting the central nervous system. Advances in structural, functional, and molecular imaging using MRI, PET, fluorescence imaging, and optoacoustic imaging have enabled the interrogation of the rodent brain across a large temporal and spatial resolution scale in a non-invasively manner. However, there are still several major gaps in translating from preclinical brain imaging to the clinical setting. The hindering factors include the following: (1) intrinsic differences between biological species regarding brain size, cell type, protein expression level, and metabolism level and (2) imaging technical barriers regarding the interpretation of image contrast and limited spatiotemporal resolution. To mitigate these factors, single-cell transcriptomics and measures to identify the cellular source of PET tracers have been developed. Meanwhile, hybrid imaging techniques that provide highly complementary anatomical and molecular information are emerging. Furthermore, deep learning-based image analysis has been developed to enhance the quantification and optimization of the imaging protocol. In this mini-review, we summarize the recent developments in small animal neuroimaging toward improved translational power, with a focus on technical improvement including hybrid imaging, data processing, transcriptomics, awake animal imaging, and on-chip pharmacokinetics. We also discuss outstanding challenges in standardization and considerations toward increasing translational power and propose future outlooks.

Principles of Tracer Kinetic Analysis in Oncology, Part I: Principles and Overview of Methodology

Learning Objectives: On successful completion of this activity, participants should be able to describe (1) describe principles of PET tracer kinetic analysis for oncologic applications; (2) list methods used for PET kinetic analysis for oncology; and (3) discuss application of kinetic modeling for cancer-specific diagnostic needs.Financial Disclosure: This work was supported by KL2 TR001879, R01 CA211337, R01 CA113941, R33 CA225310, Komen SAC130060, R50 CA211270, and K01 DA040023. Dr. Pantel is a consultant or advisor for Progenics and Blue Earth Diagnostics and is a meeting participant or lecturer for Blue Earth Diagnostics. Dr. Mankoff is on the scientific advisory boards of GE Healthcare, Philips Healthcare, Reflexion, and ImaginAb and is the owner of Trevarx; his wife is the chief executive officer of Trevarx. The authors of this article have indicated no other relevant relationships that could be perceived as a real or apparent conflict of interest.CME Credit: SNMMI is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to sponsor continuing education for physicians. SNMMI designates each JNM continuing education article for a maximum of 2.0 AMA PRA Category 1 Credits. Physicians should claim only credit commensurate with the extent of their participation in the activity. For CE credit, SAM, and other credit types, participants can access this activity through the SNMMI website (http://www.snmmilearningcenter.org) through March 2025PET enables noninvasive imaging of regional in vivo cancer biology. By engineering a radiotracer to target specific biologic processes of relevance to cancer (e.g., cancer metabolism, blood flow, proliferation, and tumor receptor expression or ligand binding), PET can detect cancer spread, characterize the cancer phenotype, and assess its response to treatment. For example, imaging of glucose metabolism using the radiolabeled glucose analog ¹⁸F-FDG has widespread applications to all 3 of these tasks and plays an important role in cancer care. However, the current clinical practice of imaging at a single time point remote from tracer injection (i.e., static imaging) does not use all the information that PET cancer imaging can provide, especially to address questions beyond cancer detection. Reliance on tracer measures obtained only from static imaging may also lead to misleading results. In this 2-part continuing education paper, we describe the principles of tracer kinetic analysis for oncologic PET (part 1), followed by examples of specific implementations of kinetic analysis for cancer PET imaging that highlight the added benefits over static imaging (part 2). This review is designed to introduce nuclear medicine clinicians to basic concepts of kinetic analysis in oncologic imaging, with a goal of illustrating how kinetic analysis can augment our understanding of in vivo cancer biology, improve our approach to clinical decision making, and guide the interpretation of quantitative measures derived from static images.

Short 2-[18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer

Purpose: To test a short 2-[¹⁸F]Fluoro-2-deoxy-D-glucose (2-[¹⁸F]FDG) PET dynamic acquisition protocol to calculate K_i using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC).

Methods: 24 patients with NSCLC who underwent standard dynamic 2-[¹⁸F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), K_i was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain K_i,s using IDIF_Fitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIF_Fitted curves were modeled to assume the value of 2-[¹⁸F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between K_i and K_i,s. Finally, K_i,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions.

Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min⁻¹. The LOOCV error was 0.4%. In patients of group 2, the mean values of K_i and K_i,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R² = 0.9970). The Passing–Bablok regression for comparison between K_i and K_i,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011).

Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[¹⁸F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional K_i estimation.

Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer's disease: A cross-validation study with Chinese and ADNI cohorts

OBJECTIVE

Disease-related metabolic brain patterns have been verified for a variety of neurodegenerative diseases including Alzheimer's disease (AD). This study aimed to explore and validate the pattern derived from cognitively normal controls (NCs) in the Alzheimer's continuum.

METHODS

This study was based on two cohorts; one from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the other from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) and [18F]florbetapir-PET imaging. Participants were binary-grouped based on β-amyloid (Aβ) status, and the positivity was defined as Aβ+. Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate the "at-risk AD-related metabolic pattern (ARADRP)" for NCs. The pattern expression score was obtained and compared between the groups, and receiver operating characteristic curves were drawn. Notably, we conducted cross-validation to verify the robustness and correlation analyses to explore the relationships between the score and AD-related pathological biomarkers.

RESULTS

Forty-eight Aβ+ NCs and 48 Aβ- NCs were included in the ADNI cohort, and 25 Aβ+ NCs and 30 Aβ- NCs were included in the SILCODE cohort. The ARADRPs were identified from the combined cohorts and the two separate cohorts, characterized by relatively lower regional loadings in the posterior parts of the precuneus, posterior cingulate, and regions of the temporal gyrus, as well as relatively higher values in the superior/middle frontal gyrus and other areas. Patterns identified from the two separate cohorts showed some regional differences, including the temporal gyrus, basal ganglia regions, anterior parts of the precuneus, and middle cingulate. Cross-validation suggested that the pattern expression score was significantly higher in the Aβ+ group of both cohorts (p < 0.01), and contributed to the diagnosis of Aβ+ NCs (with area under the curve values of 0.696-0.815). The correlation analysis revealed that the score was related to tau pathology measured in cerebrospinal fluid (p-tau: p < 0.02; t-tau: p < 0.03), but not Aβ pathology assessed with [18F]florbetapir-PET (p > 0.23).

CONCLUSIONS

ARADRP exists for NCs, and the acquired pattern expression score shows a certain ability to discriminate Aβ+ NCs from Aβ- NCs. The SSM/PCA method is expected to be helpful in the ultra-early diagnosis of AD in clinical practice.

Can we learn lessons from the FDA's approval of aducanumab?

On 7 June 2021, aducanumab was granted accelerated approval for the treatment of Alzheimer disease (AD) by the FDA on the basis of amyloid-lowering effects considered reasonably likely to confer clinical benefit. This decision makes aducanumab the first new drug to be approved for the treatment of AD since 2003 and the first drug to ever be approved for modification of the course of AD. Many have questioned how scientific evidence, expert advice and the best interests of patients and families were considered in the approval decision. In this article, we argue that prior to approval, the FDA and Biogen's shared interpretation of clinical trial data - that high-dose aducanumab was substantially clinically effective - avoided conventional scientific scrutiny, was prominently advanced by patient representative groups who had been major recipients of Biogen funds, and raised concerns that safeguards were insufficient to mitigate regulatory capture within the FDA. Here, we reflect on events leading to the FDA's decision on 7 June 2021 and consider whether any lessons can be learned for the field.

Fully Automated Gross Tumor Volume Delineation From PET in Head and Neck Cancer Using Deep Learning Algorithms

PURPOSE

The availability of automated, accurate, and robust gross tumor volume (GTV) segmentation algorithms is critical for the management of head and neck cancer (HNC) patients. In this work, we evaluated 3 state-of-the-art deep learning algorithms combined with 8 different loss functions for PET image segmentation using a comprehensive training set and evaluated its performance on an external validation set of HNC patients.

PATIENTS AND METHODS

18F-FDG PET/CT images of 470 patients presenting with HNC on which manually defined GTVs serving as standard of reference were used for training (340 patients), evaluation (30 patients), and testing (100 patients from different centers) of these algorithms. PET image intensity was converted to SUVs and normalized in the range (0-1) using the SUVmax of the whole data set. PET images were cropped to 12 × 12 × 12 cm3 subvolumes using isotropic voxel spacing of 3 × 3 × 3 mm3 containing the whole tumor and neighboring background including lymph nodes. We used different approaches for data augmentation, including rotation (-15 degrees, +15 degrees), scaling (-20%, 20%), random flipping (3 axes), and elastic deformation (sigma = 1 and proportion to deform = 0.7) to increase the number of training sets. Three state-of-the-art networks, including Dense-VNet, NN-UNet, and Res-Net, with 8 different loss functions, including Dice, generalized Wasserstein Dice loss, Dice plus XEnt loss, generalized Dice loss, cross-entropy, sensitivity-specificity, and Tversky, were used. Overall, 28 different networks were built. Standard image segmentation metrics, including Dice similarity, image-derived PET metrics, first-order, and shape radiomic features, were used for performance assessment of these algorithms.

RESULTS

The best results in terms of Dice coefficient (mean ± SD) were achieved by cross-entropy for Res-Net (0.86 ± 0.05; 95% confidence interval [CI], 0.85-0.87), Dense-VNet (0.85 ± 0.058; 95% CI, 0.84-0.86), and Dice plus XEnt for NN-UNet (0.87 ± 0.05; 95% CI, 0.86-0.88). The difference between the 3 networks was not statistically significant (P > 0.05). The percent relative error (RE%) of SUVmax quantification was less than 5% in networks with a Dice coefficient more than 0.84, whereas a lower RE% (0.41%) was achieved by Res-Net with cross-entropy loss. For maximum 3-dimensional diameter and sphericity shape features, all networks achieved a RE ≤ 5% and ≤10%, respectively, reflecting a small variability.

CONCLUSIONS

Deep learning algorithms exhibited promising performance for automated GTV delineation on HNC PET images. Different loss functions performed competitively when using different networks and cross-entropy for Res-Net, Dense-VNet, and Dice plus XEnt for NN-UNet emerged as reliable networks for GTV delineation. Caution should be exercised for clinical deployment owing to the occurrence of outliers in deep learning-based algorithms.

Use of dynamic reconstruction for parametric Patlak imaging in dynamic whole body PET

Dynamic whole body (DWB) PET acquisition protocols enable the use of whole body parametric imaging for clinical applications. In FDG imaging, accurate parametric images of PatlakK_ican be complementary to regular standardised uptake value images and improve on current applications or enable new ones. In this study we consider DWB protocols implemented on clinical scanners with a limited axial field of view with the use of multiple whole body sweeps. These protocols result in temporal gaps in the dynamic data which produce noisier and potentially more biased parametric images, compared to single bed (SB) dynamic protocols. Dynamic reconstruction using the Patlak model has been previously proposed to overcome these limits and shown improved DWB parametric images ofK_i. In this work, we propose and make use of a spectral analysis based model for dynamic reconstruction and parametric imaging of PatlakK_i. Both dynamic reconstruction methods were evaluated for DWB FDG protocols and compared against 3D reconstruction based parametric imaging from SB dynamic protocols. This work was conducted on simulated data and results were tested against real FDG dynamic data. We showed that dynamic reconstruction can achieve levels of parametric image noise and bias comparable to 3D reconstruction in SB dynamic studies, with the spectral model offering additional flexibility and further reduction of image noise. Comparisons were also made between step and shoot and continuous bed motion (CBM) protocols, which showed that CBM can achieve lower parametric image noise due to reduced acquisition temporal gaps. Finally, our results showed that dynamic reconstruction improved VOI parametric mean estimates but did not result to fully converged values before resulting in undesirable levels of noise. Additional regularisation methods need to be considered for DWB protocols to ensure both accurate quantification and acceptable noise levels for clinical applications.