Performance evaluation of the PennPET explorer with expanded axial coverage.

Objective.This work evaluated the updated PennPET Explorer total-body (TB) PET scanner, which was extended to 6 rings with updated readout firmware to achieve a 142 cm axial field of view (AFOV) without 7.6 cm inter-ring axial gaps.Approach.National Electrical Manufacturers Association (NEMA) NU 2-2018 measurements were performed with modifications including longer phantoms for sensitivity and count-rate measurements and additional positions for spatial resolution and image quality. A long uniform phantom and the clinical trials network (CTN) phantom were also used.Main results.The total sensitivity increased to 140 kcps MBq-1for a 70 cm line, a gain of 1.8x compared to the same system with axial gaps; an additional 47% increase in total counts was observed with a 142 cm line at the same activity per cm. The noise equivalent count rate (NECR) increased by 1.8x without axial gaps. The peak NECR is 1550 kcps at 25 kBq cc-1for a 140 cm phantom; due to increased randoms, the NECR is lower than with a 70 cm phantom, for which NECR is 2156 kcps cc-1at 25 kBq cc-1and continues increasing. The time-of-flight resolution is 250 ps, increasing by <10 ps at the highest activity. The axial spatial resolution degrades by 0.6 mm near the center of the AFOV, compared to 4 mm resolution near the end. The NEMA image quality phantom showed consistent contrast recovery throughout the AFOV. A long uniform phantom demonstrated axial uniformity of uptake and noise, and the CTN phantom demonstrated quantitative accuracy for both18F and89Zr.Significance. The performance evaluation of the updated PennPET Explorer demonstrates significant gains compared to conventional scanners and shows where the current NEMA standard needs to be updated for TB-PET systems. The comparisons of systems with and without inter-ring gaps demonstrate the performance trade-offs of a more cost-effective TB-PET system with incomplete detector coverage.

A personalized deep learning denoising strategy for low-count PET images.

Objective. Deep learning denoising networks are typically trained with images that are representative of the testing data. Due to the large variability of the noise levels in positron emission tomography (PET) images, it is challenging to develop a proper training set for general clinical use. Our work aims to develop a personalized denoising strategy for the low-count PET images at various noise levels.Approach.We first investigated the impact of the noise level in the training images on the model performance. Five 3D U-Net models were trained on five groups of images at different noise levels, and a one-size-fits-all model was trained on images covering a wider range of noise levels. We then developed a personalized weighting method by linearly blending the results from two models trained on 20%-count level images and 60%-count level images to balance the trade-off between noise reduction and spatial blurring. By adjusting the weighting factor, denoising can be conducted in a personalized and task-dependent way.Main results.The evaluation results of the six models showed that models trained on noisier images had better performance in denoising but introduced more spatial blurriness, and the one-size-fits-all model did not generalize well when deployed for testing images with a wide range of noise levels. The personalized denoising results showed that noisier images require higher weights on noise reduction to maximize the structural similarity and mean squared error. And model trained on 20%-count level images can produce the best liver lesion detectability.Significance.Our study demonstrated that in deep learning-based low dose PET denoising, noise levels in the training input images have a substantial impact on the model performance. The proposed personalized denoising strategy utilized two training sets to overcome the drawbacks introduced by each individual network and provided a series of denoised results for clinical reading.

Principles of Tracer Kinetic Analysis in Oncology, Part II: Examples and Future Directions.

Learning Objectives: On successful completion of this activity, participants should be able to (1) describe examples of the application of PET tracer kinetic analysis to oncology; (2) list applications research and possible clinical applications in oncology where kinetic analysis is helpful; and (3) discuss future applications of kinetic modeling to cancer research and possible clinical cancer imaging practice.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 April 2025.Kinetic analysis of dynamic PET imaging enables the estimation of biologic processes relevant to disease. Through mathematic analysis of the interactions of a radiotracer with tissue, information can be gleaned from PET imaging beyond static uptake measures. Part I of this 2-part continuing education paper reviewed the underlying principles and methodology of kinetic modeling. In this second part, the benefits of kinetic modeling for oncologic imaging are illustrated through representative case examples that demonstrate the principles and benefits of kinetic analysis in oncology. Examples of the model types discussed in part I are reviewed here: a 1-tissue-compartment model (15O-water), an irreversible 2-tissue-compartment model (18F-FDG), and a reversible 2-tissue-compartment model (3'-deoxy-3'-18F-fluorothymidine). Kinetic approaches are contrasted with static uptake measures typically used in the clinic. Overall, this 2-part review provides the reader with background in kinetic analysis to understand related research and improve the interpretation of clinical nuclear medicine studies with a focus on oncologic imaging.

Abbreviated scan protocols to capture 18F-FDG kinetics for long axial FOV PET scanners.

PURPOSE: Kinetic parameters from dynamic 18F-fluorodeoxyglucose (FDG) imaging offer complementary insights to the study of disease compared to static clinical imaging. However, dynamic imaging protocols are cumbersome due to the long acquisition time. Long axial field-of-view (LAFOV) PET scanners (> 70 cm) have two advantages for dynamic imaging over clinical PET scanners with a standard axial field-of-view (SAFOV; 16-30 cm). The large axial coverage enables multi-organ dynamic imaging in a single bed position, and the high sensitivity may enable clinically routine abbreviated dynamic imaging protocols. METHODS: In this work, we studied two abbreviated protocols using data from a 65-min dynamic 18F-FDG scan: (A) dynamic imaging immediately post-injection (p.i.) for variable durations, and (B) dynamic imaging immediately p.i. for variable durations plus a 1-h p.i. (5-min-long) datapoint. Nine cancer patients were imaged on the Biograph Vision Quadra (Siemens Healthineers). Time-activity curves over the lesions (N = 39) were fitted using the Patlak graphical analysis and a 2-tissue-compartment (2C, k4 = 0) model for variable scan durations (5-60 min). Kinetic parameters from the complete dataset served as the reference. Lesions from all cancers were grouped into low, medium, and high flux groups, and bias and precision of Ki (Patlak) and Ki, K1, k2, and k3 (2C) were calculated for each group. RESULTS: Using only early dynamic data with the 2C (or Patlak) model, accurate quantification of Ki required at least 50 (or 55) min of dynamic data for low flux lesions, at least 30 (or 40) min for medium flux lesions, and at least 15 (or 20) min for high flux lesions to achieve both 10% bias and precision. The addition of the final (5-min) datapoint allowed for accurate quantification of Ki with a bias and precision of 10% using only 10-15 min of early dynamic data for either model. CONCLUSION: Dynamic imaging for 10-15 min immediately p.i. followed by a 5-min scan at 1-h p.i can accurately and precisely quantify 18F-FDG on a long axial FOV scanner, potentially allowing for more widespread use of dynamic 18F-FDG imaging.

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.

Kinetic and Static Analysis of Poly-(Adenosine Diphosphate-Ribose) Polymerase-1-Targeted 18F-Fluorthanatrace PET Images of Ovarian Cancer.

The poly-(adenosine diphosphate-ribose) polymerase (PARP) family of proteins participates in numerous functions, most notably the DNA damage response. Cancer vulnerability to DNA damage has led to development of several PARP inhibitors (PARPi). This class of drugs has demonstrated therapeutic efficacy in ovarian, breast, and prostate cancers, but with variable response. Consequently, clinics need to select patients likely to benefit from these targeted therapies. In vivo imaging of 18F-fluorthanatrace uptake has been shown to correspond to PARP-1 expression in tissue. This study characterized the pharmacokinetics of 18F-fluorthanatrace and tested kinetic and static models to guide metric selection in future studies assessing 18F-fluorthanatrace as a biomarker of response to PARPi therapy. Methods: Fourteen prospectively enrolled ovarian cancer patients were injected with 18F-fluorthanatrace and imaged dynamically for 60 min after injection followed by up to 2 whole-body scans, with venous blood activity and metabolite measurements. SUVmax and SUVpeak were extracted from dynamic images and whole-body scans. Kinetic parameter estimates and SUVs were assessed for correlations with tissue PARP-1 immunofluorescence (n = 7). Simulations of population kinetic parameters enabled estimation of measurement bias and precision in parameter estimates. Results:18F-fluorthanatrace blood clearance was variable, but labeled metabolite profiles were similar across patients, supporting use of a population parent fraction curve. The total distribution volume from a reversible 2-tissue-compartment model and Logan reference tissue distribution volume ratio (DVR) from the first hour of PET acquisition correlated with tumor PARP-1 expression by immunofluorescence (r = 0.76 and 0.83, respectively; P < 0.05). DVR bias and precision estimates were 6.4% and 29.1%, respectively. SUVmax and SUVpeak acquired from images with midpoints of 57.5, 110 ± 3, and 199 ± 4 min highly correlated with PARP-1 expression (mean ± SD, r ≥ 0.79; P < 0.05). Conclusion: Tumor SUVmax and SUVpeak at 55-60 min after injection and later and DVR from at least 60 min appear to be robust noninvasive measures of PARP-1 binding. 18F-fluorthanatrace uptake in ovarian cancer was best described by models of reversible binding. However, pharmacokinetic patterns of tracer uptake were somewhat variable, especially at later time points.

Performance Characteristics of Long Axial Field-of-View PET Scanners with Axial Gaps.

The introduction of long (>60 cm) axial field-of-view (LAFOV) PET systems has shown their potential for clinical and research applications. LAFOV scanners are expensive, so there is interest in designing systems with longer axial coverage while mitigating cost by introducing detector gaps. We used measurements on the PennPET Explorer (64-cm AFOV prototype) and simulations of scanners up to 143-cm long to assess scanner performance with axial gaps introduced by varying the number of detector rows in each ring. Removing detectors reduces the total sensitivity and results in a non-uniform axial noise profile. Axial resolution shows small (<0.5 mm) loss from the edge of the AFOV to the center, even for a 143-cm AFOV scanner with an unrestricted acceptance angle. The presence of large axial gaps increases the variability in axial resolution and contrast recovery across the AFOV compared to a system without gaps. More modest axial gaps show less variable behavior. The results suggest that designs where the gap is no larger than one-half of the width of a detector ring may be preferred, although the optimal choice of scanner design with the trade-offs of performance and AFOV will depend on its intended usage.

Functional 4-D clustering for characterizing intratumor heterogeneity in dynamic imaging: evaluation in FDG PET as a prognostic biomarker for breast cancer.

PURPOSE: Probe-based dynamic (4-D) imaging modalities capture breast intratumor heterogeneity both spatially and kinetically. Characterizing heterogeneity through tumor sub-populations with distinct functional behavior may elucidate tumor biology to improve targeted therapy specificity and enable precision clinical decision making. METHODS: We propose an unsupervised clustering algorithm for 4-D imaging that integrates Markov-Random Field (MRF) image segmentation with time-series analysis to characterize kinetic intratumor heterogeneity. We applied this to dynamic FDG PET scans by identifying distinct time-activity curve (TAC) profiles with spatial proximity constraints. We first evaluated algorithm performance using simulated dynamic data. We then applied our algorithm to a dataset of 50 women with locally advanced breast cancer imaged by dynamic FDG PET prior to treatment and followed to monitor for disease recurrence. A functional tumor heterogeneity (FTH) signature was then extracted from functionally distinct sub-regions within each tumor. Cross-validated time-to-event analysis was performed to assess the prognostic value of FTH signatures compared to established histopathological and kinetic prognostic markers. RESULTS: Adding FTH signatures to a baseline model of known predictors of disease recurrence and established FDG PET uptake and kinetic markers improved the concordance statistic (C-statistic) from 0.59 to 0.74 (p = 0.005). Unsupervised hierarchical clustering of the FTH signatures identified two significant (p < 0.001) phenotypes of tumor heterogeneity corresponding to high and low FTH. Distributions of FDG flux, or Ki, were significantly different (p = 0.04) across the two phenotypes. CONCLUSIONS: Our findings suggest that imaging markers of FTH add independent value beyond standard PET imaging metrics in predicting recurrence-free survival in breast cancer and thus merit further study.

Update on the PennPET Explorer: A Whole-body Imager with Scalable Axial Field-of-View.

Following successful performance testing and human imaging of a prototype PennPET Explorer, the scanner has been expanded to a current axial field of view of 1.12 m. Initial studies on this instrument have demonstrated encouraging results for total-body positron emission tomography imaging. Planned studies will test the capabilities of the PennPET Explorer further and inform the design of further human imaging protocols.

Analysis of Four-Dimensional Data for Total Body PET Imaging.

The high sensitivity and total-body coverage of total-body PET scanners will be valuable for a number of clinical and research applications outlined in this article.

Kinetic Modeling of 18F-(2S,4R)4-Fluoroglutamine in Mouse Models of Breast Cancer to Estimate Glutamine Pool Size as an Indicator of Tumor Glutamine Metabolism.

The PET radiotracer 18F-(2S,4R)4-fluoroglutamine (18F-Gln) reflects glutamine transport and can be used to infer glutamine metabolism. Mouse xenograft studies have demonstrated that 18F-Gln uptake correlates directly with glutamine pool size and is inversely related to glutamine metabolism through the glutaminase enzyme. To provide a framework for the analysis of 18F-Gln-PET, we have examined 18F-Gln uptake kinetics in mouse models of breast cancer at baseline and after inhibition of glutaminase. We describe results of the preclinical analysis and computer simulations with the goal of model validation and performance assessment in anticipation of human breast cancer patient studies. Methods: Triple-negative breast cancer and receptor-positive xenografts were implanted in athymic mice. PET mouse imaging was performed at baseline and after treatment with a glutaminase inhibitor or a vehicle solution for 4 mouse groups. Dynamic PET images were obtained for 1 h beginning at the time of intravenous injection of 18F-Gln. Kinetic analysis and computer simulations were performed on representative time-activity curves, testing 1- and 2-compartment models to describe kinetics. Results: Dynamic imaging for 1 h captured blood and tumor time-activity curves indicative of largely reversible uptake of 18F-Gln in tumors. Consistent with this observation, a 2-compartment model indicated a relatively low estimate of the rate constant of tracer trapping, suggesting that the 1-compartment model is preferable. Logan plot graphical analysis demonstrated late linearity, supporting reversible kinetics and modeling with a single compartment. Analysis of the mouse data and simulations suggests that estimates of glutamine pool size, specifically the distribution volume (VD) for 18F-Gln, were more reliable using the 1-compartment reversible model than the 2-compartment irreversible model. Tumor-to-blood ratios, a more practical potential proxy of VD, correlated well with volume of distribution from single-compartment models and Logan analyses. Conclusion: Kinetic analysis of dynamic 18F-Gln-PET images demonstrated the ability to measure VD to estimate glutamine pool size, a key indicator of cellular glutamine metabolism, by both a 1-compartment model and Logan analysis. Changes in VD with glutaminase inhibition support the ability to assess response to glutamine metabolism-targeted therapy. Concordance of kinetic measures with tumor-to-blood ratios provides a clinically feasible approach to human imaging.

Quantifying bias and precision of kinetic parameter estimation on the PennPET Explorer, a long axial field-of-view scanner.

Long axial field-of-view (AFOV) PET scanners allow for full-body dynamic imaging in a single bed-position at very high sensitivity. However, the benefits for kinetic parameter estimation have yet to be studied. This work uses (1) a dynamic GATE simulation of [18F]-fluorothymidine (FLT) in a modified NEMA IQ phantom and (2) a lesion embedding study of spheres in a dynamic [18F]-fluorodeoxyglucose (FDG) human subject imaged on the PennPET Explorer. Both studies were designed using published kinetic data of lung and liver cancers and modeled using two tissue compartments. Data were reconstructed at various emulated doses. Sphere time-activity curves (TACs) were measured on resulting dynamic images, and TACs were fit using a two-tissue-compartment model (k4 ≠ 0) for the FLT study and both a two-tissue-compartment model (k4 = 0) and Patlak graphical analysis for the FDG study to estimate flux (Ki) and delivery (K1) parameters. Quantification of flux and K1 shows lower bias and better precision for both radiotracers on the long AFOV scanner, especially at low doses. Dynamic imaging on a long AFOV system can be achieved for a greater range of injected doses, as low as 0.5-2 mCi depending on the sphere size and flux, compared to a standard AFOV scanner, while maintaining good kinetic parameter estimation.

Benefit of Improved Performance with State-of-the Art Digital PET/CT for Lesion Detection in Oncology.

The latest digital whole-body PET scanners provide a combination of higher sensitivity and improved spatial and timing resolution. We performed a lesion detectability study on two generations of Biograph PET/CT scanners, the mCT Flow and the Vision, to study the impact of improved physical performance on clinical performance. Our hypothesis was that the improved performance of the Vision would result in improved lesion detectability, allowing shorter imaging times or, equivalently, a lower injected dose. Methods: Data were acquired with the Society of Nuclear Medicine and Molecular Imaging Clinical Trials Network torso phantom combined with a 20-cm-diameter cylindrical phantom. Spherical lesions were emulated by acquiring sphere-in-air data and combining them with the phantom data to generate combined datasets with embedded lesions of known contrast. Two sphere sizes and uptakes were used: 9.89-mm-diameter spheres with 6:1 (lung) and 3:1 (cylinder) local activity concentration uptakes and 4.95-mm-diameter spheres with 9.6:1 (lung) and 4.5:1 (cylinder) local activity concentration uptakes. Standard image reconstruction was performed: an ordinary Poisson ordered-subsets expectation maximization algorithm with point-spread function and time-of-flight modeling and postreconstruction smoothing with a 5-mm gaussian filter. The Vision images were also generated without any postreconstruction smoothing. Generalized scan statistics methodology was used to estimate the area under the localized receiver-operating-characteristic curve (ALROC). Results: The higher sensitivity and improved time-of-flight performance of the Vision leads to reduced contrast in the background noise nodule distribution. Measured lesion contrast is also higher on the Vision because of its improved spatial resolution. Hence, the ALROC is noticeably higher for the Vision than for the mCT Flow. Conclusion: Improved overall performance of the Vision provides a factor of 4-6 reduction in imaging time (or injected dose) over the mCT Flow when using the ALROC metric for lesions at least 9.89 mm in diameter. Smaller lesions are barely detected in the mCT Flow, leading to even higher ALROC gains with the Vision. The improved spatial resolution of the Vision also leads to a higher measured contrast that is closer to the real uptake, implying improved quantification. Postreconstruction smoothing, however, reduces this improvement in measured contrast, thereby reducing the ALROC for small, high-uptake lesions.

PennPET Explorer: Human Imaging on a Whole-Body Imager.

The PennPET Explorer, a prototype whole-body imager currently operating with a 64-cm axial field of view, can image the major body organs simultaneously with higher sensitivity than that of commercial devices. We report here the initial human imaging studies on the PennPET Explorer, with each study designed to test specific capabilities of the device. Methods: Healthy subjects were imaged with FDG on the PennPET Explorer. Subsequently, clinical subjects with disease were imaged with 18F-FDG and 68Ga-DOTATATE, and research subjects were imaged with experimental radiotracers. Results: We demonstrated the ability to scan for a shorter duration or, alternatively, with less activity, without a compromise in image quality. Delayed images, up to 10 half-lives with 18F-FDG, revealed biologic insight and supported the ability to track biologic processes over time. In a clinical subject, the PennPET Explorer better delineated the extent of 18F-FDG-avid disease. In a second clinical study with 68Ga-DOTATATE, we demonstrated comparable diagnostic image quality between the PennPET scan and the clinical scan, but with one fifth the activity. Dynamic imaging studies captured relatively noise-free input functions for kinetic modeling approaches. Additional studies with experimental research radiotracers illustrated the benefits from the combination of large axial coverage and high sensitivity. Conclusion: These studies provided a proof of concept for many proposed applications for a PET scanner with a long axial field of view.

Numerical observer study of lesion detectability for a long axial field-of-view whole-body PET imager using the PennPET Explorer.

This work uses lesion detectability to characterize the performance of long axial field of view (AFOV) PET scanners which have increased sensitivity compared to clinical scanners. Studies were performed using the PennPET Explorer, a 70 cm long AFOV scanner built at the University of Pennsylvania, for small lesions distributed in a uniform water-filled cylinder (simulations and measurements), an anthropomorphic torso phantom (measurement), and a human subject (measurement). The lesion localization and detection task was quantified numerically using a generalized scan statistics methodology. Detectability was studied as a function of background activity distribution, scan duration for a single bed position, and axial location of the lesions. For the cylindrical phantom, the areas under the localization receiver operating curve (ALROCs) of lesions placed at various axial locations in the scanner were greater than 0.8-a value considered to be clinically acceptable (i.e. 80% probability of detecting lesion)-for scan times of 60 s or longer for standard-of-care (SoC) clinical dose levels. 10 mm diameter lesions placed in the anthropomorphic phantom and human subject resulted in ALROCs of 0.8 or greater for scan times longer than 30 s in the lung region and 60 s in the liver region, also for SoC doses. ALROC results from all three activity distributions show similar trends as a function of counts detected per axial location. These results will be used to guide decisions on imaging parameters, such as scan time and patient dose, when imaging patients in a single bed position on long AFOV systems and can also be applied to clinical scanners with consideration of the sensitivity differences.

PennPET Explorer: Design and Preliminary Performance of a Whole-Body Imager.

We report on the development of the PennPET Explorer whole-body imager. Methods: The PennPET Explorer is a multiring system designed with a long axial field of view. The imager is scalable and comprises multiple 22.9-cm-long ring segments, each with 18 detector modules based on a commercial digital silicon photomultiplier. A prototype 3-segment imager has been completed and tested with an active 64-cm axial field of view. Results: The instrument design is described, and its physical performance measurements are presented. These include sensitivity of 55 kcps/MBq, spatial resolution of 4.0 mm, energy resolution of 12%, timing resolution of 256 ps, and a noise-equivalent count rate above 1,000 kcps beyond 30 kBq/mL. After an evaluation of lesion torso phantoms to characterize quantitative accuracy, human studies were performed on healthy volunteers. Conclusion: The physical performance measurements validated the system design and led to high-quality human studies.

Advances in PET Diagnostics for Guiding Targeted Cancer Therapy and Studying In Vivo Cancer Biology.

Purpose of the Review: We present an overview of recent advances in positron emission tomography (PET) diagnostics as applied to the study of cancer, specifically as a tool to study in vivo cancer biology and to direct targeted cancer therapy. The review is directed to translational and clinical cancer investigators who may not be familiar with these applications of PET cancer diagnostics, but whose research might benefit from these advancing tools. Recent Findings: We highlight recent advances in 3 areas: (1) the translation of PET imaging cancer biomarkers to clinical trials; (2) methods for measuring cancer metabolism in vivo in patients; and (3) advances in PET instrumentation, including total-body PET, that enable new methodologies. We emphasize approaches that have been translated to human studies. Summary: PET imaging methodology enables unique in vivo cancer diagnostics that go beyond cancer detection and staging, providing an improved ability to guide cancer treatment and an increased understanding of in vivo human cancer biology.

Timing properties of phosphor-coated polished LSO crystals.

This study investigates a time-of-flight (TOF)-depth-of-interaction (DOI) detector design for positron emission tomography (PET), based on phosphor-coated lutetium oxyorthosilicate (LSO) scintillator crystals coupled to fast single channel photomultiplier tubes. Interaction of the scintillation light with the phosphor coating changes the pulse shape in a depth-dependent manner. 3 × 3 × 10 mm(3) LSO scintillation crystals with polished surfaces were characterized, with and without phosphor coating, to assess DOI capability and timing properties. Two different phosphor coating geometries were studied: coating of the top surface of the crystal, and the top plus half of the crystal sides. There was negligible depth dependency in the decay time when coating only the top surface, however there was a ∼10 ns difference in end-to-end decay time when coating the top plus half of the crystal sides, sufficient to support the use of three DOI bins (3.3 mm DOI bin width). The rise time of the half-coated phosphor crystal was slightly faster at all depths, compared to uncoated crystals, however the signal amplitude was lower. Phosphor coating resulted in depth-dependent photopeak positions with an energy resolution of 13.7%, at a depth of 1 mm, and 15.3%, at a depth of 9 mm, for the half-coated crystal. Uncoated LSO crystals showed no change in photopeak position as a function of depth, with an energy resolution of 10.4%. The head-on coincidence timing resolution (CTR) of two uncoated LSO crystals was 287 ps using constant fraction discrimination for time pick-off. With phosphor coating, the CTR of the top-coated crystal was 314 ps, compared to 384 ps for the half-coated crystal. We demonstrate that the trade-off between timing resolution and DOI resolution can be controlled by the phosphor coating geometry. Here we present preliminary results demonstrating that good DOI resolution can be achieved with only a modest 26% degradation in CTR.