Whole-body PET Imaging Research Articles

18F-FDG PET Maximum-Intensity Projections and Artificial Intelligence: A Win-Win Combination to Easily Measure Prognostic Biomarkers in DLBCL Patients.

Total metabolic tumor volume (TMTV) and tumor dissemination (Dmax) calculated from baseline 18F-FDG PET/CT images are prognostic biomarkers in diffuse large B-cell lymphoma (DLBCL) patients. Yet, their automated calculation remains challenging. The purpose of this study was to investigate whether TMTV and Dmax features could be replaced by surrogate features automatically calculated using an artificial intelligence (AI) algorithm from only 2 maximum-intensity projections (MIPs) of the whole-body 18F-FDG PET images. Methods: Two cohorts of DLBCL patients from the REMARC (NCT01122472) and LNH073B (NCT00498043) trials were retrospectively analyzed. Experts delineated lymphoma lesions from the baseline whole-body 18F-FDG PET/CT images, from which TMTV and Dmax were measured. Coronal and sagittal MIP images and associated 2-dimensional reference lesion masks were calculated. An AI algorithm was trained on the REMARC MIP data to segment lymphoma regions. The AI algorithm was then used to estimate surrogate TMTV (sTMTV) and surrogate Dmax (sDmax) on both datasets. The ability of the original and surrogate TMTV and Dmax to stratify patients was compared. Results: Three hundred eighty-two patients (mean age ± SD, 62.1 y ± 13.4 y; 207 men) were evaluated. sTMTV was highly correlated with TMTV for REMARC and LNH073B datasets (Spearman r = 0.878 and 0.752, respectively), and so were sDmax and Dmax (r = 0.709 and 0.714, respectively). The hazard ratios for progression free survival of volume and MIP-based features derived using AI were similar, for example, TMTV: 11.24 (95% CI: 2.10-46.20), sTMTV: 11.81 (95% CI: 3.29-31.77), and Dmax: 9.0 (95% CI: 2.53-23.63), sDmax: 12.49 (95% CI: 3.42-34.50). Conclusion: Surrogate TMTV and Dmax calculated from only 2 PET MIP images are prognostic biomarkers in DLBCL patients and can be automatically estimated using an AI algorithm.

Identifying the individual metabolic abnormities from a systemic perspective using whole-body PET imaging

IntroductionDistinct physiological states arise from complex interactions among the various organs present in the human body. PET is a non-invasive modality with numerous successful applications in oncology, neurology, and cardiology. However, while PET imaging has been applied extensively in detecting focal lesions or diseases, its potential in detecting systemic abnormalities is seldom explored, mostly because total-body imaging was not possible until recently.MethodsIn this context, the present study proposes a framework capable of constructing an individual metabolic abnormality network using a subject’s whole-body 18F-FDG SUV image and a normal control database. The developed framework was evaluated in the patients with lung cancer, the one discharged after suffering from Covid-19 disease, and the one that had gastrointestinal bleeding with the underlying cause unknown.ResultsThe framework could successfully capture the deviation of these patients from healthy subjects at the level of both system and organ. The strength of the altered network edges revealed the abnormal metabolic connection between organs. The overall deviation of the network nodes was observed to be highly correlated to the organ SUV measures. Therefore, the molecular connectivity of glucose metabolism was characterized at a single subject level.ConclusionThe proposed framework represents a significant step toward the use of PET imaging for identifying metabolic dysfunction from a systemic perspective. A better understanding of the underlying biological mechanisms and the physiological interpretation of the interregional connections identified in the present study warrant further research.

Continuous bed motion in a silicon photomultiplier-based scanner provides equivalent spatial resolution and image quality in whole body PET images at similar acquisition times using the step-and-shoot method.

This study investigated the spatial resolution and image quality of the continuous bed motion (CBM) method in a sensitive silicon photomultiplier (SiPM)-based positron emission tomography (PET)/computed tomography (CT) system compared with the traditional step-and-shoot (SS) method. Methods: Siemens Biograph Vision was used in this study. Data acquisition using the SS method was performed for 3 min per bed. In the CBM method, the bed speed ranged from 0.5 to 3.3 mm/s. The acquisition time equivalent to the SS method was 1.1 mm/s for 2-bed ranges and 0.8 mm/s for seven-bed ranges. The spatial resolution was investigated using 18F point sources and evaluated using the full width at half maximum. Image quality was investigated using a National Electrical Manufacturers Association International Electrotechnical Commission body phantom with six spheres 10-, 13-, 17-, 22-, 28-, and 37-mm inner diameters. The radioactivity concentration ratio of the 18F solution in all spheres and the background was approximately 4:1. The detectability of each sphere was visually evaluated on a five-step score. Image quality was physically evaluated using the noise equivalent count rate (NECphantom), contrast percentage of the 10-mm hot sphere (QH,10mm), background variability percentage (N10mm), and contrast-noise ratio (QH,10mm/N10mm). Results: The spatial resolution was not affected by the difference of acquisition methods and bed speeds. The detectability of the 10-mm sphere with a bed speed of 2.2 mm/s or faster was significantly inferior to that of the SS 2-bed method. In evaluating image quality, no significant difference in the contrast percentage was observed among the acquisition methods and speeds in the CBM method. However, the increasing bed speed in the CBM method increased the N10mm and decreased the NECphantom. When comparing the SS 2-bed method with the CBM method at 0.8 mm/s, no significant differences in all parameters were observed. Conclusion: In a SiPM-based PET/CT scanner, the CBM method provides equivalent spatial resolution and image quality in whole body PET images with same acquisition time using the SS method.

Noninvasive estimation of human radiation dosimetry of 18F-FDG by whole-body small animal PET imaging in rats

PurposeSmall animal PET provides the biodistribution of administrated radiotracer in vivo and have a potential to contribute on dosimetry study. The aim of this study is to investigate the effect of region-of-interest (ROI)-delineation in whole-body rat PET image toward non-invasive estimation of human dosimetry of 18F-FDG. MethodAfter administration of 18F-FDG (averaged 11.7 MBq), 3.5-h PET and 20-min CT scans were sequentially performed for three rats by Clairvivo PET/CT system. Seven source organs, and the remainder of the body, were studied to extrapolate %ID(t) and estimate time-integrated activity coefficients [kBq-h/MBq] in human. The mean absorbed dose in each target organ and the effective dose were estimated by MIRD method. Effects of ROI-definitions on both extrapolated %ID(t) in human and estimated doses were also investigated by using (i) small ROIs of high uptake region and (ii) whole organ ROIs. ResultsAveraged effective doses of 18F-FDG in human by using high-uptake and whole-organ ROIs were 27.8 ± 6.54 and 19.3 ± 2.72 μSv/MBq, respectively. ConclusionThe use of small animal PET scanner, which allows repeatedly PET scans, have a potential to contribute on the reduction of the number of experimental animals. However, the ways of ROI drawing influences on the estimated effective dose and safe-side ROI definition may be preferred.

Whole-body PET Imaging of T-cell Response to Glioblastoma.

Immunotherapy is a promising approach for many oncological malignancies, including glioblastoma, however, there are currently no available tools or biomarkers to accurately assess whole-body immune responses in patients with glioblastoma treated with immunotherapy. Here, the utility of OX40, a costimulatory molecule mainly expressed on activated effector T cells known to play an important role in eliminating cancer cells, was evaluated as a PET imaging biomarker to quantify and track response to immunotherapy. A subcutaneous vaccination approach of CpG oligodeoxynucleotide, OX40 mAb, and tumor lysate at a remote site in a murine orthotopic glioma model was developed to induce activation of T cells distantly while monitoring their distribution in stimulated lymphoid organs with respect to observed therapeutic effects. To detect OX40-positive T cells, we utilized our in-house-developed 89Zr-DFO-OX40 mAb and in vivo PET/CT imaging. ImmunoPET with 89Zr-DFO-OX40 mAb revealed strong OX40-positive responses with high specificity, not only in the nearest lymph node from vaccinated area (mean, 20.8%ID/cc) but also in the spleen (16.7%ID/cc) and the tumor draining lymph node (11.4%ID/cc). When the tumor was small (<106 p/sec/cm2/sr in bioluminescence imaging), a high number of responders and percentage shrinkage in tumor signal was indicated after only a single cycle of vaccination. The results highlight the promise of clinically translating cancer vaccination as a potential glioma therapy, as well as the benefits of monitoring efficacy of these treatments using immunoPET imaging of T-cell activation.

Fluorodeoxyglucose positron-emission tomography-magnetic resonance hybrid imaging: An emerging tool for staging of cancer of the uterine cervix.

Positron-emission tomography-magnetic resonance imaging (PET-MRI) is an emerging hybrid imaging modality that utilizes the superior soft tissue resolution of MR with the metabolic data from PET. In this study, we sought to assess the clinical value of fluorodeoxyglucose (FDG) PET-MRI with dedicated pelvic PET-MR in the initial staging of cervical cancer. In this institutional-approved study, we identified 23 adult females who underwent FDG PET-MRI on hybrid camera for staging of primary uterine cervical cancer that included a dedicated PET-MR of the pelvis. A nuclear medicine physician and a radiologist reviewed the PET, MRI, and fusion-body and pelvis images alone and then with consensus read characterizing PET and MR abnormal findings. There were 23 patients who underwent FDG PET-MRI for initial staging of cervical cancer with an average age of 52.2 ± 14.0 years. A total of 23 suspected lymph nodes in eight different patients were detected within the pelvis with increased metabolic activity on PET. Both the dedicated pelvis and whole-body PET imaging detected the same corresponding pelvic lymph nodes, although the pelvic PET imaging had better lymph node uptake delineation due to longer acquisition time. Using a 10-mm short-axis criterion, MRI identified only 43.5% of the FDG avid lymph nodes. The average SUVmax on the pelvis PET sequences was higher with SUV 8.9 ± 5.2 compared to the whole-body PET with SUV 7.8 ± 5.4 but was not statistically significant (P > 0.05). Primary cervical cancer was identified in 18 patients on both PET imaging and MRI with dedicated MR pelvis providing better characterization. Based on our results of the patients with cervical cancer evaluated for initial staging, combining dedicated pelvic PET-MRI with whole-body PET/MR provides the most complete status of malignant disease in reference to delineation of primary tumor, involvement of surrounding tissues, and regional lymph nodes.

Classification of Myocardial 18F-FDG PET Uptake Patterns Using Deep Learning.

To perform automated myocardial segmentation and uptake classification from whole-body fluorine 18 fluorodeoxyglucose (FDG) PET. In this retrospective study, consecutive patients who underwent FDG PET imaging for oncologic indications were included (July-August 2018). The left ventricle (LV) on whole-body FDG PET images was manually segmented and classified as showing no myocardial uptake, diffuse uptake, or partial uptake. A total of 609 patients (mean age, 64 years ± 14 [standard deviation]; 309 women) were included and split between training (60%, 365 patients), validation (20%, 122 patients), and testing (20%, 122 patients) datasets. Two sequential neural networks were developed to automatically segment the LV and classify the myocardial uptake pattern using segmentation and classification training data provided by human experts. Linear regression was performed to correlate findings from human experts and deep learning. Classification performance was evaluated using receiver operating characteristic (ROC) analysis. There was moderate agreement of uptake pattern between experts and deep learning (as a fraction of correctly categorized images) with 78% (36 of 46) for no uptake, 71% (34 of 48) for diffuse uptake, and 71% (20 of 28) for partial uptake. There was no bias in LV volume for partial or diffuse uptake categories (P = .56); however, deep learning underestimated LV volumes in the no uptake category. There was good correlation for LV volume (R 2 = 0.35, b = .71). ROC analysis showed the area under the curve for classifying no uptake and diffuse uptake was high (> 0.90) but lower for partial uptake (0.77). The feasibility of a myocardial uptake index (MUI) for quantifying the degree of myocardial activity patterns was shown, and there was excellent visual agreement between MUI and uptake patterns. Deep learning was able to segment and classify myocardial uptake patterns on FDG PET images.Keywords: PET, Heart, Computer Aided Diagnosis, Computer Application-Detection/DiagnosisSupplemental material is available for this article.©RSNA, 2021.

PET imaging of 11C-labeled thiamine tetrahydrofurfuryl disulfide, vitamin B1 derivative: First-in-human study

Vitamine B1 thiamine is an essential component for glucose metabolism and energy production. The disulfide derivative, thiamine tetrahydrofurfuryl disulfide (TTFD), is more absorbent compared to readily-available water-soluble thiamine salts since it does not require the rate-limiting transport system required for thiamine absorption. However, the detailed pharmacokinetics of thiamine and TTFD under normal and pathological conditions were not clarified yet. Recently, 11C-labeled thiamine and TTFD were synthesized by our group, and their pharmacokinetics were investigated by PET imaging in normal rats. In this study, to clarify the whole body pharmacokinetics of [11C]TTFD in human healthy volunteers, we performed first-in-human PET imaging study with [11C]TTFD, along with radiation dosimetry of [11C]TTFD in humans. MethodsSynthesis of [11C]TTFD was improved for clinical study. Dynamic whole-body PET images were acquired on three young male normal subjects after intravenous injection of [11C]TTFD. VOIs were defined for source organs on the PET images to measure time-course of [11C]TTFD uptake as percentage injected dose and the number of disintegrations for each organ. Radiation dosimetry was calculated with OLINDA/EXM. ResultsWe succeeded in developing the improved synthetic method of [11C]TTFD for the first-in-human PET study. In the whole body imaging, uptake of [11C]TTFD by various tissues was almost plateaued at 10 min after intravenous injection, afterward gradually increased for the brain and urinary bladder (urine). %Injected dose was high in the liver, kidney, urinary bladder, heart, spine, brain, spleen, pancreas, stomach, and salivary glands, in this order. %Injected dose per gram of tissue was high also in the pituitary. By dosimetry, the effective radiation dose of [11C]TTFD calculated was 5.5 μSv/MBq (range 5.2–5.7). ConclusionNovel synthetic method enabled clinical PET study with [11C]TTFD, which is a safe PET tracer with a dosimetry profile comparable to other common 11C-PET tracers. Pharmacokinetics of TTFD in the pharmacological dose and at different nutritional states could be further investigated by future quantitative PET studies. Noninvasive in vivo PET imaging for pathophysiology of thiamine-related function may provide diagnostic evidence of novel information about vitamin B1 deficiency in human tissues.

Feasibility of Deep Learning-Guided Attenuation and Scatter Correction of Whole-Body 68Ga-PSMA PET Studies in the Image Domain.

This study evaluates the feasibility of direct scatter and attenuation correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning. Whole-body 68Ga-PSMA PET images of 399 subjects were used to train a residual deep learning model, taking PET non-attenuation-corrected images (PET-nonAC) as input and CT-based attenuation-corrected PET images (PET-CTAC) as target (reference). Forty-six whole-body 68Ga-PSMA PET images were used as an independent validation dataset. For validation, synthetic deep learning-based attenuation-corrected PET images were assessed considering the corresponding PET-CTAC images as reference. The evaluation metrics included the mean absolute error (MAE) of the SUV, peak signal-to-noise ratio, and structural similarity index (SSIM) in the whole body, as well as in different regions of the body, namely, head and neck, chest, and abdomen and pelvis. The deep learning-guided direct attenuation and scatter correction produced images of comparable visual quality to PET-CTAC images. It achieved an MAE, relative error (RE%), SSIM, and peak signal-to-noise ratio of 0.91 ± 0.29 (SUV), -2.46% ± 10.10%, 0.973 ± 0.034, and 48.171 ± 2.964, respectively, within whole-body images of the independent external validation dataset. The largest RE% was observed in the head and neck region (-5.62% ± 11.73%), although this region exhibited the highest value of SSIM metric (0.982 ± 0.024). The MAE (SUV) and RE% within the different regions of the body were less than 2.0% and 6%, respectively, indicating acceptable performance of the deep learning model. This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 68Ga-PSMA PET images in the image domain using deep learning with clinically tolerable errors. The technique has the potential of performing attenuation correction on stand-alone PET or PET/MRI systems.

Dopamine D1 Receptor Agonist PET Tracer Development: Assessment in Nonhuman Primates.

Non-catechol-based high-affinity selective dopamine D1 receptor (D1R) agonists were recently described, and candidate PET ligands were selected on the basis of favorable properties. The objective of this study was to characterize invivo in nonhuman primates 2 novel D1R agonist PET radiotracers, racemic 18F-MNI-800 and its more active atropisomeric (-)-enantiomer, 18F-MNI-968. Methods: Ten brain PET experiments were conducted with 18F-MNI-800 on 2 adult rhesus macaques and 2 adult cynomolgus macaques, and 8 brain PET experiments were conducted with 18F-MNI-968 on 2 adult rhesus macaques and 2 adult cynomolgus macaques. PET data were analyzed with both plasma-input-based methods and reference-region-based methods. Whole-body PET images were acquired with 18F-MNI-800 from 2 adult rhesus macaques for radiation dosimetry estimates. Results:18F-MNI-800 and 18F-MNI-968 exhibited regional uptake consistent with D1R distribution. Specificity and selectivity were demonstrated by dose-dependent blocking with the D1 antagonist SCH-23390. 18F-MNI-968 showed a 30% higher specific signal than 18F-MNI-800, with a nondisplaceable binding potential of approximately 0.3 in the cortex and approximately 1.1 in the striatum. Dosimetry radiation exposure was favorable, with an effective dose of about 0.023 mSv/MBq. Conclusion:18F-MNI-968 has significant potential as a D1R agonist PET radiotracer, and further characterization in human subjects is warranted.

Deep learning\u2013based metal artefact reduction in PET/CT imaging

ObjectivesThe susceptibility of CT imaging to metallic objects gives rise to strong streak artefacts and skewed information about the attenuation medium around the metallic implants. This metal-induced artefact in CT images leads to inaccurate attenuation correction in PET/CT imaging. This study investigates the potential of deep learning–based metal artefact reduction (MAR) in quantitative PET/CT imaging.MethodsDeep learning–based metal artefact reduction approaches were implemented in the image (DLI-MAR) and projection (DLP-MAR) domains. The proposed algorithms were quantitatively compared to the normalized MAR (NMAR) method using simulated and clinical studies. Eighty metal-free CT images were employed for simulation of metal artefact as well as training and evaluation of the aforementioned MAR approaches. Thirty 18F-FDG PET/CT images affected by the presence of metallic implants were retrospectively employed for clinical assessment of the MAR techniques.ResultsThe evaluation of MAR techniques on the simulation dataset demonstrated the superior performance of the DLI-MAR approach (structural similarity (SSIM) = 0.95 ± 0.2 compared to 0.94 ± 0.2 and 0.93 ± 0.3 obtained using DLP-MAR and NMAR, respectively) in minimizing metal artefacts in CT images. The presence of metallic artefacts in CT images or PET attenuation correction maps led to quantitative bias, image artefacts and under- and overestimation of scatter correction of PET images. The DLI-MAR technique led to a quantitative PET bias of 1.3 ± 3% compared to 10.5 ± 6% without MAR and 3.2 ± 0.5% achieved by NMAR.ConclusionThe DLI-MAR technique was able to reduce the adverse effects of metal artefacts on PET images through the generation of accurate attenuation maps from corrupted CT images.Key Points• The presence of metallic objects, such as dental implants, gives rise to severe photon starvation, beam hardening and scattering, thus leading to adverse artefacts in reconstructed CT images.• The aim of this work is to develop and evaluate a deep learning–based MAR to improve CT-based attenuation and scatter correction in PET/CT imaging.• Deep learning–based MAR in the image (DLI-MAR) domain outperformed its counterpart implemented in the projection (DLP-MAR) domain. The DLI-MAR approach minimized the adverse impact of metal artefacts on whole-body PET images through generating accurate attenuation maps from corrupted CT images.

Synthesis and Preclinical Evaluation of a 68Ga-Labeled Adnectin, 68Ga-BMS-986192, as a PET Agent for Imaging PD-L1 Expression.

Blocking the interaction of the immune checkpoint molecule programmed cell death protein-1 and its ligand, PD-L1, using specific antibodies has been a major breakthrough for immune oncology. Whole-body PD-L1 expression PET imaging may potentially allow for a better prediction of response to programmed cell death protein-1-targeted therapies. Imaging of PD-L1 expression is feasible by PET with the adnectin protein 18F-BMS-986192. However, radiofluorination of proteins such as BMS-986192 remains complex and labeling yields are low. The goal of this study was therefore the development and preclinical evaluation of a 68Ga-labeled adnectin protein (68Ga-BMS-986192) to facilitate clinical trials. Methods:68Ga labeling of DOTA-conjugated adnectin (BXA-206362) was performed in NaOAc-buffer at pH 5.5 (50°C, 15 min). In vitro stability in human serum at 37°C was analyzed using radio-thin layer chromatography and radio-high-performance liquid chromatography. PD-L1 binding assays were performed using the transduced PD-L1-expressing lymphoma cell line U-698-M and wild-type U-698-M cells as a negative control. Immunohistochemical staining studies, biodistribution studies, and small-animal PET studies of 68Ga-BMS-986192 were performed using PD-L1-positive and PD-L1-negative U-698-M-bearing NSG mice. Results:68Ga-BMS-986192 was obtained with quantitative radiochemical yields of more than 97% and with high radiochemical purity. In vitro stability in human serum was at least 95% after 4 h of incubation. High and specific binding of 68Ga-BMS-986192 to human PD-L1-expressing cancer cells was confirmed, which closely correlates with the respective PD-L1 expression level determined by flow cytometry and immunohistochemistry staining. In vivo, 68Ga-BMS-986192 uptake was high at 1 h after injection in PD-L1-positive tumors (9.0 ± 2.1 percentage injected dose [%ID]/g) and kidneys (56.9 ± 9.2 %ID/g), with negligible uptake in other tissues. PD-L1-negative tumors demonstrated only background uptake of radioactivity (0.6 ± 0.1 %ID/g). Coinjection of an excess of unlabeled adnectin reduced tumor uptake of PD-L1 by more than 80%. Conclusion:68Ga-BMS-986192 enables easy radiosynthesis and shows excellent invitro and invivo PD-L1-targeting characteristics. The high tumor uptake combined with low background accumulation at early imaging time points demonstrates the feasibility of 68Ga-BMS-986192 for imaging of PD-L1 expression in tumors and is encouraging for further clinical applications of PD-L1 ligands.

Deep learning-assisted ultra-fast/low-dose whole-body PET/CT imaging

PurposeTendency is to moderate the injected activity and/or reduce acquisition time in PET examinations to minimize potential radiation hazards and increase patient comfort. This work aims to assess the performance of regular full-dose (FD) synthesis from fast/low-dose (LD) whole-body (WB) PET images using deep learning techniques.MethodsInstead of using synthetic LD scans, two separate clinical WB 18F-Fluorodeoxyglucose (18F-FDG) PET/CT studies of 100 patients were acquired: one regular FD (~ 27 min) and one fast or LD (~ 3 min) consisting of 1/8th of the standard acquisition time. A modified cycle-consistent generative adversarial network (CycleGAN) and residual neural network (ResNET) models, denoted as CGAN and RNET, respectively, were implemented to predict FD PET images. The quality of the predicted PET images was assessed by two nuclear medicine physicians. Moreover, the diagnostic quality of the predicted PET images was evaluated using a pass/fail scheme for lesion detectability task. Quantitative analysis using established metrics including standardized uptake value (SUV) bias was performed for the liver, left/right lung, brain, and 400 malignant lesions from the test and evaluation datasets.ResultsCGAN scored 4.92 and 3.88 (out of 5) (adequate to good) for brain and neck + trunk, respectively. The average SUV bias calculated over normal tissues was 3.39 ± 0.71% and − 3.83 ± 1.25% for CGAN and RNET, respectively. Bland-Altman analysis reported the lowest SUV bias (0.01%) and 95% confidence interval of − 0.36, + 0.47 for CGAN compared with the reference FD images for malignant lesions.ConclusionCycleGAN is able to synthesize clinical FD WB PET images from LD images with 1/8th of standard injected activity or acquisition time. The predicted FD images present almost similar performance in terms of lesion detectability, qualitative scores, and quantification bias and variance.

The Role of 89Zr-Immuno-PET in Navigating and Derisking the Development of Biopharmaceuticals.

The identification of molecular drivers of disease and the compelling rise of biotherapeutics have impacted clinical care but have also come with challenges. Such therapeutics include peptides, monoclonal antibodies, antibody fragments and nontraditional binding scaffolds, activatable antibodies, bispecific antibodies, immunocytokines, antibody-drug conjugates, enzymes, polynucleotides, and therapeutic cells, as well as alternative drug carriers such as nanoparticles. Drug development is expensive, attrition rates are high, and efficacy rates are lower than desired. Almost all these drugs, which in general have a long residence time in the body, can stably be labeled with 89Zr for whole-body PET imaging and quantification. Although not restricted to monoclonal antibodies, this approach is called 89Zr-immuno-PET. This review summarizes the state of the art of the technical aspects of 89Zr-immuno-PET and illustrates why it has potential for steering the design, development, and application of biologic drugs. Appealing showcases are discussed to illustrate what can be learned with this emerging technology during preclinical and especially clinical studies about biologic drug formats and disease targets. In addition, an overview of ongoing and completed clinical trials is provided. Although 89Zr-immuno-PET is a young tool in drug development, its application is rapidly expanding, with first clinical experiences giving insight on why certain drug-target combinations might have better perspectives than others.

2020 RSNA Outstanding Researcher.

HomeRadiologyVol. 298, No. 1 Next CommunicationsFree AccessSpecial Communications2020 RSNA Outstanding ResearcherJames P. BorgstedeJames P. BorgstedeJames P. BorgstedePublished Online:Dec 1 2020https://doi.org/10.1148/radiol.2020209023MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In David Mankoff, MD, PhDDownload as PowerPointOpen in Image Viewer As one of the foremost authorities in nuclear medicine/PET, David Mankoff, MD, PhD, is a leader in molecular imaging science and a pioneer in the development of whole-body PET imaging.“Dr Mankoff is a true renaissance imaging specialist,” said RSNA President James P. Borgstede, MD. “He is a researcher, an inventor, an educator and a clinical specialist. All of imaging is fortunate to have Dr Mankoff as a colleague.”Dr Mankoff is the Gerd Muehllehner Professor of Radiology and vice chair of research at the University of Pennsylvania (Penn) Perelman School of Medicine and associate director of education and research at the Abramson Cancer Center, both in Philadelphia.While earning a PhD in bioengineering, Dr Mankoff worked with his thesis advisor, the late Gerd Muehllehner, PhD, to develop a commercial version of the scanner that later formed the basis for the PennPET Explorer, a whole-body PET imaging system recently developed at Penn.After earning his PhD and MD at Penn, Dr Mankoff completed his residency in internal medicine and nuclear medicine at the University of Washington in Seattle. He remained there, eventually becoming a full professor of radiology, medicine, and bioengineering.During his time in Seattle, Dr Mankoff developed research interests in translational cancer molecular imaging and quantitative PET imaging and image analysis. In 2013, he returned to Penn School of Medicine as a professor of radiology and the PET Center director.A leading researcher with a particular interest in breast cancer, Dr Mankoff has maintained more than 20 years of continuous funding from federal sources and has been recognized as a Susan G. Komen Scholar since 2012.Key research achievements include defining applications of FDG PET to breast cancer staging and response evaluation, translating novel tracers to early human trials, studying the in vivo biology of cancer metabolism, and imaging estrogen receptor expression in breast cancer using PET.At the national level, Dr Mankoff has served as a committee leader and scientific planning co-chair of the Eastern Cooperative Oncology Group and the American College of Radiology Imaging Network (ECOG-ACRIN). He is a former steering committee chair of the NCI Quantitative Imaging Executive Committee, former member of the NCI Clinical Trials Advisory Committee, and former past president of the American Board of Nuclear Medicine.Dr Mankoff is associate editor for RSNA’s newest journal, Radiology: Imaging Cancer. He also serves on the editorial boards of several scientific journals. Dr Mankoff is co-chair of the RSNA Vice Chairs of Research Group and a member of the RSNA Committee on Scientific Affairs. He has also been active in the RSNA Clinical Trial Methodology Workshop and he has served as workshop co-director.Recognized for his many contributions to the field, Dr Mankoff was named an RSNA Honored Educator in 2013, 2018, and 2019. He received the Distinguished Investigator Award from the Academy of Radiology Research in 2018.Article HistoryPublished online: Dec 01 2020Published in print: Jan 2021 FiguresReferencesRelatedDetailsRecommended Articles Linda Moy, MD, Named New Editor of RadiologyRadioGraphics2022Volume: 42Issue: 2pp. E37-E38Linda Moy, MD, Named New Editor of RadiologyRadiology2022Volume: 303Issue: 1pp. 3-4PET Molecular Imaging as a Tool for Precision OncologyRadiology2020Volume: 296Issue: 2pp. 379-380Bruce G. Haffty, MD, President, Radiological Society of North America, 2022Radiology2022Volume: 302Issue: 3pp. 492-493Mutation Profiles of Urothelial Cancer: Will Genomics Change Radiology or Vice Versa?Radiology2020Volume: 295Issue: 3pp. 581-582See More RSNA Education Exhibits Breast Cancer in Younger Women: Screening and Diagnostic Imaging ExaminationsDigital Posters2018Non-BRCA Early Onset Breast Cancer in Young Women: Challenges and OpportunitiesDigital Posters2020Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics: Whence and WitherDigital Posters2019 RSNA Case Collection Occult Breast CancerRSNA Case Collection2022Inflammatory breast cancerRSNA Case Collection2020Primary breast lymphomaRSNA Case Collection2020 Vol. 298, No. 1 Metrics Altmetric Score PDF download

Augmented Whole-Body Scanning via Magnifying PET.

A novel technique, called augmented whole-body scanning via magnifying PET (AWSM-PET), that improves the sensitivity and lesion detectability of a PET scanner for whole-body imaging is proposed and evaluated. A Siemens Biograph Vision PET/CT scanner equipped with one or two high-resolution panel-detectors was simulated to study the effectiveness of AWSM-PET technology. The detector panels are located immediately outside the scanner's axial field-of-view (FOV). A detector panel contains 2 ×8 detector modules each consisting of 32 ×64 LSO crystals ( 1.0 ×1.0 ×10.0 mm3 each). A 22Na point source was stepped across the scanner's FOV axially to measure sensitivity profiles at different locations. An elliptical torso phantom containing 7×9 spherical lesions was imaged at different axial locations to mimic a multi-bed-position whole-body imaging protocol. Receiver operating characteristic (ROC) curves were analyzed to evaluate the improvement in lesion detectability by the AWSM-PET technology. Experimental validation was conducted using an existing flat-panel detector integrated with a Siemens Biograph 40 PET/CT scanner to image a torso phantom containing spherical lesions with diameters ranging from 3.3 to 11.4 mm. The contrast-recovery-coefficient (CRC) of the lesions was evaluated for the scanner with or without the AWSM-PET technology. Monte Carlo simulation shows 36%-42% improvement in system sensitivity by a dual-panel AWSM-PET device. The area under the ROC curve is 0.962 by a native scanner for the detection of 4 mm diameter lesions with 5:1 tumor-to-background activity concentration. It was improved to 0.977 and 0.991 with a single- and dual-panel AWSM-PET system, respectively. Experimental studies showed that the average CRC of 3.3 mm and 4.3 mm diameter tumors were improved from 2.8% and 4.2% to 7.9% and 11.0%, respectively, by a single-panel AWSM-PET device. With a high-sensitivity dual-panel device, the corresponding CRC can be further improved to 11.0% and 15.9%, respectively. The principle of the AWSM-PET technology has been developed and validated. Enhanced system sensitivity, CRC and tumor detectability were demonstrated by Monte Carlo simulations and imaging experiments. This technology may offer a cost-effective path to realize high-resolution whole-body PET imaging clinically.

Dynamic Whole-Body 18F-FDG PET for Minimizing Patient Motion Artifact.

Recent PET/CT systems permit short acquisition with acceptable image quality. In addition, continuous-bed-motion mode is available for whole-body PET imaging with some PET cameras providing better uniformity and reproducibility throughout the field of view. We demonstrate a case with patient motion during the dynamic scan. Although the total summation of serial whole-body scan showed image artifact, the first 15 minutes' summation of the serial scans provided high-quality images by eliminating the frames with motion. This acquisition mode seems to be particularly useful to perform for those who may likely to move during PET scan.

Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network

We demonstrate the feasibility of direct generation of attenuation and scatter-corrected images from uncorrected images (PET-nonASC) using deep residual networks in whole-body 18F-FDG PET imaging. Two- and three-dimensional deep residual networks using 2D successive slices (DL-2DS), 3D slices (DL-3DS) and 3D patches (DL-3DP) as input were constructed to perform joint attenuation and scatter correction on uncorrected whole-body images in an end-to-end fashion. We included 1150 clinical whole-body 18F-FDG PET/CT studies, among which 900, 100 and 150 patients were randomly partitioned into training, validation and independent validation sets, respectively. The images generated by the proposed approach were assessed using various evaluation metrics, including the root-mean-squared-error (RMSE) and absolute relative error (ARE %) using CT-based attenuation and scatter-corrected (CTAC) PET images as reference. PET image quantification variability was also assessed through voxel-wise standardized uptake value (SUV) bias calculation in different regions of the body (head, neck, chest, liver-lung, abdomen and pelvis). Our proposed attenuation and scatter correction (Deep-JASC) algorithm provided good image quality, comparable with those produced by CTAC. Across the 150 patients of the independent external validation set, the voxel-wise REs (%) were - 1.72 ± 4.22%, 3.75 ± 6.91% and - 3.08 ± 5.64 for DL-2DS, DL-3DS and DL-3DP, respectively. Overall, the DL-2DS approach led to superior performance compared with the other two 3D approaches. The brain and neck regions had the highest and lowest RMSE values between Deep-JASC and CTAC images, respectively. However, the largest ARE was observed in the chest (15.16 ± 3.96%) and liver/lung (11.18 ± 3.23%) regions for DL-2DS. DL-3DS and DL-3DP performed slightly better in the chest region, leading to AREs of 11.16 ± 3.42% and 11.69 ± 2.71%, respectively (p value < 0.05). The joint histogram analysis resulted in correlation coefficients of 0.985, 0.980 and 0.981 for DL-2DS, DL-3DS and DL-3DP approaches, respectively. This work demonstrated the feasibility of direct attenuation and scatter correction of whole-body 18F-FDG PET images using emission-only data via a deep residual network. The proposed approach achieved accurate attenuation and scatter correction without the need for anatomical images, such as CT and MRI. The technique is applicable in a clinical setting on standalone PET or PET/MRI systems. Nevertheless, Deep-JASC showing promising quantitative accuracy, vulnerability to noise was observed, leading to pseudo hot/cold spots and/or poor organ boundary definition in the resulting PET images.

Assessment of Lesion Detectability in Dynamic Whole-Body PET Imaging Using Compartmental and Patlak Parametric Mapping.

Hybrid dynamic imaging allows not only the estimation of whole-body (WB) macroparametric maps but also the estimation of microparameters in the initial bed position targeting the blood pool region containing the pathology owing to the limited axial field of view of PET scanners. In this work, we assessed the capability of multipass WB F-FDG PET parametric imaging in terms of lesion detectability through qualitative and quantitative evaluation of simulation and clinical studies. Simulation studies were conducted by generating data incorporating 3 liver and 3 lung lesions produced by 3 noise levels and 20 noise realizations for each noise level to estimate bias and lesion detection features. The total scan time for the clinical studies of 8 patients addressed for lung and liver lesions staging, including dynamic and static WB imaging, lasted 80 minutes. An in-house-developed MATLAB code was utilized to derive the microparametric and macroparametric maps. We compared lesion detectability and different image-derived PET metrics including the SUVs, Patlak-derived influx rate constant (Ki) and distribution volume (V) and K1, k2, k3, blood volume (bv) microparameters, and Ki estimated using the generalized linear least square approach. In total, 104 lesions were detected, among which 47 were located in the targeted blood pool bed position where all quantitative parameters were calculated, thus enabling comparative analysis across all parameters. The evaluation encompassed visual interpretation performed by an expert nuclear medicine specialist and quantitative analysis. High correlation coefficients were observed between SUVmax and Kimax derived from the generalized linear least square approach, as well as Ki generated by Patlak graphical analysis. Moreover, 3 contrast-enhanced CT-proven malignant lesions located in the liver and a biopsy-proven malignant liver lesion not visible on static SUV images and Patlak maps were clearly pinpointed on K1 and k2 maps. Our results demonstrate that full compartmental modeling for the region containing the pathology has the potential of providing complementary information and, in some cases, more accurate diagnosis than conventional static SUV imaging, favorably comparing to Patlak graphical analysis.

Impact of Attenuation Correction on Quantification Accuracy in Preclinical Whole-Body PET Images

Background Whole-body PET images can be obtained by using the “step-and-shoot” (SaS) method (using multiple bed positions) or continuous bed motion (CBM). As transmission scans are not always feasible, an alternative method where attenuation data can be generated via emission-based attenuation correction (AC) maps is of interest. The aim of this preclinical study was to investigate the influence of the acquisition method and AC on the quantitation accuracy of [18F]FDG-PET. Methods [18F]FDG-PET phantom images were acquired using either SaS or CBM. Transmission scans were recorded for the SaS method using a 57Co-point source. Emission-based attenuation sinograms were obtained from the image after segmentation and inverse Fourier rebinning. PET images were reconstructed without AC, transmission based (TX-AC) and emission-based (EM-AC) attenuation correction. Moreover, [18F]FDG-PET scans of rats bearing mammary carcinomas acquired using either SaS or CBM were analyzed retrospectively and quantification in tissues was compared. Results Phantom recovery coefficients (RC) varied greatly, ranging from 0.49±0.01 to 1.15 ± 0.07, dependent on acquisition method, reconstruction algorithm and AC method. In CBM acquired images, EM-AC improved quantification accuracy when compared to no-AC images in the phantom studies (RC 0.79±0.02 vs 0.49±0.01, respectively) and in tumors of rats (DMBA model: 1.16±0.42 SUV vs 0.86±0.28 SUV, respectively). Conclusion The method of AC has a strong influence on the quantification of [18F]FDG. Our data indicates that EM-AC improves quantification in images obtained by CBM and SaS. However, the obtained values were still underestimated when compared to TX-AC corrected images.