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Zhao Y, Lv T, Xu Y, Yin J, Wang X, Xue Y, Zhu G, Yu W, Wang H, Li X. Application of Dynamic [ 18F]FDG PET/CT Multiparametric Imaging Leads to an Improved Differentiation of Benign and Malignant Lung Lesions. Mol Imaging Biol 2024; 26:790-801. [PMID: 39174787 DOI: 10.1007/s11307-024-01942-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2024] [Revised: 07/18/2024] [Accepted: 07/24/2024] [Indexed: 08/24/2024]
Abstract
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.
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Affiliation(s)
- Yihan Zhao
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Tao Lv
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yue Xu
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Jiankang Yin
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xin Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Yangyang Xue
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Gan Zhu
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Wenjing Yu
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Hui Wang
- Department of Nuclear Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xiaohu Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Research Center of Clinical Medical Imaging, Anhui Province Clinical Image Quality Control Center, Hefei, China.
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Fraum TJ, Sari H, Dias AH, Munk OL, Pyka T, Smith AM, Mawlawi OR, Laforest R, Wang G. Whole-Body Multiparametric PET in Clinical Oncology: Current Status, Challenges, and Opportunities. AJR Am J Roentgenol 2024. [PMID: 39230403 DOI: 10.2214/ajr.24.31712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
The interpretation of clinical oncologic PET studies has historically used static reconstructions based on SUVs. SUVs and SUV-based images have important limitations, including dependence on uptake times and reduced conspicuity of tracer-avid lesions in organs with high background uptake. The acquisition of dynamic PET images enables additional PET reconstructions via Patlak modeling, which assumes that a tracer is irreversibly trapped by tissues of interest. The resulting multiparametric PET images capture a tracer's net trapping rate (Ki) and apparent volume of distribution (VD), separating the contributions of bound and free tracer fractions to the PET signal captured in the SUV. Potential benefits of multiparametric PET include higher quantitative stability, superior lesion conspicuity, and greater accuracy for differentiating malignant and benign lesions. However, the imaging protocols necessary for multiparametric PET are inherently more complex and time-intensive, despite the recent introduction of automated or semiautomated scanner-based reconstruction packages. In this Review, we examine the current state of multiparametric PET in whole-body oncologic imaging. We summarize the Patlak methodology and relevant tracer kinetics, discuss clinical workflows and protocol considerations, and highlight clinical challenges and opportunities. We aim to help oncologic imagers make informed decisions about whether to implement multiparametric PET in their clinical practices.
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Affiliation(s)
- Tyler J Fraum
- Department of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St. Louis, MO, 63110, USA
| | - Hasan Sari
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
- Siemens Healthineers International AG, 8047, Zurich, Switzerland
| | - André H Dias
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Blvd 165, 8200, Aarhus, Denmark
| | - Ole L Munk
- Department of Nuclear Medicine & PET-Centre, Aarhus University Hospital, Palle-Juul-Jensens Blvd 165, 8200, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Palle-Juul-Jensens Blvd 99, 8200, Aarhus, Denmark
| | - Thomas Pyka
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
- TUM School of Medicine and Health, Ismaninger Str. 22, 81675, Munich, Germany
| | - Anne M Smith
- Siemens Medical Solutions USA, Inc., 810 Innovation Drive, Knoxville, TN, 37932, USA
| | - Osama R Mawlawi
- Department of Imaging Physics, UT MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Richard Laforest
- Department of Radiology, Washington University School of Medicine, 510 S. Kingshighway Blvd, St. Louis, MO, 63110, USA
| | - Guobao Wang
- Department of Radiology, University of California Davis Health, 4860 Y St, Sacramento, CA, 95817, USA
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Chavan R, Hyman G, Qureshi Z, Jayakumar N, Terrell W, Wardius M, Berr S, Schiff D, Fountain N, Eluvathingal Muttikkal T, Quigg M, Zhang M, K Kundu B. An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping. Biomed Phys Eng Express 2024; 10:055028. [PMID: 39094595 PMCID: PMC11333809 DOI: 10.1088/2057-1976/ad6a64] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Revised: 07/13/2024] [Accepted: 08/02/2024] [Indexed: 08/04/2024]
Abstract
Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure's distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.
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Affiliation(s)
- Rugved Chavan
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - Gabriel Hyman
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - Zoraiz Qureshi
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - Nivetha Jayakumar
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - William Terrell
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
| | - Megan Wardius
- Brain Institute, University of Virginia, Charlottesville, VA, United States of America
| | - Stuart Berr
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - David Schiff
- Department of Neurology, University of Virginia, Charlottesville, VA, United States of America
| | - Nathan Fountain
- Department of Neurology, University of Virginia, Charlottesville, VA, United States of America
| | | | - Mark Quigg
- Department of Neurology, University of Virginia, Charlottesville, VA, United States of America
| | - Miaomiao Zhang
- Department of Computer Science and Engineering, University of Virginia, Charlottesville, VA, United States of America
| | - Bijoy K Kundu
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, United States of America
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, United States of America
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Li S, Hamdi M, Dutta K, Fraum TJ, Luo J, Laforest R, Shoghi KI. FAST (fast analytical simulator of tracer)-PET: an accurate and efficient PET analytical simulation tool. Phys Med Biol 2024; 69:165020. [PMID: 39047765 DOI: 10.1088/1361-6560/ad6743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Accepted: 07/23/2024] [Indexed: 07/27/2024]
Abstract
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.
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Affiliation(s)
- Suya Li
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States of America
- Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St Louis, MO, United States of America
| | - Mahdjoub Hamdi
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States of America
| | - Kaushik Dutta
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States of America
- Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St Louis, MO, United States of America
| | - Tyler J Fraum
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States of America
| | - Jingqin Luo
- Department of Surgery, Washington University School of Medicine, St Louis, MO, United States of America
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States of America
- Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St Louis, MO, United States of America
| | - Kooresh I Shoghi
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States of America
- Imaging Science Program, McKelvey School of Engineering, Washington University in St Louis, St Louis, MO, United States of America
- Department of Biomedical Engineering, Washington University in St Louis, St Louis, MO, United States of America
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Yin H, Liu G, Mao W, Lv J, Yu H, Cheng D, Cai L, Shi H. Parametric net influx rate imaging of 68Ga-DOTATATE in patients with neuroendocrine tumors: assessment of lesion detectability. Ann Nucl Med 2024; 38:483-492. [PMID: 38573411 DOI: 10.1007/s12149-024-01922-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
OBJECTIVES There has been developed a clinical dynamic total-body 68Ga-DOTATATE PET/CT imaging protocol that allows quantitative imaging of net influx rate (Ki). Using qualitative and quantitative analyses of clinical studies, this retrospective study aims to assess whether parametric Ki images improve lesion detectability. METHODS Using a 194-cm axial field-of-view PET/CT scanner, 52 patients with neuroendocrine tumors underwent a 60-min dynamic total-body 68Ga-DOTATATE scan. Parametric Ki images and static standardized uptake value (SUV) images were generated. In addition to visual inspection of both sets of images, a quantitative analysis of 249 individual lesions was conducted using the target-to-background (TBR) metric. RESULTS There were 52 patients who underwent dynamic total-body 68Ga-DOTATATE PET/CT scans. A total of 249 lesions were evaluated, of which 66 lesions were biopsy-proven and 183 lesions were unproven. Ki images produced two fewer false positives than the SUV images. Overall, our results from 66 proven NET lesions suggested similar sensitivity (98.5%) but improved accuracy (from 95.6 to 97.1%) and potentially enhanced specificity with Ki over SUV imaging. Besides, there was no difference in the number of pathological lesions identified visually in both images. However, Ki TBR was significantly higher than SUV TBR quantitatively (P < 0.001). CONCLUSIONS Patlak Ki imaging provides nuclear physicians with a PET image with higher tumor contrast which may enhance confidence in diagnosis with possibly reduced false positive results, albeit an equivalent detectability, compared to static SUV image.
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Affiliation(s)
- Hongyan Yin
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China
| | - Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China
| | - Wujian Mao
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China
| | - Jing Lv
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China
| | - Haojun Yu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China
| | - Dengfeng Cheng
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China
| | - Liang Cai
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, No. 180 in Fenglin Road, Shanghai, 200032, People's Republic of China.
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Wang Z, Wu Y, Xia Z, Chen X, Li X, Bai Y, Zhou Y, Liang D, Zheng H, Yang Y, Wang S, Wang M, Sun T. Non-Invasive Quantification of the Brain [¹⁸F]FDG-PET Using Inferred Blood Input Function Learned From Total-Body Data With Physical Constraint. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2563-2573. [PMID: 38386580 DOI: 10.1109/tmi.2024.3368431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2024]
Abstract
Full quantification of brain PET requires the blood input function (IF), which is traditionally achieved through an invasive and time-consuming arterial catheter procedure, making it unfeasible for clinical routine. This study presents a deep learning based method to estimate the input function (DLIF) for a dynamic brain FDG scan. A long short-term memory combined with a fully connected network was used. The dataset for training was generated from 85 total-body dynamic scans obtained on a uEXPLORER scanner. Time-activity curves from 8 brain regions and the carotid served as the input of the model, and labelled IF was generated from the ascending aorta defined on CT image. We emphasize the goodness-of-fitting of kinetic modeling as an additional physical loss to reduce the bias and the need for large training samples. DLIF was evaluated together with existing methods in terms of RMSE, area under the curve, regional and parametric image quantifications. The results revealed that the proposed model can generate IFs that closer to the reference ones in terms of shape and amplitude compared with the IFs generated using existing methods. All regional kinetic parameters calculated using DLIF agreed with reference values, with the correlation coefficient being 0.961 (0.913) and relative bias being 1.68±8.74% (0.37±4.93%) for [Formula: see text] ( [Formula: see text]. In terms of the visual appearance and quantification, parametric images were also highly identical to the reference images. In conclusion, our experiments indicate that a trained model can infer an image-derived IF from dynamic brain PET data, which enables subsequent reliable kinetic modeling.
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Samimi R, Kamali-Asl A, Ahmadyar Y, van den Hoff J, Geramifar P, Rahmim A. Dual time-point [ 18F]FDG PET imaging for quantification of metabolic uptake rate: Evaluation of a simple, clinically feasible method. Phys Med 2024; 121:103336. [PMID: 38626637 DOI: 10.1016/j.ejmp.2024.103336] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/18/2024] Open
Abstract
PURPOSE We aimed to investigate whether a clinically feasible dual time-point (DTP) approach can accurately estimate the metabolic uptake rate constant (Ki) and to explore reliable acquisition times through simulations and clinical assessment considering patient comfort and quantification accuracy. METHODS We simulated uptake kinetics in different tumors for four sets of DTP PET images within the routine clinical static acquisition at 60-min post-injection (p.i.). We determined Ki for a total of 81 lesions. Ki quantification from full dynamic PET data (Patlak-Ki) and Ki from DTP (DTP-Ki) were compared. In addition, we scaled a population-based input function (PBIFscl) with the image-derived blood pool activity sampled at different time points to assess the best scaling time-point for Ki quantifications in the simulation data. RESULTS In the simulation study, Ki estimated using DTP via (30,60-min), (30,90-min), (60,90-min), and (60,120-min) samples showed strong correlations (r ≥ 0.944, P < 0.0001) with the true value of Ki. The DTP results with the PBIFscl at 60-min time-point in (30,60-min), (60,90-min), and (60,120-min) were linearly related to the true Ki with a slope of 1.037, 1.008, 1.013 and intercept of -6 × 10-4, 2 × 10-5, 5 × 10-5, respectively. In a clinical study, strong correlations (r ≥ 0.833, P < 0.0001) were observed between Patlak-Ki and DTP-Ki. The Patlak-derived mean values of Ki, tumor-to-background-ratio, signal-to-noise-ratio, and contrast-to-noise-ratio were linearly correlated with the DTP method. CONCLUSIONS Besides calculating the retention index as a commonly used quantification parameter inDTP imaging,our DTP method can accurately estimate Ki.
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Affiliation(s)
- Rezvan Samimi
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Alireza Kamali-Asl
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran.
| | - Yashar Ahmadyar
- Department of Radiation Medicine Engineering, Shahid Beheshti University, Tehran, Iran
| | - Jörg van den Hoff
- Institute of Radiopharmaceutical Cancer Research, Helmholtz-Zentrum Dresden-Rossendorf, Dresden 01328, Germany; Department of Nuclear Medicine, University Hospital Carl Gustav Carus, Technische Universität Dresden, Dresden 01307, Germany
| | - Parham Geramifar
- Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran.
| | - Arman Rahmim
- Departments of Radiology and Physics, University of British Columbia, Vancouver, BC, Canada; Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
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Moradi H, Vashistha R, Ghosh S, O'Brien K, Hammond A, Rominger A, Sari H, Shi K, Vegh V, Reutens D. Automated extraction of the arterial input function from brain images for parametric PET studies. EJNMMI Res 2024; 14:33. [PMID: 38558200 PMCID: PMC11372015 DOI: 10.1186/s13550-024-01100-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. RESULTS Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07. CONCLUSIONS Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.
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Affiliation(s)
- Hamed Moradi
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Rajat Vashistha
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Soumen Ghosh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Amanda Hammond
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Hasan Sari
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Viktor Vegh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
| | - David Reutens
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
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El Ouaridi A, Ait Elcadi Z, Mkimel M, Bougteb M, El Baydaoui R. The detection instrumentation and geometric design of clinical PET scanner: towards better performance and broader clinical applications. Biomed Phys Eng Express 2024; 10:032002. [PMID: 38412520 DOI: 10.1088/2057-1976/ad2d61] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 02/27/2024] [Indexed: 02/29/2024]
Abstract
Positron emission tomography (PET) is a powerful medical imaging modality used in nuclear medicine to diagnose and monitor various clinical diseases in patients. It is more sensitive and produces a highly quantitative mapping of the three-dimensional biodistribution of positron-emitting radiotracers inside the human body. The underlying technology is constantly evolving, and recent advances in detection instrumentation and PET scanner design have significantly improved the medical diagnosis capabilities of this imaging modality, making it more efficient and opening the way to broader, innovative, and promising clinical applications. Some significant achievements related to detection instrumentation include introducing new scintillators and photodetectors as well as developing innovative detector designs and coupling configurations. Other advances in scanner design include moving towards a cylindrical geometry, 3D acquisition mode, and the trend towards a wider axial field of view and a shorter diameter. Further research on PET camera instrumentation and design will be required to advance this technology by improving its performance and extending its clinical applications while optimising radiation dose, image acquisition time, and manufacturing cost. This article comprehensively reviews the various parameters of detection instrumentation and PET system design. Firstly, an overview of the historical innovation of the PET system has been presented, focusing on instrumental technology. Secondly, we have characterised the main performance parameters of current clinical PET and detailed recent instrumental innovations and trends that affect these performances and clinical practice. Finally, prospects for this medical imaging modality are presented and discussed. This overview of the PET system's instrumental parameters enables us to draw solid conclusions on achieving the best possible performance for the different needs of different clinical applications.
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Affiliation(s)
- Abdallah El Ouaridi
- Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Health Sciences and Technologies, Settat, Morocco
| | - Zakaria Ait Elcadi
- Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Health Sciences and Technologies, Settat, Morocco
- Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, 23874, Qatar
| | - Mounir Mkimel
- Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Health Sciences and Technologies, Settat, Morocco
| | - Mustapha Bougteb
- Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Health Sciences and Technologies, Settat, Morocco
| | - Redouane El Baydaoui
- Hassan First University of Settat, High Institute of Health Sciences, Laboratory of Health Sciences and Technologies, Settat, Morocco
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10
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Reshtebar N, Hosseini SA, Zhuang M, Sheikhzadeh P. Estimation of kinetic parameters in dynamic FDG PET imaging based on shortened protocols: a virtual clinical study. Phys Eng Sci Med 2024; 47:199-213. [PMID: 38078995 DOI: 10.1007/s13246-023-01356-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 11/12/2023] [Indexed: 03/26/2024]
Abstract
This study investigated the estimation of kinetic parameters and production of related parametric Ki images in FDG PET imaging using the proposed shortened protocol (three 3-min/bed routine static images) by means of the simulated annealing (SA) algorithm. Six realistic heterogeneous tumors and various levels of [18F] FDG uptake were simulated by the XCAT phantom. An irreversible two-tissue compartment model (2TCM) using population-based input function was employed. By keeping two routine clinical scans fixed (60-min and 90-min post injection), the effect of the early scan time on optimizing the estimation of the pharmacokinetic parameters was investigated. The SA optimization algorithm was applied to estimate micro- and macro-parameters (K1, k2, k3, Ki). The minimum bias for most parameters was observed at a scan time of 20-min, which was < 10%. A highly significant correlation (> 0.9) as well as limited bias (< 10%) were observed between kinetic parameters generated from two methods [two-tissue compartment full dynamic scan (2TCM-full) and two-tissue compartment by SA algorithm (2TCM-SA)]. The analysis showed a strong correlation (> 0.8) between (2TCM-SA) Ki and SUV images. In addition, the tumor-to-background ratio (TBR) metric in the parametric (2TCM-SA) Ki images was significantly higher than SUV, although the SUV images provide better Contrast-to-noise ratio relative to parametric (2TCM-SA) Ki images. The proposed shortened protocol by the SA algorithm can estimate the kinetic parameters in FDG PET scan with high accuracy and robustness. It was also concluded that the parametric Ki images obtained from the 2TCM-SA as a complementary image of the SUV possess more quantification information than SUV images and can be used by the nuclear medicine specialist. This method has the potential to be an alternative to a full dynamic PET scan.
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Affiliation(s)
- Niloufar Reshtebar
- Department of Energy Engineering, Sharif University of Technology, Tehran, 8639-11365, Iran
| | - Seyed Abolfazl Hosseini
- Department of Energy Engineering, Sharif University of Technology, Tehran, 8639-11365, Iran.
| | - Mingzan Zhuang
- Department of Nuclear Medicine, Meizhou People's Hospital, Meizhou, 514011, China
| | - Peyman Sheikhzadeh
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Nuclear Medicine Department, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
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Mohr P, van Sluis J, Lub-de Hooge MN, Lammertsma AA, Brouwers AH, Tsoumpas C. Advances and challenges in immunoPET methodology. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2024; 4:1360710. [PMID: 39355220 PMCID: PMC11440922 DOI: 10.3389/fnume.2024.1360710] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 02/05/2024] [Indexed: 10/03/2024]
Abstract
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.
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Affiliation(s)
- Philipp Mohr
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Marjolijn N Lub-de Hooge
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Adriaan A Lammertsma
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Adrienne H Brouwers
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Charalampos Tsoumpas
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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12
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Wang J, Bermudez D, Chen W, Durgavarjhula D, Randell C, Uyanik M, McMillan A. Motion-correction strategies for enhancing whole-body PET imaging. FRONTIERS IN NUCLEAR MEDICINE (LAUSANNE, SWITZERLAND) 2024; 4:1257880. [PMID: 39118964 PMCID: PMC11308502 DOI: 10.3389/fnume.2024.1257880] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/10/2024]
Abstract
Positron Emission Tomography (PET) is a powerful medical imaging technique widely used for detection and monitoring of disease. However, PET imaging can be adversely affected by patient motion, leading to degraded image quality and diagnostic capability. Hence, motion gating schemes have been developed to monitor various motion sources including head motion, respiratory motion, and cardiac motion. The approaches for these techniques have commonly come in the form of hardware-driven gating and data-driven gating, where the distinguishing aspect is the use of external hardware to make motion measurements vs. deriving these measures from the data itself. The implementation of these techniques helps correct for motion artifacts and improves tracer uptake measurements. With the great impact that these methods have on the diagnostic and quantitative quality of PET images, much research has been performed in this area, and this paper outlines the various approaches that have been developed as applied to whole-body PET imaging.
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Affiliation(s)
- James Wang
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Dalton Bermudez
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Weijie Chen
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, WI, United States
| | - Divya Durgavarjhula
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Computer Science, University of Wisconsin Madison, Madison, WI, United States
| | - Caitlin Randell
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States
| | - Meltem Uyanik
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
| | - Alan McMillan
- Department of Radiology, University of Wisconsin Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin Madison, Madison, WI, United States
- Department of Electrical and Computer Engineering, University of Wisconsin Madison, Madison, WI, United States
- Department of Biomedical Engineering, University of Wisconsin Madison, Madison, WI, United States
- Data Science Institute, University of Wisconsin Madison, Madison, WI, United States
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Kaneko K, Nagao M, Yamamoto A, Yano K, Honda G, Tokushige K, Sakai S. Patlak Reconstruction Using Dynamic 18 F-FDG PET Imaging for Evaluation of Malignant Liver Tumors : A Comparison of HCC, ICC, and Metastatic Liver Tumors. Clin Nucl Med 2024; 49:116-123. [PMID: 38108830 DOI: 10.1097/rlu.0000000000005013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
PURPOSE OF THE REPORT The aim of this study was to explore the different patterns of dynamic whole-body (D-WB) FDG PET/CT parameters among liver malignancy types as potential diagnostic clues and investigate the association between static and dynamic PET/CT parameters for each tumor histology. PATIENTS AND METHODS Seventy-one patients with intrahepatic cholangiocarcinoma (ICC), metastatic liver tumor (MLT), or hepatocellular carcinoma (HCC) who underwent D-WB and static dual-time-point FDG PET/CT were enrolled. We obtained Pearson correlation coefficients between the metabolic rate of FDG (MR FDG ; mg/min/ 100ml) or distribution volume of free FDG (DV FDG , %) and static PET/CT parameters. We compared MR FDG and DV FDG values by tumor type and performed receiver operating characteristic analyses for MR FDG and static images. RESULTS A total of 12 ICC, 116 MLT, and 36 HCC lesions were analyzed. MR FDG and DV FDG showed excellent correlation with early (SUV e ) and delayed SUV max (SUV d ) ( r = 0.71~0.97), but DV FDG in the HCC lesions did not ( r = 0.62 and 0.69 for SUV e and SUV d , respectively) ( P < 0.001 for all). HCC lesions showed significantly lower MR FDG (2.43 ± 1.98) and DV FDG (139.95 ± 62.58) than ICC (5.02 ± 3.56, 207.06 ± 97.13) and MLT lesions (4.51 ± 2.47, 180.13 ± 75.58) ( P < 0.01 for all). The optimal MR FDG could differentiate HCC from ICC and MLT with areas under the curve of 0.84 and 0.80, respectively. Metastatic liver tumor lesions showed the widest distribution of MR FDG and DV FDG values but with no significant difference among most primary sites. CONCLUSIONS MR FDG was strongly correlated with SUV max in the 3 malignancies and showed utility for differentiating HCC from ICC and MLT. Each tumor type has a different glucose metabolism, and D-WB FDG PET/CT imaging has the potential to visualize those differences.
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Affiliation(s)
- Koichiro Kaneko
- From the Department of Diagnostic Imaging & Nuclear Medicine
| | - Michinobu Nagao
- From the Department of Diagnostic Imaging & Nuclear Medicine
| | | | - Kyoko Yano
- From the Department of Diagnostic Imaging & Nuclear Medicine
| | - Goro Honda
- Department of Surgery, Institute of Gastroenterology
| | - Katsutoshi Tokushige
- Department of Internal Medicine, Institute of Gastroenterology, Tokyo Women's Medical University, Tokyo, Japan
| | - Shuji Sakai
- From the Department of Diagnostic Imaging & Nuclear Medicine
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Chen R, Ng YL, Yang X, Zhu Y, Li L, Zhao H, Huang G, Liu J. Assessing dynamic metabolic heterogeneity in prostate cancer patients via total-body [ 68Ga]Ga-PSMA-11 PET/CT imaging: quantitative analysis of [ 68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs. Eur J Nucl Med Mol Imaging 2024; 51:896-906. [PMID: 37889299 DOI: 10.1007/s00259-023-06475-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 10/13/2023] [Indexed: 10/28/2023]
Abstract
PURPOSE This study aimed to quantitatively assess [68Ga]Ga-PSMA-11 uptake in pathological lesions and normal organs in prostate cancer using the total-body [68Ga]Ga-PSMA-11 PET/CT and to characterize the dynamic metabolic heterogeneity of prostate cancer. METHODS Dynamic total-body [68Ga]Ga-PSMA-11 PET/CT scans were performed on ten prostate cancer patients. Manual delineation of volume-of-interests (VOIs) was performed on multiple normal organs displaying high [68Ga]Ga-PSMA-11 uptake, as well as pathological lesions. Time-to-activity curves (TACs) were generated, and the four compartment models including one-tissue compartmental model (1T1k), reversible one-tissue compartmental model (1T2k), irreversible two-tissue compartment model (2T3k) and reversible two-tissue compartmental model (2T4k) were fitted to each tissue TAC. Various rate constants, including K1 (forward transport rate from plasma to the reversible compartment), k2 (reverse transport rate from the reversible compartment to plasma), k3 (tracer binding on the PSMA-receptor and its internalization), k4 (the externalization rate of the tracer) and Ki (net influx rate), were obtained. The selection of the optimal model for describing the uptake of both lesions and normal organs was determined using the Akaike information criteria (AIC). Receiver operating characteristic (ROC) curve analysis was performed to determine the cut-off values for differentiating physiological and pathological [68Ga]Ga-PSMA-11 uptake. RESULTS Both 1T1k and 1T2k models showed relatively high AIC values compared to the 2T3k and 2T4k models in both pathological lesions and normal organs. The kinetic behavior of pathological lesions was better described by the 2T3k model compared to the 2T4k model, while the normal organs were better described by the 2T4k model. Significant variations in kinetic metrics, such as K1, k2, and k3, and Ki, were observed among normal organs with high [68Ga]Ga-PSMA-11 uptake and pathological lesions. The high Ki value in normal organs was primarily determined by elevated K1 and low k3, rather than k2. Conversely, the high Ki value in pathological lesions, ranking second to the kidney and similar to salivary glands and spleen, was predominantly determined by the highest k3 value. Notably, k3 exhibited the highest performance in distinguishing between physiological and pathological [68Ga]Ga-PSMA-11 uptake, with an area under the curve (AUC) of 0.844 (95% CI, 0.773-0.915), sensitivity of 82.9%, and specificity of 74.1%. The k3 values showed better performance than SUVmean (AUC, 0.659), SUVmax (AUC, 0.637), and other kinetic parameter including K1 (AUC, 0.604), k2 (AUC, 0.634), and Ki (AUC, 0.651). CONCLUSIONS Significant discrepancies in kinetic metrics were detected between pathological lesions and normal organs, despite their shared high uptake of [68Ga]Ga-PSMA-11. Notably, the k3 value exhibits a noteworthy capability to distinguish between pathological lesions and normal organs with elevated [68Ga]Ga-PSMA-11 uptake. This discovery implies that k3 holds promise as a prospective imaging biomarker for distinguishing between pathologic and non-specific [68Ga]Ga-PSMA-11 uptake in patients with prostate cancer.
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Affiliation(s)
- Ruohua Chen
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Yee Ling Ng
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China
| | - Xinlan Yang
- Central Research Institute, United Imaging Healthcare Group Co., Ltd, Shanghai, China
| | - Yinjie Zhu
- Department of Urology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Lianghua Li
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Haitao Zhao
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China
| | - Gang Huang
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
| | - Jianjun Liu
- Department of Nuclear Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujian Road, Shanghai, 200127, China.
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15
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Moradi H, Vashistha R, O'Brien K, Hammond A, Vegh V, Reutens D. A short 18F-FDG imaging window triple injection neuroimaging protocol for parametric mapping in PET. EJNMMI Res 2024; 14:1. [PMID: 38169031 PMCID: PMC10761663 DOI: 10.1186/s13550-023-01061-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Accepted: 12/14/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND In parametric PET, kinetic parameters are extracted from dynamic PET images. It is not commonly used in clinical practice because of long scan times and the requirement for an arterial input function (AIF). To address these limitations, we designed an 18F-fluorodeoxyglucose (18F-FDG) triple injection dynamic PET protocol for brain imaging with a standard field of view PET scanner using a 24-min imaging window and an input function modeled using measurements from a region of interest placed over the left ventricle. METHODS To test the protocol in 6 healthy participants, we examined the quality of voxel-based maps of kinetic parameters in the brain generated using the two-tissue compartment model and compared estimated parameter values with previously published values. We also utilized data from a 36-min validation imaging window to compare (1) the modeled AIF against the input function measured in the validation window; and (2) the net influx rate ([Formula: see text]) computed using parameter estimates from the short imaging window against the net influx rate obtained using Patlak analysis in the validation window. RESULTS Compared to the AIF measured in the validation window, the input function estimated from the short imaging window achieved a mean area under the curve error of 9%. The voxel-wise Pearson's correlation between [Formula: see text] estimates from the short imaging window and the validation imaging window exceeded 0.95. CONCLUSION The proposed 24-min triple injection protocol enables parametric 18F-FDG neuroimaging with noninvasive estimation of the AIF from cardiac images using a standard field of view PET scanner.
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Affiliation(s)
- Hamed Moradi
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Rajat Vashistha
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Amanda Hammond
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
| | - David Reutens
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
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16
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Singh MK. A review of digital PET-CT technology: Comparing performance parameters in SiPM integrated digital PET-CT systems. Radiography (Lond) 2024; 30:13-20. [PMID: 37864986 DOI: 10.1016/j.radi.2023.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 09/29/2023] [Accepted: 10/03/2023] [Indexed: 10/23/2023]
Abstract
OBJECTIVE The objective of this study was to perform a narrative review of digital Positron emission tomography-computed tomography (PET-CT) scanners, focussing on the current development in the technology of optimized crystal size and design, the time of flight (ToF) resolution, sensitivity, and axial field of view (AFOV). KEY FINDINGS It was observed that significant developments were carried out on the optimization of scintillation crystal size which results in the improvement of spatial resolution. such developments include the upgrade in the AFOV after the integration of SiPM technology, which results in dynamic parametric imaging acquisition in PET and sensitivity boost. The improvement in ToF resolution and the better ToF resolution values, which result in a boost in adequate sensitivity and signal-to-noise ratio (SNR). Other upgrades include the use of the smallest crystal size of 2.76 × 2.76 mm, and the use of the lowest ToF resolution of 214 ps. The use of the largest AFOV of 194 cm with the highest observed NEMA sensitivity of 225 cps/kBq for the total body PET-CT system. CONCLUSION Digital PET-CT systems offer various advantages such as a reduction in radiation dose from injected radiopharmaceuticals doses and the overall PET acquisition time with an improved diagnostic certainty. This is because of the better performance of the SiPM detector. Digital PET-CT also has added benefits of the dynamic acquisition and Patlak modeling capabilities into routine clinical practice with the advancement in higher AFOV PET systems. IMPLICATION This will help the users choose the best system during the evaluation of the PET-CT for purchase in clinical and research applications. This review will further help in teaching the latest technology and developments in PET-CT systems.
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Affiliation(s)
- M K Singh
- AECC University College, Parkwood Road, Bournemouth, UK.
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17
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Maurer A, Kotasidis F, Deibel A, Burger IA, Huellner MW. Whole-Body 18 F-FDG PET/CT Patlak Parametric Imaging of Hepatic Alveolar Echinococcosis. Clin Nucl Med 2023; 48:1089-1090. [PMID: 37801583 DOI: 10.1097/rlu.0000000000004878] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
ABSTRACT We present dynamic 18 F-FDG PET/CT acquisition in a 52-year-old old woman with histologically proven hepatic alveolar echinococcosis (AE). Metabolic rate of FDG images generated with traditional and relative Patlak analysis show the AE manifestation in the liver significantly better the static SUV images. Dynamic PET may thus have the potential to increase sensitivity in the assessment of hepatic AE manifestations. Such parametric images may offer complementary qualitative information and quantification superior to SUV images.
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Affiliation(s)
- Alexander Maurer
- From the Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | | | - Ansgar Deibel
- Clinic for Gastroenterology and Hepatology and Swiss HPB and Transplant Center, University Hospital Zurich, University of Zurich, Zurich
| | | | - Martin W Huellner
- From the Department of Nuclear Medicine, University Hospital Zurich, University of Zurich, Zurich, Switzerland
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18
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Tingen HSA, van Praagh GD, Nienhuis PH, Tubben A, van Rijsewijk ND, ten Hove D, Mushari NA, Martinez-Lucio TS, Mendoza-Ibañez OI, van Sluis J, Tsoumpas C, Glaudemans AW, Slart RH. The clinical value of quantitative cardiovascular molecular imaging: a step towards precision medicine. Br J Radiol 2023; 96:20230704. [PMID: 37786997 PMCID: PMC10646628 DOI: 10.1259/bjr.20230704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/05/2023] [Accepted: 09/05/2023] [Indexed: 10/04/2023] Open
Abstract
Cardiovascular diseases (CVD) are the leading cause of death worldwide and have an increasing impact on society. Precision medicine, in which optimal care is identified for an individual or a group of individuals rather than for the average population, might provide significant health benefits for this patient group and decrease CVD morbidity and mortality. Molecular imaging provides the opportunity to assess biological processes in individuals in addition to anatomical context provided by other imaging modalities and could prove to be essential in the implementation of precision medicine in CVD. New developments in single-photon emission computed tomography (SPECT) and positron emission tomography (PET) systems, combined with rapid innovations in promising and specific radiopharmaceuticals, provide an impressive improvement of diagnostic accuracy and therapy evaluation. This may result in improved health outcomes in CVD patients, thereby reducing societal impact. Furthermore, recent technical advances have led to new possibilities for accurate image quantification, dynamic imaging, and quantification of radiotracer kinetics. This potentially allows for better evaluation of disease activity over time and treatment response monitoring. However, the clinical implementation of these new methods has been slow. This review describes the recent advances in molecular imaging and the clinical value of quantitative PET and SPECT in various fields in cardiovascular molecular imaging, such as atherosclerosis, myocardial perfusion and ischemia, infiltrative cardiomyopathies, systemic vascular diseases, and infectious cardiovascular diseases. Moreover, the challenges that need to be overcome to achieve clinical translation are addressed, and future directions are provided.
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Affiliation(s)
- Hendrea Sanne Aletta Tingen
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Gijs D. van Praagh
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Pieter H. Nienhuis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Alwin Tubben
- Department of Cardiology, University Medical Centre Groningen, Groningen, The Netherlands
| | - Nick D. van Rijsewijk
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Derk ten Hove
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Nouf A. Mushari
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - T. Samara Martinez-Lucio
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Oscar I. Mendoza-Ibañez
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | - Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
| | | | - Andor W.J.M. Glaudemans
- Department of Nuclear Medicine and Molecular Imaging, University Medical Centre Groningen, Groningen, The Netherlands
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19
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Tonda K, Iwabuchi Y, Shiga T, Owaki Y, Fujita A, Nakahara T, Sakurai R, Shimizu A, Yamada Y, Okada M, Jinzaki M. Impact of patient characteristic factors on the dynamics of liver glucose metabolism: Evaluation of multiparametric imaging with dynamic whole-body 18 F-fluorodeoxyglucose-positron emission tomography. Diabetes Obes Metab 2023; 25:3521-3528. [PMID: 37589247 DOI: 10.1111/dom.15247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2023] [Revised: 07/20/2023] [Accepted: 07/30/2023] [Indexed: 08/18/2023]
Abstract
AIMS To assess the impact of various patient characteristics on the dynamics of liver glucose metabolism using automated multiparametric imaging with whole-body dynamic 18 F-fluorodeoxyglucose (FDG)-positron emission tomography (PET). MATERIALS AND METHODS We retrospectively enrolled 540 patients who underwent whole-body dynamic FDG-PET. Three quantitative indices representing hepatic glucose metabolism [mean standardized uptake value normalized by lean body mass (SULmean), metabolic glucose rate (kinetic index) and distribution volume (DV)] were measured from multiparametric PET images produced automatically based on the Patlak plot model. Patient characteristics including age, sex, body mass index, fasting time, blood glucose level, and the presence of diabetes mellitus (DM) or hepatic steatosis (HS) were collected. We examined the correlations between the characteristic factors and three quantitative indices using multiple regression analysis. RESULTS The success rate of kinetic analysis using multiparametric PET imaging was 93.3% (504/540). Hepatic SULmean was significantly correlated with age (p < .001), sex (p < .001) and blood glucose level (p = .002). DV was significantly correlated with age (p = .033), sex (p < .001), body mass index (p = .002), fasting time (p = .043) and the presence of HS (p = .002). The kinetic index was significantly correlated with age (p < .001) and sex (p = .004). In the comparison of the healthy, DM and HS groups, patients with DM had a significantly increased SULmean, whereas patients with HS had a significantly decreased DV. CONCLUSIONS Our results showed that liver glucose metabolism was influenced by various patient characteristic factors. Multiparametric FDG-PET imaging can be used to analyse the kinetics of liver glucose metabolism in routine clinical practice.
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Affiliation(s)
- Kai Tonda
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Yu Iwabuchi
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Tohru Shiga
- Advanced Clinical Research Center, Fukushima Global Medical Science Center, Fukushima Medical University, Fukushima, Japan
| | - Yoshiki Owaki
- Office of Radiation Technology, Keio University Hospital, Tokyo, Japan
| | - Arashi Fujita
- Office of Radiation Technology, Keio University Hospital, Tokyo, Japan
| | - Takehiro Nakahara
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Ryosuke Sakurai
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Atsushi Shimizu
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Yoshitake Yamada
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Okada
- Department of Radiology, Nihon University School of Medicine, Tokyo, Japan
| | - Masahiro Jinzaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
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Cumming P, Dias AH, Gormsen LC, Hansen AK, Alberts I, Rominger A, Munk OL, Sari H. Single time point quantitation of cerebral glucose metabolism by FDG-PET without arterial sampling. EJNMMI Res 2023; 13:104. [PMID: 38032409 PMCID: PMC10689590 DOI: 10.1186/s13550-023-01049-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 11/01/2023] [Indexed: 12/01/2023] Open
Abstract
BACKGROUND Until recently, quantitation of the net influx of 2-[18F]fluorodeoxyglucose (FDG) to brain (Ki) and the cerebrometabolic rate for glucose (CMRglc) required serial arterial blood sampling in conjunction with dynamic positron emission tomography (PET) recordings. Recent technical innovations enable the identification of an image-derived input function (IDIF) from vascular structures, but are frequently still encumbered by the need for interrupted sequences or prolonged recordings that are seldom available outside of a research setting. In this study, we tested simplified methods for quantitation of FDG-Ki by linear graphic analysis relative to the descending aorta IDIF in oncology patients examined using a Biograph Vision 600 PET/CT with continuous bed motion (Aarhus) or using a recently installed Biograph Vision Quadra long-axial field-of-view (FOV) scanner (Bern). RESULTS Correlation analysis of the coefficients of a tri-exponential decomposition of the IDIFs measured during 67 min revealed strong relationships among the total area under the curve (AUC), the terminal normalized arterial integral (theta(52-67 min)), and the terminal image-derived arterial FDG concentration (Ca(52-67 min)). These relationships enabled estimation of the missing AUC from late recordings of the IDIF, from which we then calculated FDG-Ki in brain by two-point linear graphic analysis using a population mean ordinate intercept and the single late frame. Furthermore, certain aspects of the IDIF data from Aarhus showed a marked age-dependence, which was not hitherto reported for the case of FDG pharmacokinetics. CONCLUSIONS The observed interrelationships between pharmacokinetic parameters in the IDIF measured during the PET recording support quantitation of FDG-Ki in brain using a single averaged frame from the interval 52-67 min post-injection, with minimal error relative to calculation from the complete dynamic sequences.
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Affiliation(s)
- Paul Cumming
- Department of Nuclear Medicine, Bern University Hospital, Freiburgstrasse 18, INO B 214.C, 3010, Bern, Switzerland.
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia.
| | - André H Dias
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Lars C Gormsen
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Allan K Hansen
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Ian Alberts
- Department of Nuclear Medicine, Bern University Hospital, Freiburgstrasse 18, INO B 214.C, 3010, Bern, Switzerland
| | - Axel Rominger
- Department of Nuclear Medicine, Bern University Hospital, Freiburgstrasse 18, INO B 214.C, 3010, Bern, Switzerland
| | - Ole L Munk
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Hasan Sari
- Department of Nuclear Medicine, Bern University Hospital, Freiburgstrasse 18, INO B 214.C, 3010, Bern, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
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21
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Ye Q, Zeng H, Zhao Y, Zhang W, Dong Y, Fan W, Lu Y. Framing protocol optimization in oncological Patlak parametric imaging with uKinetics. EJNMMI Phys 2023; 10:54. [PMID: 37698773 PMCID: PMC10497476 DOI: 10.1186/s40658-023-00577-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 09/05/2023] [Indexed: 09/13/2023] Open
Abstract
PURPOSE Total-body PET imaging with ultra-high sensitivity makes high-temporal-resolution framing protocols possible for the first time, which allows to capture rapid tracer dynamic changes. However, whether protocols with higher number of temporal frames can justify the efficacy with substantially added computation burden for clinical application remains unclear. We have developed a kinetic modeling software package (uKinetics) with the advantage of practical, fast, and automatic workflow for dynamic total-body studies. The aim of this work is to verify the uKinetics with PMOD and to perform framing protocol optimization for the oncological Patlak parametric imaging. METHODS Six different protocols with 100, 61, 48, 29, 19 and 12 temporal frames were applied to analyze 60-min dynamic 18F-FDG PET scans of 10 patients, respectively. Voxel-based Patlak analysis coupled with automatically extracted image-derived input function was applied to generate parametric images. Normal tissues and lesions were segmented manually or automatically to perform correlation analysis and Bland-Altman plots. Different protocols were compared with the protocol of 100 frames as reference. RESULTS Minor differences were found between uKinetics and PMOD in the Patlak parametric imaging. Compared with the protocol with 100 frames, the relative difference of the input function and quantitative kinetic parameters remained low for protocols with at least 29 frames, but increased for the protocols with 19 and 12 frames. Significant difference of lesion Ki values was found between the protocols with 100 frames and 12 frames. CONCLUSION uKinetics was proved providing equivalent oncological Patlak parametric imaging comparing to PMOD. Minor differences were found between protocols with 100 and 29 frames, which indicated that 29-frame protocol is sufficient and efficient for the oncological 18F-FDG Patlak applications, and the protocols with more frames are not needed. The protocol with 19 frames yielded acceptable results, while that with 12 frames is not recommended.
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Affiliation(s)
- Qing Ye
- Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Hao Zeng
- Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Yizhang Zhao
- Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
| | | | - Yun Dong
- Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China
| | - Wei Fan
- Sun Yat-Sen University Cancer Center, Guangzhou, China
| | - Yihuan Lu
- Shanghai United Imaging Healthcare Co., Ltd, Shanghai, China.
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22
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Yu X, Sun H, Xu L, Han Y, Wang C, Li L, Ng YL, Shi F, Qiu J, Huang G, Zhou Y, Chen Y, Liu J. Improved accuracy of the biodistribution and internal radiation dosimetry of 13 N-ammonia using a total-body PET/CT scanner. Med Phys 2023; 50:5865-5874. [PMID: 37177847 DOI: 10.1002/mp.16450] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 04/23/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023] Open
Abstract
BACKGROUND Conventional short-axis PET typically utilizes multi-bed multi-pass acquisition to produce quantitative whole-body dynamic images and cannot record all the uptake information simultaneously, resulting in errors when fitting the time-activity curves (TACs) and calculating radiation doses. PURPOSE The aim of this study is to evaluate the 13 N-ammonia biodistribution and the internal radiation doses using a 194 cm long total-body PET/CT scanner (uEXPLORER), and make a comparison with the previous short-axis PET results. METHODS Ten subjects (age 40-74 years) received 13 N-NH3 injection (418.1-670.81 MBq) and were under a dynamic scan for about 60 min with using a 3-dimensional whole-body protocol. ROIs were drawn visually on 11 major organs (brain, thyroid, gallbladder, heart wall, kidneys, liver, pancreas, spleen, lungs, bone marrow, and urinary bladder content) for each subject. TACs were generated using Pmod and the absorbed radiation doses were calculated using Olinda 2.2. To compare with the conventional PET/CT, five points were sampled on uEXPLORER's TACs to mimic the result of a short-axis PET/CT (15 cm axial FOV, consisted of 9 or 10 bed positions). Then the TACs were obtained using the multi-exponential fitting method, and the residence time and radiation dose were also calculated and compared with uEXPLORER. RESULTS The highest absorbed organ doses were the pancreas, thyroid, spleen, heart wall, and kidneys for the male. For the female, the first five highest absorbed organ dose coefficients were the pancreas, heart wall, spleen, lungs, and kidneys. The lowest absorbed dose was found in red marrow both for male and female. The simulated short-axis PET can fit TACs well for the gradually-changed uptake organs but typically underestimated for the rapid-uptake organs during the first-10 min, resulting in errors in the calculated radiation dose. CONCLUSION uEXPLORER PET/CT can measure 13 N-ammonia's TACs simultaneously in all organs of the whole body, which can provide more accurate biodistribution and radiation dose estimation compared with the conventional short-axis scanners.
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Affiliation(s)
- Xiaofeng Yu
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Hongyan Sun
- Central Research Institute, United Imaging Healthcare, Shanghai, People's Republic of China
| | - Lian Xu
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yuan Han
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Cheng Wang
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Lianghua Li
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yee Ling Ng
- Central Research Institute, United Imaging Healthcare, Shanghai, People's Republic of China
| | - Fuxiao Shi
- Central Research Institute, United Imaging Healthcare, Shanghai, People's Republic of China
| | - Ju Qiu
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Gang Huang
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare, Shanghai, People's Republic of China
| | - Yumei Chen
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
| | - Jianjun Liu
- Department of Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, People's Republic of China
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23
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Li G, Yang S, Wang S, Jiang R, Xu X. Diagnostic Value of Dynamic 18F-Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography ( 18F-FDG PET-CT) in Cervical Lymph Node Metastasis of Nasopharyngeal Cancer. Diagnostics (Basel) 2023; 13:2530. [PMID: 37568893 PMCID: PMC10417831 DOI: 10.3390/diagnostics13152530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/21/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND AND PURPOSE Dynamic 18F-FDG PET-CT scanning can accurately quantify 18F-FDG uptake and has been successfully applied in diagnosing and evaluating therapeutic effects in various malignant tumors. There is no conclusion as to whether it can accurately distinguish benign and malignant lymph nodes in nasopharyngeal cancer. The main purpose of this study is to reveal the diagnostic value of dynamic PET-CT in cervical lymph node metastasis of nasopharyngeal cancer through analysis. METHOD We first searched for cervical lymph nodes interested in static PET-CT, measured their SUV-Max values, and found the corresponding lymph nodes in magnetic resonance images before and after treatment. The valid or invalid groups were included according to the changes in lymph node size before and after treatment. If the change in the product of the maximum diameter and maximum vertical transverse diameter of the lymph node before and after treatment was greater than or equal to 50%, they would be included in the valid group. If the change was less than 50%, they would be included in the invalid group. Their Ki values were measured on dynamic PET-CT and compared under different conditions. Then, we conducted a correlation analysis between various factors and Ki values. Finally, diagnostic tests were conducted to compare the sensitivity and specificity of Ki and SUV-Max. RESULT We included 67 cervical lymph nodes from different regions of 51 nasopharyngeal cancer patients and divided them into valid and invalid groups based on changes before treatment. The valid group included 50 lymph nodes, while the invalid group included 17. There wer significant differences (p < 0.001) between the valid and the invalid groups in SUV-Max, Ki-Mean, and Ki-Max values. When the SUV-Max was ≤4.5, there was no significant difference in the Ki-Mean and Ki-Max between the two groups (p > 0.05). When the SUV-Max was ≤4.5 and pre-treatment lymph nodes were <1.0 cm, the valid group had significantly higher Ki-Mean (0.00910) and Ki-Maximum (0.01004) values than the invalid group (Ki-Mean = 0.00716, Ki-Max = 0.00767) (p < 0.05). When the SUV-Max was ≤4.5, the pre-treatment lymph nodes < 1.0 cm, and the EBV DNA replication normal, Ki-Mean (0.01060) and Ki-Max (0.01149) in the valid group were still significantly higher than the invalid group (Ki-Mean = 0.00670, Ki-Max = 0.00719) (p < 0.05). The correlation analysis between different factors (SUV-Max, T-stage, normal EB virus DNA replication, age, and pre-treatment lymph node < 1.0 cm) and the Ki value showed that SUV-Max and a pre-treatment lymph node < 1.0 cm were related to Ki-Mean and Ki-Max. Diagnostic testing was conducted; the AUC value of the SUV-Max value was 0.8259 (95% confidence interval: 0.7296-0.9222), the AUC value of the Ki-Mean was 0.8759 (95% confidence interval: 0.7950-0.9567), and the AUC value of the Ki-Max was 0.8859 (95% confidence interval: 0.8089-0.9629). After comparison, it was found that there was no significant difference in AUC values between Ki-Mean and SUV-Max (p = 0.220 > 0.05), and there was also no significant difference in AUC values between Ki max and SUV-Max (p = 0.159 > 0.05). By calculating the Youden index, we identified the optimal cut-off value. It was found that the sensitivity of SUV-Max was 100% and the specificity was 66%, the sensitivity of Ki-Mean was 100% and the specificity was 70%, and the sensitivity of Ki-Max was 100% and the specificity was 72%. After Chi-Square analysis, it was found that there was no significant difference in specificity between Ki-Mean and SUV-Max (p = 0.712), and there was also no significant difference in specificity between Ki-Max and SUV-Max (p = 0.755). CONCLUSION Dynamic PET-CT has shown a significant diagnostic value in diagnosing cervical lymph node metastasis of nasopharyngeal cancer, especially for the small SUV value, and lymph nodes do not meet the metastasis criteria before treatment, and EBV DNA replication is normal. Although the diagnostic accuracy, sensitivity, and specificity of dynamic PET-CT were not significantly different from traditional static PET-CT, the dynamic PET-CT had a more accurate tendency.
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Affiliation(s)
- Guanglie Li
- Department of Head and Neck Oncology, The Fifth Hospital of Sun Yat-sen University, Zhuhai 519000, China; (G.L.); (S.W.)
| | - Shuai Yang
- Department of Radiotherapy Physics, The Fifth Hospital of Sun Yat-sen University, Zhuhai 519000, China;
| | - Siyang Wang
- Department of Head and Neck Oncology, The Fifth Hospital of Sun Yat-sen University, Zhuhai 519000, China; (G.L.); (S.W.)
| | - Renwei Jiang
- Department of Radiotherapy Physics, The Fifth Hospital of Sun Yat-sen University, Zhuhai 519000, China;
| | - Xiwei Xu
- Department of Head and Neck Oncology, The Fifth Hospital of Sun Yat-sen University, Zhuhai 519000, China; (G.L.); (S.W.)
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24
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Guo X, Zhou B, Chen X, Chen MK, Liu C, Dvornek NC. MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; PP:10.1109/TMI.2023.3290003. [PMID: 37368811 PMCID: PMC10751388 DOI: 10.1109/tmi.2023.3290003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
In whole-body dynamic positron emission tomography (PET), inter-frame subject motion causes spatial misalignment and affects parametric imaging. Many of the current deep learning inter-frame motion correction techniques focus solely on the anatomy-based registration problem, neglecting the tracer kinetics that contains functional information. To directly reduce the Patlak fitting error for 18F-FDG and further improve model performance, we propose an interframe motion correction framework with Patlak loss optimization integrated into the neural network (MCP-Net). The MCP-Net consists of a multiple-frame motion estimation block, an image-warping block, and an analytical Patlak block that estimates Patlak fitting using motion-corrected frames and the input function. A novel Patlak loss penalty component utilizing mean squared percentage fitting error is added to the loss function to reinforce the motion correction. The parametric images were generated using standard Patlak analysis following motion correction. Our framework enhanced the spatial alignment in both dynamic frames and parametric images and lowered normalized fitting error when compared to both conventional and deep learning benchmarks. MCP-Net also achieved the lowest motion prediction error and showed the best generalization capability. The potential of enhancing network performance and improving the quantitative accuracy of dynamic PET by directly utilizing tracer kinetics is suggested.
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25
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Weissinger M, Atmanspacher M, Spengler W, Seith F, Von Beschwitz S, Dittmann H, Zender L, Smith AM, Casey ME, Nikolaou K, Castaneda-Vega S, la Fougère C. Diagnostic Performance of Dynamic Whole-Body Patlak [ 18F]FDG-PET/CT in Patients with Indeterminate Lung Lesions and Lymph Nodes. J Clin Med 2023; 12:3942. [PMID: 37373636 DOI: 10.3390/jcm12123942] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 05/22/2023] [Accepted: 05/30/2023] [Indexed: 06/29/2023] Open
Abstract
BACKGROUND Static [18F]FDG-PET/CT is the imaging method of choice for the evaluation of indeterminate lung lesions and NSCLC staging; however, histological confirmation of PET-positive lesions is needed in most cases due to its limited specificity. Therefore, we aimed to evaluate the diagnostic performance of additional dynamic whole-body PET. METHODS A total of 34 consecutive patients with indeterminate pulmonary lesions were enrolled in this prospective trial. All patients underwent static (60 min p.i.) and dynamic (0-60 min p.i.) whole-body [18F]FDG-PET/CT (300 MBq) using the multi-bed-multi-timepoint technique (Siemens mCT FlowMotion). Histology and follow-up served as ground truth. Kinetic modeling factors were calculated using a two-compartment linear Patlak model (FDG influx rate constant = Ki, metabolic rate = MR-FDG, distribution volume = DV-FDG) and compared to SUV using ROC analysis. RESULTS MR-FDGmean provided the best discriminatory power between benign and malignant lung lesions with an AUC of 0.887. The AUC of DV-FDGmean (0.818) and SUVmean (0.827) was non-significantly lower. For LNM, the AUCs for MR-FDGmean (0.987) and SUVmean (0.993) were comparable. Moreover, the DV-FDGmean in liver metastases was three times higher than in bone or lung metastases. CONCLUSIONS Metabolic rate quantification was shown to be a reliable method to detect malignant lung tumors, LNM, and distant metastases at least as accurately as the established SUV or dual-time-point PET scans.
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Affiliation(s)
- Matthias Weissinger
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, 72076 Tuebingen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany
| | - Max Atmanspacher
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, 72076 Tuebingen, Germany
| | - Werner Spengler
- Department for Internal Medicine VIII, University Hospital Tuebingen, 72076 Tuebingen, Germany
| | - Ferdinand Seith
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany
| | - Sebastian Von Beschwitz
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, 72076 Tuebingen, Germany
| | - Helmut Dittmann
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, 72076 Tuebingen, Germany
| | - Lars Zender
- Department for Internal Medicine VIII, University Hospital Tuebingen, 72076 Tuebingen, Germany
| | - Anne M Smith
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN 37932, USA
| | - Michael E Casey
- Siemens Medical Solutions USA, Inc., Molecular Imaging, Knoxville, TN 37932, USA
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, University Hospital Tuebingen, 72076 Tuebingen, Germany
- iFIT-Cluster of Excellence, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tuebingen, 72076 Tuebingen, Germany
| | - Salvador Castaneda-Vega
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, 72076 Tuebingen, Germany
- Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
| | - Christian la Fougère
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital Tuebingen, 72076 Tuebingen, Germany
- iFIT-Cluster of Excellence, Eberhard Karls University Tuebingen, 72076 Tuebingen, Germany
- German Cancer Consortium (DKTK), Partner Site Tuebingen, 72076 Tuebingen, Germany
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Miao T, Zhou B, Liu J, Guo X, Liu Q, Xie H, Chen X, Chen MK, Wu J, Carson RE, Liu C. Generation of Whole-Body FDG Parametric Ki Images from Static PET Images Using Deep Learning. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023; 7:465-472. [PMID: 37997577 PMCID: PMC10665031 DOI: 10.1109/trpms.2023.3243576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2023]
Abstract
FDG parametric Ki images show great advantage over static SUV images, due to the higher contrast and better accuracy in tracer uptake rate estimation. In this study, we explored the feasibility of generating synthetic Ki images from static SUV ratio (SUVR) images using three configurations of U-Nets with different sets of input and output image patches, which were the U-Nets with single input and single output (SISO), multiple inputs and single output (MISO), and single input and multiple outputs (SIMO). SUVR images were generated by averaging three 5-min dynamic SUV frames starting at 60 minutes post-injection, and then normalized by the mean SUV values in the blood pool. The corresponding ground truth Ki images were derived using Patlak graphical analysis with input functions from measurement of arterial blood samples. Even though the synthetic Ki values were not quantitatively accurate compared with ground truth, the linear regression analysis of joint histograms in the voxels of body regions showed that the mean R2 values were higher between U-Net prediction and ground truth (0.596, 0.580, 0.576 in SISO, MISO and SIMO), than that between SUVR and ground truth Ki (0.571). In terms of similarity metrics, the synthetic Ki images were closer to the ground truth Ki images (mean SSIM = 0.729, 0.704, 0.704 in SISO, MISO and MISO) than the input SUVR images (mean SSIM = 0.691). Therefore, it is feasible to use deep learning networks to estimate surrogate map of parametric Ki images from static SUVR images.
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Affiliation(s)
- Tianshun Miao
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Juan Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA
| | - Xueqi Guo
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Qiong Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Huidong Xie
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Xiongchao Chen
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA
| | - Jing Wu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA
- Department of Physics, Beijing Normal University, Beijing 100875, China
| | - Richard E. Carson
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Chi Liu
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
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Dias AH, Jochumsen MR, Zacho HD, Munk OL, Gormsen LC. Multiparametric dynamic whole-body PSMA PET/CT using [ 68Ga]Ga-PSMA-11 and [ 18F]PSMA-1007. EJNMMI Res 2023; 13:31. [PMID: 37060394 PMCID: PMC10105814 DOI: 10.1186/s13550-023-00981-8] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 03/31/2023] [Indexed: 04/16/2023] Open
Abstract
BACKGROUND Routine prostate-specific membrane antigen (PSMA) positron emission tomography (PET) performed for primary staging or restaging of prostate cancer patients is usually done as a single static image acquisition 60 min after tracer administration. In this study, we employ dynamic whole-body (D-WB) PET imaging to compare the pharmacokinetics of [68Ga]Ga-PSMA-11 and [18F]PSMA-1007 in various tissues and lesions, and to assess whether Patlak parametric images are quantitative and improve lesion detection and image readability. METHODS Twenty male patients with prostate cancer were examined using a D-WB PSMA PET protocol. Ten patients were scanned with [68Ga]Ga-PSMA-11 and ten with [18F]PSMA-1007. Kinetic analyses were made using time-activity curves (TACs) extracted from organs (liver, spleen, bone, and muscle) and lesions. For each patient, three images were produced: SUV + Patlak parametric images (Ki and DV). All images were reviewed visually to compare lesion detection, image readability was quantified using target-to-background ratios (TBR), and Ki and DV values were compared. RESULTS The two PSMA tracers exhibited markedly different pharmacokinetics in organs: reversible for [68Ga]Ga-PSMA-11 and irreversible for [18F]PSMA-1007. For both tracers, lesions kinetics were best described by an irreversible model. All parametric images were of good visual quality using both radiotracers. In general, Ki images were characterized by reduced vascular signal and increased lesion TBR compared with SUV images. No additional malignant lesions were identified on the parametric images. CONCLUSION D-WB PET/CT is feasible for both PSMA tracers allowing for direct reconstruction of parametric Ki images. The use of multiparametric PSMA images increased TBR but did not lead to the detection of more lesions. For quantitative whole-body Ki imaging, [18F]PSMA-1007 should be preferred over [68Ga]Ga-PSMA-11 due to its irreversible kinetics in organs and lesions.
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Affiliation(s)
- André H Dias
- Department of Nuclear Medicine & PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200, Aarhus N, Denmark.
| | - Mads R Jochumsen
- Department of Nuclear Medicine & PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Helle D Zacho
- Department of Nuclear Medicine and Clinical Cancer Research Centre, Aalborg University Hospital, Aalborg, Denmark
- Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | - Ole L Munk
- Department of Nuclear Medicine & PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Lars C Gormsen
- Department of Nuclear Medicine & PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200, Aarhus N, Denmark
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
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Guo X, Wu J, Chen MK, Liu Q, Onofrey JA, Pucar D, Pang Y, Pigg D, Casey ME, Dvornek NC, Liu C. Inter-pass motion correction for whole-body dynamic PET and parametric imaging. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2023; 7:344-353. [PMID: 37842204 PMCID: PMC10569406 DOI: 10.1109/trpms.2022.3227576] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2023]
Abstract
Whole-body dynamic FDG-PET imaging through continuous-bed-motion (CBM) mode multi-pass acquisition protocol is a promising metabolism measurement. However, inter-pass misalignment originating from body movement could degrade parametric quantification. We aim to apply a non-rigid registration method for inter-pass motion correction in whole-body dynamic PET. 27 subjects underwent a 90-min whole-body FDG CBM PET scan on a Biograph mCT (Siemens Healthineers), acquiring 9 over-the-heart single-bed passes and subsequently 19 CBM passes (frames). The inter-pass motion correction was executed using non-rigid image registration with multi-resolution, B-spline free-form deformations. The parametric images were then generated by Patlak analysis. The overlaid Patlak slope Ki and y-intercept Vb images were visualized to qualitatively evaluate motion impact and correction effect. The normalized weighted mean squared Patlak fitting errors (NFE) were compared in the whole body, head, and hypermetabolic regions of interest (ROI). In Ki images, ROI statistics were collected and malignancy discrimination capacity was estimated by the area under the receiver operating characteristic curve (AUC). After the inter-pass motion correction was applied, the spatial misalignment appearance between Ki and Vb images was successfully reduced. Voxel-wise normalized fitting error maps showed global error reduction after motion correction. The NFE in the whole body (p = 0.0013), head (p = 0.0021), and ROIs (p = 0.0377) significantly decreased. The visual performance of each hypermetabolic ROI in Ki images was enhanced, while 3.59% and 3.67% average absolute percentage changes were observed in mean and maximum Ki values, respectively, across all evaluated ROIs. The estimated mean Ki values had substantial changes with motion correction (p = 0.0021). The AUC of both mean Ki and maximum Ki after motion correction increased, possibly suggesting the potential of enhancing oncological discrimination capacity through inter-pass motion correction.
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Affiliation(s)
- Xueqi Guo
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
| | - Jing Wu
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA, and the Center for Advanced Quantum Studies and Department of Physics, Beijing Normal University, Beijing, China
| | - Ming-Kai Chen
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
| | - Qiong Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA
| | - John A Onofrey
- Department of Biomedical Engineering, the Department of Radiology and Biomedical Imaging, and the Department of Urology, Yale University, New Haven, CT, 06511, USA
| | - Darko Pucar
- Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
| | - Yulei Pang
- Department of Biomedical Engineering, Yale University, New Haven, CT, 06511, USA, and Southern Connecticut State University, New Haven, CT, 06515, USA
| | - David Pigg
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | - Michael E Casey
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | - Nicha C Dvornek
- Department of Biomedical Engineering and the Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
| | - Chi Liu
- Department of Biomedical Engineering and the Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, 06511, USA
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Tamaki N, Hirata K, Kotani T, Nakai Y, Matsushima S, Yamada K. Four-dimensional quantitative analysis using FDG-PET in clinical oncology. Jpn J Radiol 2023:10.1007/s11604-023-01411-4. [PMID: 36947283 PMCID: PMC10366296 DOI: 10.1007/s11604-023-01411-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/02/2023] [Indexed: 03/23/2023]
Abstract
Positron emission tomography (PET) with F-18 fluorodeoxyglucose (FDG) has been commonly used in many oncological areas. High-resolution PET permits a three-dimensional analysis of FDG distributions on various lesions in vivo, which can be applied for tissue characterization, risk analysis, and treatment monitoring after chemoradiotherapy and immunotherapy. Metabolic changes can be assessed using the tumor absolute FDG uptake as standardized uptake value (SUV) and metabolic tumor volume (MTV). In addition, tumor heterogeneity assessment can potentially estimate tumor aggressiveness and resistance to chemoradiotherapy. Attempts have been made to quantify intratumoral heterogeneity using radiomics. Recent reports have indicated the clinical feasibility of a dynamic FDG PET-computed tomography (CT) in pilot cohort studies of oncological cases. Dynamic imaging permits the assessment of temporal changes in FDG uptake after administration, which is particularly useful for differentiating pathological from physiological uptakes with high diagnostic accuracy. In addition, several new parameters have been introduced for the in vivo quantitative analysis of FDG metabolic processes. Thus, a four-dimensional FDG PET-CT is available for precise tissue characterization of various lesions. This review introduces various new techniques for the quantitative analysis of FDG distribution and glucose metabolism using a four-dimensional FDG analysis with PET-CT. This elegant study reveals the important role of tissue characterization and treatment strategies in oncology.
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Affiliation(s)
- Nagara Tamaki
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan.
| | - Kenji Hirata
- Department of Diagnostic Imaging, Hokkaido University Graduate School of Medicine, Sapporo, Japan
| | - Tomoya Kotani
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Yoshitomo Nakai
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Shigenori Matsushima
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kei Yamada
- Department of Radiology, Graduate School of Medical Science, Kyoto Prefectural University of Medicine, Kyoto, Japan
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Li Y, Hu J, Sari H, Xue S, Ma R, Kandarpa S, Visvikis D, Rominger A, Liu H, Shi K. A deep neural network for parametric image reconstruction on a large axial field-of-view PET. Eur J Nucl Med Mol Imaging 2023; 50:701-714. [PMID: 36326869 DOI: 10.1007/s00259-022-06003-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 10/09/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE The PET scanners with long axial field of view (AFOV) having ~ 20 times higher sensitivity than conventional scanners provide new opportunities for enhanced parametric imaging but suffer from the dramatically increased volume and complexity of dynamic data. This study reconstructed a high-quality direct Patlak Ki image from five-frame sinograms without input function by a deep learning framework based on DeepPET to explore the potential of artificial intelligence reducing the acquisition time and the dependence of input function in parametric imaging. METHODS This study was implemented on a large AFOV PET/CT scanner (Biograph Vision Quadra) and twenty patients were recruited with 18F-fluorodeoxyglucose (18F-FDG) dynamic scans. During training and testing of the proposed deep learning framework, the last five-frame (25 min, 40-65 min post-injection) sinograms were set as input and the reconstructed Patlak Ki images by a nested EM algorithm on the vendor were set as ground truth. To evaluate the image quality of predicted Ki images, mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) were calculated. Meanwhile, a linear regression process was applied between predicted and true Ki means on avid malignant lesions and tumor volume of interests (VOIs). RESULTS In the testing phase, the proposed method achieved excellent MSE of less than 0.03%, high SSIM, and PSNR of ~ 0.98 and ~ 38 dB, respectively. Moreover, there was a high correlation (DeepPET: [Formula: see text]= 0.73, self-attention DeepPET: [Formula: see text]=0.82) between predicted Ki and traditionally reconstructed Patlak Ki means over eleven lesions. CONCLUSIONS The results show that the deep learning-based method produced high-quality parametric images from small frames of projection data without input function. It has much potential to address the dilemma of the long scan time and dependency on input function that still hamper the clinical translation of dynamic PET.
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Affiliation(s)
- Y Li
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, People's Republic of China.,College of Optical Science and Engineering, Zhejiang University, Hangzhou, People's Republic of China
| | - J Hu
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - H Sari
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - S Xue
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - R Ma
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland.,Department of Engineering Physics, Tsinghua University, Beijing, China
| | - S Kandarpa
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - D Visvikis
- LaTIM, INSERM, UMR 1101, University of Brest, Brest, France
| | - A Rominger
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - H Liu
- College of Optical Science and Engineering, Zhejiang University, Hangzhou, People's Republic of China.
| | - K Shi
- Department of Nuclear Medicine, Inselpital, Bern University Hospital, University of Bern, Bern, Switzerland.,Computer Aided Medical Procedures and Augmented Reality, Institute of Informatics I16, Technical University of Munich, Munich, Germany
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The valuable role of dynamic 18F FDG PET/CT-derived kinetic parameter K i in patients with nasopharyngeal carcinoma prior to radiotherapy: A prospective study. Radiother Oncol 2023; 179:109440. [PMID: 36566989 DOI: 10.1016/j.radonc.2022.109440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 11/02/2022] [Accepted: 12/03/2022] [Indexed: 12/24/2022]
Abstract
BACKGROUND AND PURPOSE Dynamic positron emission tomography/computed tomography (PET/CT) served the potential role of characterizing malignant foci. The main objective of this prospective study was to explore the advantage of dynamic PET/CT imaging in characterizing nasopharyngeal carcinoma (NPC). METHODS AND MATERIALS Patients with probable head and neck disease underwent a local dynamic PET/CT scan followed by a whole-body static scan. Patlak analysis was used to generate parametric influx rate constant (Ki) images from 48 frames obtained from a dynamic PET/CT scan. By delineating the volumes-of-interest (VOIs) of: primary tumor (PT), lymph node (LN), and normal nasopharyngeal tissues (N), we acquired the corresponding Ki mean and SUVmean of each site respectively to perform the quantitative statistical analysis. RESULTS Qualified images of 71 patients with newly diagnosed NPC and 8 without nasopharyngeal malignant lesions were finally included. We found the correlations between Ki mean-PT and critical clinical features, including clinical stage (r = 0.368), T category (r = 0.643) and EBV-DNA copy status (r = 0.351), and Ki mean-PT differed within the group. SUVmean-PT showed correlations with clinical stage (r = 0.280) and T category (r = 0.472), but could hardly differ systematically within group of clinical features except T category. Ki mean-LN offered the positive correlations with N category (r = 0.294), M category (r = 0.238) and EBV-DNA copy status (r = 0.446), and differed within the group. In addition, Ki mean represented a sensitivity of 94.4 % and a specificity of 100 %, in distinguishing NPC from the non-NPC, when the cut-off was defined as 0.0106. When the cut-off of SUV being defined as 2.03, the sensitivity and specificity were both 100 %. CONCLUSION Our research confirmed Ki compared favorably to SUV in characterizing NPC and found that Ki can serve as an effective imaging marker of NPC.
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Vandenberghe S, Karakatsanis NA, Akl MA, Maebe J, Surti S, Dierckx RA, Pryma DA, Nehmeh SA, Bouhali O, Karp JS. The potential of a medium-cost long axial FOV PET system for nuclear medicine departments. Eur J Nucl Med Mol Imaging 2023; 50:652-660. [PMID: 36178535 DOI: 10.1007/s00259-022-05981-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 09/19/2022] [Indexed: 01/24/2023]
Abstract
PURPOSE Total body positron emission tomography (TB-PET) has recently been introduced in nuclear medicine departments. There is a large interest in these systems, but for many centers, the high acquisition cost makes it very difficult to justify their current operational budget. Here, we propose medium-cost long axial FOV scanners as an alternative. METHODS Several medium-cost long axial FOV designs are described with their advantages and drawbacks. We describe their potential for higher throughput, more cost-effective scanning, a larger group of indications, and novel research opportunities. The wider spread of TB-PET can also lead to the fast introduction of new tracers (at a low dose), new methodologies, and optimized workflows. CONCLUSIONS A medium-cost TB-PET would be positioned between the current standard PET-CT and the full TB-PET systems in investment but recapitulate most advantages of full TB-PET. These systems could be more easily justified financially in a standard academic or large private nuclear medicine department and still have ample research options.
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Affiliation(s)
- Stefaan Vandenberghe
- Medical Image and Signal Processing, Ghent University, Corneel Heymans Laan 10, 9000, Ghent, Belgium.
| | | | - Maya Abi Akl
- Medical Image and Signal Processing, Ghent University, Corneel Heymans Laan 10, 9000, Ghent, Belgium
- Science Program, Texas A&M University at Qatar, Doha, Qatar
| | - Jens Maebe
- Medical Image and Signal Processing, Ghent University, Corneel Heymans Laan 10, 9000, Ghent, Belgium
| | - Suleman Surti
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Rudi A Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Daniel A Pryma
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
| | - Sadek A Nehmeh
- Weill Cornell Medical College, Cornell University, NY, USA
| | | | - Joel S Karp
- Department of Radiology, University of Pennsylvania, Philadelphia, USA
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Du J, Jones T. Technical opportunities and challenges in developing total-body PET scanners for mice and rats. EJNMMI Phys 2023; 10:2. [PMID: 36592266 PMCID: PMC9807733 DOI: 10.1186/s40658-022-00523-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 12/20/2022] [Indexed: 01/03/2023] Open
Abstract
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.
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Affiliation(s)
- Junwei Du
- grid.27860.3b0000 0004 1936 9684Department of Biomedical Engineering, University of California at Davis, Davis, CA 95616 USA
| | - Terry Jones
- grid.27860.3b0000 0004 1936 9684Department of Radiology, University of California at Davis, Davis, CA 95616 USA
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34
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Wumener X, Zhang Y, Wang Z, Zhang M, Zang Z, Huang B, Liu M, Huang S, Huang Y, Wang P, Liang Y, Sun T. Dynamic FDG-PET imaging for differentiating metastatic from non-metastatic lymph nodes of lung cancer. Front Oncol 2022; 12:1005924. [PMID: 36439506 PMCID: PMC9686335 DOI: 10.3389/fonc.2022.1005924] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/25/2022] [Indexed: 08/13/2023] Open
Abstract
OBJECTIVES 18F-fluorodeoxyglucose (FDG) PET/CT has been widely used in tumor diagnosis, staging, and response evaluation. To determine an optimal therapeutic strategy for lung cancer patients, accurate staging is essential. Semi-quantitative standardized uptake value (SUV) is known to be affected by multiple factors and may fail to differentiate between benign and malignant lesions. Lymph nodes (LNs) in the mediastinal and pulmonary hilar regions with high FDG uptake due to granulomatous lesions such as tuberculosis, which has a high prevalence in China, pose a diagnostic challenge. This study aims to evaluate the diagnostic value of the quantitative metabolic parameters derived from dynamic 18F-FDG PET/CT in differentiating metastatic and non-metastatic LNs in lung cancer. METHODS One hundred and eight patients with pulmonary nodules were enrolled to perform 18F-FDG PET/CT dynamic + static imaging with informed consent. One hundred and thirty-five LNs in 29 lung cancer patients were confirmed by pathology. Static image analysis parameters including LN-SUVmax, LN-SUVmax/primary tumor SUVmax (LN-SUVmax/PT-SUVmax), mediastinal blood pool SUVmax (MBP-SUVmax), LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter. Quantitative parameters including K1, k2, k3 and Ki and of each LN were obtained by applying the irreversible two-tissue compartment model using in-house Matlab software. Ki/K1 was computed subsequently as a separate marker. We further divided the LNs into mediastinal LNs (N=82) and pulmonary hilar LNs (N=53). Wilcoxon rank-sum test or Independent-samples T-test and receiver-operating characteristic (ROC) analysis was performed on each parameter to compare the diagnostic efficacy in differentiating lymph node metastases from inflammatory uptake. P<0.05 were considered statistically significant. RESULTS Among the 135 FDG-avid LNs confirmed by pathology, 49 LNs were non-metastatic, and 86 LNs were metastatic. LN-SUVmax, MBP-SUVmax, LN-SUVmax/MBP-SUVmax, and LN-SUVmax/short diameter couldn't well differentiate metastatic from non-metastatic LNs (P>0.05). However, LN-SUVmax/PT-SUVmax have good performance in the differential diagnosis of non-metastatic and metastatic LNs (P=0.039). Dynamic metabolic parameters in addition to k3, the parameters including K1, k2, Ki, and Ki/K1, on the other hand, have good performance in the differential diagnosis of metastatic and non-metastatic LNs (P=0.045, P=0.001, P=0.001, P=0.001, respectively). For ROC analysis, the metabolic parameters Ki (AUC of 0.672 [0.579-0.765], sensitivity 0.395, specificity 0.918) and Ki/K1 (AUC of 0.673 [0.580-0.767], sensitivity 0.570, specificity 0.776) have good performance in the differential diagnosis of metastatic from non-metastatic LNs than SUVmax (AUC of 0.596 [0.498-0.696], sensitivity 0.826, specificity 0.388), included the mediastinal region and pulmonary hilar region. CONCLUSION Compared with SUVmax, quantitative parameters such as K1, k2, Ki and Ki/K1 showed promising results for differentiation of metastatic and non-metastatic LNs with high uptake. The Ki and Ki/K1 had a high differential diagnostic value both in the mediastinal region and pulmonary hilar region.
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Affiliation(s)
- Xieraili Wumener
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Yarong Zhang
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Zhenguo Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Maoqun Zhang
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | | | - Bin Huang
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Ming Liu
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Shengyun Huang
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Yong Huang
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Peng Wang
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Ying Liang
- Department of Nuclear Medicine, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Shenzhen, China
| | - Tao Sun
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
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Tanaka T, Nakajo M, Kawakami H, Motomura E, Fujisaka T, Ojima S, Saigo Y, Yoshiura T. Short-time-window Patlak imaging using a population-based arterial input function and optimized Bayesian penalized likelihood reconstruction: a feasibility study. EJNMMI Res 2022; 12:57. [PMID: 36075998 PMCID: PMC9458796 DOI: 10.1186/s13550-022-00933-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 08/31/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND To explore the feasibility of short-time-window Ki imaging using a population-based arterial input function (IF) and optimized Bayesian penalized likelihood (BPL) reconstruction as a practical alternative to long-time-window Ki imaging with an individual patient-based IF. Myocardial Ki images were generated from 73 dynamic 18F-FDG-PET/CT scans of 30 patients with cardiac sarcoidosis. For each dynamic scan, the Ki images were obtained using the IF from each individual patient and a long time window (10-60 min). In addition, Ki images were obtained using the normalized averaged population-based IF and BPL algorithms with different beta values (350, 700, and 1000) with a short time window (40-60 min). The visual quality of each image was visually rated using a 4-point scale (0, not visible; 1, poor; 2, moderate; and 3, good), and the Ki parameters (Ki-max, Ki-mean, Ki-volume) of positive myocardial lesions were measured independently by two readers. Wilcoxon's rank sum test, McNemar's test, or linear regression analysis were performed to assess the differences or relationships between two quantitative variables. RESULTS Both readers similarly rated 51 scans as positive (scores = 1-3) and 22 scans as negative (score = 0) for all four Ki images. Among the three types of population-based IF Ki images, the proportion of images with scores of 3 was highest with a beta of 1000 (78.4 and 72.5%, respectively) and lowest with a beta of 350 (33.3 and 23.5%) for both readers (all p < 0.001). The coefficients of determination between the Ki parameters obtained with the individual patient-based IF and those obtained with the population-based IF were highest with a beta of 1000 for both readers (Ki-max, 0.91 and 0.92, respectively; Ki-mean, 0.91 and 0.92, respectively; Ki-volume, 0.75 and 0.60, respectively; and all p < 0.001). CONCLUSIONS Short-time-window Ki images with a population-based IF reconstructed using the BPL algorithm and a high beta value were closely correlated with long-time-window Ki images generated with an individual patient-based IF. Short-time-window Ki images using a population-based IF and BPL reconstruction might represent practical alternatives to long-time-window Ki images generated using an individual patient-based IF.
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Affiliation(s)
- Takato Tanaka
- Department of Radiation Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Masatoyo Nakajo
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan.
| | - Hirofumi Kawakami
- Academic Department, GE Healthcare Japan, 4-7-127 Asahigaoka-Hinoshi, Tokyo, 191-8503, Japan
| | - Eriko Motomura
- Department of Radiation Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Tomofumi Fujisaka
- Department of Radiation Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Satoko Ojima
- Department of Cardiovascular Medicine and Hypertension, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Yasumasa Saigo
- Department of Radiation Technology, Kagoshima University Hospital, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
| | - Takashi Yoshiura
- Department of Radiology, Graduate School of Medical and Dental Sciences, Kagoshima University, 8-35-1 Sakuragaoka, Kagoshima, 890-8544, Japan
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Dias AH, Smith AM, Shah V, Pigg D, Gormsen LC, Munk OL. Clinical validation of a population-based input function for 20-min dynamic whole-body 18F-FDG multiparametric PET imaging. EJNMMI Phys 2022; 9:60. [PMID: 36076097 PMCID: PMC9458803 DOI: 10.1186/s40658-022-00490-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/29/2022] [Indexed: 11/26/2022] Open
Abstract
Purpose Contemporary PET/CT scanners can use 70-min dynamic whole-body (D-WB) PET to generate more quantitative information about FDG uptake than just the SUV by generating parametric images of FDG metabolic rate (MRFDG). The analysis requires the late (50–70 min) D-WB tissue data combined with the full (0–70 min) arterial input function (AIF). Our aim was to assess whether the use of a scaled population-based input function (sPBIF) obviates the need for the early D-WB PET acquisition and allows for a clinically feasible 20-min D-WB PET examination.
Methods A PBIF was calculated based on AIFs from 20 patients that were D-WB PET scanned for 120 min with simultaneous arterial blood sampling. MRFDG imaging using PBIF requires that the area under the curve (AUC) of the sPBIF is equal to the AUC of the individual patient’s input function because sPBIF AUC bias translates into MRFDG bias. Special patient characteristics could affect the shape of their AIF. Thus, we validated the use of PBIF in 171 patients that were divided into 12 subgroups according to the following characteristics: diabetes, cardiac ejection fraction, blood pressure, weight, eGFR and age. For each patient, the PBIF was scaled to the aorta image-derived input function (IDIF) to calculate a sPBIF, and the AUC bias was calculated. Results We found excellent agreement between the AIF and IDIF at all times. For the clinical validation, the use of sPBIF led to an acceptable AUC bias of 1–5% in most subgroups except for patients with diabetes or patients with low eGFR, where the biases were marginally higher at 7%. Multiparametric MRFDG images based on a short 20-min D-WB PET and sPBIF were visually indistinguishable from images produced by the full 70-min D-WB PET and individual IDIF. Conclusions A short 20-min D-WB PET examination using PBIF can be used for multiparametric imaging without compromising the image quality or precision of MRFDG. The D-WB PET examination may therefore be used in clinical routine for a wide range of patients, potentially allowing for more precise quantification in e.g. treatment response imaging. Supplementary Information The online version contains supplementary material available at 10.1186/s40658-022-00490-y.
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Affiliation(s)
- André H Dias
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200, Aarhus N, Denmark
| | - Anne M Smith
- Siemens Medical Solutions USA, Inc., Knoxville, TN, USA
| | - Vijay Shah
- Siemens Medical Solutions USA, Inc., Knoxville, TN, USA
| | - David Pigg
- Siemens Medical Solutions USA, Inc., Knoxville, TN, USA
| | - Lars C Gormsen
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200, Aarhus N, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark
| | - Ole L Munk
- Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Palle Juul-Jensens Boulevard 165, 8200, Aarhus N, Denmark. .,Department of Clinical Medicine, Aarhus University, Aarhus N, Denmark.
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Skawran S, Messerli M, Kotasidis F, Trinckauf J, Weyermann C, Kudura K, Ferraro DA, Pitteloud J, Treyer V, Maurer A, Huellner MW, Burger IA. Can Dynamic Whole-Body FDG PET Imaging Differentiate between Malignant and Inflammatory Lesions? Life (Basel) 2022; 12:life12091350. [PMID: 36143386 PMCID: PMC9501027 DOI: 10.3390/life12091350] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/16/2022] [Accepted: 08/29/2022] [Indexed: 11/18/2022] Open
Abstract
Background: Investigation of the clinical feasibility of dynamic whole-body (WB) [18F]FDG PET, including standardized uptake value (SUV), rate of irreversible uptake (Ki), and apparent distribution volume (Vd) in physiologic tissues, and comparison between inflammatory/infectious and cancer lesions. Methods: Twenty-four patients were prospectively included to undergo dynamic WB [18F]FDG PET/CT for clinically indicated re-/staging of oncological diseases. Parametric maps of Ki and Vd were generated using Patlak analysis alongside SUV images. Maximum parameter values (SUVmax, Kimax, and Vdmax) were measured in liver parenchyma and in malignant or inflammatory/infectious lesions. Lesion-to-background ratios (LBRs) were calculated by dividing the measurements by their respective mean in the liver tissue. Results: Seventy-seven clinical target lesions were identified, 60 malignant and 17 inflammatory/infectious. Kimax was significantly higher in cancer than in inflammatory/infections lesions (3.0 vs. 2.0, p = 0.002) while LBRs of SUVmax, Kimax, and Vdmax did not differ significantly between the etiologies: LBR (SUVmax) 3.3 vs. 2.9, p = 0.06; LBR (Kimax) 5.0 vs. 4.4, p = 0.05, LBR (Vdmax) 1.1 vs. 1.0, p = 0.18). LBR of inflammatory/infectious and cancer lesions was higher in Kimax than in SUVmax (4.5 vs. 3.2, p < 0.001). LBRs of Kimax and SUVmax showed a strong correlation (Spearman’s rho = 0.83, p < 0.001). Conclusions: Dynamic WB [18F]FDG PET/CT is feasible in a clinical setting. LBRs of Kimax were higher than SUVmax. Kimax was higher in malignant than in inflammatory/infectious lesions but demonstrated a large overlap between the etiologies.
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Affiliation(s)
- Stephan Skawran
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Michael Messerli
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | | | - Josephine Trinckauf
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Corina Weyermann
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
| | - Ken Kudura
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
- Claraspital, 4058 Basel, Switzerland
| | - Daniela A. Ferraro
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Janique Pitteloud
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Valerie Treyer
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Alexander Maurer
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Martin W. Huellner
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
| | - Irene A. Burger
- Department of Nuclear Medicine, University Hospital Zurich, 8091 Zurich, Switzerland
- Faculty of Medicine, University of Zurich, 8006 Zurich, Switzerland
- Department of Nuclear Medicine, Kantonsspital Baden, 5404 Baden, Switzerland
- Correspondence:
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Wang G, Nardo L, Parikh M, Abdelhafez YG, Li E, Spencer BA, Qi J, Jones T, Cherry SR, Badawi RD. Total-Body PET Multiparametric Imaging of Cancer Using a Voxelwise Strategy of Compartmental Modeling. J Nucl Med 2022; 63:1274-1281. [PMID: 34795014 PMCID: PMC9364337 DOI: 10.2967/jnumed.121.262668] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 11/08/2021] [Indexed: 02/03/2023] Open
Abstract
Quantitative dynamic PET with compartmental modeling has the potential to enable multiparametric imaging and more accurate quantification than static PET imaging. Conventional methods for parametric imaging commonly use a single kinetic model for all image voxels and neglect the heterogeneity of physiologic models, which can work well for single-organ parametric imaging but may significantly compromise total-body parametric imaging on a scanner with a long axial field of view. In this paper, we evaluate the necessity of voxelwise compartmental modeling strategies, including time delay correction (TDC) and model selection, for total-body multiparametric imaging. Methods: Ten subjects (5 patients with metastatic cancer and 5 healthy volunteers) were scanned on a total-body PET/CT system after injection of 370 MBq of 18F-FDG. Dynamic data were acquired for 60 min. Total-body parametric imaging was performed using 2 approaches. One was the conventional method that uses a single irreversible 2-tissue-compartment model with and without TDC. The second approach selects the best kinetic model from 3 candidate models for individual voxels. The differences between the 2 approaches were evaluated for parametric imaging of microkinetic parameters and the 18F-FDG net influx rate, KiResults: TDC had a nonnegligible effect on kinetic quantification of various organs and lesions. The effect was larger in lesions with a higher blood volume. Parametric imaging of Ki with the standard 2-tissue-compartment model introduced vascular-region artifacts, which were overcome by the voxelwise model selection strategy. Conclusion: The time delay and appropriate kinetic model vary in different organs and lesions. Modeling of the time delay of the blood input function and model selection improved total-body multiparametric imaging.
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Affiliation(s)
- Guobao Wang
- Department of Radiology, University of California Davis Medical Center, Sacramento, California;
| | - Lorenzo Nardo
- Department of Radiology, University of California Davis Medical Center, Sacramento, California
| | - Mamta Parikh
- UC Davis Comprehensive Cancer Center, Sacramento, California; and
| | - Yasser G Abdelhafez
- Department of Radiology, University of California Davis Medical Center, Sacramento, California
| | - Elizabeth Li
- Department of Biomedical Engineering, University of California at Davis, Davis, California
| | - Benjamin A Spencer
- Department of Radiology, University of California Davis Medical Center, Sacramento, California
- Department of Biomedical Engineering, University of California at Davis, Davis, California
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California at Davis, Davis, California
| | - Terry Jones
- Department of Radiology, University of California Davis Medical Center, Sacramento, California
| | - Simon R Cherry
- Department of Radiology, University of California Davis Medical Center, Sacramento, California
- Department of Biomedical Engineering, University of California at Davis, Davis, California
| | - Ramsey D Badawi
- Department of Radiology, University of California Davis Medical Center, Sacramento, California
- Department of Biomedical Engineering, University of California at Davis, Davis, California
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Guo X, Zhou B, Pigg D, Spottiswoode B, Casey ME, Liu C, Dvornek NC. Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network. Med Image Anal 2022; 80:102524. [PMID: 35797734 PMCID: PMC10923189 DOI: 10.1016/j.media.2022.102524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 06/08/2022] [Accepted: 06/24/2022] [Indexed: 11/24/2022]
Abstract
Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, but have yet been investigated with consideration for tracer distribution changes or in the whole-body scope. In this work, we developed an unsupervised automatic deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. Our dataset contains 27 subjects each under a 90-min FDG whole-body dynamic PET scan. Evaluating performance in motion simulation studies and a 9-fold cross-validation on the human subject dataset, compared with both traditional and deep learning baselines, we demonstrated that the proposed network achieved the lowest motion prediction error, obtained superior performance in enhanced qualitative and quantitative spatial alignment between parametric Ki and Vb images, and significantly reduced parametric fitting error. We also showed the potential of the proposed motion correction method for impacting downstream analysis of the estimated parametric images, improving the ability to distinguish malignant from benign hypermetabolic regions of interest. Once trained, the motion estimation inference time of our proposed network was around 460 times faster than the conventional registration baseline, showing its potential to be easily applied in clinical settings.
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Affiliation(s)
- Xueqi Guo
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - Bo Zhou
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA
| | - David Pigg
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | | | - Michael E Casey
- Siemens Medical Solutions USA, Inc., Knoxville, TN, 37932, USA
| | - Chi Liu
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA.
| | - Nicha C Dvornek
- Department of Biomedical Engineering, Yale University, New Haven, CT 06511, USA; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT 06511, USA.
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Inglese M, Duggento A, Boccato T, Ferrante M, Toschi N. Spatiotemporal learning of dynamic positron emission tomography data improves diagnostic accuracy in breast cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:186-189. [PMID: 36086343 DOI: 10.1109/embc48229.2022.9871033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Positron emission tomography (PET) can reveal metabolic activity in a voxelwise manner. PET analysis is commonly performed in a static manner by analyzing the standardized uptake value (SUV) obtained from the plateau region of PET acquisitions. A dynamic PET acquisition can provide a map of the spatiotemporal concentration of the tracer in vivo, hence conveying information about radiotracer delivery to tissue, its interaction with the target and washout. Therefore, tissue-specific biochemical properties are embedded in the shape of time activity curves (TACs), which are generally used for kinetic analysis. Conventionally, TACs are employed along with information about blood plasma activity concentration, i.e., the arterial input function (AIF), and specific compartmental models to obtain a full quantitative analysis of PET data. The main drawback of this approach is the need for invasive procedures requiring arterial blood sample collection during the whole PET scan. In this paper, we address the challenge of improving PET diagnostic accuracy through an alternative approach based on the analysis of time signal intensity patterns. Specifically, we demonstrate the diagnostic potential of tissue TACs provided by dynamic PET acquisition using various deep learning models. Our framework is shown to outperform the discriminative potential of classical SUV analysis, hence paving the way for more accurate PET-based lesion discrimination without additional acquisition time or invasive procedures. Clinical Relevance- The diagnostic accuracy of dynamic PET data exploited by deep-learning based time signal intensity pattern analysis is superior to that of static SUV imaging.
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Liu G, Yu H, Shi D, Hu P, Hu Y, Tan H, Zhang Y, Yin H, Shi H. Short-time total-body dynamic PET imaging performance in quantifying the kinetic metrics of 18F-FDG in healthy volunteers. Eur J Nucl Med Mol Imaging 2022; 49:2493-2503. [PMID: 34417855 DOI: 10.1007/s00259-021-05500-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2021] [Accepted: 07/18/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To investigate the performance of short-time dynamic imaging in quantifying kinetic metrics of 2-[18F]-fluoro-2-deoxy-D-glucose (18F-FDG). METHODS Dynamic total-body positron emission tomography (PET) imaging was performed in 11 healthy volunteers for 75 min. The data were divided into 30-, 45- and 75-min groups. Nonlinear regression (NLR) generated constant rates (k1 to k3) and NLR-based Ki in various organs. The Patlak method calculated parametric Ki images to generate Patlak-based Ki values. Paired samples t-test or the Wilcoxon signed-rank test compared the kinetic metrics between the groups, depending on data normality. Deming regression and Bland-Altman analysis assessed the correlation and agreement between NLR- and Patlak-based Ki. A two-sided P < 0.05 was considered statistically significant. RESULTS The 45- and 75-min groups were similar in NLR-based kinetic metrics. The relative difference ranges were as follows: k1, from 3.4% (P = 0.627) in the spleen to 57.9% (P = 0.130) in the white matter; k2, from 6.0% (P = 0.904) in the spleen to 60.7% (P = 0.235) in the left ventricle (LV) myocardium; k3, from 45.6% (P = 0.302) in the LV myocardium to 96.3% (P = 0.478) in the liver; Ki, from 14.0% (P = 0.488) in the liver to 77.8% (P = 0.067) in the kidney. Patlak-based Ki values were also similar between these groups in all organs, except the grey matter (9.6%, P = 0.029) and cerebellum (14.4%, P = 0.002). However, significant differences in kinetic metrics were found between the 30-min and 75-min groups in most organs both in NLR- and Patlak-based analyses. The NLR- and Patlak-based Ki values significantly correlated, with no bias in any of the organs. CONCLUSION Dynamic imaging using a high-sensitivity total-body PET scanner for a shorter time of 45 min could achieve relevant kinetic metrics of 18F-FDG as done by long-time imaging.
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Affiliation(s)
- Guobing Liu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Haojun Yu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Dai Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Pengcheng Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yan Hu
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hui Tan
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Yiqiu Zhang
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hongyan Yin
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Hongcheng Shi
- Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, People's Republic of China.
- Institute of Nuclear Medicine, Fudan University, Shanghai, 200032, China.
- Shanghai Institute of Medical Imaging, Shanghai, 200032, China.
- Cancer Prevention and Treatment Center, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
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Zaker N, Haddad K, Faghihi R, Arabi H, Zaidi H. Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks. Eur J Nucl Med Mol Imaging 2022; 49:4048-4063. [PMID: 35716176 PMCID: PMC9525418 DOI: 10.1007/s00259-022-05867-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 06/09/2022] [Indexed: 11/20/2022]
Abstract
Purpose This study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant (Ki) from standardized uptake value (SUV) and/or dynamic PET image series. Methods Whole-body 18F-FDG dynamic PET images of 19 subjects consisting of 13 frames or passes were employed for training a residual deep learning model with SUV and/or dynamic series as input and Ki-Patlak (slope) images as output. The training and evaluation were performed using a nine-fold cross-validation scheme. Owing to the availability of SUV images acquired 60 min post-injection (20 min total acquisition time), the data sets used for the training of the models were split into two groups: “With SUV” and “Without SUV.” For “With SUV” group, the model was first trained using only SUV images and then the passes (starting from pass 13, the last pass, to pass 9) were added to the training of the model (one pass each time). For this group, 6 models were developed with input data consisting of SUV, SUV plus pass 13, SUV plus passes 13 and 12, SUV plus passes 13 to 11, SUV plus passes 13 to 10, and SUV plus passes 13 to 9. For the “Without SUV” group, the same trend was followed, but without using the SUV images (5 models were developed with input data of passes 13 to 9). For model performance evaluation, the mean absolute error (MAE), mean error (ME), mean relative absolute error (MRAE%), relative error (RE%), mean squared error (MSE), root mean squared error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) were calculated between the predicted Ki-Patlak images by the two groups and the reference Ki-Patlak images generated through Patlak analysis using the whole acquired data sets. For specific evaluation of the method, regions of interest (ROIs) were drawn on representative organs, including the lung, liver, brain, and heart and around the identified malignant lesions. Results The MRAE%, RE%, PSNR, and SSIM indices across all patients were estimated as 7.45 ± 0.94%, 4.54 ± 2.93%, 46.89 ± 2.93, and 1.00 ± 6.7 × 10−7, respectively, for models predicted using SUV plus passes 13 to 9 as input. The predicted parameters using passes 13 to 11 as input exhibited almost similar results compared to the predicted models using SUV plus passes 13 to 9 as input. Yet, the bias was continuously reduced by adding passes until pass 11, after which the magnitude of error reduction was negligible. Hence, the predicted model with SUV plus passes 13 to 9 had the lowest quantification bias. Lesions invisible in one or both of SUV and Ki-Patlak images appeared similarly through visual inspection in the predicted images with tolerable bias. Conclusion This study concluded the feasibility of direct deep learning-based approach to estimate Ki-Patlak parametric maps without requiring the input function and with a fewer number of passes. This would lead to shorter acquisition times for WB dynamic imaging with acceptable bias and comparable lesion detectability performance. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-022-05867-w.
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Affiliation(s)
- Neda Zaker
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland.,School of Mechanical Engineering, Department of Nuclear Engineering, Shiraz University, Shiraz, Iran
| | - Kamal Haddad
- School of Mechanical Engineering, Department of Nuclear Engineering, Shiraz University, Shiraz, Iran
| | - Reza Faghihi
- School of Mechanical Engineering, Department of Nuclear Engineering, Shiraz University, Shiraz, Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. .,Geneva University Neurocenter, Geneva University, Geneva, Switzerland. .,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands. .,Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
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Karakatsanis NA, Nehmeh MH, Conti M, Bal G, González AJ, Nehmeh SA. Physical performance of adaptive axial FOV PET scanners with a sparse detector block rings or a checkerboard configuration. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6aa1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 04/26/2022] [Indexed: 11/12/2022]
Abstract
Abstract
Objective. Using Monte-Carlo simulations, we evaluated the physical performance of a hypothetical state-of-the-art clinical PET scanner with adaptive axial field-of-view (AFOV) based on the validated GATE model of the Siemens Biograph VisionTM PET/CT scanner. Approach. Vision consists of 16 compact PET rings, each consisting of 152 mini-blocks of 5 × 5 Lutetium Oxyorthosilicate crystals (3.2 × 3.2 × 20 mm3). The Vision 25.6 cm AFOV was extended by adopting (i) a sparse mini-block ring (SBR) configuration of 49.6 cm AFOV, with all mini-block rings interleaved with 16 mm axial gaps, or (ii) a sparse mini-block checkerboard (SCB) configuration of 51.2 cm AFOV, with all mini-blocks interleaved with gaps of 16 mm (transaxial) × 16 mm (axial) width in checkerboard pattern. For sparse configurations, a ‘limited’ continuous bed motion (limited-CBM) acquisition was employed to extend AFOVs by 2.9 cm. Spatial resolution, sensitivity, image quality (IQ), NECR and scatter fraction were assessed per NEMA NU2-2012. Main Results. All IQ phantom spheres were distinguishable with all configurations. SBR and SCB percent contrast recovery (% CR) and background variability (% BV) were similar (p-value > 0.05). Compared to Vision, SBR and SCB %CRs were similar (p-values > 0.05). However, SBR and SCB %BVs were deteriorated by 30% and 26% respectively (p-values < 0.05). SBR, SCB and Vision exhibited system sensitivities of 16.6, 16.8, and 15.8 kcps MBq−1, NECRs of 311 kcps @35 kBq cc−1, 266 kcps @25.8 kBq cc−1, and 260 kcps @27.8 kBq cc−1, and scatter fractions of 31.2%, 32.4%, and 32.6%, respectively. SBR and SCB exhibited a smoother sensitivity reduction and noise enhancement rate from AFOV center to its edges. SBR and SCB attained comparable spatial resolution in all directions (p-value > 0.05), yet, up to 1.5 mm worse than Vision (p-values < 0.05). Significance. The proposed sparse configurations may offer a clinically adoptable solution for cost-effective adaptive AFOV PET with either highly-sensitive or long-AFOV acquisitions.
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Wang H, Miao Y, Yu W, Zhu G, Wu T, Zhao X, Yuan G, Li B, Xu H. Improved Clinical Workflow for Whole-Body Patlak Parametric Imaging Using Two Short Dynamic Acquisitions. Front Oncol 2022; 12:822708. [PMID: 35574350 PMCID: PMC9097952 DOI: 10.3389/fonc.2022.822708] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Accepted: 03/30/2022] [Indexed: 11/13/2022] Open
Abstract
Objective We sought to explore the feasibility of shorter acquisition times using two short dynamic scans for a multiparametric PET study and the influence of quantitative performance in shortened dynamic PET. Methods Twenty-one patients underwent whole-body dynamic 18F-FDG PET/CT examinations on a PET/CT (Siemens Biograph Vision) with a total scan time of 75 min using continuous bed motion for Patlak multiparametric imaging. Two sets of Patlak multiparametric images were produced: the standard MRFDG and DVFDG images (MRFDG-std and DVFDG-std) and two short dynamic MRFDG and DVFDG images (MRFDG-tsd and DVFDG-tsd), which were generated by a 0–75 min post injection (p.i.) dynamic PET series and a 0–6 min + 60–75 min p.i. dynamic PET series, respectively. The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were obtained and compared using Passing–Bablok regression and Bland–Altman analysis. Results High correlations were obtained between MRFDG-tsd and MRFDG-std, and between DVFDG-tsd and DVFDG-std for both normal organs and all lesions (0.962 ≦ Spearman’s rho ≦ 0.982, p < 0.0001). The maximum, mean, and peak values of the standard and two short dynamic multiparametric acquisitions were also in agreement. For normal organs, the Bland–Altman plot showed that the mean bias of MRFDG-max, MRFDG-mean, and MRFDG-peak was -0.002 (95% CI: -0.032–0.027), -0.002 (95% CI: -0.026–0.023), and -0.002 (95% CI: -0.026–0.022), respectively. The mean bias of DVFDG-max, DVFDG-mean, and DVFDG-peak was -3.3 (95% CI: -24.8–18.2), -1.4 (95% CI: -12.1–9.2), and -2.3 (95% CI: -15–10.4), respectively. For lesions, the Bland–Altman plot showed that the mean bias of MRFDG-max, MRFDG-mean, and MRFDG-peak was -0.009 (95% CI: -0.056–0.038), -0.004 (95% CI: -0.039–0.031), and -0.004 (95% CI: -0.036–0.028), respectively. The mean bias of DVFDG-max, DVFDG-mean, and DVFDG-peak was -8.4 (95% CI: -42.6–25.9), -4.8 (95% CI: -20.2–10.6), and -4.0 (95% CI: -23.7–15.6), respectively. Conclusions This study demonstrates the feasibility of using two short dynamic scans that include the first 0–6 min and 60–75 min scans p.i. for Patlak multiparametric images, which can increase patient throughout for parametric analysis.
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Affiliation(s)
- Hui Wang
- Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Ying Miao
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Wenjing Yu
- Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Gan Zhu
- Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Tao Wu
- Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Xuefeng Zhao
- Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Guangjie Yuan
- Department of Nuclear Medicine, First Affiliated Hospital of Anhui Medical University, Hefei, China
| | - Biao Li
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Huiqin Xu
- Department of Nuclear Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Clinical Evaluation of Nuclear Imaging Agents in Breast Cancer. Cancers (Basel) 2022; 14:cancers14092103. [PMID: 35565232 PMCID: PMC9101155 DOI: 10.3390/cancers14092103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/16/2022] [Accepted: 04/20/2022] [Indexed: 12/07/2022] Open
Abstract
Precision medicine is the customization of therapy for specific groups of patients using genetic or molecular profiling. Noninvasive imaging is one strategy for molecular profiling and is the focus of this review. The combination of imaging and therapy for precision medicine gave rise to the field of theranostics. In breast cancer, the detection and quantification of therapeutic targets can help assess their heterogeneity, especially in metastatic disease, and may help guide clinical decisions for targeted treatments. Positron emission tomography (PET) or single-photon emission tomography (SPECT) imaging has the potential to play an important role in the molecular profiling of therapeutic targets in vivo for the selection of patients who are likely to respond to corresponding targeted therapy. In this review, we discuss the state-of-the-art nuclear imaging agents in clinical research for breast cancer. We reviewed 17 clinical studies on PET or SPECT agents that target 10 different receptors in breast cancer. We also discuss the limitations of the study designs and of the imaging agents in these studies. Finally, we offer our perspective on which imaging agents have the highest potential to be used in clinical practice in the future.
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Pantel AR, Viswanath V, Muzi M, Doot RK, Mankoff DA. Principles of Tracer Kinetic Analysis in Oncology, Part I: Principles and Overview of Methodology. J Nucl Med 2022; 63:342-352. [PMID: 35232879 DOI: 10.2967/jnumed.121.263518] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 01/12/2022] [Indexed: 12/12/2022] Open
Abstract
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.
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Affiliation(s)
- Austin R Pantel
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Varsha Viswanath
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - Mark Muzi
- Department of Radiology, University of Washington, Seattle, Washington
| | - Robert K Doot
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
| | - David A Mankoff
- Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania; and
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Ivanidze J, Roytman M, Skafida M, Kim S, Glynn S, Osborne JR, Pannullo SC, Nehmeh S, Ramakrishna R, Schwartz TH, Knisely JPS, Lin E, Karakatsanis NA. Dynamic 68Ga-DOTATATE PET/MRI in the Diagnosis and Management of Intracranial Meningiomas. Radiol Imaging Cancer 2022; 4:e210067. [PMID: 35275019 DOI: 10.1148/rycan.210067] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
Purpose To evaluate dynamic gallium 68 (68Ga) tetraazacyclododecane tetraacetic acid octreotate (DOTATATE) brain PET/MRI as an adjunct modality in meningioma, enabling multiparametric standardized uptake value (SUV) and Patlak net binding rate constant (Ki) imaging, and to optimize static acquisition period. Materials and Methods In this prospective study (ClinicalTrials.gov no. NCT04081701, DOMINO-START), 68Ga-DOTATATE PET/MRI-derived time-activity curves (TACs) were measured in 84 volumes of interest in 19 participants (mean age, 63 years; range, 36-89 years; 13 women; 2019-2021) with meningiomas. Region- and voxel-specific Ki were determined using Patlak analysis with a validated population-based reference tissue TAC model built from an independent data set of nine participants. Mean and maximum absolute and relative-to-superior-sagittal-sinus SUVs were extracted from the entire 50 minutes (SUV50) and last 10 minutes (SUV10) of acquisition. SUV versus Ki Spearman correlation, SUV and Ki meningioma versus posttreatment-change Mann-Whitney U tests, and SUV50 versus SUV10 Wilcoxon matched-pairs signed rank tests were performed. Results Absolute and relative maximum SUV50 demonstrated a strong positive correlation with Patlak Ki in meningioma (r = 0.82, P < .001 and r = 0.85, P < .001, respectively) and posttreatment-change lesions (r = 0.88, P = .007 and r = 0.83, P = .02, respectively). Patlak Ki images yielded higher lesion contrast by mitigating nonspecific background signal. All SUV50 and SUV10 metrics differed between meningioma and posttreatment-change regions (P < .001). Within the meningioma group, SUV10 attained higher mean scores than SUV50 (P < .001). Conclusion Combined SUV and Patlak K i 68Ga-DOTATATE PET/MRI enabled multiparametric evaluation of meningioma, offering the potential to enhance lesion contrast with Ki imaging and optimize the SUV measurement postinjection window. Keywords: Molecular Imaging-Clinical Translation, Neuro-Oncology, PET/MRI, Dynamic, Patlak ClinicalTrials.gov registration no. NCT04081701 © RSNA, 2022.
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Affiliation(s)
- Jana Ivanidze
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Michelle Roytman
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Myrto Skafida
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Sean Kim
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Shannon Glynn
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Joseph R Osborne
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Susan C Pannullo
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Sadek Nehmeh
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Rohan Ramakrishna
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Theodore H Schwartz
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Jonathan P S Knisely
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Eaton Lin
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
| | - Nicolas A Karakatsanis
- From the Departments of Radiology (J.I., M.R., M.S., J.R.O., S.N., E.L., N.A.K.), Neurologic Surgery (S.C.P., R.R., T.H.S.), and Radiation Oncology (J.P.S.K.), NewYork-Presbyterian/Weill Cornell Medical Center, 515 E 71st St, S-120, New York, NY 10021; Weill Cornell Medical College, New York, NY (S.K., S.G.); and Department of Biomedical Engineering, Cornell University, Ithaca, NY (S.C.P.)
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Katal S, Eibschutz LS, Saboury B, Gholamrezanezhad A, Alavi A. Advantages and Applications of Total-Body PET Scanning. Diagnostics (Basel) 2022; 12:diagnostics12020426. [PMID: 35204517 PMCID: PMC8871405 DOI: 10.3390/diagnostics12020426] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 02/04/2023] Open
Abstract
Recent studies have focused on the development of total-body PET scanning in a variety of fields such as clinical oncology, cardiology, personalized medicine, drug development and toxicology, and inflammatory/infectious disease. Given its ultrahigh detection sensitivity, enhanced temporal resolution, and long scan range (1940 mm), total-body PET scanning can not only image faster than traditional techniques with less administered radioactivity but also perform total-body dynamic acquisition at a longer delayed time point. These unique characteristics create several opportunities to improve image quality and can provide a deeper understanding regarding disease detection, diagnosis, staging/restaging, response to treatment, and prognostication. By reviewing the advantages of total-body PET scanning and discussing the potential clinical applications for this innovative technology, we can address specific issues encountered in routine clinical practice and ultimately improve patient care.
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Affiliation(s)
- Sanaz Katal
- Independent Researcher, Melbourne 3000, Australia;
| | - Liesl S. Eibschutz
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90007, USA; (L.S.E.); (A.G.)
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health (NIH), Bethesda, MD 20892, USA;
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90007, USA; (L.S.E.); (A.G.)
| | - Abass Alavi
- Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, PA 19104, USA
- Correspondence:
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Dynamic whole-body FDG-PET imaging for oncology studies. Clin Transl Imaging 2022. [DOI: 10.1007/s40336-022-00479-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Abstract
Introduction
Recent PET/CT systems have improved sensitivity and spatial resolution by smaller PET detectors and improved reconstruction software. In addition, continuous-bed-motion mode is now available in some PET systems for whole-body PET imaging. In this review, we describe the advantages of dynamic whole-body FDG-PET in oncology studies.
Methods
PET–CT imaging was obtained at 60 min after FDG administration. Dynamic whole-body imaging with continuous bed motion in 3 min each with flow motion was obtained over 400 oncology cases. For routine image analysis, these dynamic phases (usually four phases) were summed as early FDG imaging. The image quality of each serial dynamic imaging was visually evaluated. In addition, changes in FDG uptake were analyzed in consecutive dynamic imaging and also in early delayed (90 min after FDG administration) time point imaging (dual-time-point imaging; DTPI). Image interpretation was performed by consensus of two nuclear medicine physicians.
Result
All consecutive dynamic whole-body PET images of 3 min duration had acceptable image quality. Many of the areas with physiologically high FDG uptake had altered uptake on serial images. On the other hand, most of the benign and malignant lesions did not show visual changes on serial images. In the study of 60 patients with suspected colorectal cancer, unchanged uptake was noted in almost all regions with pathologically proved FDG uptake, indicating high sensitivity with high negative predictive value on both serial dynamic imaging and on DTPI. We proposed another application of serial dynamic imaging for minimizing motion artifacts for patients who may be likely to move during PET studies.
Discussion
Dynamic whole-body imaging has several advantages over the static imaging. Serial assessment of changes in FDG uptake over a short period of time is useful for distinguishing pathological from physiological uptake, especially in the abdominal regions. These dynamic PET studies may minimize the need for DPTI. In addition, continuous dynamic imaging has the potential to reduce motion artifacts in patients who are likely to move during PET imaging. Furthermore, kinetic analysis of the FDG distribution in tumor areas has a potential for precise tissue characterization.
Conclusion
Dynamic whole-body FDG-PET imaging permits assessment of serial FDG uptake change which is particularly useful for differentiation of pathological uptake from physiological uptake with high diagnostic accuracy. This imaging can be applied for minimizing motion artifacts. Wide clinical applications of such serial, dynamic whole-body PET imaging is expected in oncological studies in the near future.
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Sari H, Mingels C, Alberts I, Hu J, Buesser D, Shah V, Schepers R, Caluori P, Panin V, Conti M, Afshar-Oromieh A, Shi K, Eriksson L, Rominger A, Cumming P. First results on kinetic modelling and parametric imaging of dynamic 18F-FDG datasets from a long axial FOV PET scanner in oncological patients. Eur J Nucl Med Mol Imaging 2022; 49:1997-2009. [PMID: 34981164 DOI: 10.1007/s00259-021-05623-6] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 11/15/2021] [Indexed: 11/26/2022]
Abstract
PURPOSE To investigate the kinetics of 18F-fluorodeoxyglucose (18F-FDG) by positron emission tomography (PET) in multiple organs and test the feasibility of total-body parametric imaging using an image-derived input function (IDIF). METHODS Twenty-four oncological patients underwent dynamic 18F-FDG scans lasting 65 min using a long axial FOV (LAFOV) PET/CT system. Time activity curves (TAC) were extracted from semi-automated segmentations of multiple organs, cerebral grey and white matter, and from vascular structures. The tissue and tumor lesion TACs were fitted using an irreversible two-tissue compartment (2TC) and a Patlak model. Parametric images were also generated using direct and indirect Patlak methods and their performances were evaluated. RESULTS We report estimates of kinetic parameters and metabolic rate of glucose consumption (MRFDG) for different organs and tumor lesions. In some organs, there were significant differences between MRFDG values estimated using 2TC and Patlak models. No statistically significant difference was seen between MRFDG values estimated using 2TC and Patlak methods in tumor lesions (paired t-test, P = 0.65). Parametric imaging showed that net influx (Ki) images generated using direct and indirect Patlak methods had superior tumor-to-background ratio (TBR) to standard uptake value (SUV) images (3.1- and 3.0-fold mean increases in TBRmean, respectively). Influx images generated using the direct Patlak method had twofold higher contrast-to-noise ratio in tumor lesions compared to images generated using the indirect Patlak method. CONCLUSION We performed pharmacokinetic modelling of multiple organs using linear and non-linear models using dynamic total-body 18F-FDG images. Although parametric images did not reveal more tumors than SUV images, the results confirmed that parametric imaging furnishes improved tumor contrast. We thus demonstrate the feasibility of total-body kinetic modelling and parametric imaging in basic research and oncological studies. LAFOV PET can enhance dynamic imaging capabilities by providing high sensitivity parametric images and allowing total-body pharmacokinetic analysis.
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Affiliation(s)
- Hasan Sari
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland.
| | - Clemens Mingels
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Ian Alberts
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Jicun Hu
- Siemens Medical Solutions, USA Inc., Knoxville, TN, USA
| | - Dorothee Buesser
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Vijay Shah
- Siemens Medical Solutions, USA Inc., Knoxville, TN, USA
| | - Robin Schepers
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Patrik Caluori
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | | | | | - Ali Afshar-Oromieh
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Lars Eriksson
- Siemens Medical Solutions, USA Inc., Knoxville, TN, USA
- Department of Oncology and Pathology, Medical Radiation Physics, Karolinska Institutet, Stockholm, Sweden
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Paul Cumming
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
- School of Psychology and Counselling, Queensland University of Technology, Brisbane, Australia
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