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Reed MB, Handschuh PA, Schmidt C, Murgaš M, Gomola D, Milz C, Klug S, Eggerstorfer B, Aichinger L, Godbersen GM, Nics L, Traub-Weidinger T, Hacker M, Lanzenberger R, Hahn A. Validation of cardiac image-derived input functions for functional PET quantification. Eur J Nucl Med Mol Imaging 2024; 51:2625-2637. [PMID: 38676734 PMCID: PMC11224076 DOI: 10.1007/s00259-024-06716-8] [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: 01/15/2024] [Accepted: 04/14/2024] [Indexed: 04/29/2024]
Abstract
PURPOSE Functional PET (fPET) is a novel technique for studying dynamic changes in brain metabolism and neurotransmitter signaling. Accurate quantification of fPET relies on measuring the arterial input function (AIF), traditionally achieved through invasive arterial blood sampling. While non-invasive image-derived input functions (IDIF) offer an alternative, they suffer from limited spatial resolution and field of view. To overcome these issues, we developed and validated a scan protocol for brain fPET utilizing cardiac IDIF, aiming to mitigate known IDIF limitations. METHODS Twenty healthy individuals underwent fPET/MR scans using [18F]FDG or 6-[18F]FDOPA, utilizing bed motion shuttling to capture cardiac IDIF and brain task-induced changes. Arterial and venous blood sampling was used to validate IDIFs. Participants performed a monetary incentive delay task. IDIFs from various blood pools and composites estimated from a linear fit over all IDIF blood pools (3VOI) and further supplemented with venous blood samples (3VOIVB) were compared to the AIF. Quantitative task-specific images from both tracers were compared to assess the performance of each input function to the gold standard. RESULTS For both radiotracer cohorts, moderate to high agreement (r: 0.60-0.89) between IDIFs and AIF for both radiotracer cohorts was observed, with further improvement (r: 0.87-0.93) for composite IDIFs (3VOI and 3VOIVB). Both methods showed equivalent quantitative values and high agreement (r: 0.975-0.998) with AIF-derived measurements. CONCLUSION Our proposed protocol enables accurate non-invasive estimation of the input function with full quantification of task-specific changes, addressing the limitations of IDIF for brain imaging by sampling larger blood pools over the thorax. These advancements increase applicability to any PET scanner and clinical research setting by reducing experimental complexity and increasing patient comfort.
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Affiliation(s)
- Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Patricia Anna Handschuh
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Clemens Schmidt
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Matej Murgaš
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - David Gomola
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Christian Milz
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Sebastian Klug
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Benjamin Eggerstorfer
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Lisa Aichinger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Godber Mathis Godbersen
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Lukas Nics
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria.
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria.
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health (C3NMH), Medical University of Vienna, Vienna, Austria
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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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Gu F, Wu Q. Quantitation of dynamic total-body PET imaging: recent developments and future perspectives. Eur J Nucl Med Mol Imaging 2023; 50:3538-3557. [PMID: 37460750 PMCID: PMC10547641 DOI: 10.1007/s00259-023-06299-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 06/05/2023] [Indexed: 10/04/2023]
Abstract
BACKGROUND Positron emission tomography (PET) scanning is an important diagnostic imaging technique used in disease diagnosis, therapy planning, treatment monitoring, and medical research. The standardized uptake value (SUV) obtained at a single time frame has been widely employed in clinical practice. Well beyond this simple static measure, more detailed metabolic information can be recovered from dynamic PET scans, followed by the recovery of arterial input function and application of appropriate tracer kinetic models. Many efforts have been devoted to the development of quantitative techniques over the last couple of decades. CHALLENGES The advent of new-generation total-body PET scanners characterized by ultra-high sensitivity and long axial field of view, i.e., uEXPLORER (United Imaging Healthcare), PennPET Explorer (University of Pennsylvania), and Biograph Vision Quadra (Siemens Healthineers), further stimulates valuable inspiration to derive kinetics for multiple organs simultaneously. But some emerging issues also need to be addressed, e.g., the large-scale data size and organ-specific physiology. The direct implementation of classical methods for total-body PET imaging without proper validation may lead to less accurate results. CONCLUSIONS In this contribution, the published dynamic total-body PET datasets are outlined, and several challenges/opportunities for quantitation of such types of studies are presented. An overview of the basic equation, calculation of input function (based on blood sampling, image, population or mathematical model), and kinetic analysis encompassing parametric (compartmental model, graphical plot and spectral analysis) and non-parametric (B-spline and piece-wise basis elements) approaches is provided. The discussion mainly focuses on the feasibilities, recent developments, and future perspectives of these methodologies for a diverse-tissue environment.
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Affiliation(s)
- Fengyun Gu
- School of Mathematics and Physics, North China Electric Power University, 102206, Beijing, China.
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland.
| | - Qi Wu
- School of Mathematical Sciences, University College Cork, T12XF62, Cork, Ireland
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Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Long axial field of view (LAFOV) PET-CT: implementation in static and dynamic oncological studies. Eur J Nucl Med Mol Imaging 2023; 50:3354-3362. [PMID: 37079129 PMCID: PMC10541341 DOI: 10.1007/s00259-023-06222-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 04/02/2023] [Indexed: 04/21/2023]
Abstract
Long axial field of view (LAFOV) PET-CT scanners have been recently developed and are already in clinical use in few centers worldwide. Although still limited, the hitherto acquired experience with these novel systems highlights an increased sensitivity as their main advantage, which results in an increased lesion detectability. This attribute, alternatively, allows a reduction in PET acquisition time and/or administered radiotracer dose, while it renders delayed scanning of satisfying diagnostic accuracy possible. Another potential advantage of the new generation scanners is CT-less approaches for attenuation correction with the impact of marked reduction of radiation exposure, which may in turn lead to greater acceptance of longitudinal PET studies in the oncological setting. Further, the possibility for the first time of whole-body dynamic imaging, improved compartment modeling, and whole-body parametric imaging represent unique characteristics of the LAFOV PET-CT scanners. On the other hand, the advent of the novel LAFOV scanners is linked to specific challenges, such as the high purchase price and issues related to logistics and their optimal operation in a nuclear medicine department. Moreover, with regard to its research applications in oncology, the full potential of the new scanners can only be reached if different radiopharmaceuticals, both short and long-lived ones, as well as novel tracers, are available for use, which would, in turn, require the appropriate infrastructure in the area of radiochemistry. Although the novel LAFOV scanners are not yet widely used, this development represents an important step in the evolution of molecular imaging. This review presents the advantages and challenges of LAFOV PET-CT imaging for oncological applications with respect to static and dynamic acquisition protocols as well as to new tracers, while it provides an overview of the literature in the field.
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Affiliation(s)
- Antonia Dimitrakopoulou-Strauss
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany.
| | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
| | - Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Im Neuenheimer Feld 280, D-69120, Heidelberg, Germany
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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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Reed MB, Godbersen GM, Vraka C, Rausch I, Ponce de León M, Popper V, Geist B, Nics L, Komorowski A, Karanikas G, Beyer T, Traub-Weidinger T, Hahn A, Langsteger W, Hacker M, Lanzenberger R. Comparison of cardiac image-derived input functions for quantitative whole body [ 18F]FDG imaging with arterial blood sampling. Front Physiol 2023; 14:1074052. [PMID: 37035658 PMCID: PMC10073457 DOI: 10.3389/fphys.2023.1074052] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Introduction: Dynamic positron emission tomography (PET) and the application of kinetic models can provide important quantitative information based on its temporal information. This however requires arterial blood sampling, which can be challenging to acquire. Nowadays, state-of-the-art PET/CT systems offer fully automated, whole-body (WB) kinetic modelling protocols using image-derived input functions (IDIF) to replace arterial blood sampling. Here, we compared the validity of an automatic WB kinetic model protocol to the reference standard arterial input function (AIF) for both clinical and research settings. Methods: Sixteen healthy participants underwent dynamic WB [18F]FDG scans using a continuous bed motion PET/CT system with simultaneous arterial blood sampling. Multiple processing pipelines that included automatic and manually generated IDIFs derived from the aorta and left ventricle, with and without motion correction were compared to the AIF. Subsequently generated quantitative images of glucose metabolism were compared to evaluate performance of the different input functions. Results: We observed moderate to high correlations between IDIFs and the AIF regarding area under the curve (r = 0.49-0.89) as well as for the cerebral metabolic rate of glucose (CMRGlu) (r = 0.68-0.95). Manual placing of IDIFs and motion correction further improved their similarity to the AIF. Discussion: In general, the automatic vendor protocol is a feasible approach for the quantification of CMRGlu for both, clinical and research settings where expertise or time is not available. However, we advise on a rigorous inspection of the placement of the volume of interest, the resulting IDIF, and the quantitative values to ensure valid interpretations. In protocols requiring longer scan times or where cohorts are prone to involuntary movement, manual IDIF definition with additional motion correction is recommended, as this has greater accuracy and reliability.
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Affiliation(s)
- Murray Bruce Reed
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | | | - Chrysoula Vraka
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | | | - Valentin Popper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Barbara Geist
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Lukas Nics
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Arkadiusz Komorowski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Georgios Karanikas
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Werner Langsteger
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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van Sluis J, van Snick JH, Brouwers AH, Noordzij W, Dierckx RAJO, Borra RJH, Lammertsma AA, Glaudemans AWJM, Slart RHJA, Yaqub M, Tsoumpas C, Boellaard R. Shortened duration whole body 18F-FDG PET Patlak imaging on the Biograph Vision Quadra PET/CT using a population-averaged input function. EJNMMI Phys 2022; 9:74. [PMID: 36308568 PMCID: PMC9618000 DOI: 10.1186/s40658-022-00504-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022] Open
Abstract
Background Excellent performance characteristics of the Vision Quadra PET/CT, e.g. a substantial increase in sensitivity, allow for precise measurements of image-derived input functions (IDIF) and tissue time activity curves. Previously we have proposed a method for a reduced 30 min (as opposed to 60 min) whole body 18F-FDG Patlak PET imaging procedure using a previously published population-averaged input function (PIF) scaled to IDIF values at 30–60 min post-injection (p.i.). The aim of the present study was to apply this method using the Vision Quadra PET/CT, including the use of a PIF to allow for shortened scan durations. Methods Twelve patients with suspected lung malignancy were included and received a weight-based injection of 18F-FDG. Patients underwent a 65-min dynamic PET acquisition which were reconstructed using European Association of Nuclear Medicine Research Ltd. (EARL) standards 2 reconstruction settings. A volume of interest (VOI) was placed in the ascending aorta (AA) to obtain the IDIF. An external PIF was scaled to IDIF values at 30–60, 40–60, and 50–60 min p.i., respectively, and parametric 18F-FDG influx rate constant (Ki) images were generated using a t* of 30, 40 or 50 min, respectively. Herein, tumour lesions as well as healthy tissues, i.e. liver, muscle tissue, spleen and grey matter, were segmented. Results Good agreement between the IDIF and corresponding PIF scaled to 30–60 min p.i. and 40–60 min p.i. was obtained with 7.38% deviation in Ki. Bland–Altman plots showed excellent agreement in Ki obtained using the PIF scaled to the IDIF at 30–60 min p.i. and at 40–60 min p.i. as all data points were within the limits of agreement (LOA) (− 0.004–0.002, bias: − 0.001); for the 50–60 min p.i. Ki, all except one data point fell in between the LOA (− 0.021–0.012, bias: − 0.005). Conclusions Parametric whole body 18F-FDG Patlak Ki images can be generated non-invasively on a Vision Quadra PET/CT system. In addition, using a scaled PIF allows for a substantial (factor 2 to 3) reduction in scan time without substantial loss of accuracy (7.38% bias) and precision (image quality and noise interference). Supplementary Information The online version contains supplementary material available at 10.1186/s40658-022-00504-9.
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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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Chen Z, Cheng Z, Duan Y, Zhang Q, Zhang N, Gu F, Wang Y, Zhou Y, Wang H, Liang D, Zheng H, Hu Z. FDG PET Scan Durations via Effective Data Processing. Med Phys 2022; 50:2121-2134. [PMID: 35950784 DOI: 10.1002/mp.15893] [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: 01/10/2022] [Revised: 07/20/2022] [Accepted: 07/25/2022] [Indexed: 11/08/2022] Open
Abstract
BACKGROUND Total-body dynamic PET (dPET) imaging using 18 F-fluorodeoxyglucose (18 F-FDG) has received widespread attention in clinical oncology. However, the conventionally required scan duration of approximately 1 hour seriously limits the application and promotion of this imaging technique. In this study, we investigated the possibility and feasibility of shortening the total-body dynamic scan duration to 30 min post-injection (PI) with the help of a novel Patlak data processing algorithm for accurate Ki estimations of tumor lesions. METHODS Total-body dPET images acquired by uEXPLORER (United Imaging Healthcare Inc.) using 18 F-FDG of 15 patients with different tumor types were analyzed in this study. Dynamic images were reconstructed into 25 frames with a specific temporal dividing protocol for the scan data acquired 1 hour PI. Patlak analysis-based Ki parametric imaging was conducted based on the imaging data corresponding to the first 30 min PI, during which a Patlak data processing method based on cubit Hermite interpolation (THI) was applied. The resultant Ki images acquired by 30-min dynamic PET data and the standard 1-hour Ki images were compared in terms of visual imaging effect, region signal-to-noise ratio (SNR), and Ki estimation accuracy to evaluate the performance of the proposed Ki imaging method with a shortened scan duration. RESULTS With the help of Patlak data processing, acceptable Ki parametric images were obtained from dynamic PET data acquired with a scan duration of 30 min PI. Compared with Ki images obtained from unprocessed Patlak data, the resulting images from the proposed method performed better in terms of noise reduction. Moreover, Bland-Altman (BA) plot and Person correlation coefficient (PPC) analysis showed that that 30-min Ki images obtained from the processed Patlak data had higher accuracy for tumor lesions. CONCLUSION Satisfactory Ki parametric images with high tumor accuracy can be acquired from dynamic imaging data corresponding to the first 30 min PI. Patlak data processing can help achieve higher Ki imaging quality and higher accuracy regarding tumor lesion Ki values. Clinically, it is possible to shorten the dynamic scan duration of 18 F-FDG PET to 30 min to acquire an accurate tumor Ki and further effective tumor detection with uEXPLORER scanners. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Zixiang Chen
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,University of Chinese Academy of Sciences
| | - Zhaoping Cheng
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital
| | - Yanhua Duan
- Department of PET/CT, The First Affiliated Hospital of Shandong First Medical University, Shandong Provincial Qianfoshan Hospital
| | - Qiyang Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,National Innovation Center for High Performance Medical Devices
| | - Na Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,United Imaging Research Institute of Innovative Medical Equipment
| | - Fengyun Gu
- Central Research Institute, United Imaging Healthcare Group
| | - Ying Wang
- Central Research Institute, United Imaging Healthcare Group
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group
| | - Haining Wang
- United Imaging Research Institute of Innovative Medical Equipment
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,United Imaging Research Institute of Innovative Medical Equipment
| | - Hairong Zheng
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Science.,United Imaging Research Institute of Innovative Medical Equipment
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10
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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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11
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Wu Y, Feng T, Shen Y, Fu F, Meng N, Li X, Xu T, Sun T, Gu F, Wu Q, Zhou Y, Han H, Bai Y, Wang M. Total-body parametric imaging using the patlak model: Feasibility of reduced scan time. Med Phys 2022; 49:4529-4539. [PMID: 35394071 DOI: 10.1002/mp.15647] [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: 07/22/2021] [Revised: 01/17/2022] [Accepted: 03/19/2022] [Indexed: 11/08/2022] Open
Abstract
PURPOSE This study explored the feasibility of reducing the scan time of Patlak parametric imaging on the uEXPLORER. METHODS A total of 65 patients (27 females and 38 males, age 56.1±10.4) were recruited in this study. 18 F-FDG was injected and its dose was adjusted by body weight (4.07 MBq / kg).Total-body dynamic scanning was performed on the uEXPLORER Total-Body PET/CT scanner with a total scan time of 60 minutes from the injection. The image derived input function (IDIF) was obtained from the aortic arch. The voxelwise Patlak analysis was applied to generate the Ki images designated as GIDIF with different acquisition times (20-60, 30-60, 40-60, and 44-60 min). The population-based input function (PBIF) was constructed from the mean value of the IDIF from the population, and Ki images designated as GPBIF were generated using the PBIF. Non-localmeans (NLM) denoising was applied to the generated images to get two extra groups of (NLM-designated) images: GIDIF+NLM and GPBIF+NLM . Two radiologists evaluated the overall image quality, noise, and lesion detectability of the Ki images from different groups. The 20-60 min scans in GIDIF were selected as the gold standard for each patient. We determined that image quality is at sufficient level if all the lesions can be recognized and meet the clinical criteria. Ki values in muscle and lesion were compared across different groups to evaluate the quantitative accuracy. RESULTS The overall image quality, image noise, and lesion conspicuity were significantly better in long time series than short time series in all 4 groups (all p<0.001). The Ki images in the GIDIF and GPBIF groups generated from 30-min scans showed diagnostic value equivalent to the 40-min scans of GIDIF . While the image quality of the 16-min scans was poor, all lesions could still be detected. No significant difference was found between Ki values estimated with GIDIF and GPBIF in muscle and lesion regions (all p>0.5). After applying the NLM filter, The coefficient of variation could be reduced on the order of [1%, 15%, 19%,37%] and [110%, 125%, 94%, 69%] with four acquisition time schemes for lesion and muscle. The reduction percentage did not have a substantial difference in IDIF and PBIF group. The Ki images in the GIDIF+NLM and GPBIF+NLM groups generated from the 20-min acquisitions showed acceptable quality. All lesions could be found on the NLM processed images of the 16-min scans. No significant difference was found between Ki values produced with GIDIF+NLM and GPBIF+NLM in muscle and lesion regions(all p>0.7). CONCLUSIONS The Ki images generated by the PBIF-based Patlak model using a 20-min dynamic scan with the NLM filter achieved a similar diagnostic efficiency to images with GIDIF from 40-min dynamic data, and there is no significant difference between Ki images generated using IDIF or PBIF (p>0.5). This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Tao Feng
- UIH America Inc., Houston, TX, 77054, USA
| | - Yu Shen
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Xiaochen Li
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Tianyi Xu
- United Imaging Healthcare Group, Shanghai, 201807, China
| | - Tao Sun
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Fengyun Gu
- United Imaging Healthcare Group, Shanghai, 201807, China.,Department of Statistics, School of Mathematical Sciences, University College Cork, Cork, T12XF62, Ireland
| | - Qi Wu
- United Imaging Healthcare Group, Shanghai, 201807, China.,Department of Statistics, School of Mathematical Sciences, University College Cork, Cork, T12XF62, Ireland
| | - Yun Zhou
- United Imaging Healthcare Group, Shanghai, 201807, China
| | - Hui Han
- Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Yan Bai
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, Henan, 450003, China
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Abstract
Abstract
Theragnostics in nuclear medicine constitute an essential element of precision medicine. This notion integrates radionuclide diagnostics procedures and radionuclide therapies using appropriate radiopharmaceutics and treatment targeting specific biological pathways or receptors. The term theragnostics should also include another aspect of treatment: not only whether a given radioisotopic drug can be used, but also in what dose it ought to be used. Theragnostic procedures also allow predicting the effects of treatment based on the assessment of specific receptor density or the metabolic profile of neoplastic cells. The future of theragnostics depends not only on the use of new radiopharmaceuticals, but also on new gamma cameras. Modern theragnostics already require unambiguous pharmacokinetic and pharmacodynamic measurements based on absolute values. Only dynamic studies provide such a possibility. The introduction of the dynamic total-body PET-CT will enable this type of measurements characterizing metabolic processes and receptor expression on the basis of Patlak plot.
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Affiliation(s)
- Leszek Królicki
- Department of Nuclear Medicine , Medical University of Warsaw , Warszawa , Poland
| | - Jolanta Kunikowska
- Department of Nuclear Medicine , Medical University of Warsaw , Warszawa , Poland
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13
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Dimitrakopoulou-Strauss A, Pan L, Sachpekidis C. Parametric Imaging With Dynamic PET for Oncological Applications: Protocols, Interpretation, Current Applications and Limitations for Clinical Use. Semin Nucl Med 2021; 52:312-329. [PMID: 34809877 DOI: 10.1053/j.semnuclmed.2021.10.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Nuclear medicine imaging modalities, and in particular positron emission tomography (PET), provide functional images that demonstrate the mean radioactivity distribution at a defined point in time. With the help of mathematical model's, it is possible to depict isolated parameters of the radiotracers' pharmacokinetics and to visualize them. These so called parametric images add a new dimension to the existing conventional PET images and provide more detailed information about the tracer distribution over time and space. Prerequisite for the calculation of parametric images, which reflect specific pharmacokinetic parameters, is the dynamic PET (dPET) data acquisition. Hitherto, PET parametric imaging has mainly found use for research purposes. However, it has not been yet implemented into clinical routine, since it is more time-consuming, it requires a complicated analysis and still lacks a clear benefit over conventional PET imaging. However, the recent introduction of new PET-CT scanners with an ultralong field of view, which allow a faster data acquisition and are associated with higher sensitivity, as well as the development of more sophisticated evaluation software packages will probably lead to a renaissance of dPET and parametric maps even of the whole body. The implementation of dPET imaging in daily routine with appropriate acquisition protocols, as well as the calculation, interpretation and potential clinical applications of parametric images will be discussed in this review article.
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Affiliation(s)
| | - Leyun Pan
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany
| | - Christos Sachpekidis
- Clinical Cooperation Unit Nuclear Medicine, German Cancer Research Center, Heidelberg, Germany
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14
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Wu Y, Feng T, Zhao Y, Xu T, Fu F, Huang Z, Meng N, Li H, Shao F, Wang M. Whole-body Parametric Imaging of FDG PET using uEXPLORER with Reduced Scan Time. J Nucl Med 2021; 63:622-628. [PMID: 34385335 PMCID: PMC8973287 DOI: 10.2967/jnumed.120.261651] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 06/14/2021] [Indexed: 11/25/2022] Open
Abstract
Parametric imaging of the net influx rate (Ki) in 18F-FDG PET has been shown to provide improved quantification and specificity for cancer detection compared with SUV imaging. Current methods of generating parametric images usually require a long dynamic scanning time. With the recently developed uEXPLORER scanner, a dramatic increase in sensitivity has reduced the noise in dynamic imaging, making it more robust to use a nonlinear estimation method and flexible protocols. In this work, we explored 2 new possible protocols besides the standard 60-min one for the possibility of reducing scanning time for Ki imaging. Methods: The gold standard protocol (protocol 1) was conventional dynamic scanning with a 60-min scanning time. The first proposed protocol (protocol 2) included 2 scanning periods: 0–4 min and 54–60 min after injection. The second proposed protocol (protocol 3) consisted of a single scanning period from 50 to 60 min after injection, with a second injection applied at 56 min. The 2 new protocols were simulated from the 60-min standard scans. A hybrid input function combining the population-based input function and the image-derived input function (IDIF) was used. The results were also compared with the IDIF acquired from protocol 1. A previously developed maximum-likelihood approach was used to estimate the Ki images. In total, 7 cancer patients imaged using the uEXPLORER scanner were enrolled in this study. Lesions were identified from the patient data, and the lesion Ki values were compared among the different protocols. Results: The acquired hybrid input function was comparable in shape to the IDIF for each patient. The average difference in area under the curve was about 3%, suggesting good quantitative accuracy. The visual difference between the Ki images generated using IDIF and those generated using the hybrid input function was also minimal. The acquired Ki images using different protocols were visually comparable. The average Ki difference in the lesions was 2.8% ± 2.1% for protocol 2 and 1% ± 2.2% for protocol 3. Conclusion: The results suggest that it is possible to acquire Ki images using the nonlinear estimation approach with a much-reduced scanning time. Between the 2 new protocols, the protocol with dual injection shows the greatest promise in terms of practicality.
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Affiliation(s)
- Yaping Wu
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | | | | | | | - Fangfang Fu
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | - Zhun Huang
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | - Nan Meng
- Department of Medical Imaging, Henan Provincial People's Hospital, China
| | | | - Fengmin Shao
- Department of Medical Imaging, Henan Provincial People's Hospital, China, China
| | - Meiyun Wang
- Department of Medical Imaging, Henan Provincial People's Hospital, China
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15
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Feng T, Yao S, Xi C, Zhao Y, Wang R, Wu S, Li C, Xu B. Deep learning-based image reconstruction for TOF PET with DIRECT data partitioning format. Phys Med Biol 2021; 66. [PMID: 34256356 DOI: 10.1088/1361-6560/ac13fe] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 07/13/2021] [Indexed: 11/12/2022]
Abstract
Conventional positron emission tomography (PET) image reconstruction is achieved by the statistical iterative method. Deep learning provides another opportunity for speeding up the image reconstruction process. However, conventional deep learning-based image reconstruction requires a fully connected network for learning the Radon transform. The use of fully connected networks greatly complicated the network and increased hardware cost. In this study, we proposed a novel deep learning-based image reconstruction method by utilizing the DIRECT data partitioning method. The U-net structure with only convolutional layers was used in our approach. Patch-based model training and testing were used to achieve 3D reconstructions within current hardware limitations. Time-of-flight (TOF)-histoimages were first generated from the listmode data to replace conventional sinograms. Different projection angles were used as different channels in the input. A total of 15 patient data were used in this study. For each patient, the dynamic whole-body scanning protocol was used to expand the training dataset and a total of 372 separate scans were included. The leave-one-patient-out validation method was used. Two separate studies were carried out. In the first study, the measured TOF-histoimages were directly used for model training and testing, to study the performance of the method in real-world applications. In the second study, TOF-histoimages were simulated from already reconstructed images to exclude the scatters, randoms, attenuation-activity mismatch effects. This study was used to evaluate the optimal performance when all other corrections are ideal. Volumes of interests were placed in the liver and lesion region to study image noise and lesion quantitations. The reconstructed images using the proposed deep learning method showed similar image quality when compared with the conventional expectation-maximization approach. A minimal difference was observed when the simulated TOF-histoimages were used as model input and testing, suggesting the deep learning model can indeed learn the reconstruction process. Some quantitative difference was observed when the measured TOF-histoimages were used. The two studies suggested that the major difference is caused by inaccurate corrections performed by the network itself, which indicated that physics-based corrections are still required for better quantitative performance. In conclusion, we have proposed a novel deep learning-based image reconstruction method for TOF PET. With the help of the DIRECT data partitioning method, no fully connected layers were used and 3D image reconstruction can be directly achieved within the limits of the current hardware.
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Affiliation(s)
- Tao Feng
- UIH America, Inc., Houston, TX, United States of America
| | - Shulin Yao
- PLA General Hospital, Beijing, People's Republic of China
| | - Chen Xi
- Shanghai United Imaging Healthcare, Shanghai, People's Republic of China
| | - Yizhang Zhao
- Shanghai United Imaging Healthcare, Shanghai, People's Republic of China
| | - Ruimin Wang
- PLA General Hospital, Beijing, People's Republic of China
| | - Shina Wu
- PLA General Hospital, Beijing, People's Republic of China
| | - Can Li
- PLA General Hospital, Beijing, People's Republic of China
| | - Baixuan Xu
- PLA General Hospital, Beijing, People's Republic of China
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16
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van Sluis J, Yaqub M, Brouwers AH, Dierckx RAJO, Noordzij W, Boellaard R. Use of population input functions for reduced scan duration whole-body Patlak 18F-FDG PET imaging. EJNMMI Phys 2021; 8:11. [PMID: 33547518 PMCID: PMC7865035 DOI: 10.1186/s40658-021-00357-8] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 01/22/2021] [Indexed: 11/10/2022] Open
Abstract
Abstract Whole-body Patlak images can be obtained from an acquisition of first 6 min of dynamic imaging over the heart to obtain the arterial input function (IF), followed by multiple whole-body sweeps up to 60 min pi. The use of a population-averaged IF (PIF) could exclude the first dynamic scan and minimize whole-body sweeps to 30–60 min pi. Here, the effects of (incorrect) PIFs on the accuracy of the proposed Patlak method were assessed. In addition, the extent of mitigating these biases through rescaling of the PIF to image-derived IF values at 30–60 min pi was evaluated. Methods Using a representative IF and rate constants from the literature, various tumour time-activity curves (TACs) were simulated. Variations included multiplication of the IF with a positive and negative gradual linear bias over 60 min of 5, 10, 15, 20, and 25% (generating TACs using an IF different from the PIF); use of rate constants (K1, k3, and both K1 and k2) multiplied by 2, 1.5, and 0.75; and addition of noise (μ = 0 and σ = 5, 10 and 15%). Subsequent Patlak analysis using the original IF (representing the PIF) was used to obtain the influx constant (Ki) for the differently simulated TACs. Next, the PIF was scaled towards the (simulated) IF value using the 30–60-min pi time interval, simulating scaling of the PIF to image-derived values. Influence of variabilities in IF and rate constants, and rescaling the PIF on bias in Ki was evaluated. Results Percentage bias in Ki observed using simulated modified IFs varied from − 16 to 16% depending on the simulated amplitude and direction of the IF modifications. Subsequent scaling of the PIF reduced these Ki biases in most cases (287 out of 290) to < 5%. Conclusions Simulations suggest that scaling of a (possibly incorrect) PIF to IF values seen in whole-body dynamic imaging from 30 to 60 min pi can provide accurate Ki estimates. Consequently, dynamic Patlak imaging protocols may be performed for 30–60 min pi making whole-body Patlak imaging clinically feasible.
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Affiliation(s)
- Joyce van Sluis
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Adrienne H Brouwers
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Rudi A J O Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Walter Noordzij
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, University of Groningen, Hanzeplein 1, 9713 GZ, Groningen, The Netherlands.,Department of Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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Wang G, Rahmim A, Gunn RN. PET Parametric Imaging: Past, Present, and Future. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020; 4:663-675. [PMID: 33763624 PMCID: PMC7983029 DOI: 10.1109/trpms.2020.3025086] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Positron emission tomography (PET) is actively used in a diverse range of applications in oncology, cardiology, and neurology. The use of PET in the clinical setting focuses on static (single time frame) imaging at a specific time-point post radiotracer injection and is typically considered as semi-quantitative; e.g. standardized uptake value (SUV) measures. In contrast, dynamic PET imaging requires increased acquisition times but has the advantage that it measures the full spatiotemporal distribution of a radiotracer and, in combination with tracer kinetic modeling, enables the generation of multiparametric images that more directly quantify underlying biological parameters of interest, such as blood flow, glucose metabolism, and receptor binding. Parametric images have the potential for improved detection and for more accurate and earlier therapeutic response assessment. Parametric imaging with dynamic PET has witnessed extensive research in the past four decades. In this paper, we provide an overview of past and present activities and discuss emerging opportunities in the field of parametric imaging for the future.
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Affiliation(s)
- Guobao Wang
- Department of Radiology, University of California Davis Health, Sacramento, CA 95817, USA
| | - Arman Rahmim
- University of British Columbia, Vancouver, BC, Canada
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