1
|
Volpi T, Maccioni L, Colpo M, Debiasi G, Capotosti A, Ciceri T, Carson RE, DeLorenzo C, Hahn A, Knudsen GM, Lammertsma AA, Price JC, Sossi V, Wang G, Zanotti-Fregonara P, Bertoldo A, Veronese M. An update on the use of image-derived input functions for human PET studies: new hopes or old illusions? EJNMMI Res 2023; 13:97. [PMID: 37947880 PMCID: PMC10638226 DOI: 10.1186/s13550-023-01050-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 11/02/2023] [Indexed: 11/12/2023] Open
Abstract
BACKGROUND The need for arterial blood data in quantitative PET research limits the wider usability of this imaging method in clinical research settings. Image-derived input function (IDIF) approaches have been proposed as a cost-effective and non-invasive alternative to gold-standard arterial sampling. However, this approach comes with its own limitations-partial volume effects and radiometabolite correction among the most important-and varying rates of success, and the use of IDIF for brain PET has been particularly troublesome. MAIN BODY This paper summarizes the limitations of IDIF methods for quantitative PET imaging and discusses some of the advances that may make IDIF extraction more reliable. The introduction of automated pipelines (both commercial and open-source) for clinical PET scanners is discussed as a way to improve the reliability of IDIF approaches and their utility for quantitative purposes. Survey data gathered from the PET community are then presented to understand whether the field's opinion of the usefulness and validity of IDIF is improving. Finally, as the introduction of next-generation PET scanners with long axial fields of view, ultra-high sensitivity, and improved spatial and temporal resolution, has also brought IDIF methods back into the spotlight, a discussion of the possibilities offered by these state-of-the-art scanners-inclusion of large vessels, less partial volume in small vessels, better description of the full IDIF kinetics, whole-body modeling of radiometabolite production-is included, providing a pathway for future use of IDIF. CONCLUSION Improvements in PET scanner technology and software for automated IDIF extraction may allow to solve some of the major limitations associated with IDIF, such as partial volume effects and poor temporal sampling, with the exciting potential for accurate estimation of single kinetic rates. Nevertheless, until individualized radiometabolite correction can be performed effectively, IDIF approaches remain confined at best to a few tracers.
Collapse
Affiliation(s)
- Tommaso Volpi
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, PO Box 208048, New Haven, CT, 06520-8048, USA.
| | - Lucia Maccioni
- Department of Information Engineering, University of Padova, Padua, Italy
| | - Maria Colpo
- Department of Information Engineering, University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
| | - Giulia Debiasi
- Department of Information Engineering, University of Padova, Padua, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padua, Italy
| | - Amedeo Capotosti
- Department of Information Engineering, University of Padova, Padua, Italy
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Tommaso Ciceri
- Department of Information Engineering, University of Padova, Padua, Italy
- Neuroimaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, LC, Italy
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale University, 801 Howard Avenue, PO Box 208048, New Haven, CT, 06520-8048, USA
| | - Christine DeLorenzo
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Comprehensive Center for Clinical Neurosciences and Mental Healthy (C3NMH), Medical University of Vienna, Vienna, Austria
| | - Gitte Moos Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Adriaan A Lammertsma
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, Groningen, Netherlands
| | - Julie C Price
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, USA
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, BC, Canada
| | - Guobao Wang
- Department of Radiology, University of California Davis Medical Center, Sacramento, CA, USA
| | | | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, Padua, Italy
- Padova Neuroscience Center, University of Padova, Padua, Italy
| | - Mattia Veronese
- Department of Information Engineering, University of Padova, Padua, Italy
- Department of Neuroimaging, King's College London, London, UK
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Hepatic Positron Emission Tomography: Applications in Metabolism, Haemodynamics and Cancer. Metabolites 2022; 12:metabo12040321. [PMID: 35448508 PMCID: PMC9026326 DOI: 10.3390/metabo12040321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 03/29/2022] [Accepted: 03/31/2022] [Indexed: 11/28/2022] Open
Abstract
Evaluating in vivo the metabolic rates of the human liver has been a challenge due to its unique perfusion system. Positron emission tomography (PET) represents the current gold standard for assessing non-invasively tissue metabolic rates in vivo. Here, we review the existing literature on the assessment of hepatic metabolism, haemodynamics and cancer with PET. The tracer mainly used in metabolic studies has been [18F]2-fluoro-2-deoxy-D-glucose (18F-FDG). Its application not only enables the evaluation of hepatic glucose uptake in a variety of metabolic conditions and interventions, but based on the kinetics of 18F-FDG, endogenous glucose production can also be assessed. 14(R,S)-[18F]fluoro-6-thia-Heptadecanoic acid (18F-FTHA), 11C-Palmitate and 11C-Acetate have also been applied for the assessment of hepatic fatty acid uptake rates (18F-FTHA and 11C-Palmitate) and blood flow and oxidation (11C-Acetate). Oxygen-15 labelled water (15O-H2O) has been used for the quantification of hepatic perfusion. 18F-FDG is also the most common tracer used for hepatic cancer diagnostics, whereas 11C-Acetate has also shown some promising applications in imaging liver malignancies. The modelling approaches used to analyse PET data and also the challenges in utilizing PET in the assessment of hepatic metabolism are presented.
Collapse
|
4
|
Indovina L, Scolozzi V, Capotosti A, Sestini S, Taralli S, Cusumano D, Giancipoli RG, Ciasca G, Cardillo G, Calcagni ML. Short 2-[ 18F]Fluoro-2-Deoxy-D-Glucose PET Dynamic Acquisition Protocol to Evaluate the Influx Rate Constant by Regional Patlak Graphical Analysis in Patients With Non-Small-Cell Lung Cancer. Front Med (Lausanne) 2021; 8:725387. [PMID: 34881253 PMCID: PMC8647994 DOI: 10.3389/fmed.2021.725387] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 10/04/2021] [Indexed: 11/25/2022] Open
Abstract
Purpose: To test a short 2-[18F]Fluoro-2-deoxy-D-glucose (2-[18F]FDG) PET dynamic acquisition protocol to calculate Ki using regional Patlak graphical analysis in patients with non-small-cell lung cancer (NSCLC). Methods: 24 patients with NSCLC who underwent standard dynamic 2-[18F]FDG acquisitions (60 min) were randomly divided into two groups. In group 1 (n = 10), a population-based image-derived input function (pIDIF) was built using a monoexponential trend (10–60 min), and a leave-one-out cross-validation (LOOCV) method was performed to validate the pIDIF model. In group 2 (n = 14), Ki was obtained by standard regional Patlak plot analysis using IDIF (0–60 min) and tissue response (10–60 min) curves from the volume of interests (VOIs) placed on descending thoracic aorta and tumor tissue, respectively. Moreover, with our method, the Patlak analysis was performed to obtain Ki,s using IDIFFitted curve obtained from PET counts (0–10 min) followed by monoexponential coefficients of pIDIF (10–60 min) and tissue response curve obtained from PET counts at 10 min and between 40 and 60 min, simulating two short dynamic acquisitions. Both IDIF and IDIFFitted curves were modeled to assume the value of 2-[18F]FDG plasma activity measured in the venous blood sampling performed at 45 min in each patient. Spearman's rank correlation, coefficient of determination, and Passing–Bablok regression were used for the comparison between Ki and Ki,s. Finally, Ki,s was obtained with our method in a separate group of patients (group 3, n = 8) that perform two short dynamic acquisitions. Results: Population-based image-derived input function (10–60 min) was modeled with a monoexponential curve with the following fitted parameters obtained in group 1: a = 9.684, b = 16.410, and c = 0.068 min−1. The LOOCV error was 0.4%. In patients of group 2, the mean values of Ki and Ki,s were 0.0442 ± 0.0302 and 0.33 ± 0.0298, respectively (R2 = 0.9970). The Passing–Bablok regression for comparison between Ki and Ki,s showed a slope of 0.992 (95% CI: 0.94–1.06) and intercept value of −0.0003 (95% CI: −0.0033–0.0011). Conclusions: Despite several practical limitations, like the need to position the patient twice and to perform two CT scans, our method contemplates two short 2-[18F]FDG dynamic acquisitions, a population-based input function model, and a late venous blood sample to obtain robust and personalized input function and tissue response curves and to provide reliable regional Ki estimation.
Collapse
Affiliation(s)
- Luca Indovina
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Valentina Scolozzi
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Amedeo Capotosti
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | | | - Silvia Taralli
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Romina Grazia Giancipoli
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
| | - Gabriele Ciasca
- Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuseppe Cardillo
- Unit of Thoracic Surgery, San Camillo Forlanini Hospital, Rome, Italy
| | - Maria Lucia Calcagni
- Unità Operativa Complessa (UOC) di Medicina Nucleare, Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.,Dipartimento Universitario di Scienze Radiologiche ed Ematologiche, Università Cattolica del Sacro Cuore, Rome, Italy
| |
Collapse
|
5
|
Meikle SR, Sossi V, Roncali E, Cherry SR, Banati R, Mankoff D, Jones T, James M, Sutcliffe J, Ouyang J, Petibon Y, Ma C, El Fakhri G, Surti S, Karp JS, Badawi RD, Yamaya T, Akamatsu G, Schramm G, Rezaei A, Nuyts J, Fulton R, Kyme A, Lois C, Sari H, Price J, Boellaard R, Jeraj R, Bailey DL, Eslick E, Willowson KP, Dutta J. Quantitative PET in the 2020s: a roadmap. Phys Med Biol 2021; 66:06RM01. [PMID: 33339012 PMCID: PMC9358699 DOI: 10.1088/1361-6560/abd4f7] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Positron emission tomography (PET) plays an increasingly important role in research and clinical applications, catalysed by remarkable technical advances and a growing appreciation of the need for reliable, sensitive biomarkers of human function in health and disease. Over the last 30 years, a large amount of the physics and engineering effort in PET has been motivated by the dominant clinical application during that period, oncology. This has led to important developments such as PET/CT, whole-body PET, 3D PET, accelerated statistical image reconstruction, and time-of-flight PET. Despite impressive improvements in image quality as a result of these advances, the emphasis on static, semi-quantitative 'hot spot' imaging for oncologic applications has meant that the capability of PET to quantify biologically relevant parameters based on tracer kinetics has not been fully exploited. More recent advances, such as PET/MR and total-body PET, have opened up the ability to address a vast range of new research questions, from which a future expansion of applications and radiotracers appears highly likely. Many of these new applications and tracers will, at least initially, require quantitative analyses that more fully exploit the exquisite sensitivity of PET and the tracer principle on which it is based. It is also expected that they will require more sophisticated quantitative analysis methods than those that are currently available. At the same time, artificial intelligence is revolutionizing data analysis and impacting the relationship between the statistical quality of the acquired data and the information we can extract from the data. In this roadmap, leaders of the key sub-disciplines of the field identify the challenges and opportunities to be addressed over the next ten years that will enable PET to realise its full quantitative potential, initially in research laboratories and, ultimately, in clinical practice.
Collapse
Affiliation(s)
- Steven R Meikle
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
- Brain and Mind Centre, The University of Sydney, Australia
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Canada
| | - Emilie Roncali
- Department of Biomedical Engineering, University of California, Davis, United States of America
| | - Simon R Cherry
- Department of Biomedical Engineering, University of California, Davis, United States of America
- Department of Radiology, University of California, Davis, United States of America
| | - Richard Banati
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
- Brain and Mind Centre, The University of Sydney, Australia
- Australian Nuclear Science and Technology Organisation, Sydney, Australia
| | - David Mankoff
- Department of Radiology, University of Pennsylvania, United States of America
| | - Terry Jones
- Department of Radiology, University of California, Davis, United States of America
| | - Michelle James
- Department of Radiology, Molecular Imaging Program at Stanford (MIPS), CA, United States of America
- Department of Neurology and Neurological Sciences, Stanford University, CA, United States of America
| | - Julie Sutcliffe
- Department of Biomedical Engineering, University of California, Davis, United States of America
- Department of Internal Medicine, University of California, Davis, CA, United States of America
| | - Jinsong Ouyang
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Yoann Petibon
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Chao Ma
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Suleman Surti
- Department of Radiology, University of Pennsylvania, United States of America
| | - Joel S Karp
- Department of Radiology, University of Pennsylvania, United States of America
| | - Ramsey D Badawi
- Department of Biomedical Engineering, University of California, Davis, United States of America
- Department of Radiology, University of California, Davis, United States of America
| | - Taiga Yamaya
- National Institute of Radiological Sciences (NIRS), National Institutes for Quantum and Radiological Science and Technology (QST), Chiba, Japan
| | - Go Akamatsu
- National Institute of Radiological Sciences (NIRS), National Institutes for Quantum and Radiological Science and Technology (QST), Chiba, Japan
| | - Georg Schramm
- Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium
| | - Ahmadreza Rezaei
- Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium
| | - Johan Nuyts
- Department of Imaging and Pathology, Nuclear Medicine & Molecular imaging, KU Leuven, Belgium
| | - Roger Fulton
- Brain and Mind Centre, The University of Sydney, Australia
- Department of Medical Physics, Westmead Hospital, Sydney, Australia
| | - André Kyme
- Brain and Mind Centre, The University of Sydney, Australia
- School of Biomedical Engineering, Faculty of Engineering and IT, The University of Sydney, Australia
| | - Cristina Lois
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, United States of America
| | - Hasan Sari
- Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
- Athinoula A. Martinos Center, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
| | - Julie Price
- Department of Radiology, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
- Athinoula A. Martinos Center, Massachusetts General Hospital & Harvard Medical School, Boston, MA, United States of America
| | - Ronald Boellaard
- Radiology and Nuclear Medicine, Cancer Center Amsterdam, Amsterdam University Medical Center, location VUMC, Netherlands
| | - Robert Jeraj
- Departments of Medical Physics, Human Oncology and Radiology, University of Wisconsin, United States of America
- Faculty of Mathematics and Physics, University of Ljubljana, Slovenia
| | - Dale L Bailey
- Sydney School of Health Sciences, Faculty of Medicine and Health, The University of Sydney, Australia
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia
- Faculty of Science, The University of Sydney, Australia
| | - Enid Eslick
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia
| | - Kathy P Willowson
- Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia
- Faculty of Science, The University of Sydney, Australia
| | - Joyita Dutta
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, United States of America
| |
Collapse
|
6
|
Wang B, Liu H. FBP-Net for direct reconstruction of dynamic PET images. Phys Med Biol 2020; 65. [PMID: 33049720 DOI: 10.1088/1361-6560/abc09d] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 10/13/2020] [Indexed: 12/22/2022]
Abstract
Dynamic positron emission tomography (PET) imaging can provide information about metabolic changes over time, used for kinetic analysis and auxiliary diagnosis. Existing deep learning-based reconstruction methods have too many trainable parameters and poor generalization, and require mass data to train the neural network. However, obtaining large amounts of medical data is expensive and time-consuming. To reduce the need for data and improve the generalization of network, we combined the filtered back-projection (FBP) algorithm with neural network, and proposed FBP-Net which could directly reconstruct PET images from sinograms instead of post-processing the rough reconstruction images obtained by traditional methods. The FBP-Net contained two parts: the FBP part and the denoiser part. The FBP part adaptively learned the frequency filter to realize the transformation from the detector domain to the image domain, and normalized the coarse reconstruction images obtained. The denoiser part merged the information of all time frames to improve the quality of dynamic PET reconstruction images, especially the early time frames. The proposed FBP-Net was performed on simulation and real dataset, and the results were compared with the state-of-art U-net and DeepPET. The results showed that FBP-Net did not tend to overfit the training set and had a stronger generalization.
Collapse
Affiliation(s)
- Bo Wang
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 310027 Hangzhou, People's Republic of China
| | - Huafeng Liu
- State Key Laboratory of Modern Optical Instrumentation, College of Optical Science and Engineering, Zhejiang University, 310027 Hangzhou, People's Republic of China.,Author to whom any correspondence should be addressed
| |
Collapse
|
7
|
Cui J, Qin Z, Chen S, Chen Y, Liu H. Structure and Tracer Kinetics-Driven Dynamic PET Reconstruction. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2020. [DOI: 10.1109/trpms.2019.2947860] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
|
8
|
Real-time control of respiratory motion: Beyond radiation therapy. Phys Med 2019; 66:104-112. [PMID: 31586767 DOI: 10.1016/j.ejmp.2019.09.241] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 09/23/2019] [Accepted: 09/26/2019] [Indexed: 12/16/2022] Open
Abstract
Motion management in radiation oncology is an important aspect of modern treatment planning and delivery. Special attention has been paid to control respiratory motion in recent years. However, other medical procedures related to both diagnosis and treatment are likely to benefit from the explicit control of breathing motion. Quantitative imaging - including increasingly important tools in radiology and nuclear medicine - is among the fields where a rapid development of motion control is most likely, due to the need for quantification accuracy. Emerging treatment modalities like focussed-ultrasound tumor ablation are also likely to benefit from a significant evolution of motion control in the near future. In the present article an overview of available respiratory motion systems along with ongoing research in this area is provided. Furthermore, an attempt is made to envision some of the most expected developments in this field in the near future.
Collapse
|
9
|
Cysouw MCF, Golla SVS, Frings V, Smit EF, Hoekstra OS, Kramer GM, Boellaard R. Partial-volume correction in dynamic PET-CT: effect on tumor kinetic parameter estimation and validation of simplified metrics. EJNMMI Res 2019; 9:12. [PMID: 30715647 PMCID: PMC6362178 DOI: 10.1186/s13550-019-0483-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 01/25/2019] [Indexed: 12/27/2022] Open
Abstract
Background Partial-volume effects generally result in an underestimation of tumor tracer uptake on PET-CT for small lesions, necessitating partial-volume correction (PVC) for accurate quantification. However, investigation of PVC in dynamic oncological PET studies to date is scarce. The aim of this study was to investigate PVC’s impact on tumor kinetic parameter estimation from dynamic PET-CT acquisitions and subsequent validation of simplified semi-quantitative metrics. Ten patients with EGFR-mutated non-small cell lung cancer underwent dynamic 18F-fluorothymidine PET-CT before, 7 days after, and 28 days after commencing treatment with a tyrosine kinase inhibitor. Parametric PVC was applied using iterative deconvolution without and with highly constrained backprojection (HYPR) denoising, respectively. Using an image-derived input function with venous parent plasma calibration, we estimated full kinetic parameters VT, K1, and k3/k4 (BPND) using a reversible two-tissue compartment model, and simplified metrics (SUV and tumor-to-blood ratio) at 50–60 min post-injection. Results PVC had a non-linear effect on measured activity concentrations per timeframe. PVC significantly changed each kinetic parameter, with a median increase in VT of 11.8% (up to 25.1%) and 10.8% (up to 21.7%) without and with HYPR, respectively. Relative changes in kinetic parameter estimates vs. simplified metrics after applying PVC were poorly correlated (correlations 0.36–0.62; p < 0.01). PVC increased correlations between simplified metrics and VT from 0.82 and 0.81 (p < 0.01) to 0.90 and 0.88 (p < 0.01) for SUV and TBR, respectively, albeit non-significantly. PVC also increased correlations between treatment-induced changes in simplified metrics vs. VT at 7 (SUV) and 28 (SUV and TBR) days after treatment start non-significantly. Delineation on partial-volume corrected PET images resulted in a median decrease in metabolic tumor volume of 14.3% (IQR − 22.1 to − 7.5%), and increased the effect of PVC on kinetic parameter estimates. Conclusion PVC has a significant impact on tumor kinetic parameter estimation from dynamic PET-CT data, which differs from its effect on simplified metrics. However, it affected validation of these simplified metrics both as single measurements and as biomarkers of treatment response only to a small extent. Future dynamic PET studies should preferably incorporate PVC. Trial registration Dutch Trial Register, NTR3557. Electronic supplementary material The online version of this article (10.1186/s13550-019-0483-z) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- M C F Cysouw
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands.
| | - S V S Golla
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - V Frings
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - E F Smit
- Department of Thoracic Oncology, Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, the Netherlands
| | - O S Hoekstra
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - G M Kramer
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | - R Boellaard
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Vrije Universiteit Amsterdam, Cancer Center Amsterdam, De Boelelaan 1117, Amsterdam, the Netherlands
| | | |
Collapse
|