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Moradi H, Vashistha R, Ghosh S, O'Brien K, Hammond A, Rominger A, Sari H, Shi K, Vegh V, Reutens D. Automated extraction of the arterial input function from brain images for parametric PET studies. EJNMMI Res 2024; 14:33. [PMID: 38558200 PMCID: PMC11372015 DOI: 10.1186/s13550-024-01100-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Accepted: 03/23/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Accurate measurement of the arterial input function (AIF) is crucial for parametric PET studies, but the AIF is commonly derived from invasive arterial blood sampling. It is possible to use an image-derived input function (IDIF) obtained by imaging a large blood pool, but IDIF measurement in PET brain studies performed on standard field of view scanners is challenging due to lack of a large blood pool in the field-of-view. Here we describe a novel automated approach to estimate the AIF from brain images. RESULTS Total body 18F-FDG PET data from 12 subjects were split into a model adjustment group (n = 6) and a validation group (n = 6). We developed an AIF estimation framework using wavelet-based methods and unsupervised machine learning to distinguish arterial and venous activity curves, compared to the IDIF from the descending aorta. All of the automatically extracted AIFs in the validation group had similar shape to the IDIF derived from the descending aorta IDIF. The average area under the curve error and normalised root mean square error across validation data were - 1.59 ± 2.93% and 0.17 ± 0.07. CONCLUSIONS Our automated AIF framework accurately estimates the AIF from brain images. It reduces operator-dependence, and could facilitate the clinical adoption of parametric PET.
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Affiliation(s)
- Hamed Moradi
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Rajat Vashistha
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Soumen Ghosh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Amanda Hammond
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
- Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Hasan Sari
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Freiburgstrasse 18, 3010, Bern, Switzerland
| | - Viktor Vegh
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia.
| | - David Reutens
- Centre for Advanced Imaging, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, Brisbane, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Australia
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Narciso L, Deller G, Dassanayake P, Liu L, Pinto S, Anazodo U, Soddu A, Lawrence KS. Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [ 18F]FDG PET. EJNMMI Phys 2024; 11:11. [PMID: 38285319 PMCID: PMC10825104 DOI: 10.1186/s40658-024-00614-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 01/15/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [18F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data. METHODS Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs (n = 5). Using dynamic [18F]FDG PET data from neurologically healthy volunteers (n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach. RESULTS Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [18F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns. CONCLUSIONS A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.
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Affiliation(s)
- Lucas Narciso
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Graham Deller
- Imaging Program, Lawson Health Research Institute, 268 Grosvenor St, London, ON, N6A 4V2, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Praveen Dassanayake
- Imaging Program, Lawson Health Research Institute, 268 Grosvenor St, London, ON, N6A 4V2, Canada
- Department of Medical Biophysics, Western University, London, ON, Canada
| | - Linshan Liu
- Imaging Program, Lawson Health Research Institute, 268 Grosvenor St, London, ON, N6A 4V2, Canada
| | - Samara Pinto
- Department of Biomedical Gerontology, PUCRS, Porto Alegre, Rio Grande do Sul, Brazil
| | - Udunna Anazodo
- Imaging Program, Lawson Health Research Institute, 268 Grosvenor St, London, ON, N6A 4V2, Canada
- Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Andrea Soddu
- Department of Physics and Astronomy, Western University, London, ON, Canada
| | - Keith St Lawrence
- Imaging Program, Lawson Health Research Institute, 268 Grosvenor St, London, ON, N6A 4V2, Canada.
- Department of Medical Biophysics, Western University, London, ON, Canada.
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3
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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.
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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
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Naganawa M, Gallezot JD, Shah V, Mulnix T, Young C, Dias M, Chen MK, Smith AM, Carson RE. Assessment of population-based input functions for Patlak imaging of whole body dynamic 18F-FDG PET. EJNMMI Phys 2020; 7:67. [PMID: 33226522 PMCID: PMC7683759 DOI: 10.1186/s40658-020-00330-x] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2020] [Accepted: 09/16/2020] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Arterial blood sampling is the gold standard method to obtain the arterial input function (AIF) for quantification of whole body (WB) dynamic 18F-FDG PET imaging. However, this procedure is invasive and not typically available in clinical environments. As an alternative, we compared AIFs to population-based input functions (PBIFs) using two normalization methods: area under the curve (AUC) and extrapolated initial plasma concentration (CP*(0)). To scale the PBIFs, we tested two methods: (1) the AUC of the image-derived input function (IDIF) and (2) the estimated CP*(0). The aim of this study was to validate IDIF and PBIF for FDG oncological WB PET studies by comparing to the gold standard arterial blood sampling. METHODS The Feng 18F-FDG plasma concentration model was applied to estimate AIF parameters (n = 23). AIF normalization used either AUC(0-60 min) or CP*(0), estimated from an exponential fit. CP*(0) is also described as the ratio of the injected dose (ID) to initial distribution volume (iDV). iDV was modeled using the subject height and weight, with coefficients that were estimated in 23 subjects. In 12 oncological patients, we computed IDIF (from the aorta) and PBIFs with scaling by the AUC of the IDIF from 4 time windows (15-45, 30-60, 45-75, 60-90 min) (PBIFAUC) and estimated CP*(0) (PBIFiDV). The IDIF and PBIFs were compared with the gold standard AIF, using AUC values and Patlak Ki values. RESULTS The IDIF underestimated the AIF at early times and overestimated it at later times. Thus, based on the AUC and Ki comparison, 30-60 min was the most accurate time window for PBIFAUC; later time windows for scaling underestimated Ki (- 6 ± 8 to - 13 ± 9%). Correlations of AUC between AIF and IDIF, PBIFAUC(30-60), and PBIFiDV were 0.91, 0.94, and 0.90, respectively. The bias of Ki was - 9 ± 10%, - 1 ± 8%, and 3 ± 9%, respectively. CONCLUSIONS Both PBIF scaling methods provided good mean performance with moderate variation. Improved performance can be obtained by refining IDIF methods and by evaluating PBIFs with test-retest data.
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Affiliation(s)
- Mika Naganawa
- PET Center, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA.
| | - Jean-Dominique Gallezot
- PET Center, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Vijay Shah
- Molecular Imaging, Siemens Medical Solutions USA, Inc., Knoxville, TN, USA
| | - Tim Mulnix
- PET Center, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Colin Young
- PET Center, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Mark Dias
- PET Center, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Ming-Kai Chen
- PET Center, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
| | - Anne M Smith
- Molecular Imaging, Siemens Medical Solutions USA, Inc., Knoxville, TN, USA
| | - Richard E Carson
- PET Center, Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA
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5
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He X, Wedekind F, Kroll T, Oskamp A, Beer S, Drzezga A, Ermert J, Neumaier B, Bauer A, Elmenhorst D. Image-Derived Input Functions for Quantification of A 1 Adenosine Receptors Availability in Mice Brains Using PET and [ 18F]CPFPX. Front Physiol 2020; 10:1617. [PMID: 32063864 PMCID: PMC7000659 DOI: 10.3389/fphys.2019.01617] [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: 10/30/2019] [Accepted: 12/23/2019] [Indexed: 12/28/2022] Open
Abstract
Purpose In vivo imaging for the A1 adenosine receptors (A1ARs) with positron emission tomography (PET) using 8-cyclopentyl-3-(3-[18F]fluoropropyl)-1-propylxan- thine ([18F]CPFPX) has become an important tool for studying physiological processes quantitatively in mice. However, the measurement of arterial input functions (AIFs) on mice is a method with restricted applicability because of the small total blood volume and the related difficulties in withdrawing blood. Therefore, the aim of this study was to extract an appropriate [18F]CPFPX image-derived input function (IDIF) from dynamic PET images of mice. Procedures In this study, five mice were scanned with [18F]CPFPX for 60 min. Arterial blood samples (n = 7 per animal) were collected from the femoral artery and corrected for metabolites. To generate IDIFs, three different approaches were selected: (A) volume of interest (VOI) placed over the heart (cube, 10 mm); (B) VOI set over abdominal vena cava/aorta region with a cuboid (5 × 5 × 15 mm); and (C) with 1 × 1 × 1 mm voxels on five consecutive slices. A calculated scaling factor (α) was used to correct for partial volume effect; the method of obtaining the total metabolite correction of [18F]CPFPX for IDIFs was developed. Three IDIFs were validated by comparison with AIF. Validation included the following: visual performance; computing area under the curve (AUC) ratios (IDIF/AIF) of whole-blood curves and parent curves; and the mean distribution volume (VT) ratios (IDIF/AIF) of A1ARs calculated by Logan plot and two-tissue compartment model. Results Compared with the AIF, the IDIF with VOI over heart showed the best performance among the three IDIFs after scaling by 1.77 (α) in terms of visual analysis, AUC ratios (IDIF/AIF; whole-blood AUC ratio, 1.03 ± 0.06; parent curve AUC ratio, 1.01 ± 0.10) and VT ratios (IDIF/AIF; Logan VT ratio, 1.00 ± 0.17; two-tissue compartment model VT ratio, 1.00 ± 0.13) evaluation. The A1ARs distribution of average parametric images was in good accordance to autoradiography of the mouse brain. Conclusion The proposed study provides evidence that IDIF with VOI over heart can replace AIF effectively for quantification of A1ARs using PET and [18F]CPFPX in mice brains.
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Affiliation(s)
- Xuan He
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany.,Department of Neurophysiology, Institute of Zoology (Bio-II), RWTH Aachen University, Aachen, Germany
| | - Franziska Wedekind
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany
| | - Tina Kroll
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany
| | - Angela Oskamp
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany
| | - Simone Beer
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany
| | - Alexander Drzezga
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany.,Department of Nuclear Medicine, University Hospital of Cologne, Cologne, Germany
| | - Johannes Ermert
- Institut für Neurowissenschaften und Medizin (INM-5), Forschungszentrum Jülich, Jülich, Germany
| | - Bernd Neumaier
- Institut für Neurowissenschaften und Medizin (INM-5), Forschungszentrum Jülich, Jülich, Germany
| | - Andreas Bauer
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany.,Neurological Department, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - David Elmenhorst
- Institut für Neurowissenschaften und Medizin (INM-2), Forschungszentrum Jülich, Jülich, Germany.,Division of Medical Psychology, University of Bonn, Bonn, Germany
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Richard MA, Fouquet JP, Lebel R, Lepage M. MRI-Guided Derivation of the Input Function for PET Kinetic Modeling. PET Clin 2016; 11:193-202. [DOI: 10.1016/j.cpet.2015.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
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Mateos-Pérez JM, Soto-Montenegro ML, Peña-Zalbidea S, Desco M, Vaquero JJ. Functional segmentation of dynamic PET studies: Open source implementation and validation of a leader-follower-based algorithm. Comput Biol Med 2016; 69:181-8. [PMID: 26773940 DOI: 10.1016/j.compbiomed.2015.12.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2015] [Revised: 12/15/2015] [Accepted: 12/17/2015] [Indexed: 11/26/2022]
Abstract
UNLABELLED We present a novel segmentation algorithm for dynamic PET studies that groups pixels according to the similarity of their time-activity curves. METHODS Sixteen mice bearing a human tumor cell line xenograft (CH-157MN) were imaged with three different (68)Ga-DOTA-peptides (DOTANOC, DOTATATE, DOTATOC) using a small animal PET-CT scanner. Regional activities (input function and tumor) were obtained after manual delineation of regions of interest over the image. The algorithm was implemented under the jClustering framework and used to extract the same regional activities as in the manual approach. The volume of distribution in the tumor was computed using the Logan linear method. A Kruskal-Wallis test was used to investigate significant differences between the manually and automatically obtained volumes of distribution. RESULTS The algorithm successfully segmented all the studies. No significant differences were found for the same tracer across different segmentation methods. Manual delineation revealed significant differences between DOTANOC and the other two tracers (DOTANOC - DOTATATE, p=0.020; DOTANOC - DOTATOC, p=0.033). Similar differences were found using the leader-follower algorithm. CONCLUSION An open implementation of a novel segmentation method for dynamic PET studies is presented and validated in rodent studies. It successfully replicated the manual results obtained in small-animal studies, thus making it a reliable substitute for this task and, potentially, for other dynamic segmentation procedures.
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Affiliation(s)
- José María Mateos-Pérez
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Montreal Neurological Institute, McGill University, Montreal, Québec, Canada.
| | - María Luisa Soto-Montenegro
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain
| | - Santiago Peña-Zalbidea
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Manuel Desco
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Juan José Vaquero
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
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Bertrán M, Martínez N, Carbajal G, Fernández A, Gómez Á. An open tool for input function estimation and quantification of dynamic PET FDG brain scans. Int J Comput Assist Radiol Surg 2015; 11:1419-30. [PMID: 26514683 DOI: 10.1007/s11548-015-1307-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2015] [Accepted: 09/23/2015] [Indexed: 10/22/2022]
Abstract
PURPOSE Positron emission tomography (PET) analysis of clinical studies is mostly restricted to qualitative evaluation. Quantitative analysis of PET studies is highly desirable to be able to compute an objective measurement of the process of interest in order to evaluate treatment response and/or compare patient data. But implementation of quantitative analysis generally requires the determination of the input function: the arterial blood or plasma activity which indicates how much tracer is available for uptake in the brain. The purpose of our work was to share with the community an open software tool that can assist in the estimation of this input function, and the derivation of a quantitative map from the dynamic PET study. METHODS Arterial blood sampling during the PET study is the gold standard method to get the input function, but is uncomfortable and risky for the patient so it is rarely used in routine studies. To overcome the lack of a direct input function, different alternatives have been devised and are available in the literature. These alternatives derive the input function from the PET image itself (image-derived input function) or from data gathered from previous similar studies (population-based input function). In this article, we present ongoing work that includes the development of a software tool that integrates several methods with novel strategies for the segmentation of blood pools and parameter estimation. RESULTS The tool is available as an extension to the 3D Slicer software. Tests on phantoms were conducted in order to validate the implemented methods. We evaluated the segmentation algorithms over a range of acquisition conditions and vasculature size. Input function estimation algorithms were evaluated against ground truth of the phantoms, as well as on their impact over the final quantification map. End-to-end use of the tool yields quantification maps with [Formula: see text] relative error in the estimated influx versus ground truth on phantoms. CONCLUSIONS The main contribution of this article is the development of an open-source, free to use tool that encapsulates several well-known methods for the estimation of the input function and the quantification of dynamic PET FDG studies. Some alternative strategies are also proposed and implemented in the tool for the segmentation of blood pools and parameter estimation. The tool was tested on phantoms with encouraging results that suggest that even bloodless estimators could provide a viable alternative to blood sampling for quantification using graphical analysis. The open tool is a promising opportunity for collaboration among investigators and further validation on real studies.
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Affiliation(s)
- Martín Bertrán
- Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
| | - Natalia Martínez
- Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
| | - Guillermo Carbajal
- Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay.
| | - Alicia Fernández
- Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
| | - Álvaro Gómez
- Instituto de Ingeniería Eléctrica, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
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Evans E, Buonincontri G, Izquierdo D, Methner C, Hawkes RC, Ansorge RE, Krieg T, Carpenter TA, Sawiak SJ. Combining MRI with PET for partial volume correction improves image-derived input functions in mice. IEEE TRANSACTIONS ON NUCLEAR SCIENCE 2015; 62:628-633. [PMID: 26213413 PMCID: PMC4510926 DOI: 10.1109/tns.2015.2433897] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Accurate kinetic modelling using dynamic PET requires knowledge of the tracer concentration in plasma, known as the arterial input function (AIF). AIFs are usually determined by invasive blood sampling, but this is prohibitive in murine studies due to low total blood volumes. As a result of the low spatial resolution of PET, image-derived input functions (IDIFs) must be extracted from left ventricular blood pool (LVBP) ROIs of the mouse heart. This is challenging because of partial volume and spillover effects between the LVBP and myocardium, contaminating IDIFs with tissue signal. We have applied the geometric transfer matrix (GTM) method of partial volume correction (PVC) to 12 mice injected with 18F-FDG affected by a Myocardial Infarction (MI), of which 6 were treated with a drug which reduced infarction size [1]. We utilised high resolution MRI to assist in segmenting mouse hearts into 5 classes: LVBP, infarcted myocardium, healthy myocardium, lungs/body and background. The signal contribution from these 5 classes was convolved with the point spread function (PSF) of the Cambridge split magnet PET scanner and a non-linear fit was performed on the 5 measured signal components. The corrected IDIF was taken as the fitted LVBP component. It was found that the GTM PVC method could recover an IDIF with less contamination from spillover than an IDIF extracted from PET data alone. More realistic values of Ki were achieved using GTM IDIFs, which were shown to be significantly different (p<0.05) between the treated and untreated groups.
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Affiliation(s)
- Eleanor Evans
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK, CB2 0QQ ( )
| | - Guido Buonincontri
- Wolfson Brain Imaging Centre and the Department of Medicine, University of Cambridge, Cambridge, UK, CB2 0QQ ( )
| | - David Izquierdo
- Athinoula A. Martinos Center for Biomedical Imaging, 149 Thirteenth Street, Suite 2301, Charlestown, MA, 02129 ( )
| | - Carmen Methner
- Department of Medicine, University of Cambridge and is now at Oregon Health and Science University, Portland, OR, 97239 ( )
| | - Rob C Hawkes
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK, CB2 0QQ ( )
| | - Richard E Ansorge
- Department of Physics, University of Cambridge, Cambridge, UK, CB3 0HE ( )
| | - Thomas Krieg
- Member of the Department of Medicine, University of Cambridge, Cambridge, UK, CB2 0QQ ( )
| | - T Adrian Carpenter
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK, CB2 0QQ ( )
| | - Stephen J Sawiak
- Member of both the Wolfson Brain Imaging Centre, and the Behavioural and Clinical Neurosciences Institute, University of Cambridge, Cambridge, UK, CB2 3EB ( )
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10
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Su Y, Blazey TM, Snyder AZ, Raichle ME, Hornbeck RC, Aldea P, Morris JC, Benzinger TLS. Quantitative amyloid imaging using image-derived arterial input function. PLoS One 2015; 10:e0122920. [PMID: 25849581 PMCID: PMC4388540 DOI: 10.1371/journal.pone.0122920] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Accepted: 02/24/2015] [Indexed: 11/19/2022] Open
Abstract
Amyloid PET imaging is an indispensable tool widely used in the investigation, diagnosis and monitoring of Alzheimer’s disease (AD). Currently, a reference region based approach is used as the mainstream quantification technique for amyloid imaging. This approach assumes the reference region is amyloid free and has the same tracer influx and washout kinetics as the regions of interest. However, this assumption may not always be valid. The goal of this work is to evaluate an amyloid imaging quantification technique that uses arterial region of interest as the reference to avoid potential bias caused by specific binding in the reference region. 21 participants, age 58 and up, underwent Pittsburgh compound B (PiB) PET imaging and MR imaging including a time-of-flight (TOF) MR angiography (MRA) scan and a structural scan. FreeSurfer based regional analysis was performed to quantify PiB PET data. Arterial input function was estimated based on coregistered TOF MRA using a modeling based technique. Regional distribution volume (VT) was calculated using Logan graphical analysis with estimated arterial input function. Kinetic modeling was also performed using the estimated arterial input function as a way to evaluate PiB binding (DVRkinetic) without a reference region. As a comparison, Logan graphical analysis was also performed with cerebellar cortex as reference to obtain DVRREF. Excellent agreement was observed between the two distribution volume ratio measurements (r>0.89, ICC>0.80). The estimated cerebellum VT was in line with literature reported values and the variability of cerebellum VT in the control group was comparable to reported variability using arterial sampling data. This study suggests that image-based arterial input function is a viable approach to quantify amyloid imaging data, without the need of arterial sampling or a reference region. This technique can be a valuable tool for amyloid imaging, particularly in population where reference normalization may not be accurate.
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Affiliation(s)
- Yi Su
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
- * E-mail:
| | - Tyler M. Blazey
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Abraham Z. Snyder
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Marcus E. Raichle
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Russ C. Hornbeck
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Patricia Aldea
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - John C. Morris
- Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
| | - Tammie L. S. Benzinger
- Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Knight Alzheimer’s Disease Research Center (ADRC), Washington University School of Medicine, Saint Louis, Missouri, United States of America
- Department of Neurosurgery, Washington University School of Medicine, Saint Louis, Missouri, United States of America
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11
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Mateos-Pérez JM, Desco M, Dae MW, García-Villalba C, Cussó L, Vaquero JJ. Automatic TAC extraction from dynamic cardiac PET imaging using iterative correlation from a population template. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 111:308-314. [PMID: 23693137 DOI: 10.1016/j.cmpb.2013.04.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2012] [Revised: 04/01/2013] [Accepted: 04/18/2013] [Indexed: 06/02/2023]
Abstract
This work describes a new iterative method for extracting time-activity curves (TAC) from dynamic imaging studies using a priori information from generic models obtained from TAC templates. Analytical expressions of the TAC templates were derived from TACs obtained by manual segmentation of three (13)NH3 pig studies (gold standard). An iterative method for extracting both ventricular and myocardial TACs using models of the curves obtained as an initial template was then implemented and tested. These TACs were extracted from masked and unmasked images; masking was applied to remove the lungs and surrounding non-relevant structures. The resulting TACs were then compared with TACs obtained manually; the results of kinetic analysis were also compared. Extraction of TACs for each region was sensitive to the presence of other organs (e.g., lungs) in the image. Masking the volume of interest noticeably reduces error. The proposed method yields good results in terms of TAC definition and kinetic parameter estimation, even when the initial TAC templates do not accurately match specific tracer kinetics.
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12
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Back to the future: the absolute quantification of cerebral metabolic rate of glucose. Clin Transl Imaging 2013. [DOI: 10.1007/s40336-013-0030-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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13
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Fung EK, Carson RE. Cerebral blood flow with [15O]water PET studies using an image-derived input function and MR-defined carotid centerlines. Phys Med Biol 2013; 58:1903-23. [PMID: 23442733 DOI: 10.1088/0031-9155/58/6/1903] [Citation(s) in RCA: 46] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Full quantitative analysis of brain PET data requires knowledge of the arterial input function into the brain. Such data are normally acquired by arterial sampling with corrections for delay and dispersion to account for the distant sampling site. Several attempts have been made to extract an image-derived input function (IDIF) directly from the internal carotid arteries that supply the brain and are often visible in brain PET images. We have devised a method of delineating the internal carotids in co-registered magnetic resonance (MR) images using the level-set method and applying the segmentations to PET images using a novel centerline approach. Centerlines of the segmented carotids were modeled as cubic splines and re-registered in PET images summed over the early portion of the scan. Using information from the anatomical center of the vessel should minimize partial volume and spillover effects. Centerline time-activity curves were taken as the mean of the values for points along the centerline interpolated from neighboring voxels. A scale factor correction was derived from calculation of cerebral blood flow (CBF) using gold standard arterial blood measurements. We have applied the method to human subject data from multiple injections of [(15)O]water on the HRRT. The method was assessed by calculating the area under the curve (AUC) of the IDIF and the CBF, and comparing these to values computed using the gold standard arterial input curve. The average ratio of IDIF to arterial AUC (apparent recovery coefficient: aRC) across 9 subjects with multiple (n = 69) injections was 0.49 ± 0.09 at 0-30 s post tracer arrival, 0.45 ± 0.09 at 30-60 s, and 0.46 ± 0.09 at 60-90 s. Gray and white matter CBF values were 61.4 ± 11.0 and 15.6 ± 3.0 mL/min/100 g tissue using sampled blood data. Using IDIF centerlines scaled by the average aRC over each subjects' injections, gray and white matter CBF values were 61.3 ± 13.5 and 15.5 ± 3.4 mL/min/100 g tissue. Using global average aRC values, the means were unchanged, and intersubject variability was noticeably reduced. This MR-based centerline method with local re-registration to [(15)O]water PET yields a consistent IDIF over multiple injections in the same subject, thus permitting the absolute quantification of CBF without arterial input function measurements.
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Affiliation(s)
- Edward K Fung
- Department of Biomedical Engineering, Yale University, 801 Howard Avenue, New Haven, CT 06520, USA.
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14
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Gonzalez ME, Dinelle K, Vafai N, Heffernan N, McKenzie J, Appel-Cresswell S, McKeown MJ, Stoessl AJ, Sossi V. Novel spatial analysis method for PET images using 3D moment invariants: applications to Parkinson's disease. Neuroimage 2012; 68:11-21. [PMID: 23246861 DOI: 10.1016/j.neuroimage.2012.11.055] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2012] [Revised: 10/16/2012] [Accepted: 11/22/2012] [Indexed: 11/27/2022] Open
Abstract
We present a novel analysis method for positron emission tomography (PET) data that uses the spatial characteristics of the radiotracer's distribution within anatomically-defined regions of interest (ROIs) to provide an independent feature that may aid in characterizing pathological and normal states. The analysis of PET data for research purposes traditionally involves kinetic modeling of the concentration of the radiotracer over time within a ROI to derive parameters related to the uptake/binding of the radiotracer in the body. Here we describe an analysis method to quantify the spatial changes present in PET images based on 3D shape descriptors that are invariant to translation, scaling, and rotation, called 3D moment invariants (3DMIs). An ROI can therefore be characterized not only by the radiotracer's uptake rate constant or binding potential within the ROI, but also the 3D spatial shape and distribution of the radioactivity throughout the ROI. This is particularly relevant in Parkinson's disease (PD), where both the kinetic and the spatial distribution of the tracer are known to change due to disease: the posterior parts of the striatum (in particular in the putamen) are affected before the anterior parts. Here we show that 3DMIs are able to quantify the spatial distribution of PET radiotracer images allowing for discrimination between healthy controls and PD subjects. More importantly, 3DMIs are found to be well correlated with subjects' scores on the United Parkinson's Disease Rating Scale (a clinical measure of disease severity) in all anatomical regions studied here (putamen, caudate and ventral striatum). On the other hand, kinetic parameters only show significant correlation to clinically-assessed PD severity in the putamen. We also find that 3DMI-characterized changes in spatial patterns of dopamine release in response to l-dopa medication are significantly correlated with PD severity. These findings suggest that quantitative studies of a radiotracer's spatial distribution may provide complementary information to kinetic modeling that is relatively robust to intersubject variability and may contribute novel information in PET neuroimaging studies.
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Affiliation(s)
- Marjorie E Gonzalez
- Department of Physics and Astronomy, University of British Columbia, 6224 Agricultural Rd, Vancouver, Canada BC V6T 1Z1.
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Physical and organizational provision for installation, regulatory requirements and implementation of a simultaneous hybrid PET/MR-imaging system in an integrated research and clinical setting. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2012; 26:159-71. [PMID: 23053713 DOI: 10.1007/s10334-012-0347-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2012] [Revised: 07/22/2012] [Accepted: 08/09/2012] [Indexed: 10/27/2022]
Abstract
The implementation of hybrid imaging systems requires thorough and anticipatory planning at local and regional levels. For installation of combined positron emission and magnetic resonance imaging systems (PET/MRI), a number of physical and constructional provisions concerning shielding of electromagnetic fields (RF- and high-field) as well as handling of radionuclides have to be met, the latter of which includes shielding for the emitted 511 keV gamma rays. Based on our experiences with a SIEMENS Biograph mMR system, a step-by-step approach is required to allow a trouble-free installation. In this article, we present a proposal for a standardized step-by-step plan to accomplish the installation of a combined PET/MRI system. Moreover, guidelines for the smooth operation of combined PET/MRI in an integrated research and clinical setting will be proposed. Overall, the most important preconditions for the successful implementation of PET/MRI in an integrated research and clinical setting is the interdisciplinary target-oriented cooperation between nuclear medicine, radiology, and all referring and collaborating institutions at all levels of interaction (personnel, imaging protocols, reporting, selection of the data transfer and communication methods).
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16
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Minimally invasive input function for 2-18F-fluoro-A-85380 brain PET studies. Eur J Nucl Med Mol Imaging 2012; 39:651-9. [PMID: 22231015 DOI: 10.1007/s00259-011-2004-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2011] [Accepted: 11/08/2011] [Indexed: 10/14/2022]
Abstract
PURPOSE Quantitative neuroreceptor positron emission tomography (PET) studies often require arterial cannulation to measure input function. While population-based input function (PBIF) would be a less invasive alternative, it has only rarely been used in conjunction with neuroreceptor PET tracers. The aims of this study were (1) to validate the use of PBIF for 2-(18)F-fluoro-A-85380, a tracer for nicotinic receptors; (2) to compare the accuracy of measures obtained via PBIF to those obtained via blood-scaled image-derived input function (IDIF) from carotid arteries; and (3) to explore the possibility of using venous instead of arterial samples for both PBIF and IDIF. METHODS Ten healthy volunteers underwent a dynamic 2-(18)F-fluoro-A-85380 brain PET scan with arterial and, in seven subjects, concurrent venous serial blood sampling. PBIF was obtained by averaging the normalized metabolite-corrected arterial input function and subsequently scaling each curve with individual blood samples. IDIF was obtained from the carotid arteries using a blood-scaling method. Estimated Logan distribution volume (V(T)) values were compared to the reference values obtained from arterial cannulation. RESULTS For all subjects, PBIF curves scaled with arterial samples were similar in shape and magnitude to the reference arterial input function. The Logan V(T) ratio was 1.00 ± 0.05; all subjects had an estimation error <10%. IDIF gave slightly less accurate results (V(T) ratio 1.03 ± 0.07; eight of ten subjects had an error <10%). PBIF scaled with venous samples yielded inaccurate results (V(T) ratio 1.13 ± 0.13; only three of seven subjects had an error <10%). Due to arteriovenous differences at early time points, IDIF could not be calculated using venous samples. CONCLUSION PBIF scaled with arterial samples accurately estimates Logan V(T) for 2-(18)F-fluoro-A-85380. Results obtained with PBIF were slightly better than those obtained with IDIF. Due to arteriovenous concentration differences, venous samples cannot be substituted for arterial samples.
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Zanotti-Fregonara P, Chen K, Liow JS, Fujita M, Innis RB. Image-derived input function for brain PET studies: many challenges and few opportunities. J Cereb Blood Flow Metab 2011; 31:1986-98. [PMID: 21811289 PMCID: PMC3208145 DOI: 10.1038/jcbfm.2011.107] [Citation(s) in RCA: 154] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Quantitative positron emission tomography (PET) brain studies often require that the input function be measured, typically via arterial cannulation. Image-derived input function (IDIF) is an elegant and attractive noninvasive alternative to arterial sampling. However, IDIF is also a very challenging technique associated with several problems that must be overcome before it can be successfully implemented in clinical practice. As a result, IDIF is rarely used as a tool to reduce invasiveness in patients. The aim of the present review was to identify the methodological problems that hinder widespread use of IDIF in PET brain studies. We conclude that IDIF can be successfully implemented only with a minority of PET tracers. Even in those cases, it only rarely translates into a less-invasive procedure for the patient. Finally, we discuss some possible alternative methods for obtaining less-invasive input function.
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Sanabria-Bohórquez SM, Joshi AD, Holahan M, Daneker L, Riffel K, Williams M, Li W, Cook JJ, Hamill TG. Quantification of the glycine transporter 1 in rhesus monkey brain using [18F]MK-6577 and a model-based input function. Neuroimage 2011; 59:2589-99. [PMID: 21930214 DOI: 10.1016/j.neuroimage.2011.08.080] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2011] [Revised: 08/23/2011] [Accepted: 08/25/2011] [Indexed: 10/17/2022] Open
Abstract
BACKGROUND Glycine transporter 1 (GlyT1) inhibitors have emerged as potential treatments for schizophrenia due to their potentiation of NMDA receptor activity by modulating the local concentrations of the NMDA co-agonist glycine. [18F]MK-6577 is a potent and selective GlyT1 inhibitor PET tracer. Although differences in ligand kinetics can be expected between non-human primates and humans, the tracer pre-clinical evaluation can provide valuable information supporting protocol design and quantification in the clinical space. The main objective of this work was to evaluate the in vivo kinetics of [18F]MK-6577 in rhesus monkey brain. Additionally, a method for estimating the tracer input function from the tracer brain tissue kinetics and venous sampling was validated. This technique was applied for determination of the dose-occupancy relationship of a GlyT1 inhibitor in monkey brain. METHODS Compartmental and Logan graphical analysis were utilized for quantification of the [18F]MK-6577 binding using the measured tracer arterial input function. The stability of the tracer volume of distribution relative to scan length was assessed. The proposed model-based input function method takes advantage of the agreement between the tracer concentration in arterial and venous plasma from ~5 min. The approach estimates the initial peak of the input curve by adding a gamma like function term to the measured venous curve. The parameters of the model function were estimated by simultaneously fitting several brain time activity curves to a compartmental model. RESULTS Good agreement was found between the model-based and the measured arterial plasma curve and the corresponding distribution volumes. The Logan analysis was the preferred method of analysis providing reliable and stable volume of distribution and occupancy results using a 90 and possibly 60 min scan length. CONCLUSION The model-based input function method and Logan analysis are well suited for quantification of [18F]MK-6577 binding and GlyT1 occupancy in monkey brain.
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Zanotti-Fregonara P, Liow JS, Fujita M, Dusch E, Zoghbi SS, Luong E, Boellaard R, Pike VW, Comtat C, Innis RB. Image-derived input function for human brain using high resolution PET imaging with [C](R)-rolipram and [C]PBR28. PLoS One 2011; 6:e17056. [PMID: 21364880 PMCID: PMC3045425 DOI: 10.1371/journal.pone.0017056] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2010] [Accepted: 01/13/2011] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND The aim of this study was to test seven previously published image-input methods in state-of-the-art high resolution PET brain images. Images were obtained with a High Resolution Research Tomograph plus a resolution-recovery reconstruction algorithm using two different radioligands with different radiometabolite fractions. Three of the methods required arterial blood samples to scale the image-input, and four were blood-free methods. METHODS All seven methods were tested on twelve scans with [(11)C](R)-rolipram, which has a low radiometabolite fraction, and on nineteen scans with [(11)C]PBR28 (high radiometabolite fraction). Logan V(T) values for both blood and image inputs were calculated using the metabolite-corrected input functions. The agreement of image-derived Logan V(T) values with the reference blood-derived Logan V(T) values was quantified using a scoring system. Using the image input methods that gave the most accurate results with Logan analysis, we also performed kinetic modelling with a two-tissue compartment model. RESULTS For both radioligands the highest scores were obtained with two blood-based methods, while the blood-free methods generally performed poorly. All methods gave higher scores with [(11)C](R)-rolipram, which has a lower metabolite fraction. Compartment modeling gave less reliable results, especially for the estimation of individual rate constants. CONCLUSION OUR STUDY SHOWS THAT: 1) Image input methods that are validated for a specific tracer and a specific machine may not perform equally well in a different setting; 2) despite the use of high resolution PET images, blood samples are still necessary to obtain a reliable image input function; 3) the accuracy of image input may also vary between radioligands depending on the magnitude of the radiometabolite fraction: the higher the metabolite fraction of a given tracer (e.g., [(11)C]PBR28), the more difficult it is to obtain a reliable image-derived input function; and 4) in association with image inputs, graphical analyses should be preferred over compartmental modelling.
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Affiliation(s)
- Paolo Zanotti-Fregonara
- Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
- * E-mail:
| | - Jeih-San Liow
- Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Masahiro Fujita
- Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | | | - Sami S. Zoghbi
- Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Elise Luong
- Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | - Ronald Boellaard
- Department of Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Victor W. Pike
- Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
| | | | - Robert B. Innis
- Molecular Imaging Branch, National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, Maryland, United States of America
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20
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Zanotti-Fregonara P, Zoghbi SS, Liow JS, Luong E, Boellaard R, Gladding RL, Pike VW, Innis RB, Fujita M. Kinetic analysis in human brain of [11C](R)-rolipram, a positron emission tomographic radioligand to image phosphodiesterase 4: a retest study and use of an image-derived input function. Neuroimage 2010; 54:1903-9. [PMID: 21034834 DOI: 10.1016/j.neuroimage.2010.10.064] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2010] [Revised: 09/22/2010] [Accepted: 10/20/2010] [Indexed: 10/18/2022] Open
Abstract
UNLABELLED [(11)C](R)-rolipram provides a measure of the density of phosphodiesterase 4 (PDE4) in brain, an enzyme that metabolizes cAMP. The aims of this study were to perform kinetic modeling of [(11)C](R)-rolipram in healthy humans using an arterial input function and to replace this arterial input in humans with an image-derived input function. METHODS Twelve humans had two injections of [(11)C](R)-rolipram. An image-derived input function was obtained from the carotid arteries and four blood samples. The samples were used for partial volume correction and for estimating the parent concentration using HPLC analysis. RESULTS An unconstrained two-compartment model and Logan analysis measured distribution volume V(T), with good identifiability but with moderately high retest variability (15%). Similar results were obtained using the image input (ratio image/arterial V(T)=1.00±0.06). CONCLUSIONS Binding of [(11)C](R)-rolipram to PDE4 can be quantified in human brain using kinetic modeling and an arterial input function. Image input function from carotid arteries provides an equally accurate and reproducible method to quantify PDE4.
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Affiliation(s)
- Paolo Zanotti-Fregonara
- Molecular Imaging Branch, National Institute of Mental Health, Bethesda, Maryland 20892-2035, USA
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Zheng X, Tian G, Huang SC, Feng D. A hybrid clustering method for ROI delineation in small-animal dynamic PET images: application to the automatic estimation of FDG input functions. ACTA ACUST UNITED AC 2010; 15:195-205. [PMID: 20952342 DOI: 10.1109/titb.2010.2087343] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Tracer kinetic modeling with dynamic positron emission tomography (PET) requires a plasma time-activity curve (PTAC) as an input function. Several image-derived input function (IDIF) methods that rely on drawing the region of interest (ROI) in large vascular structures have been proposed to overcome the problems caused by the invasive approach for obtaining the PTAC, especially for small-animal studies. However, the manual placement of ROIs for estimating IDIF is subjective and labor-intensive, making it an undesirable and unreliable process. In this paper, we propose a novel hybrid clustering method (HCM) that objectively delineates ROIs in dynamic PET images for the estimation of IDIFs, and demonstrate its application to the mouse PET studies acquired with [ (18)F]Fluoro-2-deoxy-2-D-glucose (FDG). We begin our HCM using k-means clustering for background removal. We then model the time-activity curves using polynomial regression mixture models in curve clustering for heart structure detection. The hierarchical clustering is finally applied for ROI refinements. The HCM achieved accurate ROI delineation in both computer simulations and experimental mouse studies. In the mouse studies, the predicted IDIF had a high correlation with the gold standard, the PTAC derived from the invasive blood samples. The results indicate that the proposed HCM has a great potential in ROI delineation for automatic estimation of IDIF in dynamic FDG-PET studies.
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Affiliation(s)
- Xiujuan Zheng
- Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong Kong.
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22
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Maroy R, Boisgard R, Comtat C, Jego B, Fontyn Y, Jan S, Dubois A, Trébossen R, Tavitian B. Quantitative organ time activity curve extraction from rodent PET images without anatomical prior. Med Phys 2010; 37:1507-17. [PMID: 20443471 DOI: 10.1118/1.3327454] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Numerous new drug candidates fail because of inadequate pharmacokinetics. Positron emission tomography (PET) enables the noninvasive characterization of the drug in humans and animals. The aim of the present work was the comparison of methods for the extraction of organ time activity curves from rodent PET images without requiring resort to anatomical information. METHODS The rodent organs were segmented using the local means analysis method and the accuracy of the time activity curve (TAC) estimated using four methods was compared: The mean TAC (Mean), the TAC computed in a selection of organ voxels (ROIopt), and the TAC corrected for partial volume effect using the geometric transfer matrix (GTM) method. The accuracy of the TAC estimated using the three methods was compared on phantom simulations and on experimental data sets on mice injected with fluorothymidine. RESULTS The segmentation quality measured on phantom simulation was 80% of overlap between segmented and gold standard organs. On the phantom simulations, the error on the TAC estimation on phantom simulations was lower for ROIopt (8%) than using the GTM (18%) and the Mean (27%) methods. Similar results were achieved on the experimental data sets: ROIopt (5.8%), GTM (9.7%), and Mean (12%). CONCLUSIONS The new ROI optimization method was fast and precise for all homogeneous organs, while mean organ TAC computation led as expected to important errors. GTM improved the quantification accuracy but showed instabilities due to segmentation errors and to small organ sizes. Partial volume effect correction or limitation is thus possible for the extraction of precise organ TACs without requiring either manual delineation or an anatomical modality.
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Affiliation(s)
- R Maroy
- Commissariat à l'Energie Atomique, Service Hospitalier Frédéric Joliot, 91401 Orsay, France.
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Croteau E, Lavallée É, Labbe SM, Hubert L, Pifferi F, Rousseau JA, Cunnane SC, Carpentier AC, Lecomte R, Bénard F. Image-derived input function in dynamic human PET/CT: methodology and validation with 11C-acetate and 18F-fluorothioheptadecanoic acid in muscle and 18F-fluorodeoxyglucose in brain. Eur J Nucl Med Mol Imaging 2010; 37:1539-50. [PMID: 20437239 PMCID: PMC2914861 DOI: 10.1007/s00259-010-1443-z] [Citation(s) in RCA: 74] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2009] [Accepted: 03/08/2010] [Indexed: 01/05/2023]
Abstract
Purpose Despite current advances in PET/CT systems, blood sampling still remains the standard method to obtain the radiotracer input function for tracer kinetic modelling. The purpose of this study was to validate the use of image-derived input functions (IDIF) of the carotid and femoral arteries to measure the arterial input function (AIF) in PET imaging. The data were obtained from two different research studies, one using 18F-FDG for brain imaging and the other using 11C-acetate and 18F-fluoro-6-thioheptadecanoic acid (18F-FTHA) in femoral muscles. Methods The method was validated with two phantom systems. First, a static phantom consisting of syringes of different diameters containing radioactivity was used to determine the recovery coefficient (RC) and spill-in factors. Second, a dynamic phantom built to model bolus injection and clearance of tracers was used to establish the correlation between blood sampling, AIF and IDIF. The RC was then applied to the femoral artery data from PET imaging studies with 11C-acetate and 18F-FTHA and to carotid artery data from brain imaging with 18F-FDG. These IDIF data were then compared to actual AIFs from patients. Results With 11C-acetate, the perfusion index in the femoral muscle was 0.34±0.18 min−1 when estimated from the actual time–activity blood curve, 0.29±0.15 min−1 when estimated from the corrected IDIF, and 0.66±0.41 min−1 when the IDIF data were not corrected for RC. A one-way repeated measures (ANOVA) and Tukey’s test showed a statistically significant difference for the IDIF not corrected for RC (p<0.0001). With 18F-FTHA there was a strong correlation between Patlak slopes, the plasma to tissue transfer rate calculated using the true plasma radioactivity content and the corrected IDIF for the femoral muscles (vastus lateralis r=0.86, p=0.027; biceps femoris r=0.90, p=0.017). On the other hand, there was no correlation between the values derived using the AIF and those derived using the uncorrected IDIF. Finally, in the brain imaging study with 18F-FDG, the cerebral metabolic rate of glucose (CMRglc) measured using the uncorrected IDIF was consistently overestimated. The CMRglc obtained using blood sampling was 13.1±3.9 mg/100 g per minute and 14.0±5.7 mg/100 g per minute using the corrected IDIF (r2=0.90). Conclusion Correctly obtained, carotid and femoral artery IDIFs can be used as a substitute for AIFs to perform tracer kinetic modelling in skeletal femoral muscles and brain analyses.
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Affiliation(s)
- Etienne Croteau
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC Canada
- Sherbrooke Molecular Imaging Center, Centre de recherche clinique Étienne-LeBel, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - Éric Lavallée
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC Canada
- Sherbrooke Molecular Imaging Center, Centre de recherche clinique Étienne-LeBel, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - Sébastien M. Labbe
- Department of Medicine, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - Laurent Hubert
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC Canada
- Sherbrooke Molecular Imaging Center, Centre de recherche clinique Étienne-LeBel, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - Fabien Pifferi
- Research Center on Aging, Université de Sherbrooke, Sherbrooke, QC Canada
- Mécanismes Adaptatifs et Évolution, MNHN-CNRS, Brunoy, France
| | - Jacques A. Rousseau
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC Canada
- Sherbrooke Molecular Imaging Center, Centre de recherche clinique Étienne-LeBel, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - Stephen C. Cunnane
- Research Center on Aging, Université de Sherbrooke, Sherbrooke, QC Canada
- Department of Medicine, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - André C. Carpentier
- Department of Medicine, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - Roger Lecomte
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Sciences, Université de Sherbrooke, Sherbrooke, QC Canada
- Sherbrooke Molecular Imaging Center, Centre de recherche clinique Étienne-LeBel, Centre Hospitalier Universitaire de Sherbrooke, Sherbrooke, QC Canada
| | - François Bénard
- Division of Nuclear Medicine, Department of Radiology, University of British Columbia, Vancouver, BC Canada
- BC Cancer Agency, 675 West 10th Avenue, Vancouver, BC V5Z 1L3 Canada
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Comparison of eight methods for the estimation of the image-derived input function in dynamic [(18)F]-FDG PET human brain studies. J Cereb Blood Flow Metab 2009; 29:1825-35. [PMID: 19584890 DOI: 10.1038/jcbfm.2009.93] [Citation(s) in RCA: 63] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The aim of this study was to compare eight methods for the estimation of the image-derived input function (IDIF) in [(18)F]-FDG positron emission tomography (PET) dynamic brain studies. The methods were tested on two digital phantoms and on four healthy volunteers. Image-derived input functions obtained with each method were compared with the reference input functions, that is, the activity in the carotid labels of the phantoms and arterial blood samples for the volunteers, in terms of visual inspection, areas under the curve, cerebral metabolic rates of glucose (CMRglc), and individual rate constants. Blood-sample-free methods provided less reliable results as compared with those obtained using the methods that require the use of blood samples. For some of the blood-sample-free methods, CMRglc estimations considerably improved when the IDIF was calibrated with a single blood sample. Only one of the methods tested in this study, and only in phantom studies, allowed a reliable calculation of the individual rate constants. For the estimation of CMRglc values using an IDIF in [(18)F]-FDG PET brain studies, a reliable absolute blood-sample-free procedure is not available yet.
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