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Albert NL, Preusser M, Traub-Weidinger T, Tolboom N, Law I, Palmer JD, Guedj E, Furtner J, Fraioli F, Huang RY, Johnson DR, Deroose CM, Herrmann K, Vogelbaum M, Chang S, Tonn JC, Weller M, Wen PY, van den Bent MJ, Verger A, Ivanidze J, Galldiks N. Joint EANM/EANO/RANO/SNMMI practice guideline/procedure standards for diagnostics and therapy (theranostics) of meningiomas using radiolabeled somatostatin receptor ligands: version 1.0. Eur J Nucl Med Mol Imaging 2024:10.1007/s00259-024-06783-x. [PMID: 38898354 DOI: 10.1007/s00259-024-06783-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Accepted: 05/30/2024] [Indexed: 06/21/2024]
Abstract
PURPOSE To provide practice guideline/procedure standards for diagnostics and therapy (theranostics) of meningiomas using radiolabeled somatostatin receptor (SSTR) ligands. METHODS This joint practice guideline/procedure standard was collaboratively developed by the European Association of Nuclear Medicine (EANM), the Society of Nuclear Medicine and Molecular Imaging (SNMMI), the European Association of Neurooncology (EANO), and the PET task force of the Response Assessment in Neurooncology Working Group (PET/RANO). RESULTS Positron emission tomography (PET) using somatostatin receptor (SSTR) ligands can detect meningioma tissue with high sensitivity and specificity and may provide clinically relevant information beyond that obtained from structural magnetic resonance imaging (MRI) or computed tomography (CT) imaging alone. SSTR-directed PET imaging can be particularly useful for differential diagnosis, delineation of meningioma extent, detection of osseous involvement, and the differentiation between posttherapeutic scar tissue and tumour recurrence. Moreover, SSTR-peptide receptor radionuclide therapy (PRRT) is an emerging investigational treatment approach for meningioma. CONCLUSION These practice guidelines will define procedure standards for the application of PET imaging in patients with meningiomas and related SSTR-targeted PRRTs in routine practice and clinical trials and will help to harmonize data acquisition and interpretation across centers, facilitate comparability of studies, and to collect larger databases. The current document provides additional information to the evidence-based recommendations from the PET/RANO Working Group regarding the utilization of PET imaging in meningiomas Galldiks (Neuro Oncol. 2017;19(12):1576-87). The information provided should be considered in the context of local conditions and regulations.
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Affiliation(s)
- Nathalie L Albert
- Department of Nuclear Medicine, LMU Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
| | - Matthias Preusser
- Department of Medicine I, Division of Oncology, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
- Department of Diagnostic and Therapeutic Nuclear Medicine, Clinic Donaustadt, Vienna Health Care Group, Vienna, Austria
| | - Nelleke Tolboom
- Princess Máxima Centre for Paediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, Netherlands
- Division Imaging & Oncology, University Medical Centre Utrecht, Utrecht, Netherlands
| | - Ian Law
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Joshua D Palmer
- Department of Radiation Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Eric Guedj
- Institut Fresnel, Nuclear Medicine Department, APHM, CNRS, Timone Hospital, CERIMED, Aix Marseille Univ, Marseille, France
| | - Julia Furtner
- Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Faculty of Medicine and Dentistry, Danube Private University, 3500, Krems, Austria
| | - Francesco Fraioli
- Institute of Nuclear Medicine, University College London (UCL), London, UK
| | - Raymond Y Huang
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | | | - Christophe M Deroose
- Nuclear Medicine and Molecular Imaging, Department of Imaging and Pathology, University Hospitals Leuven, KU Leuven, Leuven, Belgium
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK) - University Hospital Essen, Essen, Germany
| | | | - Susan Chang
- Department of Neurological Surgery, University of California at San Francisco, San Francisco, CA, USA
| | - Joerg-Christian Tonn
- Department of Neurosurgery, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Michael Weller
- Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Martin J van den Bent
- Department of Neurology, Brain Tumor Center at Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Antoine Verger
- Department of Nuclear Medicine and Nancyclotep Imaging Platform, CHRU Nancy and IADI INSERM UMR 1254, Université de Lorraine, Nancy, France
| | - Jana Ivanidze
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Norbert Galldiks
- Institute of Neuroscience and Medicine (INM-3), Research Center Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital Cologne, Cologne, Germany
- Center for Integrated Oncology (CIO), Universities of Aachen, Bonn, Cologne, and Duesseldorf, Cologne, Germany
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Bach MJ, Jakubauskaite A, Law I, Henriksen OM, Havsteen I, Henriksen AC, Rosenbaum S, Marner L. Long-term prognostic value of [ 15O]H 2O PET imaging in patients suspected for cerebral hemodynamic insufficiency. J Stroke Cerebrovasc Dis 2024; 33:107466. [PMID: 38029459 DOI: 10.1016/j.jstrokecerebrovasdis.2023.107466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/20/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
OBJECTIVES Quantitative regional cerebral perfusion (rCBF) measurements using [15O]H2O PET with arterial cannulation and acetazolamide (ACZ) challenge have been reserved to identify high-risk patients that are candidates for by-pass operation. We aimed to assess the prognostic value of various parameters in quantitative [15O]H2O PET measurements in patients not subsequently undergoing surgery. METHODS We identified 32 eligible patients who underwent [15O]H2O brain PET imaging for suspicion of hemodynamic insufficiency between 2009 and 2020. Cerebrovascular events were defined as new ischemic lesions on MRI, stroke, transient ischemic attack, vascular dementia. Follow-up period was 91 months (range: 26-146). rCBF before (rCBFbase) and after (rCBFacz) ACZ challenge and the relative increase (CVR), were examined in the anterior (ACA), middle (MCA), and posterior (PCA) cerebral artery territories of the affected hemisphere, and the most recent MRI scans were scored for infarcts and white matter lesions. RESULTS Receiver operating characteristic (ROC) curve analysis showed higher prognostic accuracy for rCBFacz(AUC:0.82) compared to CVR (AUC:0.72) and rCBFbase (AUC:0.77). ROC AUC, optimal thresholds (and corresponding sensitivity/specificity/accuracy) for rCBFacz after ACZ in individual territories were 0.79 and 37.8 mL 100g-1 min-1 (0.81/0.63/0.72) for the ACA, 0.84 and 32 mL 100g-1 min-1 (0.81/0.75/0.78) for the MCA, and 0.70 and 43.9 ml/(mL 100g-1 min-1 (0.81/0.43/0,62) for the PCA. Kaplan Meier survival curve showed longer event-free survival in patients with rCBFacz below cut-off (p=0.007). In multivariate analysis rCBFacz remained a significant predictor when correcting for age. CONCLUSION Quantitative rCBF measurements after ACZ challenge with [15O]H2O PET provided high prognostic value for future cerebrovascular events.
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Affiliation(s)
- Mathias Jacobsen Bach
- Department of Neurology, Copenhagen University Hospital Bispebjerg, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Bispebjerg, Denmark
| | | | - Ian Law
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Physiology and Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology and Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Denmark
| | - Inger Havsteen
- Department of Radiology Copenhagen University Hospital Bispebjerg, Denmark
| | - Alexander Cuculiza Henriksen
- Department of Neurology, Copenhagen University Hospital Bispebjerg, Denmark; Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Bispebjerg, Denmark
| | - Sverre Rosenbaum
- Department of Neurology, Copenhagen University Hospital Bispebjerg, Denmark
| | - Lisbeth Marner
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; Department of Clinical Physiology and Nuclear Medicine, Copenhagen University Hospital Bispebjerg, Denmark.
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Arabi H, Zaidi H. Recent Advances in Positron Emission Tomography/Magnetic Resonance Imaging Technology. Magn Reson Imaging Clin N Am 2023; 31:503-515. [PMID: 37741638 DOI: 10.1016/j.mric.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/25/2023]
Abstract
More than a decade has passed since the clinical deployment of the first commercial whole-body hybrid PET/MR scanner in the clinic. The major advantages and limitations of this technology have been investigated from technical and medical perspectives. Despite the remarkable advantages associated with hybrid PET/MR imaging, such as reduced radiation dose and fully simultaneous functional and structural imaging, this technology faced major challenges in terms of mutual interference between MRI and PET components, in addition to the complexity of achieving quantitative imaging owing to the intricate MRI-guided attenuation correction in PET/MRI. In this review, the latest technical developments in PET/MRI technology as well as the state-of-the-art solutions to the major challenges of quantitative PET/MR imaging are discussed.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, Geneva 4 CH-1211, Switzerland; Geneva University Neurocenter, Geneva University, Geneva CH-1205, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, Odense 500, Denmark.
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Veit-Haibach P, Ahlström H, Boellaard R, Delgado Bolton RC, Hesse S, Hope T, Huellner MW, Iagaru A, Johnson GB, Kjaer A, Law I, Metser U, Quick HH, Sattler B, Umutlu L, Zaharchuk G, Herrmann K. International EANM-SNMMI-ISMRM consensus recommendation for PET/MRI in oncology. Eur J Nucl Med Mol Imaging 2023; 50:3513-3537. [PMID: 37624384 PMCID: PMC10547645 DOI: 10.1007/s00259-023-06406-x] [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: 06/30/2023] [Accepted: 08/16/2023] [Indexed: 08/26/2023]
Abstract
PREAMBLE The Society of Nuclear Medicine and Molecular Imaging (SNMMI) is an international scientific and professional organization founded in 1954 to promote the science, technology, and practical application of nuclear medicine. The European Association of Nuclear Medicine (EANM) is a professional non-profit medical association that facilitates communication worldwide between individuals pursuing clinical and research excellence in nuclear medicine. The EANM was founded in 1985. The merged International Society for Magnetic Resonance in Medicine (ISMRM) is an international, nonprofit, scientific association whose purpose is to promote communication, research, development, and applications in the field of magnetic resonance in medicine and biology and other related topics and to develop and provide channels and facilities for continuing education in the field.The ISMRM was founded in 1994 through the merger of the Society of Magnetic Resonance in Medicine and the Society of Magnetic Resonance Imaging. SNMMI, ISMRM, and EANM members are physicians, technologists, and scientists specializing in the research and practice of nuclear medicine and/or magnetic resonance imaging. The SNMMI, ISMRM, and EANM will periodically define new guidelines for nuclear medicine practice to help advance the science of nuclear medicine and/or magnetic resonance imaging and to improve the quality of service to patients throughout the world. Existing practice guidelines will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated. Each practice guideline, representing a policy statement by the SNMMI/EANM/ISMRM, has undergone a thorough consensus process in which it has been subjected to extensive review. The SNMMI, ISMRM, and EANM recognize that the safe and effective use of diagnostic nuclear medicine imaging and magnetic resonance imaging requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guideline by those entities not providing these services is not authorized. These guidelines are an educational tool designed to assist practitioners in providing appropriate care for patients. They are not inflexible rules or requirements of practice and are not intended, nor should they be used, to establish a legal standard of care. For these reasons and those set forth below, the SNMMI, the ISMRM, and the EANM caution against the use of these guidelines in litigation in which the clinical decisions of a practitioner are called into question. The ultimate judgment regarding the propriety of any specific procedure or course of action must be made by the physician or medical physicist in light of all the circumstances presented. Thus, there is no implication that an approach differing from the guidelines, standing alone, is below the standard of care. To the contrary, a conscientious practitioner may responsibly adopt a course of action different from that set forth in the guidelines when, in the reasonable judgment of the practitioner, such course of action is indicated by the condition of the patient, limitations of available resources, or advances in knowledge or technology subsequent to publication of the guidelines. The practice of medicine includes both the art and the science of the prevention, diagnosis, alleviation, and treatment of disease. The variety and complexity of human conditions make it impossible to always reach the most appropriate diagnosis or to predict with certainty a particular response to treatment. Therefore, it should be recognized that adherence to these guidelines will not ensure an accurate diagnosis or a successful outcome. All that should be expected is that the practitioner will follow a reasonable course of action based on current knowledge, available resources, and the needs of the patient to deliver effective and safe medical care. The sole purpose of these guidelines is to assist practitioners in achieving this objective.
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Affiliation(s)
- Patrick Veit-Haibach
- Joint Department Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, Toronto General Hospital, 1 PMB-275, 585 University Avenue, Toronto, Ontario, M5G 2N2, Canada
- Joint Department of Medical Imaging, University of Toronto, Toronto, Canada
| | - Håkan Ahlström
- Department of Surgical Sciences, Uppsala University, 751 85, Uppsala, Sweden
- Antaros Medical AB, BioVenture Hub, 431 53, Mölndal, Sweden
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Roberto C Delgado Bolton
- Department of Diagnostic Imaging (Radiology) and Nuclear Medicine, University Hospital San Pedro and Centre for Biomedical Research of La Rioja (CIBIR), Logroño, La Rioja, Spain
| | - Swen Hesse
- Department of Nuclear Medicine, University of Leipzig Medical Center, Leipzig, Germany
| | - Thomas Hope
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Martin W Huellner
- Department of Nuclear Medicine, University Hospital Zürich, University of Zürich, Rämistrasse 100, 8091, Zurich, Switzerland
| | - Andrei Iagaru
- Department of Radiology, Division of Nuclear Medicine, Stanford University Medical Center, Stanford, CA, USA
| | - Geoffrey B Johnson
- Division of Nuclear Medicine, Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - Andreas Kjaer
- Department of Clinical Physiology, Nuclear Medicine & PET and Cluster for Molecular Imaging, Rigshospitalet and University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, Copenhagen, Denmark
| | - Ur Metser
- Joint Department of Medical Imaging, University Health Network, Mount Sinai Hospital and Women's College Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Harald H Quick
- High-Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
- Erwin L. Hahn Institute for MR Imaging, University of Duisburg-Essen, Essen, Germany
| | - Bernhard Sattler
- Department of Nuclear Medicine, University Hospital Leipzig, Leipzig, Germany
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Greg Zaharchuk
- Division of Neuroradiology, Department of Radiology, Stanford University, 300 Pasteur Drive, Room S047, Stanford, CA, 94305-5105, USA
| | - Ken Herrmann
- Department of Nuclear Medicine, University of Duisburg-Essen and German Cancer Consortium (DKTK), University Hospital Essen, Essen, Germany.
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Krokos G, MacKewn J, Dunn J, Marsden P. A review of PET attenuation correction methods for PET-MR. EJNMMI Phys 2023; 10:52. [PMID: 37695384 PMCID: PMC10495310 DOI: 10.1186/s40658-023-00569-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 08/07/2023] [Indexed: 09/12/2023] Open
Abstract
Despite being thirteen years since the installation of the first PET-MR system, the scanners constitute a very small proportion of the total hybrid PET systems installed. This is in stark contrast to the rapid expansion of the PET-CT scanner, which quickly established its importance in patient diagnosis within a similar timeframe. One of the main hurdles is the development of an accurate, reproducible and easy-to-use method for attenuation correction. Quantitative discrepancies in PET images between the manufacturer-provided MR methods and the more established CT- or transmission-based attenuation correction methods have led the scientific community in a continuous effort to develop a robust and accurate alternative. These can be divided into four broad categories: (i) MR-based, (ii) emission-based, (iii) atlas-based and the (iv) machine learning-based attenuation correction, which is rapidly gaining momentum. The first is based on segmenting the MR images in various tissues and allocating a predefined attenuation coefficient for each tissue. Emission-based attenuation correction methods aim in utilising the PET emission data by simultaneously reconstructing the radioactivity distribution and the attenuation image. Atlas-based attenuation correction methods aim to predict a CT or transmission image given an MR image of a new patient, by using databases containing CT or transmission images from the general population. Finally, in machine learning methods, a model that could predict the required image given the acquired MR or non-attenuation-corrected PET image is developed by exploiting the underlying features of the images. Deep learning methods are the dominant approach in this category. Compared to the more traditional machine learning, which uses structured data for building a model, deep learning makes direct use of the acquired images to identify underlying features. This up-to-date review goes through the literature of attenuation correction approaches in PET-MR after categorising them. The various approaches in each category are described and discussed. After exploring each category separately, a general overview is given of the current status and potential future approaches along with a comparison of the four outlined categories.
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Affiliation(s)
- Georgios Krokos
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK.
| | - Jane MacKewn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Joel Dunn
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
| | - Paul Marsden
- School of Biomedical Engineering and Imaging Sciences, The PET Centre at St Thomas' Hospital London, King's College London, 1st Floor Lambeth Wing, Westminster Bridge Road, London, SE1 7EH, UK
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DE Luca F, Bolin M, Blomqvist L, Wassberg C, Martin H, Falk Delgado A. 11C-methionine PET/MRI in postoperative patients after craniotomy: zero echo time and head atlas versus CT-based attenuation correction. THE QUARTERLY JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING : OFFICIAL PUBLICATION OF THE ITALIAN ASSOCIATION OF NUCLEAR MEDICINE (AIMN) [AND] THE INTERNATIONAL ASSOCIATION OF RADIOPHARMACOLOGY (IAR), [AND] SECTION OF THE SOCIETY OF... 2023; 67:215-222. [PMID: 35119249 DOI: 10.23736/s1824-4785.22.03389-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Attenuation correction (AC) is an important topic in PET/MRI and particularly challenging after brain tumor surgery, near metal implants, adjacent bone and burr holes. In this study, we evaluated the performance of two MR-driven AC methods, zero-echo-time AC (ZTE-AC) and atlas-AC, in comparison to reference standard CT-AC in patients with surgically treated brain tumors at 11C-methionine PET/MRI. METHODS This retrospective study investigated seven postoperative patients with neuropathologically confirmed brain tumor at 11C-methionine PET/MRI. Three AC maps - ZTE-AC, atlas-AC and reference standard CT-AC - were generated for each patient. Standardized uptake values (SUV) were obtained at the metal implant, adjacent bone and burr hole. Standard uptake ratio (SUR) SURmetal/mirror, SURbone/mirror and SURburrhole/mirror were then calculated and analyzed with Bland-Altman, Pearson correlation and intraclass correlation reliability. RESULTS Smaller mean percent bias range (Bland-Altman) was found for ZTE-AC than atlas-AC in all analyses (metal ZTE -0.46 to -0.02, metal atlas -3.57 to -3.26; bone ZTE -4.60 to -2.16, bone atlas -5.25 to -3.81; burr hole ZTE -0.95 to -0.52, burr hole atlas 7.86 to 8.87). Percent SD range (Bland-Altman) was large for both methods in all analyses, with lower absolute values for ZTE-AC (ZTE 7.02-8.49; atlas 11.47-14.83). A very strong correlation (Pearson correlation) was demonstrated for both methods compared to CT-AC (ZTE ρ 0.97-0.99, P<0.001; atlas ρ 0.88-0.91, P≤0.009) with higher absolute values for ZTE. An excellent intraclass correlation coefficient was found across all analyses for ZTE, atlas and CT maps (ICC ≥0.88). CONCLUSIONS ZTE for MR-driven PET attenuation correction presented a more comparable performance to reference standard CT-AC at the postoperative site. ZTE-AC may serve as a useful diagnostic tool for MR-driven AC in patients with surgically treated brain tumors.
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Affiliation(s)
- Francesca DE Luca
- Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden -
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden -
| | - Martin Bolin
- Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Lennart Blomqvist
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
- Department of Molecular Medicine and Surgery Karolinska Institute, Solna, Sweden
| | - Cecilia Wassberg
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Heather Martin
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Anna Falk Delgado
- Department of Clinical Neuroscience, Karolinska Institute, Solna, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
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Ladefoged CN, Anderberg L, Madsen K, Henriksen OM, Hasselbalch SG, Andersen FL, Højgaard L, Law I. Estimation of brain amyloid accumulation using deep learning in clinical [ 11C]PiB PET imaging. EJNMMI Phys 2023; 10:44. [PMID: 37450069 PMCID: PMC10348957 DOI: 10.1186/s40658-023-00562-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 07/05/2023] [Indexed: 07/18/2023] Open
Abstract
INTRODUCTION Estimation of brain amyloid accumulation is valuable for evaluation of patients with cognitive impairment in both research and clinical routine. The development of high throughput and accurate strategies for the determination of amyloid status could be an important tool in patient selection for clinical trials and amyloid directed treatment. Here, we propose the use of deep learning to quantify amyloid accumulation using standardized uptake value ratio (SUVR) and classify amyloid status based on their PET images. METHODS A total of 1309 patients with cognitive impairment scanned with [11C]PIB PET/CT or PET/MRI were included. Two convolutional neural networks (CNNs) for reading-based amyloid status and SUVR prediction were trained using 75% of the PET/CT data. The remaining PET/CT (n = 300) and all PET/MRI (n = 100) data was used for evaluation. RESULTS The prevalence of amyloid positive patients was 61%. The amyloid status classification model reproduced the expert reader's classification with 99% accuracy. There was a high correlation between reference and predicted SUVR (R2 = 0.96). Both reference and predicted SUVR had an accuracy of 97% compared to expert classification when applying a predetermined SUVR threshold of 1.35 for binary classification of amyloid status. CONCLUSION The proposed CNN models reproduced both the expert classification and quantitative measure of amyloid accumulation in a large local dataset. This method has the potential to replace or simplify existing clinical routines and can facilitate fast and accurate classification well-suited for a high throughput pipeline.
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Affiliation(s)
- Claes Nøhr Ladefoged
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Lasse Anderberg
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Karine Madsen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Steen Gregers Hasselbalch
- Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Flemming Littrup Andersen
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Liselotte Højgaard
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology and Nuclear Medicine, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Raymond C, Jurkiewicz MT, Orunmuyi A, Liu L, Dada MO, Ladefoged CN, Teuho J, Anazodo UC. The performance of machine learning approaches for attenuation correction of PET in neuroimaging: A meta-analysis. J Neuroradiol 2023; 50:315-326. [PMID: 36738990 DOI: 10.1016/j.neurad.2023.01.157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/28/2023] [Indexed: 02/05/2023]
Abstract
PURPOSE This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards. METHODS Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022. Reported outcomes for image quality, tissue classification performance, regional and global bias were extracted to evaluate ML-AC performance. Methodological quality of included studies and the quality of evidence of analysed outcomes were assessed using QUADAS-2 and GRADE, respectively. RESULTS A total of 19 studies (2371 participants) met the inclusion criteria. Overall, the global bias of ML methods was 0.76 ± 1.2%. For image quality, the relative mean square error (RMSE) was 0.20 ± 0.4 while for tissues classification, the Dice similarity coefficient (DSC) for bone/soft tissue/air were 0.82 ± 0.1 / 0.95 ± 0.03 / 0.85 ± 0.14. CONCLUSIONS In general, ML-AC performance is within acceptable limits for clinical PET imaging. The sparse information on ML-AC robustness and its limited qualitative clinical evaluation may hinder clinical implementation in neuroimaging, especially for PET/MRI or emerging brain PET systems where standard AC approaches are not readily available.
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Affiliation(s)
- Confidence Raymond
- Department of Medical Biophysics, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada
| | - Michael T Jurkiewicz
- Department of Medical Biophysics, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada; Department of Medical Imaging, Western University, London, ON, Canada
| | - Akintunde Orunmuyi
- Kenyatta University Teaching, Research and Referral Hospital, Nairobi, Kenya
| | - Linshan Liu
- Lawson Health Research Institute, London, ON, Canada
| | | | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine, and PET, Rigshospitalet, Copenhagen, Denmark
| | - Jarmo Teuho
- Turku PET Centre, Turku University, Turku, Finland; Turku University Hospital, Turku, Finland
| | - Udunna C Anazodo
- Department of Medical Biophysics, Western University, London, ON, Canada; Lawson Health Research Institute, London, ON, Canada; Montreal Neurological Institute, 3801 Rue University, Montreal, QC H3A 2B4, Canada.
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9
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Ladefoged CN, Andersen FL, Andersen TL, Anderberg L, Engkebølle C, Madsen K, Højgaard L, Henriksen OM, Law I. DeepDixon synthetic CT for [ 18F]FET PET/MRI attenuation correction of post-surgery glioma patients with metal implants. Front Neurosci 2023; 17:1142383. [PMID: 37090806 PMCID: PMC10115992 DOI: 10.3389/fnins.2023.1142383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Accepted: 03/08/2023] [Indexed: 04/25/2023] Open
Abstract
Purpose Conventional magnetic resonance imaging (MRI) can for glioma assessment be supplemented by positron emission tomography (PET) imaging with radiolabeled amino acids such as O-(2-[18F]fluoroethyl)-L-tyrosine ([18F]FET), which provides additional information on metabolic properties. In neuro-oncology, patients often undergo brain and skull altering treatment, which is known to challenge MRI-based attenuation correction (MR-AC) methods and thereby impact the simplified semi-quantitative measures such as tumor-to-brain ratio (TBR) used in clinical routine. The aim of the present study was to examine the applicability of our deep learning method, DeepDixon, for MR-AC in [18F]FET PET/MRI scans of a post-surgery glioma cohort with metal implants. Methods The MR-AC maps were assessed for all 194 included post-surgery glioma patients (318 studies). The subgroup of 147 patients (222 studies, 200 MBq [18F]FET PET/MRI) with tracer uptake above 1 ml were subsequently reconstructed with DeepDixon, vendor-default atlas-based method, and a low-dose computed tomography (CT) used as reference. The biological tumor volume (BTV) was delineated on each patient by isocontouring tracer uptake above a TBR threshold of 1.6. We evaluated the MR-AC methods using the recommended clinical metrics BTV and mean and maximum TBR on a patient-by-patient basis against the reference with CT-AC. Results Ninety-seven percent of the studies (310/318) did not have any major artifacts using DeepDixon, which resulted in a Dice coefficient of 0.89/0.83 for tissue/bone, respectively, compared to 0.84/0.57 when using atlas. The average difference between DeepDixon and CT-AC was within 0.2% across all clinical metrics, and no statistically significant difference was found. When using DeepDixon, only 3 out of 222 studies (1%) exceeded our acceptance criteria compared to 72 of the 222 studies (32%) with the atlas method. Conclusion We evaluated the performance of a state-of-the-art MR-AC method on the largest post-surgical glioma patient cohort to date. We found that DeepDixon could overcome most of the issues arising from irregular anatomy and metal artifacts present in the cohort resulting in clinical metrics within acceptable limits of the reference CT-AC in almost all cases. This is a significant improvement over the vendor-provided atlas method and of particular importance in response assessment.
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10
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Hinge C, Henriksen OM, Lindberg U, Hasselbalch SG, Højgaard L, Law I, Andersen FL, Ladefoged CN. A zero-dose synthetic baseline for the personalized analysis of 2-Deoxy-2-[18F]fluoroglucose: Application in Alzheimer’s disease. Front Neurosci 2022; 16:1053783. [DOI: 10.3389/fnins.2022.1053783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/07/2022] [Indexed: 11/25/2022] Open
Abstract
PurposeBrain 2-Deoxy-2-[18F]fluoroglucose ([18F]FDG-PET) is widely used in the diagnostic workup of Alzheimer’s disease (AD). Current tools for uptake analysis rely on non-personalized templates, which poses a challenge as decreased glucose uptake could reflect neuronal dysfunction, or heterogeneous brain morphology associated with normal aging. Overcoming this, we propose a deep learning method for synthesizing a personalized [18F]FDG-PET baseline from the patient’s own MRI, and showcase its applicability in detecting AD pathology.MethodsWe included [18F]FDG-PET/MRI data from 123 patients of a local cohort and 600 patients from ADNI. A supervised, adversarial model with two connected Generative Adversarial Networks (GANs) was trained on cognitive normal (CN) patients with transfer-learning to generate full synthetic baseline volumes (sbPET) (192 × 192 × 192) which reflect healthy uptake conditioned on brain anatomy. Synthetic accuracy was measured by absolute relative %-difference (Abs%), relative %-difference (RD%), and peak signal-to-noise ratio (PSNR). Lastly, we deployed the sbPET images in a fully personalized method for localizing metabolic abnormalities.ResultsThe model achieved a spatially uniform Abs% of 9.4%, RD% of 0.5%, and a PSNR of 26.3 for CN subjects. The sbPET images conformed to the anatomical information dictated by the MRI and proved robust in presence of atrophy. The personalized abnormality method correctly mapped the pathology of AD subjects while showing little to no anomalies for CN subjects.ConclusionThis work demonstrated the feasibility of synthesizing fully personalized, healthy-appearing [18F]FDG-PET images. Using these, we showcased a promising application in diagnosing AD, and theorized the potential value of sbPET images in other neuroimaging routines.
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11
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Zainudin NA, Zulkifli N, Hamid K, Hashim H, Mansor S. Experimental evaluation of absolute quantification in 99m Tc-TRODAT-1 SPECT/CT brain dopamine transporter (DAT) studies. J Appl Clin Med Phys 2022; 23:e13723. [PMID: 35833589 PMCID: PMC9359040 DOI: 10.1002/acm2.13723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 06/05/2022] [Accepted: 06/23/2022] [Indexed: 11/10/2022] Open
Abstract
Objective To evaluate the quantitative accuracy of clinical brain dopamine transporters (DAT) investigations utilizing 99mTc‐TRODAT‐1 single‐photon emission computed tomography (SPECT)/computed tomography (CT) in experimental and clinical settings. Materials and methods The study used an experimental phantom evaluation and a clinical dataset. Three‐dimensional‐ordered subsets expectation–maximization reconstructed the original and resampled datasets using attenuation correction, scatter correction, and resolution recovery. The reconstructed data were analyzed and reported as percentage difference, standardized uptake value reference (SUVr), and a coefficient of variation (CoV). The Taguchi method tested the impact of the three different parameters on signal‐to‐noise ratio (SNR) and SUVr, including number iteration, Poisson resampling, and phantom setup, with and without the plaster of Paris (POP). Six 99mTc‐TRODAT‐1 SPECT/CT scans were acquired in healthy subjects for verification purposes. Results The percentage activity difference between the phantom with and without POP is 20% and 5%, respectively. The SUVr reveals a 10% underestimate for both with and without POP. When it comes to the influence of Poisson resampling, the SUVr value for 75% Poisson resampling indicates 10% underestimation on both sides of the caudate and putamen area, with and without POP. When 25% of Poisson resampling is applied, the SUVr value is overestimated (±35%). In the Taguchi analysis, iteration numbers were the most dominant factor with the F‐value of 9.41 and the contribution rate of 52.66% (p < 0.05) for SNR. In comparison, F‐value of 9.1 for Poisson resampled with contribution rate of 58.91% (p < 0.05) for SUVr. Reducing counts by 25% from the original dataset resulted in a minimal bias in SUVr, compared to 50% and 75%. Conclusion The optimal absolute SPECT/CT quantification of brain DAT studies using 99mTc‐TRODAT‐1 appears achievable with at least 4i10s and SUVr as the surrogate parameter. In clinical investigations, it is possible to reduce the recommended administered dose by up to 25% while maintaining accurate measurement.
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Affiliation(s)
- Norasma Amira Zainudin
- Departmen of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia
| | - Nadiah Zulkifli
- Departmen of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia
| | - Khadijah Hamid
- Departmen of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia.,Nuclear Medicine Unit, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia
| | - Hazlin Hashim
- Departmen of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia.,Nuclear Medicine Unit, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia
| | - Syahir Mansor
- Departmen of Biomedical Imaging, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia.,Nuclear Medicine Unit, Advanced Medical and Dental Institute, Universiti Sains Malaysia, Kepala Batas, Penang, 13200, Malaysia
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12
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Zhao MY, Woodward A, Fan AP, Chen KT, Yu Y, Chen DY, Moseley ME, Zaharchuk G. Reproducibility of cerebrovascular reactivity measurements: A systematic review of neuroimaging techniques . J Cereb Blood Flow Metab 2022; 42:700-717. [PMID: 34806918 PMCID: PMC9254040 DOI: 10.1177/0271678x211056702] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Cerebrovascular reactivity (CVR), the capacity of the brain to increase cerebral blood flow (CBF) to meet changes in physiological demand, is an important biomarker to evaluate brain health. Typically, this brain "stress test" is performed by using a medical imaging modality to measure the CBF change between two states: at baseline and after vasodilation. However, since there are many imaging modalities and many ways to augment CBF, a wide range of CVR values have been reported. An understanding of CVR reproducibility is critical to determine the most reliable methods to measure CVR as a clinical biomarker. This review focuses on CVR reproducibility studies using neuroimaging techniques in 32 articles comprising 427 total subjects. The literature search was performed in PubMed, Embase, and Scopus. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). We identified 5 factors of the experimental subjects (such as sex, blood characteristics, and smoking) and 9 factors of the measuring technique (such as the imaging modality, the type of the vasodilator, and the quantification method) that have strong effects on CVR reproducibility. Based on this review, we recommend several best practices to improve the reproducibility of CVR quantification in neuroimaging studies.
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Affiliation(s)
- Moss Y Zhao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Amanda Woodward
- Lane Medical Library, Stanford University, Stanford, CA, USA
| | - Audrey P Fan
- Department of Biomedical Engineering, University of California Davis, Davis, CA, USA.,Department of Neurology, University of California Davis, Davis, CA, USA
| | - Kevin T Chen
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Yannan Yu
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - David Y Chen
- Department of Medical Imaging, Taipei Medical University - Shuan-Ho Hospital, New Taipei City.,Department of Radiology, School of Medicine, Taipei Medical University, Taipei *Research materials supporting this publication can be accessed at https://doi.org/10.25740/hd852bg4538
| | | | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA, USA
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13
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Deng S, Franklin CG, O'Boyle M, Zhang W, Heyl BL, Jerabek PA, Lu H, Fox PT. Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults. Neuroimage 2022; 250:118923. [PMID: 35066157 PMCID: PMC9201851 DOI: 10.1016/j.neuroimage.2022.118923] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 01/07/2022] [Accepted: 01/11/2022] [Indexed: 12/18/2022] Open
Abstract
Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.
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Affiliation(s)
- Shengwen Deng
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Crystal G Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Michael O'Boyle
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Wei Zhang
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Betty L Heyl
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Paul A Jerabek
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; South Texas Veterans Health Care System, San Antonio, TX, USA.
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14
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Tanaka A, Sekine T, Ter Voert EEGW, Zeimpekis KG, Delso G, de Galiza Barbosa F, Warnock G, Kumita SI, Veit Haibach P, Huellner M. Reproducibility of Standardized Uptake Values Including Volume Metrics Between TOF-PET-MR and TOF-PET-CT. Front Med (Lausanne) 2022; 9:796085. [PMID: 35308500 PMCID: PMC8924656 DOI: 10.3389/fmed.2022.796085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 02/07/2022] [Indexed: 11/30/2022] Open
Abstract
Purpose To investigate the reproducibility of tracer uptake measurements, including volume metrics, such as metabolic tumor volume (MTV) and tumor lesion glycolysis (TLG) obtained by TOF-PET-CT and TOF-PET-MR. Materials and Methods Eighty consecutive patients with different oncologic diagnoses underwent TOF-PET-CT (Discovery 690; GE Healthcare) and TOF-PET-MR (SIGNA PET-MR; GE Healthcare) on the same day with single dose−18F-FDG injection. The scan order, PET-CT following or followed by PET-MR, was randomly assigned. A spherical volume of interest (VOI) of 30 mm was placed on the liver in accordance with the PERCIST criteria. For liver, the maximum and mean standard uptake value for body weight (SUV) and lean body mass (SUL) were obtained. For tumor delineation, VOI with a threshold of 40 and 50% of SUVmax was used (VOI40 and VOI50). The SUVmax, SUVmean, SUVpeak, MTV and TLG were calculated. The measurements were compared between the two scanners. Results In total, 80 tumor lesions from 35 patients were evaluated. There was no statistical difference observed in liver regions, whereas in tumor lesions, SUVmax, SUV mean, and SUVpeak of PET-MR were significantly underestimated (p < 0.001) in both VOI40 and VOI50. Among volume metrics, there was no statistical difference observed except TLG on VOI50 (p = 0.03). Correlation between PET-CT and PET-MR of each metrics were calculated. There was a moderate correlation of the liver SUV and SUL metrics (r = 0.63–0.78). In tumor lesions, SUVmax and SUVmean had a stronger correlation with underestimation in PET-MR on VOI 40 (SUVmax and SUVmean; r = 0.92 and 0.91 with slope = 0.71 and 0.72, respectively). In the evaluation of MTV and TLG, the stronger correlations were observed both on VOI40 (MTV and TLG; r = 0.75 and 0.92) and VOI50 (MTV and TLG; r = 0.88 and 0.95) between PET-CT and PET-MR. Conclusion PET metrics on TOF-PET-MR showed a good correlation with that of TOF-PET-CT. SUVmax and SUVpeak of tumor lesions were underestimated by 16% on PET-MRI. MTV with % threshold can be regarded as identical volumetric markers for both TOF-PET-CT and TOF-PET-MR.
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Affiliation(s)
- Aruki Tanaka
- Department of Radiology, Nippon Medical School Hospital, Tokyo, Japan
| | - Tetsuro Sekine
- Department of Radiology, Nippon Medical School Hospital, Tokyo, Japan.,Department of Radiology, Nippon Medical School Musashi Kosugi Hospital, Kanagawa, Japan.,Departments of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Edwin E G W Ter Voert
- Departments of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Konstantinos G Zeimpekis
- Departments of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland.,Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | | | - Felipe de Galiza Barbosa
- Departments of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
| | - Geoffrey Warnock
- Departments of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland.,PMOD Technologies Ltd., Zurich, Switzerland
| | | | - Patrick Veit Haibach
- Departments of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland.,Toronto Joint Department Medical Imaging, University Health Network, Sinai Health System, Women's College Hospital, Toronto, ON, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Martin Huellner
- Departments of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland.,University of Zurich, Zurich, Switzerland
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15
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Olin AB, Hansen AE, Rasmussen JH, Jakoby B, Berthelsen AK, Ladefoged CN, Kjær A, Fischer BM, Andersen FL. Deep learning for Dixon MRI-based attenuation correction in PET/MRI of head and neck cancer patients. EJNMMI Phys 2022; 9:20. [PMID: 35294629 PMCID: PMC8927520 DOI: 10.1186/s40658-022-00449-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 03/02/2022] [Indexed: 11/10/2022] Open
Abstract
Background Quantitative whole-body PET/MRI relies on accurate patient-specific MRI-based attenuation correction (AC) of PET, which is a non-trivial challenge, especially for the anatomically complex head and neck region. We used a deep learning model developed for dose planning in radiation oncology to derive MRI-based attenuation maps of head and neck cancer patients and evaluated its performance on PET AC. Methods Eleven head and neck cancer patients, referred for radiotherapy, underwent CT followed by PET/MRI with acquisition of Dixon MRI. Both scans were performed in radiotherapy position. PET AC was performed with three different patient-specific attenuation maps derived from: (1) Dixon MRI using a deep learning network (PETDeep). (2) Dixon MRI using the vendor-provided atlas-based method (PETAtlas). (3) CT, serving as reference (PETCT). We analyzed the effect of the MRI-based AC methods on PET quantification by assessing the average voxelwise error within the entire body, and the error as a function of distance to bone/air. The error in mean uptake within anatomical regions of interest and the tumor was also assessed. Results The average (± standard deviation) PET voxel error was 0.0 ± 11.4% for PETDeep and −1.3 ± 21.8% for PETAtlas. The error in mean PET uptake in bone/air was much lower for PETDeep (−4%/12%) than for PETAtlas (−15%/84%) and PETDeep also demonstrated a more rapidly decreasing error with distance to bone/air affecting only the immediate surroundings (less than 1 cm). The regions with the largest error in mean uptake were those containing bone (mandible) and air (larynx) for both methods, and the error in tumor mean uptake was −0.6 ± 2.0% for PETDeep and −3.5 ± 4.6% for PETAtlas. Conclusion The deep learning network for deriving MRI-based attenuation maps of head and neck cancer patients demonstrated accurate AC and exceeded the performance of the vendor-provided atlas-based method both overall, on a lesion-level, and in vicinity of challenging regions such as bone and air.
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Affiliation(s)
- Anders B Olin
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.,Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark.,Department of Radiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Jacob H Rasmussen
- Department of Otorhinolaryngology, Head and Neck Surgery and Audiology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Björn Jakoby
- Siemens Healthcare GmbH, Erlangen, Germany.,University of Surrey, Guildford, Surrey, UK
| | - Anne K Berthelsen
- Department of Oncology, Section of Radiotherapy, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Andreas Kjær
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
| | - Barbara M Fischer
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark.,King's College London and Guy's and St Thomas' PET Centre, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, London, UK
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET & Cluster for Molecular Imaging, Rigshospitalet, University of Copenhagen, Blegdamsvej 9, 2100, Copenhagen, Denmark
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Renner A, Rausch I, Cal Gonzalez J, Laistler E, Moser E, Jochimsen T, Sattler T, Sabri O, Beyer T, Figl M, Birkfellner W, Sattler B. Technical Note: A PET/MR coil with an integrated, orbiting 511 keV transmission source for PET/MR imaging validated in an animal study. Med Phys 2022; 49:2366-2372. [PMID: 35224747 PMCID: PMC9310742 DOI: 10.1002/mp.15586] [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: 03/29/2021] [Revised: 02/04/2022] [Accepted: 02/04/2022] [Indexed: 11/11/2022] Open
Abstract
Background Purpose Methods Results Conclusion
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Affiliation(s)
- Andreas Renner
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
- Department of Radiation Oncology Medical University Vienna Austria
| | - Ivo Rausch
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Jacobo Cal Gonzalez
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Elmar Laistler
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Ewald Moser
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Thies Jochimsen
- Department of Nuclear Medicine University Hospital Leipzig Germany
| | - Tatjana Sattler
- Clinic for Ruminants and Swine University of Leipzig Germany
| | - Osama Sabri
- Department of Nuclear Medicine University Hospital Leipzig Germany
| | - Thomas Beyer
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Michael Figl
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Wolfgang Birkfellner
- Center for Medical Physics and Biomedical Engineering Medical University Vienna Austria
| | - Bernhard Sattler
- Department of Nuclear Medicine University Hospital Leipzig Germany
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Puig O, Henriksen OM, Andersen FL, Lindberg U, Højgaard L, Law I, Ladefoged CN. Deep-learning-based attenuation correction in dynamic [ 15O]H 2O studies using PET/MRI in healthy volunteers. J Cereb Blood Flow Metab 2021; 41:3314-3323. [PMID: 34250821 PMCID: PMC8669198 DOI: 10.1177/0271678x211029178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Quantitative [15O]H2O positron emission tomography (PET) is the accepted reference method for regional cerebral blood flow (rCBF) quantification. To perform reliable quantitative [15O]H2O-PET studies in PET/MRI scanners, MRI-based attenuation-correction (MRAC) is required. Our aim was to compare two MRAC methods (RESOLUTE and DeepUTE) based on ultrashort echo-time with computed tomography-based reference standard AC (CTAC) in dynamic and static [15O]H2O-PET. We compared rCBF from quantitative perfusion maps and activity concentration distribution from static images between AC methods in 25 resting [15O]H2O-PET scans from 14 healthy men at whole-brain, regions of interest and voxel-wise levels. Average whole-brain CBF was 39.9 ± 6.0, 39.0 ± 5.8 and 40.0 ± 5.6 ml/100 g/min for CTAC, RESOLUTE and DeepUTE corrected studies respectively. RESOLUTE underestimated whole-brain CBF by 2.1 ± 1.50% and rCBF in all regions of interest (range -2.4%- -1%) compared to CTAC. DeepUTE showed significant rCBF overestimation only in the occipital lobe (0.6 ± 1.1%). Both MRAC methods showed excellent correlation on rCBF and activity concentration with CTAC, with slopes of linear regression lines between 0.97 and 1.01 and R2 over 0.99. In conclusion, RESOLUTE and DeepUTE provide AC information comparable to CTAC in dynamic [15O]H2O-PET but RESOLUTE is associated with a small but systematic underestimation.
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Affiliation(s)
- Oriol Puig
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Otto M Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ulrich Lindberg
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Liselotte Højgaard
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
| | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
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Zhang L, Xiao Z, Zhou C, Yuan J, He Q, Yang Y, Liu X, Liang D, Zheng H, Fan W, Zhang X, Hu Z. Spatial adaptive and transformer fusion network (STFNet) for low-count PET blind denoising with MRI. Med Phys 2021; 49:343-356. [PMID: 34796526 DOI: 10.1002/mp.15368] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 01/12/2023] Open
Abstract
PURPOSE Positron emission tomography (PET) has been widely used in various clinical applications. PET is a type of emission computed tomography and operates by positron annihilation radiation. With magnetic resonance imaging (MRI) providing anatomical information, joint PET/MRI reduces the radiation exposure risk of patients. Improved hardware and imaging algorithms have been proposed to further decrease the dose from radioactive tracers or the bed duration, but few methods focus on denoising low-count PET with MRI input. The existing methods are based on fixed conventional convolution and local attention, which do not sufficiently extract and fuse contextual and complementary information from multimodal input. There is still much room for improvement. Therefore, we propose a novel deep learning method for low-count PET/MRI denoising called the spatial-adaptive and transformer fusion network (STFNet), which consists of a Siamese encoder with a spatial-adaptive block (SA-block) and the transformer fusion encoder (TFE). METHODS Our proposed STFNet consists of a Siamese encoder with an SA-block, TFE, and two branches of the decoder. First, in the encoder, we adapt the SA-block in the Siamese encoder. The SA-block comprises deformable convolution with fusion modulation (DCFM) and two convolutional operations, which can promote network extraction of more relative and long-range contextual features. Second, the pixel-to-pixel TFE helps the network establish a local and global relationship between high-level feature maps of PET and MRI. In the decoder part, we design two branches for PET denoising and MRI translation, and predictions are obtained by trainable weighted summation. This proposed algorithm is implemented to predict synthetic standard-dose neck PET images from low-count neck PET images and MRI. Additionally, this method is compared with the existing U-Net and residual U-Net methods with and without MRI input. RESULTS To demonstrate the advantages of our method, we introduce configuration studies about TFE, ablation studies, and empirical comparative studies. Quantitative analyses are based on root mean square error (RSME), peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and Pearson correlation coefficient (PCC). Additionally, qualitative results show the comparisons between our proposed method and other existing methods. All experimental results and visualizations show that our method achieves state-of-the-art performance in quantification and qualification. CONCLUSIONS Based on our experiments, STFNet performs better than existing methods in measurement and visualization. However, our proposed method may still be suboptimal because we apply only the L1 loss to train our data set, and the data set includes corrupted PET with different low counts. In the future, we may exploit a generative adversarial network (GAN)-based paradigm in our STFNet to further improve the visual quality.
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Affiliation(s)
- Lipei Zhang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Zizheng Xiao
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Chao Zhou
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Jianmin Yuan
- Central Research Institute, Shanghai United Imaging Healthcare, Shanghai, China
| | - Qiang He
- Central Research Institute, Shanghai United Imaging Healthcare, Shanghai, China
| | - Yongfeng Yang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Xin Liu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Dong Liang
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Hairong Zheng
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
| | - Wei Fan
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Xu Zhang
- Department of Nuclear Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Zhanli Hu
- Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.,Chinese Academy of Sciences Key Laboratory of Health Informatics, Shenzhen, China
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19
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Hamdi M, Natsuaki Y, Wangerin KA, An H, St James S, Kinahan PE, Sunderland JJ, Larson PEZ, Hope TA, Laforest R. Evaluation of attenuation correction in PET/MRI with synthetic lesion insertion. J Med Imaging (Bellingham) 2021; 8:056001. [PMID: 34568511 DOI: 10.1117/1.jmi.8.5.056001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Accepted: 09/02/2021] [Indexed: 11/14/2022] Open
Abstract
Purpose: One major challenge facing simultaneous positron emission tomography (PET)/ magnetic resonance imaging (MRI) is PET attenuation correction (AC) measurement and evaluation of its accuracy. There is a crucial need for the evaluation of current and emergent PET AC methodologies in terms of absolute quantitative accuracy in the reconstructed PET images. Approach: To address this need, we developed and evaluated a lesion insertion tool for PET/MRI that will facilitate this evaluation process. This tool was developed for the Biograph mMR and evaluated using phantom and patient data. Contrast recovery coefficients (CRC) from the NEMA IEC phantom of synthesized lesions were compared to measurements. In addition, SUV biases of lesions inserted in human brain and pelvis images were assessed from PET images reconstructed with MRI-based AC (MRAC) and CT-based AC (CTAC). Results: For cross-comparison PET/MRI scanners AC evaluation, we demonstrated that the developed lesion insertion tool can be harmonized with the GE-SIGNA lesion insertion tool. About < 3 % CRC curves difference between simulation and measurement was achieved. An average of 1.6% between harmonized simulated CRC curves obtained with mMR and SIGNA lesion insertion tools was achieved. A range of - 5 % to 12% MRAC to CTAC SUV bias was respectively achieved in the vicinity and inside bone tissues in patient images in two anatomical regions, the brain, and pelvis. Conclusions: A lesion insertion tool was developed for the Biograph mMR PET/MRI scanner and harmonized with the SIGNA PET/MRI lesion insertion tool. These tools will allow for an accurate evaluation of different PET/MRI AC approaches and permit exploration of subtle attenuation correction differences across systems.
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Affiliation(s)
- Mahdjoub Hamdi
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Yutaka Natsuaki
- University of California San Francisco, Department of Radiation Oncology, San Francisco, California, United States
| | | | - Hongyu An
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
| | - Sarah St James
- University of California San Francisco, Department of Radiation Oncology, San Francisco, California, United States
| | - Paul E Kinahan
- University of Washington Seattle, Seattle, Washington, United States
| | - John J Sunderland
- University of Iowa, Carver College of Medicine, Department of Radiology, Iowa City, Iowa, United States
| | - Peder E Z Larson
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
| | - Thomas A Hope
- University of California San Francisco, Department of Radiology and Biomedical Imaging, San Francisco, California, United States
| | - Richard Laforest
- Washington University in St. Louis, Mallinckrodt Institute of Radiology, St. Louis, Missouri, United States
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20
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Brancato V, Borrelli P, Alfano V, Picardi M, Mascalchi M, Nicolai E, Salvatore M, Aiello M. The impact of MR-based attenuation correction in spinal cord FDG-PET/MR imaging for neurological studies. Med Phys 2021; 48:5924-5934. [PMID: 34369590 PMCID: PMC9293017 DOI: 10.1002/mp.15149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/30/2021] [Accepted: 07/24/2021] [Indexed: 11/21/2022] Open
Abstract
Purpose Positron emission tomography (PET) attenuation correction (AC) in positron emission tomography‐magnetic resonance (PET/MR) scanners constitutes a critical and barely explored issue in spinal cord investigation, mainly due to the limitations in accounting for highly attenuating bone structures which surround the spinal canal. Our study aims at evaluating the clinical suitability of MR‐driven AC (MRAC) for 18‐fluorodeoxy‐glucose positron emission tomography (18F‐FDG‐PET) in spinal cord. Methods Thirty‐six patients, undergoing positron emission tomography‐computed tomography (PET/CT) and PET/MR in the same session for oncological examination, were retrospectively analyzed. For each patient, raw PET data from PET/MR scanner were reconstructed with 4‐ and 5‐class MRAC maps, generated by hybrid PET/MR system (PET_MRAC4 and PET_MRAC5, respectively, where PET_MRAC is PET images reconstructed using MR‐based attenuation correction map), and an AC map derived from CT data after a custom co‐registration pipeline (PET_rCTAC, where PET_rCTAC is PET images reconstructed using CT‐based attenuation correction map), which served as reference. Mean PET standardized uptake values (SUVm) were extracted from the three reconstructed PET images by regions of interest (ROIs) identified on T2‐weighted MRI, in the spinal cord, lumbar cerebrospinal fluid (CSF), and vertebral marrow at five levels (C2, C5, T6, T12, and L3). SUVm values from PET_MRAC4 and PET_MRAC5 were compared with each other and with the reference by means of paired t‐test, and correlated using Pearson's correlation (r) to assess their consistency. Cohen's d was calculated to assess the magnitude of differences between PET images. Results SUVmvalues from PET_MRAC4 were lower than those from PET_MRAC5 in almost all analyzed ROIs, with a mean difference ranging from 0.03 to 0.26 (statistically significant in the vertebral marrow at C2 and C5, spinal cord at T6 and T2, and CSF at L3). This was also confirmed by the effect size, with highest values at low spinal levels (d = 0.45 at T12 in spinal cord, d = 0.95 at L3 in CSF). SUVm values from PET_MRAC4 and PET_MRAC5 showed a very good correlation (0.81 < r < 0.97, p < 0.05) in all spinal ROIs. Underestimation of SUVm between PET_MRAC4 and PET_rCTAC was observed at each level, with a mean difference ranging from 0.02 to 0.32 (statistically significant in the vertebral marrow at C2 and T6, and CSF at L3). Although PET_MRAC5 underestimates PET_rCTAC (mean difference ranging from 0.02 to 0.3), an overall decrease in effect size could be observed for PET_MRAC5, mainly at lower spinal levels (T12, L3). SUVm from both PET_MRAC4 and PET_MRAC5 methods showed r value from good to very good with respect to PET_rCTAC (0.67 < r < 0.9 and 0.73 < r < 0.94, p < 0.05, respectively). Conclusions Our results showed that neglecting bones in AC can underestimate the FDG uptake measurement of the spinal cord. The inclusion of bones in MRAC is far from negligible and improves the AC in spinal cord, mainly at low spinal levels. Therefore, care must be taken in the spinal canal region, and the use of AC map reconstruction methods accounting for bone structures could be beneficial.
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Affiliation(s)
| | | | | | - Marco Picardi
- Department of Clinical Medicine and Surgery, Federico II University Medical School, Naples, Italy
| | - Mario Mascalchi
- «Mario Serio» Department of Experimental and Clinical Biomedical Sciences, University of Florence, Italy
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21
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Hybrid 2-[18F] FDG PET/MRI in premanifest Huntington's disease gene-expansion carriers: The significance of partial volume correction. PLoS One 2021; 16:e0252683. [PMID: 34115782 PMCID: PMC8195345 DOI: 10.1371/journal.pone.0252683] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 05/19/2021] [Indexed: 11/19/2022] Open
Abstract
Background Huntington’s disease (HD) is an inherited, progressive neurodegenerative disease that has no cure. Striatal atrophy and hypometabolism has been described in HD as far as 15 years before clinical onset and therefore structural and functional imaging biomarkers are the most applied biomarker modalities which call for these to be exact; however, most studies are not considering the partial volume effect and thereby tend to overestimate metabolic reductions, which may bias imaging outcome measures of interventions. Objective Evaluation of partial volume effects in a cohort of premanifest HD gene-expansion carriers (HDGECs). Methods 21 HDGECs and 17 controls had a hybrid 2-[18F]FDG PET/MRI scan performed. Volume measurements and striatal metabolism, both corrected and uncorrected for partial volume effect were correlated to an estimate of disease burden, the CAG age product scaled (CAPS). Results We found significantly reduced striatal metabolism in HDGECs, but not in striatal volume. There was a negative correlation between the CAPS and striatal metabolism, both corrected and uncorrected for the partial volume effect. The partial volume effect was largest in the smallest structures and increased the difference in metabolism between the HDGEC with high and low CAPS scores. Statistical parametric mapping confirmed the results. Conclusions A hybrid 2-[18F]FDG PET/MRI scan provides simultaneous information on structure and metabolism. Using this approach for the first time on HDGECs, we highlight the importance of partial volume effect correction in order not to underestimate the standardized uptake value and thereby the risk of overestimating the metabolic effect on the striatal structures, which potentially could bias studies determining imaging outcome measures of interventions in HDGECs and probably also symptomatic HD.
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22
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Husseini JS, Amorim BJ, Torrado-Carvajal A, Prabhu V, Groshar D, Umutlu L, Herrmann K, Cañamaque LG, Garzón JRG, Palmer WE, Heidari P, Shih TTF, Sosna J, Matushita C, Cerci J, Queiroz M, Muglia VF, Nogueira-Barbosa MH, Borra RJH, Kwee TC, Glaudemans AWJM, Evangelista L, Salvatore M, Cuocolo A, Soricelli A, Herold C, Laghi A, Mayerhoefer M, Mahmood U, Catana C, Daldrup-Link HE, Rosen B, Catalano OA. An international expert opinion statement on the utility of PET/MR for imaging of skeletal metastases. Eur J Nucl Med Mol Imaging 2021; 48:1522-1537. [PMID: 33619599 PMCID: PMC8240455 DOI: 10.1007/s00259-021-05198-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 01/10/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND MR is an important imaging modality for evaluating musculoskeletal malignancies owing to its high soft tissue contrast and its ability to acquire multiparametric information. PET provides quantitative molecular and physiologic information and is a critical tool in the diagnosis and staging of several malignancies. PET/MR, which can take advantage of its constituent modalities, is uniquely suited for evaluating skeletal metastases. We reviewed the current evidence of PET/MR in assessing for skeletal metastases and provided recommendations for its use. METHODS We searched for the peer reviewed literature related to the usage of PET/MR in the settings of osseous metastases. In addition, expert opinions, practices, and protocols of major research institutions performing research on PET/MR of skeletal metastases were considered. RESULTS Peer-reviewed published literature was included. Nuclear medicine and radiology experts, including those from 13 major PET/MR centers, shared the gained expertise on PET/MR use for evaluating skeletal metastases and contributed to a consensus expert opinion statement. [18F]-FDG and non [18F]-FDG PET/MR may provide key advantages over PET/CT in the evaluation for osseous metastases in several primary malignancies. CONCLUSION PET/MR should be considered for staging of malignancies where there is a high likelihood of osseous metastatic disease based on the characteristics of the primary malignancy, hight clinical suspicious and in case, where the presence of osseous metastases will have an impact on patient management. Appropriate choice of tumor-specific radiopharmaceuticals, as well as stringent adherence to PET and MR protocols, should be employed.
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Affiliation(s)
- Jad S Husseini
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Bárbara Juarez Amorim
- Division of Nuclear Medicine, Department of Radiology, School of Medical Sciences,, State University of Campinas, Campinas, Brazil
| | - Angel Torrado-Carvajal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Medical Image Analysis and Biometry Laboratory, Universidad Rey Juan Carlos, Madrid, Spain
| | - Vinay Prabhu
- Department of Radiology, NYU Langone Health, New York, NY, USA
| | - David Groshar
- Department of Nuclear Medicine, Assuta Medical Center, Tel Aviv, and Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Lale Umutlu
- Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany
| | - Ken Herrmann
- Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
| | - Lina García Cañamaque
- Department of Nuclear Medicine, Hospital Universitario Madrid Sanchinarro, Madrid, Spain
| | | | - William E Palmer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Pedram Heidari
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Tiffany Ting-Fang Shih
- Department of Radiology and Medical Imaging, National Taiwan University College of Medicine and Hospital, Taipei City, Taiwan
| | - Jacob Sosna
- Department of Radiology, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Cristina Matushita
- Department of Nuclear Medicine, Hospital São Lucas of Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, Brazil
| | - Juliano Cerci
- Department of Nuclear Medicine, Quanta Diagnóstico Nuclear, Curitiba, Brazil
| | - Marcelo Queiroz
- Department of Radiology and Oncology, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Valdair Francisco Muglia
- Department of Medical Images, Radiation Therapy and Oncohematology, Ribeirao Preto Medical School, Hospital Clinicas, University of São Paulo, Ribeirão Prêto, Brazil
| | - Marcello H Nogueira-Barbosa
- Department of Medical Imaging, Hematology and Clinical Oncology, Ribeirão Preto Medical School. University of São Paulo (USP), Ribeirão Prêto, Brazil
| | - Ronald J H Borra
- Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Thomas C Kwee
- Medical Imaging Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Andor W J M Glaudemans
- Medical Imaging Center, Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, The Netherlands
| | - Laura Evangelista
- Department of Clinical and Experimental Medicine, University of Padova, Padua, Italy
| | - Marco Salvatore
- Department of Radiology and Nuclear Medicine, Università Suor Orsola Benincasa di Napoli, Naples, Italy
- Department of Radiology and Nuclear Medicine, Institute for Hospitalization and Healthcare (IRCCS) SDN, Istituto di Ricerca, Naples, Italy
| | - Alberto Cuocolo
- Department of Radiology and Nuclear Medicine, Institute for Hospitalization and Healthcare (IRCCS) SDN, Istituto di Ricerca, Naples, Italy
- Department of Advanced Biomedical Science, University of Naples Federico II, Naples, Italy
| | - Andrea Soricelli
- Department of Radiology and Nuclear Medicine, Institute for Hospitalization and Healthcare (IRCCS) SDN, Istituto di Ricerca, Naples, Italy
- Department of Movement and Wellness Sciences, Parthenope University of Naples, Naples, Italy
| | - Christian Herold
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna General Hospital, Vienna, Austria
| | - Andrea Laghi
- Department of Radiology, University of Rome "La Sapienza", Rome, Italy
| | - Marius Mayerhoefer
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Umar Mahmood
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Bruce Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Onofrio A Catalano
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
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23
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Vestergaard MB, Calvo OP, Hansen AE, Rosenbaum S, Larsson HBW, Henriksen OM, Law I. Validation of kinetic modeling of [ 15O]H 2O PET using an image derived input function on hybrid PET/MRI. Neuroimage 2021; 233:117950. [PMID: 33716159 DOI: 10.1016/j.neuroimage.2021.117950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 02/23/2021] [Accepted: 03/05/2021] [Indexed: 11/15/2022] Open
Abstract
In present study we aimed to validate the use of image-derived input functions (IDIF) in the kinetic modeling of cerebral blood flow (CBF) measured by [15O]H2O PET by comparing with the accepted reference standard arterial input function (AIF). Additional comparisons were made to mean cohort AIF and CBF values acquired by methodologically independent phase-contrast mapping (PCM) MRI. Using hybrid PET/MRI an IDIF was generated by measuring the radiotracer concentration in the internal carotid arteries and correcting for partial volume effects using the intravascular volume measured from MRI-angiograms. Seven patients with carotid steno-occlusive disease and twelve healthy controls were examined at rest, after administration of acetazolamide, and, in the control group, during hyperventilation. Agreement between the techniques was examined by linear regression and Bland-Altman analysis. Global CBF values modeled using IDIF correlated with values from AIF across perfusion states in both patients (p<10-6, R2=0.82, 95% limits of agreement (LoA)=[-11.3-9.9] ml/100 g/min) and controls (p<10-6, R2=0.87, 95% LoA=[-17.1-13.7] ml/100 g/min). The reproducibility of gCBF using IDIF was identical to AIF (15.8%). Values from IDIF and AIF had equally good correlation to measurements by PCM MRI, R2=0.86 and R2=0.84, (p<10-6), respectively. Mean cohort AIF performed substantially worse than individual IDIFs (p<10-6, R2=0.63, LoA=[-12.8-25.3] ml/100 g/min). In the patient group, use of IDIF provided similar reactivity maps compared to AIF. In conclusion, global CBF values modeled using IDIF correlated with values modeled by AIF and similar perfusion deficits could be established in a patient group.
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Affiliation(s)
- Mark B Vestergaard
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark.
| | - Oriol P Calvo
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark; Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Sverre Rosenbaum
- Department of Neurology, Copenhagen University Hospital Bispebjerg, Copenhagen, Denmark
| | - Henrik B W Larsson
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark; Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Otto M Henriksen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital Rigshospitalet, Blegdamsvej 9, 2100 Copenhagen Ø, Denmark; Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
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Poirier SE, Kwan BYM, Jurkiewicz MT, Samargandy L, Iacobelli M, Steven DA, Lam Shin Cheung V, Moran G, Prato FS, Thompson RT, Burneo JG, Anazodo UC, Thiessen JD. An evaluation of the diagnostic equivalence of 18F-FDG-PET between hybrid PET/MRI and PET/CT in drug-resistant epilepsy: A pilot study. Epilepsy Res 2021; 172:106583. [PMID: 33636504 DOI: 10.1016/j.eplepsyres.2021.106583] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Revised: 01/27/2021] [Accepted: 02/09/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Hybrid PET/MRI may improve detection of seizure-onset zone (SOZ) in drug-resistant epilepsy (DRE), however, concerns over PET bias from MRI-based attenuation correction (MRAC) have limited clinical adoption of PET/MRI. This study evaluated the diagnostic equivalency and potential clinical value of PET/MRI against PET/CT in DRE. MATERIALS AND METHODS MRI, FDG-PET and CT images (n = 18) were acquired using a hybrid PET/MRI and a CT scanner. To assess diagnostic equivalency, PET was reconstructed using MRAC (RESOLUTE) and CT-based attenuation correction (CTAC) to generate PET/MRI and PET/CT images, respectively. PET/MRI and PET/CT images were compared qualitatively through visual assessment and quantitatively through regional standardized uptake value (SUV) and z-score assessment. Diagnostic accuracy and sensitivity of PET/MRI and PET/CT for SOZ detection were calculated through comparison to reference standards (clinical hypothesis and histopathology, respectively). RESULTS Inter-reader agreement in visual assessment of PET/MRI and PET/CT images was 78 % and 81 %, respectively. PET/MRI and PET/CT were strongly correlated in mean SUV (r = 0.99, p < 0.001) and z-scores (r = 0.92, p < 0.001) across all brain regions. MRAC SUV bias was <5% in most brain regions except the inferior temporal gyrus, temporal pole, and cerebellum. Diagnostic accuracy and sensitivity were similar between PET/MRI and PET/CT (87 % vs. 85 % and 83 % vs. 83 %, respectively). CONCLUSION We demonstrate here that PET/MRI with optimal MRAC can yield similar diagnostic performance as PET/CT. Nevertheless, further exploration of the potential added value of PET/MRI is necessary before clinical adoption of PET/MRI for epilepsy imaging.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.
| | - Benjamin Y M Kwan
- Department of Diagnostic Radiology, Queen's University, Kingston, ON, Canada
| | - Michael T Jurkiewicz
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Lina Samargandy
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Maryssa Iacobelli
- Lawson Imaging, Lawson Health Research Institute, London, ON, Canada
| | - David A Steven
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Victor Lam Shin Cheung
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | | | - Frank S Prato
- Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - R Terry Thompson
- Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Jorge G Burneo
- Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Research Centre for Studies in Aging, McGill University, Montréal, QC, Canada.
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, London, ON, Canada; Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada; Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada
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Sousa JM, Appel L, Merida I, Heckemann RA, Costes N, Engström M, Papadimitriou S, Nyholm D, Ahlström H, Hammers A, Lubberink M. Accuracy and precision of zero-echo-time, single- and multi-atlas attenuation correction for dynamic [ 11C]PE2I PET-MR brain imaging. EJNMMI Phys 2020; 7:77. [PMID: 33369700 PMCID: PMC7769756 DOI: 10.1186/s40658-020-00347-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 12/09/2020] [Indexed: 01/12/2023] Open
Abstract
BACKGROUND A valid photon attenuation correction (AC) method is instrumental for obtaining quantitatively correct PET images. Integrated PET/MR systems provide no direct information on attenuation, and novel methods for MR-based AC (MRAC) are still under investigation. Evaluations of various AC methods have mainly focused on static brain PET acquisitions. In this study, we determined the validity of three MRAC methods in a dynamic PET/MR study of the brain. METHODS Nine participants underwent dynamic brain PET/MR scanning using the dopamine transporter radioligand [11C]PE2I. Three MRAC methods were evaluated: single-atlas (Atlas), multi-atlas (MaxProb) and zero-echo-time (ZTE). The 68Ge-transmission data from a previous stand-alone PET scan was used as reference method. Parametric relative delivery (R1) images and binding potential (BPND) maps were generated using cerebellar grey matter as reference region. Evaluation was based on bias in MRAC maps, accuracy and precision of [11C]PE2I BPND and R1 estimates, and [11C]PE2I time-activity curves. BPND was examined for striatal regions and R1 in clusters of regions across the brain. RESULTS For BPND, ZTE-MRAC showed the highest accuracy (bias < 2%) in striatal regions. Atlas-MRAC exhibited a significant bias in caudate nucleus (- 12%) while MaxProb-MRAC revealed a substantial, non-significant bias in the putamen (9%). R1 estimates had a marginal bias for all MRAC methods (- 1.0-3.2%). MaxProb-MRAC showed the largest intersubject variability for both R1 and BPND. Standardized uptake values (SUV) of striatal regions displayed the strongest average bias for ZTE-MRAC (~ 10%), although constant over time and with the smallest intersubject variability. Atlas-MRAC had highest variation in bias over time (+10 to - 10%), followed by MaxProb-MRAC (+5 to - 5%), but MaxProb showed the lowest mean bias. For the cerebellum, MaxProb-MRAC showed the highest variability while bias was constant over time for Atlas- and ZTE-MRAC. CONCLUSIONS Both Maxprob- and ZTE-MRAC performed better than Atlas-MRAC when using a 68Ge transmission scan as reference method. Overall, ZTE-MRAC showed the highest precision and accuracy in outcome parameters of dynamic [11C]PE2I PET analysis with use of kinetic modelling.
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Affiliation(s)
- João M Sousa
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
| | - Lieuwe Appel
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Medical Imaging Centre, Uppsala University Hospital, Uppsala, Sweden
| | | | - Rolf A Heckemann
- Department of Radiation Physics, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | | | | | - Dag Nyholm
- Department of Neurology, Uppsala University Hospital, Uppsala, Sweden
- Department of Neurosciences, Uppsala University, Uppsala, Sweden
| | - Håkan Ahlström
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Medical Imaging Centre, Uppsala University Hospital, Uppsala, Sweden
| | - Alexander Hammers
- King's College London & Guy's and St Thomas' PET Centre, King's College, London, UK
| | - Mark Lubberink
- Department of Surgical Sciences, Uppsala University, Uppsala, Sweden
- Medical Physics, Uppsala University Hospital, Uppsala, Sweden
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Ando T, Kemp B, Warnock G, Sekine T, Kaushik S, Wiesinger F, Delso G. Zero Echo Time MRAC on FDG-PET/MR Maintains Diagnostic Accuracy for Alzheimer's Disease; A Simulation Study Combining ADNI-Data. Front Neurosci 2020; 14:569706. [PMID: 33324141 PMCID: PMC7725704 DOI: 10.3389/fnins.2020.569706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 11/03/2020] [Indexed: 11/13/2022] Open
Abstract
Aim Attenuation correction using zero-echo time (ZTE) - magnetic resonance imaging (MRI) (ZTE-MRAC) has become one of the standard methods for brain-positron emission tomography (PET) on commercial PET/MR scanners. Although the accuracy of the net tracer-uptake quantification based on ZTE-MRAC has been validated, that of the diagnosis for dementia has not yet been clarified, especially in terms of automated statistical analysis. The aim of this study was to clarify the impact of ZTE-MRAC on the diagnosis of Alzheimer's disease (AD) by performing simulation study. Methods We recruited 27 subjects, who underwent both PET/computed tomography (CT) and PET/MR (GE SIGNA) examinations. Additionally, we extracted 107 subjects from the Alzheimer Disease Neuroimaging Initiative (ADNI) dataset. From the PET raw data acquired on PET/MR, three FDG-PET series were generated, using two vendor-provided MRAC methods (ZTE and Atlas) and CT-based AC. Following spatial normalization to Montreal Neurological Institute (MNI) space, we calculated each patient's specific error maps, which correspond to the difference between the PET image corrected using the CTAC method and the PET images corrected using the MRAC methods. To simulate PET maps as if ADNI data had been corrected using MRAC methods, we multiplied each of these 27 error maps with each of the 107 ADNI cases in MNI space. To evaluate the probability of AD in each resulting image, we calculated a cumulative t-value using a fully automated method which had been validated not only in the original ADNI dataset but several multi-center studies. In the method, PET score = 1 is the 95% prediction limit of AD. PET score and diagnostic accuracy for the discrimination of AD were evaluated in simulated images using the original ADNI dataset as reference. Results Positron emission tomography score was slightly underestimated both in ZTE and Atlas group compared with reference CTAC (-0.0796 ± 0.0938 vs. -0.0784 ± 0.1724). The absolute error of PET score was lower in ZTE than Atlas group (0.098 ± 0.075 vs. 0.145 ± 0.122, p < 0.001). A higher correlation to the original PET score was observed in ZTE vs. Atlas group (R 2: 0.982 vs. 0.961). The accuracy for the discrimination of AD patients from normal control was maintained in ZTE and Atlas compared to CTAC (ZTE vs. Atlas. vs. original; 82.5% vs. 82.1% vs. 83.2% (CI 81.8-84.5%), respectively). Conclusion For FDG-PET images on PET/MR, attenuation correction using ZTE-MRI had superior accuracy to an atlas-based method in classification for dementia. ZTE maintains the diagnostic accuracy for AD.
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Affiliation(s)
- Takahiro Ando
- Department of Radiology, Nippon Medical School, Tokyo, Japan
| | - Bradley Kemp
- Department of Radiology, Mayo Clinic, Rochester, MN, United States
| | - Geoffrey Warnock
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.,PMOD Technologies Ltd., Zurich, Switzerland
| | - Tetsuro Sekine
- Department of Radiology, Nippon Medical School, Tokyo, Japan.,Department of Radiology, Nippon Medical School Musashi-Kosugi Hospital, Kawasaki, Japan.,Department of Nuclear Medicine, University Hospital Zurich, Zurich, Switzerland
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27
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Abstract
Attenuation correction has been one of the main methodological challenges in the integrated positron emission tomography and magnetic resonance imaging (PET/MRI) field. As standard transmission or computed tomography approaches are not available in integrated PET/MRI scanners, MR-based attenuation correction approaches had to be developed. Aspects that have to be considered for implementing accurate methods include the need to account for attenuation in bone tissue, normal and pathological lung and the MR hardware present in the PET field-of-view, to reduce the impact of subject motion, to minimize truncation and susceptibility artifacts, and to address issues related to the data acquisition and processing both on the PET and MRI sides. The standard MR-based attenuation correction techniques implemented by the PET/MRI equipment manufacturers and their impact on clinical and research PET data interpretation and quantification are first discussed. Next, the more advanced methods, including the latest generation deep learning-based approaches that have been proposed for further minimizing the attenuation correction related bias are described. Finally, a future perspective focused on the needed developments in the field is given.
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Affiliation(s)
- Ciprian Catana
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States of America
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28
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Zhang YD, Dong Z, Wang SH, Yu X, Yao X, Zhou Q, Hu H, Li M, Jiménez-Mesa C, Ramirez J, Martinez FJ, Gorriz JM. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2020; 64:149-187. [PMID: 32834795 PMCID: PMC7366126 DOI: 10.1016/j.inffus.2020.07.006] [Citation(s) in RCA: 115] [Impact Index Per Article: 28.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/06/2020] [Accepted: 07/14/2020] [Indexed: 05/13/2023]
Abstract
Multimodal fusion in neuroimaging combines data from multiple imaging modalities to overcome the fundamental limitations of individual modalities. Neuroimaging fusion can achieve higher temporal and spatial resolution, enhance contrast, correct imaging distortions, and bridge physiological and cognitive information. In this study, we analyzed over 450 references from PubMed, Google Scholar, IEEE, ScienceDirect, Web of Science, and various sources published from 1978 to 2020. We provide a review that encompasses (1) an overview of current challenges in multimodal fusion (2) the current medical applications of fusion for specific neurological diseases, (3) strengths and limitations of available imaging modalities, (4) fundamental fusion rules, (5) fusion quality assessment methods, and (6) the applications of fusion for atlas-based segmentation and quantification. Overall, multimodal fusion shows significant benefits in clinical diagnosis and neuroscience research. Widespread education and further research amongst engineers, researchers and clinicians will benefit the field of multimodal neuroimaging.
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Affiliation(s)
- Yu-Dong Zhang
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Zhengchao Dong
- Department of Psychiatry, Columbia University, USA
- New York State Psychiatric Institute, New York, NY 10032, USA
| | - Shui-Hua Wang
- Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- School of Architecture Building and Civil engineering, Loughborough University, Loughborough, LE11 3TU, UK
- School of Mathematics and Actuarial Science, University of Leicester, LE1 7RH, UK
| | - Xiang Yu
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Xujing Yao
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Qinghua Zhou
- School of Informatics, University of Leicester, Leicester, LE1 7RH, Leicestershire, UK
| | - Hua Hu
- Department of Psychiatry, Columbia University, USA
- Department of Neurology, The Second Affiliated Hospital of Soochow University, China
| | - Min Li
- Department of Psychiatry, Columbia University, USA
- School of Internet of Things, Hohai University, Changzhou, China
| | - Carmen Jiménez-Mesa
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Javier Ramirez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Francisco J Martinez
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
| | - Juan Manuel Gorriz
- Department of Signal Theory, Networking and Communications, University of Granada, Granada, Spain
- Department of Psychiatry, University of Cambridge, Cambridge CB21TN, UK
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29
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Mecheter I, Alic L, Abbod M, Amira A, Ji J. MR Image-Based Attenuation Correction of Brain PET Imaging: Review of Literature on Machine Learning Approaches for Segmentation. J Digit Imaging 2020; 33:1224-1241. [PMID: 32607906 PMCID: PMC7573060 DOI: 10.1007/s10278-020-00361-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Recent emerging hybrid technology of positron emission tomography/magnetic resonance (PET/MR) imaging has generated a great need for an accurate MR image-based PET attenuation correction. MR image segmentation, as a robust and simple method for PET attenuation correction, has been clinically adopted in commercial PET/MR scanners. The general approach in this method is to segment the MR image into different tissue types, each assigned an attenuation constant as in an X-ray CT image. Machine learning techniques such as clustering, classification and deep networks are extensively used for brain MR image segmentation. However, only limited work has been reported on using deep learning in brain PET attenuation correction. In addition, there is a lack of clinical evaluation of machine learning methods in this application. The aim of this review is to study the use of machine learning methods for MR image segmentation and its application in attenuation correction for PET brain imaging. Furthermore, challenges and future opportunities in MR image-based PET attenuation correction are discussed.
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Affiliation(s)
- Imene Mecheter
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK.
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar.
| | - Lejla Alic
- Magnetic Detection and Imaging Group, Faculty of Science and Technology, University of Twente, Enschede, Netherlands
| | - Maysam Abbod
- Department of Electronic and Computer Engineering, Brunel University London, Uxbridge, UK
| | - Abbes Amira
- Institute of Artificial Intelligence, De Montfort University, Leicester, UK
| | - Jim Ji
- Department of Electrical and Computer Engineering, Texas A & M University at Qatar, Doha, Qatar
- Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX, USA
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30
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Harries J, Jochimsen TH, Scholz T, Schlender T, Barthel H, Sabri O, Sattler B. A realistic phantom of the human head for PET-MRI. EJNMMI Phys 2020; 7:52. [PMID: 32757099 PMCID: PMC7406590 DOI: 10.1186/s40658-020-00320-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 07/16/2020] [Indexed: 12/27/2022] Open
Abstract
Background The combination of positron emission tomography (PET) and magnetic resonance imaging (MRI) (PET-MRI) is a unique hybrid imaging modality mainly used in oncology and neurology. The MRI-based attenuation correction (MRAC) is crucial for correct quantification of PET data. A suitable phantom to validate quantitative results in PET-MRI is currently missing. In particular, the correction of attenuation due to bone is usually not verified by commonly available phantoms. The aim of this work was, thus, the development of such a phantom and to explore whether such a phantom might be used to validate MRACs. Method Various materials were investigated for their attenuation and MR properties. For the substitution of bone, water-saturated gypsum plaster was used. The attenuation of 511 keV annihilation photons was regulated by addition of iodine. Adipose tissue was imitated by silicone and brain tissue by agarose gel, respectively. The practicability with respect to the comparison of MRACs was checked as follows: A small flask inserted into the phantom and a large spherical phantom (serving as a reference with negligible error in MRAC) were filled with the very same activity concentration. The activity concentration was measured and compared using clinical protocols on PET-MRI and different built-in and offline MRACs. The same measurements were carried out using PET-CT for comparison. Results The phantom imitates the human head in sufficient detail. All tissue types including bone were detected as such so that the phantom-based comparison of the quantification accuracy of PET-MRI was possible. Quantitatively, the activity concentration in the brain, which was determined using different MRACs, showed a deviation of about 5% on average and a maximum deviation of 11% compared to the spherical phantom. For PET-CT, the deviation was 5%. Conclusions The comparatively small error in quantification indicates that it is possible to construct a brain PET-MRI phantom that leads to MR-based attenuation-corrected images with reasonable accuracy.
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Affiliation(s)
- Johanna Harries
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany.,Department of Radiation Safety and Medical Physics, Medizinische Hochschule Hannover, Carl-Neuberg Straße 1, Hannover, Germany
| | - Thies H Jochimsen
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany.
| | - Thomas Scholz
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Tina Schlender
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
| | - Bernhard Sattler
- Department of Nuclear Medicine, Leipzig University Hospital, Liebigstr. 18, Leipzig, Germany
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31
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Lassen ML, Slomka PJ. PET-derived bone information from 18F-sodium fluoride: A perfect match for whole-body PET/MR attenuation correction? J Nucl Cardiol 2020; 27:1142-1144. [PMID: 31897993 DOI: 10.1007/s12350-019-01994-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 12/04/2019] [Indexed: 11/25/2022]
Affiliation(s)
- Martin Lyngby Lassen
- Artificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, 8700 Beverly Blvd. Ste. A047, Los Angeles, CA, 90048, USA.
| | - Piotr J Slomka
- Artificial Intelligence in Medicine Program, Cedars-Sinai Medical Center, 8700 Beverly Blvd. Ste. A047, Los Angeles, CA, 90048, USA
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32
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Ladefoged CN, Hansen AE, Henriksen OM, Bruun FJ, Eikenes L, Øen SK, Karlberg A, Højgaard L, Law I, Andersen FL. AI-driven attenuation correction for brain PET/MRI: Clinical evaluation of a dementia cohort and importance of the training group size. Neuroimage 2020; 222:117221. [PMID: 32750498 DOI: 10.1016/j.neuroimage.2020.117221] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 07/15/2020] [Accepted: 07/28/2020] [Indexed: 11/27/2022] Open
Abstract
INTRODUCTION Robust and reliable attenuation correction (AC) is a prerequisite for accurate quantification of activity concentration. In combined PET/MRI, AC is challenged by the lack of bone signal in the MRI from which the AC maps has to be derived. Deep learning-based image-to-image translation networks present itself as an optimal solution for MRI-derived AC (MR-AC). High robustness and generalizability of these networks are expected to be achieved through large training cohorts. In this study, we implemented an MR-AC method based on deep learning, and investigated how training cohort size, transfer learning, and MR input affected robustness, and subsequently evaluated the method in a clinical setup, with the overall aim to explore if this method could be implemented in clinical routine for PET/MRI examinations. METHODS A total cohort of 1037 adult subjects from the Siemens Biograph mMR with two different software versions (VB20P and VE11P) was used. The software upgrade included updates to all MRI sequences. The impact of training group size was investigated by training a convolutional neural network (CNN) on an increasing training group size from 10 to 403. The ability to adapt to changes in the input images between software versions were evaluated using transfer learning from a large cohort to a smaller cohort, by varying training group size from 5 to 91 subjects. The impact of MRI sequence was evaluated by training three networks based on the Dixon VIBE sequence (DeepDixon), T1-weighted MPRAGE (DeepT1), and ultra-short echo time (UTE) sequence (DeepUTE). Blinded clinical evaluation relative to the reference low-dose CT (CT-AC) was performed for DeepDixon in 104 independent 2-[18F]fluoro-2-deoxy-d-glucose ([18F]FDG) PET patient studies performed for suspected neurodegenerative disorder using statistical surface projections. RESULTS Robustness increased with group size in the training data set: 100 subjects were required to reduce the number of outliers compared to a state-of-the-art segmentation-based method, and a cohort >400 subjects further increased robustness in terms of reduced variation and number of outliers. When using transfer learning to adapt to changes in the MRI input, as few as five subjects were sufficient to minimize outliers. Full robustness was achieved at 20 subjects. Comparable robust and accurate results were obtained using all three types of MRI input with a bias below 1% relative to CT-AC in any brain region. The clinical PET evaluation using DeepDixon showed no clinically relevant differences compared to CT-AC. CONCLUSION Deep learning based AC requires a large training cohort to achieve accurate and robust performance. Using transfer learning, only five subjects were needed to fine-tune the method to large changes to the input images. No clinically relevant differences were found compared to CT-AC, indicating that clinical implementation of our deep learning-based MR-AC method will be feasible across MRI system types using transfer learning and a limited number of subjects.
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Affiliation(s)
- Claes Nøhr Ladefoged
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark.
| | - Adam Espe Hansen
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
| | - Frederik Jager Bruun
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Silje Kjærnes Øen
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anna Karlberg
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway; (c)Department of Radiology and Nuclear Medicine, St. Olavs hospital, Trondheim University Hospital, Trondheim, Norway
| | - Liselotte Højgaard
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
| | - Flemming Littrup Andersen
- Department of Clinical Physiology, Nuclear Medicine & PET, Rigshospitalet, University of Copenhagen, Denmark
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33
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Puig O, Henriksen OM, Vestergaard MB, Hansen AE, Andersen FL, Ladefoged CN, Rostrup E, Larsson HB, Lindberg U, Law I. Comparison of simultaneous arterial spin labeling MRI and 15O-H 2O PET measurements of regional cerebral blood flow in rest and altered perfusion states. J Cereb Blood Flow Metab 2020; 40:1621-1633. [PMID: 31500521 PMCID: PMC7370368 DOI: 10.1177/0271678x19874643] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Arterial spin labelling (ASL) is a non-invasive magnetic resonance imaging (MRI) technique that may provide fully quantitative regional cerebral blood flow (rCBF) images. However, before its application in clinical routine, ASL needs to be validated against the clinical gold standard, 15O-H2O positron emission tomography (PET). We aimed to compare the two techniques by performing simultaneous quantitative ASL-MRI and 15O-H2O-PET examinations in a hybrid PET/MRI scanner. Duplicate rCBF measurements were performed in healthy young subjects (n = 14) in rest, during hyperventilation, and after acetazolamide (post-ACZ), yielding 63 combined PET/MRI datasets in total. Average global CBF by ASL-MRI and 15O-H2O-PET was not significantly different in any state (40.0 ± 6.5 and 40.6 ± 4.1 mL/100 g/min, respectively in rest, 24.5 ± 5.1 and 23.4 ± 4.8 mL/100 g/min, respectively, during hyperventilation, and 59.1 ± 10.4 and 64.7 ± 10.0 mL/100 g/min, respectively, post-ACZ). Overall, strong correlation between the two methods was found across all states (slope = 1.01, R2 = 0.82), while the correlations within individual states and of reactivity measures were weaker, in particular in rest (R2 = 0.05, p = 0.03). Regional distribution was similar, although ASL yielded higher perfusion and absolute reactivity in highly vascularized areas. In conclusion, ASL-MRI and 15O-H2O-PET measurements of rCBF are highly correlated across different perfusion states, but with variable correlation within and between hemodynamic states, and systematic differences in regional distribution.
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Affiliation(s)
- Oriol Puig
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Otto M Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Mark B Vestergaard
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Claes N Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Egill Rostrup
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Henrik Bw Larsson
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Ulrich Lindberg
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
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34
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Poirier SE, Kwan BYM, Jurkiewicz MT, Samargandy L, Steven DA, Suller-Marti A, Lam Shin Cheung V, Khan AR, Romsa J, Prato FS, Burneo JG, Thiessen JD, Anazodo UC. 18F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation. Eur J Hybrid Imaging 2020; 4:10. [PMID: 34191151 PMCID: PMC8218143 DOI: 10.1186/s41824-020-00079-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/12/2020] [Indexed: 02/28/2023] Open
Abstract
BACKGROUND Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic focus (EF) prior to surgical resection in medically refractory epilepsy (MRE), especially when MRI is negative or equivocal. In this study, we developed a PET-guided diffusion tractography (PET/DTI) approach combining 18F-fluorodeoxyglucose PET (FDG-PET) and diffusion MRI to investigate white matter (WM) integrity in MRI-negative MRE patients and its potential impact on epilepsy surgical planning. METHODS FDG-PET and diffusion MRI of 14 MRI-negative or equivocal MRE patients were used to retrospectively pilot the PET/DTI approach. We used asymmetry index (AI) mapping of FDG-PET to detect the EF as brain areas showing the largest decrease in FDG uptake between hemispheres. Seed-based WM fiber tracking was performed on DTI images with a seed location in WM 3 mm from the EF. Fiber tractography was repeated in the contralateral brain region (opposite to EF), which served as a control for this study. WM fibers were quantified by calculating the fiber count, mean fractional anisotropy (FA), mean fiber length, and mean cross-section of each fiber bundle. WM integrity was assessed through fiber visualization and by normalizing ipsilateral fiber measurements to contralateral fiber measurements. The added value of PET/DTI in clinical decision-making was evaluated by a senior neurologist. RESULTS In over 60% of the patient cohort, AI mapping findings were concordant with clinical reports on seizure-onset localization and lateralization. Mean FA, fiber count, and mean fiber length were decreased in 14/14 (100%), 13/14 (93%), and 12/14 (86%) patients, respectively. PET/DTI improved diagnostic confidence in 10/14 (71%) patients and indicated that surgical candidacy be reassessed in 3/6 (50%) patients who had not undergone surgery. CONCLUSIONS We demonstrate here the utility of AI mapping in detecting the EF based on brain regions showing decreased FDG-PET activity and, when coupled with DTI, could be a powerful tool for detecting EF and assessing WM integrity in MRI-negative epilepsy. PET/DTI could be used to further enhance clinical decision-making in epilepsy surgery.
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Affiliation(s)
- Stefan E Poirier
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada. .,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
| | - Benjamin Y M Kwan
- Department of Diagnostic Radiology, Queen's University, Kingston, Ontario, Canada
| | - Michael T Jurkiewicz
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Lina Samargandy
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - David A Steven
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Ana Suller-Marti
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | | | - Ali R Khan
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Imaging Research Laboratories, Robarts Research Institute, London, Ontario, Canada
| | - Jonathan Romsa
- Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Frank S Prato
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jorge G Burneo
- Epilepsy Program, Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Jonathan D Thiessen
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.,Department of Medical Imaging, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Udunna C Anazodo
- Lawson Imaging, Lawson Health Research Institute, 268 Grosvenor St., London, Ontario, N6A 4 V2, Canada. .,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
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Tiepolt S, Luthardt J, Patt M, Hesse S, Hoffmann KT, Weise D, Gertz HJ, Sabri O, Barthel H. Early after Administration [11C]PiB PET Images Correlate with Cognitive Dysfunction Measured by the CERAD Test Battery. J Alzheimers Dis 2020; 68:65-76. [PMID: 30636731 DOI: 10.3233/jad-180217] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
BACKGROUND Amyloid-β (Aβ) and [18F]FDG PET are established as amyloid pathology and neuronal injury biomarkers. Early after administration Aβ PET images have the potential to replace [18F]FDG PET images allowing dual biomarker delivery by the administration of a single tracer. For [18F]FDG PET data, a correlation with cognitive performance is known. OBJECTIVE The aim of this study was to investigate whether early after administration [11C]PiB PET data also correlate with cognitive performance. METHODS The early after administration [11C]PiB PET data of 31 patients with cognitive impairment were evaluated. CERAD subtests were summarized to five cognitive domains. The resulting z scores were correlated with the PET data on a voxel- and VOI-based approach. Additional subgroup analyses (MCI versus dementia, Aβ-positive versus Aβ-negative subjects) were performed. RESULTS Significant correlations between cognitive performance and early after administration [11C]PiB PET data were found between left temporo-parietal SUVR and language domain, bilateral occipital as well as left temporal SUVR and executive function, left pre- and postcentral SUVRs, and visuospatial abilities. For the episodic and immediate memory domains, the analysis at the high significance level did not show any correlated cluster, however, the exploratory analysis did. CONCLUSION Our study revealed correlations between deficits in different cognitive domains and regional early after administration [11C]PiB PET data similar to those known from [18F]FDG PET studies. Thus, our data support the assumption that early [11C]PiB PET data have a potential as neuronal injury biomarker. Head-to-head double-tracer studies of larger cohorts are needed to confirm this assumption.
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Affiliation(s)
- Solveig Tiepolt
- Department of Nuclear Medicine, University of Leipzig, University of Leipzig, Leipzig, Germany
| | - Julia Luthardt
- Department of Nuclear Medicine, University of Leipzig, University of Leipzig, Leipzig, Germany
| | - Marianne Patt
- Department of Nuclear Medicine, University of Leipzig, University of Leipzig, Leipzig, Germany
| | - Swen Hesse
- Department of Nuclear Medicine, University of Leipzig, University of Leipzig, Leipzig, Germany
| | | | - David Weise
- Department of Psychiatry, University of Leipzig, Leipzig, Germany.,Department of Neurology, University of Leipzig, Leipzig, Germany
| | | | - Osama Sabri
- Department of Nuclear Medicine, University of Leipzig, University of Leipzig, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, University of Leipzig, University of Leipzig, Leipzig, Germany
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The impact of atlas-based MR attenuation correction on the diagnosis of FDG-PET/MR for Alzheimer's diseases- A simulation study combining multi-center data and ADNI-data. PLoS One 2020; 15:e0233886. [PMID: 32492074 PMCID: PMC7269241 DOI: 10.1371/journal.pone.0233886] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 05/14/2020] [Indexed: 11/19/2022] Open
Abstract
Background The purpose of this study was to assess the impact of vendor-provided atlas-based MRAC on FDG PET/MR for the evaluation of Alzheimer’s disease (AD) by using simulated images. Methods We recruited 47 patients, from two institutions, who underwent PET/CT and PET/MR (GE SIGNA) examination for oncological staging. From the PET raw data acquired on PET/MR, two FDG-PET series were generated, using vendor-provided MRAC (atlas-based) and CTAC. The following simulation steps were performed in MNI space: After spatial normalization and smoothing of the PET datasets, we calculated the error map for each patient, PETMRAC/PETCTAC. We multiplied each of these 47 error maps with each of the 203 Alzheimer’s Disease Neuroimaging Initiative (ADNI) cases after the identical normalization and smoothing. This resulted in 203*47 = 9541 datasets. To evaluate the probability of AD in each resulting image, a cumulative t-value was calculated automatically using commercially-available software (PMOD PALZ) which has been used in multiple large cohort studies. The diagnostic accuracy for the discrimination of AD and predicting progression from mild cognitive impairment (MCI) to AD were evaluated in simulated images compared with ADNI original images. Results The accuracy and specificity for the discrimination of AD-patients from normal controls were not substantially impaired, but sensitivity was slightly impaired in 5 out of 47 datasets (original vs. error; 83.2% [CI 75.0%-89.0%], 83.3% [CI 74.2%-89.8%] and 83.1% [CI 75.6%-88.3%] vs. 82.7% [range 80.4–85.0%], 78.5% [range 72.9–83.3%,] and 86.1% [range 81.4–89.8%]). The accuracy, sensitivity and specificity for predicting progression from MCI to AD during 2-year follow-up was not impaired (original vs. error; 62.5% [CI 53.3%-69.3%], 78.8% [CI 65.4%-88.6%] and 54.0% [CI 47.0%-69.1%] vs. 64.8% [range 61.5–66.7%], 75.7% [range 66.7–81.8%,] and 59.0% [range 50.8–63.5%]). The worst 3 error maps show a tendency towards underestimation of PET scores. Conclusion FDG-PET/MR based on atlas-based MR attenuation correction showed similar diagnostic accuracy to the CT-based method for the diagnosis of AD and the prediction of progression of MCI to AD using commercially-available software, although with a minor reduction in sensitivity.
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Torrado-Carvajal A. Importance of attenuation correction in PET/MR image quantification: Methods and applications. Rev Esp Med Nucl Imagen Mol 2020. [DOI: 10.1016/j.remnie.2020.03.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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38
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Torrado-Carvajal A. Importance of attenuation correction in PET/MR image quantification: Methods and applications. Rev Esp Med Nucl Imagen Mol 2020; 39:163-168. [PMID: 32345573 DOI: 10.1016/j.remn.2020.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 03/02/2020] [Accepted: 03/03/2020] [Indexed: 10/24/2022]
Abstract
The generation of accurate attenuation correction (AC) maps is a basic step to allow for quantitative PET/MR imaging. However, generating MR-based AC maps is a challenge because there is no direct relationship between the PET attenuation coefficients (μ) and the intensity of the MR signal, contrary to what happens with the intensity of CT images. In fact, ignoring the bone causes a distorted and biased distribution of the calculated SUV values. To solve this problem, several MR-based AC methods have been proposed in the literature. In this paper we describe how these methods work, and the challenge they faced to translate into full body applications. Currently, in research environments, the accuracy of AC methods is no longer a limiting factor to solve in order to carry out quantitative in vivo molecular imaging studies. However, many of these methods present a series of limitations for their real implementation in the clinical practice due to insufficient clinical validation and the difficulty of their implementation in a real environment (as described in the examples of clinical applications). Thus, we need the PET/MR community to work on the standardization of the use and assessment of different AC methods. In this scenario, the opening and access by vendors to the implementation of new AC methods in their PET/MR scanners plays a crucial role.
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Affiliation(s)
- A Torrado-Carvajal
- Laboratorio de Análisis de Imagen Médica y Biometría, Universidad Rey Juan Carlos, Madrid, España; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, Estados Unidos.
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A visual rating scale for cingulate island sign on 18F-FDG-PET to differentiate dementia with Lewy bodies and Alzheimer's disease. J Neurol Sci 2019; 410:116645. [PMID: 31911283 DOI: 10.1016/j.jns.2019.116645] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 12/17/2019] [Accepted: 12/21/2019] [Indexed: 12/27/2022]
Abstract
Valid diagnosis of dementia with Lewy bodies (DLB) is essential to establish appropriate treatment and care. However, the diagnostic accuracy is complicated by clinical and pathological overlap with Alzheimer's disease (AD). Cingulate island sign (CIS), defined as sparing of posterior cingulate cortex (PCC) relative to precuneus and cuneus on 18F-fluoro-deoxy-glucose positron emission tomography (18F-FDG-PET), is included in the revised diagnostic DLB criteria. There are no guidelines for the visual grading of CIS, although visual rating is a fast-applicable method in a clinical setting. The objective was to develop a robust visual CIS scale and evaluate the performance in differentiating DLB with and without amyloid beta pathology (Aβ+/-), and AD. 18F-FDG-PET scans from 35 DLB patients, 36 AD patients, and 23 healthy controls were rated according to a visual CIS scale based on specific reading criteria. The visual CIS scale was validated against a quantitative CIS ratio derived from a region of interest analysis of PCC, precuneus, and cuneus. DLB patients had a significantly higher visual CIS score compared to AD patients, and controls. A cut-off visual CIS score of 4 significantly differentiated DLB Aβ- patients from DLB Aβ+ patients. In conclusion, the visual CIS scale is clinically useful to differentiate DLB from AD. The degree of CIS may be related to Aβ pathology in DLB patients.
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40
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Rischka L, Gryglewski G, Berroterán-Infante N, Rausch I, James GM, Klöbl M, Sigurdardottir H, Hartenbach M, Hahn A, Wadsak W, Mitterhauser M, Beyer T, Kasper S, Prayer D, Hacker M, Lanzenberger R. Attenuation Correction Approaches for Serotonin Transporter Quantification With PET/MRI. Front Physiol 2019; 10:1422. [PMID: 31824335 PMCID: PMC6883225 DOI: 10.3389/fphys.2019.01422] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2019] [Accepted: 11/04/2019] [Indexed: 12/26/2022] Open
Abstract
Background Several MR-based attenuation correction (AC) approaches were developed to conquer the challenging AC in hybrid PET/MR imaging. These AC methods are commonly evaluated on standardized uptake values or tissue concentration. However, in neurotransmitter system studies absolute quantification is more favorable due to its accuracy. Therefore, our aim was to investigate the accuracy of segmentation- and atlas-based MR AC approaches on serotonin transporter (SERT) distribution volumes and occupancy after a drug challenge. Methods 18 healthy subjects (7 male) underwent two [11C]DASB PET/MRI measurements in a double-blinded, placebo controlled, cross-over design. After 70 min the selective serotonin reuptake inhibitor (SSRI) citalopram or a placebo was infused. The parameters total and specific volume of distribution (VT, VS = BPP) and occupancy were quantified. All subjects underwent a low-dose CT scan as reference AC method. Besides the standard AC approaches DIXON and UTE, a T1-weighted structural image was recorded to estimate a pseudo-CT based on an MR/CT database (pseudoCT). Another evaluated AC approach superimposed a bone model on AC DIXON. Lastly, an approach optimizing the segmentation of UTE images was analyzed (RESOLUTE). PET emission data were reconstructed with all 6 AC methods. The accuracy of the AC approaches was evaluated on a region of interest-basis for the parameters VT, BPP, and occupancy with respect to the results of AC CT. Results Variations for VT and BPP were found with all AC methods with bias ranging from -15 to 17%. The smallest relative errors for all regions were found with AC pseudoCT (<|5%|). Although the bias between BPP SSRI and BPP placebo varied markedly with AC DIXON (<|12%|) and AC UTE (<|9%|), a high correlation to AC CT was obtained (r 2∼1). The relative difference of the occupancy for all tested AC methods was small for SERT high binding regions (<|4%|). Conclusion The high correlation might offer a rescaling from the biased parameters VT and BPP to the true values. Overall, the pseudoCT approach yielded smallest errors and the best agreement with AC CT. For SERT occupancy, all AC methods showed little bias in high binding regions, indicating that errors may cancel out in longitudinal assessments.
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Affiliation(s)
- Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Gregor Gryglewski
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Neydher Berroterán-Infante
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Ivo Rausch
- QIMP Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Gregory Miles James
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Manfred Klöbl
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Helen Sigurdardottir
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Markus Hartenbach
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Andreas Hahn
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Wolfgang Wadsak
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.,CBmed, Graz, Austria
| | - Markus Mitterhauser
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.,Ludwig Boltzmann Institute Applied Diagnostics, Vienna, Austria
| | - Thomas Beyer
- QIMP Group, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Siegfried Kasper
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Rupert Lanzenberger
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
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Puig O, Vestergaard MB, Lindberg U, Hansen AE, Ulrich A, Andersen FL, Johannesen HH, Rostrup E, Law I, Larsson HBW, Henriksen OM. Phase contrast mapping MRI measurements of global cerebral blood flow across different perfusion states - A direct comparison with 15O-H 2O positron emission tomography using a hybrid PET/MR system. J Cereb Blood Flow Metab 2019; 39:2368-2378. [PMID: 30200799 PMCID: PMC6890999 DOI: 10.1177/0271678x18798762] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Revised: 05/25/2018] [Accepted: 07/29/2018] [Indexed: 11/29/2022]
Abstract
Phase-contrast mapping (PCM) magnetic resonance imaging (MRI) provides easy-access non-invasive quantification of global cerebral blood flow (gCBF) but its accuracy in altered perfusion states is not established. We aimed to compare paired PCM MRI and 15O-H2O positron emission tomography (PET) measurements of gCBF in different perfusion states in a single scanning session. Duplicate combined gCBF PCM-MRI and 15O-H2O PET measurements were performed in the resting condition, during hyperventilation and after acetazolamide administration (post-ACZ) using a 3T hybrid PET/MR system. A total of 62 paired gCBF measurements were acquired in 14 healthy young male volunteers. Average gCBF in resting state measured by PCM-MRI and 15O-H2O PET were 58.5 ± 10.7 and 38.6 ± 5.7 mL/100 g/min, respectively, during hyperventilation 33 ± 8.6 and 24.7 ± 5.8 mL/100 g/min, respectively, and post-ACZ 89.6 ± 27.1 and 57.3 ± 9.6 mL/100 g/min, respectively. On average, gCBF measured by PCM-MRI was 49% higher compared to 15O-H2O PET. A strong correlation between the two methods across all states was observed (R2 = 0.72, p < 0.001). Bland-Altman analysis suggested a perfusion dependent relative bias resulting in higher relative difference at higher CBF values. In conclusion, measurements of gCBF by PCM-MRI in healthy volunteers show a strong correlation with 15O-H2O PET, but are associated with a large and non-linear perfusion-dependent difference.
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Affiliation(s)
- Oriol Puig
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Mark B Vestergaard
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ulrich Lindberg
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Adam E Hansen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Annette Ulrich
- Department of Cardiothoracic Anesthesiology, Copenhagen University Hospital Rigshospitalet Blegdamsvej, Copenhagen, Denmark
| | - Flemming L Andersen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Helle H Johannesen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Egill Rostrup
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Henrik BW Larsson
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Otto M Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
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Schramm G, Ladefoged CN. Metal artifact correction strategies in MRI-based attenuation correction in PET/MRI. BJR Open 2019; 1:20190033. [PMID: 33178954 PMCID: PMC7592486 DOI: 10.1259/bjro.20190033] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Revised: 09/27/2019] [Accepted: 10/20/2019] [Indexed: 12/31/2022] Open
Abstract
In hybrid positron emission tomography (PET) and MRI systems, attenuation correction for PET image reconstruction is commonly based on processing of dedicated MR images. The image quality of the latter is strongly affected by metallic objects inside the body, such as e.g. dental implants, endoprostheses, or surgical clips which all lead to substantial artifacts that propagate into MRI-based attenuation images. In this work, we review publications about metal artifact correction strategies in MRI-based attenuation correction in PET/MRI. Moreover, we also give an overview about publications investigating the impact of MRI-based attenuation correction metal artifacts on the reconstructed PET image quality and quantification.
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Affiliation(s)
- Georg Schramm
- Department of Imaging and Pathology, Division of Nuclear Medicine, KU/UZ Leuven, Leuven, Belgium
| | - Claes Nøhr Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
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43
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Lei Y, Dong X, Wang T, Higgins K, Liu T, Curran WJ, Mao H, Nye JA, Yang X. Whole-body PET estimation from low count statistics using cycle-consistent generative adversarial networks. Phys Med Biol 2019; 64:215017. [PMID: 31561244 DOI: 10.1088/1361-6560/ab4891] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Lowering either the administered activity or scan time is desirable in PET imaging as it decreases the patient's radiation burden or improves patient comfort and reduces motion artifacts. But reducing these parameters lowers overall photon counts and increases noise, adversely impacting image contrast and quantification. To address this low count statistics problem, we propose a cycle-consistent generative adversarial network (Cycle GAN) model to estimate diagnostic quality PET images using low count data. Cycle GAN learns a transformation to synthesize diagnostic PET images using low count data that would be indistinguishable from our standard clinical protocol. The algorithm also learns an inverse transformation such that cycle low count PET data (inverse of synthetic estimate) generated from synthetic full count PET is close to the true low count PET. We introduced residual blocks into the generator to catch the differences between low count and full count PET in the training dataset and better handle noise. The average mean error and normalized mean square error in whole body were -0.14% ± 1.43% and 0.52% ± 0.19% with Cycle GAN model, compared to 5.59% ± 2.11% and 3.51% ± 4.14% on the original low count PET images. Normalized cross-correlation is improved from 0.970 to 0.996, and peak signal-to-noise ratio is increased from 39.4 dB to 46.0 dB with proposed method. We developed a deep learning-based approach to accurately estimate diagnostic quality PET datasets from one eighth of photon counts, and has great potential to improve low count PET image quality to the level of diagnostic PET used in clinical settings.
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Affiliation(s)
- Yang Lei
- Department of Radiation Oncology, Emory University, Atlanta, GA, United States of America. Co-first author
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Cabello J, Avram M, Brandl F, Mustafa M, Scherr M, Leucht C, Leucht S, Sorg C, Ziegler SI. Impact of non-uniform attenuation correction in a dynamic [ 18F]-FDOPA brain PET/MRI study. EJNMMI Res 2019; 9:77. [PMID: 31428975 PMCID: PMC6702490 DOI: 10.1186/s13550-019-0547-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Accepted: 07/25/2019] [Indexed: 12/31/2022] Open
Abstract
Background PET (positron emission tomography) biokinetic modelling relies on accurate quantitative data. One of the main corrections required in PET imaging to obtain high quantitative accuracy is tissue attenuation correction (AC). Incorrect non-uniform PET-AC may result in local bias in the emission images, and thus in relative activity distributions and time activity curves for different regions. MRI (magnetic resonance imaging)-based AC is an active area of research in PET/MRI neuroimaging, where several groups developed in the last few years different methods to calculate accurate attenuation (μ-)maps. Some AC methods have been evaluated for different PET radioisotopes and pathologies. However, AC in PET/MRI has scantly been investigated in dynamic PET studies where the aim is to get quantitative kinetic parameters, rather than semi-quantitative parameters from static PET studies. In this work, we investigated the impact of AC accuracy in PET image absolute quantification and, more importantly, in the slope of the Patlak analysis based on the simplified reference tissue model, from a dynamic [18F]-fluorodopa (FDOPA) PET/MRI study. In the study, we considered the two AC methods provided by the vendor and an in-house AC method based on the dual ultrashort time echo MRI sequence, using as reference a multi-atlas-based AC method based on a T1-weighted MRI sequence. Results Non-uniform bias in absolute PET quantification across the brain, from − 20% near the skull to − 10% in the central region, was observed using the two vendor’s μ-maps. The AC method developed in-house showed a − 5% and 1% bias, respectively. Our study resulted in a 5–9% overestimation of the PET kinetic parameters with the vendor-provided μ-maps, while our in-house-developed AC method showed < 2% overestimation compared to the atlas-based AC method, using the cerebellar cortex as reference region. The overestimation obtained using the occipital pole as reference region resulted in a 7–10% with the vendor-provided μ-maps, while our in-house-developed AC method showed < 6% overestimation. Conclusions PET kinetic analyses based on a reference region are especially sensitive to the non-uniform bias in PET quantification from AC inaccuracies in brain PET/MRI. Depending on the position of the reference region and the bias with respect to the analysed region, kinetic analyses suffer different levels of bias. Considering bone in the μ-map can potentially result in larger errors, compared to the absence of bone, when non-uniformities in PET quantification are introduced.
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Affiliation(s)
- Jorge Cabello
- Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany. .,Present Address: Siemens Healthineers Molecular Imaging, Knoxville, TN, USA.
| | - Mihai Avram
- Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Felix Brandl
- Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Mona Mustafa
- Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Martin Scherr
- Klinik und Poliklinik für Psychiatrie, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Universitätsklinik für Psychiatrie und Psychotherapie, Paracelsus Medical University, Salzburg, Austria
| | - Claudia Leucht
- Klinik und Poliklinik für Psychiatrie, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Stefan Leucht
- Klinik und Poliklinik für Psychiatrie, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Christian Sorg
- Neuroradiology, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Neuroimaging Center (TUM-NIC), Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Klinik und Poliklinik für Psychiatrie, Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Sibylle I Ziegler
- Nuklearmedizinische Klinik und Poliklinik, Klinikum rechts der Isar, Technische Universität München, Munich, Germany.,Klinik und Poliklinik für Nuklearmedizin, Klinikum der Universität München, Ludwig-Maximilians-Universität, Munich, Germany
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45
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Navarro de Lara LI, Frass-Kriegl R, Renner A, Sieg J, Pichler M, Bogner T, Moser E, Beyer T, Birkfellner W, Figl M, Laistler E. Design, Implementation, and Evaluation of a Head and Neck MRI RF Array Integrated with a 511 keV Transmission Source for Attenuation Correction in PET/MR. SENSORS 2019; 19:s19153297. [PMID: 31357545 PMCID: PMC6696210 DOI: 10.3390/s19153297] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Revised: 07/23/2019] [Accepted: 07/25/2019] [Indexed: 01/13/2023]
Abstract
The goal of this work is to further improve positron emission tomography (PET) attenuation correction and magnetic resonance (MR) sensitivity for head and neck applications of PET/MR. A dedicated 24-channel receive-only array, fully-integrated with a hydraulic system to move a transmission source helically around the patient and radiofrequency (RF) coil array, is designed, implemented, and evaluated. The device enables the calculation of attenuation coefficients from PET measurements at 511 keV including the RF coil and the particular patient. The RF coil design is PET-optimized by minimizing photon attenuation from coil components and housing. The functionality of the presented device is successfully demonstrated by calculating the attenuation map of a water bottle based on PET transmission measurements; results are in excellent agreement with reference values. It is shown that the device itself has marginal influence on the static magnetic field B0 and the radiofrequency transmit field B1 of the 3T PET/MR system. Furthermore, the developed RF array is shown to outperform a standard commercial 16-channel head and neck coil in terms of signal-to-noise ratio (SNR) and parallel imaging performance. In conclusion, the presented hardware enables accurate calculation of attenuation maps for PET/MR systems while improving the SNR of corresponding MR images in a single device without degrading the B0 and B1 homogeneity of the scanner.
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Affiliation(s)
- Lucia Isabel Navarro de Lara
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Roberta Frass-Kriegl
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Andreas Renner
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
- Institute of Applied Physics, Vienna University of Technology, Wiedner Hauptstrasse 8-10/134, 1040 Vienna, Austria
| | - Jürgen Sieg
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Michael Pichler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Thomas Bogner
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Ewald Moser
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Thomas Beyer
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Wolfgang Birkfellner
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Michael Figl
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria
| | - Elmar Laistler
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Waehringer Guertel 18-20, 1090 Vienna, Austria.
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Delso G, Gillett D, Bashari W, Matys T, Mendichovszky I, Gurnell M. Clinical Evaluation of 11C-Met-Avid Pituitary Lesions Using a ZTE-Based AC Method. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2019. [DOI: 10.1109/trpms.2018.2886838] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Kaltoft NS, Marner L, Larsen VA, Hasselbalch SG, Law I, Henriksen OM. Hybrid FDG PET/MRI vs. FDG PET and CT in patients with suspected dementia - A comparison of diagnostic yield and propagated influence on clinical diagnosis and patient management. PLoS One 2019; 14:e0216409. [PMID: 31048902 PMCID: PMC6497285 DOI: 10.1371/journal.pone.0216409] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 04/21/2019] [Indexed: 12/03/2022] Open
Abstract
Background Both 18F-fluoro-deoxy-glucose (FDG) positron emission tomography (PET), computed tomography (CT) and magnetic resonance imaging (MRI) are routinely used in the evaluation of memory clinic patients. Hybrid PET/MR systems now allow simultaneous PET and MRI imaging within the duration of the PET emission scan. Purpose To compare the diagnostic yield of PET/MRI using an abbreviated MR protocol with that of separate PET and CT in a mixed memory clinic population, and the propagated influences on clinical diagnosis and patient management. Material and methods Consecutive memory clinic patients (n = 78) undergoing both CT and hybrid FDG PET/MRI scans were identified retrospectively. MRI and CT were separately evaluated for vascular and structural pathology. PET scans were classified according to the presence of neurodegenerative or vascular disease using CT or MRI, respectively, for anatomical guiding. A memory clinic expert assessed the clinical impact of the additional findings and/or change of PET classification achieved by MRI anatomical guiding as compared to CT guiding. Results MRI lead to significantly higher Fazekas scores, higher medial temporal and global cortical atrophy scores, and identified more patients with infarcts (28 vs 8, p<0.001) compared to CT. MRI changed PET classification in 13 (17%) patients. Addition of MRI to CT had minor clinical impact in 4/78 (5%) and major clinical impact in 13/78 (17%) of patients. Conclusion The study demonstrates the capabilities of PET/MRI systems for routine clinical imaging of memory clinic patients, and that even an abbreviated hybrid PET/MRI protocol provides significant additional information influencing clinical diagnosis and patient management in a substantial fraction of patients when compared to separate PET and CT.
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Affiliation(s)
- Nicolai Stefan Kaltoft
- Department of Radiology, Rigshospitalet Blegdamsvej, Copenhagen University Hospital, Copenhagen, Denmark
| | - Lisbeth Marner
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Blegdamsvej, Copenhagen University Hospital, Copenhagen, Denmark
| | - Vibeke Andree Larsen
- Department of Radiology, Rigshospitalet Blegdamsvej, Copenhagen University Hospital, Copenhagen, Denmark
| | - Steen Gregers Hasselbalch
- Danish Dementia Research Centre, Dept. of Neurology, Rigshospitalet Blegdamsvej, Copenhagen University Hospital, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Blegdamsvej, Copenhagen University Hospital, Copenhagen, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet Blegdamsvej, Copenhagen University Hospital, Copenhagen, Denmark
- * E-mail:
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Marner L, Nysom K, Sehested A, Borgwardt L, Mathiasen R, Henriksen OM, Lundemann M, Munck Af Rosenschöld P, Thomsen C, Bøgeskov L, Skjøth-Rasmussen J, Juhler M, Kruse A, Broholm H, Scheie D, Lauritsen T, Forman JL, Wehner PS, Højgaard L, Law I. Early Postoperative 18F-FET PET/MRI for Pediatric Brain and Spinal Cord Tumors. J Nucl Med 2019; 60:1053-1058. [PMID: 30683767 DOI: 10.2967/jnumed.118.220293] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2018] [Accepted: 12/11/2018] [Indexed: 11/16/2022] Open
Abstract
Complete resection is the treatment of choice for most pediatric brain tumors, but early postoperative MRI for detection of residual tumor may be misleading because of MRI signal changes caused by the operation. PET imaging with amino acid tracers in adults increases the diagnostic accuracy for brain tumors, but the literature in pediatric neurooncology is limited. A hybrid PET/MRI system is highly beneficial in children, reducing the number of scanning procedures, and this is to our knowledge the first larger study using PET/MRI in pediatric neurooncology. We evaluated if additional postoperative 18F-fluoro-ethyl-tyrosine (18F-FET) PET in children and adolescents would improve diagnostic accuracy for the detection of residual tumor as compared with MRI alone and would assist clinical management. Methods: Twenty-two patients (7 male; mean age, 9.5 y; range, 0-19 y) were included prospectively and consecutively in the study and had 27 early postoperative 18F-FET PET exams performed preferentially in a hybrid PET/MRI system (NCT03402425). Results: Using follow-up (93%) or reoperation (7%) as the reference standard, PET combined with MRI discriminated tumor from treatment effects with a lesion-based sensitivity/specificity/accuracy (95% confidence intervals) of 0.73 (0.50-1.00)/1.00 (0.74-1.00)/0.87 (0.73-1.00) compared with MRI alone: 0.80 (0.57-1.00)/0.75 (0.53-0.94)/0.77 (0.65-0.90); that is, the specificity for PET/MRI was 1.00 as compared with 0.75 for MRI alone (P = 0.13). In 11 of 27 cases (41%), results from the 18F-FET PET scans added relevant clinical information, including one scan that directly influenced clinical management because an additional residual tumor site was identified. 18F-FET uptake in reactive changes was frequent (52%), but correct interpretation was possible in all cases. Conclusion: The high specificity for detecting residual tumor suggests that supplementary 18F-FET PET is relevant in cases where reoperation for residual tumor is considered.
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Affiliation(s)
- Lisbeth Marner
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Karsten Nysom
- Department of Paediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Astrid Sehested
- Department of Paediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Lise Borgwardt
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - René Mathiasen
- Department of Paediatrics and Adolescent Medicine, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Otto Mølby Henriksen
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Michael Lundemann
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital, Rigshospitalet, Denmark
| | | | - Carsten Thomsen
- Department of Diagnostic Radiology, Copenhagen University Hospital, Rigshospitalet, Denmark.,Department of Radiology, Zealand University Hospital, Køge, Denmark
| | - Lars Bøgeskov
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Denmark
| | | | - Marianne Juhler
- Department of Neurosurgery, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Anders Kruse
- Department of Orthopaedic Surgery, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Helle Broholm
- Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - David Scheie
- Department of Pathology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Torsten Lauritsen
- Department of Anaesthesiology, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Julie Lyng Forman
- Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark; and
| | - Peder Skov Wehner
- Hans Christian Andersen Children's Hospital, Odense University Hospital, Odense, Denmark
| | - Liselotte Højgaard
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital, Rigshospitalet, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine, and PET, Copenhagen University Hospital, Rigshospitalet, Denmark
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Rausch I, Zitterl A, Berroterán-Infante N, Rischka L, Prayer D, Fenchel M, Sareshgi RA, Haug AR, Hacker M, Beyer T, Traub-Weidinger T. Dynamic [18F]FET-PET/MRI using standard MRI-based attenuation correction methods. Eur Radiol 2019; 29:4276-4285. [PMID: 30635757 PMCID: PMC6610265 DOI: 10.1007/s00330-018-5942-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Revised: 11/14/2018] [Accepted: 11/30/2018] [Indexed: 11/30/2022]
Abstract
AIM To assess if tumour grading based on dynamic [18F]FET positron emission tomography/magnetic resonance imaging (PET/MRI) studies is affected by different MRI-based attenuation correction (AC) methods. METHODS Twenty-four patients with suspected brain tumours underwent dynamic [18F]FET-PET/MRI examinations and subsequent low-dose computed tomography (CT) scans of the head. The dynamic PET data was reconstructed using the following AC methods: standard Dixon-based AC and ultra-short echo time MRI-based AC (MR-AC) and a model-based AC approach. All data were reconstructed also using CT-based AC (reference). For all lesions and reconstructions, time-activity curves (TACs) and time to peak (TTP) were extracted using different region-of-interest (ROI) and volume-of-interest (VOI) definitions. According to the most common evaluation approaches, TACs were categorised into two or three distinct curve patterns. Changes in TTP and TAC patterns compared to PET using CT-based AC were reported. RESULTS In the majority of cases, TAC patterns did not change. However, TAC pattern changes as well as changes in TTP were observed in up to 8% and 17% of the cases when using different MR-AC methods and ROI/VOI definitions, respectively. However, these changes in TTP and TAC pattern were attributed to different delineations of the ROIs/VOIs in PET corrected with different AC methods. CONCLUSION PET/MRI using different MR-AC methods can be used for the assessment of TAC patterns in dynamic [18F]FET studies, as long as a meaningful delineation of the area of interest within the tumour is ensured. KEY POINTS • PET/MRI using different MR-AC methods can be used for dynamic [18F]FET studies. • A meaningful segmentation of the area of interest needs to be ensured, mandating a visual validation of the delineation by an experienced reader.
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Affiliation(s)
- Ivo Rausch
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Andreas Zitterl
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Neydher Berroterán-Infante
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Lucas Rischka
- Department of Psychiatry and Psychotherapy, Medical University of Vienna, Vienna, Austria
| | - Daniela Prayer
- Division of Neuroradiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | | | - Reza A Sareshgi
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.,Division of Radiology-Technique, University of Applied Science, Vienna, Austria
| | - Alexander R Haug
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Marcus Hacker
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria
| | - Thomas Beyer
- QIMP Team, Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria
| | - Tatjana Traub-Weidinger
- Division of Nuclear Medicine, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, 1090, Vienna, Austria.
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50
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Ladefoged CN, Marner L, Hindsholm A, Law I, Højgaard L, Andersen FL. Deep Learning Based Attenuation Correction of PET/MRI in Pediatric Brain Tumor Patients: Evaluation in a Clinical Setting. Front Neurosci 2019; 12:1005. [PMID: 30666184 PMCID: PMC6330282 DOI: 10.3389/fnins.2018.01005] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 12/13/2018] [Indexed: 11/13/2022] Open
Abstract
Aim: Positron emission tomography (PET) imaging is a useful tool for assisting in correct differentiation of tumor progression from reactive changes. O-(2-18F-fluoroethyl)-L-tyrosine (FET)-PET in combination with MRI can add valuable information for clinical decision making. Acquiring FET-PET/MRI simultaneously allows for a one-stop-shop that limits the need for a second sedation or anesthesia as with PET and MRI in sequence. PET/MRI is challenged by lack of a direct measure of photon attenuation. Accepted solutions for attenuation correction (AC) might not be applicable to pediatrics. The aim of this study was to evaluate the use of the subject-specific MR-derived AC method RESOLUTE, modified to a pediatric cohort, against the performance of an MR-AC technique based on deep learning in a pediatric brain tumor cohort. Methods: The modifications to RESOLUTE and the implementation of a deep learning method were performed using 79 pediatric patient examinations. We analyzed the 36 of these with active brain tumor area above 1 mL. We measured background (B), tumor mean and maximal activity (TMEAN, TMAX), biological tumor volume (BTV), and calculated the clinical metrics TMEAN/B and TMAX/B. Results: Overall, we found both RESOLUTE and our DeepUTE methodologies to accurately reproduce the CT-AC clinical metrics. Regardless of age, both methods were able to obtain AC maps similar to the CT-AC, albeit with DeepUTE producing the most similar based on both quantitative metrics and visual inspection. In the patient-by-patient analysis DeepUTE was the only technique with all patients inside the predefined acceptable clinical limits. It also had a higher precision with relative %-difference to the reference CT-AC (TMAX/B mean: -0.1%, CI: [-0.8%, 0.5%], p = 0.54) compared to RESOLUTE (TMAX/B mean: 0.3%, CI: [-0.6%, 1.2%], p = 0.67) and DIXON-AC (TMAX/B mean: 5.9%, CI: [4.5%, 7.4%], p < 0.0001). Conclusion: Overall, we found DeepUTE to be the AC method that most robustly reproduced the CT-AC clinical metrics per se, closely followed by RESOLUTE modified to pediatric cohorts. The added accuracy due to better noise handling of DeepUTE, ease of use, as well as the improved runtime makes DeepUTE the method of choice for PET/MRI attenuation correction.
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Affiliation(s)
- Claes Nøhr Ladefoged
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Lisbeth Marner
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Amalie Hindsholm
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Ian Law
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
| | - Liselotte Højgaard
- Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet, Copenhagen, Denmark
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