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Lo YW, Lin KH, Lee CY, Li CW, Lin CY, Chen YW, Wang LW, Wu YH, Huang WS. The impact of ZTE-based MR attenuation correction compared to CT-AC in 18F-FBPA PET before boron neutron capture therapy. Sci Rep 2024; 14:13950. [PMID: 38886395 PMCID: PMC11183148 DOI: 10.1038/s41598-024-63248-9] [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: 09/12/2023] [Accepted: 05/27/2024] [Indexed: 06/20/2024] Open
Abstract
Tumor-to-normal ratio (T/N) measurement of 18F-FBPA is crucial for patient eligibility to receive boron neutron capture therapy. This study aims to compare the difference in standard uptake value ratios on brain tumors and normal brains using PET/MR ZTE and atlas-based attenuation correction with the current standard PET/CT attenuation correction. Regarding the normal brain uptake, the difference was not significant between PET/CT and PET/MR attenuation correction methods. The T/N ratio of PET/CT-AC, PET/MR ZTE-AC and PET/MR AB-AC were 2.34 ± 0.95, 2.29 ± 0.88, and 2.19 ± 0.80, respectively. The T/N ratio comparison showed no significance using PET/CT-AC and PET/MR ZTE-AC. As for the PET/MRI AB-AC, significantly lower T/N ratio was observed (- 5.18 ± 9.52%; p < 0.05). The T/N difference between ZTE-AC and AB-AC was also significant (4.71 ± 5.80%; p < 0.01). Our findings suggested PET/MRI imaging using ZTE-AC provided superior quantification on 18F-FBPA-PET compared to atlas-based AC. Using ZTE-AC on 18F-FBPA-PET /MRI might be crucial for BNCT pre-treatment planning.
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Affiliation(s)
- Yi-Wen Lo
- Integrated PET/MR Imaging Center, Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan ROC
- Clinical Imaging Research Center (CIRC), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ko-Han Lin
- Integrated PET/MR Imaging Center, Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan ROC.
| | - Chien-Ying Lee
- Integrated PET/MR Imaging Center, Department of Nuclear Medicine, Taipei Veterans General Hospital, Taipei, Taiwan ROC
- Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming Chiao Tung University, Taipei, Taiwan ROC
| | | | | | - Yi-Wei Chen
- Division of Radiotherapy, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan ROC
| | - Ling-Wei Wang
- Division of Radiotherapy, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan ROC
| | - Yuan-Hung Wu
- Division of Radiotherapy, Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan ROC
| | - Wen-Sheng Huang
- Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Taipei, Taiwan ROC.
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Sousa JM, Appel L, Engström M, Nyholm D, Ahlström H, Lubberink M. Comparison of quantitative [ 11C]PE2I brain PET studies between an integrated PET/MR and a stand-alone PET system. Phys Med 2024; 117:103185. [PMID: 38042064 DOI: 10.1016/j.ejmp.2023.103185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/03/2023] [Accepted: 11/20/2023] [Indexed: 12/04/2023] Open
Abstract
PET/MR systems demanded great efforts for accurate attenuation correction (AC) but differences in technology, geometry and hardware attenuation may also affect quantitative results. Dedicated PET systems using transmission-based AC are regarded as the gold standard for quantitative brain PET. The study aim was to investigate the agreement between quantitative PET outcomes from a PET/MR scanner against a stand-alone PET system. Nine patients with Parkinsonism underwent two 80-min dynamic PET scans with the dopamine transporter ligand [11C]PE2I. Images were reconstructed with resolution-matched settings using 68Ge-transmission (stand-alone PET), and zero-echo-time MR (PET/MR) scans for AC. Non-displaceable binding potential (BPND) and relative delivery (R1) were evaluated using volumes of interest and voxel-wise analysis. Correlations between systems were high (r ≥ 0.85) for both quantitative outcome parameters in all brain regions. Striatal BPND was significantly lower on PET/MR than on stand-alone PET (-7%). R1 was significantly overestimated in posterior cortical regions (9%) and underestimated in striatal (-9%) and limbic areas (-6%). The voxel-wise evaluation revealed that the MR-safe headphones caused a negative bias in both parametric BPND and R1 images. Additionally, a significant positive bias of R1 was found in the auditory cortex, most likely due to the acoustic background noise during MR imaging. The relative bias of the quantitative [11C]PE2I PET data acquired from a SIGNA PET/MR system was in the same order as the expected test-retest reproducibility of [11C]PE2I BPND and R1, compared to a stand-alone ECAT PET scanner. MR headphones and background noise are potential sources of error in functional PET/MR studies.
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Affiliation(s)
- João M Sousa
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Physics, Uppsala University Hospital, Uppsala, Sweden.
| | - Lieuwe Appel
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Imaging Centre, Uppsala University Hospital, Uppsala, Sweden
| | | | - Dag Nyholm
- Department of Neurology, Uppsala University Hospital, Uppsala, Sweden; Department of Medical Sciences, Neurology, Uppsala University, Uppsala, Sweden
| | - Håkan Ahlström
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Imaging Centre, Uppsala University Hospital, Uppsala, Sweden; Antaros Medical AB, BioVenture Hub, Mölndal, Sweden
| | - Mark Lubberink
- Nuclear Medicine & PET, Department of Surgical Sciences, Uppsala University, Uppsala, Sweden; Medical Physics, Uppsala University Hospital, Uppsala, Sweden
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Cheval M, Rodrigo S, Taussig D, Caillé F, Petrescu AM, Bottlaender M, Tournier N, Besson FL, Leroy C, Bouilleret V. [ 18F]DPA-714 PET Imaging in the Presurgical Evaluation of Patients With Drug-Resistant Focal Epilepsy. Neurology 2023; 101:e1893-e1904. [PMID: 37748889 PMCID: PMC10663012 DOI: 10.1212/wnl.0000000000207811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 07/17/2023] [Indexed: 09/27/2023] Open
Abstract
BACKGROUND AND OBJECTIVES Translocator protein 18 kDa (TSPO) PET imaging is used to monitor glial activation. Recent studies have proposed TSPO PET as a marker of the epileptogenic zone (EZ) in drug-resistant focal epilepsy (DRFE). This study aims to assess the contributions of TSPO imaging using [18F]DPA-714 PET and [18F]FDG PET for localizing the EZ during presurgical assessment of DRFE, when phase 1 presurgical assessment does not provide enough information. METHODS We compared [18F]FDG and [18F]DPA-714 PET images of 23 patients who had undergone a phase 1 presurgical assessment, using qualitative visual analysis and quantitative analysis, at both the voxel and the regional levels. PET abnormalities (increase in binding for [18F]DPA-714 vs decrease in binding for [18F]FDG) were compared with clinical hypotheses concerning the localization of the EZ based on phase 1 presurgical assessment. The additional value of [18F]DPA-714 PET imaging to [18F]FDG for refining the localization of the EZ was assessed. To strengthen the visual analysis, [18F]DPA-714 PET imaging was also reviewed by 2 experienced clinicians blind to the EZ location. RESULTS The study included 23 patients. Visual analysis of [18F]DPA-714 PET was significantly more accurate than [18F]FDG PET to both, show anomalies (95.7% vs 56.5%, p = 0.022), and provide additional information to refine the EZ localization (65.2% vs 17.4%, p = 0.019). All 10 patients with normal [18F]FDG PET had anomalies when using [18F]DPA-714 PET. The additional value of [18F]DPA-714 PET seemed to be greater in patients with normal brain MRI or with neocortical EZ (especially if insula is involved). Regional analysis of [18F]DPA-714 and [18F]FDG PET provided similar results. However, using voxel-wise analysis, [18F]DPA-714 was more effective than [18F]FDG for unveiling clusters whose localization was more often consistent with the EZ hypothesis (87.0% vs 39.1%, p = 0.019). Nonrelevant bindings were seen in 14 of 23 patients in visual analysis and 9 patients of 23 patients in voxel-wise analysis. DISCUSSION [18F]DPA-714 PET imaging provides valuable information for presurgical assessments of patients with DRFE. TSPO PET could become an additional tool to help to the localization of the EZ, especially in patients with negative [18F]FDG PET. TRIAL REGISTRATION INFORMATION Eudract 2017-003381-27. Inclusion of the first patient: September 24, 2018. CLASSIFICATION OF EVIDENCE This study provides Class IV evidence on the utility of [18F]DPA-714 PET compared with [18F]FDG PET in identifying the epileptic zone in patients undergoing phase 1 presurgical evaluation for intractable epilepsy.
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Affiliation(s)
- Margaux Cheval
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France.
| | - Sebastian Rodrigo
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Delphine Taussig
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Fabien Caillé
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Ana Maria Petrescu
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Michel Bottlaender
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Nicolas Tournier
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Florent L Besson
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Claire Leroy
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
| | - Viviane Bouilleret
- From the Université Paris-Saclay (M.C., C.L., M.B., N.T.); BioMAPS (S.R., F.C., F.L.B.); Bicetre University Hospital (D.T., A.M.P.), Paris; and Imagerie Moléculaire In Vivo (V.B.), SHFJ, CEA, Orsay, France
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Mueller C, Fang YHD, Jones C, McConathy JE, Raman F, Lapi SE, Younger JW. Evidence of neuroinflammation in fibromyalgia syndrome: a [ 18 F]DPA-714 positron emission tomography study. Pain 2023; 164:2285-2295. [PMID: 37326674 PMCID: PMC10502894 DOI: 10.1097/j.pain.0000000000002927] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 03/21/2023] [Accepted: 03/28/2023] [Indexed: 06/17/2023]
Abstract
ABSTRACT This observational study aimed to determine whether individuals with fibromyalgia (FM) exhibit higher levels of neuroinflammation than healthy controls (HCs), as measured with positron emission tomography using [ 18 F]DPA-714, a second-generation radioligand for the translocator protein (TSPO). Fifteen women with FM and 10 HCs underwent neuroimaging. Distribution volume (V T ) was calculated for in 28 regions of interest (ROIs) using Logan graphical analysis and compared between groups using multiple linear regressions. Group (FM vs HC) was the main predictor of interest and TSPO binding status (high- vs mixed-affinity) was added as a covariate. The FM group had higher V T in the right postcentral gyrus ( b = 0.477, P = 0.033), right occipital gray matter (GM; b = 0.438, P = 0.039), and the right temporal GM ( b = 0.466, P = 0.042). The FM group also had lower V T than HCs in the left isthmus of the cingulate gyrus ( b = -0.553, P = 0.014). In the subgroup of high-affinity binders, the FM group had higher V T in the bilateral precuneus, postcentral gyrus, parietal GM, occipital GM, and supramarginal gyrus. Group differences in the right parietal GM were associated with decreased quality of life, higher pain severity and interference, and cognitive problems. In support of our hypothesis, we found increased radioligand binding (V T ) in the FM group compared with HCs in several brain regions regardless of participants' TSPO binding status. The ROIs overlapped with prior reports of increased TSPO binding in FM. Overall, increasing evidence supports the hypothesis that FM involves microglia-mediated neuroinflammation in the brain.
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Affiliation(s)
| | - Yu-Hua D. Fang
- Radiology and Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Chloe Jones
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jonathan E. McConathy
- Department of Radiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Fabio Raman
- Department of Radiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Suzanne E. Lapi
- Department of Radiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jarred W. Younger
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, United States
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5
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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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Yacoubian TA, Fang YHD, Gerstenecker A, Amara A, Stover N, Ruffrage L, Collette C, Kennedy R, Zhang Y, Hong H, Qin H, McConathy J, Benveniste EN, Standaert DG. Brain and Systemic Inflammation in De Novo Parkinson's Disease. Mov Disord 2023. [PMID: 36853618 DOI: 10.1002/mds.29363] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 03/01/2023] Open
Abstract
OBJECTIVE To assess the presence of brain and systemic inflammation in subjects newly diagnosed with Parkinson's disease (PD). BACKGROUND Evidence for a pathophysiologic role of inflammation in PD is growing. However, several key gaps remain as to the role of inflammation in PD, including the extent of immune activation at early stages, potential effects of PD treatments on inflammation and whether pro-inflammatory signals are associated with clinical features and/or predict more rapid progression. METHODS We enrolled subjects with de novo PD (n = 58) and age-matched controls (n = 62). Subjects underwent clinical assessments, including the Movement Disorder Society-United Parkinson's Disease rating scale (MDS-UPDRS). Comprehensive cognitive assessment meeting MDS Level II criteria for mild cognitive impairment testing was performed. Blood was obtained for flow cytometry and cytokine/chemokine analyses. Subjects underwent imaging with 18 F-DPA-714, a translocator protein 18kd ligand, and lumbar puncture if eligible and consented. RESULTS Baseline demographics and medical history were comparable between groups. PD subjects showed significant differences in University of Pennsylvania Smell Identification Test, Schwab and England Activities of Daily Living, Scales for Outcomes in PD autonomic dysfunction, and MDS-UPDRS scores. Cognitive testing demonstrated significant differences in cognitive composite, executive function, and visuospatial domain scores at baseline. Positron emission tomography imaging showed increased 18 F-DPA-714 signal in PD subjects. 18 F-DPA-714 signal correlated with several cognitive measures and some chemokines. CONCLUSIONS 18 F-DPA-714 imaging demonstrated increased central inflammation in de novo PD subjects compared to controls. Longitudinal follow-up will be important to determine whether the presence of inflammation predicts cognitive decline. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Talene A Yacoubian
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Yu-Hua Dean Fang
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Adam Gerstenecker
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Amy Amara
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Natividad Stover
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Lauren Ruffrage
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Christopher Collette
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Richard Kennedy
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Yue Zhang
- Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Huixian Hong
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Hongwei Qin
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jonathan McConathy
- Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Etty N Benveniste
- Department of Cell, Developmental, and Integrative Biology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - David G Standaert
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
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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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Akamatsu G, Tsutsui Y, Daisaki H, Mitsumoto K, Baba S, Sasaki M. A review of harmonization strategies for quantitative PET. Ann Nucl Med 2023; 37:71-88. [PMID: 36607466 PMCID: PMC9902332 DOI: 10.1007/s12149-022-01820-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Accepted: 12/27/2022] [Indexed: 01/07/2023]
Abstract
PET can reveal in vivo biological processes at the molecular level. PET-derived quantitative values have been used as a surrogate marker for clinical decision-making in numerous clinical studies and trials. However, quantitative values in PET are variable depending on technical, biological, and physical factors. The variability may have a significant impact on a study outcome. Appropriate scanner calibration and quality control, standardization of imaging protocols, and any necessary harmonization strategies are essential to make use of PET as a biomarker with low bias and variability. This review summarizes benefits, limitations, and remaining challenges for harmonization of quantitative PET, including whole-body PET in oncology, brain PET in neurology, PET/MR, and non-18F PET imaging. This review is expected to facilitate harmonization of quantitative PET and to promote the contribution of PET-derived biomarkers to research and development in medicine.
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Affiliation(s)
- Go Akamatsu
- Department of Advanced Nuclear Medicine Sciences, Institute for Quantum Medical Sciences, National Institutes for Quantum Science and Technology (QST), 4-9-1 Anagawa, Inage-ku, Chiba, 263-8555, Japan. .,Department of Molecular Imaging Research, Kobe City Medical Center General Hospital, 2-1-1 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan.
| | - Yuji Tsutsui
- Department of Radiological Science, Faculty of Health Science, Junshin Gakuen University, 1-1-1 Chikushigaoka, Minami-ku, Fukuoka, 815-8510 Japan
| | - Hiromitsu Daisaki
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, 323-1 Kamioki-machi, Maebashi, Gunma 371-0052 Japan
| | - Katsuhiko Mitsumoto
- Department of Clinical Radiology Service, Kyoto University Hospital, 54 Shogoin-Kawaharacho, Sakyo-ku, Kyoto, 606-8507 Japan
| | - Shingo Baba
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582 Japan
| | - Masayuki Sasaki
- Department of Medical Quantum Science, Faculty of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582 Japan
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11
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Leynes AP, Ahn S, Wangerin KA, Kaushik SS, Wiesinger F, Hope TA, Larson PEZ. Attenuation Coefficient Estimation for PET/MRI With Bayesian Deep Learning Pseudo-CT and Maximum-Likelihood Estimation of Activity and Attenuation. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2022; 6:678-689. [PMID: 38223528 PMCID: PMC10785227 DOI: 10.1109/trpms.2021.3118325] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024]
Abstract
A major remaining challenge for magnetic resonance-based attenuation correction methods (MRAC) is their susceptibility to sources of magnetic resonance imaging (MRI) artifacts (e.g., implants and motion) and uncertainties due to the limitations of MRI contrast (e.g., accurate bone delineation and density, and separation of air/bone). We propose using a Bayesian deep convolutional neural network that in addition to generating an initial pseudo-CT from MR data, it also produces uncertainty estimates of the pseudo-CT to quantify the limitations of the MR data. These outputs are combined with the maximum-likelihood estimation of activity and attenuation (MLAA) reconstruction that uses the PET emission data to improve the attenuation maps. With the proposed approach uncertainty estimation and pseudo-CT prior for robust MLAA (UpCT-MLAA), we demonstrate accurate estimation of PET uptake in pelvic lesions and show recovery of metal implants. In patients without implants, UpCT-MLAA had acceptable but slightly higher root-mean-squared-error (RMSE) than Zero-echotime and Dixon Deep pseudo-CT when compared to CTAC. In patients with metal implants, MLAA recovered the metal implant; however, anatomy outside the implant region was obscured by noise and crosstalk artifacts. Attenuation coefficients from the pseudo-CT from Dixon MRI were accurate in normal anatomy; however, the metal implant region was estimated to have attenuation coefficients of air. UpCT-MLAA estimated attenuation coefficients of metal implants alongside accurate anatomic depiction outside of implant regions.
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Affiliation(s)
- Andrew P Leynes
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
- UC Berkeley-UC San Francisco Joint Graduate Program in Bioengineering, University of California at Berkeley, Berkeley, CA 94720 USA
| | - Sangtae Ahn
- Biology and Physics Department, GE Research, Niskayuna, NY 12309 USA
| | | | - Sandeep S Kaushik
- MR Applications Science Laboratory Europe, GE Healthcare, 80807 Munich, Germany
- Department of Computer Science, Technical University of Munich, 80333 Munich, Germany
- Department of Quantitative Biomedicine, University of Zurich, 8057 Zurich, Switzerland
| | - Florian Wiesinger
- MR Applications Science Laboratory Europe, GE Healthcare, 80807 Munich, Germany
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA, USA
- Department of Radiology, San Francisco VA Medical Center, San Francisco, CA 94121 USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California at San Francisco, San Francisco, CA 94158 USA
- UC Berkeley-UC San Francisco Joint Graduate Program in Bioengineering, University of California at Berkeley, Berkeley, CA 94720 USA
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12
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Matsuo H, Nishio M, Nogami M, Zeng F, Kurimoto T, Kaushik S, Wiesinger F, Kono AK, Murakami T. Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks. Sci Rep 2022; 12:11090. [PMID: 35773366 PMCID: PMC9247083 DOI: 10.1038/s41598-022-14677-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 06/10/2022] [Indexed: 01/04/2023] Open
Abstract
The integrated positron emission tomography/magnetic resonance imaging (PET/MRI) scanner simultaneously acquires metabolic information via PET and morphological information using MRI. However, attenuation correction, which is necessary for quantitative PET evaluation, is difficult as it requires the generation of attenuation-correction maps from MRI, which has no direct relationship with the gamma-ray attenuation information. MRI-based bone tissue segmentation is potentially available for attenuation correction in relatively rigid and fixed organs such as the head and pelvis regions. However, this is challenging for the chest region because of respiratory and cardiac motions in the chest, its anatomically complicated structure, and the thin bone cortex. We propose a new method using unsupervised generative attentional networks with adaptive layer-instance normalisation for image-to-image translation (U-GAT-IT), which specialised in unpaired image transformation based on attention maps for image transformation. We added the modality-independent neighbourhood descriptor (MIND) to the loss of U-GAT-IT to guarantee anatomical consistency in the image transformation between different domains. Our proposed method obtained a synthesised computed tomography of the chest. Experimental results showed that our method outperforms current approaches. The study findings suggest the possibility of synthesising clinically acceptable computed tomography images from chest MRI with minimal changes in anatomical structures without human annotation.
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Affiliation(s)
- Hidetoshi Matsuo
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan.
| | - Mizuho Nishio
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Munenobu Nogami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Feibi Zeng
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | | | | | | | - Atsushi K Kono
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Takamichi Murakami
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Japan
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13
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Presotto L, Bettinardi V, Bagnalasta M, Scifo P, Savi A, Vanoli EG, Fallanca F, Picchio M, Perani D, Gianolli L, De Bernardi E. Evaluation of a 2D UNet-Based Attenuation Correction Methodology for PET/MR Brain Studies. J Digit Imaging 2022; 35:432-445. [PMID: 35091873 PMCID: PMC9156597 DOI: 10.1007/s10278-021-00551-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 11/10/2021] [Accepted: 11/16/2021] [Indexed: 12/15/2022] Open
Abstract
Deep learning (DL) strategies applied to magnetic resonance (MR) images in positron emission tomography (PET)/MR can provide synthetic attenuation correction (AC) maps, and consequently PET images, more accurate than segmentation or atlas-registration strategies. As first objective, we aim to investigate the best MR image to be used and the best point of the AC pipeline to insert the synthetic map in. Sixteen patients underwent a 18F-fluorodeoxyglucose (FDG) PET/computed tomography (CT) and a PET/MR brain study in the same day. PET/CT images were reconstructed with attenuation maps obtained: (1) from CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with a 2D UNet trained on MR image/attenuation map pairs. As for MR, T1-weighted and Zero Time Echo (ZTE) images were considered; as for attenuation maps, CTs and 511 keV low-resolution attenuation maps were assessed. As second objective, we assessed the ability of DL strategies to provide proper AC maps in presence of cranial anatomy alterations due to surgery. Three 11C-methionine (METH) PET/MR studies were considered. PET images were reconstructed with attenuation maps obtained: (1) from diagnostic coregistered CT (reference), (2) from MR with an atlas-based and a segmentation-based method and (3) with 2D UNets trained on the sixteen FDG anatomically normal patients. Only UNets taking ZTE images in input were considered. FDG and METH PET images were quantitatively evaluated. As for anatomically normal FDG patients, UNet AC models generally provide an uptake estimate with lower bias than atlas-based or segmentation-based methods. The intersubject average bias on images corrected with UNet AC maps is always smaller than 1.5%, except for AC maps generated on too coarse grids. The intersubject bias variability is the lowest (always lower than 2%) for UNet AC maps coming from ZTE images, larger for other methods. UNet models working on MR ZTE images and generating synthetic CT or 511 keV low-resolution attenuation maps therefore provide the best results in terms of both accuracy and variability. As for METH anatomically altered patients, DL properly reconstructs anatomical alterations. Quantitative results on PET images confirm those found on anatomically normal FDG patients.
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Affiliation(s)
- Luca Presotto
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Valentino Bettinardi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Matteo Bagnalasta
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Paola Scifo
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Annarita Savi
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | | | - Federico Fallanca
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Maria Picchio
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy ,Vita-Salute San Raffaele University, Milan, Italy
| | - Daniela Perani
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy ,Vita-Salute San Raffaele University, Milan, Italy
| | - Luigi Gianolli
- Nuclear Medicine Department, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Elisabetta De Bernardi
- School of Medicine and Surgery, University of Milano-Bicocca, via Cadore 48, Monza, 20900 Italy ,Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4, University of Milan-Bicocca, Monza, Italy
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14
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Fang YHD, McConathy JE, Yacoubian TA, Zhang Y, Kennedy RE, Standaert DG. Image Quantification for TSPO PET with a Novel Image-Derived Input Function Method. Diagnostics (Basel) 2022; 12:1161. [PMID: 35626315 PMCID: PMC9140104 DOI: 10.3390/diagnostics12051161] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 05/02/2022] [Accepted: 05/03/2022] [Indexed: 01/27/2023] Open
Abstract
There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling analysis with an arterial input function (AIF) measured with blood sampling procedures, the invasiveness of such procedures has been an obstacle for wide application. To address these challenges, we developed an image-derived input function (IDIF) that noninvasively estimates the arterial input function from the images acquired for 18F-DPA-714 quantification. Methods: The method entails three fully automatic steps to extract the IDIF, including a segmentation of voxels with highest likelihood of being the arterial blood over the carotid artery, a model-based matrix factorization to extract the arterial blood signal, and a scaling optimization procedure to scale the extracted arterial blood signal into the activity concentration unit. Two cohorts of human subjects were used to evaluate the extracted IDIF. In the first cohort of five subjects, arterial blood sampling was performed, and the calculated IDIF was validated against the measured AIF through the comparison of distribution volumes from AIF (VT,AIF) and IDIF (VT,IDIF). In the second cohort, PET studies from twenty-eight healthy controls without arterial blood sampling were used to compare VT,IDIF with VT,REF measured using a reference region-based analysis to evaluate whether it can distinguish high-affinity (HAB) and mixed-affinity (MAB) binders. Results: In the arterial blood-sampling cohort, VT derived from IDIF was found to be an accurate surrogate of the VT from AIF. The bias of VT, IDIF was −5.8 ± 7.8% when compared to VT,AIF, and the linear mixed effect model showed a high correlation between VT,AIF and VT, IDIF (p < 0.001). In the nonblood-sampling cohort, VT, IDIF showed a significance difference between the HAB and MAB healthy controls. VT, IDIF and standard uptake values (SUV) showed superior results in distinguishing HAB from MAB subjects than VT,REF. Conclusions: A novel IDIF method for 18F-DPA-714 PET quantification was developed and evaluated in this study. This IDIF provides a noninvasive alternative measurement of VT to quantify the TSPO binding of 18F-DPA-714 in the human brain through dynamic PET scans.
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Affiliation(s)
- Yu-Hua Dean Fang
- Department of Radiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
- Center for Neurodegeneration and Experimental Therapeutics, Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.A.Y.); (D.G.S.)
| | - Jonathan E. McConathy
- Department of Radiology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA;
| | - Talene A. Yacoubian
- Center for Neurodegeneration and Experimental Therapeutics, Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.A.Y.); (D.G.S.)
| | - Yue Zhang
- Department of Medicine, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (Y.Z.); (R.E.K.)
| | - Richard E. Kennedy
- Department of Medicine, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (Y.Z.); (R.E.K.)
| | - David G. Standaert
- Center for Neurodegeneration and Experimental Therapeutics, Department of Neurology, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA; (T.A.Y.); (D.G.S.)
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15
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Composite attenuation correction method using a 68Ge-transmission multi-atlas for quantitative brain PET/MR. Phys Med 2022; 97:36-43. [PMID: 35339864 DOI: 10.1016/j.ejmp.2022.03.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2021] [Revised: 02/18/2022] [Accepted: 03/14/2022] [Indexed: 01/06/2023] Open
Abstract
In positron emission tomography (PET), 68Ge-transmission scanning is considered the gold standard in attenuation correction (AC) though not available in current dual imaging systems. In this experimental study we evaluated a novel AC method for PET/magnetic resonance (MR) imaging which is essentially based on a composite database of multiple 68Ge-transmission maps and T1-weighted (T1w) MR image-pairs (composite transmission, CTR-AC). This proof-of-concept study used retrospectively a database with 125 pairs of co-registered 68Ge-AC maps and T1w MR images from anatomical normal subjects and a validation dataset comprising dynamic [11C]PE2I PET data from nine patients with Parkinsonism. CTR-AC maps were generated by non-rigid image registration of all database T1w MRI to each subject's T1w, applying the same transformation to every 68Ge-AC map, and averaging the resulting 68Ge-AC maps. [11C]PE2I PET images were reconstructed using CTR-AC and a patient-specific 68Ge-AC map as the reference standard. Standardized uptake values (SUV) and quantitative parameters of kinetic analysis were compared, i.e., relative delivery (R1) and non-displaceable binding potential (BPND). CTR-AC showed high accuracy for whole-brain SUV (mean %bias ± SD: 0.5 ± 3.5%), whole-brain R1 (-0.1 ± 3.2%), and putamen BPND (3.7 ± 8.1%). SUV and R1 precision (SD of %bias) were modest and lowest in the anterior cortex, with an R1 %bias of -1.1 ± 6.4%). The prototype CTR-AC is capable of providing accurate MRAC-maps with continuous linear attenuation coefficients though still experimental. The method's accuracy is comparable to the best MRAC methods published so far, both in SUV and as found for ZTE-AC in quantitative parameters of kinetic modelling.
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16
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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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Sandberg JK, Young VA, Yuan J, Hargreaves BA, Wishah F, Vasanawala SS. Zero echo time pediatric musculoskeletal magnetic resonance imaging: initial experience. Pediatr Radiol 2021; 51:2549-2560. [PMID: 34156504 DOI: 10.1007/s00247-021-05125-5] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/24/2021] [Accepted: 06/10/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND Projection radiography (XR) is often supplemented by both CT (to evaluate osseous structures with ionizing radiation) and MRI (for marrow and soft-tissue assessment). Zero echo time (ZTE) MR imaging produces a "CT-like" osseous contrast that might obviate CT. OBJECTIVE This study investigated our institution's initial experience in implementing an isotropic ZTE MR imaging sequence for pediatric musculoskeletal examinations. MATERIALS AND METHODS Pediatric patients referred for extremity MRI at 3 tesla (T) underwent ZTE MR imaging to yield images with contrast similar to that of CT. A radiograph-like image was also created with ray-sum image processing. We assessed ZTE-CT/XR anatomical image quality (Sanat) from 0 (nondiagnostic) to 5 (outstanding). Further, we made image comparisons on a 5-point scale (Scomp) (range of -2 = conventional CT/XR greater anatomical delineation to +2 = ZTE-CT/XR greater anatomical delineation; 0=same) for three cohorts: (1) ZTE-XR to conventional radiography, (2) ZTE-CT to conventional CT and (3) pathological lesion assessment on ZTE-XR to conventional radiography. We measured cortical thickness of ZTE-XR and ZTE-CT and compared these with conventional imaging. We calculated confidence interval of proportions, Wilcoxon rank sum test and intraclass correlation coefficients for inter-reader agreement. RESULTS Cohorts 1, 2 and 3 consisted of 40, 20 and 35 cases, respectively (age range 0.6-23.0 years). ZTE-CT versus CT and ZTE-XR versus radiography of cortical thicknesses were not significantly different (P=0.55 and P=0.31, respectively). Cortical delineation was rated diagnostic or better (score of 3, 4 or 5) in all cases (confidence interval of proportions = 100%) for ZTE-CT/XR. Similarly, intramedullary cavity delineation was rated diagnostic or better in all cases for ZTE-CT, and ZTE-XR was at least diagnostic in 58-63% of cases. For cohort 2, cortex and intramedullary cavity Scomp for ZTE-CT was comparable to those of conventional CT, with confidence interval of proportion (sum of score of -1 to +2) of 93-100% and 95%, respectively. Pathology visualized on ZTE-CT/XR was comparable; Scomp confidence interval of proportions was 95%/97-100%, with improved delineation of non-displaced fractures on ZTE-XR. Readers had moderate to near-perfect intraclass correlation coefficient (range=0.60-0.93). CONCLUSION Implementation of a diagnostic-quality ZTE MRI sequence in the pediatric population is feasible and can be performed as a complementary pulse sequence to enhance musculoskeletal MRI studies. Compared to conventional CT, ZTE has comparable cortical delineation, intramedullary cavity and pathology visualization. While not intended as a replacement for conventional radiography, ZTE-XR provides similar visualization of pathology.
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Affiliation(s)
- Jesse K Sandberg
- Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Room 1844, Stanford, CA, 94305, USA.
| | - Victoria A Young
- Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Room 1844, Stanford, CA, 94305, USA
| | - Jianmin Yuan
- Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Room 1844, Stanford, CA, 94305, USA
| | - Brian A Hargreaves
- Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Room 1844, Stanford, CA, 94305, USA
| | - Fidaa Wishah
- Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Room 1844, Stanford, CA, 94305, USA
| | - Shreyas S Vasanawala
- Department of Radiology, Stanford University School of Medicine, 725 Welch Road, Room 1844, Stanford, CA, 94305, USA
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18F-FDG PET/MR in focal epilepsy: A new step for improving the detection of epileptogenic lesions. Epilepsy Res 2021; 178:106819. [PMID: 34847426 DOI: 10.1016/j.eplepsyres.2021.106819] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/19/2021] [Accepted: 11/15/2021] [Indexed: 11/20/2022]
Abstract
PURPOSE Hybrid PET/MR is a promising tool in focal drug-resistant epilepsy, however the additional value for the detection of epileptogenic lesions and surgical decision-making remains to be established. METHODS We retrospectively compared 18F-FDG PET/MR images with those obtained by a previous 18F-FDG PET co-registered with MRI (PET+MR) in 25 consecutive patients (16 females, 13-60 years) investigated for focal drug-resistant epilepsy. Visual analysis was performed by two readers blinded from imaging modalities, asked to assess the technical characteristics (co-registration, quality of images), the confidence in results, the location of PET abnormalities and the presence of a structural lesion on MRI. Clinical impact on surgical strategy and outcome was assessed independently. RESULTS The location of epileptic focus was temporal in 9 patients and extra-temporal in 16 others. MRI was initially considered negative in 21 patients. PET stand-alone demonstrated metabolic abnormalities in 19 cases (76%), and the co-registration with MRI allowed the detection of 4 additional structural lesions. Compared to PET+MR, the PET/MR sensitivity was increased by 13% and new structural lesions (mainly focal cortical dysplasias) were detected in 6 patients (24%). Change of surgical decision-making was substantial for 10 patients (40%), consisting in avoiding invasive monitoring in 6 patients and modifying the planning in 4 others. Seizure-free outcome (follow-up>1 year) was obtained in 12/14 patients who underwent a cortical resection. CONCLUSION Hybrid PET/MR may improve the detection of epileptogenic lesions, allowing to optimize the presurgical work-up and to increase the proportion of successful surgery even in the more complex cases.
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Elisevich K, Davoodi-Bojd E, Heredia JG, Soltanian-Zadeh H. Prospective Quantitative Neuroimaging Analysis of Putative Temporal Lobe Epilepsy. Front Neurol 2021; 12:747580. [PMID: 34803885 PMCID: PMC8602195 DOI: 10.3389/fneur.2021.747580] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 09/20/2021] [Indexed: 11/22/2022] Open
Abstract
Purpose: A prospective study of individual and combined quantitative imaging applications for lateralizing epileptogenicity was performed in a cohort of consecutive patients with a putative diagnosis of mesial temporal lobe epilepsy (mTLE). Methods: Quantitative metrics were applied to MRI and nuclear medicine imaging studies as part of a comprehensive presurgical investigation. The neuroimaging analytics were conducted remotely to remove bias. All quantitative lateralizing tools were trained using a separate dataset. Outcomes were determined after 2 years. Of those treated, some underwent resection, and others were implanted with a responsive neurostimulation (RNS) device. Results: Forty-eight consecutive cases underwent evaluation using nine attributes of individual or combinations of neuroimaging modalities: 1) hippocampal volume, 2) FLAIR signal, 3) PET profile, 4) multistructural analysis (MSA), 5) multimodal model analysis (MMM), 6) DTI uncertainty analysis, 7) DTI connectivity, and 9) fMRI connectivity. Of the 24 patients undergoing resection, MSA, MMM, and PET proved most effective in predicting an Engel class 1 outcome (>80% accuracy). Both hippocampal volume and FLAIR signal analysis showed 76% and 69% concordance with an Engel class 1 outcome, respectively. Conclusion: Quantitative multimodal neuroimaging in the context of a putative mTLE aids in declaring laterality. The degree to which there is disagreement among the various quantitative neuroimaging metrics will judge whether epileptogenicity can be confined sufficiently to a particular temporal lobe to warrant further study and choice of therapy. Prediction models will improve with continued exploration of combined optimal neuroimaging metrics.
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Affiliation(s)
- Kost Elisevich
- Department of Clinical Neurosciences, Spectrum Health, Grand Rapids, MI, United States
- Department of Surgery, College of Human Medicine, Michigan State University, Grand Rapids, MI, United States
| | - Esmaeil Davoodi-Bojd
- Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
| | - John G. Heredia
- Imaging Physics, Department of Radiology, Spectrum Health, Grand Rapids, MI, United States
| | - Hamid Soltanian-Zadeh
- Radiology and Research Administration, Henry Ford Health System, Detroit, MI, United States
- Control and Intelligent Processing Center of Excellence (CIPCE), School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran
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20
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Chong LR, Lee K, Sim FY. 3D MRI with CT-like bone contrast - An overview of current approaches and practical clinical implementation. Eur J Radiol 2021; 143:109915. [PMID: 34461599 DOI: 10.1016/j.ejrad.2021.109915] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Revised: 08/13/2021] [Accepted: 08/15/2021] [Indexed: 12/24/2022]
Abstract
CT is the imaging modality of choice for assessment of 3D bony morphology but incurs the penalty of ionizing radiation. Improving the ability of 3D MRI to provide high-resolution images of cortical bone with CT-like bone contrast has been a focus of recent research. The ability of 3D MRI to deliver cortical bone information with similar diagnostic performance to CT would complement assessment of soft tissues and medullary bone from a single MRI examination, simplifying evaluation and obviating radiation exposure from additional CT. This article presents an overview of current 3D MRI approaches for imaging cortical bone with CT-like bone contrast including ultrashort echo time, zero echo time, T1-weighted gradient recalled echo, susceptibility-weighted imaging and deep learning techniques. We also discuss clinical implementation of an optimized stack-of-stars 3D gradient recalled echo pulse sequence (3D-Bone) on commercially available MRI scanners for rendering 3D MRI with CT-like bone contrast in our institutional practice.
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Affiliation(s)
- Le Roy Chong
- Department of Radiology, Changi General Hospital, 2 Simei Street 3, Singapore 529889, Republic of Singapore.
| | - Kathy Lee
- Department of Radiology, Changi General Hospital, 2 Simei Street 3, Singapore 529889, Republic of Singapore.
| | - Fang Yang Sim
- Department of Radiology, Changi General Hospital, 2 Simei Street 3, Singapore 529889, Republic of Singapore.
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21
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Catana C, Laforest R, An H, Boada F, Cao T, Faul D, Jakoby B, Jansen FP, Kemp BJ, Kinahan PE, Larson PEZ, Levine MA, Maniawski P, Mawlawi O, McConathy J, McMillan A, Price JC, Rajagopal A, Sunderland J, Veit-Haibach P, Wangerin KA, Ying C, Hope TA. A Path to Qualification of PET/MR Scanners for Multicenter Brain Imaging Studies: Evaluation of MR-based Attenuation Correction Methods Using a Patient Phantom. J Nucl Med 2021; 63:615-621. [PMID: 34301784 PMCID: PMC8973286 DOI: 10.2967/jnumed.120.261881] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 06/06/2021] [Indexed: 11/25/2022] Open
Abstract
PET/MRI scanners cannot be qualified in the manner adopted for hybrid PET/CT devices. The main hurdle with qualification in PET/MRI is that attenuation correction (AC) cannot be adequately measured in conventional PET phantoms because of the difficulty in converting the MR images of the physical structures (e.g., plastic) into electron density maps. Over the last decade, a plethora of novel MRI-based algorithms has been developed to more accurately derive the attenuation properties of the human head, including the skull. Although promising, none of these techniques has yet emerged as an optimal and universally adopted strategy for AC in PET/MRI. In this work, we propose a path for PET/MRI qualification for multicenter brain imaging studies. Specifically, our solution is to separate the head AC from the other factors that affect PET data quantification and use a patient as a phantom to assess the former. The emission data collected on the integrated PET/MRI scanner to be qualified should be reconstructed using both MRI- and CT-based AC methods, and whole-brain qualitative and quantitative (both voxelwise and regional) analyses should be performed. The MRI-based approach will be considered satisfactory if the PET quantification bias is within the acceptance criteria specified here. We have implemented this approach successfully across 2 PET/MRI scanner manufacturers at 2 sites.
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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, United States
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine
| | | | - Fernando Boada
- Department of Radiology, Center for Advanced Imaging Innovation and Research, New York University Langone Medical Center
| | - Tuoyu Cao
- Shanghai United Imaging Healthcare Co., Ltd., China
| | | | | | | | | | | | | | | | - Piotr Maniawski
- Philips Healthcare, Advanced Molecular Imaging, United States
| | | | | | - Alan McMillan
- University of Wisconsin School of Medicine and Public Health
| | | | - Abhejit Rajagopal
- Department of Radiology and Biomedical Imaging, University of California, San Francisco
| | | | | | | | - Chunwei Ying
- Department of Biomedical Engineering, Washington University in St. Louis
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22
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Arabi H, Zaidi H. Assessment of deep learning-based PET attenuation correction frameworks in the sinogram domain. Phys Med Biol 2021; 66. [PMID: 34167094 DOI: 10.1088/1361-6560/ac0e79] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2021] [Accepted: 06/24/2021] [Indexed: 02/04/2023]
Abstract
This study set out to investigate various deep learning frameworks for PET attenuation correction in the sinogram domain. Different models for both time-of-flight (TOF) and non-TOF PET emission data were implemented, including direct estimation of the attenuation corrected (AC) emission sinograms from the nonAC sinograms, estimation of the attenuation correction factors (ACFs) from PET emission data, correction of scattered photons prior to training of the models, and separate training of the models for each segment of the emission sinograms. A segmentation-based 2-class AC map was included as a bottom-line technique for comparison of the different models considering PET/CT AC as reference. Fifty clinical TOF PET/CT brain scans were employed for training whereas 20 were used for evaluation of the models. Quantitative analysis of the resulting PET images was carried out through region-wise standardized uptake value (SUV) bias calculation. The models relying on TOF information significantly outperformed the nonTOF models as well as the segmentation-based AC map resulting in maximum SUV bias of 6.5%, 9.5%, and 14.0%, respectively. Estimation of ACFs from either TOF or nonTOF PET emission data was very sensitive to prior scatter correction. However, direct estimation of AC sinograms from nonAC sinograms revealed no sensitivity to scatter correction, thus obviating the need for prior scatter estimation. For TOF PET data, though direct prediction of the AC sinograms does not require prior estimation of scattered photons, it requires input/output channels equal to the number of TOF bins which might be computationally or memory-wise expensive. Prediction of the ACF matrices from TOF emission data is less demanding in terms of memory as it requires only a single channel for output. AC in the sinogram domain of TOF PET data exhibited superior performance compared to both nonTOF and segmentation-based methods. However, such models require multiple input/output channels.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland.,Geneva Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland.,Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, The Netherlands.,Department of Nuclear Medicine, University of Southern Denmark, DK-500, Odense, Denmark
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23
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Chen Y, Ying C, Binkley MM, Juttukonda MR, Flores S, Laforest R, Benzinger TL, An H. Deep learning-based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) for PET/MR attenuation correction in dementia neuroimaging. Magn Reson Med 2021; 86:499-513. [PMID: 33559218 PMCID: PMC8091494 DOI: 10.1002/mrm.28689] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 12/23/2020] [Accepted: 12/29/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE The accuracy of existing PET/MR attenuation correction (AC) has been limited by a lack of correlation between MR signal and tissue electron density. Based on our finding that longitudinal relaxation rate, or R1 , is associated with CT Hounsfield unit in bone and soft tissues in the brain, we propose a deep learning T1 -enhanced selection of linear attenuation coefficients (DL-TESLA) method to incorporate quantitative R1 for PET/MR AC and evaluate its accuracy and longitudinal test-retest repeatability in brain PET/MR imaging. METHODS DL-TESLA uses a 3D residual UNet (ResUNet) for pseudo-CT (pCT) estimation. With a total of 174 participants, we compared PET AC accuracy of DL-TESLA to 3 other methods adopting similar 3D ResUNet structures but using UTE R 2 ∗ , or Dixon, or T1 -MPRAGE as input. With images from 23 additional participants repeatedly scanned, the test-retest differences and within-subject coefficient of variation of standardized uptake value ratios (SUVR) were compared between PET images reconstructed using either DL-TESLA or CT for AC. RESULTS DL-TESLA had (1) significantly lower mean absolute error in pCT, (2) the highest Dice coefficients in both bone and air, (3) significantly lower PET relative absolute error in whole brain and various brain regions, (4) the highest percentage of voxels with a PET relative error within both ±3% and ±5%, (5) similar to CT test-retest differences in SUVRs from the cerebrum and mean cortical (MC) region, and (6) similar to CT within-subject coefficient of variation in cerebrum and MC. CONCLUSION DL-TESLA demonstrates excellent PET/MR AC accuracy and test-retest repeatability.
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Affiliation(s)
- Yasheng Chen
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Chunwei Ying
- Dept. of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Michael M. Binkley
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Meher R. Juttukonda
- Athinoula A. Martinos Center for Biomedical Imaging, Dept. of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA
- Dept. of Radiology, Harvard Medical School, Boston, Massachusetts 02115, USA
| | - Shaney Flores
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Tammie L.S. Benzinger
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
| | - Hongyu An
- Dept. of Neurology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Dept. of Biomedical Engineering, Washington University in St. Louis, St. Louis, Missouri 63110, USA
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, Missouri 63110, USA
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Evaluation of Arterial Spin Labeling MRI-Comparison with 15O-Water PET on an Integrated PET/MR Scanner. Diagnostics (Basel) 2021; 11:diagnostics11050821. [PMID: 34062847 PMCID: PMC8147295 DOI: 10.3390/diagnostics11050821] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 11/16/2022] Open
Abstract
Cerebral blood flow (CBF) measurements are of high clinical value and can be acquired non-invasively with no radiation exposure using pseudo-continuous arterial spin labeling (ASL). The aim of this study was to evaluate accordance in resting state CBF between ASL (CBFASL) and 15O-water positron emission tomography (PET) (CBFPET) acquired simultaneously on an integrated 3T PET/MR system. The data comprised ASL and dynamic 15O-water PET data with arterial blood sampling of eighteen subjects (eight patients with focal epilepsy and ten healthy controls, age 21 to 61 years). 15O-water PET parametric CBF images were generated using a basis function implementation of the single tissue compartment model. Cortical and subcortical regions were automatically segmented using Freesurfer. Average CBFASL and CBFPET in grey matter were 60 ± 20 and 75 ± 22 mL/100 g/min respectively, with a relatively high correlation (r = 0.78, p < 0.001). Bland-Altman analysis revealed poor agreement (bias = −15 mL/100 g/min, lower and upper limits of agreements = −16 and 45 mL/100 g/min, respectively) with a negative relationship. Accounting for the negative relationship, the width of the limits of agreement could be narrowed from 61 mL/100 g/min to 35 mL/100 g/min using regression-based limits of agreements. Although a high correlation between CBFASL and CBFPET was found, the agreement in absolute CBF values was not sufficient for ASL to be used interchangeably with 15O-water PET.
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25
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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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Gong K, Han PK, Johnson KA, El Fakhri G, Ma C, Li Q. Attenuation correction using deep Learning and integrated UTE/multi-echo Dixon sequence: evaluation in amyloid and tau PET imaging. Eur J Nucl Med Mol Imaging 2021; 48:1351-1361. [PMID: 33108475 PMCID: PMC8411350 DOI: 10.1007/s00259-020-05061-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2020] [Accepted: 09/30/2020] [Indexed: 02/07/2023]
Abstract
PURPOSE PET measures of amyloid and tau pathologies are powerful biomarkers for the diagnosis and monitoring of Alzheimer's disease (AD). Because cortical regions are close to bone, quantitation accuracy of amyloid and tau PET imaging can be significantly influenced by errors of attenuation correction (AC). This work presents an MR-based AC method that combines deep learning with a novel ultrashort time-to-echo (UTE)/multi-echo Dixon (mUTE) sequence for amyloid and tau imaging. METHODS Thirty-five subjects that underwent both 11C-PiB and 18F-MK6240 scans were included in this study. The proposed method was compared with Dixon-based atlas method as well as magnetization-prepared rapid acquisition with gradient echo (MPRAGE)- or Dixon-based deep learning methods. The Dice coefficient and validation loss of the generated pseudo-CT images were used for comparison. PET error images regarding standardized uptake value ratio (SUVR) were quantified through regional and surface analysis to evaluate the final AC accuracy. RESULTS The Dice coefficients of the deep learning methods based on MPRAGE, Dixon, and mUTE images were 0.84 (0.91), 0.84 (0.92), and 0.87 (0.94) for the whole-brain (above-eye) bone regions, respectively, higher than the atlas method of 0.52 (0.64). The regional SUVR error for the atlas method was around 6%, higher than the regional SUV error. The regional SUV and SUVR errors for all deep learning methods were below 2%, with mUTE-based deep learning method performing the best. As for the surface analysis, the atlas method showed the largest error (> 10%) near vertices inside superior frontal, lateral occipital, superior parietal, and inferior temporal cortices. The mUTE-based deep learning method resulted in the least number of regions with error higher than 1%, with the largest error (> 5%) showing up near the inferior temporal and medial orbitofrontal cortices. CONCLUSION Deep learning with mUTE can generate accurate AC for amyloid and tau imaging in PET/MR.
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Affiliation(s)
- Kuang Gong
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Paul Kyu Han
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Keith A Johnson
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Chao Ma
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Quanzheng Li
- Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.
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Gong K, Yang J, Larson PEZ, Behr SC, Hope TA, Seo Y, Li Q. MR-based Attenuation Correction for Brain PET Using 3D Cycle-Consistent Adversarial Network. IEEE TRANSACTIONS ON RADIATION AND PLASMA MEDICAL SCIENCES 2021; 5:185-192. [PMID: 33778235 PMCID: PMC7993643 DOI: 10.1109/trpms.2020.3006844] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Attenuation correction (AC) is important for the quantitative merits of positron emission tomography (PET). However, attenuation coefficients cannot be derived from magnetic resonance (MR) images directly for PET/MR systems. In this work, we aimed to derive continuous AC maps from Dixon MR images without the requirement of MR and computed tomography (CT) image registration. To achieve this, a 3D generative adversarial network with both discriminative and cycle-consistency loss (Cycle-GAN) was developed. The modified 3D U-net was employed as the structure of the generative networks to generate the pseudo CT/MR images. The 3D patch-based discriminative networks were used to distinguish the generated pseudo CT/MR images from the true CT/MR images. To evaluate its performance, datasets from 32 patients were used in the experiment. The Dixon segmentation and atlas methods provided by the vendor and the convolutional neural network (CNN) method which utilized registered MR and CT images were employed as the reference methods. Dice coefficients of the pseudo-CT image and the regional quantification in the reconstructed PET images were compared. Results show that the Cycle-GAN framework can generate better AC compared to the Dixon segmentation and atlas methods, and shows comparable performance compared to the CNN method.
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Affiliation(s)
- Kuang Gong
- Center for Advanced Medical Computing and Analysis, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 USA
| | - Jaewon Yang
- Physics Research Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143 USA
| | - Peder E Z Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143 USA
| | - Spencer C Behr
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143 USA
| | - Thomas A Hope
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143 USA
| | - Youngho Seo
- Physics Research Laboratory, Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143 USA
| | - Quanzheng Li
- Center for Advanced Medical Computing and Analysis, Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114 USA
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28
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Mannheim JG, Cheng JCK, Vafai N, Shahinfard E, English C, McKenzie J, Zhang J, Barlow L, Sossi V. Cross-validation study between the HRRT and the PET component of the SIGNA PET/MRI system with focus on neuroimaging. EJNMMI Phys 2021; 8:20. [PMID: 33635449 PMCID: PMC7910400 DOI: 10.1186/s40658-020-00349-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Accepted: 12/16/2020] [Indexed: 01/20/2023] Open
Abstract
Background The Siemens high-resolution research tomograph (HRRT - a dedicated brain PET scanner) is to this day one of the highest resolution PET scanners; thus, it can serve as useful benchmark when evaluating performance of newer scanners. Here, we report results from a cross-validation study between the HRRT and the whole-body GE SIGNA PET/MR focusing on brain imaging. Phantom data were acquired to determine recovery coefficients (RCs), % background variability (%BG), and image voxel noise (%). Cross-validation studies were performed with six healthy volunteers using [11C]DTBZ, [11C]raclopride, and [18F]FDG. Line profiles, regional time-activity curves, regional non-displaceable binding potentials (BPND) for [11C]DTBZ and [11C]raclopride scans, and radioactivity ratios for [18F]FDG scans were calculated and compared between the HRRT and the SIGNA PET/MR. Results Phantom data showed that the PET/MR images reconstructed with an ordered subset expectation maximization (OSEM) algorithm with time-of-flight (TOF) and TOF + point spread function (PSF) + filter revealed similar RCs for the hot spheres compared to those obtained on the HRRT reconstructed with an ordinary Poisson-OSEM algorithm with PSF and PSF + filter. The PET/MR TOF + PSF reconstruction revealed the highest RCs for all hot spheres. Image voxel noise of the PET/MR system was significantly lower. Line profiles revealed excellent spatial agreement between the two systems. BPND values revealed variability of less than 10% for the [11C]DTBZ scans and 19% for [11C]raclopride (based on one subject only). Mean [18F]FDG ratios to pons showed less than 12% differences. Conclusions These results demonstrated comparable performances of the two systems in terms of RCs with lower voxel-level noise (%) present in the PET/MR system. Comparison of in vivo human data confirmed the comparability of the two systems. The whole-body GE SIGNA PET/MR system is well suited for high-resolution brain imaging as no significant performance degradation was found compared to that of the reference standard HRRT.
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Affiliation(s)
- Julia G Mannheim
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada. .,Werner Siemens Imaging Center, Department of Preclinical Imaging and Radiopharmacy, Eberhard-Karls University Tuebingen, Tuebingen, Germany. .,Cluster of Excellence iFIT (EXC 2180) "Image Guided and Functionally Instructed Tumor Therapies", University of Tuebingen, Tuebingen, Germany.
| | - Ju-Chieh Kevin Cheng
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada.,Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Nasim Vafai
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Elham Shahinfard
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Carolyn English
- Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Jessamyn McKenzie
- Djavad Mowafaghian Centre for Brain Health, Pacific Parkinson's Research Centre, University of British Columbia & Vancouver Coastal Health, Vancouver, British Columbia, Canada
| | - Jing Zhang
- Global MR Applications & Workflow, GE Healthcare Canada, Vancouver, British Columbia, Canada
| | - Laura Barlow
- UBC MRI Research Centre, University of British Columbia, Vancouver, British Columbia, Canada
| | - Vesna Sossi
- Department of Physics and Astronomy, University of British Columbia, Vancouver, British Columbia, Canada
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Kubo H, Nemoto A, Ukon N, Ito H. Evaluation of a model-based attenuation correction method on whole-body 18F-fluorodeoxyglucose positron emission tomography/magnetic resonance imaging. Radiol Phys Technol 2021; 14:70-81. [PMID: 33400065 DOI: 10.1007/s12194-020-00605-z] [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: 07/24/2020] [Revised: 12/19/2020] [Accepted: 12/22/2020] [Indexed: 10/22/2022]
Abstract
The bone cannot be evaluated using magnetic resonance attenuation correction (MRAC) with the Dixon sequence. To solve this issue, the present study aimed to evaluate model-based AC for whole-body 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET)/magnetic resonance imaging (MRI) by creating bone segmentation. We analyzed and evaluated the data of 31 consecutive patients. The Biograph mMR (Siemens Healthcare) was used for clinical whole-body 18F-FDG PET/MRI with the conventional MRAC method, and OSIRIX MD software was used to analyze the images. After the examination, the new model-based post-processing MRAC was applied to create μ-maps with bone segmentation, and retrospective PET reconstruction was performed using this μ-map. The bone structures of all patients created using model-based MRAC were visually evaluated. Standard uptake values (SUVs) at 11 anatomical positions in PET images, corrected using the μ-map with and without bone segmentation, were measured and compared. The model-based post-processing MRAC was run for all patients, without errors. Visual evaluation revealed that the model-based post-processing MRAC exhibited poor results for six patients. Furthermore, it exhibited an increasing trend of SUV in the brain compared to the conventional method. Locations other than the brain indicated a similar or decreasing trend. The two methods showed a good linear correlation for all patients. However, patients aged < 20 years exhibited a different trend from those aged ≥ 20 years. We should exercise caution when applying this model-based MRAC for younger patients.
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Affiliation(s)
- Hitoshi Kubo
- Preparing Section for New Faculty of Medical Science, Fukushima Medical University, 1 Hikariga-oka, Fukushima, Fukushima, 960-1295, Japan. .,Advanced Clinical Research Center, Fukushima Medical University, Fukushima, Japan.
| | - Ayaka Nemoto
- Advanced Clinical Research Center, Fukushima Medical University, Fukushima, Japan
| | - Naoyuki Ukon
- Advanced Clinical Research Center, Fukushima Medical University, Fukushima, Japan
| | - Hiroshi Ito
- Advanced Clinical Research Center, Fukushima Medical University, Fukushima, Japan.,Department of Radiology and Nuclear Medicine, Fukushima Medical University, Fukushima, Japan
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30
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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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31
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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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32
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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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33
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De Luca F, Bolin M, Blomqvist L, Wassberg C, Martin H, Falk Delgado A. Validation of PET/MRI attenuation correction methodology in the study of brain tumours. BMC Med Imaging 2020; 20:126. [PMID: 33238917 PMCID: PMC7690209 DOI: 10.1186/s12880-020-00526-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 11/17/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND This study aims to compare proton density weighted magnetic resonance imaging (MRI) zero echo time (ZTE) and head atlas attenuation correction (AC) to the reference standard computed tomography (CT) based AC for 11C-methionine positron emission tomography (PET)/MRI. METHODS A retrospective cohort of 14 patients with suspected or confirmed brain tumour and 11C-Methionine PET/MRI was included in the study. For each scan, three AC maps were generated: ZTE-AC, atlas-AC and reference standard CT-AC. Maximum and mean standardised uptake values (SUV) were measured in the hotspot, mirror region and frontal cortex. In postoperative patients (n = 8), SUV values were additionally obtained adjacent to the metal implant and mirror region. Standardised uptake ratios (SUR) hotspot/mirror, hotspot/cortex and metal/mirror were then calculated and analysed with Bland-Altman, Pearson correlation and intraclass correlation reliability in the overall group and subgroups. RESULTS ZTE-AC demonstrated narrower SD and 95% CI (Bland-Altman) than atlas-AC in the hotspot analysis for all groups (ZTE overall ≤ 2.84, - 1.41 to 1.70; metal ≤ 1.67, - 3.00 to 2.20; non-metal ≤ 3.04, - 0.96 to 3.38; Atlas overall ≤ 4.56, - 1.05 to 3.83; metal ≤ 3.87, - 3.81 to 4.64; non-metal ≤ 4.90, - 1.68 to 5.86). The mean bias for both ZTE-AC and atlas-AC was ≤ 2.4% compared to CT-AC. In the metal region analysis, ZTE-AC demonstrated a narrower mean bias range-closer to zero-and narrower SD and 95% CI (ZTE 0.21-0.48, ≤ 2.50, - 1.70 to 2.57; Atlas 0.56-1.54, ≤ 4.01, - 1.81 to 4.89). The mean bias for both ZTE-AC and atlas-AC was within 1.6%. A perfect correlation (Pearson correlation) was found for both ZTE-AC and atlas-AC compared to CT-AC in the hotspot and metal analysis (ZTE ρ 1.00, p < 0.0001; atlas ρ 1.00, p < 0.0001). An almost perfect intraclass correlation coefficient for absolute agreement was found between Atlas-, ZTE and CT maps for maxSUR and meanSUR values in all the analyses (ICC > 0.99). CONCLUSIONS Both ZTE and atlas-AC showed a good performance against CT-AC in patients with brain tumour.
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Affiliation(s)
- Francesca De Luca
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden.
| | - Martin Bolin
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- Department of Molecular Medicine and Surger, Karolinska Institutet, Stockholm, Sweden
| | - Lennart Blomqvist
- Department of Molecular Medicine and Surger, Karolinska Institutet, Stockholm, Sweden
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Stockholm, 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 Institutet, Stockholm, Sweden
- Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
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Wallstén E, Axelsson J, Jonsson J, Karlsson CT, Nyholm T, Larsson A. Improved PET/MRI attenuation correction in the pelvic region using a statistical decomposition method on T2-weighted images. EJNMMI Phys 2020; 7:68. [PMID: 33226495 PMCID: PMC7683750 DOI: 10.1186/s40658-020-00336-5] [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: 06/12/2020] [Accepted: 11/04/2020] [Indexed: 11/29/2022] Open
Abstract
Background Attenuation correction of PET/MRI is a remaining problem for whole-body PET/MRI. The statistical decomposition algorithm (SDA) is a probabilistic atlas-based method that calculates synthetic CTs from T2-weighted MRI scans. In this study, we evaluated the application of SDA for attenuation correction of PET images in the pelvic region. Materials and method Twelve patients were retrospectively selected from an ongoing prostate cancer research study. The patients had same-day scans of [11C]acetate PET/MRI and CT. The CT images were non-rigidly registered to the PET/MRI geometry, and PET images were reconstructed with attenuation correction employing CT, SDA-generated CT, and the built-in Dixon sequence-based method of the scanner. The PET images reconstructed using CT-based attenuation correction were used as ground truth. Results The mean whole-image PET uptake error was reduced from − 5.4% for Dixon-PET to − 0.9% for SDA-PET. The prostate standardized uptake value (SUV) quantification error was significantly reduced from − 5.6% for Dixon-PET to − 2.3% for SDA-PET. Conclusion Attenuation correction with SDA improves quantification of PET/MR images in the pelvic region compared to the Dixon-based method.
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Affiliation(s)
- Elin Wallstén
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden.
| | - Jan Axelsson
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
| | | | - Tufve Nyholm
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
| | - Anne Larsson
- Department of Radiation Sciences, Radiation Physics, Umeå University, 901 85, Umeå, Sweden
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35
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Nguyen NC, Beriwal S, Moon CH, D'Ardenne N, Mountz JM, Furlan A, Muthukrishnan A, Rangaswamy B. Diagnostic Value of FDG PET/MRI in Females With Pelvic Malignancy-A Systematic Review of the Literature. Front Oncol 2020; 10:519440. [PMID: 33123460 PMCID: PMC7571667 DOI: 10.3389/fonc.2020.519440] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Accepted: 08/28/2020] [Indexed: 11/13/2022] Open
Abstract
Hybrid imaging with F-18 fludeoxyglucose positron emission tomography/magnetic resonance imaging (FDG PET/MRI) has increasing clinical applications supplementing conventional ultrasound, CT, and MRI imaging as well as hybrid PET/CT imaging in assessing cervical, endometrial, and ovarian cancer. This article summarizes the existing literature and discusses the emerging role of hybrid PET/MRI in gynecologic malignancies. Thus, far, the published literature on the applications of FDG PET/MRI shows that it can have a significant impact on patient management by improving the staging of the cancers compared with PET/CT, influencing clinical decision and treatment strategy. For disease restaging, current literature indicates that PET/MRI performs equivalently to PET/CT. There appears to be a mild-moderate inverse correlation between standard-uptake-value (SUV) and apparent-diffusion-coefficient (ADC) values, which could be used to predict tumor grading and risk stratification. It remains to be seen as to whether multi-parametric PET/MRI imaging could prove valuable for prognostication and outcome. PET/MRI provides the opportunity for reduced radiation exposure, which is particularly relevant for a young female in need of multiple scans for treatment monitoring and follow-up. Fast acquisition protocols and optimized methods for attenuation correction are still evolving. Major limitations of PET/MRI remains such as suboptimal detection of small pulmonary nodules and lack of utility for radiation treatment planning, which pose an impediment in making PET/MRI a viable one-stop-shop imaging option to compete with PET/CT.
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Affiliation(s)
- Nghi Co Nguyen
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Sushil Beriwal
- Department of Radiation Oncology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States
| | - Chan-Hong Moon
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Nicholas D'Ardenne
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - James M Mountz
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Alessandro Furlan
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ashok Muthukrishnan
- Department of Radiology, University of Pittsburgh, Pittsburgh, PA, United States
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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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Song X, Qian P, Zheng J, Jiang Y, Xia K, Traughber B, Wu D, Muzic RF. mDixon-based synthetic CT generation via transfer and patch learning. Pattern Recognit Lett 2020. [DOI: 10.1016/j.patrec.2020.06.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Bahrami A, Karimian A, Fatemizadeh E, Arabi H, Zaidi H. A new deep convolutional neural network design with efficient learning capability: Application to CT image synthesis from MRI. Med Phys 2020; 47:5158-5171. [DOI: 10.1002/mp.14418] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 07/03/2020] [Accepted: 07/17/2020] [Indexed: 12/26/2022] Open
Affiliation(s)
- Abass Bahrami
- Faculty of Physics University of Isfahan Isfahan Iran
| | - Alireza Karimian
- Department of Biomedical Engineering Faculty of Engineering University of Isfahan Isfahan Iran
| | - Emad Fatemizadeh
- School of Electrical Engineering Sharif University of Technology Tehran Iran
| | - Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital GenevaCH‐1211 Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital GenevaCH‐1211 Switzerland
- Geneva University NeurocenterGeneva University Geneva1205 Switzerland
- Department of Nuclear Medicine and Molecular Imaging University of GroningenUniversity Medical Center Groningen Groningen Netherlands
- Department of Nuclear Medicine University of Southern Denmark OdenseDK‐500 Denmark
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Aizaz M, Moonen RPM, van der Pol JAJ, Prieto C, Botnar RM, Kooi ME. PET/MRI of atherosclerosis. Cardiovasc Diagn Ther 2020; 10:1120-1139. [PMID: 32968664 DOI: 10.21037/cdt.2020.02.09] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Myocardial infarction and stroke are the most prevalent global causes of death. Each year 15 million people worldwide die due to myocardial infarction or stroke. Rupture of a vulnerable atherosclerotic plaque is the main underlying cause of stroke and myocardial infarction. Key features of a vulnerable plaque are inflammation, a large lipid-rich necrotic core (LRNC) with a thin or ruptured overlying fibrous cap, and intraplaque hemorrhage (IPH). Noninvasive imaging of these features could have a role in risk stratification of myocardial infarction and stroke and can potentially be utilized for treatment guidance and monitoring. The recent development of hybrid PET/MRI combining the superior soft tissue contrast of MRI with the opportunity to visualize specific plaque features using various radioactive tracers, paves the way for comprehensive plaque imaging. In this review, the use of hybrid PET/MRI for atherosclerotic plaque imaging in carotid and coronary arteries is discussed. The pros and cons of different hybrid PET/MRI systems are reviewed. The challenges in the development of PET/MRI and potential solutions are described. An overview of PET and MRI acquisition techniques for imaging of atherosclerosis including motion correction is provided, followed by a summary of vessel wall imaging PET/MRI studies in patients with carotid and coronary artery disease. Finally, the future of imaging of atherosclerosis with PET/MRI is discussed.
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Affiliation(s)
- Mueez Aizaz
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Rik P M Moonen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Jochem A J van der Pol
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Claudia Prieto
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - René M Botnar
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.,Escuela de Ingenieria, Pontificia Universidad Catolica de Chile, Santiago, Chile
| | - M Eline Kooi
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Center, Maastricht, The Netherlands.,CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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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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Vanhaute H, Ceccarini J, Michiels L, Koole M, Sunaert S, Lemmens R, Triau E, Emsell L, Vandenbulcke M, Van Laere K. In vivo synaptic density loss is related to tau deposition in amnestic mild cognitive impairment. Neurology 2020; 95:e545-e553. [PMID: 32493717 DOI: 10.1212/wnl.0000000000009818] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Accepted: 01/09/2020] [Indexed: 12/18/2022] Open
Abstract
OBJECTIVE To investigate in vivo whether synaptic loss and neurofibrillary tangle load spatially overlap and correlate with clinical symptoms in patients with amnestic mild cognitive impairment (aMCI). METHODS In this cross-sectional study, 10 patients with aMCI and 10 healthy controls underwent triple PET-MRI with 11C-UCB-J (synaptic vesicle protein 2A), 18F-MK-6240 (tau deposition), and 11C-Pittsburgh compound B (β-amyloid) and neuropsychological assessment. Gray matter atrophy was assessed by voxel-based morphometry with T1-weighted MRIs. Voxel-wise and volume-of-interest analyses were conducted on PET data. The interrelationship of synaptic density and tau deposition was investigated. We also investigated correlations of 18F-MK-6240 and 11C-UCB-J binding with cognitive performance. RESULTS Compared to controls, patients with aMCI showed a decreased 11C-UCB-J binding mainly in substructures of the medial temporal lobe (MTL; 48%-51%, p cluster = 0.02). Increased 18F-MK6240 binding in the same region was observed (42%-44%, p cluster = 0.0003), spreading to association cortices. In the MTL, higher 18F-MK-6240 binding inversely related to lower 11C-UCB-J binding (p = 0.02, r = -0.76). Decreased performance on cognitive tests was associated with both increased 18F-MK-6240 and decreased 11C-UCB-J binding in the hippocampus (p < 0.01, r > 0.7), although in a multivariate analysis only 18F-MK-6240 binding was significantly related to cognitive performance. CONCLUSIONS Patients with aMCI have high tau deposition and synaptic density loss mainly in key regions known to be involved in early cognitive impairment, indicating that these are interrelated in the MTL, while tau binding had already spread toward association cortices. Longitudinal data are needed to provide further insight into the temporal aspects of this relationship.
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Affiliation(s)
- Heleen Vanhaute
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium.
| | - Jenny Ceccarini
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Laura Michiels
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Michel Koole
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Stefan Sunaert
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Robin Lemmens
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Eric Triau
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Louise Emsell
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Mathieu Vandenbulcke
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
| | - Koen Van Laere
- From the Division of Nuclear Medicine (H.V., J.C., M.K., K.V.L.) and Department of Neurology (L.M., R.L.), University Hospitals Leuven; Nuclear Medicine and Molecular Imaging (H.V., J.C., M.K., S.S., L.E., K.V.L.) and Translational MRI (S.S., L.E.), Department of Imaging and Pathology, and Department of Geriatric Psychiatry (H.V., L.E., M.V.), University Psychiatric Centre, Laboratory for Neurobiology (L.M., R.L.), KU Leuven; and Center for Brain and Disease Research (L.M., R.L.), VIB-KU Leuven, Belgium. Dr. Triau is in private practice in Leuven, Belgium
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Zero Echo Time–Based PET/MRI Attenuation Correction in Patients With Oral Cavity Cancer. Clin Nucl Med 2020; 45:501-505. [DOI: 10.1097/rlu.0000000000003091] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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Arabi H, Zaidi H. Deep learning-guided estimation of attenuation correction factors from time-of-flight PET emission data. Med Image Anal 2020; 64:101718. [PMID: 32492585 DOI: 10.1016/j.media.2020.101718] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 03/30/2020] [Accepted: 04/30/2020] [Indexed: 01/09/2023]
Abstract
PURPOSE Attenuation correction (AC) is essential for quantitative PET imaging. In the absence of concurrent CT scanning, for instance on hybrid PET/MRI systems or dedicated brain PET scanners, an accurate approach for synthetic CT generation is highly desired. In this work, a novel framework is proposed wherein attenuation correction factors (ACF) are estimated from time-of-flight (TOF) PET emission data using deep learning. METHODS In this approach, referred to as called DL-EM), the different TOF sinogram bins pertinent to the same slice are fed into a multi-input channel deep convolutional network to estimate a single ACF sinogram associated with the same slice. The clinical evaluation of the proposed DL-EM approach consisted of 68 clinical brain TOF PET/CT studies, where CT-based attenuation correction (CTAC) served as reference. A two-tissue class consisting of background-air and soft-tissue segmentation of the TOF PET non-AC images (SEG) as a proxy of the technique used in the clinic was also included in the comparative evaluation. Qualitative and quantitative PET analysis was performed through SUV bias maps quantification in 63 different brain regions. RESULTS The DL-EM approach resulted in 6.1 ± 9.7% relative mean absolute error (RMAE) in bony structures compared to SEG AC method with RMAE of 16.1 ± 8.2% (p-value <0.001). Considering the entire head region, DL-EM led to a root mean square error (RMSE) of 0.3 ± 0.01 outperforming the SEG method with RMSE of 0.8 ± 0.02 SUV (p-value <0.001). The region-wise analysis of brain PET studies revealed less than 7% absolute SUV bias for the DL-EM approach, whereas the SEG method resulted in more than 14% absolute SUV bias (p-value <0.05). CONCLUSIONS Qualitative assessment and quantitative PET analysis demonstrated the superior performance of the DL-EM approach over the segmentation-based technique with clinically acceptable SUV bias. The results obtained using the DL-EM approach are comparable to state-of-the-art MRI-guided AC methods. Yet, this approach enables the extraction of interesting features about patient-specific attenuation which could be employed not only as a stand-alone AC approach but also as complementary/prior information in other AC algorithms.
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Affiliation(s)
- Hossein Arabi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Department of Medical Imaging, Geneva University Hospital, CH-1211 Geneva 4, Switzerland; Geneva Neuroscience Center, Geneva University, CH-1205 Geneva, Switzerland; Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, 9700 RB Groningen, Netherlands; Department of Nuclear Medicine, University of Southern Denmark, DK-500 Odense, Denmark.
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Qian P, Chen Y, Kuo JW, Zhang YD, Jiang Y, Zhao K, Al Helo R, Friel H, Baydoun A, Zhou F, Heo JU, Avril N, Herrmann K, Ellis R, Traughber B, Jones RS, Wang S, Su KH, Muzic RF. mDixon-Based Synthetic CT Generation for PET Attenuation Correction on Abdomen and Pelvis Jointly Using Transfer Fuzzy Clustering and Active Learning-Based Classification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:819-832. [PMID: 31425065 PMCID: PMC7284852 DOI: 10.1109/tmi.2019.2935916] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multi-center trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less. These experimental results distinctively demonstrate the effectiveness of our proposed TFCALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence.
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Clinical utility of new three-dimensional model using a zero-echo-time sequence in endoscopic endonasal transsphenoidal surgery. Clin Neurol Neurosurg 2020; 190:105743. [PMID: 32113079 DOI: 10.1016/j.clineuro.2020.105743] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 02/11/2020] [Accepted: 02/16/2020] [Indexed: 11/23/2022]
Abstract
OBJECTIVES Recognizing the anatomical orientation surrounding the sellar floor is crucial in endoscopic endonasal transsphenoidal surgery (ETSS). Zero-echo-time (ZTE) sequences were recently suggested for a new bone identification technique on magnetic resonance imaging (MRI). This study aimed to evaluate the clinical usefulness of three-dimensional (3D)-ZTE-based MRI models in providing anatomical guidance for ETSS. PATIENTS AND METHODS ZTE-based MRI and magnetic resonance angiography (MRA) data from 15 consecutive patients with pituitary tumor treated between September 2018 and May 2019 were used to create 3D-MRI models. From these, the architecture surrounding the sellar floor, particularly anatomical relationships between tumors and internal carotid arteries (ICAs), was visualized to preoperatively plan surgical procedures. In addition, 3D-ZTE-based MRI models were compared to actual surgical views during ETSS to evaluate model applicability. RESULTS These 3D-ZTE-based MRI models clearly demonstrated the morphology of the sellar floor and matched well with intraoperative views, including pituitary tumor, by successively eliminating sphenoidal structures. The models also permitted determination of the maximum marginal line of the opening of the sellar floor by presenting vital structures such as ICAs and tumors. With such 3D-MRI models, the surgeon could access the intracranial area through the sellar floor more safely, and resect the pituitary tumor maximally without complications. CONCLUSION Our 3D-MRI models based on ZTE sequences allowed distinct visualization of vital structures and pituitary tumor around the sellar floor. This new method using 3D-ZTE-based MRI models showed low invasiveness for patients and was useful in preoperative planning for ETSS, facilitating maximum tumor resection without complications.
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Gong K, Berg E, Cherry SR, Qi J. Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction. PROCEEDINGS OF THE IEEE. INSTITUTE OF ELECTRICAL AND ELECTRONICS ENGINEERS 2020; 108:51-68. [PMID: 38045770 PMCID: PMC10691821 DOI: 10.1109/jproc.2019.2936809] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. While there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time and position information from the detector signals. Now with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the abovementioned applications of machine learning in nuclear medicine.
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Affiliation(s)
- Kuang Gong
- Department of Biomedical Engineering, University of California, Davis, CA, USA and is now with Massachusetts General Hospital, Boston, MA, USA
| | - Eric Berg
- Department of Biomedical Engineering, University of California, Davis, CA, USA
| | - Simon R. Cherry
- Department of Biomedical Engineering and Department of Radiology, University of California, Davis, CA, USA
| | - Jinyi Qi
- Department of Biomedical Engineering, University of California, Davis, CA, USA
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Sgard B, Khalifé M, Bouchut A, Fernandez B, Soret M, Giron A, Zaslavsky C, Delso G, Habert MO, Kas A. ZTE MR-based attenuation correction in brain FDG-PET/MR: performance in patients with cognitive impairment. Eur Radiol 2019; 30:1770-1779. [DOI: 10.1007/s00330-019-06514-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 08/28/2019] [Accepted: 10/15/2019] [Indexed: 10/25/2022]
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Blanc-Durand P, Khalife M, Sgard B, Kaushik S, Soret M, Tiss A, El Fakhri G, Habert MO, Wiesinger F, Kas A. Attenuation correction using 3D deep convolutional neural network for brain 18F-FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction. PLoS One 2019; 14:e0223141. [PMID: 31589623 PMCID: PMC6779234 DOI: 10.1371/journal.pone.0223141] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 09/13/2019] [Indexed: 11/23/2022] Open
Abstract
One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI-based surrogate computed tomography (CT) from which AC-maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC-maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for 18FDG-PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two-point LAVA-flex MRI (for atlas-based AC) and ZTE-MRI were available in all patients. Three AC methods were evaluated and compared to CT-based AC (CTAC): one based on a single head-atlas, one based on ZTE-segmentation and one CNN with a 3D U-net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions-of-interest based analyses with CTAC set as reference. The U-net AC method yielded the lowest bias, the lowest inter-individual and inter-regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of -0.2% in most cortical regions. These results suggest that the U-net AC is more reliable for correcting photon attenuation in brain FDG-PET/MR than atlas-AC and ZTE-AC methods.
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Affiliation(s)
- Paul Blanc-Durand
- Nuclear Medicine Department, Groupe Hospitalier Pitié-Salpêtrière C. Foix, APHP, Paris, France
- * E-mail:
| | - Maya Khalife
- Centre de Neuroimagerie de Recherche (CENIR), Institut du Cerveau et de la Moëlle, Paris, France
| | - Brian Sgard
- Nuclear Medicine Department, Groupe Hospitalier Pitié-Salpêtrière C. Foix, APHP, Paris, France
| | | | - Marine Soret
- Nuclear Medicine Department, Groupe Hospitalier Pitié-Salpêtrière C. Foix, APHP, Paris, France
| | - Amal Tiss
- Gordon Center for Medical Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Georges El Fakhri
- Gordon Center for Medical Imaging, Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Marie-Odile Habert
- Nuclear Medicine Department, Groupe Hospitalier Pitié-Salpêtrière C. Foix, APHP, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Université, Paris, France
| | | | - Aurélie Kas
- Nuclear Medicine Department, Groupe Hospitalier Pitié-Salpêtrière C. Foix, APHP, Paris, France
- Laboratoire d’Imagerie Biomédicale, Sorbonne Université, Paris, France
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Rezaei A, Schramm G, Willekens SMA, Delso G, Van Laere K, Nuyts J. A Quantitative Evaluation of Joint Activity and Attenuation Reconstruction in TOF PET/MR Brain Imaging. J Nucl Med 2019; 60:1649-1655. [PMID: 30979823 DOI: 10.2967/jnumed.118.220871] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 04/04/2019] [Indexed: 11/16/2022] Open
Abstract
Time-of-flight (TOF) PET data provide an effective means for attenuation correction (AC) when no (or incomplete or inaccurate) attenuation information is available. Since MR scanners provide little information on photon attenuation of different tissue types, AC in hybrid PET/MR scanners has always been challenging. In this contribution, we aim at validating the activity reconstructions of the maximum-likelihood ordered-subsets activity and attenuation (OSAA) reconstruction algorithm on a patient brain data set. We present a quantitative comparison of joint reconstructions with the current clinical gold standard-ordered-subsets expectation maximization-using CT-based AC in PET/CT, as well as the current state of the art in PET/MR, that is, zero time echo (ZTE)-based AC. Methods: The TOF PET emission data were initially used in a preprocessing stage to estimate crystal maps of efficiencies, timing offsets, and timing resolutions. Applying these additional corrections during reconstructions, OSAA, ZTE-based, and the vendor-provided atlas-based AC techniques were analyzed and compared with CT-based AC. In our initial study, we used the CT-based estimate of the expected scatter and later used the ZTE-based and OSAA attenuation estimates to compute the expected scatter contribution of the data during reconstructions. In all reconstructions, a maximum-likelihood scaling of the single-scatter simulation estimate to the emission data was used for scatter correction. The reconstruction results were analyzed in the 86 segmented regions of interest of the Hammers atlas. Results: Our quantitative analysis showed that, in practice, a tracer activity difference of +0.5% (±2.1%) and +0.1% (±2.3%) could be expected for the state-of-the-art ZTE-based and OSAA AC methods, respectively, in PET/MR compared with the clinical gold standard in PET/CT. Conclusion: Joint activity and attenuation estimation methods can provide an effective solution to the challenging AC problem for brain studies in hybrid TOF PET/MR scanners. With an accurate TOF-based (timing offsets and timing resolutions) calibration, and similar to the results of the state-of-the-art method in PET/MR, regional errors of joint TOF PET reconstructions are within a few percentage points.
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Affiliation(s)
- Ahmadreza Rezaei
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Georg Schramm
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Stefanie M A Willekens
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Gaspar Delso
- MR Applications and Workflow, GE Healthcare, Waukesha, Wisconsin
| | - Koen Van Laere
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
| | - Johan Nuyts
- KU Leuven - University of Leuven, Department of Imaging and Pathology, Division of Nuclear Medicine & Molecular Imaging (NMMI), Medical Imaging Research Center (MIRC), B-3000, Leuven, Belgium; and
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No significant difference found in PET/MRI CBF values reconstructed with CT-atlas-based and ZTE MR attenuation correction. EJNMMI Res 2019; 9:26. [PMID: 30888559 PMCID: PMC6424990 DOI: 10.1186/s13550-019-0494-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Accepted: 03/06/2019] [Indexed: 01/31/2023] Open
Abstract
Background Accurate attenuation correction (AC) is one of the most important issues to be addressed in quantitative brain PET/MRI imaging. Atlas-based MRI AC (AB-MRAC), one of the representative MRAC methods, has been used to estimate the skull attenuation in brain scans. The zero echo time (ZTE) pulse sequence is also expected to provide a better MRAC estimation in brain PET scans. The difference in quantitative measurements of cerebral blood flow (CBF) using H215O-PET/MRI was compared between the two MRAC methods, AB and ZTE. Method Twelve patients with cerebrovascular disease (4 males, 43.2 ± 11.7 years) underwent H215O-PET/MRI studies with a 3-min PET scan and MRI scans including the ZTE sequence. Eleven of them were also studied under the conditions of baseline and 10 min after acetazolamide administration, and 2 of them were followed up after several months interval. A total of 25 PET images were reconstructed as dynamic data using 2 sets of reconstruction parameters to obtain the image-derived input function (IDIF), the time-activity curves of the major cerebral artery extracted from images, and CBF images. The CBF images from AB- and ZTE-MRAC were then compared for global and regional differences. Results The mean differences of IDIF curves at each point obtained from AB- and ZTE-MRAC dynamic data were less than 5%, and the differences in time-activity curves were very small. The means of CBF from AB- and ZTE-MRAC reconstructions calculated using each IDIF showed differences of less than 5% for all cortical regions. CBF images from AB-MRAC tended to show greater values in the parietal region and smaller values in the skull base region. Conclusion The CBF images from AB- and ZTE-MRAC reconstruction showed no significant differences in regional values, although the parietal region tended to show greater values in AB-MRAC reconstruction. Quantitative values in the skull base region were very close, and almost the same IDIFs were obtained.
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