1
|
Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Nuklearmedizin 2023; 62:306-313. [PMID: 37802058 DOI: 10.1055/a-2157-6670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/08/2023]
Abstract
BACKGROUND Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. RESULTS AND CONCLUSION In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. KEY POINTS · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET..
Collapse
Affiliation(s)
- Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Tobias Hepp
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Ferdinand Seith
- Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| |
Collapse
|
2
|
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.
Collapse
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.
| |
Collapse
|
3
|
Mirshahvalad SA, Metser U, Basso Dias A, Ortega C, Yeung J, Veit-Haibach P. 18F-FDG PET/MRI in Detection of Pulmonary Malignancies: A Systematic Review and Meta-Analysis. Radiology 2023; 307:e221598. [PMID: 36692397 DOI: 10.1148/radiol.221598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Background There have been conflicting results regarding fluorine 18-labeled fluorodeoxyglucose (18F-FDG) PET/MRI diagnostic performance in lung malignant neoplasms. Purpose To evaluate the diagnostic performance of 18F-FDG PET/MRI for the detection of pulmonary malignant neoplasms. Materials and Methods A systematic search was conducted within the Scopus, Web of Science, and PubMed databases until December 31, 2021. Published original articles that met the following criteria were considered eligible for meta-analysis: (a) detecting malignant lesions in the lung, (b) comparing 18F-FDG PET/MRI with a valid reference standard, and (c) providing data for the meta-analytic calculations. A hierarchical method was used to pool the performances. The bivariate model was used to find the summary points and 95% CIs. The hierarchical summary receiver operating characteristic model was used to draw the summary receiver operating characteristic curve and calculate the area under the curve. The Higgins I2 statistic and Cochran Q test were used for heterogeneity assessment. Results A total of 43 studies involving 1278 patients met the inclusion criteria and were included in the meta-analysis. 18F-FDG PET/MRI had a pooled sensitivity and specificity of 96% (95% CI: 84, 99) and 100% (95% CI: 98, 100), respectively. 18F-FDG PET/CT had a pooled sensitivity and specificity of 99% (95% CI: 61, 100) and 99% (95% CI: 94, 100), respectively, which were comparable with those of 18F-FDG PET/MRI. At meta-regression, studies in which contrast media (P = .03) and diffusion-weighted imaging (P = .04) were used as a part of a pulmonary 18F-FDG PET/MRI protocol showed significantly higher sensitivities. Conclusion Fluorine 18-labeled fluorodeoxyglucose (18F-FDG) PET/MRI was found to be accurate and comparable with 18F-FDG PET/CT in the detection of malignant pulmonary lesions, with significantly improved sensitivity when advanced acquisition protocols were used. © RSNA, 2023 Supplemental material is available for this article.
Collapse
Affiliation(s)
- Seyed Ali Mirshahvalad
- From the Joint Department of Medical Imaging (S.A.M., U.R., A.B.D., C.O., P.V.H.) and Division of Thoracic Surgery, Department of Surgery (J.Y.), Toronto General Hospital, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2
| | - Ur Metser
- From the Joint Department of Medical Imaging (S.A.M., U.R., A.B.D., C.O., P.V.H.) and Division of Thoracic Surgery, Department of Surgery (J.Y.), Toronto General Hospital, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2
| | - Adriano Basso Dias
- From the Joint Department of Medical Imaging (S.A.M., U.R., A.B.D., C.O., P.V.H.) and Division of Thoracic Surgery, Department of Surgery (J.Y.), Toronto General Hospital, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2
| | - Claudia Ortega
- From the Joint Department of Medical Imaging (S.A.M., U.R., A.B.D., C.O., P.V.H.) and Division of Thoracic Surgery, Department of Surgery (J.Y.), Toronto General Hospital, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2
| | - Jonathan Yeung
- From the Joint Department of Medical Imaging (S.A.M., U.R., A.B.D., C.O., P.V.H.) and Division of Thoracic Surgery, Department of Surgery (J.Y.), Toronto General Hospital, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2
| | - Patrick Veit-Haibach
- From the Joint Department of Medical Imaging (S.A.M., U.R., A.B.D., C.O., P.V.H.) and Division of Thoracic Surgery, Department of Surgery (J.Y.), Toronto General Hospital, University Health Network, University of Toronto, 585 University Ave, Toronto, ON, Canada M5G 2N2
| |
Collapse
|
4
|
Grootjans W, Rietbergen DDD, van Velden FHP. Added Value of Respiratory Gating in Positron Emission Tomography for the Clinical Management of Lung Cancer Patients. Semin Nucl Med 2022; 52:745-758. [DOI: 10.1053/j.semnuclmed.2022.04.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 04/21/2022] [Indexed: 12/24/2022]
|
5
|
Chen S, Fraum TJ, Eldeniz C, Mhlanga J, Gan W, Vahle T, Krishnamurthy UB, Faul D, Gach HM, Binkley MM, Kamilov US, Laforest R, An H. MR-assisted PET respiratory motion correction using deep-learning based short-scan motion fields. Magn Reson Med 2022; 88:676-690. [PMID: 35344592 DOI: 10.1002/mrm.29233] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 02/03/2022] [Accepted: 02/23/2022] [Indexed: 11/11/2022]
Abstract
PURPOSE We evaluated the impact of PET respiratory motion correction (MoCo) in a phantom and patients. Moreover, we proposed and examined a PET MoCo approach using motion vector fields (MVFs) from a deep-learning reconstructed short MRI scan. METHODS The evaluation of PET MoCo was performed in a respiratory motion phantom study with varying lesion sizes and tumor to background ratios (TBRs) using a static scan as the ground truth. MRI-based MVFs were derived from either 2000 spokes (MoCo2000 , 5-6 min acquisition time) using a Fourier transform reconstruction or 200 spokes (MoCoP2P200 , 30-40 s acquisition time) using a deep-learning Phase2Phase (P2P) reconstruction and then incorporated into PET MoCo reconstruction. For six patients with hepatic lesions, the performance of PET MoCo was evaluated using quantitative metrics (SUVmax , SUVpeak , SUVmean , lesion volume) and a blinded radiological review on lesion conspicuity. RESULTS MRI-assisted PET MoCo methods provided similar results to static scans across most lesions with varying TBRs in the phantom. Both MoCo2000 and MoCoP2P200 PET images had significantly higher SUVmax , SUVpeak , SUVmean and significantly lower lesion volume than non-motion-corrected (non-MoCo) PET images. There was no statistical difference between MoCo2000 and MoCoP2P200 PET images for SUVmax , SUVpeak , SUVmean or lesion volume. Both radiological reviewers found that MoCo2000 and MoCoP2P200 PET significantly improved lesion conspicuity. CONCLUSION An MRI-assisted PET MoCo method was evaluated using the ground truth in a phantom study. In patients with hepatic lesions, PET MoCo images improved quantitative and qualitative metrics based on only 30-40 s of MRI motion modeling data.
Collapse
Affiliation(s)
- Sihao Chen
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Tyler J Fraum
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Cihat Eldeniz
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Joyce Mhlanga
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Weijie Gan
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | | | | | - David Faul
- Siemens Medical Solutions USA, Inc., Malvern, PA, USA
| | - H Michael Gach
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.,Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.,Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael M Binkley
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
| | - Ulugbek S Kamilov
- Department of Computer Science & Engineering, Washington University in St. Louis, St. Louis, MO, USA.,Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA
| | - Hongyu An
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.,Mallinckrodt Institute of Radiology, Washington University in St. Louis, St. Louis, MO, USA.,Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA.,Department of Electrical & Systems Engineering, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
6
|
Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. ROFO-FORTSCHR RONTG 2022; 194:605-612. [PMID: 35211929 DOI: 10.1055/a-1718-4128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
BACKGROUND Machine learning (ML) is considered an important technology for future data analysis in health care. METHODS The inherently technology-driven fields of diagnostic radiology and nuclear medicine will both benefit from ML in terms of image acquisition and reconstruction. Within the next few years, this will lead to accelerated image acquisition, improved image quality, a reduction of motion artifacts and - for PET imaging - reduced radiation exposure and new approaches for attenuation correction. Furthermore, ML has the potential to support decision making by a combined analysis of data derived from different modalities, especially in oncology. In this context, we see great potential for ML in multiparametric hybrid imaging and the development of imaging biomarkers. RESULTS AND CONCLUSION In this review, we will describe the basics of ML, present approaches in hybrid imaging of MRI, CT, and PET, and discuss the specific challenges associated with it and the steps ahead to make ML a diagnostic and clinical tool in the future. KEY POINTS · ML provides a viable clinical solution for the reconstruction, processing, and analysis of hybrid imaging obtained from MRI, CT, and PET.. CITATION FORMAT · Küstner T, Hepp T, Seith F. Multiparametric Oncologic Hybrid Imaging: Machine Learning Challenges and Opportunities. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1718-4128.
Collapse
Affiliation(s)
- Thomas Küstner
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Tobias Hepp
- Medical Image and Data Analysis (MIDAS.lab), Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| | - Ferdinand Seith
- Department of Diagnostic and Interventional Radiology, University Hospitals Tubingen, Germany
| |
Collapse
|
7
|
Polycarpou I, Soultanidis G, Tsoumpas C. Synergistic motion compensation strategies for positron emission tomography when acquired simultaneously with magnetic resonance imaging. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200207. [PMID: 34218675 PMCID: PMC8255946 DOI: 10.1098/rsta.2020.0207] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/15/2021] [Indexed: 05/04/2023]
Abstract
Subject motion in positron emission tomography (PET) is a key factor that degrades image resolution and quality, limiting its potential capabilities. Correcting for it is complicated due to the lack of sufficient measured PET data from each position. This poses a significant barrier in calculating the amount of motion occurring during a scan. Motion correction can be implemented at different stages of data processing either during or after image reconstruction, and once applied accurately can substantially improve image quality and information accuracy. With the development of integrated PET-MRI (magnetic resonance imaging) scanners, internal organ motion can be measured concurrently with both PET and MRI. In this review paper, we explore the synergistic use of PET and MRI data to correct for any motion that affects the PET images. Different types of motion that can occur during PET-MRI acquisitions are presented and the associated motion detection, estimation and correction methods are reviewed. Finally, some highlights from recent literature in selected human and animal imaging applications are presented and the importance of motion correction for accurate kinetic modelling in dynamic PET-MRI is emphasized. This article is part of the theme issue 'Synergistic tomographic image reconstruction: part 2'.
Collapse
Affiliation(s)
- Irene Polycarpou
- Department of Health Sciences, European University of Cyprus, Nicosia, Cyprus
| | - Georgios Soultanidis
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Charalampos Tsoumpas
- Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Biomedical Imaging Science Department, University of Leeds, West Yorkshire, UK
- Invicro, London, UK
| |
Collapse
|
8
|
Perkins T, Lee D, Simpson J, Greer P, Goodwin J. Experimental evaluation of four-dimensional Magnetic Resonance Imaging for radiotherapy planning of lung cancer. PHYSICS & IMAGING IN RADIATION ONCOLOGY 2021; 17:32-35. [PMID: 33898775 PMCID: PMC8058028 DOI: 10.1016/j.phro.2020.12.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 12/18/2020] [Accepted: 12/22/2020] [Indexed: 12/25/2022]
Abstract
Radiotherapy planning for lung cancer typically requires both 3D and 4D Computed Tomography (CT) to account for respiratory related movement. 4D Magnetic Resonance Imaging (MRI) with self-navigation offers a potential alternative with greater reliability in patients with irregular breathing patterns and improved soft tissue contrast. In this study 4D-CT and a 4D-MRI Radial Volumetric Interpolated Breath-hold Examination (VIBE) sequence was evaluated with a 4D phantom and 13 patient respiratory patterns, simulating tumour motion. Quantification of motion related tumour displacement in 4D-MRI and 4D-CT found no statistically significant difference in mean motion range. The results demonstrated the potential viability of 4D-MRI for lung cancer treatment planning.
Collapse
Affiliation(s)
- Terry Perkins
- Blacktown Cancer & Haematology Centre, Blacktown Hospital, NSW, Australia.,School of Physics, University of Sydney, Australia
| | - Danny Lee
- School of Mathematical and Physical Science, University of Newcastle, Australia
| | - John Simpson
- Radiation Oncology, Calvary Mater Newcastle, Australia.,School of Mathematical and Physical Science, University of Newcastle, Australia
| | - Peter Greer
- Radiation Oncology, Calvary Mater Newcastle, Australia.,School of Mathematical and Physical Science, University of Newcastle, Australia
| | - Jonathan Goodwin
- Radiation Oncology, Calvary Mater Newcastle, Australia.,School of Mathematical and Physical Science, University of Newcastle, Australia
| |
Collapse
|