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Lucas A, Campbell Arnold T, Okar SV, Vadali C, Kawatra KD, Ren Z, Cao Q, Shinohara RT, Schindler MK, Davis KA, Litt B, Reich DS, Stein JM. Multi-contrast high-field quality image synthesis for portable low-field MRI using generative adversarial networks and paired data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.12.28.23300409. [PMID: 38234785 PMCID: PMC10793526 DOI: 10.1101/2023.12.28.23300409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Introduction Portable low-field strength (64mT) MRI scanners promise to increase access to neuroimaging for clinical and research purposes, however these devices produce lower quality images compared to high-field scanners. In this study, we developed and evaluated a deep learning architecture to generate high-field quality brain images from low-field inputs using a paired dataset of multiple sclerosis (MS) patients scanned at 64mT and 3T. Methods A total of 49 MS patients were scanned on portable 64mT and standard 3T scanners at Penn (n=25) or the National Institutes of Health (NIH, n=24) with T1-weighted, T2-weighted and FLAIR acquisitions. Using this paired data, we developed a generative adversarial network (GAN) architecture for low- to high-field image translation (LowGAN). We then evaluated synthesized images with respect to image quality, brain morphometry, and white matter lesions. Results Synthetic high-field images demonstrated visually superior quality compared to low-field inputs and significantly higher normalized cross-correlation (NCC) to actual high-field images for T1 (p=0.001) and FLAIR (p<0.001) contrasts. LowGAN generally outperformed the current state-of-the-art for low-field volumetrics. For example, thalamic, lateral ventricle, and total cortical volumes in LowGAN outputs did not differ significantly from 3T measurements. Synthetic outputs preserved MS lesions and captured a known inverse relationship between total lesion volume and thalamic volume. Conclusions LowGAN generates synthetic high-field images with comparable visual and quantitative quality to actual high-field scans. Enhancing portable MRI image quality could add value and boost clinician confidence, enabling wider adoption of this technology.
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Affiliation(s)
- Alfredo Lucas
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
| | - Serhat V Okar
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Chetan Vadali
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Radiology, University of Pennsylvania
| | - Karan D Kawatra
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Zheng Ren
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Quy Cao
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania
| | - Matthew K Schindler
- Perelman School of Medicine, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Kathryn A Davis
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Brian Litt
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Neurology, University of Pennsylvania
| | - Daniel S Reich
- National Institute of Neurological Disorders and Stroke, National Institutes of Health
| | - Joel M Stein
- Perelman School of Medicine, University of Pennsylvania
- Center for Neuroengineering and Therapeutics, Departments of Bioengineering and Neurology, University of Pennsylvania
- Department of Radiology, University of Pennsylvania
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Kimberly WT, Sorby-Adams AJ, Webb AG, Wu EX, Beekman R, Bowry R, Schiff SJ, de Havenon A, Shen FX, Sze G, Schaefer P, Iglesias JE, Rosen MS, Sheth KN. Brain imaging with portable low-field MRI. NATURE REVIEWS BIOENGINEERING 2023; 1:617-630. [PMID: 37705717 PMCID: PMC10497072 DOI: 10.1038/s44222-023-00086-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/06/2023] [Indexed: 09/15/2023]
Abstract
The advent of portable, low-field MRI (LF-MRI) heralds new opportunities in neuroimaging. Low power requirements and transportability have enabled scanning outside the controlled environment of a conventional MRI suite, enhancing access to neuroimaging for indications that are not well suited to existing technologies. Maximizing the information extracted from the reduced signal-to-noise ratio of LF-MRI is crucial to developing clinically useful diagnostic images. Progress in electromagnetic noise cancellation and machine learning reconstruction algorithms from sparse k-space data as well as new approaches to image enhancement have now enabled these advancements. Coupling technological innovation with bedside imaging creates new prospects in visualizing the healthy brain and detecting acute and chronic pathological changes. Ongoing development of hardware, improvements in pulse sequences and image reconstruction, and validation of clinical utility will continue to accelerate this field. As further innovation occurs, portable LF-MRI will facilitate the democratization of MRI and create new applications not previously feasible with conventional systems.
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Affiliation(s)
- W Taylor Kimberly
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Annabel J Sorby-Adams
- Department of Neurology and the Center for Genomic Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Andrew G Webb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Ed X Wu
- Laboratory of Biomedical Imaging and Signal Processing, Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Rachel Beekman
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, Yale Center for Brain & Mind Health, New Haven, CT, USA
| | - Ritvij Bowry
- Departments of Neurosurgery and Neurology, McGovern Medical School, University of Texas Health Neurosciences, Houston, TX, USA
| | - Steven J Schiff
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Adam de Havenon
- Division of Vascular Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA
| | - Francis X Shen
- Harvard Medical School Center for Bioethics, Harvard law School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Gordon Sze
- Department of Radiology, Yale New Haven Hospital and Yale School of Medicine, New Haven, CT, USA
| | - Pamela Schaefer
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Centre for Medical Image Computing, University College London, London, UK
- Computer Science and AI Laboratory, Massachusetts Institute of Technology, Boston, MA, USA
| | - Matthew S Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kevin N Sheth
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Yale New Haven Hospital and Yale School of Medicine, Yale Center for Brain & Mind Health, New Haven, CT, USA
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Anazodo UC, Ng JJ, Ehiogu B, Obungoloch J, Fatade A, Mutsaerts HJMM, Secca MF, Diop M, Opadele A, Alexander DC, Dada MO, Ogbole G, Nunes R, Figueiredo P, Figini M, Aribisala B, Awojoyogbe BO, Aduluwa H, Sprenger C, Wagner R, Olakunle A, Romeo D, Sun Y, Fezeu F, Orunmuyi AT, Geethanath S, Gulani V, Nganga EC, Adeleke S, Ntobeuko N, Minja FJ, Webb AG, Asllani I, Dako F. A framework for advancing sustainable magnetic resonance imaging access in Africa. NMR IN BIOMEDICINE 2023; 36:e4846. [PMID: 36259628 DOI: 10.1002/nbm.4846] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 10/03/2022] [Accepted: 10/05/2022] [Indexed: 06/16/2023]
Abstract
Magnetic resonance imaging (MRI) technology has profoundly transformed current healthcare systems globally, owing to advances in hardware and software research innovations. Despite these advances, MRI remains largely inaccessible to clinicians, patients, and researchers in low-resource areas, such as Africa. The rapidly growing burden of noncommunicable diseases in Africa underscores the importance of improving access to MRI equipment as well as training and research opportunities on the continent. The Consortium for Advancement of MRI Education and Research in Africa (CAMERA) is a network of African biomedical imaging experts and global partners, implementing novel strategies to advance MRI access and research in Africa. Upon its inception in 2019, CAMERA sets out to identify challenges to MRI usage and provide a framework for addressing MRI needs in the region. To this end, CAMERA conducted a needs assessment survey (NAS) and a series of symposia at international MRI society meetings over a 2-year period. The 68-question NAS was distributed to MRI users in Africa and was completed by 157 clinicians and scientists from across Sub-Saharan Africa (SSA). On average, the number of MRI scanners per million people remained at less than one, of which 39% were obsolete low-field systems but still in use to meet daily clinical needs. The feasibility of coupling stable energy supplies from various sources has contributed to the growing number of higher-field (1.5 T) MRI scanners in the region. However, these systems are underutilized, with only 8% of facilities reporting clinical scans of 15 or more patients per day, per scanner. The most frequently reported MRI scans were neurological and musculoskeletal. The CAMERA NAS combined with the World Health Organization and International Atomic Energy Agency data provides the most up-to-date data on MRI density in Africa and offers a unique insight into Africa's MRI needs. Reported gaps in training, maintenance, and research capacity indicate ongoing challenges in providing sustainable high-value MRI access in SSA. Findings from the NAS and focused discussions at international MRI society meetings provided the basis for the framework presented here for advancing MRI capacity in SSA. While these findings pertain to SSA, the framework provides a model for advancing imaging needs in other low-resource settings.
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Affiliation(s)
- Udunna C Anazodo
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Jinggang J Ng
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Boaz Ehiogu
- Lawson Health Research Institute, London, Ontario, Canada
| | | | | | - Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Amsterdam UMC Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, The Netherlands
| | | | - Mamadou Diop
- Lawson Health Research Institute, London, Ontario, Canada
- Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Abayomi Opadele
- Molecular and Cellular Dynamics Research, Graduate School of Biomedical Science and Engineering, Hokkaido University, Hokkaido, Japan
| | | | - Michael O Dada
- Department of Physics, Federal University of Technology, Minna, Niger State, Nigeria
| | - Godwin Ogbole
- Department of Radiology, University College Hospital Ibadan, Ibadan, Nigeria
| | - Rita Nunes
- Department of Bioengineering, Instituto Superior, Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Patricia Figueiredo
- Department of Bioengineering, Instituto Superior, Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Matteo Figini
- Department of Computer Science, University College London, London, UK
| | | | - Bamidele O Awojoyogbe
- Department of Physics, Federal University of Technology, Minna, Niger State, Nigeria
| | | | - Christian Sprenger
- Department of Anesthesiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Rachel Wagner
- Mbarara University of Science and Technology, Mbarara, Uganda
- Department of Medical Biophysics, Western University, London, Ontario, Canada
| | | | - Dominic Romeo
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yusha Sun
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Francis Fezeu
- Neurosurgery & Neurology, BRAIN Global, Salisbury, Maryland, USA
| | - Akintunde T Orunmuyi
- Department of Nuclear Medicine, University of Ibadan, Ibadan, Oyo State, Nigeria
| | - Sairam Geethanath
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, USA
| | - Vikas Gulani
- Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA
| | | | - Sola Adeleke
- Department of Oncology, Guy's & St Thomas' Hospital, London, UK
| | - Ntusi Ntobeuko
- Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Frank J Minja
- Department of Radiology and Imaging Sciences, Emory University, Atlanta, Georgia, USA
| | - Andrew G Webb
- Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands
| | - Iris Asllani
- Department of Neuroscience, University of Sussex, Brighton, UK
- Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, New York, USA
| | - Farouk Dako
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
- RAD-AID International, Chevy Chase, Maryland, USA
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