| 08:15 | | IntroductionDavid Waddington University of Sydney, Australia |
| 08:27 | 0416.
 | MRI advancement and research in Africa: Report on the Inaugural ISMRM African Chapter conference Johnes Obungoloch1, Adesola Emmauel Adepoju2, Petronella Samuels3,4, Mary-Jane Orevaoghene Amadi5, Segun Joseph Ayilara 6, Mary Kamuzora 7, Klenam Dzefi-Tettey 8, Anthonia A Ikpeme 9, Frances Robertson3, Udunna Anazodo 10, Chip Truwit 11, Farouk Dako12, Andrew Webb13, Yaw B Mensah 8, Iris Asllani14,15, Ernesta Meintjes 3,16, and Godwin Ogbole2
1Mbarara University of Science and Technology, Mbarara, Uganda, Mbarara, Uganda, 2Department of Radiology, College of Medicine, University of Ibadan, Ibadan, Nigeria, 3Biomedical Engineering Research Centre, Division of Biomedical Engineering, University of Cape Town, South Africa, Cape Town, South Africa, 4Department of Human Biology, Faculty of Health Sciences, University of Cape Town, South Africa, CapeTown, South Africa, 5Rivers State University Teaching Hospital, Port Harcourt, Nigeria, 6Department of Radiology, University College Hospital, Ibadan, Nigeria, 7Department of Radiology, Muhimbili National Hospital-Mloganzila, Dar-es-salaam, Tanzania, 8Department of Radiology, Korle Bu Teaching Hospital, Accra, Ghana, 9Department of Radiology, University of Calabar teaching Hospital, Calabar, Nigeria, 10Montreal Neurological Institute, McGill University, Montreal, QC, Canada, 11Hyperfine Inc, St. Guilford, CT, United States, 12Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States, 13C. J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, Netherlands, 14Clinical Imaging Science Centre, University of Sussex,, Sussex, United Kingdom, 15Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY, United States, 16Cape Universities Body Imaging Centre, University of Cape Town, CapeTown, South Africa Keywords: Low-Field MRI, Low-Field MRI, Networking, Innovation Motivation: Due to rising non-communicable diseases, limited MRI accessibility, and Africa's underrepresentation in ISMRM, the African Chapter (AC) was founded in 2023. An inaugural conference in Ghana focused on emerging MRI technology for improved accessibility. Goal(s): To provide the inaugural conference report of AC-ISMRM, with the identification of challenges and barriers to MRI access and propose solutions toward democratization of MRI across Africa. Approach: A white paper approach was adopted Results: Over 100 scientists from 12 African countries met to identify challenges and propose solutions for advancing MRI access and value in Africa. Low-field MRI was identified as a breakthrough innovation toward this goal. Impact: The AC-ISMRM conference marks a pioneer event, convening African scientists and clinicians, aimed at establishing a network dedicated to rectify Africa's underrepresentation in MRI research, seeking solutions to challenges on the continent and promoting collaboration and MRI advancements |
| 08:39 | 0417.
 | Bridging Health Disparities: Accessible MRI in Underserved African Countries Israa S. Hissein1,2, Jingting Yao3, Ming Zhao1,4, Foksouna Sakadi5, André J.W. van der Kouwe3, and Jerome L. Ackerman3
1Massachusetts General Hospital, Boston, MA, United States, 2National Institutes of Health/ National Institute of Environmental Health Sciences, Durham, NC, United States, 3Massachusetts General Hospital/ Harvard Medical School, Boston, MA, United States, 4FxMasse Associates, Inc., Boston, MA, United States, 5Nationale Référence Teaching Hospital, N'Djamena, Chad Keywords: New Devices, Neuro, Healthcare Disparities, Neurological Disorders, Accessible MRI Motivation: Neurological disorders, including cerebral malaria and HIV/AIDS-associated complications, are leading causes of death in underserved African nations, hindered by a lack of medical equipment, particularly MRI facilities. Goal(s): We seek to address two key questions: the need for MRI facilities in underserved African regions and the potential of accessible MRI in reducing healthcare disparities related to medical imaging. Approach: We employed a multifaceted approach, involving literature review, interviews, and evaluations of accessible scanner benefits and enhancements. Results: The marked disparities in MRI capabilities in Africa underscore the pressing need for investment in enhanced MRI infrastructure and customized imaging technologies, tailored to resource-limited settings. Impact: This research underscores the urgent need for MRI facilities to address neurological disorders in African countries, highlighting infrastructure gaps and the potential for innovative, compact MRI solutions to improve healthcare in resource-limited settings. |
| 08:51 | 0418.
 | Medical physics mentoring through a RAD-AID International partnership with Intermed Hospital in Mongolia: Development and initial visit Joseph Weygand1,2, Batnasan Shagdarsuren3, Tamir Munkhtuvshin3, Bayarbaatar Bold3, Khulan Khurelsukh3, Eman Suliman4, John M. Bryant5, Gage Redler5, Benjamin C. Musall6, Shauna M. McVorran1, Travis C. Salzillo7, Sharon Mohammed2,8, and Daniel J. Mollura2
1Department of Radiation Oncology and Applied Sciences, Dartmouth-Hitchcock Medical Center, Lebanon, NH, United States, 2RAD-AID International, Chevy Chase, MD, United States, 3Department of Radiology, Intermed Hospital, Ulaanbaatar, Mongolia, 4Department of Medicine, Al-Zahraa Hospital University Medical Center, Cairo, Egypt, 5Department of Radiation Oncology, Moffitt Cancer Center, Tampa, FL, United States, 6Department of Diagnostic and Interventional Imaging, University of Texas Health Science Center at Houston McGovern Medical School, Houston, TX, United States, 7Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, United States, 8Department of Radiology, Bellevue Hospital, New York, NY, United States Keywords: Safety, Health Care Economics, Global Health, Outreach Motivation: Despite wide application in high-income countries, MRI is largely underutilized in low- and middle-income countries3 (LMIC’s). One reason is a lack of expertise in MRI physics in LMIC’s. RAD-AID International is an organization engaged in radiological outreach and is active in over twenty countries. Goal(s): In this abstract, a roadmap for an MRI physics mentorship partnership is presented and illustrated at a private hospital in Northeast Asia. Approach: A physicist volunteering for RAD-AID International visited Intermed Hospital in Mongolia. Results: He taught the basics of MRI physics, optimized their imaging protocols, and established a quality assurance program. Collaboration will be ongoing. Impact: MRI is underutilized in low-resource settings. One
impediment to its utilization is lack of physics expertise. In this work, a
roadmap is presented whereby a philanthropic organization partners with an Asian
hospital to better incorporate physics concepts into clinic practice. |
| 09:03 | 0419.
 | Parallel Imaging Reconstruction in Public Datasets Biases Downstream Analysis in Retrospective Sampling Studies Evan Frenklak1, Yamin Arefeen1,2, and Jonathan I Tamir1
1Chandra Family Department of Electrical and Computer Engineering, UT Austin, Austin, TX, United States, 2MD Anderson, Houston, TX, United States Keywords: Data Processing, Image Reconstruction, implicit data crimes Motivation: We explore the “Implicit Data Crime” of datasets whose subsampled k-space is filled using parallel imaging. These datasets are treated as fully-sampled, but their points derive from (1)prospective sampling, and (2)reconstruction of un-sampled points, creating artificial data correlations given low SNR or high acceleration. Goal(s): How will downstream tasks, including reconstruction algorithm comparison and optimal trajectory design, be biased by effects of parallel imaging on a prospectively undersampled dataset? Approach: Comparing reconstruction performance using data that are fully sampled with data that are completed using the SENSE algorithm. Results: Utilizing parallel imaging filled k-space results in biased downstream perception of algorithm performance. Impact: This study demonstrates evidence of overly-optimistic bias resulting from the use of k-space filled in with parallel imaging as ground truth data. Researchers should be aware of this possibility and carefully examine the computational pipeline behind datasets they use. |
| 09:15 | 0420.
 | Facilitating access to neuroimaging and computational resources in low-resource settings through a centralised biomedical imaging platform Niall J Bourke1, Jonathan O'Muircheartaigh1, Sean Deoni2, Pablo Velasco3, Doug Dean III4, Emil Ljungberg1,5, Jessica E Ringshaw6, Maclean Vokhiwa7,8, Marc Seal9, Richard Beare9, Victoria Nankabirwa10, Francesco Padormo11, Costas Tsougarakis3, Can Akgun3, Carly Bennallick12, František Váša1, Layla E Bradford6, Marlie Miles13, Michal Zieff6, Thandeka Mazubane6, Zayaan Goolam Nabi6, Simone Williams6, Yaw Mensah 14, Samuel A Oppong15, Levente Baljer1, Muriel Bruchhage16, Natasha Lepore17, Khula South Africa Data Collection Team6, Daniel Alexander18, Derek Jones19, Kirsten A Donald6, and Steven Williams1
1Department of Neuroimaging, King's College London, London, United Kingdom, 2Maternal, Newborn, and Child Health Discovery & Tools, Bill & Melinda Gates Foundation, Seattle, WA, United States, 3Flywheel, Minneapolis, MN, United States, 4Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Wisconsin, WI, United States, 5Department of Medical Radiation Physics, Lund University, Lund, Sweden, 6Department of Paediatrics and Child Health, University of Cape Town, Cape Town, South Africa, 7Neuroscience, Training & Research Unit of Excellence, Blantyre, Malawi, 8Kamuzu University of Health Sciences, Blantyre, Malawi, 9Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Victoria, Australia, 10Makerere University, Kampala, Uganda, 11Hyperfine, London, United Kingdom, 12King's College London, London, United Kingdom, 13University of Cape Town, Cape Town, South Africa, 14Department of Radiology, Korle Bu Teach Hospital, Accra, Ghana, 15Department of Obstetrics and Gynecology, Korle Bu Teach Hospital, Accra, Ghana, 16Stavanger University, Stavanger, Norway, 17University of Southern California, Los Angeles, CA, United States, 18Department of Computer Science, University College London, London, United Kingdom, 19Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom Keywords: Neuro, Low-Field MRI Motivation: MRI remains inaccessible in many parts of the world, as are the computational resources to perform neuroimaging analysis. We hope to develop resources for a growing neuroimaging community in low-resource settings. Goal(s): To develop scalable neuroimaging tools, building capacity across low-resource settings and supporting a community for neuroimaging research. Approach: Partnership with Hyperfine, Flywheel and numerous collaborators in sub-Saharan Africa and south Asia to collect and process MRI scans of children in the early years of life. Results: Containerised workflows optimised for ultra-low field paediatric imaging were developed. Derived volume estimates were generated from geographically dispersed regions for further global health questions. Impact: Multiple low-resource sites in a global consortium have generated derived volume estimates through standardised workflows of ultra-low field MR images. This enables answering of locally relevant clinical questions on factors affecting neurodevelopment, such as maternal anaemia, HIV exposure, malnutrition etc. |
| 09:27 | 0421.
 | Research use of an ultra-low-field MRI to measure child neurodevelopment at 3 and 12 months of age in Southern Malawi, Sub-Saharan Africa Maclean Vokhiwa1,2, Able Khosa1, Blessings Nthulula1, Karen Chetcuti3, Louise Randall4, Steven Greenstein5, Marc Seal5, Richard Beare5, Niall Bourke6, Francesco Padormo7, John Rogers7, Pip Torelli7, Sean Deoni8, Sant-Rayn Pasricha4, and Kamija S. Phiri1,2
1Neuroscience, Training & Research Unit of Excellence (TRUE), Blantyre, Malawi, 2Kamuzu University of Health Sciences (KUHeS), Blantyre, Malawi, 3Radiology, Kamuzu University of Health Sciences (KUHeS), Blantyre, Malawi, 4Pasricha Lab, Population Health and Immunity, Walter and Eliza Hall Institute (WEHI), Voctoria, Australia, 5Developmental Imaging, Murdoch Children's Research Institute, The Royal Children's Hospital, Voctoria, Australia, 6Centre for Neuroimaging Sciences, Psychology and Neuroscience, King’s College London, London, United Kingdom, 7Hyperfine.io, London, United Kingdom, 85. Maternal, Newborn, and Child Health Discovery & Tools, Bill & Melinda Gates Foundation, Seattle, WA, United States Keywords: Neuro, Low-Field MRI, Infant Brain Development; Brain MRI; Ultra-Low-Field MRI Motivation: In Sub-Saharan Africa, limited MRI access and expertise can be addressed through international collaborations to enhance quality neuroimaging data collection in brain research. Goal(s): We describe research usability and reliability of an ultra-low-field (64mT) MRI data collection from Zomba, Malawi. Approach: We scanned ~481 children at 3 and 12 months of age, using hyperfine Swoop ULF-MRI (64T) for neuroimaging data to augment traditional randomized control trial outcome measures. We summarize procedures, participant responsiveness, and neuroimaging quality. Results: Full-scan success was in over 88% of participants within 55 weeks, with 87.4 to 99.6% completing all 5 scanning sequences. Full-brain quality scans were in >79%. Impact: International
collaborations, such as UNITY project, utilizing ultra-low-field MRI improves
research capacity and enables reliable measurement of brain development in
Sub-Saharan Africa. This significantly promotes advancement of developmental
neuroscience in the region. |
| 09:39 | 0422.
 | Portable Ultra-Low Field MRI is Sensitive to Distinct Profiles in Brain Development of Malnutrition and Nutritional Intervention Muriel Bruchhage1, Hang Zhou2, Yidong Zhou2, Daniel Elijah Scheiene1, Niall J. Bourke3, Jonathan O’Muircheartaigh4,5, James Cole6, Kristofer E. Bouchard7,8, Susanne Martin-Herz9, Victoria Laleau9, Valerie Flaherman9, Sean C. L. Deoni10, Hans-Georg Müller2, Joan Murungi11, and Victoria Nankabirwa11
1Institute for Social Sciences, University of Stavanger, Stavanger, Norway, 2Department of Statistics, University of California Davis, Davis, CA, United States, 3Centre for Neuroimaging Sciences, King's College London, London, United Kingdom, 4Department of Forensic and Neurodevelopmental Sciences, King's College London, London, United Kingdom, 5Department of Perinatal Imaging and Health, King's College London, London, United Kingdom, 6Department of Computer Science, UCL, London, United Kingdom, 7Scientific Data Division and Biological Systems & Engineering Division, Lawrence Berkeley National Laboratory, University of California Berkeley, Berkeley, CA, United States, 8Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience, University of California Berkeley, Berkeley, CA, United States, 9Department of Pediatrics, Division of Developmental Medicine, University of California San Francisco, San Francisco, CA, United States, 10Maternal, Newborn, and Health Discovery Tolls, Bill and Melinda Gates Foundation, Seattle, WA, United States, 11School of Public Health, Makerere University, Kampala, Uganda Keywords: Neuro, Pediatric, Low-Field MRI Motivation: The first years are essential for a child’s development and adverse factors, including malnutrition, can affect neurodevelopment and survival rates. Access to high-field MRI scanners in Sub-Saharan Africa is highly limited. Goal(s): To detect distinct profiles in brain development of malnutrition and nutritional intervention using ultra-low field MRI. Approach: We used ultra-low field MRI in a pediatric cohort in Uganda of 71 infants (<1.5 years) with and without history of malnourishment, imaged before and after receiving an intervention. Results: Using PACE brain-for-age growth percentiles, we demonstrate that ultra-low field MRI is sensitive to distinct profiles in brain development of malnutrition and nutritional intervention. Impact: The distinct profiles of early malnourishment on neurodevelopment and their changes after nutritional intervention derived by ultra-low field MRI could allow for more appropriate neurodevelopmental burden estimates in LMIC pediatric populations and support early intervention evaluation. |
| 09:51 | 0423.
 | MRI4All: A Week-Long Hackathon for the Development of an Open-Source Ultra-Low-Field MRI System Sai Abitha Srinivas1, Leeor Alon2,3, Akbar Alipour4, Anais Artiges3,5, Kai Tobias Block3,5, Fernando Boada6, Doug Bratner5, Ryan Brown5, Jingjia Chen7, Vito Ciancia8, Clarissa Cooley9, Tarun Dutt5, David Garrett10, Sairam Geethanath11, Bernhard Gruber9,12, Dinank Gupta13, Carlotta Ianniello14, Ilknur Icke15, Kalina Jordanova16, Hector Lise de Moura5, Yvonne Lui5, Andrew Mao5, Jonathan Martin17, Anmol Monga5, Amritha Musipatla5, Shounak Nandi4, Aaron Purchase9, Thiago Rubio18, Amanpreet Saimbhi5, Anja Samardzija19, Charlotte Sappo20, Greg Shakar21, Yun Shang22, Jeff Short9, Daniel Sodickson5, Jason Stockmann9, Zach Stoebner23, Heng Sun19, Florin Teleanu18, Sebastian Theilenberg14, Radhika Tibrewala5, Antonio Verdone5, George Verghese5, Roy Wiggins5, Bingyu Xin24, Guang Yang25, Chengtong Zhang18, Horace Zhang19, Ruoxun Zi5, Riccardo Lattanzi5, Nora Krassnig-Plass12, Karthik Lakshmanan5, Kranthi Kiran21, Lavanya Umapathy5, Luoyao Chen5, and Alex Nwigwe26
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York City, NY, United States, 4Icahn School of Medicine at Mount Sinai, New York City, NY, United States, 5Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York City, NY, United States, 6Radiology, Stanford., Stanford, CA, United States, 7Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States, 8LaGuardia Studio, New York City, NY, United States, 9A. A. Martinos Center for Biomedical Imaging, Boston, MA, United States, 10Department of Neurosurgery, Baylor Scott & White Medical Center, Temple, TX, United States, 11Accessible MR Laboratory, Biomedical Engineering, and Imaging Institute, Dept. of Diagnostic, Molecular and Interventional Radiology, Mount Sinai Hospital, New York City, NY, United States, 12BARNLabs, Muenzkirchen, Austria, 13Biomedical Engineering, University of Michiga, Ann Arbor, MI, United States, 14Department of Biomedical Engineering, Columbia University in the City of New York, New York City, NY, United States, 15Bayer, Cambridge, MA, United States, 16NIST: National Institudes of standards and Techonology, Boulder, CO, United States, 17Division of Vascular & Interventional Radiology, Department of Radiology, Duke University Medical Center, Durnham, NC, United States, 18Department of Chemistry, New York University, New York City, NY, United States, 19Department of Biomedical Engineering, Yale University, New Haven, CT, United States, 20Vanderbilt University, Nashville, TN, United States, 21New York University, New York City, NY, United States, 22Columbia University, New York City, NY, United States, 23Electrical & Computer Engineering, University of Texas at Austin, Austin, TX, United States, 24Department of Computer Science, Rutgers University, Pitscataway, NJ, United States, 25Harvard University, Cambridge, MA, United States, 26Massachusetts Institute of Technology, Boston, MA, United States Keywords: Low-Field MRI, Low-Field MRI, open-source Motivation: To break the accessibility barrier of high-field MRI, we demonstrate that hardware and software systems necessary for an affordable MRI system, can be designed and constructed within a week using open-source tools and conventional 3D printing approaches. Goal(s): To illustrate the realization of an ultra low field MRI system fully operational using open source tools Approach: Over the course of a week, researchers across the USA have assembled to build the main magnet, field homogenization, gradient, radio-frequency (RF), and software systems needed for the creation of MRI. Results: We present, for the first time, a community-driven open-source MRI system built within a week. Impact: to introduce a community-driven open-source MRI low-field systems have the capability to widely democratize MRI throughout the community and the world. |
| 10:03 | | DiscussionDavid Waddington University of Sydney, Australia |