Abstract #0423

MRI4All: A Week-Long Hackathon for the Development of an Open-Source Ultra-Low-Field MRI System

Sai Abitha Srinivas¹, Leeor Alon^2,3, Akbar Alipour⁴, Anais Artiges^3,5, Kai Tobias Block^3,5, Fernando Boada⁶, Doug Bratner⁵, Ryan Brown⁵, Jingjia Chen⁷, Vito Ciancia⁸, Clarissa Cooley⁹, Tarun Dutt⁵, David Garrett¹⁰, Sairam Geethanath¹¹, Bernhard Gruber^9,12, 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

Synopsis

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.

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