Abstract #1188

3D quantitative-amplified Magnetic Resonance Imaging (3D q-aMRI)

Itamar Terem¹, Nan Wang², Paul Condron³, Kyan Younes⁴, Javid Abderezaei⁵, Berthy Feng ⁶, Hari Kumar^3,7,8, Hillary Vossler⁹, Mehmet Kurt⁵, Katherine L. Bouman ⁶, Elizabeth Mormino⁴, Samantha Holdsworth^3,10, and Kawin Setsompop^1,2

¹Electrical Engineering, Stanford, Stanford, CA, United States, ²Radiology, Stanford, Stanford, CA, United States, ³Mātai Medical Research Institute, Tairāwhiti-Gisborne, New Zealand, ⁴Neurology & Neurological Sciences, Stanford, Stanford, CA, United States, ⁵Mechanical Engineering, University of Washington, Seattle, WA, United States, ⁶Computing and Mathematical Sciences (CMS), Cal Tech, Pasadena, CA, United States, ⁷Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, ⁸General Electric Healthcare, Victoria, Australia, ⁹Neurology, Stanford, Stanford, CA, United States, ¹⁰Faculty of Medical & Health Sciences & Centre for Brain Research, University of Auckland, Auckland, New Zealand

Synopsis

Keywords: Neurofluids, Data Processing, amplified MRI (aMRI), Alzheimer`s diseaseAmplified Magnetic Resonance Imaging (aMRI) is a pulsatile brain motion visualization method that delivers ‘videos’ with high contrast and temporal resolution. aMRI has been shown to be a promising tool in various neurological disorders. However, aMRI currently lacks the ability to quantify the sub-voxel motion field in physical units. Here, we introduce a quantitative-aMRI (q-aMRI) algorithm, which quantifies the sub-voxel motion of the 3D aMRI signal and validates its precision using phantom simulations with realistic noise. In-vivo experiments on healthy volunteers demonstrated repeatability of the measurements, and differences in brain motion were observed in subjects with positive/negative amyloid PET.

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