The MIMRTL team from the University of Wisconsin School of Medicine and Public Health Department of Radiology participated in the 2024 International Society for Magnetic Resonance in Medicine (ISMRM) conference, held in Singapore from May 4-9. The team presented a series of research projects that highlighted technical advancements in magnetic resonance imaging (MRI).
Contributions included:
“Improvement in Fat-Water Separation Using Modeled Gradient Impulse Response with Two-Point Dixon Radial Imaging,” authored by James Hao Wang (MIMRTL team member), Ali Pirasteh, and Alan McMillan (MIMRTL team member). This study focused on improving the separation of fat and water signals in MRI using reconstructions enhanced by modeling gradient imperfections with a gradient impulse response function. The enhanced two-point Dixon radial imaging technique developed by the team offers improved accurate fat-water separation combined with the motion robustness of radial imaging.
“Quantitively Accurate Bipolar Quantitative Chemical Shift Encoded Imaging Using the Gradient Impulse Response Function,” by James Hao Wang (MIMRTL team member), Diego Hernando, Ali Pirasteh, and Alan McMillan (MIMRTL team member). This research aimed to address the challenges in quantitative chemical shift-encoded imaging, particularly the inaccuracies introduced by gradient imperfections. By incorporating the gradient impulse response function improved quantitative fat-water separation was enabled using a bipolar readout, which could reduce scan time by up to 50%.
“Addressing Gradient Imperfection Related Bias in Stack-of-Stars MRI for Free-Breathing, Confounder-Corrected T1 Mapping,” authored by Yavuz Muslu, James H. Wang, Ty A. Cashen, Diego Hernando, Alan McMillan (MIMRTL team member), and Scott B. Reeder. This study tackled the biases introduced by gradient imperfections in stack-of-stars MRI, a technique often used for free-breathing to enhance T1 mapping.
“Vendor-Neutral Development and Cross-Center Validation of Flip Angle Modulated 2D Sequential CSE-MRI Technique for Liver Fat Quantification,” by Jiayi Tang, Daiki Tamada, Xingwang Yong, Yuting Chen, Shohei Fujita, Jitka Starekova, Jeff Kammerman, Jean H. Brittain, Alan McMillan (MIMRTL team member), Jon-Fredrik Nielsen, Maxim Zaitsev, Scott B. Reeder, Berkin Bilgic, and Diego Hernando. This research introduced a vendor-neutral, cross-center validated technique for liver fat quantification using flip angle modulated 2D sequential chemical shift-encoded MRI. The approach demonstrated consistent and accurate liver fat measurements across different MRI systems and centers utilizing the PulseSeq rapid pulse sequence prototyping framework.