# Comprehensive noninvasive assessment of liver histopathology in nonalcoholic fatty liver disease (NAFLD) via magnetic resonance imaging, cytometry and elastography (MR-ICE) > **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2022 · $357,750 ## Abstract PROJECT SUMMARY / ABSTRACT The growing prevalence of nonalcoholic fatty liver disease (NAFLD) creates an imperative to reliably distinguish between patients with simple steatosis and those with nonalcoholic steatohepatitis (NASH). However, hepatic inflammation and cellular injury diagnoses often require invasive liver biopsies for histopathologic staging. There is an urgent need for safe and reliable noninvasive imaging methods for diagnosing NASH and longitudinal assessing hepatic inflammation and hepatocellular injury to monitor treatment efficacy. The presence of and severity of steatosis and fiborosis are now well addressed with chemical shift imaging and magnetic resonance elastography (MRE). Our current cycle of research has confirmed that MRE-assessed loss modulus is a very promising biomarker for inflammation. In this renewal application, we propose to continue validation of this biomarker for inflammation and to add noninvasive assessment of cell injury (ballooning) by introducing a novel MR cytometry modification into the MRE protocol. The overall goal of this work is to develop and validate multiparametric MR-ICE imaging technologies for fully assessing disease states during NAFLD evolution, especially inflammation and hepatocellular injury. • In Aim 1, we will develop the MR-ICE imaging protocol. Fat fraction will be evaluated with a 6-point Dixon method. A dual-frequency, self-navigating, and hybrid radial-Cartesian 3D vector hepatic MRE technique will be optimized for characterizing multiple mechanical properties of viscoelasticity and nonlinearity. MRC sequence and reconstruction will be developed with gradient waveforms and diffusion signal fitting that are specifically designed for hepatocyte cytometry, with or without simultaneous MRE acquisition. • In Aim 2, we will perform longitudinal application of the MR-ICE in an in vivo rat model (N=96, diet-induced NASH). Technical integrity and diagnostic performance will be assessed by comparing multiple in vivo imaging biomarkers (fat fraction, hepatocyte size, viscoelasticity, nonlinearity) with ex vivo tissue composition (water and fat contents), dynamic mechanical analysis (DMA) testing and histologic features (steatosis, inflammation, ballooning, fibrosis), respectively. Statistical models will be trained to diagnose NASH. • Prior to pilot clinical evaluation, we will aseess the repeatability of MR-ICE biomarkers in 5 controls and 5 clinical patients using a test-retest strategy. Finally, a pilot clinical evaluation in 10 controls and 40 patients with biopsy-proven NAFLD/NASH will be performed to provide preliminary evidence of the diagnostic performance of the MR-ICE protocol for staging NAFLD/NASH. Emerging therapeutic interventions may require life-long treatment, creating the need for more precise non- invasive methods for identifying those patients who need such interventions. The development of MR-ICE will make it possible for us and other investigators to advance... ## Key facts - **NIH application ID:** 10517042 - **Project number:** 2R01EB017197-09 - **Recipient organization:** MAYO CLINIC ROCHESTER - **Principal Investigator:** Meng Yin - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $357,750 - **Award type:** 2 - **Project period:** 2014-05-01 → 2026-05-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10517042 ## Citation > US National Institutes of Health, RePORTER application 10517042, Comprehensive noninvasive assessment of liver histopathology in nonalcoholic fatty liver disease (NAFLD) via magnetic resonance imaging, cytometry and elastography (MR-ICE) (2R01EB017197-09). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10517042. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*