UCLA

Characteristic features of chronic pancreatitis (CP) may be absent on standard imaging studies. Quantitative Magnetic Resonance Imaging (MRI) techniques such as T₁ mapping, extracellular volume (ECV) fraction, diffusion-weighted imaging (DWI) with apparent diffusion coefficient map (ADC), MR elastography (MRE), and T₁-weighted signal intensity ratio (SIR) have shown promise for the diagnosis and grading severity of CP. However, radiologists still use the Cambridge classification which is based on traditional ductal imaging alone. There is an urgent need to develop new diagnostic criteria that incorporate both parenchymal and ductal features of CP seen by MRI/MRCP. Designed to fulfill this clinical need, we present the MINIMAP study, which was funded in September 2018 by the National Institutes of Health. This is a comprehensive quantitative MR imaging study which will be performed at multiple institutions in well-phenotyped CP patient cohorts. We hypothesize that quantitative MRI/MRCP features can serve as valuable non-invasive imaging biomarkers to detect and grade CP. We will evaluate the role of T₁ relaxometry, ECV, T₁-weighted gradient echo SIR, MRE, arteriovenous enhancement ratio, ADC, pancreas volume/atrophy, pancreatic fat fraction, ductal features, and pancreatic exocrine output following secretin stimulation in the assessment of CP. We will attempt to generate a multi-parametric pancreatic tissue fibrosis (PTF) scoring system. We anticipate that a quantitative scoring system may serve as a biomarker of pancreatic fibrosis; hence this imaging technique can be used in clinical practice as well as clinical trials to evaluate the efficacy of agents which may slow the progression or reverse measures of CP.