Acceleration of chemical shift encoding-based water-fat imaging for pancreatic proton density fat fraction mapping in a single breath-hold: Data from the LION study

Abstract

Purpose: With the rising prevalence of obesity and metabolic syndrome, there is an increasing need for noninvasive quantification of pancreatic fat as a marker of metabolic risk. Chemical shift encoding (CSE)-based water-fat separation enables pancreatic proton density fat fraction (PDFF) mapping. This study evaluates techniques for accelerating high-resolution, single-breath-hold PDFF mapping using sparse sampling with compressed sensing with sensitivity encoding (C-SENSE) and a deep learning (DL)-assisted reconstruction algorithm, focusing on reproducibility, precision, and clinical applicability.

Methods: 104 abdominal MRI datasets were obtained from 71 adults (58 % female; age 18-65 years; body mass index (BMI) 30.0-39.9 kg/m²; without diabetes) enrolled in a lifestyle intervention trial. Imaging was performed at 3 T (Ingenia Elition X, Philips) using two six-echo gradient-echo acquisitions (2 × 2 × 3 mm³, identical TR/TE/echo spacing). Acceleration factors of R = 6 (16.9 s) and R = 10 (10.3 s) were reconstructed using vendor compressed sensing (C-SENSE6, C-SENSE10); the DL-assisted reconstruction (C-SENSE AI10) was applied only to R = 10 to evaluate denoising of higher-acceleration data. PDFF maps were analyzed using three regional regions of interest (ROIs) (head, body, tail) and whole-pancreas segmentation.

Results: A Mean pancreatic PDFF measured with C-SENSE6 was 15.0 [10.9 - 23.0] % at baseline (V1) and 8.2 [7.1 - 11.4] % after one year (V3). Across all reconstructions, PDFF ranged 3.5 - 47.6 %. Strong linearity was observed between C-SENSE10 and C-SENSE AI10 compared with C-SENSE6 (R² ≥ 0.99). Whole-pancreas analysis showed high reproducibility (intraclass correlation coefficient = 0.87 - 1.00 across methods). The DL-assisted reconstruction reduced map noise compared with conventional C-SENSE10 without affecting PDFF accuracy.

Conclusion: Accelerated CSE-based pancreatic PDFF mapping enables precise, reproducible, and clinically feasible single-breath-hold fat quantification. The approach provides a robust tool for evaluating pancreatic steatosis in obesity and metabolic disease research.

Keywords: Compressed sensing; Obesity; Pancreatic fat; magnetic resonance imaging (MRI); nonalcoholic fatty pancreas disease (NAFPD); proton density fat fraction (PDFF).