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Updated: Jan 13, 2026
Fat-Water Phantoms for Magnetic Resonance Imaging Validation: A Flexible and Scalable Protocol
Published on: September 7, 2018
Acceleration of chemical shift encoding-based water-fat imaging for pancreatic proton density fat fraction mapping in
Selina Rupp1, Jessie Han1, Stella Marlene Naebauer1
1Institute for Diagnostic and Interventional Radiology, School of Medicine and Health, Technical University of Munich, Munich, Germany.
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.
