A 3D Quantification Technique for Liver Fat Fraction Distribution Analysis Using Dixon Magnetic Resonance Imaging

Summary

This study introduces a unique 3D quantification method for liver fat fraction (LFF) distribution using Dixon Magnetic Resonance Imaging (Dixon MRI). LFF maps, derived from in-phase and water-phase images, are integrated with 3D liver contours to differentiate LFF patterns between normal and steatotic livers, enabling precise assessment of liver fat content.

Abstract

This study presents a 3D quantification methodology for the distribution of liver fat fraction (LFF) through the utilization of Dixon MRI image analysis. The central aim is to offer a highly accurate and non-invasive means of evaluating liver fat content. The process involves the acquisition of In-phase and Water-phase images from a Dixon sequence. LFF maps are then meticulously computed voxel by voxel by dividing the Lipid Phase images by the In-phase images. Simultaneously, 3D liver contours are extracted from the In-phase images. These crucial components are seamlessly integrated to construct a comprehensive 3D-LFF distribution model. This technique is not limited to healthy livers but extends to those afflicted by hepatic steatosis. The results obtained demonstrate the remarkable effectiveness of this approach in both visualizing and quantifying liver fat content. It distinctly discerns patterns that differentiate between normal and steatotic livers. By harnessing Dixon MRI to extract the 3D structure of the liver, this method offers precise LFF assessments spanning the entirety of the organ, thereby holding great promise for the diagnosis of hepatic steatosis with remarkable effectiveness.

Introduction

Non-Alcoholic Fatty Liver Disease (NAFLD) encompasses a spectrum of pathological conditions, ranging from the abnormal accumulation of triglycerides in liver cells (hepatic steatosis) to the development of inflammation and damage to liver cells, known as non-alcoholic Steatohepatitis (NASH). In some cases, NAFLD can progress to more severe stages, including fibrosis, cirrhosis, end-stage liver disease, or even Hepatocellular carcinoma (HCC)¹. Published data from the World Health Organization and the Global Burden of Disease suggest that approximately 1,235.7 million individuals worldwide are affected by NAFLD across all age groups

Protocol

The study was approved, and the patient was recruited from the Department of Infectious Diseases at Dongzhimen Hospital, Beijing University of Chinese Medicine, in Beijing, China. The patient underwent a routine abdominal Dixon MRI scan after providing informed consent. In this investigation, a 3D distribution modeling approach is employed to reconstruct the liver fat fraction (LFF) in a standard patient with medically diagnosed hepatic steatosis. Furthermore, the study provides a quantitative assessment comparing the patient's liver with a healthy liver. The software tools utilized in this research are listed in the Table of Materials.

Results

This investigation utilizes actual patient datasets acquired using a commercially available MRI scanner to validate the 3D liver fat fraction quantification methodology (Figure 1). The MRI protocol included Dixon's four-phase imaging^9,10: In-phase, Out-of-phase, Water-only, and Fat-only (Figure 2). The fat fraction (FF) of each voxel is computed by dividing the In-phase minus Water-only voxel signal by t.......

Discussion

This research presents an innovative 3D quantification technique for analyzing the distribution of liver fat fraction (LFF) using Dixon MRI^9,10. By integrating LFF maps, which are generated from in-phase and water-phase images, with 3D liver contours, this method distinguishes between LFF patterns in normal and steatotic livers⁶. Consequently, it facilitates a precise evaluation of liver fat content.

Step 3 repr.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
MATLAB	MathWorks	2022B	Computing and visualization
Mimics	Materialise	Mimics Research V20	Model format transformation
Tools for 3D_LFF	Intelligent Entropy	HepaticSteatosis V1.0	Beijing Intelligent Entropy Science & Technology Co Ltd. Modeling for CT/MRI fusion