A Three-Dimensional Digital Model for Early Diagnosis of Hepatic Fibrosis Based on Magnetic Resonance Elastography
July 21st, 2023
The objective of this study was to develop a novel three-dimensional digital model for the early diagnosis of hepatic fibrosis, which includes the stiffness of each voxel in the patient's liver and can thus, be used to calculate the distribution ratio of the patient's liver at different fibrosis stages.
The scope of the research is to develop a three-dimensional digital model for the early diagnosis of hepatic fibrosis based on magnetic resonance elastography. The research aims to address the limitations of current models and provide a measurable and quantitative approach for the accurate assessment and monitoring of hepatic fibrosis. Some of the current experimental challenges in the field of hepatic fibrosis research include improving the scanning accuracy and imaging techniques of non-invasive methods, like magnetic resonance elastography, standardizing computational protocols for consistent results and accumulating more comprehensive and that was data size for accurate classification and diagnosis.
The liver MRI structural images were accurately aligned with MRE images, enabling the three-dimensional distribution of liver stiffness distribution and achieving precise quantification in 3D space. It is possible to accurately establish a liver stiffness distribution map for a healthy liver and conveniently utilize it for the diagnosis and the treatment of patients with different status of liver cirrhosis. In the future, we want to address AI-driven diagnosis, treatment, and prognosis of liver diseases, as well as the development of herbal medicines related to liver diseases.
To begin open MATLAB software. Ensure that the ideal data have a horizontal resolution of 256 by 256 pixels with a pixel spacing of 1.5625 millimeters and a slice thickness of 10 millimeters. Copy all DICOM data to a customized working directory and navigate to the directory containing the data in MATLAB's working directory.
Then execute the description name function to add descriptive names to the folders for each sequence. Add a description name to each image sequence folder. To check images of ideal, change the directory of different phase folders.
Execute the slice view function to view the impact sequences for each phase. Next, quickly browse the different sequences using the scroll bar at the bottom of the GUI. Select the MRI ideal out phase sequence to depict liver tissue boundaries.
Initiate the Mimic software, select New Project, and in the ensuing dialogue box, navigate to the folder containing the ideal out phase images. Click Next, followed by the Convert button to enter into the sequence editing state. To create an empty mask from the Mask dialogue box, click on the New button and select the maximum threshold.
Using the Edit Masks tool located beneath the segment label, delimit the area of the liver in all horizontal views. To generate the 3D spatial part of the liver, select the outline liver mask, and click on the Calculate part from mask button. Next, click File then Export.
And select the DICOM command. In the pop-up dialogue box, choose the Liver Mask and set the file path and names. Then click the OK button to complete the export of the 3D region of the liver to the specified DICOM files.
Begin by changing the directory to the folder, SE27_ST8K_PA, which contains the liver stiffness map sequence. To browse through each stiffness map, execute the MRE show function, with the functions argument being the file name located in the specified path. Observe the liver stiffness map in an RGB true color image, where each pixel point has three values representing the three primary colors.
Calculate the exact stiffness for each pixel using their respective correlations. Then establish the spatial relationship between MRE and ideal sequences. To obtain the 3D volume of liver stiffness distribution, invoke the LSD slice function with the 3D liver region and the liver stiffness map as input parameters.
Then drag the scroll bar below the GUI to view the stiffness map of each layer of the liver. Next, execute the LSD volume function with the same input as LSD slice to obtain the spatial distribution of the 3D liver LSD. View the 3D volume of LSD from any perspective by holding down the left mouse button.
After determining the numerical ranges of stiffness values from four different stages of hepatic fibrosis, using the hepatic fibrosis function with the 3D volume of LSD as the input parameter, calculate the distribution of the patient's entire liver voxels across different fibrosis stages. Then calculate and compare the results between a completely healthy liver and the liver of a typical fibrosis patient. Quantitative results of the patient's hepatic fibrosis indicate the proportion of the patient's liver in different stages of hepatic fibrosis.
Comparison of LSD fully reflects the degree of hepatic fibrosis in the patient compared to a healthy liver.
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Overview
This study develops a three-dimensional digital model for early diagnosis of hepatic fibrosis using magnetic resonance elastography. The model aims to improve the accuracy of liver stiffness assessment and monitoring across different fibrosis stages.
Key Study Components
Area of Science
- Neuroscience
- Biology
- Medical Imaging
Background
- Current models for diagnosing hepatic fibrosis have limitations.
- Non-invasive methods like magnetic resonance elastography need improved accuracy.
- Standardization of computational protocols is essential for consistent results.
- Comprehensive data accumulation is necessary for accurate classification.
Purpose of Study
- To develop a digital model for early diagnosis of hepatic fibrosis.
- To provide a measurable approach for assessing liver stiffness.
- To facilitate diagnosis and treatment of liver cirrhosis.
Methods Used
- Magnetic resonance elastography for imaging liver stiffness.
- Alignment of MRI structural images with MRE images.
- Creation of a three-dimensional liver stiffness distribution map.
- Quantification of liver stiffness in 3D space.
Main Results
- Accurate establishment of liver stiffness distribution for healthy livers.
- Facilitated diagnosis for patients with varying liver conditions.
- Potential for AI-driven diagnosis and treatment in future research.
- Exploration of herbal medicines related to liver diseases.
Conclusions
- The developed model enhances early diagnosis of hepatic fibrosis.
- It addresses current challenges in non-invasive liver assessment.
- Future work will focus on AI applications in liver disease management.
Frequently Asked Questions
Executive Industry Relevance
Quantitative, non-invasive assessment of hepatic fibrosis is a critical inflection point for early-stage liver disease drug discovery and translational research. The integration of three-dimensional digital modeling with magnetic resonance elastography (MRE) enables precise, whole-organ mapping of liver stiffness, directly supporting predictive confidence in preclinical and biomarker-driven programs. This approach addresses longstanding challenges in standardization, reproducibility, and comprehensive disease quantification for biopharma R&D portfolios.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables robust, quantitative interrogation of liver tissue mechanics for target validation in fibrosis research.
- Supports biological de-risking by providing spatially resolved, organ-wide stiffness data aligned with disease progression.
- Facilitates predictive confidence in early-stage compound triage and mechanistic studies.
Screening & Assay Development
- Provides standardized, reproducible 3D liver models for downstream assay development and screening workflows.
- Delivers quantitative outputs that can be benchmarked across compounds and experimental conditions.
- Enables scalable, platform-ready datasets for high-content screening and comparative analysis.
Translational & Preclinical Research
- Aligns imaging-derived biomarkers with disease-relevant endpoints for translational continuity.
- Supports risk-adjusted advancement decisions by quantifying fibrosis stages across the entire liver volume.
- Improves predictive de-risking for preclinical candidate selection and biomarker validation.
Pipeline & Workflow Integration
This 3D digital modeling workflow positions MRE-based liver stiffness mapping as a bridge from early discovery through preclinical validation, supporting both hypothesis testing and translational biomarker development.
- Discovery Biology: Enables hypothesis-driven assessment of fibrosis mechanisms and target engagement.
- Screening: Provides assay-ready, quantitative 3D data for compound evaluation and reproducibility.
- Analytics: Delivers spatially resolved, quantitative stiffness measurements for robust statistical comparison.
- Translational Research: Facilitates alignment of imaging biomarkers with preclinical and clinical endpoints.
- Enterprise Reuse: Establishes a reusable digital infrastructure for liver disease research across programs.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in fibrosis research.
- Operational Value: Standardizes imaging and computational protocols for reproducibility and scalability.
- Strategic Value: Enables more informed go/no-go decisions and capital-efficient portfolio management.
- Portfolio Impact: Supports risk-adjusted prioritization and advancement of liver disease assets.
Implementation Considerations
- Requires expertise in MRI/MRE imaging and computational modeling.
- Demands access to advanced imaging instrumentation and analytical software (e.g., MATLAB, Mimic).
- Necessitates cross-team standardization of imaging and data processing protocols.
- Adaptation may be needed for different liver disease models or imaging platforms.
- Dependent on high-quality, well-aligned imaging data for optimal quantification.
