Neural Network Liver Tissue Characterization via MRI
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Solution Overview
Problem
Current methods for characterizing liver tissue, particularly in nonalcoholic steatohepatitis (NASH), rely on a limited number of quantitative magnetic resonance parameters, failing to effectively utilize the full range of MRI data and neglecting the correlation between morphological and quantitative features, leading to inaccurate and incomplete assessments.
Innovation Solution
A computer-implemented method using a neural network to analyze both morphological magnetic resonance image data and parameter maps, incorporating convolutional and dense layers to generate tissue scores for inflammation, fibrosis, and NASH presence, thereby leveraging artificial intelligence to extract and correlate relevant features from high-dimensional data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional thresholding methods with limited quantitative parameters are used, then the assessment process is simple, but the diagnostic accuracy and completeness are insufficient
Solution Approach 1:
The patent combines multiple MRI sequences (T1-weighted, T2-weighted, PD-weighted) and merges morphological image data with quantitative parameter maps into a unified evaluation framework. This integration allows comprehensive tissue characterization by simultaneously analyzing structural and functional information, thereby improving diagnostic accuracy while managing system complexity through automated processing.
Solution Approach 2:
The evaluation system is designed to perform multiple functions: it characterizes tissue composition, assesses pathological changes, and provides diagnostic recommendations. By making the system multi-functional and adaptable to different MRI sequences and parameter types, it achieves high diagnostic accuracy without requiring separate specialized systems for each assessment type.
2Loss of information
If only a small number of quantitative values are used, then the evaluation is computationally efficient, but the information utilization is incomplete and correlation between features is lost
Solution Approach 1:
The patent performs preliminary processing of MRI data by generating quantitative parameter maps (T1, T2, PD values) and extracting morphological features before the main evaluation stage. This preliminary action organizes the data in advance, enabling efficient subsequent analysis while ensuring comprehensive information utilization from all MRI sequences and parameters.
Solution Approach 2:
The system creates multiple representations of the liver tissue data: original images, quantitative parameter maps, and extracted feature sets. These copies allow different analysis approaches to be applied simultaneously without processing the same raw data multiple times, thereby reducing computational redundancy while maintaining complete information utilization.
3Measurement precision
If invasive liver biopsy is performed, then definitive diagnosis of NASH is achieved, but patient discomfort and procedural risk increase
Solution Approach 1:
The patent replaces the mechanical invasive procedure of liver biopsy with a non-invasive magnetic resonance imaging-based evaluation system. By substituting physical tissue sampling with advanced MRI sequences and computational analysis, the system achieves comparable diagnostic accuracy while eliminating procedural risks and patient discomfort associated with biopsy.
Solution Approach 2:
The system changes the measurement parameters from direct tissue sampling (biopsy) to indirect non-invasive MRI parameter measurement. By utilizing multiple MRI sequences and quantitative parameter maps (T1, T2, PD values, fat fraction, iron content), the system extracts diagnostic information without physical intervention, thereby maintaining accuracy while reducing harm.
Data Source
AI summary
The disclosure relates to techniques for automatically characterizing liver tissue of a patient, comprising receiving morphological magnetic resonance image data set and at least one magnetic resonance parameter map of an imaging region comprising at least partially the liver of the patient, each acquired by a magnetic resonance imaging device, via a first interface. The techniques further include applying a trained function comprising a neural network to input data comprising at least the image data set and the parameter map. At least one tissue score describing the liver tissue is generated as output data, which is provided using a second interface.