MRI Signal Processing System for Automated Soft Tissue Segmentation
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Solution Overview
Problem
Current radiological diagnosis relies heavily on subjective visual inspection and is laborious, with existing automated programs providing inadequate segmentation of soft tissue structures, hindering efficient detection and characterization of abnormalities, particularly in neurological diagnoses like Alzheimer's disease.
Innovation Solution
A magnetic resonance imaging (MRI) system with a signal processing system that utilizes templates and integrating features to automatically segment soft tissue regions, incorporating preprocessing and normalization engines to generate accurate segmented views of soft tissue structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation is performed by radiologists, then segmentation accuracy can be maintained, but the process becomes laborious and time-consuming
Solution Approach 1:
The system enables automated self-segmentation of soft tissue structures by having the computer system perform the segmentation task independently without requiring manual radiologist intervention for each case, thus resolving the contradiction between maintaining accuracy and reducing time loss
Solution Approach 2:
The patent replaces the manual mechanical process of radiologist segmentation with an automated computational system using algorithms and image processing techniques, eliminating the need for human labor while maintaining segmentation quality
2Productivity
If existing automated segmentation programs are used, then processing time is reduced, but segmentation quality becomes inadequate for clinical adoption
Solution Approach 1:
The system improves segmentation quality by changing key parameters including using multi-contrast MRI data (T1, T2, PD images), implementing advanced normalization techniques, and adjusting algorithmic parameters to optimize the balance between processing speed and segmentation accuracy for clinical adoption
Solution Approach 2:
The patent combines multiple image contrast types (T1-weighted, T2-weighted, proton density images) and integrates them into a composite segmentation approach, where each contrast type contributes different tissue information that together produces superior segmentation quality compared to single-contrast methods
3Reliability
If quantitative analysis is implemented, then detection and characterization of abnormalities is improved, but the complexity of analysis increases
Solution Approach 1:
The system divides the complex analysis task into distinct segmented regions of soft tissue structures, allowing quantitative analysis to be performed on specific anatomical regions independently, which improves abnormality detection reliability while managing complexity through modular region-based analysis
Data Source
AI summary
A magnetic resonance imaging (MRI) system, comprising: a magnetic resonance imaging scanner configured to generate a plurality of signals for forming at least one magnetic resonance image of a soft tissue region from a subject under observation, wherein the at least one magnetic resonance image provides at least one integrating feature to facilitate automatic segmentation; a signal processing system in communication with the magnetic resonance imaging scanner to receive the plurality of signals; and a data storage unit in communication with the signal processing system, wherein the data storage unit contains at least one template corresponding to the soft tissue region, wherein the signal processing system is adapted to process the plurality of signals received from the magnetic resonance imaging scanner to automatically perform segmentation for the soft tissue region of the subject under observation by utilizing the at least one template and the at least one integrating feature.