MRI Pulse Sequence Classification via Machine Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current MRI pulse sequence classification methods rely on proprietary and non-universal naming conventions, leading to difficulties in identifying and retrieving related scans due to human error and variability in data management practices, which hampers radiologists' ability to efficiently search and display medical images.
Innovation Solution
A machine learning-based system that classifies MRI pulse sequences by analyzing medical imaging data using a convolutional neural network (CNN) to extract features and generate custom DICOM tags, reducing reliance on manual metadata entry and proprietary names, thereby standardizing the classification of characteristics like fat suppression, echo type, and contrast presence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based engines using manually-entered DICOM metadata are used to classify MRI pulse sequences, then classification can be performed, but the system becomes fragile and unreliable due to human error and variability in data entry
Solution Approach 1:
The patent replaces the manual mechanical process of entering DICOM metadata with an automated machine learning system. The ML model automatically extracts pulse sequence characteristics directly from MRI images, eliminating the need for manual data entry and the associated human errors, thus improving reliability while reducing data management complexity
Solution Approach 2:
The system enables MRI images to self-classify by having the machine learning model automatically extract and determine pulse sequence characteristics directly from the image data itself, without requiring external manual annotation or metadata entry, making the classification process self-sufficient and reliable
2Productivity
If proprietary and non-universal naming conventions are used for MRI pulse sequences, then specific sequences can be identified, but searching and retrieving related scans becomes difficult and inefficient
Solution Approach 1:
The patent creates a universal classification schema that works across different MRI vendors and proprietary naming conventions. The ML model extracts fundamental pulse sequence characteristics (echo type, fat suppression, contrast presence) that are vendor-agnostic, enabling consistent search and retrieval of related scans regardless of the original proprietary naming used
Solution Approach 2:
The system transforms the classification from relying on variable proprietary names and descriptors to using standardized physical parameters (echo type, fat suppression status, contrast presence). This parameter transformation creates a universal language for pulse sequence classification that improves searchability and retrieval efficiency across different institutions and vendors
3Measurement precision
If manual metadata entry is required for pulse sequence classification, then classification can be performed, but human error and variability increase, reducing classification accuracy
Solution Approach 1:
The patent replaces the manual mechanical process of metadata entry with automated machine learning-based extraction. The system automatically determines pulse sequence characteristics by analyzing MRI images through a trained ML model, eliminating human error and significantly improving classification precision while achieving full automation
Solution Approach 2:
Instead of relying on manually copied metadata that may contain errors, the system creates a new copy of the classification information by directly analyzing the MRI image content itself. The ML model extracts characteristics directly from the image data, creating a more accurate and reliable classification that is independent of manual metadata copying
Data Source
AI summary
A method for classifying magnetic resonance imaging (MRI) pulse sequences includes receiving medical imaging data associated with an MRI scan, determining one or more pulse sequence characteristics for the medical imaging data using a machine learning model, wherein the medical imaging data is provided as an input to the machine learning model and wherein the machine learning model outputs a classification for each of the one or more pulse sequence characteristics, and updating a database containing the medical imaging data to include the one or more pulse sequence characteristics in association with the medical imaging data.