MRI Scan Prediction for Dynamic Contrast Liver Imaging
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Solution Overview
Problem
Existing radiological examinations using dynamic contrast-enhanced MRI for focal liver lesions require prolonged patient immobilization due to the need for multiple scans over time, leading to discomfort and potential motion artifacts.
Innovation Solution
A machine learning model is trained to predict subsequent MRI scans based on previous scans, learning the dynamics of contrast agent distribution, allowing for calculated scans rather than real-time acquisitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple MRI scans are acquired over time to monitor contrast agent distribution, then diagnostic information quality is improved, but examination time increases and patient comfort deteriorates
Solution Approach 1:
The patent uses a machine learning model to generate synthetic MRI scans that copy the appearance and diagnostic information of real scans. The model learns from a training set of real MRI scans and generates synthetic images that mimic the contrast agent distribution patterns, thereby providing diagnostic information without requiring actual prolonged scanning.
Solution Approach 2:
The patent performs preliminary training of the machine learning model using a training set of MRI scans before the actual examination. This preliminary action enables the model to predict contrast agent distribution patterns, allowing the system to generate synthetic scans quickly during the examination without requiring prolonged real-time scanning.
2Measurement precision
If multiple MRI scans are acquired over time to monitor contrast agent distribution, then diagnostic information quality is improved, but patient comfort deteriorates due to prolonged immobilization
Solution Approach 1:
The patent generates synthetic MRI scans that replicate the diagnostic information of real scans, eliminating the need for patients to remain immobilized for extended periods. The machine learning model creates copies of the required diagnostic images through computation rather than physical scanning.
Solution Approach 2:
The model is trained in advance on a comprehensive training set, enabling it to predict contrast enhancement patterns without requiring patients to undergo prolonged scanning sessions. This preliminary preparation transfers the time burden from the examination phase to the training phase.
3Measurement precision
If multiple MRI scans are acquired over time to monitor contrast agent distribution, then diagnostic information quality is improved, but motion artifacts increase
Solution Approach 1:
The patent generates synthetic MRI scans through machine learning computation rather than physical scanning. Since these images are computationally generated rather than acquired from moving patients, they are free from motion artifacts that plagues traditional prolonged MRI examinations.
4Loss of time
If a machine learning model is trained to predict subsequent MRI scans, then examination time is reduced, but model complexity increases
Solution Approach 1:
The patent employs a machine learning model that learns to copy the patterns of contrast agent distribution from a training set of MRI scans. This copying capability allows the model to generate synthetic scans that replicate real scan outcomes, reducing examination time while managing complexity through pattern recognition rather than complex physical scanning protocols.
Data Source
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AI summary
The present invention relates to the technical field of radiology, and in particular to assisting radiologists in radiological examinations using artificial intelligence methods. The present invention also relates to training a machine learning model and using the trained model to predict representations of an examination area in one or more states of a sequence of states in a radiological examination.