Synthetic MRI Data Generation for Longitudinal Studies
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Solution Overview
Problem
Magnetic resonance devices used for generating medical image data are costly and time-consuming, making repeated captures for longitudinal studies inefficient, and existing methods struggle to maintain consistency when using different devices with varying magnetic field strengths and acquisition methods.
Innovation Solution
A method for generating synthetic medical image data by acquiring image data from multiple magnetic resonance devices at different times, modifying the data to match the properties of the first image data, using trained functions like convolutional neural networks and generative adversarial networks to ensure consistency and quality, allowing for flexible and cost-effective follow-up examinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If repeated medical image data captures are performed using magnetic resonance devices for longitudinal studies, then diagnostic accuracy and disease monitoring capability are improved, but examination costs and time consumption increase significantly
Solution Approach 1:
The patent creates synthetic copies of medical image data through deep learning models. A reference image from one time point is used to generate synthetic images at other time points, eliminating the need for repeated physical scans. This copying approach maintains diagnostic quality while eliminating time loss associated with repeated examinations.
Solution Approach 2:
The system performs preliminary action by acquiring reference image data at a single time point and using it to generate multiple synthetic time-point images in advance. This preliminary acquisition enables longitudinal studies without requiring repeated patient examinations, thus improving reliability while reducing time consumption.
2Adaptability or versatility
If different magnetic resonance devices are used for repeated examinations, then flexibility and accessibility are improved, but data consistency and comparability deteriorate due to varying magnetic field strengths and acquisition methods
Solution Approach 1:
The patent introduces a deep learning-based synthetic image generation system as an intermediary between different magnetic resonance devices. This intermediary translates images from devices with varying magnetic field strengths into a consistent reference framework, maintaining data consistency while preserving device flexibility and accessibility.
Solution Approach 2:
The system changes parameters by transforming images acquired with different magnetic field strengths (1.5T, 3T, 7T) into a unified reference format. The deep learning model adjusts image characteristics to match the reference device parameters, ensuring data consistency across diverse imaging conditions while maintaining adaptability to different devices.
3Measurement precision
If high-resolution medical image data is acquired repeatedly, then diagnostic precision is improved, but cost and resource consumption increase
Solution Approach 1:
The patent creates high-resolution synthetic copies of medical images through deep learning super-resolution techniques. Instead of repeatedly acquiring expensive high-resolution scans, the system generates high-resolution images computationally from lower-resolution inputs, maintaining measurement precision while dramatically reducing energy loss and examination costs.
Solution Approach 2:
The system replaces expensive, resource-intensive repeated high-resolution acquisitions with inexpensive computational synthesis. The synthetic images serve as disposable, on-demand replacements for costly physical scans, maintaining diagnostic precision while eliminating recurring resource consumption.
Data Source
AI summary
In a method for generating synthetic medical image data, first image data of an object under examination including a first value for a property is acquired, second image data of the object under examination including a second value for the property is acquired, the second value of the property of the second image data is matched to the first value to modify the second image data to generate synthetic image data, and the synthetic image data is provided (e.g. in electronic form as a data file). The first image data can be captured with a first magnetic resonance device at a first point in time, and the second image data can be captured with a second magnetic resonance device at a second point in time.