MRI Simulator for Synthetic Training Data Generation
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Solution Overview
Problem
Current methods for generating training datasets for artificial intelligence in Magnetic Resonance Imaging (MRI) are limited in variability and realism, making it difficult to create comprehensive and high-quality datasets for AI applications, especially due to factors like noise, limited spatial and temporal resolution, and the need for real patient data.
Innovation Solution
A method involving an MRI simulator that uses pulse sequences and anatomical models to generate simulated MR images and label maps, allowing for repetition with different pulse sequences, anatomical model variations, and simulation parameters, utilizing a simulation-based reconstruction framework to produce a diverse and accurate training dataset.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real patient data is used for training datasets, then the realism and clinical relevance is improved, but the availability and ease of generation is worsened due to limited access and ethical constraints
Solution Approach 1:
The patent creates synthetic copies of medical images using simulation software that replicates the appearance and characteristics of real patient data without requiring actual patient information. The simulation generates artificial images that mimic real clinical scenarios, providing unlimited training data while maintaining privacy and ethical standards.
Solution Approach 2:
The system enables self-service generation of training datasets through automated simulation processes. Researchers can independently generate diverse training data without needing to collect, store, or process actual patient data, eliminating ethical constraints and availability limitations while maintaining data quality.
2Adaptability or versatility
If more diverse training data is generated with different pulse sequences and anatomical models, then the comprehensiveness and AI performance is improved, but the computational resources and time required is worsened
Solution Approach 1:
The patent pre-configures multiple pulse sequences and anatomical models within the simulation framework, allowing diverse training data to be generated systematically. By preparing the simulation environment in advance with various parameters and configurations, the system can efficiently produce diverse datasets without requiring extensive real-time computational resources for each variation.
3Manufacturing precision
If simulated images are used instead of real images, then the control over image quality and noise levels is improved, but the clinical realism and authenticity is worsened
Solution Approach 1:
The patent employs parameter changes to adjust simulation settings, including noise levels, resolution, and imaging characteristics. By carefully tuning these parameters, the simulated images can match the statistical properties and visual characteristics of real patient data, achieving both quality control and clinical realism simultaneously.
Data Source
AI summary
A method for generating training datasets for AI applications includes providing an MRI simulator and input thereto. The input is a pulse sequence and an anatomical model. The method includes selecting the position and/or orientation of a plane/volume of interest of the anatomical model, and executing the MRI simulator, producing a simulated MR image. The method includes obtaining produced synthesized MR images and/or label maps for each synthesized MR image, obtaining a training dataset based on all obtained produced MR images and/or label maps possible to produce for each MR image, and building a computer model for synthesized MR images and/or label maps using an SBR framework. The procedure may be repeated with different pulse sequences and/or the same pulse sequence, by amending the position and/or orientation of a plane/volume of interest of the anatomical model, and/or by amending properties of the anatomical model, and/or with another anatomical model.