Synthesizing MRI Images via qMRI Parameter Modulation
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Solution Overview
Problem
Current magnetic resonance imaging (MRI) techniques face limitations in quantitatively characterizing tissue pathologies, particularly in detecting subtle changes in relaxation times, which can lead to missed diagnoses and inefficiencies in training artificial neural networks for image analysis.
Innovation Solution
A method for synthesizing MRI images by modulating MRI parameter values within a quantitative MRI map to mimic tissue pathologies, using a database to select appropriate values and patterns, and generating synthetic images that can be used to train neural networks, thereby enhancing diagnostic sensitivity and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional MRI techniques are used to detect tissue pathologies, then the imaging process is straightforward, but the sensitivity for detecting subtle changes in relaxation times is insufficient leading to missed diagnoses
Solution Approach 1:
The patent applies preliminary action by pre-modulating qMRI map values to mimic various tissue pathologies before generating synthetic MRI images. This allows the neural network to be trained in advance on a wide range of pathological conditions, improving its ability to detect subtle changes in real clinical images without requiring actual patient data for each condition.
Solution Approach 2:
The patent utilizes parameter changes by systematically varying qMRI map values (such as T1, T2 relaxation times) within physiological ranges to simulate different pathological states. This enables the generation of diverse synthetic images with controlled parameters, enhancing the measurement precision for detecting subtle tissue changes while maintaining diagnostic reliability.
2Measurement precision
If real patient MRI data is used to train neural networks, then the training data is authentic, but the need for extensive manual annotation increases time consumption and reduces productivity
Solution Approach 1:
The patent applies copying by generating synthetic MRI images that replicate the appearance and characteristics of real pathological conditions. These synthetic copies serve as training data for neural networks, providing authentic-looking examples without requiring actual patient data or manual annotation, thus dramatically improving training productivity while maintaining data quality.
Solution Approach 2:
The patent implements self-service by enabling the system to automatically generate its own training data through modulation of qMRI maps. This eliminates the need for external manual annotation processes, as the system autonomously produces labeled synthetic images with known ground truth, significantly enhancing training efficiency and productivity.
3Adaptability or versatility
If the range of pathology severity levels is expanded in training data, then the neural network becomes more robust, but the complexity of data collection and annotation increases
Solution Approach 1:
The patent utilizes parameter changes by systematically varying qMRI map values across different severity levels within physiological ranges. This allows the generation of synthetic images representing mild, moderate, and severe pathologies without requiring actual patient data for each severity level, thereby enhancing neural network robustness while avoiding the complexity of collecting and annotating diverse real-world data.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach improves the sensitivity of MRI diagnostics for subtle pathologies and reduces the need for extensive manual annotation, enabling more effective training of neural networks with realistically simulated disease manifestations at varying severity levels.
Implementation Method 1
Magnetic Resonance Imaging (MRI) is a method to obtain an image representing the chemical and physical microscopic properties of materials, by utilizing a quantum mechanical phenomenon, named Nuclear Magnetic Resonance (NMR), in which a system of spins, placed in a magnetic field resonantly absorb energy
Implementation Method 2
An additional time constant, T2 (≤T1), called 'spin-spin relaxation time' or 'transverse relaxation time', controls the elapsed time in which the transverse magnetization diminishes
Data Source
AI summary
A method of synthesizing a magnetic resonance (MR) image, comprises obtaining a quantitative MRI (qMRI) map of values of an MRI parameter, modulating values of the MRI parameter within a region of the qMRI map to mimic a tissue pathology therein, and generating an MR image based on the modulated qMRI map.