Synthetic MRI Image Generation from Single Scan Data
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Solution Overview
Problem
Current MRI techniques require multiple imaging sequences to capture clinically relevant information, which increases scan time and costs, and often results in incomplete data sets.
Innovation Solution
A system and method utilizing a processor module to process and analyze MRI patient scan data, generating synthetic T1-weighted and CT images, reducing the need for multiple imaging sequences by using deep learning to produce high-quality images from a single set of MRI data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple MRI imaging sequences are used to capture clinically relevant information, then the completeness and quality of medical imaging data is improved, but the scan time and costs increase significantly
Solution Approach 1:
The patent applies the copying principle by using a trained processor module to generate synthetic T1-weighted and CT images from a single set of MRI scan data. Instead of acquiring multiple real MRI sequences, the system creates synthetic copies that replicate the diagnostic information of multiple imaging sequences, thereby reducing scan time while maintaining data completeness
Solution Approach 2:
The patent employs parameter changes by transforming the input MRI scan data through a trained processor module that adjusts imaging parameters to produce different image types (T1-weighted, CT) from the same source data. This allows multiple imaging contrasts to be derived from a single acquisition, reducing the need for multiple sequences
2Reliability
If multiple MRI imaging sequences are used to assess different tissue types, then the diagnostic information completeness is improved, but the costs increase significantly
Solution Approach 1:
The patent implements universality by designing a processor module that can generate multiple types of medical images (T1-weighted, CT, and other contrasts) from a single set of MRI scan data. This multi-functional approach allows one imaging sequence to serve multiple diagnostic purposes, reducing the need for multiple specialized sequences and thereby lowering costs
Solution Approach 2:
The system creates synthetic copies of different image types from a single MRI acquisition, eliminating the need to perform multiple expensive imaging sequences. The synthetic images replicate the diagnostic value of multiple sequences at a fraction of the cost
3Productivity
If a single set of MRI scan data is used to generate multiple image types, then the scan time and costs are reduced, but the quality and completeness of imaging data may be compromised
Solution Approach 1:
The patent applies preliminary action by training the processor module in advance using a large dataset of paired MRI images and corresponding T1-weighted/CT images. This pre-training ensures that when the system generates synthetic images from a single scan, the output quality matches or exceeds clinical standards, thereby maintaining reliability while improving productivity
Solution Approach 2:
The system incorporates feedback mechanisms where the generated synthetic images are compared against ground truth images from multiple sequences, and the processor module is continuously refined. This feedback loop ensures that the synthetic images maintain high diagnostic quality and completeness
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 significantly reduces scan time and costs while maintaining or improving the quality of medical imaging data, enabling more efficient assessment of bodily tissues and fluids.
Implementation Method 1
utilizing a processor module to process and analyze MRI patient scan data, generating synthetic T1-weighted and CT images, reducing the need for multiple imaging sequences by using deep learning to produce high-quality images from a single set of MRI data
Data Source
AI summary
A system for assessment of different kinds of bodily tissues and/or bodily fluids and/or air, in particular air being associated with a human body, wherein, for primary data assessment, the processor module is configured to process and/or analyse at least the MRI patient scan data of the first patient such that at least one set of Bone MRI data, in particular synthetic CT data, of the first patient is provided, and/or wherein, for a secondary data assessment, the processor module is configured to process and/or analyse at least the MRI patient scan data of the first patient such that at least one set of synthetic T1-weighted image data of the first patient is provided. Furthermore, the present disclosure refers to a method for training of a processor module, a method for providing a primary data assessment and/or a secondary data assessment by such a system and a computer-readable medium.