Synthetic Medical Image Generation with Conditional Generative Models
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Solution Overview
Problem
Dynamic medical imaging can be lengthy and uncomfortable for patients due to the need to minimize movement, and it is challenging to replace poor-quality images without repeating the entire examination.
Innovation Solution
A computer-implemented method using a conditional generative model trained with medical image data sets to generate synthetic medical images based on image embeddings, allowing for the reconstruction of examination regions at different time points, reducing the need for prolonged patient immobility and image repetition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If dynamic medical imaging is performed to capture changes in the examination region over time, then diagnostic information is improved, but examination time increases and patient comfort deteriorates
Solution Approach 1:
The system performs preliminary actions by generating synthetic intermediate time-point images from a limited set of actual captured images. The generative model is trained in advance to learn the temporal evolution patterns, enabling it to synthesize realistic intermediate frames that represent the examination region at uncaptured time points, thereby reducing the need for prolonged continuous imaging.
Solution Approach 2:
The system creates synthetic copies of medical images at intermediate time points by using a generative model that learns from actual captured images. These synthetic images serve as realistic replicas that fill temporal gaps, allowing the system to reconstruct the dynamic examination sequence without capturing every moment, thus shortening examination duration while preserving diagnostic quality.
2Reliability
If dynamic medical imaging is performed to capture changes in the examination region over time, then diagnostic information is improved, but patient comfort deteriorates due to movement restriction
Solution Approach 1:
The system performs preliminary actions by generating synthetic intermediate time-point images from a limited set of actual captured images. The generative model is trained in advance to learn the temporal evolution patterns, enabling it to synthesize realistic intermediate frames that represent the examination region at uncaptured time points, thereby reducing the need for prolonged continuous imaging.
Solution Approach 2:
The system creates synthetic copies of medical images at intermediate time points by using a generative model that learns from actual captured images. These synthetic images serve as realistic replicas that fill temporal gaps, allowing the system to reconstruct the dynamic examination sequence without capturing every moment, thus shortening examination duration while preserving diagnostic quality.
3Manufacturing precision
If traditional image replacement methods are used for poor quality images, then image quality is improved, but examination time increases due to repetition
Solution Approach 1:
The system creates synthetic copies of medical images at intermediate time points by using a generative model that learns from actual captured images. These synthetic images serve as realistic replicas that fill temporal gaps, allowing the system to reconstruct the dynamic examination sequence without capturing every moment, thus shortening examination duration while preserving diagnostic quality.
Solution Approach 2:
The system performs preliminary actions by generating synthetic intermediate time-point images from a limited set of actual captured images. The generative model is trained in advance to learn the temporal evolution patterns, enabling it to synthesize realistic intermediate frames that represent the examination region at uncaptured time points, thereby reducing the need for prolonged continuous imaging.
4Measurement precision
If more time points are captured during dynamic examination, then temporal resolution is improved, but examination duration increases
Solution Approach 1:
The system creates synthetic copies of medical images at intermediate time points by using a generative model that learns from actual captured images. These synthetic images serve as realistic replicas that fill temporal gaps, allowing the system to reconstruct the dynamic examination sequence without capturing every moment, thus shortening examination duration while preserving diagnostic quality.
Solution Approach 2:
The system performs preliminary actions by generating synthetic intermediate time-point images from a limited set of actual captured images. The generative model is trained in advance to learn the temporal evolution patterns, enabling it to synthesize realistic intermediate frames that represent the examination region at uncaptured time points, thereby reducing the need for prolonged continuous imaging.
Data Source
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AI summary
Systems, methods, and computer programs disclosed herein relate to training a machine learning model and using the trained machine learning model to generate a synthetic medical image of an examination region of an examination object representing the examination region at one point in time during a dynamic examination of the examination region based on one or more medical images representing the examination region at another point in time or multiple other points in time.