Cross-Modality Image Synthesis via GAN
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Solution Overview
Problem
Current cross-modality image synthesis techniques, such as registration-based methods, are time-consuming and dependent on accurate image registration, which can be complex and burdensome, especially in medical imaging where separate acquisition of images like CT and MR images is costly and exposes patients to radiation.
Innovation Solution
A system and method using a trained machine learning model, specifically a generative adversarial network (GAN), to generate images of one modality from another without the need for registration, by processing images through a trained generative and discriminative model to produce high-accuracy synthetic images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If registration-based cross-modality synthesis is used, then synthetic images can be generated from different modalities, but the processing time becomes excessively long and the system complexity increases
Solution Approach 1:
The patent replaces the mechanical image registration process with a deep learning-based direct synthesis approach. Instead of performing complex geometric alignment and intensity matching between MR and CT images, a trained neural network directly generates synthetic CT images from MR images, eliminating the need for manual or algorithmic registration steps while maintaining synthesis quality.
Solution Approach 2:
The patent performs preliminary training of the deep learning model using paired MR-CT image data before actual synthesis. This preliminary action of training the network on registered image pairs enables the model to learn the mapping between modalities, so that during actual use, no registration is needed - the model directly produces accurate synthetic images from unregistered input images.
2Loss of information
If multiple separate image acquisitions (CT and MR) are performed, then comprehensive diagnostic information is obtained, but patient exposure to radiation increases and the process becomes burdensome
Solution Approach 1:
The patent creates a synthetic copy of the CT image from the MR image using deep learning. Instead of acquiring the actual CT image that would expose the patient to radiation, the system generates a realistic synthetic CT image that preserves the anatomical and density information needed for treatment planning, thereby eliminating radiation exposure while maintaining diagnostic value.
Solution Approach 2:
The patent makes the MR image serve multiple functions by using it both as the primary diagnostic image and as the input for generating the synthetic CT image. This multi-functionality eliminates the need for separate CT acquisition, as the single MR image provides both the anatomical detail for segmentation and the basis for generating attenuation maps for dose calculation.
3Manufacturing precision
If registration-based synthesis is used, then cross-modality images can be aligned, but the registration accuracy requirement creates additional complexity and potential errors
Solution Approach 1:
The patent substitutes the complex registration machinery with a simplified deep learning pipeline. Instead of implementing and tuning multiple registration algorithms (rigid, affine, non-rigid) with various parameters and convergence criteria, the system uses a trained neural network that automatically learns the transformation and synthesis in one step, dramatically reducing system complexity.
Solution Approach 2:
The patent enables the synthesis system to be self-sufficient by training the model on registered image pairs during the training phase, then using the trained model to perform both alignment and synthesis simultaneously during inference. The model internally handles the registration task without requiring external registration tools or manual intervention, making the system self-contained and simpler to operate.
Data Source
AI summary
The present disclosure is related to systems and methods for image processing. The method includes obtaining a first image of a first modality. The method includes generating a second image of a second modality by processing, based on a trained machine learning model, the first image. The second modality may be different from the first modality.