CT to MRI Image Transformation via Dual Neural Network Models
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Solution Overview
Problem
Current medical image transformation technologies fail to accurately convert Computer Tomography (CT) images into Magnetic Resonance Imaging (MRI) images, as they lack effective methods to align and transform the distinct features and properties of both imaging modalities.
Innovation Solution
An apparatus and method utilizing an artificial neural network technology, comprising a preprocessing module and two artificial neural network models, where the first model generates an intermediate transformed image from a CT image to reveal global features, and the second model generates an MRI image from this intermediate image to reflect regional features, using generators and discriminators for adversarial training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a single artificial neural network model is used for direct transformation, then the transformation process is simple, but the accuracy of converting CT images to MRI images is insufficient
Solution Approach 1:
The transformation process is divided into two separate models: a first model that extracts global features and a second model that generates the final MRI image with regional details. This segmentation allows each model to specialize in specific tasks, improving overall transformation accuracy while managing complexity through functional division.
Solution Approach 2:
An intermediate transformed image is introduced as a bridge between the input CT image and the final MRI image. The first model generates this intermediate image containing global features, which then serves as input to the second model. This intermediary structure enables progressive transformation and improves final image quality.
2Shape
If global features are emphasized in the transformation, then the overall structure is preserved, but regional details are lost
Solution Approach 1:
The transformation task is segmented into two stages: the first model focuses on preserving global structural features, while the second model concentrates on generating accurate regional details. This functional segmentation allows each model to optimize for its specific purpose without compromise.
Solution Approach 2:
The first model performs preliminary transformation to establish the global structure and overall anatomy before the second model refines the image with detailed regional features. This preliminary action ensures that the foundation is laid correctly before detailed work begins.
Data Source
AI summary
An apparatus for image transformation for transforming a first medical image into a second medical image based on an artificial neural network technology includes a preprocessing module that receives the first medical image and second medical image obtained by photographing the same part of a body using different photographing techniques and performs preprocessing on the first and second medical images, and an artificial neural network module that receives the preprocessed first medical image and second medical image, respectively, and transforms the first medical image into a second medical image.