Synthetic CT Angiography Imaging With Focal Learning and Image Alignment
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Solution Overview
Problem
Current image-to-image conversion models for CT angiography fail to effectively highlight critical regions and require multiple CT scans with contrast agents, leading to inefficiency and high costs.
Innovation Solution
A focal learning-based method using an adversarial network with a generator, corrector, and discriminator, employing a joint focal learning loss function to enhance the generation of CT angiography images from non-contrast CT images, focusing on vascular tissues and improving registration and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple CT scans with contrast agents are performed to obtain CTA images, then imaging quality and vascular visualization are improved, but examination time and cost increase substantially
Solution Approach 1:
The patent uses image-to-image conversion models (Pix2Pix, CycleGAN, RegGAN) to generate synthetic CTA images from NCCT images. The generator network learns to copy and transform features from non-contrast CT images to create contrast-enhanced angiography images, eliminating the need for actual contrast agent administration and multiple scans while maintaining vascular visualization quality
Solution Approach 2:
The patent replaces the physical mechanical process of contrast agent injection and multiple CT scans with an artificial intelligence-based image transformation system. The adversarial network model substitutes the clinical procedure with a computational process that converts NCCT images to synthetic CTA images through learned feature mappings
2Adaptability or versatility
If unpaired medical image conversion models (CycleGAN) are used to convert NCCT to CTA, then paired image requirements are relaxed, but conversion effectiveness is limited
Solution Approach 1:
The patent employs an adversarial framework where a discriminator network provides feedback to the generator network. The discriminator critiques the authenticity and quality of generated CTA images, and the generator uses this feedback to iteratively improve its output. This feedback mechanism enables the model to achieve high conversion effectiveness while maintaining flexibility in handling unpaired data
Solution Approach 2:
The patent introduces a focal learning mechanism that dynamically adjusts the importance weights of different spatial regions during training. By changing the parameter emphasis on critical regions (such as vascular areas), the model optimizes its attention to generate more accurate and clinically relevant image conversions from unpaired data
3Ease of manufacture
If conventional image conversion models are trained without region-specific weighting, then training process is simplified, but critical region visualization is not highlighted
Solution Approach 1:
The patent implements focal learning with region-specific weighting factors that assign different importance levels to different spatial locations in the image. During training, the loss function is weighted according to the clinical significance of each region, enabling the model to prioritize accurate visualization of critical areas such as blood vessels while maintaining overall image quality
Data Source
AI summary
The present invention discloses a focal learning-based method for intelligent CT angiography imaging. (1) Acquiring NCCT images and their corresponding real CTA images; (2) Constructing an adversarial network model comprising a generator, a corrector, and a discriminator; (3) Formulating a joint focal learning loss function for the generator-corrector pair and a separate loss function for the discriminator; (4) Training the adversarial network model using the training set, and validating the trained model using the validation set; (5) Identifying the generator with the best test performance by virtue to the test set. The invention establishes a joint focal learning loss function, which allows the generator to create synthetic CTA images that more effectively emphasize target areas, like vascular tissues. Furthermore, a corrector is incorporated into the invention to facilitate improved registration and alignment between NCCT images and CTA images.