Virtual DWI Generation From NCCT for Early Ischemic Core Detection
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Solution Overview
Problem
Current imaging methods for detecting the infarct core in ischemic stroke, such as NCCT, DWI, and CTP, face challenges including high cost, limited accessibility, long scanning times, radiation exposure, and accuracy issues, necessitating a more affordable and efficient alternative.
Innovation Solution
An image processing system and method that generates virtual diffusion-weighted magnetic resonance imaging (DWI) from non-contrast-enhanced CT scans using a generative and discriminative model training process, leveraging autoencoders and patch GANs to create accurate virtual DWI without the need for expensive MRIs or contrast agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If diffusion-weighted magnetic resonance imaging (DWI) is used to detect the infarct core, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent uses a generative adversarial network (GAN) to create a virtual DWI image that copies the diagnostic information of real DWI images. The model learns the mapping relationship between NCCT and DWI images through training, then generates synthetic DWI images from new NCCT images, providing comparable diagnostic accuracy without requiring actual DWI scanning equipment
Solution Approach 2:
The patent replaces expensive, complex DWI imaging equipment with a computational model that runs on standard computing hardware. The virtual DWI images are generated through software processing of readily available NCCT images, eliminating the need for specialized MRI scanners and reducing overall system cost and complexity
2Measurement precision
If diffusion-weighted magnetic resonance imaging (DWI) is used to detect the infarct core, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent performs preliminary training of the GAN model using paired NCCT and DWI images to learn the transformation relationship. Once trained, the model can rapidly generate virtual DWI images from new NCCT images without requiring the time-consuming DWI scanning process, as the heavy computational work was done in advance during training
Solution Approach 2:
The generated virtual DWI images replicate the diagnostic information of real DWI images but can be produced much faster once the model is trained, eliminating the need for time-consuming MRI scanning while maintaining diagnostic accuracy
3Productivity
If tomographic cerebral blood flow perfusion (CTP) imaging is used to detect the infarct core, then productivity is improved, but object-affected harmful factors increase
Solution Approach 1:
The patent uses a generative adversarial network as an intermediary to transform NCCT images into virtual DWI images. This computational mediator enables rapid diagnosis similar to CTP but without requiring contrast agent injection or exposing patients to additional radiation, as the transformation is performed through learned image processing rather than additional physical imaging
Solution Approach 2:
The virtual DWI images copy the diagnostic information needed for rapid infarct core detection that would otherwise require CTP imaging, but are generated through software processing of non-contrast CT images, eliminating the harmful effects of contrast agents and additional radiation exposure
4Ease of operation
If non-contrast enhanced CT (NCCT) is used to evaluate the infarct core, then ease of operation is improved, but measurement precision worsens
Solution Approach 1:
The patent transforms easily interpretable NCCT images into virtual DWI images that have superior diagnostic characteristics for infarct core detection. The generated images copy the high-contrast, high-specificity features of real DWI images while retaining the ease of acquisition and initial interpretation of NCCT images
Solution Approach 2:
The generative adversarial network learns and applies parameter transformations to convert NCCT image characteristics into DWI-like image characteristics. This includes changing intensity distributions, contrast patterns, and texture features to match the diagnostic properties of true DWI images, thereby improving measurement precision while maintaining operational simplicity
Data Source
AI summary
An image processing method thereof for early detection of ischemic core of ischemic stroke patient includes a model training phase and a model inference phase. In the model training phase, it first generates a pseudo diffusion-weighted MR imaging (DWI) and a generator loss according to a non-contrast-enhanced CT (NCCT) image for training; then, it generates a discriminator loss and a discriminator result according to a DWI for training and the first pseudo-DWI; then, it judges whether to end the model training phase according to the generator loss and the discriminator loss, and if so, ends the model training phase and obtains a set of parameters for inference phase, and if not, updates the parameters used in the model training phase. In the model inference phase, it generates a pseudo DWI according to an NCCT image and the set of parameters for the inference phase.