Intraoperative MRI Noise Reduction via Pixel Weighting
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Solution Overview
Problem
Intraoperative MRI images often suffer from unpredictable noise and artifacts due to unforeseen device activations, making it difficult to suppress noise only at the occurrence location while preserving the original image information.
Innovation Solution
An image processing device that calculates a weighting value for each pixel based on the difference between the original and noise-reduced images, allowing for weighted averaging to combine these images, effectively reducing noise only at occurrence locations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If general noise reduction processing is applied to the entire image, then noise is suppressed, but original image information is lost and edge blurring occurs
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on noise detection. The weighting value calculation section computes pixel-specific weights that are higher for regions with noise artifacts and lower for clean regions, enabling localized noise suppression while preserving original image information in non-noisy areas. This resolves the contradiction by making the noise reduction effect spatially variable rather than uniform across the entire image.
2Measurement precision
If re-imaging is performed to obtain noise-free images, then image quality is improved, but surgical time is extended
Solution Approach 1:
The patent introduces an image processing device as an intermediary that post-processes existing MRI images to remove noise. Instead of requiring additional acquisitions, the system takes the originally captured image, applies noise detection and weighting-based filtering, and generates a cleaned image. This mediator approach achieves noise suppression without extending surgical time, resolving the contradiction between image quality and time loss.
3Object-affected harmful factors
If noise reduction processing with high denoising strength is applied, then noise is effectively removed, but edge blurring and artifact introduction occur
Solution Approach 1:
The patent applies noise reduction selectively rather than uniformly. The weighting value calculation determines the degree of denoising applied to each pixel based on local noise characteristics. In regions with severe noise, stronger denoising is applied; in clean regions, minimal or no denoising is applied. This partial action approach achieves effective noise removal where needed while avoiding edge blurring in regions where it is not necessary.
Data Source
AI summary
Provided is an image in which noise and artifacts, which pose a problem in intraoperative MRI, are reduced and a decrease in sharpness due to noise reduction for a tissue or a site, for which high visibility is required, is suppressed. In a case of generating and presenting a third MR image by using a first MR image acquired by an MRI apparatus and a second MR image obtained by performing processing of reducing noise and artifacts with respect to the first MR image, a difference for each pixel between the first MR image and the second MR image is taken, and a weighting value for each pixel is calculated using a generated difference image. The weighting value is used to combine the first MR image and the second MR image through weighted averaging for each pixel, and the combined image is presented.