Multi-Acquisition MRI Denoising with Spatially Informed Component Selection
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Solution Overview
Problem
Existing methods for denoising magnetic resonance imaging data using principal component analysis often discard important anatomical and pathological information due to simplistic variance-based selection criteria, leading to incomplete retention of relevant spatial information.
Innovation Solution
A method that selects principal components based on the decrease of variance after applying a smoothing filter to their scores, allowing for adaptive filtering that retains informative indicators and minimizes the exclusion of relevant components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If variance-based selection criteria are used to select principal components, then noise attenuation is achieved, but important anatomical and pathological information is discarded
Solution Approach 1:
The patent changes the selection parameter from simple variance to a composite criterion that considers both variance and spatial coherence. This is achieved by calculating a selection score that combines the variance of each principal component with a measure of its spatial continuity, thereby transforming the selection parameter to preserve both noise attenuation and important anatomical information
Solution Approach 2:
The patent introduces a feedback mechanism where the selection of principal components is guided by evaluating their contribution to both noise reduction and information preservation. The selection criterion provides feedback on the quality of each component, allowing iterative refinement of the selection to optimize the balance between denoising and information retention
2Device complexity
If simplistic variance-based selection is applied, then processing simplicity is maintained, but retention of relevant spatial information becomes incomplete
Solution Approach 1:
The patent enhances the selection parameter from simple variance to a composite metric incorporating spatial coherence measures. This involves calculating additional parameters such as spatial gradients and continuity scores, which are integrated into the selection criterion to improve reliability without excessive complexity
Solution Approach 2:
The patent applies a balanced approach by selecting a subset of principal components that provides sufficient denoising while preserving essential spatial information. Rather than selecting all components or using overly complex criteria, it chooses an optimal subset that achieves adequate performance with reasonable computational effort
Data Source
AI summary
A system and method for denoising experimental data originating from multiple acquisitions by a magnetic resonance imaging device, by analysis of selected principal components, to obtain a better compromise between the efficiency of the denoising and retention of the relevant information in the experimental data under consideration during their reconstruction to produce denoised experimental data. A selection criterion is based on the informative indicators quantifying the spatial information contained in images of scores associated with said principal components. The invention also provides for the capability to apply an adaptive filtering excluding the persistent spatial noise associated with each component selected.