K-Space Denoising for Diffusion Tensor Imaging via Sparsity Constraints
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Solution Overview
Problem
Current methods for improving the signal-to-noise ratio (SNR) in Magnetic Resonance Diffusion Tensor Imaging (DTI) either increase scanning time or affect spatial resolution, and existing denoising techniques can introduce system errors that impact the accuracy of diffusion tensor calculations.
Innovation Solution
A method that calculates a maximum likelihood estimator and a maximum posterior probability estimator of the diffusion tensor using sparsity constraints, directly from K-space data, avoiding the need for intermediate image denoising and thus minimizing errors and maintaining spatial resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sampling many times or reducing the sampling area of K space is used to increase SNR, then the signal-to-noise ratio is improved, but scanning time increases or spatial resolution is affected
Solution Approach 1:
The patent changes the parameter of noise handling from traditional image-space denoising to direct K-space data processing. By applying sparsity constraints and maximum likelihood estimation directly to the raw K-space diffusion data, the method improves SNR without requiring additional sampling or affecting spatial resolution, thus resolving the contradiction between measurement precision and time loss
2Measurement precision
If traditional image denoising methods are applied after rebuilding diffusion weighted images, then noise is reduced, but system errors are introduced that affect diffusion tensor accuracy
Solution Approach 1:
The patent applies preliminary action by performing denoising operations directly on the raw K-space data before image reconstruction and tensor calculation. By incorporating sparsity constraints and maximum likelihood estimation at the earliest possible stage (raw data level), the method reduces noise while preserving the integrity of the diffusion signal, preventing system errors from propagating through subsequent processing steps
Solution Approach 2:
The patent introduces sparsity constraints and maximum likelihood estimation as intermediary mathematical tools between the raw K-space data and the final diffusion tensor. These intermediaries enable effective noise suppression while maintaining the accuracy of diffusion parameters by providing a rigorous statistical framework that distinguishes signal from noise without introducing systematic biases
Data Source
AI summary
The application provides a method, apparatus and computer program product for denoising a magnetic resonance diffusion tensor, wherein the method comprises: collecting data of K space; calculating a maximum likelihood estimator of a diffusion tensor according to the collected data of K space; calculating a maximum posterior probability estimator of the diffusion tensor by using sparsity of the diffusion tensor and sparsity of a diffusion parameter and taking the calculating maximum likelihood estimator as an initial value; and calculating the diffusion parameter according to the calculated maximum posterior probability estimator. The application solves the technical problem in the prior art of how to realize high precision denoising of diffusion tensor while not increasing scanning time and affecting spatial resolution, achieves the technical effects of effectively suppressing noises in the diffusion tensor and improving the estimation accuracy of the diffusion tensor.