PROPELLER MRI Denoising k-Space Blades Deep Learning
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Solution Overview
Problem
Existing PROPELLER MRI techniques face challenges in denoising colored noise, which results in blurriness and degraded image quality, especially with low signal-to-noise ratio data.
Innovation Solution
A computer-implemented method and system that utilize a deep learning-based denoising network to denoise individual blades of k-space data prior to phase correcting and gridding, improving image quality by enhancing signal-to-noise ratio and sharpness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Object-affected harmful factors
If conventional denoising methods are applied to colored noise in PROPELLER images, then noise reduction is attempted, but image quality degrades with blurriness and loss of sharpness
Solution Approach 1:
The patent segments the k-space data into multiple individual blades before denoising. By applying denoising operations to each blade separately in the k-space domain rather than to the complete gridded image, the method reduces colored noise while preserving edge information and maintaining image sharpness. This segmentation allows selective noise reduction without the blurriness that occurs with conventional full-image denoising approaches.
2Reliability
If PROPELLER imaging is used to reduce motion artifacts, then motion robustness is improved, but colored noise is introduced during gridding which degrades denoising performance
Solution Approach 1:
The patent performs denoising as a preliminary action before the gridding step. By denoising individual k-space blades before they are gridded into the final image, the method prevents colored noise from being introduced during the gridding process. This preliminary denoising of individual blades maintains the motion artifact reduction benefits of PROPELLER while eliminating the colored noise problem that would otherwise degrade denoising performance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The proposed method effectively improves image quality in PROPELLER MRI by reducing noise and enhancing resolution, leading to better diagnostic images with improved signal-to-noise ratio and sharpness.
Implementation Method 1
magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradient fields vary according to the particular localization method being used. The resulting set of received nuclear magnetic resonance (NMR) signals are digitized and processed
Data Source
AI summary
A system and method for improving image quality of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER) imaging include acquiring a plurality of blades of k-space data of a region of interest in a rotational manner around a center of k-space via a magnetic resonance imaging (MRI) scanner from a coil during a PROPELLER sequence, wherein each blade of the plurality of blades of k-space data includes a plurality of parallel phase encoding lines sampled in a phase encoding order. The system and method also include utilizing a deep learning-based denoising network to denoise each blade of the plurality of blades of k-space data to generate a plurality of denoised blades. The system and method further include utilizing a PROPELLER reconstruction algorithm to generate a complex image from the plurality of denoised blades.