Parameter-free denoising of complex MR images by iterative multi-wavelet thresholding

摘要

A method for denoising Magnetic Resonance Imaging (MRI) data includes receiving a noisy image acquired using an MRI imaging device and determining a noise model comprising a non-diagonal covariance matrix based on the noisy image and calibration characteristics of the MRI imaging device. The noisy image is designated as the current best image. Then, an iterative denoising process is performed to remove noise from the noisy image. Each iteration of the iterative denoising process comprises (i) applying a bank of heterogeneous denoisers to the current best image to generate a plurality of filter outputs, (ii) creating an image matrix comprising the noisy image, the current best image, and the plurality of filter outputs, (iii) finding a linear combination of elements of the image matrix which minimizes a Stein Unbiased Risk Estimation (SURE) value for the linear combination and the noise model, (iv) designating the linear combination as the current best image, and (v) updating each respective denoiser in the bank of heterogeneous denoisers based on the SURE value. Following the iterative denoising process, the current best image is designated as a final denoised image.

主权项

1. A method for denoising Magnetic Resonance Imaging (MRI) data, the method comprising:
receiving a noisy image acquired using an MRI imaging device; determining a noise model comprising a non-diagonal covariance matrix based on the noisy image and calibration characteristics of the MRI imaging device; designating the noisy image as a current best image; performing an iterative denoising process to remove noise from the noisy image, each iteration of the iterative denoising process comprising:
applying a bank of heterogeneous denoisers to the current best image to generate a plurality of filter outputs,creating an image matrix comprising the noisy image, the current best image, and the plurality of filter outputs,finding a linear combination of elements of the image matrix which minimizes a Stein Unbiased Risk Estimation (SURE) value for the linear combination and the noise model,designating the linear combination as the current best image,updating each respective denoiser in the bank of heterogeneous denoisers based on the SURE value; and following the iterative denoising process, designating the current best image as a final denoised image.