Dataset Information

Training a neural network for Gibbs and noise removal in diffusion MRI.

ABSTRACT:

Purpose

To develop and evaluate a neural network-based method for Gibbs artifact and noise removal.

Methods

A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images.

Results

Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps. The CNN for complex images was also able to reduce artifacts in partial Fourier acquisitions.

Conclusions

The proposed CNNs extend the ability of artifact correction in diffusion MRI. The machine learning method described here can be applied on each imaging slice independently, allowing it to be used flexibly in clinical applications.

SUBMITTER: Muckley MJ

PROVIDER: S-EPMC7722184 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

Training a neural network for Gibbs and noise removal in diffusion MRI.

Muckley Matthew J MJ Ades-Aron Benjamin B Papaioannou Antonios A Lemberskiy Gregory G Solomon Eddy E Lui Yvonne W YW Sodickson Daniel K DK Fieremans Els E Novikov Dmitry S DS Knoll Florian F

Magnetic resonance in medicine 20200714 1

<h4>Purpose</h4>To develop and evaluate a neural network-based method for Gibbs artifact and noise removal.<h4>Methods</h4>A convolutional neural network (CNN) was designed for artifact removal in diffusion-weighted imaging data. Two implementations were considered: one for magnitude images and one for complex images. Both models were based on the same encoder-decoder structure and were trained by simulating MRI acquisitions on synthetic non-MRI images.<h4>Results</h4>Both machine learning metho ...[more]

PMID: 32662910

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Project description:PurposeTo investigate the feasibility of accelerating prostate diffusion-weighted imaging (DWI) by reducing the number of acquired averages and denoising the resulting image using a proposed guided denoising convolutional neural network (DnCNN).Materials and methodsRaw data from the prostate DWI scans were retrospectively gathered between July 2018 and July 2019 from six single-vendor MRI scanners. There were 103 datasets used for training (median age, 64 years; interquartile range [IQR], 11), 15 for validation (median age, 68 years; IQR, 12), and 37 for testing (median age, 64 years; IQR, 12). High b-value diffusion-weighted (hb DW) data were reconstructed into noisy images using two averages and reference images using all 16 averages. A conventional DnCNN was modified into a guided DnCNN, which uses the low b-value DW image as a guidance input. Quantitative and qualitative reader evaluations were performed on the denoised hb DW images. A cumulative link mixed regression model was used to compare the readers' scores. The agreement between the apparent diffusion coefficient (ADC) maps (denoised vs reference) was analyzed using Bland-Altman analysis.ResultsCompared with the original DnCNN, the guided DnCNN produced denoised hb DW images with higher peak signal-to-noise ratio (32.79 ± 3.64 [standard deviation] vs 33.74 ± 3.64), higher structural similarity index (0.92 ± 0.05 vs 0.93 ± 0.04), and lower normalized mean square error (3.9% ± 10 vs 1.6% ± 1.5) (P < .001 for all). Compared with the reference images, the denoised images received higher image quality scores from the readers (P < .0001). The ADC values based on the denoised hb DW images were in good agreement with the reference ADC values (mean ADC difference ranged from -0.04 to 0.02 × 10-3 mm2/sec).ConclusionAccelerating prostate DWI by reducing the number of acquired averages and denoising the resulting image using the proposed guided DnCNN is technically feasible. Supplemental material is available for this article. © RSNA, 2020.

Dataset Information

Training a neural network for Gibbs and noise removal in diffusion MRI.

Purpose

Methods

Results

Conclusions

Publications

Training a neural network for Gibbs and noise removal in diffusion MRI.

Similar Datasets

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