Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning–based filter using convolutional neural network

Abstract

Objectives To reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver. Methods This retrospective study included 192 patients (131 men, 68.7 ± 10.3 years) receiving gadoxetate disodium–enhanced liver MRI in 2017. Datasets were submitted to a newly developed filter (MARC), consisting of 7 convolutional layers, and trained on 14,190 cropped images generated from abdominal MR images. Motion artifact for training was simulated by adding periodic k-space domain noise to the images. Original and filtered images of pre-contrast and 6 arterial phases (7 image sets per patient resulting in 1344 sets in total) were evaluated regarding motion artifacts on a 4-point scale. Lesion conspicuity in original and filtered images was ranked by side-by-side comparison. Results Of the 1344 original image sets, motion artifact score was 2 in 597, 3 in 165, and 4 in 54 sets. MARC significantly improved image quality over all phases showing an average motion artifact score of 1.97 ± 0.72 compared to 2.53 ± 0.71 in original MR images (p < 0.001). MARC improved motion scores from 2 to 1 in 177/596 (29.65%), from 3 to 2 in 119/165 (72.12%), and from 4 to 3 in 34/54 sets (62.96%). Lesion conspicuity was significantly improved (p < 0.001) without removing anatomical details. Conclusions Motion artifacts and lesion conspicuity of gadoxetate disodium–enhanced arterial phase liver MRI were significantly improved by the MARC filter, especially in cases with substantial artifacts. This method can be of high clinical value in subjects with failing breath-hold in the scan. Key Points • This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network (motion artifact reduction with convolutional neural network, MARC). • MARC significantly improved MR image quality after gadoxetate disodium administration by reducing motion artifacts, especially in cases with severely degraded images. • Postprocessing with MARC led to better lesion conspicuity without removing anatomical details.

Image-based Motion Artifact Reduction on Liver Dynamic Contrast Enhanced MRI

“…This is especially not possible for contrast enhanced acquisitions. Kromrey et al added periodic phase errors to K-space lines to simulate periodic respiratory motion artifacts in abdomen MRI (20). Tamada et al used random phase error patterns to simulate more-severe, non-periodic motion (19).…”

“…Only a few studies were focused on motion artifact reduction in DCE-MRI of the liver (19,20) and used the motion artifact reduction with convolutional (MARC) network with the pixel-by-pixel MSE as the loss function in their models. Although these methods achieved relatively good denoising effect on the liver MRIs, Yang et al mentioned that using the pixel-by-pixel MSE as the loss function would lead to over-smoothing problems so that perceptually-important details were likely to be overlooked (30).…”

“…Recently, deep learning (DL) approaches have been developed in medical imaging use cases including image reconstruction and artifact reduction (11,12), motion detection and correction (13), and image quality control (14,15). DL methods utilized convolutional neural networks (CNNs) to extract features of different types of artifacts and correct them in brain (16,17), abdominal (18)(19)(20) and cardiac imaging (13). Specifically for liver DCE-MRI, Tamada et al proposed a denoising CNN on multi-phase magnitude-only image patches that learned the artifact patterns as residual feature maps and then subtracted them from the original images to obtain the motion reduced images (19).…”

“…Specifically for liver DCE-MRI, Tamada et al proposed a denoising CNN on multi-phase magnitude-only image patches that learned the artifact patterns as residual feature maps and then subtracted them from the original images to obtain the motion reduced images (19). Kromrey et al developed deep learning filters in CNNs for multi-arterial phase acquisitions and improved image quality on severely degraded images (20). However, the motion artifacts used for model training in both studies were simulated based on specific re-ordering of K-space lines, which cannot represent the full spectrum of motion artifact patterns.…”

An Overview of Artificial Intelligence Applications in Liver and Pancreatic Imaging

“…The noise, sharpness and artifacts were not statistically different between DL T2W and non-cartesian T2W [ 62 ]. DL was also used to reduce respiratory artifacts, and this led to an increase of liver lesion conspicuousness without removing anatomical details [ 63 ]. DL applied to MR may reduce acquisition time and improve image quality.…”