Reduction of respiratory motion artifacts in gadoxetate-enhanced MR with a deep learning\u2013based filter using convolutional neural network

M.-L Kromrey,U Motosugi,H Onishi,J.-P Kühn,S Funayama,N Nagata,D Tamada,S Ichikawa,H Johno

doi:10.1007/s00330-020-07006-1

Abstract

ObjectivesTo reveal the utility of motion artifact reduction with convolutional neural network (MARC) in gadoxetate disodium–enhanced multi-arterial phase MRI of the liver.MethodsThis 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.ResultsOf 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.ConclusionsMotion 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.

Highlights

Recent research demonstrated intravenous bolus injection of gadolinium-based contrast agents to be accompanied by motion-related image degradation in the arterial phase [1,2,3]
This study presents a newly developed deep learning–based filter for artifact reduction using convolutional neural network
Postprocessing with motion artifact reduction with convolutional neural network (MARC) led to better lesion conspicuity without removing anatomical details

Summary

Introduction

Recent research demonstrated intravenous bolus injection of gadolinium-based contrast agents to be accompanied by motion-related image degradation in the arterial phase [1,2,3]. This phenomenon is temporary and self-limited, wherefore the term “transient severe motion” (TSM) is often used. Dilution of the contrast agent is able to reduce the incidence of artifacts [9, 10] Another strategy can be a modulation of the data acquisition: fast scanning techniques leading to a shorter examination time minimize the risk of patient motion. Min et al [16] showed a similar incidence of TSM artifacts with multiple arterial phases using view sharing from two different vendors and conventional single arterial phase in a retrospective study

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