3 research outputs found
Respiratory Motion Correction in Abdominal MRI using a Densely Connected U-Net with GAN-guided Training
Abdominal magnetic resonance imaging (MRI) provides a straightforward way of
characterizing tissue and locating lesions of patients as in standard
diagnosis. However, abdominal MRI often suffers from respiratory motion
artifacts, which leads to blurring and ghosting that significantly deteriorate
the imaging quality. Conventional methods to reduce or eliminate these motion
artifacts include breath holding, patient sedation, respiratory gating, and
image post-processing, but these strategies inevitably involve extra scanning
time and patient discomfort. In this paper, we propose a novel
deep-learning-based model to recover MR images from respiratory motion
artifacts. The proposed model comprises a densely connected U-net with
generative adversarial network (GAN)-guided training and a perceptual loss
function. We validate the model using a diverse collection of MRI data that are
adversely affected by both synthetic and authentic respiration artifacts.
Effective outcomes of motion removal are demonstrated. Our experimental results
show the great potential of utilizing deep-learning-based methods in
respiratory motion correction for abdominal MRI.Comment: 8 pages, 4 figures, submitted to the 22nd International Conference on
Medical Image Computing and Computer Assisted Interventio
DeepResp: Deep learning solution for respiration-induced B0 fluctuation artifacts in multi-slice GRE
Respiration-induced B fluctuation corrupts MRI images by inducing phase
errors in k-space. A few approaches such as navigator have been proposed to
correct for the artifacts at the expense of sequence modification. In this
study, a new deep learning method, which is referred to as DeepResp, is
proposed for reducing the respiration-artifacts in multi-slice gradient echo
(GRE) images. DeepResp is designed to extract the respiration-induced phase
errors from a complex image using deep neural networks. Then, the
network-generated phase errors are applied to the k-space data, creating an
artifact-corrected image. For network training, the computer-simulated images
were generated using artifact-free images and respiration data. When evaluated,
both simulated images and in-vivo images of two different breathing conditions
(deep breathing and natural breathing) show improvements (simulation:
normalized root-mean-square error (NRMSE) from 7.8% to 1.3%; structural
similarity (SSIM) from 0.88 to 0.99; ghost-to-signal-ratio (GSR) from 7.9% to
0.6%; deep breathing: NRMSE from 13.9% to 5.8%; SSIM from 0.86 to 0.95; GSR
20.2% to 5.7%; natural breathing: NRMSE from 5.2% to 4.0%; SSIM from 0.94 to
0.97; GSR 5.7% to 2.8%). Our approach does not require any modification of the
sequence or additional hardware, and may therefore find useful applications.
Furthermore, the deep neural networks extract respiration-induced phase errors,
which is more interpretable and reliable than results of end-to-end trained
networks.Comment: 19 page
Review: Noise and artifact reduction for MRI using deep learning
For several years, numerous attempts have been made to reduce noise and
artifacts in MRI. Although there have been many successful methods to address
these problems, practical implementation for clinical images is still
challenging because of its complicated mechanism. Recently, deep learning
received considerable attention, emerging as a machine learning approach in
delivering robust MR image processing. The purpose here is therefore to explore
further and review noise and artifact reduction using deep learning for MRI.Comment: Submitted to Magnetic Resonance in Medical Sciences on 2/27/202