Multishot scan magnetic resonance imaging (MRI) acquisition is inherently sensitive to motion, and motion artifact reduction is essential for improving the image quality in MRI. This work proposes and validates a new end‐to‐end motion‐correction method for the multishot sequence that incorporates a conditional generative adversarial network with minimum entropy (cGANME) of MR images. The cGANME contains an encoder‐decoder generator to obtain motion‐corrected images and a PatchGAN discriminator to classify the image as either real (motion‐free) or fake (motion‐corrected). The entropy of the images is set as one loss item in the cGAN's loss as the entropy increases monotonically with the motion artifacts. An ablation experiment of the different weights of entropy loss was performed to evaluate the function of entropy loss. The preclinical dataset was acquired with a fast spin echo pulse sequence on a 7.0‐T scanner. After the simulation, we had 10,080/2880/1440 slices for training, testing, and validating, respectively. The clinical dataset was downloaded from the Human Connection Project website, and 11,300/3500/2000 slices were used for training, testing, and validating after simulation, respectively. Extensive experiments for different motion patterns, motion levels, and protocol parameters demonstrate that cGANME outperforms traditional and some state‐of‐the‐art, deep learning‐based methods. In addition, we tested cGANME on in vivo awake rats and mitigated the motion artifacts, indicating that the model has some generalizability.
The transient severe motion may cause severe image degradation during gadoxetic acid-enhanced arterial phase imaging. This work proposes a new dual-domain unsupervised motion artifacts disentanglement network for motion correction related to gadoxetic acid-enhanced MRI. We assume that motion-free images and motion-corrupted images belong to the different domains, then the motion correction is converted to the image-to-image translation problem. The image-to-image translation within the same domain is designed to constrain autoencoders to learn the feature representation. And the cross-domain translation explores the cycle consistency in the absence of paired images. Experimental results demonstrate that our method can effectively reduce artifacts in the gadoxetic acid-enhanced images.
We proposed a spectrum-to-spectrum/spectrum-to-phase phase correction method based on a neural network for magnetic resonance spectra. The former network obtains phase-corrected spectra by the end-to-end training the mapping between the manually corrected spectra and uncorrected spectra. And the latter can achieve more accurate phase correction by predicting the zero- and first-order phases for correction. The result shows that the proposed network can effectively obtain high-quality phase correction spectra even under noisy and baseline distortion conditions.
We proposed a dual-domain self-supervised motion artifacts disentanglement network (DSMAD-Net) for the liver's gadoxetic acid-enhanced arterial phase images. The motion correction is converted to the image-to-image translation problem by assuming that motion-free images and motion-corrupted images belong to different domains. Specifically, image-to-image translation within the same domain is designed to constrain auto-encoders to learn the feature representation by utilizing the input images as supervision information. Moreover, the cross-domain translation explores the cycle consistency in the absence of paired motion-free and motion-corrupted images. Experimental results demonstrate that our method remarkably removes artifacts in the gadoxetic acid-enhanced arterial phase images.
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