Correction of motion artifacts using a multiscale fully convolutional neural network
K Sommer, A Saalbach, T Brosch… - American Journal …, 2020 - Am Soc Neuroradiology
BACKGROUND AND PURPOSE: Motion artifacts are a frequent source of image
degradation in the clinical application of MR imaging (MRI). Here we implement and validate …
degradation in the clinical application of MR imaging (MRI). Here we implement and validate …
[CITATION][C] Correction of motion artifacts using a multi-resolution fully convolutional neural network
Motion artifacts are a frequent source of image degradation in clinical practice. Here we
demonstrate the feasibility of correcting motion artifacts in magnitude-only MR images using …
demonstrate the feasibility of correcting motion artifacts in magnitude-only MR images using …
Retrospective correction of motion artifact affected structural MRI images using deep learning of simulated motion
Head motion during MRI acquisition presents significant problems for subsequent
neuroimaging analyses. In this work, we propose to use convolutional neural networks …
neuroimaging analyses. In this work, we propose to use convolutional neural networks …
[HTML][HTML] Automatic brain MRI motion artifact detection based on end-to-end deep learning is similarly effective as traditional machine learning trained on image quality …
P Vakli, B Weiss, J Szalma, P Barsi, I Gyuricza… - Medical Image …, 2023 - Elsevier
Head motion artifacts in magnetic resonance imaging (MRI) are an important confounding
factor concerning brain research as well as clinical practice. For this reason, several …
factor concerning brain research as well as clinical practice. For this reason, several …
[HTML][HTML] Retrospective motion artifact correction of structural MRI images using deep learning improves the quality of cortical surface reconstructions
Head motion during MRI acquisition presents significant challenges for neuroimaging
analyses. In this work, we present a retrospective motion correction framework built on a …
analyses. In this work, we present a retrospective motion correction framework built on a …
Automatic MR image quality evaluation using a Deep CNN: A reference-free method to rate motion artifacts in neuroimaging
Motion artifacts on magnetic resonance (MR) images degrade image quality and thus
negatively affect clinical and research scanning. Considering the difficulty in preventing …
negatively affect clinical and research scanning. Considering the difficulty in preventing …
Suppressing motion artefacts in MRI using an Inception‐ResNet network with motion simulation augmentation
The suppression of motion artefacts from MR images is a challenging task. The purpose of
this paper was to develop a standalone novel technique to suppress motion artefacts in MR …
this paper was to develop a standalone novel technique to suppress motion artefacts in MR …
[PDF][PDF] Motion correction in MRI using deep convolutional neural network
Patient motion during MR data acquisition appears in the reconstructed image as blurring
and incoherent artefacts. In this work, we present a novel deep learning encoder-decoder …
and incoherent artefacts. In this work, we present a novel deep learning encoder-decoder …
Clinical utility of deep learning motion correction for T1 weighted MPRAGE MR images
Purpose To evaluate the clinical utility of the application of a deep learning motion correction
technique on 3D MPRAGE magnetic resonance images acquired in routine clinical practice …
technique on 3D MPRAGE magnetic resonance images acquired in routine clinical practice …
MoCoNet: Motion correction in 3D MPRAGE images using a convolutional neural network approach
Purpose: The suppression of motion artefacts from MR images is a challenging task. The
purpose of this paper is to develop a standalone novel technique to suppress motion …
purpose of this paper is to develop a standalone novel technique to suppress motion …
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