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Found 6 papers, 0 papers with code
Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation
In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity.
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data.
Magnetic Resonance Fingerprinting using Recurrent Neural Networks
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition.
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space.
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem.
Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI
The proposed method is a joint image reconstruction and motion correction method consisting of several steps, including a non-rigid motion extraction and a motion-compensated reconstruction.
