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HIGH-QUALITY SEGMENTATION OF LOW QUALITY CARDIAC MR IMAGES we use an end-to-end image artefact correction algorithm that uses k-space input. Our algorithm outputs high quality images (Fig.1d) and correspondingly improved segmentations (Fig.1h) com-pared to state-of-the-art image denoising techniques (Liu and Fang,2017) (Fig.1c and g).
A classic spin echo sequence fills the k-space line by line. Here is the explanation of the k-space trajectory: 90° RF pulse + Slice-selection gradient : location at origin (center) of k-space; Negative and strong phase-encoding gradient: moves to the lower bound of k-space
18 de feb. de 2023 · An in-house pipeline was developed replicating all post-processing steps done on an MR scanner to go from raw k-space data to DICOM images viewed by clinicians for diagnostic decisions.
26 de sept. de 2018 · We propose two deep architectures, an end-to-end synthesis network and a latent feature interpolation network, to predict cardiac segmentation maps from extremely undersampled dynamic MRI data, bypassing the usual image reconstruction stage altogether.
Abstract. Reconstructing magnetic resonance imaging (MRI) from un-dersampled k-space enables the accelerated acquisition of MRI but is a challenging problem. However, in many diagnostic scenarios, perfect reconstructions are not necessary as long as the images allow clinical practitioners to extract clinically relevant parameters. In this work, we
16 de sept. de 2018 · Cardiac MR Segmentation from Undersampled k-space Using Deep Latent Representation Learning. This work proposes two deep architectures, an end-to-end synthesis network and a latent feature interpolation network, to predict cardiac segmentation maps from extremely undersampled dynamic MRI data, bypassing the usual image reconstruction ...
11 de oct. de 2019 · The method is based on our recently developed joint artefact detection and reconstruction method, which reconstructs high quality MR images from k-space using a joint loss function and essentially converts the artefact correction task to an under-sampled image reconstruction task by enforcing a data consistency term.
