A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis

no code implementations • 2 May 2022 • Inês P. Machado, Esther Puyol-Antón, Kerstin Hammernik, Gastão Cruz, Devran Ugurlu, Ihsane Olakorede, Ilkay Oksuz, Bram Ruijsink, Miguel Castelo-Branco, Alistair A. Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King

Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation.

MRI Reconstruction

The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images

no code implementations • 22 Sep 2021 • Devran Ugurlu, Esther Puyol-Anton, Bram Ruijsink, Alistair Young, Ines Machado, Kerstin Hammernik, Andrew P. King, Julia A. Schnabel

Our dataset contains short axis images from 4 different MR scanners and 3 different pathology groups.

Left Ventricle Segmentation Right Ventricle Segmentation +1

Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data

no code implementations • 16 Sep 2021 • Ines Machado, Esther Puyol-Anton, Kerstin Hammernik, Gastao Cruz, Devran Ugurlu, Bram Ruijsink, Miguel Castelo-Branco, Alistair Young, Claudia Prieto, Julia A. Schnabel, Andrew P. King

The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters.

MRI Reconstruction