Assessment of qualitative and quantitative features in coronary artery MRA

Abstract

International audienceIn this paper, an analysis of the coronary trees using Magnetic Resonance Angiography (MRA) is performed. The objective is to estimate how much MRA is capable to provide insights into the vascular network. A qualitative exploration of the MRA volumes with anatomical labelling by experts is rst performed, Quantitative vessel features are then manually extracted providing a ground truth which is further compared to a semi-automatic extraction. This evaluation is carried out on 10 datasets of the SSFP MRA sequence and allows getting a more precise view on the current state-of-the- art as well as on future achievements to be done

Heart failure classification using deep learning to extract spatiotemporal features from ECG

“…In medicine, all kinds of heart diseases need to process and analyze ECGs [ 34 – 37 ]. Therefore, this method is not limited to the field of heart failure classification, but can also be extended to other fields such as arrhythmia [ 38 – 40 ] and coronary artery disease [ 41 – 44 ].…”

Automatic determination of optimal view for the visualization of coronary lesions by rotational X-ray angiography

“…We consider that a dynamic sequence of 3D coronary tree is available either from pre-segmented CT or MRA data [2][3] or by reconstruction of the 3D coronary tree from the projections acquired at each phase of the cardiac cycle [4][5][6][7][8] and proceed along the following stages to compute the optimum angular position of the C-arm: -interactive selection of the vascular segment of interest in the 3D+T space, -spatio-temporal matching of vascular branches over the 3D+T sequence (see section 3.2. ), -simulation of volume projection in a given 2D plan and in a clinically relevant gantry orientation, -computation of the foreshortening degree for the segment of interest and its overlapping rate with adjacent segments, for each relevant gantry orientation and cardiac phase (see section 3.3.…”

Arrhythmia classification detection based on multiple electrocardiograms databases

“…Cardiovascular diseases account for the most important deaths globally, with heart disease and stroke accounting for 80% of these deaths [ 3 ]. In order to reduce the risk of death, Electrocardiogram (ECG) [ 4 ], Phonocardiogram (PCG) [ 5 ], Magnetic Resonance Angiography (MRA) [ 6 ], Echocardiography [ 7 ], X-ray angiography [ 8 ] and 3D Holography [ 9 ] are effective detection approaches for heart disease in the early stages. Arrhythmia classification detection based on ECG databases has been the focus of research in recent years.…”