Cardiac magnetic resonance (CMR) sequences visualise the cardiac function
voxel-wise over time. Simultaneously, deep learning-based deformable image
registration is able to estimate discrete vector fields which warp one time
step of a CMR sequence to the following in a self-supervised manner. However,
despite the rich source of information included in these 3D+t vector fields, a
standardised interpretation is challenging and the clinical applications remain
limited so far. In this work, we show how to efficiently use a deformable
vector field to describe the underlying dynamic process of a cardiac cycle in
form of a derived 1D motion descriptor. Additionally, based on the expected
cardiovascular physiological properties of a contracting or relaxing ventricle,
we define a set of rules that enables the identification of five cardiovascular
phases including the end-systole (ES) and end-diastole (ED) without the usage
of labels. We evaluate the plausibility of the motion descriptor on two
challenging multi-disease, -center, -scanner short-axis CMR datasets. First, by
reporting quantitative measures such as the periodic frame difference for the
extracted phases. Second, by comparing qualitatively the general pattern when
we temporally resample and align the motion descriptors of all instances across
both datasets. The average periodic frame difference for the ED, ES key phases
of our approach is 0.80±0.85, 0.69±0.79 which is slightly better
than the inter-observer variability (1.07±0.86, 0.91±1.6) and the
supervised baseline method (1.18±1.91, 1.21±1.78). Code and labels
will be made available on our GitHub repository.
https://github.com/Cardio-AI/cmr-phase-detectionComment: accepted for the STACOM2022 workshop @ MICCAI202