Self-supervised motion descriptor for cardiac phase detection in 4D CMR based on discrete vector field estimations

TL;DR

This paper introduces a self-supervised method to derive a 1D motion descriptor from 3D+t vector fields in cardiac MRI, enabling automatic detection of cardiac phases without labels, validated on multi-center datasets.

Contribution

It presents a novel approach to interpret deformable vector fields as a motion descriptor and identifies cardiac phases in a label-free manner, improving over supervised methods.

Findings

Achieved low periodic frame difference scores close to inter-observer variability.

Successfully identified cardiac phases without labels across diverse datasets.

Outperformed supervised baseline in phase detection accuracy.

Abstract

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 $0.80\pm{0.85}$, $0.69\pm{0.79}$ which is slightly better than the inter-observer variability ($1.07\pm{0.86}$, $0.91\pm{1.6}$) and the supervised baseline method ($1.18\pm{1.91}$, $1.21\pm{1.78}$). Code and labels will be made available on our GitHub repository. https://github.com/Cardio-AI/cmr-phase-detection