Kinematic motion representation in Cine-MRI to support cardiac disease classification

ABSTRACT

Cine-MRI sequences provide detailed information about anatomical and movement of heart, covering one full periodic cycle, which result fundamental to support diagnosis and follow personalised treatments. From such sequences, expert cardiologists can estimate cardiac performance index manually delineating shapes, and evaluating temporal geometrical changes. This patterns nevertheless are subject to proper manual delineations of ventricles and restrict the analysis to standard dynamic index, losing sight-hidden dynamic relationships that could be related with certain cardiac diseases. This work introduces a motion cardiac descriptor that fully describes kinematic heart patterns computed from local velocity fields, along the cycle. Firstly, velocity field is recovered among consecutive basal slices, which thereafter are characterised with differential kinematics, such as velocity, acceleration, divergence, and among others. Then, a regional multiscale partition allows to recover regional motion patterns, coding incidence motion measures as kinematic occurrence histograms. The set of regional motion patterns form a motion descriptor that fully describes heart dynamic and allows to automatically classify cardiac pathologies. The motion descriptor was evaluated over two different datasets, achieving averages accuracies of $80.58\mathrm{\%}$ (45 cases, 4 conditions) and $75.23\mathrm{\%}$ (100 cases, 5 conditions) mapped to a Random Forest Classifier, and over a set of Cine-MRI volumes achieved an average accuracy of $80.58\mathrm{\%}$ among four pathologies.

KEYWORDS:

• Cine-MRI
• motion patterns
• kinematic features
• multi-scale analysis
• cardiac disease classification

Acknowledgments

Additionally, grateful acknowledgments to the Vicerrectoría de Investigación y Extensión of the Universidad Industrial de Santander for supporting this research registered by the project: Predicción de patologías cardíacas utilizando representaciones de aprendizaje profundo en secuencias de resonancia magnética cardíaca (CMR), with SIVIE code 2703.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the Universidad Industrial de Santander [2703].