The heterogeneity in myofiber helicity across the cardiac wall causes twisting (torsion) in the left ventricle (LV) during contraction, which is a significant contributor to its pumping function. Although important progress has been made in identifying and studying a quantitative “global” metric for torsion, four-dimensional (4D) torsion characteristics in the LV remain underexplored. We propose an imaging-based framework that uses myocardial motion obtained from cine cardiac magnetic resonance (CMR) scans of the LV to calculate torsion. We performed our LV torsion analysis in a mitral valve prolapse (MVP) human patient (n=1) pre- and post-MV repair. Establishing a high-fidelity torsion measure in the LV offers a rigorous regional marker to investigate the potential reversal of cardiac remodeling post-surgical interventions, such as MV repair. After image acquisition, a non-rigid image registration algorithm was used to calculate 4D LV displacements. Twisting was evaluated through (i) an in-plane rotation-based approximation (referred to as T2D) and (ii) a three-dimensional formulation involving in-plane and through-plane strains (referred to as T3D). Comparing T2D to T3D, broad regions of positive (counter-clockwise) rotation, captured through T3D, were unrepresented by T2D despite a qualitative agreement between the two metrics in capturing the average regional torsion. Also, the presence of comprehensive transmural positive torsion at the apical section was opposed to negative epicardial torsion at the midsection. Such variations in torsional behavior, captured by T3D, are expected due to transmural helicity in fiber orientation. Overall, the underlying effects of through-plane shear in characterizing LV torsion were evidenced, and the image registration framework offered a comprehensive tool to capture 4D myocardial torsion that can complement conventional LV global markers.
Cardiac gating or breath-hold MRI acquisition is challenging. In particular, data collected in a short amount of time might be insufficient for the diagnosis of patients with impaired breath-holding capabilities and/or arrhythmia. A major challenge in cardiac MRI is the motion of the heart itself, the pulsate blood flow, and the respiratory motion. Furthermore, the motion of the diaphragm in the chest moving up and down gets translated to the heart when a patient breathes. Therefore, artifacts arise due to the changes in signal intensity or phase as a function of time, resulting in blurry images. This paper describes a novel reconstruction strategy for real time cardiac MRI without requiring the use of an electro-cardiogram or of breath holding. In this research we focused on automation and evaluation of the performance of our proposed method in real time MRI data to ensure a good basis for the signal extraction. Hence, it assists in the reconstruction. The proposed method enables one to extract cardiac beating waveforms directly from real-time cardiac MRI series collected from freely breathing patients and without cardiac gating. Our method only requires minimal user involvement as initialization step. Thereafter, the method follows the registered area in every frame and updates itself.
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