Implicit Neural Representations for Registration of Left Ventricle Myocardium During a Cardiac Cycle
This provides a more efficient method for cardiac function assessment, though it is incremental as it adapts existing INR techniques to a specific medical imaging domain.
The study tackled the problem of modeling left ventricle myocardium movement during the cardiac cycle by extending implicit neural representations for deformable image registration to cardiac CT, achieving high registration accuracy.
Understanding the movement of the left ventricle myocardium (LVmyo) during the cardiac cycle is essential for assessing cardiac function. One way to model this movement is through a series of deformable image registrations (DIRs) of the LVmyo. Traditional deep learning methods for DIRs, such as those based on convolutional neural networks, often require substantial memory and computational resources. In contrast, implicit neural representations (INRs) offer an efficient approach by operating on any number of continuous points. This study extends the use of INRs for DIR to cardiac computed tomography (CT), focusing on LVmyo registration. To enhance the precision of the registration around the LVmyo, we incorporate the signed distance field of the LVmyo with the Hounsfield Unit values from the CT frames. This guides the registration of the LVmyo, while keeping the tissue information from the CT frames. Our framework demonstrates high registration accuracy and provides a robust method for temporal registration that facilitates further analysis of LVmyo motion.