FusionNet: a frame interpolation network for 4D heart models
This work addresses patient discomfort and diagnostic accuracy issues in heart disease diagnosis by improving temporal resolution in CMR imaging, though it is incremental as it builds on existing frame interpolation techniques for a specific domain.
The paper tackles the problem of low temporal resolution in cardiac magnetic resonance (CMR) imaging due to short scan times, which reduces diagnostic accuracy, by proposing FusionNet, a neural network that estimates intermediate 3D heart shapes to create high-temporal-resolution 4D cardiac motion models, achieving a Dice coefficient of over 0.897 and outperforming existing methods.
Cardiac magnetic resonance (CMR) imaging is widely used to visualise cardiac motion and diagnose heart disease. However, standard CMR imaging requires patients to lie still in a confined space inside a loud machine for 40-60 min, which increases patient discomfort. In addition, shorter scan times decrease either or both the temporal and spatial resolutions of cardiac motion, and thus, the diagnostic accuracy of the procedure. Of these, we focus on reduced temporal resolution and propose a neural network called FusionNet to obtain four-dimensional (4D) cardiac motion with high temporal resolution from CMR images captured in a short period of time. The model estimates intermediate 3D heart shapes based on adjacent shapes. The results of an experimental evaluation of the proposed FusionNet model showed that it achieved a performance of over 0.897 in terms of the Dice coefficient, confirming that it can recover shapes more precisely than existing methods. This code is available at: https://github.com/smiyauchi199/FusionNet.git