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FusionNet: a frame interpolation network for 4D heart models

Chujie Chang, Shoko Miyauchi, Ken'ichi Morooka, Ryo Kurazume, Oscar Martinez Mozos

TL;DR

This work proposes 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 and shows that it can recover shapes more precisely than existing methods.

Abstract

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

FusionNet: a frame interpolation network for 4D heart models

TL;DR

This work proposes 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 and shows that it can recover shapes more precisely than existing methods.

Abstract

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
Paper Structure (9 sections, 3 figures, 1 table)

This paper contains 9 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Dataset
  3. FusionNet architecture
  4. Experiment and discussion
  5. Comparison with existing methods
  6. Ablation study
  7. Robustness to changes in frame intervals
  8. Conclusion
  9. Acknowledgements

Figures (3)

  • Figure 1: Overview of our FusionNet.
  • Figure 2: Cross-section of the generated voxel model corresponding to the estimated frame (e.g. frames 2 and 8) and the difference image between the model and the ground truth. The last column, WP, indicates the number of white pixels in the difference image. The results of our FusionNet contain the lower values.
  • Figure 3: (a) Average volume change in ground truth 4D cardiac models. (b) Relationship between the average Dice coefficients and the input frame interval for FusionNet and the three existing methods (* p < .05).