用神经网络从短时扫描重建高帧率心脏动态模型
FusionNet: a frame interpolation network for 4D heart models
- 通过相邻心形预测中间帧，实现短时扫描下的4D心脏建模
- Dice系数超过0.897，重建精度优于现有方法
- 适合需要快速心脏成像的临床场景
心脏磁共振（CMR）成像广泛用于可视化心脏运动和诊断心脏病。然而，标准CMR检查需患者在封闭、嘈杂的机器中静躺40-60分钟，增加不适感。此外，缩短扫描时间会降低心脏运动的时空分辨率，影响诊断准确性。本文聚焦于时间分辨率下降问题，提出FusionNet神经网络，从短时间内采集的CMR图像中重建具有高时间分辨率的四维（4D）心脏运动模型。该模型基于相邻时刻的心脏三维形状，估计中间时刻的3D心形。实验评估显示，FusionNet的Dice系数超过0.897，表明其在形状恢复上优于现有方法。代码已开源：https://github.com/smiyauchi199/FusionNet.git
原文摘要 · Abstract (English)
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
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