1354796 - Space-Time Deep Learning for Automated Analysis of Free-breathing CMR Perfusion Datasets Enabled by Synthesized Nonrigid Motion Augmentation

The application of machine learning to CMR analysis has experienced rapid growth owing to advancements involving deep neural networks (DNNs). In particular, new approaches using convolutional neural networks have been developed for automated analysis of cardiac function and T1 mapping [1,2] as well as perfusion [3]. Nearly all perfusion analysis techniques rely on accurate nonrigid motion correction (MoCo) to align time frames in a stress/rest perfusion image series. However, perfect MoCo is not always achievable especially in stress studies that involve arrhythmias or have ECG miss-triggering. Herein we introduce two novel data-augmentation approaches that simulate nonrigid motion in free-breathing CMR perfusion images, yielding a highly robust DNN that can directly analyze free-breathing perfusion CMR without the need for MoCo.

Methods:

A 2D UNet is trained to localize a region of interest in a free-breathing perfusion image series which is passed forward to a 3D UNet trained to jointly segment the myocardium across all time frames. In addition to conventional data augmentation, 2 new types of augmentation are proposed in training the networks: (1) we randomly generate smooth signals that simulate realistic motion patterns in first-pass perfusion series to dynamically control the scaling rate as well as vertical/horizontal shifting for each frame in the augmented slices; (2) synthetic ECG miss triggering errors are generated by using 2 short-axis slices from the same perfusion series and randomly swapping systolic and diastolic frames between the 2 slices.

Results: A 6-segment myocardial blood flow (MBF) quantification comparison between our proposed approach and the MoCo-derived ground truth demonstrates a high level of agreement between the two (R = 0.911, p< 10^-5). In comparison, a naïve free-breathing segmentation model trained with the same data using only conventional augmentation had a significantly agreement vs. MoCo-derived MBF (R = 0.806, p< 10^-5). Fig 3 shows example results comparing the DNN trained using our proposed approach vs. the naïve approach.

Conclusion: We presented two novel augmentation techniques to vastly improve free-breathing segmentation performance in CMR stress/rest perfusion datasets. Our results demonstrate a significant improvement in accuracy of MBF quantification (correlation vs. motion-corrected ground truth). The proposed approach has the potential to eliminate the need for MoCo in DNN-based analysis of perfusion CMR datasets.