Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation

In this work, we develop a neural network-based method to convert a noisy motion signal generated from segmenting cardiac SPECT images, to that of a high-quality surrogate signal, such as those seen from external motion tracking systems (EMTs). This synthetic surrogate will be used as input to our pre-existing motion correction technique developed for EMT surrogate signals. In our method, we test two families of neural networks to perform signal-to-signal translation (noisy internal motion to external surrogate): 1) fully connected networks and 2) convolutional neural networks. Our dataset consists of cardiac perfusion SPECT acquisitions for which cardiac motion was estimated (input: COM signals) in conjunction with a respiratory surrogate motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT signals). We obtain an r-score of 0.74 between the predicted surrogate and the EMT signal and our goal is to lay a foundation to guide the optimization of neural networks for respiratory motion correction from SPECT without the need for an EMT.