Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation. (arXiv:2208.01034v1 [eess.IV])

In this work, we develop a neural network-based method to convert a noisy
motion signal generated from segmenting rebinned list-mode 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
translate 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:
center-of-count-mass – COM signals) in conjunction with a respiratory surrogate
motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT
signals). We obtained an average R-score of 0.76 between the predicted
surrogate and the EMT signal. 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.

Source: https://arxiv.org/abs/2208.01034


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