Background: A cardiac resting phase is used when performing free-breathing cardiac magnetic resonance examinations.
Purpose: The purpose of this study was to test a cardiac resting phase detection system based on neural networks in clinical practice.
Material and methods: Four chamber-view cine images were obtained from 32 patients and analyzed. The rest duration, start point, and end point were compared between that determined by the experts and general operators, and a similar comparison was done between that determined by the experts and neural networks: the normalized root-mean-square error (RMSE) was also calculated.
Results: Unlike manual detection, the neural network was able to determine the resting phase almost simultaneously as the image was obtained. The rest duration and start point were not significantly different between the neural network and expert (p = .30, .90, respectively), whereas the end point was significantly different between the two groups (p < .05). The start point was not significantly different between the general operator and expert (p = .09), whereas the rest duration and end point were significantly different between the two groups (p < .05). The normalized RMSEs of the rest duration, start point, and end point of the neural network were 0.88, 0.64, and 0.33 ms, respectively, which were lower than those of the general operator (normalized RMSE values were 0.98, 0.68, and 0.51 ms, respectively).
Conclusions: The neural network can determine the resting phase instantly with better accuracy than the manual detection of general operators.
Keywords: cardiac resting phase; deep learning; magnetic resonance image; neural network.
© The Author(s) 2022.