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.

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Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): Research Support from Siemens Healthineers GmbH. Background In bSSFP sequences commonly used for cardiac MRI, signal modulation (e.g. banding artifacts) due to B0 inhomogeneity is often observed, especially at higher field strengths. The spatial position of these artifacts can be shifted by a frequency offset to reduce artifacts in a region of interest (ROI), e.g. the heart. To this end, frequency scout (FS) scans are acquired to visually select the optimal frequency offset [1,2]. In this work, we propose a fully automated image-based system for selecting the optimal frequency offset on FS images based on machine learning. Methods The proposed prototype system consists of four main steps (Fig.1). First, a pre-trained deep-learning-based whole heart segmentation network is applied on a four chamber-view FS image to localize the ROI where artifacts should be reduced. Second, high frequency components within the ROI (for each frequency offset in the FS series) are extracted by successive processing of Fourier transformation, high-pass filtering, inverse Fourier transformation and subtraction over series. and N images with the lowest high-frequency content are selected. Third, an adaptive weighting map for each FS image is generated which penalizes signal deviations from a pixel-wise median that is calculated based on the selected images [3]. By averaging the maps and selecting the frame with maximum percentage, the optimal frequency offset is selected. A total of 38 datasets, acquired on multiple clinical 3T MRI scanners (MAGNETOM Skyra, Vida, Prisma, Lumina; Siemens Healthcare, Erlangen, Germany), were used to evaluate the proposed system. All FS series were annotated manually and used to compare with the system output. The experts were allowed to select multiple possible optimal FS images within a FS series. In case of multiple annotations, the system output was labelled as correct when it selected one of the offsets chosen by the expert. Further, the generated weighting maps were visually evaluated. Results The proposed system achieved an accuracy of 92.1% compared to experts’ ground truth annotations. From the failed cases (n=3), the maximum difference was off by 2 frames. Based on the generated weighting maps, a reasonable decision on the selection of the optimal frequency offset is made. The algorithm successfully selects an FS image with minimized banding and flow artifacts within the ROI (Fig. 2a). Further, it reveals that the generated weighting map correctly suppress areas containing artifacts (Fig. 2b). Conclusions Initial results demonstrate the feasibility of the proposed system to automatically select the optimal frequency offset on FS scans. Therefore, it can improve the automation of a cardiac MRI workflow. An example of the result of each step

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