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Low-Dose Cardiac-Gated Spect Via a Spatiotemporal Convolutional Neural Network

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IEEE ISBI 2020 Virtual Conference April 2020
In previous studies convolutional neural networks (CNN) have been demonstrated to be effective for suppressing the elevated imaging noise in low-dose single-photon emission computed tomography (SPECT). In this study, we investigate a spatiotemporal CNN model (ST-CNN) to exploit the signal redundancy in both spatial and temporal domains among the gate frames in a cardiac-gated sequence. In the experiments, we demonstrated the proposed ST-CNN model on a set of 119 clinical acquisitions with imaging dose reduced by four times. The quantitative results show that ST-CNN can lead to further improvement in the reconstructed myocardium in terms of the overall error level and the spatial resolution of the left ventricular (LV) wall. Compared to a spatial-only CNN, STCNN decreased the mean-squared-error of the reconstructed myocardium by 21.1% and the full-width at half-maximum of the LV wall by 5.3%.
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Low-Dose Cardiac-Gated Spect Via a Spatiotemporal Convolutional Neural Network

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In previous studies convolutional neural networks (CNN) have been demonstrated to be effective for suppressing the elevated imaging noise in low-dose single-photon emission computed tomography (SPECT). In this study, we investigate a spatiotemporal CNN model (ST-CNN) to exploit the signal redundancy in both spatial and temporal domains among the gate frames in a cardiac-gated sequence. In the experiments, we demonstrated the proposed ST-CNN model on a set of 119 clinical acquisitions with imaging dose reduced by four times. The quantitative results show that ST-CNN can lead to further improvement in the reconstructed myocardium in terms of the overall error level and the spatial resolution of the left ventricular (LV) wall. Compared to a spatial-only CNN, STCNN decreased the mean-squared-error of the reconstructed myocardium by 21.1% and the full-width at half-maximum of the LV wall by 5.3%.