The presented work introduces a deep-learning cardiac MRI approach to quantitative assessment of ventricular volumes from raw MRI data without image reconstruction. As the information required for volumetric measurements is less than that for image reconstruction, ventricular function may be assessed with less MRI data than conventional image-based methods. This offers the potential to improve temporal resolution for quantitatively imaging cardiac function.
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