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Abstract #3827

A Paradigm shift in MR physics with Deep Learning Reconstruction: higher image quality and spatial resolution in shorter scan time

Mo Kadbi1, Dawn Berkeley1, Brian Tymkiw1, Hung Do1, and Erin Kelly1
1Canon Medical System USA, Tustin, CA, United States

In MR physics, there is a fundamental tradeoff between image spatial resolution, signal to noise ratio, and scan time. To acquire images with high resolution and SNR, signal averaging is the most common solution, but results in longer scan time. In this study, a Deep Learning Reconstruction method was employed to remove the noise from clinical images and improve SNR. This SNR improvement was devoted to increase the spatial resolution without the need of signal averaging and increased scan time. Hence, higher resolution images with high image quality can be obtained in shorter time in clinical practice.

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