Abstract #1776

Higher Resolution with Improved Image Quality without Increased Scan Time: Is it possible with MRI Deep Learning Reconstruction?

Hung Do¹, Carly Lockard², Dawn Berkeley¹, Brian Tymkiw¹, Nathan Dulude³, Scott Tashman², Garry Gold⁴, Erin Kelly¹, and Charles Ho²

¹Canon Medical Systems USA, Inc., Tustin, CA, United States, ²Steadman Philippon Research Institute, Vail, CO, United States, ³The Steadman Clinic, Vail, CO, United States, ⁴Stanford University, Stanford, CA, United States

In magnetic resonance imaging (MRI), increased resolution leads to increased scan time and reduced signal-to-noise ratio (SNR). Parallel imaging (PI) can be used to mitigate the increased scan time but comes with an additional penalty in SNR resulting in reduced image quality. Deep Learning Reconstruction (DLR) has recently been developed to intelligently remove noise from low SNR input images producing increased SNR and quality output images. SNR gain from DLR could be used to increase resolution while maintaining scan time. This work demonstrates that DLR could be used to increase resolution and image quality without increased scan time.

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