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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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