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Abstract #2218

The Effects of Deep Learning Imaging Denoising on Quantitative Diffusion Metrics of Lower Leg Muscles

Madison Kamaile George1, Marco Barbieri2, Laurel Hales2, Anoosha Pai1, Valentina Mazzoli3, and Feliks Kogan2
1Bioengineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States, 3Radiology, New York University, New York City, NY, United States

Synopsis

Keywords: Muscle, Quantitative Imaging

Motivation: Deep learning (DL) denoising reconstruction reduces noise without added scan times. However, the effects of this denoising on quantitative diffusion tensor imaging (DTI) metrics have yet to be evaluated.

Goal(s): Determine the effects of DL-denoising, across a range of signal averages (NEX), on quantitative DTI metrics in lower leg muscles.

Approach: 6 Subjects obtained a series of DTI acquisitions. Quantitative biometrics of muscle compartments were compared with and without DL-denoising.

Results: Biometric accuracy for each NEX was similar with and without DL-denoising. All coefficients of variation fell below 5%. Concordance correlation coefficients were above 0.87.

Impact: The influence of DL-denoising on quantitative diffusion tensor imaging (DTI) metrics is unknown. We found that DL-denoising in lower leg muscles does not affect quantitative DTI measures but also does not improve accuracy at lower signal averages.

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