Abstract #3716

Ultra-Fast Submillimeter Whole Brain QSM Using Highly Accelerated 3D Echo-Planar Imaging with a Complex Convolutional Neural Network

Bryan Quah¹, Sreekanth Madhusoodhanan Nair¹, Arzu Has Silemek^1,2, Brian Renner¹, Elaina Gombos¹, Mustafa Subhi¹, Jin Jin³, Fei Han⁴, Nader Binesh⁵, Marcel Maya⁵, Debiao Li², Marwa Kaisey¹, Nancy Sicotte¹, Omar Al-Louzi¹, and Pascal Sati^1,2

¹Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ²Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, ³Siemens Healthineers, Brisbane, Australia, ⁴Siemens Healthineers, CA, United States, ⁵Department of Imaging, Cedars-Sinai Medical Center, Los Angeles, CA, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Susceptibility mapping

Motivation: To restore image quality of highly accelerated QSM for data interpretation.

Goal(s): To evaluate the feasibility of a denoising convolutional neural network for generating high-quality submillimeter isotropic QSM acquired in 2-3 minutes at 3-Tesla.

Approach: A previously developed network for denoising complex-valued MRI data was applied to accelerated 3D-EPI scans (TA: 2min and 3min). Image quality was evaluated and QSM values obtained with and without denoising were compared against reference non-accelerated non-denoised 3D-EPI (TA: 6min).

Results: SNR and structural measures demonstrated improved image quality when denoising the accelerated data. Similar QSM values were observed for both highly accelerated denoised 3D-EPI and reference 3D-EPI.

Impact: Our approach for denoising complex-valued 3D-EPI brain images shows the feasibility of producing high-quality, whole-brain, submillimeter isotropic QSM acquired in 2-3 minutes at 3-Tesla, facilitating its clinical adoption.

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