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Abstract #4690

Accelerating low-distortion diffusion MRI of the head and neck with transfer learning from a different organ, acquisition and scanner

Or Alus1, Victoria Yu1, Ricardo Otazo1,2, and Eric Aliotta1
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, 2Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Transfer learning, Head and neck

Motivation: Propeller and multi shot EPI sequences offer DWI head and neck images with reduced distortions, but suffer from longer scanning time and reduced SNR.

Goal(s): Shorten the scan time while improving SNR and without impacting ADC values.

Approach: Apply transfer learning to retrain denoising deep learning algorithm pretrained on different anatomy and vendor scanner, and using limited amount of data.

Results: Retraining has improved quantitative metrics, resulting in higher quality denoised images using a limited dataset of 5 cases.

Impact: Offsett extra time needed by distortion-reducing protocols to enhance the efficiency of MRI for cancer applications.

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Keywords