Abstract #3883

Evaluation of deep learning-based reconstruction for qualitative and quantitative DW-MRI in head and neck cancers

Ramesh Paudyal¹, Akash Deelip Shah², Amaresha Shridhar Konar¹, Jaemin Shin³, Eve LoCastro¹, Nisha Bagchi⁴, Maggie Fung³, Suchandrima Banerjee⁵, Nancy Lee⁶, and Amita Shukla-Dave^1,2

¹Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ²Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States, ³GE Health Care, New York, NY, United States, ⁴Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States, ⁵GE Health Care, Menlo Park, CA, United States, ⁶Radiation Oncology, Memorial Sloan Kettering Cancer Center, New York, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, TumorThe head and neck (HN) region have complex anatomical structures that affect the image quality of diffusion-weighted MRI. Therefore deep learning (DL)-based Reconstruction (Recon) for DW-MRI could be a promising method that can help improve image sharpness and signal-to-noise ratio (SNR) without increasing signal averaging. The present study aimed to evaluate the performance of qualitative and quantitative multiple b-value DW-MRI powered by DL-based Recon for tumors in the HN region. The DL-based recon method improved the DW image quality and SNR compared to those without DL recon.

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