Abstract #4032

Performance Evaluation of Deep Learning-based Image Reconstruction for Head and Neck Imaging Protocol

Amaresha Shridhar Konar¹, Jaemin Shin², Ramesh Paudyal¹, Abhay Dave³, Maggie Fung², Suchandrima Banerjee⁴, Vaios Hatzoglou⁵, and Amita Shukla-Dave^1,5

¹Medical Physics, Memorial Sloan Kettering Cancer Center, New York City, NY, United States, ²GE Healthcare, New York City, NY, United States, ³Touro College of Osteopathic Medicine, New York, NY, United States, ⁴GE Healthcare, Menlo Park, CA, United States, ⁵Radiology, Memorial Sloan Kettering Cancer Center, New York City, NY, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Quantitative ImagingMRI has excellent extracranial soft-tissue contrast to detect tumors in the head and neck (HN) region. Technical challenges arise due to MRI related artifacts. In routine radiological practice, HN MR imaging protocols are optimized specifically to the subsites. We aimed to evaluate the performance of the HN imaging protocol that include qualitative T₁w, T₂w, and quantitative diffusion MRI powered by a novel deep learning (DL) based reconstruction (recon) using the ACR and QIBA diffusion phantoms. This phantom study showed that qualitative T₁w and T₂w images and multiple b-value DWI data powered with DL recon substantially improves the image quality.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.