Meeting Banner
Abstract #5031

Resilience of synthetic CT DL network to varying ZTE-MRI input SNR

Sandeep Kaushik1,2, Cristina Cozzini1, Mikael Bylund3, Steven Petit4, Bjoern Menze2, and Florian Wiesinger1
1GE Healthcare, Munich, Germany, 2Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland, 3Umeå University, Umeå, Sweden, 4Erasmus MC Cancer Institute, Rotterdam, Netherlands

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Radiotherapy, MR-only RT, Synthetic CT, Multi-task CNN, PET/MRMany recent works have proposed methods to convert MRI into synthetic CT (sCT). While they have demonstrated a certain level of accuracy, not many have studied the robustness of those methods. In this work, we study the robustness of a multi-task deep learning (DL) model that computes sCT images from fast ZTE MR images under different levels of image noise. We evaluate its impact on radiation therapy planning. The proposed method demonstrates resilience against input noise variations. It makes way for a clinically acceptable dose calculation with a fast input image acquisition.

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.

Keywords