Keywords: Diagnosis/Prediction, Parkinson's Disease
Motivation: Image registration is a fundamental technique widely used in medical image analysis. However, the ANTs-based registration method has a limitation in terms of long processing time, which may make it difficult to apply in practice.
Goal(s): To address this limitation, we changed the registration method used in Heuron IPD, a commercialized Parkinson's disease diagnostic tool, from ANTs-based method to a deep learning-based method.
Approach: Specifically, we used TransMorph, an unsupervised medical image registration model and evaluated its performance on prospectively collected data.
Results: The execution time of registration process was reduced while maintaining diagnostic performance of Heuron IPD.
Impact: By changing the ANTs-based SyN registration method in Heuron IPD with TransMorph, a deep learning-based model, we reduced the registration processing time by approximately 32 times while maintaining the diagnostic performance of Heuron IPD.
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.
Keywords