Keywords: Cartilage, MSK
Motivation: Quantitative MRI (qMRI) studies of cartilage regions need both regional segmentation and pixel-wise fitting analysis, which can be time-consuming and subject to inter-individual variability.
Goal(s): To design a deep neural network for simultaneous qMRI mapping and accurate tissue segmentation.
Approach: By leveraging different scan sequences, we proposed a RMQ-net with Uncertainty-awareness(UA) module, or UA-QMR-net. A majority-voting strategy was applied for robust cartilage segmentation and accelerated qMRI analysis.
Results: The results demonstrated that the UA-RMQ-net achieved higher performance than the original RMQ-net for both UTE-T1 and UTE-T1r analyses of articular cartilage.
Impact: By leveraging information from different scan sequences, the proposed UA-RMQ-net could obtain higher performance for accelerated qMRI analysis.
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