Functional lung imaging biomarkers, such as the ventilated defect percentage, are computed from segmentations derived from spatially co-registered functional hyperpolarized gas MRI and structural 1H-MRI. Although hyperpolarized gas and 1H-MRI can be acquired in the same or similar breaths, the acquired scans are frequently misaligned. Here, we propose a multi-channel deep learning-based approach to generate accurate lung cavity estimations (LCEs). Across all evaluation metrics, the multi-channel approach significantly outperformed single-channel approaches and generated plausible LCEs across a wide range of pulmonary pathologies. In addition, correlation and Bland-Altman analyses of lung volumes demonstrated strong correlation and minimal bias with expert LCEs.
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