Conventional MR images and pseudo-CT’s (pCT’s) generated using state-of-the-art machine learning techniques poorly characterize bone anatomies, preventing applicability for orthopedic applications. We hypothesize that smart use of several specific MR contrasts will expose the information needed for diagnostic quality bone visualization. We designed a patch-based convolutional neural network taking groups of different MR contrasts -which were obtained from a single multi-gradient sequence- as inputs . It generated competitive pCT scans, capturing local anatomical variances present in the dataset. We show that Dixon reconstructed inputs appear to generate better soft-tissue visualization, while complex-valued data show promising results in bone reconstruction.
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