Four machine learning inversion algorithms with different material spatial property assumptions (trained on simulated data with homogeneous, piecewise constant, smooth, or piecewise smoothly varying material properties) were evaluated in a brain simulating phantom with stiff inclusions, a test-retest repeatability dataset, and an Alzheimer’s disease dataset. The piecewise smooth inversion produced the highest contrast to noise ratio and allowed the best visualization of inclusions in the phantom study. All four inversions produced stiffness estimates that were repeatable and sensitive to stiffness changes in Alzheimer’s disease.
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