Multi-centre heterogeneous imaging datasets are frequently required to develop image-based computer-aided diagnosis and treatment monitoring tools. However, these datasets may present large underlying variability, potentially impacting the performance of the developed tools. Here, as a proof-of-concept, we propose a machine learning method to study scan variability related to the scanner vendor and magnetic field strength in brain MR images from two cohorts of healthy subjects. Our model has high accuracy rates (>92%), confirming the presence of scan variability in heterogeneous, multi-centre datasets. This model may be further incorporated into automated diagnostic tools, potentially allowing more reliable and robust results.
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