Abstract #5042

Use of an Automated Approach for Generating vADC for a Large Patient Population Studied with 129Xe MRI

Ramtin Babaeipour¹, Maria Mihele², Keeirah Raguram², Matthew Fox^2,3, and Alexei Ouriadov^1,2,3

¹School of Biomedical Engineering, The University of Western Ontario, London, ON, Canada, ²Department of Physics and Astronomy, The University of Western Ontario, London, ON, Canada, ³Lawson Health Research Institute, London, ON, Canada

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Segmentation, Deep learning, Transfer learningHyperpolarized ¹²⁹Xe lung MRI is an efficient technique used to investigate and assess pulmonary diseases. However, the longitudinal observation of the emphysema progression using hyperpolarized gas MRI-based ADC can be problematic, as the disease-progression can lead to increasing unventilated-lung areas, which likely excludes the largest ADC estimates. One solution to this problem is to combine static-ventilation and ADC measurements following the idea of ³He MRI ventilatory ADC (vADC). We have demonstrated this method adapted for ¹²⁹Xe MRI to help overcome the above-mentioned shortcomings and provide an accurate assessment of the emphysema progression.

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.

Click here for more information on becoming a member.