Deep learning enables robust quantification of cerebral blood flow using ASL in the presence of pathology: Application to treated gliomas

Motivation: The accuracy of cerebral blood flow (CBF) quantification in arterial spin labelling (ASL) may reduce in regions exhibiting pathology-induced signal abnormalities in proton density (PD) images. Goal(s): To develop an algorithm for improved CBF quantification by correcting signal abnormalities in PD images due to pathology. Approach: To correct signal abnormalities, an image-inpainting algorithm based on deep learning (DL) was developed using healthy subject data. The algorithm was demonstrated with an application to patients post tumour treatment. Results: The developed DL algorithm was able to effectively correct signal abnormalities, resulting in improved CBF maps. Impact: The improvement in CBF accuracy through DL-corrected PD images may aid clinicians in their assessment of patients. This study demonstrates the potential benefit of the proposed method in an example application of monitoring tumour recurrence post treatment with ASL.