Keywords: Analysis/Processing, CEST / APT / NOE, Segmentation, CEST, Signature
Motivation: The segmentation of brain tissues - white matter, gray matter, cerebrospinal fluid - and tumor regions based on metabolic maps is crucial for the diagnosis, treatment planning and monitoring of neurological diseases.
Goal(s): Segmentation of white matter, gray matter, cerebrospinal fluid and tumor regions in the human brain using CEST-MRI signatures.
Approach: A voxel-wise deep learning network that performs segmentation using the B1B0-corrected acquired CEST spectrum and CEST maps
Results: In validation studies, the network has reliably differentiated between white matter, gray matter, CSF and tumor regions in different patient datasets and proven its robustness and consistency in various clinical use cases
Impact: Segmentation based on CEST signatures improves the delineation of brain tissue and identification of tumors, enabling better clinical decision-making. The approach improves neuroimaging techniques by using biochemical contrasts and can improve the results in the diagnosis of malignant brain areas.
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