Enhancing amide proton transfer imaging in ischemic stroke using a machine learning approach with partially synthetic data.

ABSTRACT: Amide proton transfer (APT) imaging, a technique sensitive to tissue pH, holds promise in the diagnosis of ischemic stroke. Achieving accurate and rapid APT imaging is crucial for this application. However, conventional APT quantification methods either lack accuracy or are time-consuming. Machine learning (ML) has recently been recognized as a potential solution to improve APT quantification. In this paper, we applied an ML model trained on a new type of partially synthetic data, along with an optimization approach utilizing recursive feature elimination, to predict APT imaging in an animal stroke model. This partially synthetic datum is not a simple blend of measured and simulated chemical exchange saturation transfer (CEST) signals. Rather, it integrates the underlying components includ

SUBMITTER: Viswanathan M 

PROVIDER: S-EPMC11602689 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature