Amide Proton Transfer (APT) imaging in tumor with a machine learning approach using partially synthetic data.

ABSTRACT:

Purpose

Machine learning (ML) has been increasingly used to quantify chemical exchange saturation transfer (CEST) effect. ML models are typically trained using either measured data or fully simulated data. However, training with measured data often lacks sufficient training data, while training with fully simulated data may introduce bias due to limited simulations pools. This study introduces a new platform that combines simulated and measured components to generate partially synthetic CEST data, and to evaluate its feasibility for training ML models to predict amide proton transfer (APT) effect.

Methods

Partially synthetic CEST signals were created using an inverse summation of APT effects from simulations and the other components from measurements. Training data were gene

SUBMITTER: Viswanathan M 

PROVIDER: S-EPMC10635304 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature