Article

Flexible and Cost-Effective Deep Learning for Fast Multi-Parametric Relaxometry using Phase-Cycled bSSFP

2024-03-21

Abstract excerpt

<title>Abstract</title> <p>To accelerate the clinical adoption of quantitative magnetic resonance imaging (qMRI), frameworks are needed that not only allow for rapid acquisition, but also flexibility, cost-efficiency, and high accuracy in parameter mapping. In this study, feed-forward deep neural network (DNN)- and iterative fitting-based frameworks are compared for multi-parametric (MP) relaxometry based on phas...

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Literature Corpus work: ef835ea6-231f-5b47-9b8d-5612feae42e5
DOI: 10.21203/rs.3.rs-4049684/v1

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