Article

Improving Glioblastoma Classification Using Quantitative Transport Mapping with a Synthetic Data Trained Deep Neural Network

2026-04-01

Abstract excerpt

<h4>Purpose</h4> To develop a deep neural network-based, AIF-free, perfusion estimation method (QTMnet) for improved performance on glioma classification. <h4>Methods</h4> A globally defined arterial input function (AIF) is needed to recover perfusion parameters in the two-compartment exchange model (2CXM). We have developed Quantitative Transport Mapping (QTM) to create an AIF-independent estimation method. QTM...

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Literature Corpus work: 0555a5f2-e0c3-5a4e-ad16-e05596b0552d
DOI: 10.64898/2026.03.31.26349864

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