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Abstract #0136

Improving the reliability of pharmacokinetic parameters in dynamic contrast-enhanced MRI in gliomas: Deep learning approach

Kyu Sung Choi1, Sung-Hye You2, Yoseob Han1, Jong Chul Ye1, Seung Hong Choi3, and Bumseok Jeong1
1Korea Advanced Institute for Science and Technology, Daejeon, Korea, Republic of, 2Korea University College of Medicine, Seoul, Korea, Republic of, 3Seoul National University Hospital, Seoul, Korea, Republic of

AIFDCE has been known to be sensitive to noise, because of the relatively weak T1 contrast-enhanced MR signal intensity (SI) compared to the T2* SI of DSC-MRI, leading to PK parameters – Ktrans, Ve, and Vp – with low reliability. In this study, we developed a neural network model generating an AIF similar to the AIF obtained from DSC-MRI – AIFgenerated DSC – and demonstrated that the accuracy and reliability of Ktrans and Ve derived from AIFgenerated DSC can be improved compared to those from AIFDCE without obtaining DSC-MRI, not leading to an additional deposition of gadolinium in the brain.

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