Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach.

ABSTRACT:

Purpose

To develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI.

Methods

A deep convolutional network was trained with numerically simulated data to estimate the CIF. The trained network was tested using simulated lesion data and used to estimate voxel-wise CIF for pharmacokinetic model analysis of breast DCE-MRI data using an abbreviated protocol from women with malignant (n = 25) and benign (n = 28) lesions. The estimated parameters were used to build a logistic regression model to detect the malignancy.

Result

The pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for a

SUBMITTER: Bae J 

PROVIDER: S-EPMC8852816 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature