ArticleMagnetic resonance in medicine2022
Estimation of the capillary level input function for dynamic contrast-enhanced MRI of the breast using a deep learning approach.
Article in Magnetic resonance in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A deep learning framework to stratify Nottingham histologic grade 2 breast tumors based on dynamic contrast-enhanced MRI.European radiology · 2026Article
- DCE-MRI of the liver with sub-second temporal resolution using GRASP-Pro with navi-stack-of-stars sampling.NMR in biomedicine · 2024Article
- Improving measurement of blood-brain barrier permeability with reduced scan time using deep-learning-derived capillary input function.NeuroImage · 2023Article
- Feasibility of measuring blood-brain barrier permeability using ultra-short echo time radial magnetic resonance imaging.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
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Authors and funding
9 authors.
Funding
Abstract
purposeTo develop a deep learning approach to estimate the local capillary-level input function (CIF) for pharmacokinetic model analysis of DCE-MRI.
methodsA 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.
resultThe pharmacokinetic parameters estimated using the network-predicted CIF from our breast DCE data showed significant differences between the malignant and benign groups for all parameters. Testing the diagnostic performance with the estimated parameters, the conventional approach with arterial input function (AIF) showed an area under the curve (AUC) between 0.76 and 0.87, and the proposed approach with CIF demonstrated similar performance with an AUC between 0.79 and 0.81.
conclusionThis study shows the feasibility of estimating voxel-wise CIF using a deep neural network. The proposed approach could eliminate the need to measure AIF manually without compromising the diagnostic performance to detect the malignancy in the clinical setting.
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