ArticleProceedings of SPIE--the International Society for Optical Engineering
Deep Learning-based Deformable Registration of Dynamic Contrast-Enhanced MR Images of the Kidney.
Article in Proceedings of SPIE--the International Society for Optical Engineering. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence-Based Arterial Input Function for the Quantitative Assessment of Myocardial Blood Flow and Perfusion Reserve in Cardiac Magnetic Resonance: A Validation Study.Diagnostics (Basel, Switzerland) · 2025Article
- L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.Biomedical engineering letters · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
Respiratory motion is a major contributor to bias in quantitative analysis of magnetic resonance imaging (MRI) acquisitions. Deformable registration of three-dimensional (3D) dynamic contrast-enhanced (DCE) MRI data improves estimation of kidney kinetic parameters. In this study, we proposed a deep learning approach with two steps: a convolutional neural network (CNN) based affine registration network, followed by a U-Net trained for deformable registration between two MR images. The proposed registration method was applied successively across consecutive dynamic phases of the 3D DCE-MRI dataset to reduce motion effects in the different kidney compartments (
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Registered trials
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