Deep Learning-based Deformable Registration of Dynamic Contrast-Enhanced MR Images of the Kidney

James Huang, Junyu Guo, Ivan Pedrosa, Baowei Fei

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 (i.e., cortex, medulla). Successful reduction in the motion effects caused by patient respiration during image acquisition allows for improved kinetic analysis of the kidney. Original and registered images were analyzed and compared using dynamic intensity curves of the kidney compartments, target registration error of anatomical markers, image subtraction, and simple visual assessment. The proposed deep learning-based approach to correct motion effects in abdominal 3D DCE-MRI data can be applied to various kidney MR imaging applications.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage-Guided Procedures, Robotic Interventions, and Modeling
EditorsCristian A. Linte, Jeffrey H. Siewerdsen
PublisherSPIE
ISBN (Electronic)9781510649439
DOIs
StatePublished - 2022
EventMedical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12034
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2022: Image-Guided Procedures, Robotic Interventions, and Modeling
CityVirtual, Online
Period3/21/223/27/22

Keywords

  • convolutional neural network (CNN)
  • deep learning
  • Deformable image registration
  • dynamic contrast enhanced (DCE) MRI
  • kidney

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Deep Learning-based Deformable Registration of Dynamic Contrast-Enhanced MR Images of the Kidney'. Together they form a unique fingerprint.

Cite this