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XeMRI to CT lung image registration enhanced with personalized 4DCT-derived motion model

  • Adam Szmul
  • , Tahreema Matin
  • , Fergus V. Gleeson
  • , Julia A. Schnabel
  • , Vicente Grau
  • , Bartłomiej W. Papież
  • University of Oxford
  • University of Oxford Medical Sciences Division
  • Oxford University Hospitals NHS Trust
  • King's College London

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

This paper presents a novel method for multi-modal lung image registration constrained by a motion model derived from lung 4DCT. The motion model is estimated based on the results of intra-patient image registration using Principal Component Analysis. The approach with a prior motion model is particularly important for regions where there is not enough information to reliably drive the registration process, as in the case of hyperpolarized Xenon MRI and proton density MRI to CT registration. Simultaneously, the method addresses local variations between images in the supervoxel-based motion model parameters optimization step. We compare our results in terms of the plausibility of the estimated deformations and correlation coefficient with 4DCT-based estimated ventilation maps using state-of-the-art multi-modal image registration methods. Our method achieves higher average correlation scores, showing that the application of Principal Component Analysis-based motion model in the deformable registration, helps to drive the registration for the regions of the lungs with insufficient amount of information.

Original languageEnglish
Title of host publicationImage Analysis for Moving Organ, Breast, and Thoracic Images - Third International Workshop, RAMBO 2018, Fourth International Workshop, BIA 2018, and First International Workshop, TIA 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsDavid Snead, Emanuele Trucco, Danail Stoyanov, Zeike Taylor, Lena Maier-Hein, Nasir Rajpoot, Hrvoje Bogunovic, Francesco Ciompi, Mitko Veta, Mona K. Garvin, Xin Jan Chen, Anne Martel, Jeroen van der Laak, Yanwu Xu, Stephen McKenna
PublisherSpringer Verlag
Pages260-271
Number of pages12
ISBN (Print)9783030009458
DOIs
StatePublished - 2018
Externally publishedYes
Event3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International Workshop on Breast Image Analysis, BIA 2018, and 1st International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: 16 Sep 201820 Sep 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11040 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2018, 4th International Workshop on Breast Image Analysis, BIA 2018, and 1st International Workshop on Thoracic Image Analysis, TIA 2018, held in conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
Country/TerritorySpain
CityGranada
Period16/09/1820/09/18

Keywords

  • Lung 4D CT
  • Lung motion model
  • Multi-modal image registration
  • Ventilation estimation
  • XeMRI

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