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Related Experiment Video

Updated: May 11, 2026

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities
07:13

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Published on: October 27, 2023

LCC-Demons: a robust and accurate symmetric diffeomorphic registration algorithm.

M Lorenzi1, N Ayache2, G B Frisoni3

  • 1Asclepios Research Project, INRIA Sophia Antipolis, 2004 route des Lucioles BP 93, 06 902 Sophia Antipolis, France; LENITEM, IRCCS San Giovanni di Dio Fatebenefratelli, via Pilastroni 4, 25125 Brescia, Italy.

Neuroimage
|May 21, 2013
PubMed
Summary

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This study introduces LCC-Demons, a fast and robust non-linear registration framework for medical imaging. It accurately measures anatomical changes, proving effective for both inter-subject and intra-subject analyses, including Alzheimer's disease research.

Area of Science:

  • Medical Imaging
  • Computational Anatomy
  • Biomedical Engineering

Background:

  • Non-linear registration is crucial for computational anatomy but requires efficiency, accuracy, and robustness for clinical use.
  • Existing registration algorithms face challenges with biases in medical images.

Purpose of the Study:

  • To develop a fast, robust, and accurate non-linear registration framework for medical image analysis.
  • To evaluate the proposed framework's performance in inter-subject and intra-subject registration tasks.

Main Methods:

  • Proposed a novel framework based on the log-Demons diffeomorphic registration algorithm.
  • Utilized stationary velocity fields (SVFs) for transformation parameterization.
  • Implemented a symmetric local correlation coefficient (LCC) as the similarity metric.
Keywords:
Alzheimer's diseaseDemonsLongitudinal atrophyNon-linear registrationOptimization

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Last Updated: May 11, 2026

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Main Results:

  • The LCC-Demons framework demonstrated efficiency, flexibility, and robustness in non-linear image registration.
  • Successfully applied to inter-subject registration, comparing favorably with state-of-the-art methods.
  • Showed effectiveness in intra-subject longitudinal registration for measuring Alzheimer's disease atrophy.

Conclusions:

  • LCC-Demons is a versatile and accurate algorithm for various medical imaging applications.
  • The framework provides stable numerical schemes for evaluating spatial changes using Jacobian determinant and flux.
  • It performs well on both intra- and inter-subject registration without additional optimization.