Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Calibration Curves: Linear Least Squares01:20

Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
Calibration Curves: Correlation Coefficient01:10

Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Learning-based non-linear registration robust to MRI-sequence contrast.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Longitudinal FreeSurfer with non-linear subject-specific template improves sensitivity to cortical thinning.

Proceedings of the International Society for Magnetic Resonance in Medicine ... Scientific Meeting and Exhibition. International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition·2026
Same author

Inflammation Associated With Obesity, Aging, and Amyloid Burden in Adults With Down Syndrome.

Obesity (Silver Spring, Md.)·2026
Same author

Structural connectome analysis using a graph-based deep model for prediction of non-imaging variables.

Frontiers in neuroscience·2026
Same author

DEEP-LEARNING CORTICAL REGISTRATION GUIDED BY STRUCTURAL AND DIFFUSION MRI AND CONNECTIVITY.

Proceedings. IEEE International Symposium on Biomedical Imaging·2026
Same author

Selective Cingulum Degeneration in Huntington's Disease: A Clinically Relevant Event.

Movement disorders : official journal of the Movement Disorder Society·2026
Same journal

Segmentation of the parasagittal dura mater on multi-center 3D-FLAIR MRI.

NeuroImage·2026
Same journal

Spatial frequency channels implement a mental ruler in spatial vision.

NeuroImage·2026
Same journal

Exploring the Link Between Intravoxel Incoherent Motion Measured Brain Diffusivity During Wakefulness and Sleep Macrostructure in the Elderly.

NeuroImage·2026
Same journal

Closed-loop adaptation of transcranial magnetic stimulation intensity with electroencephalography feedback.

NeuroImage·2026
Same journal

Volumetric postmortem MRI of the medial temporal lobe in Alzheimer's disease and related disorders: methodological advances and implications for in vivo biomarker development.

NeuroImage·2026
Same journal

Neural responses to equity and inequity when receiving vicarious rewards for self and charity during adolescence.

NeuroImage·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 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

Highly accurate inverse consistent registration: a robust approach.

Martin Reuter1, H Diana Rosas, Bruce Fischl

  • 1Massachusetts General Hospital/Harvard Medical School, Boston, MA, USA. mreuter@nmr.mgh.harvard.ed

Neuroimage
|July 20, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a robust image registration method using statistics to accurately align brain images, even with anatomical changes or distortions. The technique improves accuracy by ignoring outlier regions, outperforming existing tools.

More Related Videos

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Related Experiment Videos

Last Updated: Jun 10, 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

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
02:09

Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function

Published on: April 12, 2024

Area of Science:

  • Computational neuroimaging
  • Medical image analysis
  • Computer vision

Background:

  • Accurate image registration is crucial for computational neuroimaging, particularly for segmenting brain structures and quantifying changes over time.
  • Existing methods struggle with variations like jaw movement, MR distortions, and anatomical changes, impacting longitudinal studies and motion correction.
  • There is a need for robust registration techniques that can handle image differences and outliers effectively.

Purpose of the Study:

  • To present a novel image registration method based on robust statistics.
  • To address the challenges of image differences, intensity scaling, and outlier regions in neuroimaging.
  • To ensure inverse consistency and automatic sensitivity parameter estimation for outlier detection.

Main Methods:

  • The proposed method utilizes robust statistics, inspired by Nestares and Heeger (2000).
  • It incorporates a sensitivity parameter to automatically detect and ignore outlier regions.
  • The approach ensures inverse consistency (symmetry) and handles varying intensity scales.

Main Results:

  • The developed registration method achieves high accuracy by effectively ignoring outlier regions.
  • It demonstrates superior robustness against noise, intensity scaling, and outliers compared to state-of-the-art tools like FLIRT and SPM.
  • The method guarantees inverse consistency and automatic outlier detection.

Conclusions:

  • The robust statistics-based image registration method offers enhanced accuracy and reliability for computational neuroimaging.
  • It provides a significant improvement over current registration tools, particularly in the presence of image artifacts and anatomical variations.
  • This technique is valuable for motion correction and analyzing longitudinal neuroimaging studies.