Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Brain Iron in Nucleus Accumbens and Cognitive Function in Preeclampsia.

Hypertension (Dallas, Tex. : 1979)·2026

Same author

Arterial spin labeling cerebral blood flow quantification from quantitative transport mapping based on multiscale fluid mechanics simulation and deep learning.

IEEE transactions on bio-medical engineering·2026

Same author

QSM vs dopamine transporter PET: χ+ Mapping Shows Stronger Longitudinal Associations with Nigral Degeneration than Conventional QSM in Parkinson's Disease.

AJNR. American journal of neuroradiology·2026

Same author

Cerebral blood flow-iron susceptibility decoupling links cognition, amyloid pathology, and neuroinflammation in Alzheimer's disease.

Journal of Alzheimer's disease : JAD·2026

Same author

MRI Quantification of Liver Fibrosis Using Diamagnetic Susceptibility: An Ex Vivo Validation Study.

Tomography (Ann Arbor, Mich.)·2026

Same author

Validation of quantitative transport mapping (QTM) with an ex vivo perfused liver model.

Magnetic resonance in medicine·2026

Same journal

Topological skeleton analysis for network-based shape representation in biology and beyond.

iScience·2026

Same journal

Condition-specific neural signatures of reactivation during post-retrieval rest: An EEG study.

iScience·2026

Same journal

Multi-chaotic signal identification employing a causal cross-correlation neural network.

iScience·2026

Same journal

Repeated insertions at positions 261-280 in KPC-2 highlight a ceftazidime-avibactam resistance hotspot.

iScience·2026

Same journal

ROS inhibits microtubule dynamics and cell growth heterogeneity during Arabidopsis sepal morphogenesis.

iScience·2026

Same journal

Type 1 diabetes alters early macrophage-<i>Mycobacterium tuberculosis</i> transcriptional coordination during infection.

iScience·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Dec 5, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Spatially Adaptive Regularization in Total Field Inversion for Quantitative Susceptibility Mapping.

Priya S Balasubramanian^1,2, Pascal Spincemaille², Lingfei Guo²

¹Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA.

|October 21, 2020

Summary

This summary is machine-generated.

A new Adaptive Total Field Inversion (ATFI) method reduces artifacts in quantitative susceptibility mapping (QSM). ATFI improves accuracy in phantom and patient data, offering better shadow reduction for clearer medical imaging.

Keywords:

Algorithms Magnetism Nuclear Magnetic Resonance Physics Magnetic Resonance Imaging

More Related Videos

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Related Experiment Videos

Last Updated: Dec 5, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Area of Science:

Medical Imaging
Biophysics
Computational Science

Background:

Quantitative susceptibility mapping (QSM) is crucial for neuroimaging.
Existing QSM methods struggle with artifacts like "shadows" and require accurate reconstruction from total field data.
Spatiotemporal artifacts in QSM can obscure important pathological information.

Purpose of the Study:

To introduce and evaluate an Adaptive Total Field Inversion (ATFI) algorithm for improved QSM reconstruction.
To demonstrate ATFI's effectiveness in suppressing shadow artifacts using spatially adaptive regularization.
To compare ATFI's performance against conventional and existing regularized methods.

Main Methods:

Developed ATFI algorithm incorporating spatially adaptive regularization.
Regularization penalizes low-frequency susceptibility components in regions with low R2*-derived signal intensity.
Evaluated ATFI on numerical, phantom (gadolinium), COSMOS, and patient datasets (with and without hemorrhages).

Main Results:

ATFI demonstrated the lowest error in numerical and gadolinium phantom datasets.
The algorithm (TFIR) showed good agreement with ground truth in high-susceptibility regions in COSMOS data.
In patient data, ATFI closely matched the reference local field method in quality and outperformed other total field techniques in clinical scores and shadow reduction.

Conclusions:

ATFI offers a significant improvement for quantitative susceptibility mapping.
The method effectively suppresses shadow artifacts, enhancing image clarity and diagnostic potential.
ATFI shows promise for clinical applications, particularly in the presence of pathologies like hemorrhages.