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Related Concept Videos

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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A Fast and Quantitative Method for Post-translational Modification and Variant Enabled Mapping of Peptides to Genomes
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Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection.

Berkin Bilgic1, Audrey P Fan, Jonathan R Polimeni

  • 1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.

Magnetic Resonance in Medicine
|November 22, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a faster method for quantitative susceptibility mapping using total variation penalty and automatic regularization. The new approach significantly speeds up reconstruction, making online processing of susceptibility maps feasible.

Keywords:
L-curveQuantitative susceptibility mappingRegularizationTotal variation

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Area of Science:

  • Medical imaging
  • Magnetic Resonance Imaging (MRI)
  • Image reconstruction

Background:

  • Quantitative susceptibility mapping (QSM) is crucial for various neuroimaging applications.
  • Traditional QSM reconstruction methods are computationally intensive, limiting their clinical utility.
  • Accurate and efficient QSM is needed for advanced applications like functional blood oxygen level-dependent (BOLD) susceptibility mapping.

Purpose of the Study:

  • To develop a fast and efficient algorithm for quantitative susceptibility mapping (QSM).
  • To enable automatic selection of regularization parameters for QSM.
  • To facilitate edge-aware regularization in QSM.

Main Methods:

  • Implemented an accelerated L(1)-regularized susceptibility mapping algorithm using variable splitting.
  • Utilized soft thresholding and fast Fourier transforms for closed-form iteration evaluation.
  • Incorporated a magnitude signal-derived weighting mask for edge-aware regularization.

Main Results:

  • Achieved a 20-fold speedup in QSM reconstruction compared to nonlinear conjugate gradient (CG) solvers.
  • Completed a full QSM pipeline (0.6 mm isotropic resolution) in 1.2 minutes, versus 22 minutes with CG.
  • Enabled practical automatic regularization parameter estimation using the L-curve method in 13 minutes.
  • Demonstrated successful edge-aware regularization, solving complex problems 5 times faster than CG.
  • Showcased utility in functional BOLD susceptibility mapping, overcoming computational barriers.

Conclusions:

  • Online reconstruction of regularized susceptibility maps is now feasible.
  • The proposed dipole inversion method offers significant speed and efficiency improvements for QSM.
  • This advancement has the potential to broaden the application of QSM in clinical and research settings.