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Related Experiment Video

Updated: Oct 3, 2025

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Quantification of Mouse Heart Left Ventricular Function, Myocardial Strain, and Hemodynamic Forces by Cardiovascular Magnetic Resonance Imaging

Published on: May 24, 2021

Spatiotemporal Bayesian Regularization for Cardiac Strain Imaging: Simulation and In Vivo Results.

Rashid Al Mukaddim^1,2, Nirvedh H Meshram^1,2, Ashley M Weichmann³

¹Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53706 USA.

IEEE Open Journal of Ultrasonics, Ferroelectrics, and Frequency Control

|February 17, 2022

Summary

Spatiotemporal Bayesian regularization (STBR) algorithms improve cardiac strain imaging accuracy. These novel methods enhance displacement estimation in ultrasound RF frames, outperforming traditional normalized cross-correlation (NCC) for myocardial motion analysis.

Keywords:

Bayesian regularization Cardiac strain imaging cardiac ultrasound high frequency ultrasound multi-level block matching murine echocardiography spatiotemporal information

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Last Updated: Oct 3, 2025

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Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Magnetic Resonance Derived Myocardial Strain Assessment Using Feature Tracking

Published on: February 12, 2011

Area of Science:

Medical Imaging
Biomedical Engineering
Computational Science

Background:

Cardiac strain imaging (CSI) is crucial for detecting myocardial motion abnormalities.
Accurate displacement estimation is vital for precise strain tensor calculations.
Existing methods like normalized cross-correlation (NCC) have limitations in accuracy and precision.

Purpose of the Study:

To propose and implement Spatiotemporal Bayesian Regularization (STBR) algorithms for enhanced cardiac strain estimation.
To integrate STBR into a Lagrangian framework for 2D displacement estimation using ultrasound RF data.
To evaluate the performance of STBR against traditional NCC methods.

Main Methods:

Developed two STBR schemes (STBR-1 and STBR-2) for iterative regularization of 2D NCC matrices using local spatiotemporal information.
Utilized a finite-element-analysis (FEA) model of canine myocardial deformation to quantify strain bias and errors.
Conducted an in vivo feasibility study on mouse hearts to compare elastographic signal-to-noise ratio (SNR).

Main Results:

STBR algorithms significantly outperformed NCC in reducing strain bias and errors (p < 0.001).
Mean longitudinal total temporal relative error (TTR) for NCC was 25.41%, compared to 9.27-10.38% for STBR methods.
STBR-2 demonstrated the highest expected SNR for both radial and longitudinal strain in vivo.

Conclusions:

STBR significantly improves the accuracy and precision of cardiac strain imaging.
The proposed methods offer a robust alternative to traditional NCC for myocardial motion analysis.
STBR enhances CSI performance in vivo, particularly in terms of signal-to-noise ratio.