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IntroductionUltrasonography, or renal ultrasound, is a noninvasive medical imaging technique that uses high-frequency sound waves to visualize the kidneys, ureters, bladder, and surrounding tissues.Indications for Urinary System UltrasonographyUrinary system ultrasonography is indicated in various clinical scenarios, such as:Kidney Stones (Urolithiasis): To detect and monitor the size and presence of kidney or urinary tract stones.Hydronephrosis: To assess the dilation of the renal pelvis and...

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Blood Flow Imaging with Ultrafast Doppler

Blood Flow Imaging with Ultrafast Doppler

Published on: October 14, 2020

Sparsity driven ultrasound imaging.

Ahmet Tuysuzoglu¹, Jonathan M Kracht, Robin O Cleveland

¹Department of Electrical and Computer Engineering, Boston University, Boston, Massachusetts 02215, USA. atuysuz@bu.edu

The Journal of the Acoustical Society of America

|February 23, 2012

Summary

This summary is machine-generated.

This study introduces a new ultrasound imaging method using sparsity-driven regularization for better image quality. The approach enhances feature preservation and reduces artifacts, even with limited data.

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Wideband Optical Detector of Ultrasound for Medical Imaging Applications

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Published on: May 11, 2014

Area of Science:

Medical Imaging
Signal Processing
Computational Physics

Background:

Ultrasound imaging is crucial for medical diagnostics.
Current methods can suffer from artifacts and limited resolution, especially with sparse data.
Model-based approaches offer potential for improved reconstruction.

Purpose of the Study:

To propose a novel image formation framework for ultrasound imaging.
To leverage sparsity-driven regularization for enhanced image reconstruction.
To address challenges posed by synthetic transducer arrays and sparse data.

Main Methods:

Developed a physics-based forward model for ultrasound observations.
Formulated image formation as an optimization problem.
Employed efficient numerical algorithms to solve the optimization problem.
Utilized single-frequency Fourier domain data.

Main Results:

The sparsity-driven, model-based approach effectively estimates complex-valued reflectivity.
Physical features are preserved while spurious artifacts are suppressed.
Robust reconstructions were achieved even with sparse and reduced observation apertures.
Demonstrated effectiveness using experimental data.

Conclusions:

The proposed framework offers a robust method for ultrasound image formation.
Sparsity-driven regularization is effective in improving image quality and artifact reduction.
The approach shows promise for applications with limited transducer data.