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Imaging Studies II: Ultrasonography

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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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Wideband Optical Detector of Ultrasound for Medical Imaging Applications
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Off-Grid Ultrasound Imaging by Stochastic Optimization.

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    Inverse grid-free estimation of reflectivities (INFER) improves ultrasound imaging by solving inverse problems. This method enhances image resolution and contrast while being robust to speed of sound variations, outperforming traditional beamforming.

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    Area of Science:

    • Medical Imaging
    • Computational Physics
    • Signal Processing

    Background:

    • Delay-and-sum (DAS) beamforming in ultrasound imaging produces artifacts, requiring multiple transmissions for clarity.
    • Inverse problem approaches offer high image quality with fewer transmissions but often need fine grids and are sensitive to parameter changes.

    Purpose of the Study:

    • To introduce Inverse Grid-free Estimation of Reflectivities (INFER), an off-grid, stochastic algorithm for ultrasound inverse scattering problems.
    • To develop a method that optimizes gridpoint locations, reflectivities, and sound speed simultaneously, reducing gridpoint requirements.

    Main Methods:

    • Developed an off-grid, stochastic algorithm (INFER) for solving the inverse scattering problem in ultrasound.
    • Jointly optimized gridpoint locations, reflectivities, and sound speed, enabling fewer gridpoints than conventional methods.
    • Utilized stochastic optimization for simultaneous processing of multiple transmissions without increased computational load.

    Main Results:

    • Achieved 2x-3x higher far-field lateral resolution and 6%-68% higher generalized contrast-to-noise ratio (gCNR) on in vivo data.
    • Demonstrated robustness to speed of sound variations up to ±100 m/s.
    • Showcased superior performance compared to existing beamforming methods on both phantom and in vivo data.

    Conclusions:

    • INFER offers significant improvements in ultrasound image quality, resolution, and robustness.
    • The off-grid, stochastic approach effectively addresses limitations of traditional inverse problem methods in ultrasound.
    • INFER provides a more efficient and accurate alternative to conventional beamforming techniques.