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Related Concept Videos

Imaging Studies II: Ultrasonography01:24

Imaging Studies II: Ultrasonography

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...
Imaging Studies IV: Magnetic Resonance Imaging01:27

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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Radiological Investigation II: MRI and Ventilation Perfusion Scan

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Magnetic Resonance Imaging (MRI) and Ventilation Perfusion Scans are two radiological investigations that offer detailed diagnostic images of the body, particularly lung structures.
MRI
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Dual Raster-Scanning Photoacoustic Small-Animal Imager for Vascular Visualization
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APRIL: Adaptive Regression-Based Two-Dimensional Quantitative Anisotropy Imaging Using Acoustic Radiation Force

Md Walid Hassan, Kayla Crook, Young Jin Gi

    Biorxiv : the Preprint Server for Biology
    |July 10, 2026
    PubMed
    Summary

    This study introduces APRIL, a novel framework for 2D anisotropy imaging using acoustic radiation force impulse (ARFI) technology. APRIL enables robust, depth-resolved imaging of tissue anisotropy, extending beyond focal point estimations for clinical applications.

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    Area of Science:

    • Medical Imaging
    • Biomedical Engineering
    • Acoustics

    Background:

    • Acoustic Radiation Force Impulse (ARFI) imaging estimates tissue properties based on displacement. Current methods often provide focal estimates of anisotropy, limiting detailed spatial analysis.
    • Quantitative, depth-resolved anisotropy imaging is crucial for characterizing tissue microstructure and identifying pathologies.

    Purpose of the Study:

    • To develop and validate APRIL (Adaptive Polynomial Regression for anisotropy Imaging via ARFI-induced Displacements), a quantitative, depth-resolved anisotropy imaging framework.
    • To extend ARFI-based focal degree-of-anisotropy (DoA) estimation into two-dimensional (2D) mapping.
    • To model the depth-dependent relationship between shear modulus ratio (SMR) and peak displacement ratio (PDR).

    Main Methods:

    • Developed the APRIL framework using adaptive polynomial regression or shape-preserving spline interpolation based on excitation point spread function (PSF) asymmetry.
    • Generated training data using LS-DYNA3D + Field II simulations in homogeneous transversely isotropic media.
    • Validated experimentally in-vivo murine tumor models, ex-vivo chicken breast, and tissue-mimicking phantoms using a Verasonics system.

    Main Results:

    • APRIL achieved depth-resolved SMR prediction errors below 9% over 10-30 mm depth.
    • Demonstrated high accuracy in focal regions (MAE 2.3%) and stable performance across PSF transition zones.
    • Reconstructed anisotropy maps in heterogeneous phantoms with SSIM up to 86% and MPE below 7%, accurately delineating inclusion boundaries.
    • Showed robustness to acoustic parameter variations (MAE < 10%) and tracked tumor anisotropy progression in vivo.

    Conclusions:

    • APRIL enables robust, two-dimensional anisotropy imaging beyond focal estimates.
    • The framework is physically grounded and generalizable for clinically viable anisotropy biomarkers.
    • APRIL facilitates spatially resolved anisotropy biomarker imaging in various tissues without requiring heterogeneous training data.