# A Machine Learning Alternative to Beamforming to Improve Ultrasound Image Quality for Interventional Access to the Kidney

> **NIH NIH R21** · JOHNS HOPKINS UNIVERSITY · 2021 · $235,027

## Abstract

Project Summary
 Despite the widespread prevalence of ultrasound imaging in hospitals today, the clinical utility of ultrasound
guidance is severely hampered by clutter and reverberation artifacts that obscure structures of interest and com-
plicate anatomical measurements. Clutter is particularly problematic in overweight and obese individuals, who
account for 78.6 million adults and 12.8 million children in North America. Similarly, interventional procedures of-
ten require insertion of one or more metal tools, which generate reverberation artifacts that obfuscate instrument
location, orientation, and geometry, while obscuring nearby tissues, thus additionally hampering ultrasound im-
age quality. Although artifacts are problematic, ultrasound continues to persist primarily because of its greatest
strengths (i.e., mobility, cost, non-ionizing radiation, real-time visualization, and multiplanar views) in comparison
to existing image-guidance options, but it would be significantly more useful without problematic artifacts.
 Our long-term project goal is to use state-of-the-art machine learning techniques to provide interventional
radiologists with artifact-free ultrasound-based images. We will initially develop a new framework alternative
to the ultrasound beamforming process that removes needle tip reverberations and acoustic clutter caused by
multipath scattering in near-field tissues when guiding needles to the kidney to enable removal of painful kidney
stones. Our first aim will test convolutional neural networks (CNNs) that input raw channel data and output
human readable images with no artifacts caused by multipath scattering and reverberations. A secondary goal
of the CNNs is to learn the minimum number of parameters required to create these new CNN-based images.
Our second aim will validate the trained algorithms with ultrasound data from experimental phantom and ex vivo
tissue. Our third aim will extend our evaluation to ultrasound images of in vivo porcine kidneys. This work is the
first to propose bypassing the entire beamforming process and replacing it with machine learning and computer
vision techniques to remove traditionally problematic noise artifacts and create a fundamentally new type of
artifact-free, high-contrast, high-resolution, ultrasound-based image for guiding interventional procedures.
 This work combines the expertise of an imaging scientist, a computer scientist, and an interventional ra-
diologist to explore an untapped, understudied area that is only recently made feasible through improvements
in computing power, advances in computer vision capabilities, and new knowledge about dominant sources of
image degradation. Translation to in vivo cases is enabled by our clinical collaboration with the Department
of Radiology at the Johns Hopkins Hospital. With support from the NIH Trailblazer Award, our team will be
the first to develop these tools and capabilities to eliminate noise artifacts in interventional ultrasound, open...

## Key facts

- **NIH application ID:** 10170765
- **Project number:** 3R21EB025621-03S1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Muyinatu A. Lediju Bell
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $235,027
- **Award type:** 3
- **Project period:** 2020-12-07 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10170765

## Citation

> US National Institutes of Health, RePORTER application 10170765, A Machine Learning Alternative to Beamforming to Improve Ultrasound Image Quality for Interventional Access to the Kidney (3R21EB025621-03S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10170765. Licensed CC0.

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