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

波束成形的机器学习替代方案可提高肾脏介入治疗的超声图像质量

基本信息

批准号：
10170765
负责人：
Muyinatu A. Lediju Bell
金额：
$ 23.5万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-12-07 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10170765
关键词：
Abdomen Acoustics Adult Age Anatomy Area Award Breast biopsy Bypass Cancer Detection Child Clinical Collaborations Computer Vision Systems Computers Custom Data Evaluation Excision Family suidae Funding Geometry Goals Hospitals Human Image Intervention Interventional Ultrasonography Kidney Kidney Calculi Knowledge Learning Location Machine Learning Measurement Metals Modeling Morphologic artifacts Needles Network-based Noise Nonionizing Radiation North America Operative Surgical Procedures Output Overweight Pain Patients Prevalence Procedures Process Radiology Specialty Readability Resolution Scientist Signal Transduction Source Structure Techniques Testing Time Tissues Translations Ultrasonography United States National Institutes of Health Visualization Work algorithm training base convolutional neural network cost image guided image guided intervention imaging scientist improved in vivo innovation instrument interest metallicity obese patients obese person radiologist signal processing tool

项目摘要

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, opening the door to a new paradigm in ultrasound image formation, which will directly beneﬁt millions of patients with clearer, easier-to-interpret ultrasound images. Subsequent R01 funding will customize our innovation to addi- tional application-speciﬁc ultrasound procedures (e.g., breast biopsies, cancer detection, autonomous surgery).

项目总结

项目成果

期刊论文数量（7）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deep Learning to Obtain Simultaneous Image and Segmentation Outputs From a Single Input of Raw Ultrasound Channel Data.

DOI：
10.1109/tuffc.2020.2993779
发表时间：
2020-12
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
0
作者：
Nair AA;Washington KN;Tran TD;Reiter A;Lediju Bell MA
通讯作者：
Lediju Bell MA

The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability.

DOI：
10.1109/tuffc.2019.2956855
发表时间：
2020-04
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
0
作者：
Rodriguez-Molares A;Rindal OMH;D'hooge J;Masoy SE;Austeng A;Lediju Bell MA;Torp H
通讯作者：
Torp H

CohereNet: A Deep Learning Architecture for Ultrasound Spatial Correlation Estimation and Coherence-Based Beamforming.

DOI：
10.1109/tuffc.2020.2982848
发表时间：
2020-12
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
0
作者：
Wiacek A;Gonzalez E;Bell MAL
通讯作者：
Bell MAL

Detection of COVID-19 features in lung ultrasound images using deep neural networks.

使用深度神经网络检测肺部超声图像中的 COVID-19 特征。

DOI：
10.1038/s43856-024-00463-5
发表时间：
2024
期刊：
Communications medicine
影响因子：
0
作者：
Zhao,Lingyi;Fong,TiffanyClair;Bell,MuyinatuALediju
通讯作者：
Bell,MuyinatuALediju

Deep Learning for Ultrasound Image Formation: CUBDL Evaluation Framework and Open Datasets.

DOI：
10.1109/tuffc.2021.3094849
发表时间：
2021-12
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
0
作者：
Hyun D;Wiacek A;Goudarzi S;Rothlubbers S;Asif A;Eickel K;Eldar YC;Huang J;Mischi M;Rivaz H;Sinden D;van Sloun RJG;Strohm H;Bell MAL
通讯作者：
Bell MAL

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Muyinatu A. Lediju Bell其他文献

Overfit detection method for deep neural networks trained to beamform ultrasound images

用于训练以对超声图像进行波束形成的深度神经网络的过拟合检测方法

DOI：
10.1016/j.ultras.2024.107562
发表时间：
2025-04-01
期刊：
ULTRASONICS
影响因子：
4.100
作者：
Jiaxin Zhang;Muyinatu A. Lediju Bell
通讯作者：
Muyinatu A. Lediju Bell

Deep Learning-Based Displacement Tracking for Post-Stroke Myofascial Shear Strain Quantification

基于深度学习的位移跟踪，用于中风后肌筋膜剪切应变量化

DOI：
发表时间：
期刊：
影响因子：
0
作者：
Md Ashikuzzaman;Jonny Huang;Steve Bonwit;Azin Etemadimanesh;Preeti Raghavan;Muyinatu A. Lediju Bell
通讯作者：
Muyinatu A. Lediju Bell

Mitigating skin tone bias in linear array emin vivo/em photoacoustic imaging with short-lag spatial coherence beamforming

利用短滞后空间相干波束形成减轻线性阵列体内/体外光声成像中的肤色偏差

DOI：
10.1016/j.pacs.2023.100555
发表时间：
2023-10-01
期刊：
Photoacoustics
影响因子：
6.800
作者：
Guilherme S.P. Fernandes;João H. Uliana;Luciano Bachmann;Antonio A.O. Carneiro;Muyinatu A. Lediju Bell;Theo Z. Pavan
通讯作者：
Theo Z. Pavan