ArticleIEEE transactions on medical imaging2021
Deep Learning for Ultrasound Beamforming in Flexible Array Transducer.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Beyond Physical Contact: Non-Contact Flexible Sensors With Intelligent Perception.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- Wearable ultrasound: a review of core technologies and clinical applications in cardiovascular monitoring.Microsystems & nanoengineering · 2026Review
- Synchronous wearable ultrasound for early detection of coronary and carotid artery comorbidity.Science advances · 2026Article
- Evaluating Curvature-Induced Variation in Deep Learning-Based Beamforming for Flexible Transducers in Ultrasound-Guided Radiation Therapy.Bioengineering (Basel, Switzerland) · 2026Article
- Gas Vesicles and Acoustic Protein Nanostructures in Molecular Ultrasound Nanomedicine: Translational Archetypes, Biomaterial Design, and Barriers to Clinical Realization.International journal of nanomedicine · 2026Review
- UltraFlex: Iterative Model-Based Ultrasonic Flexible-Array Shape Calibration.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2025Article
- An in-silico simulation study to generate computed tomography images from ultrasound data by using deep learning techniques.BJR artificial intelligence · 2025Article
- Emerging Wearable Ultrasound Technology.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Review
- Flexible Ultrasonic Transducers for Wearable Biomedical Applications: A Review on Advanced Materials, Structural Designs, and Future Prospects.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Review
- FLEX: FLexible Transducer With External Tracking for Ultrasound Imaging With Patient-Specific Geometry Estimation.IEEE transactions on bio-medical engineering · 2024Article
- Article
- Investigating pulse-echo sound speed estimation in breast ultrasound with deep learning.Ultrasonics · 2024Article
- Towards Ultrasound Imaging-based Closed-loop Peripheral Nerve Stimulation for Tremor Suppression.Current opinion in biomedical engineering · 2023Article
- A flexible optoacoustic blood 'stethoscope' for noninvasive multiparametric cardiovascular monitoring.Nature communications · 2023Article
- Flexible array transducer for photoacoustic-guided interventions: phantom andBiomedical optics express · 2023Article
- Ultrasound Imaging with Flexible Array Transducer for Pancreatic Cancer Radiation Therapy.Cancers · 2023Article
- Supervised segmentation for guiding peripheral revascularization with forward-viewing, robotically steered ultrasound guidewire.Medical physics · 2023Article
- Visualizing tactile feedback: an overview of current technologies with a focus on ultrasound elastography.Frontiers in medical technology · 2023Review
- DAART: a deep learning platform for deeply accelerated adaptive radiation therapy for lung cancer.Frontiers in oncology · 2023Article
- A wearable cardiac ultrasound imager.Nature · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Ultrasound imaging has been developed for image-guided radiotherapy for tumor tracking, and the flexible array transducer is a promising tool for this task. It can reduce the user dependence and anatomical changes caused by the traditional ultrasound transducer. However, due to its flexible geometry, the conventional delay-and-sum (DAS) beamformer may apply incorrect time delay to the radio-frequency (RF) data and produce B-mode images with considerable defocusing and distortion. To address this problem, we propose a novel end-to-end deep learning approach that may alternate the conventional DAS beamformer when the transducer geometry is unknown. Different deep neural networks (DNNs) were designed to learn the proper time delays for each channel, and they were expected to reconstruct the undistorted high-quality B-mode images directly from RF channel data. We compared the DNN results to the standard DAS beamformed results using simulation and flexible array transducer scan data. With the proposed DNN approach, the averaged full-width-at-half-maximum (FWHM) of point scatters is 1.80 mm and 1.31 mm lower in simulation and scan results, respectively; the contrast-to-noise ratio (CNR) of the anechoic cyst in simulation and phantom scan is improved by 0.79 dB and 1.69 dB, respectively; and the aspect ratios of all the cysts are closer to 1. The evaluation results show that the proposed approach can effectively reduce the distortion and improve the lateral resolution and contrast of the reconstructed B-mode images.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.