ArticleUltrasound in medicine & biology2020
3-D H-Scan Ultrasound Imaging and Use of a Convolutional Neural Network for Scatterer Size Estimation.
Article in Ultrasound in medicine & biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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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.
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Who cites it
7 citing papers in PubMed, 22 citations in OpenAlex.
- Assessment of Early Breast Cancer Response to Chemotherapy with Ultrasound Radiomics.Diagnostics (Basel, Switzerland) · 2026Article
- Imaging in inflammatory arthritis: progress towards precision medicine.Nature reviews. Rheumatology · 2023Review
- H-Scan Ultrasound Monitoring of Breast Cancer Response to Chemotherapy and Validation With Diffusion-Weighted Magnetic Resonance Imaging.Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine · 2023Article
- High-Resolution Ultrasound Characterization of Local Scattering in Cancer Tissue.Ultrasound in medicine & biology · 2023Article
- Generalized mathematical framework for contrast-enhanced ultrasound imaging with pulse inversion spectral deconvolution.Ultrasonics · 2023Article
- 3-D H-scan ultrasound imaging of relative scatterer size using a matrix array transducer and sparse random aperture compounding.Computers in biology and medicine · 2022Article
- Faster motion correction of clinical contrast-enhanced ultrasound imaging using deep learning.IEEE International Ultrasonics Symposium : [proceedings]. IEEE International Ultrasonics Symposium · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
H-Scan ultrasound (US) is a new imaging technology that estimates the relative size of acoustic scattering objects and structures. The purpose of this study was to introduce a three-dimensional (3-D) H-scan US imaging approach for scatterer size estimation in volume space. Using a programmable research scanner (Vantage 256, Verasonics Inc, Kirkland, WA, USA) equipped with a custom volumetric imaging transducer (4 DL7, Vermon, Tours, France), raw radiofrequency (RF) data was collected for offline processing to generate H-scan US volumes. A deep convolutional neural network (CNN) was modified and used to achieve voxel mapping from the input H-scan US image to underlying scatterer size. Preliminary studies were conducted using homogeneous gelatin-based tissue-mimicking phantom materials embedded with acoustic scatterers of varying size (15 to 250 μm) and concentrations (0.1 to 1%). Two additional phantoms were embedded with 63 or 125 µm-sized microspheres and used to test CNN estimation accuracy. In vitro results indicate that 3-D H-scan US imaging can visualize the spatial distribution of acoustic scatterers of varying size at different concentrations (R
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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.