ArticleIEEE transactions on ultrasonics, ferroelectrics, and frequency control2022
3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography.
Article in IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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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
21 citing papers in PubMed.
- A virtual imaging framework for three-dimensional quantitative optoacoustic tomography using stochastic numerical breast phantoms.Photoacoustics · 2026Article
- Stochastic numerical head phantoms to enable virtual imaging studies of transcranial photoacoustic computed tomography.Photoacoustics · 2026Article
- Design and measurement of 3D-printed variable-morphology and variable-density breast phantoms for mammography and breast CT dose assessment.3D printing in medicine · 2026Article
- Histology-based Microstructural Tissue Phantoms for Realistic Ultrasound Simulation.Ultrasonic imaging · 2026Article
- Labeled Numerical Phantom of Abdominal Wall for Wave-Physics-Based Ultrasound Imaging: Applications to Image Reconstruction.IEEE transactions on ultrasonics · 2026Article
- Survey of Deep Learning and Physics-Based Approaches in Computational Wave Imaging.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2025Article
- Deep-Learning-Driven High Spatial Resolution Attenuation Imaging for Ultrasound Tomography (AI-UT).IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2025Article
- Review of GPU-based Monte Carlo simulation platforms for transmission and emission tomography in medicine.Physics in medicine and biology · 2025Review
- Revisiting the joint estimation of initial pressure and speed-of-sound distributions in photoacoustic computed tomography with consideration of canonical object constraints.Photoacoustics · 2025Article
- 3D Frequency-Domain Full Waveform Inversion for Whole-Breast Imaging With a Multi-Row Ring Array.IEEE open journal of ultrasonics, ferroelectrics, and frequency control · 2025Article
- Investigating the Use of Traveltime and Reflection Tomography for Deep Learning-Based Sound-Speed Estimation in Ultrasound Computed Tomography.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2024Article
- Learning a stable approximation of an existing but unknown inverse mapping: application to the half-time circular Radon transform.Inverse problems · 2024Article
- Report on the AAPM Grand Challenge on deep generative modeling for learning medical image statistics.ArXiv · 2024Article
- A High-Resolution 3D Ultrasound Imaging System Oriented towards a Specific Application in Breast Cancer Detection Based on a 1 × 256 Ring Array.Micromachines · 2024Article
- Learned Full Waveform Inversion Incorporating Task Information for Ultrasound Computed Tomography.IEEE transactions on computational imaging · 2024Article
- A Forward Model Incorporating Elevation-Focused Transducer Properties for 3-D Full-Waveform Inversion in Ultrasound Computed Tomography.IEEE transactions on ultrasonics, ferroelectrics, and frequency control · 2023Article
- Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics.IEEE transactions on medical imaging · 2023Article
- Stochastic three-dimensional numerical phantoms to enable computational studies in quantitative optoacoustic computed tomography of breast cancer.Journal of biomedical optics · 2023Article
- Dynamic Speed of Sound Adaptive Transmission-Reflection Ultrasound Computed Tomography.Sensors (Basel, Switzerland) · 2023Article
- Deep learning methods hold promise for light fluence compensation in three-dimensional optoacoustic imaging.Journal of biomedical optics · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues. Computer-simulation studies, also known as virtual imaging trials, provide researchers with an economical and convenient route to systematically explore imaging system designs and image reconstruction methods. When simulating an imaging technology intended for clinical use, it is essential to employ realistic numerical phantoms that can facilitate the objective, or task-based, assessment of image quality (IQ). Moreover, when computing objective IQ measures, an ensemble of such phantoms should be employed, which displays the variability in anatomy and object properties that are representative of the to-be-imaged patient cohort. Such stochastic phantoms for clinically relevant applications of USCT are currently lacking. In this work, a methodology for producing realistic 3-D numerical breast phantoms for enabling clinically relevant computer-simulation studies of USCT breast imaging is presented. By extending and adapting an existing stochastic 3-D breast phantom for use with USCT, methods for creating ensembles of numerical acoustic breast phantoms are established. These breast phantoms will possess clinically relevant variations in breast size, composition, acoustic properties, tumor locations, and tissue textures. To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented, which addresses the development and assessment of image reconstruction procedures. Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development.
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