Five-dimensional ultrasound system for soft tissue visualization
Abstract
Purpose A five-dimensional ultrasound (US) system is proposed as a real-time pipeline involving fusion of 3D B-mode data with the 3D ultrasound elastography (USE) data as well as visualization of these fused data and a real-time update capability over time for each consecutive scan. 3D B-mode data assist in visualizing the anatomy of the target organ, and 3D elastography data adds strain information.Methods We investigate the feasibility of such a system and show that an end-to-end real-time system, from acquisition to visualization, can be developed. We present a system that consists of (a) a real-time 3D elastography algorithm based on a normalized cross-correlation (NCC) computation on a GPU; (b) real-time 3D B-mode acquisition and network transfer; (c) scan conversion of 3D elastography and B-mode volumes (if acquired by 4D wobbler probe); and (d) visualization software that fuses, visualizes, and updates 3D B-mode and 3D elastography data in real time.Results We achieved a speed improvement of 4.45-fold for the threaded version of the NCC-based 3D USE versus the non-threaded version. The maximum speed was 79 volumes/s for 3D scan conversion. In a phantom, we validated the dimensions of a 2.2-cm-diameter sphere scan-converted to B-mode volume. Also, we validated the 5D US system visualization transfer function and detected 1- and 2-cm spherical objects (phantom lesion). Finally, we applied the system to a phantom consisting of three lesions to delineate the lesions from the surrounding background regions of the phantom.Conclusion A 5D US system is achievable with real-time performance. We can distinguish between hard and soft areas in a phantom using the transfer functions.
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The term '5D ultrasound' is uncommonly used to describe AI-assisted US imaging including image enhancement processing or automated calculations.
“the term 5D is uncommonly used to describe AI-assisted US imaging including image enhancement processing or automated calculations [10,11].”
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“While 4D technology extends the 3D view of the scanned object with a time frame, enabling motion visualization, or so-called ‘real-time 3D’ [9], the term 5D is uncommonly used to describe AI-assisted US imaging including image enhancement processing or automated calculations [10,11].”
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Deep Learning for Clinical Ultrasound Imaging: From Supervised Approaches to Foundation Models
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- Authors
- Deshmukh, N. P., Caban, J. J., Taylor, R. H., Hager, G. D., & Boctor, E. M.
- Affiliations
- Walter Reed National Military Medical Center (US)
- Johns Hopkins University (US)
- Venue
- International Journal of Computer Assisted Radiology and Surgery
- Published
- 2015-08-15
- Volume
- 10
- Issue
- 12
- Pages
- 1927-1939
- ISSN
- 1861-6410 (ISSN-L)
- 1861-6429
- OpenAlex
- W1153540639
- Cited by
- 4
- References
- 40
- Citations by year
- 2025: 2 · 2023: 1 · 2015: 1
- FWCI
- 0.27
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- International Journal of Computer Assisted Radiology and Surgery (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NIH SBIR Grant (CA134169)
- NIH Grant (CA112852)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Health Sciences › Medicine › Critical Care and Intensive Care Medicine › Ultrasound in Clinical Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- real-time image fusion, normalized cross-correlation, rasterization, soft tissue visualization, lesion delineation, phantom validation
- MeSH terms
- Algorithms, Computer Systems, Feasibility Studies, Humans, Software, Phantoms, Imaging, Imaging, Three-Dimensional, Elasticity Imaging Techniques
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