Development and translation of 3D ultrasound-based imaging systems for diagnostic and image-guided interventions
The past 50 years have witnessed unprecedented developments in new imaging systems that use 3D visualization. These new technologies have revolutionized diagnostic radiology, as they provide the clinician with information about the interior of the human body never before available. Conventional 2D ultrasound imaging is an important costeffective technique used routinely in the management of several diseases and is used globally in hospitals and diagnostic clinics. However, 2D viewing of 3D anatomy, using conventional ultrasound, limits our ability to quantify and visualize the anatomy and guide therapy, because multiple 2D images must be integrated mentally. This practice is inefficient and leads, at times, to variability and incorrect diagnoses. Also, since the 2D ultrasound image represents a thin plane at an arbitrary angle in the body, reproduction of this plane at a later time for monitoring disease progression or regression is difficult. <br/><br/> Investigators and companies have addressed these limitations by developing 3D ultrasound-based devices and techniques. In this paper, we describe our developments in 3D ultrasound (3D US) imaging instrumentation and techniques. In our approach, the conventional ultrasound transducer is scanned mechanically using various external fixtures. The 2D images are digitized and then reconstructed in real-time into a 3D image, which can be viewed and manipulated interactively. We describe the use of 3D ultrasound for diagnosis, and image-guided intervention with four examples: prostate biopsy, whole breast imaging, imaging of musculoskeletal joints, and imaging and analysis of carotid arterial atherosclerotic plaques.
Authors
- Aaron Fenster (ORCID: https://orcid.org/0000-0003-3525-2788)
- Emily Lalone (ORCID: https://orcid.org/0000-0002-0946-2493)
- Bernard Chiu (ORCID: https://orcid.org/0000-0001-5237-2410)
- Claire Park (ORCID: https://orcid.org/0000-0003-2468-7575)
- Amal Aziz
Institutions
- Brigham and Women's Hospital (US)
- Wilfrid Laurier University (CA)
Publication Details
- Published
- 2025-04-07
- DOI
- https://doi.org/10.1117/12.3055539
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Canadian Institutes of Health Research
- Natural Sciences and Engineering Research Council of Canada