The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Novel microbubble preparation technologies
Abstract
Gas microbubbles, stabilised by a surfactant or polymer coating, have become well established over the past 20–30 years as the most effective type of contrast agent available for ultrasound radiography. More recently, their potential for use in therapeutic applications including targeted drug delivery, gene therapy, thrombolysis and focused ultrasound surgery has also been investigated. Developments in both diagnostic and therapeutic applications have greatly increased the need for more advanced preparation technologies which provide a high degree of control over microbubble size, composition, stability and uniformity. Conventional processing techniques such as sonication and high shear emulsification offer high yield and low cost production but poor control over microbubble size and uniformity. This is being addressed by the development of new technologies, such as membrane emulsification, inkjet printing, electrohydrodynamic atomisation and microfluidic processing which offer significant improvements in terms of control over microbubble characteristics. The aim of this paper is to review the range of techniques available for microbubble preparation and how these have evolved to keep pace with advances in clinical practice.
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Cites
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Microbubbles in medical imaging: current applications and future directions
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Cited by
Bakhshi, P. K., Bain, J., Gul, M. O., Stride, E., Edirisinghe, M., & Staniland, S. S. (2016) · Macromolecular Bioscience · doi:10.1002/mabi.201600181 · Cited by 10
Self-citation
Metadata
- Authors
- Stride, E., & Edirisinghe, M.
- Venue
- Soft Matter
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2008
- Volume
- 4
- Issue
- 12
- Pages
- 2350
- ISSN
- 1744-683X (ISSN-L)
- 1744-6848
- OpenAlex
- W2056760267
- Cited by
- 262
- References
- 43
- FWCI
- 11.94
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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