Embolotherapy involves the occlusion of blood flow to tumors to treat a variety of cancers, including renal carcinoma and hepatocellular carcinoma. The accompanying liver cirrhosis makes the treatment of hepatocellular carcinoma by traditional methods difficult. Previous attempts at embolotherapy have used solid emboli. A major difficulty in embolotherapy is restricting delivery of the emboli to the tumor. We are developing a novel minimally invasive gas embolotherapy technique that uses gas bubbles rather than solid emboli. The bubbles originate as encapsulated liquid droplets that are small enough to pass through capillaries. The droplets can be selectively vaporized in vivo by focused high intensity ultrasound to form gas bubbles which are then sufficiently large to lodge in the tumor vasculature. We investigated the dynamics of bubble lodging in microfluidic model bifurcations made of poly(dimethylsiloxane) and in theoretical analyses. The results show that the critical driving pressure below which a bubble will lodge in a bifurcation is significantly less than the driving pressure required to dislodge it. Based these results, we estimate that gas bubbles from embolotherapy can lodge in vessels 20 μm or smaller in diameter, and conclude that bubbles may potentially be used to reduce blood flow to tumor microcirculation.
Skip Nav Destination
ASME/JSME 2007 5th Joint Fluids Engineering Conference
July 30–August 2, 2007
San Diego, California, USA
Conference Sponsors:
- Fluids Engineering Division
ISBN:
0-7918-4289-4
PROCEEDINGS PAPER
A Microfluidic Model of Cardiovascular Bubble Lodging
Joseph L. Bull,
Joseph L. Bull
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
Andre´s J. Caldero´n,
Andre´s J. Caldero´n
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
Yun Seok Heo,
Yun Seok Heo
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
Dongeun Huh,
Dongeun Huh
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
Nobuyuki Futai,
Nobuyuki Futai
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
Shuichi Takayama,
Shuichi Takayama
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
J. Brian Fowlkes
J. Brian Fowlkes
University of Michigan, Ann Arbor, MI
Search for other works by this author on:
Joseph L. Bull
University of Michigan, Ann Arbor, MI
Andre´s J. Caldero´n
University of Michigan, Ann Arbor, MI
Yun Seok Heo
University of Michigan, Ann Arbor, MI
Dongeun Huh
University of Michigan, Ann Arbor, MI
Nobuyuki Futai
University of Michigan, Ann Arbor, MI
Shuichi Takayama
University of Michigan, Ann Arbor, MI
J. Brian Fowlkes
University of Michigan, Ann Arbor, MI
Paper No:
FEDSM2007-37446, pp. 691-694; 4 pages
Published Online:
March 30, 2009
Citation
Bull, JL, Caldero´n, AJ, Heo, YS, Huh, D, Futai, N, Takayama, S, & Fowlkes, JB. "A Microfluidic Model of Cardiovascular Bubble Lodging." Proceedings of the ASME/JSME 2007 5th Joint Fluids Engineering Conference. Volume 2: Fora, Parts A and B. San Diego, California, USA. July 30–August 2, 2007. pp. 691-694. ASME. https://doi.org/10.1115/FEDSM2007-37446
Download citation file:
8
Views
Related Proceedings Papers
Related Articles
Radio-Frequency Ablation in a Realistic Reconstructed Hepatic Tissue
J Biomech Eng (June,2007)
Noninvasive Blood Perfusion Measurements of an Isolated Rat Liver and an Anesthetized Rat Kidney
J Biomech Eng (December,2008)
Microbubble Expansion in a Flexible Tube
J Biomech Eng (August,2006)
Related Chapters
Numerical Simulation of the Bubble Cloud Dynamics in an Ultrasound Field
Proceedings of the 10th International Symposium on Cavitation (CAV2018)
Bubble Dynamics and High Intensity Focused Ultrasound: Experimental Observations and Numerical Simulations using Boundary Element Method
Proceedings of the 10th International Symposium on Cavitation (CAV2018)
Using Statistical Learning Theory to Improve Treatment Response for Metastatic Colorectal Carcinoma
Intelligent Engineering Systems through Artificial Neural Networks, Volume 20