Integrated 8 channel high intensity focused ultrasound beamforming driver
Is this your thesis?
This record came from a bulk archive import. If it’s yours, link it to your profile.
Abstract (EN)
High intensity focused ultrasound technique is used to ablate pathological tissues, drug delivery, hemostasis, lithotripsy, thrombolysis. Due to the nature of HIFU, it has almost no side effects compared to other treatments. Focused ultrasound systems uses ultrasound waves and focuses them on a tiny fraction of a tissue and increases the temperature of focused area dramatically while maintaining low or no temperature rise in surrounding tissue. As the HIFU systems are advancing, their size is getting small. HIFU ablation systems can be used either invasive or non-invasive according to its purpose. For invasive HIFU ablation systems like ultrasound catheter ablation systems, size of the HIFU device is very important. In ultrasound catheter ablation systems, transducer arrays and their drivers are to be kept as small as possible due to size restrictions, while maintaining a reasonable number of channels for proper beamforming. In addition, these systems should be highly integrated with minimal power consumption for flexibility and functionality. Despite the size restrictions on these systems, such problems can be overcome by ASICs. In this work, we present a simple 8 channel high-frequency, high-voltage driver, together with its beamforming and control circuitry designed with AMS's H35 high voltage process. Keywords: beamforming, beamforming driver, high intensity focused ultrasound, HIFU, CMUT, very large scale integration, VLSI
Author
Hasan Yetik
Institution
How to Cite
Hasan Yetik (Master Thesis). Integrated 8 channel high intensity focused ultrasound beamforming driver, 2014, TOBB Economics and Technology University.
Keywords
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from TOBB Economics and Technology University
- Extracting the features of drilling foams with image processing techniques and data analysis using artificial neural networks(2007)
- Automatic web services execution engine with qos support(2007)
- Usage of information theory to control the qos and energy consumption for wireless sensor networks(2007)
- Low power techniques for superscalar microprocessors(2007)
- Hardware design for predicting widths of values produced in microprocessors(2007)
- Soft error detection by using tag comperator in microprocessors(2007)
