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Computerized Planning of Cryosurgery


Cryosurgery has been known as an invasive surgical technique since 1961, when Cooper and Lee invented the first cryoprobe. In the 1990s, new developments in Joule-Thomson cooling (the cooling effect associated with a sudden relief of a pressurized gas) led to a dramatic decrease in the size of cryoprobes and an increase in the number of cryoprobes that could be used simultaneously. A dozen or more cryoprobes operating simultaneously in a single prostate cryosurgery is already common practice. If localized effectively, one of the primary benefits of using a large number of miniaturized cryoprobes is superior control over the freezing process.


Currently, the process of selecting the correct placement of the cryoprobes for a specific procedure is an art held by the cryosurgeon, based on the surgeon’s own experience and rules of thumb. Cryoprobes are typically operated in a trail-and-error fashion, until the entire target volume is thought to be frozen. Currently, there are no means to determine the optimal locations for the cryoprobes. Suboptimal cryoprobe localization may leave regions in the target volume unfrozen, may lead to cryoinjury of healthy surrounding tissues, may require an unnecessarily large number of cryoprobes, may increase the duration of the surgical procedure, and may increase the likelihood of post cryosurgery complications, all of which affect the quality and cost of the medical treatment. Computerized planning tools would help to alleviate these difficulties.


Our goal is to develop computerized planning tools for cryosurgery that are suitable for all available cooling techniques.


·        Prostate model reconstruction for computerized planning of cryosurgery (movies

·        Computerized planning of cryosurgery via bubble packing (movies

·        Experimental verification of bioheat simulations (movies) 

·        Computerized Planning: insertion-depth effect (movies) 


This project is conducted in collaboration with the Computational Engineering & Robotics Laboratory.


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This research has been supported, in part, by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) NIH Grant # 1R01EB003563