Numerical simulation of controlled precision cryosurgery using argon Joule–Thomson and liquid nitrogen evaporation cryoprobes

Abstract

Cryoablation is the most commonly used type of cryosurgery. Today, this treatment is insufficiently automated and therefore less accurate than alternative technologies that significantly limits and gradually reduces the scope of its application. However, from low temperature engineering point of view cryosurgery has the untapped potential of the improving its accuracy. In this paper three major methodological problems of cryosurgical equipment and procedure improving are considered: the impact on target isotherm location (1) of the soft biotissue moisture content solidification latent heat variation, (2) of the cryoprobe constructions and operating at maximum cooling power including the fluid cryogen features and the accuracy of boundary condition replacing a cryoprobe and (3) target isotherm fixation approach by modifying the cryoprobe operating mode. The numerical results showed that the target isotherm location depends insignificantly on different moisture content of soft biotissue, then it is not a limiting factor for cryoexposure prediction. Also numerical results showed that the theoretically minimum temperature of cryoprobe surface significantly overestimates their movement, this leads to a significant difference between the experimental and simulation results. The typical shape and movement of cryonecrosis isotherm at refined maximum cryoprobe cooling power for two typical constructions of cryoprobes and two cryogens are presented. The novel algorithm of modifying the cryoprobe operation mode for controlled precision cryosurgery is proposed and validated. All this complements the previously obtained data and shows the prospects for performing precision cryosurgery. The total average uncertainty of target isotherm location during the studied preliminary predicting stage of temperature field movement (virtual cryoablation) is estimated no more than ±0.41 mm. This information is expected to be useful for improving the quality of cryosurgery planning algorithms (e.g. for tumor treatment).