Respiration-Induced Movement Correlation for Synchronous Noninvasive Renal Cancer Surgery

Noninvasive surgery (NIS), such as high-intensity focused ultrasound (HIFU)-based ablation or radiosurgery, is used for treating tumors and cancers in various parts of the body. The soft tissue targets (usually organs) deform and move as a result of physiological processes such as respiration. Moreover, other deformations induced during surgery by changes in patient position, changes in physical properties caused by repeated exposures and uncertainties resulting from cavitation also occur. In this paper, we present a correlation-based movement prediction technique to address respiration-induced movement of the urological organs while targeting through extracorporeal trans-abdominal route access. Among other organs, kidneys are worst affected during respiratory cycles, with significant three-dimensional displacements observed on the order of 20 mm. Remote access to renal targets such as renal carcinomas and cysts during noninvasive surgery, therefore, requires a tightly controlled real-time motion tracking and quantitative estimate for compensation routine to synchronize the energy source(s) for precise energy delivery to the intended regions. The correlation model finds a mapping between the movement patterns of external skin markers placed on the abdominal access window and the internal movement of the targeted kidney. The coarse estimate of position is then fine-tuned using the Adaptive Neuro-Fuzzy Inference System (ANFIS), thereby achieving a nonlinear mapping. The technical issues involved in this tracking scheme are threefold: the model must have sufficient accuracy in mapping the movement pattern; there must be an image-based tracking scheme to provide the organ position within allowable system latency; and the processing delay resulting from modeling and tracking must be within the achievable prediction horizon to accommodate the latency in the therapeutic delivery system. The concept was tested on ultrasound image sequences collected from 20 healthy volunteers. The results indicate that the modeling technique can be practically integrated into an image-guided noninvasive robotic surgical system with an indicative targeting accuracy of more than 94%. A comparative analysis showed the superiority of this technique over conventional linear mapping and model-free blind search techniques.