An X-Ray C-Arm Guided Automatic Targeting System for Histotripsy

Objective: Histotripsy is an emerging noninvasive, nonionizing and nonthermal focal cancer therapy that is highly precise and can create a treatment zone of virtually any size and shape. Current histotripsy systems rely on ultrasound imaging to target lesions. However, deep or isoechoic targets obstructed by bowel gas or bone can often not be treated safely using ultrasound imaging alone. This work presents an alternative x-ray Carm based targeting approach and a fully automated robotic targeting system. Methods: The approach uses conventional cone beam CT (CBCT) images to localize the target lesion and 2D fluoroscopy to determine the 3D position and orientation of the histotripsy transducer relative to the C-arm. The proposed pose estimation uses a digital model and deep learning-based feature segmentation to estimate the transducer focal point relative to the CBCT coordinate system. Additionally, the integrated robotic arm was calibrated to the C-arm by estimating the transducer pose for four preprogrammed transducer orientations and positions. The calibrated system can then automatically position the transducer such that the focal point aligns with any target selected in a CBCT image. Results: The accuracy of the proposed targeting approach was evaluated in phantom studies, where the selected target location was compared to the center of the spherical ablation zones in post-treatment CBCTs. The mean and standard deviation of the Euclidean distance was 1.4 +/- 0.5 mm. The mean absolute error of the predicted treatment radius was 0.5 +/- 0.5 mm. Conclusion: CBCT-based histotripsy targeting enables accurate and fully automated treatment without ultrasound guidance. Significance: The proposed approach could considerably decrease operator dependency and enable treatment of tumors not visible under ultrasound.