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 C-arm 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.
Insights
This study introduces a new X-ray C-arm targeting system for histotripsy (a noninvasive cancer therapy). This automated approach accurately targets lesions without ultrasound, improving safety and accessibility for deep or obstructed tumors.
Area of Science:
- Medical Physics
- Oncology
- Robotics
Background:
- Histotripsy is a precise, noninvasive cancer therapy using focused ultrasound.
- Current ultrasound-based targeting is limited by factors like bowel gas, bone, or deep tumor locations.
- A novel targeting method is needed to expand histotripsy's applicability.
Purpose of the Study:
- To develop and validate an automated X-ray C-arm based targeting system for histotripsy.
- To overcome limitations of ultrasound guidance for targeting deep or obstructed tumors.
- To enhance the precision and safety of histotripsy cancer treatment.
Main Methods:
- Utilized cone-beam CT (CBCT) for lesion localization and 2D fluoroscopy for transducer positioning.
- Employed a digital model and deep learning for precise pose estimation of the histotripsy transducer.
- Calibrated a robotic arm to the C-arm system for automated transducer alignment.
Main Results:
- Phantom studies demonstrated high accuracy, with a mean Euclidean distance of 1.4 ±0.5 mm between the target and ablation zone center.
- The mean absolute error for the predicted treatment radius was 0.5 ±0.5 mm.
- The system achieved accurate, automated targeting without reliance on ultrasound.
Conclusions:
- CBCT-based histotripsy targeting offers an accurate and automated alternative to ultrasound guidance.
- This approach reduces operator dependency and expands treatment possibilities for tumors previously inaccessible via ultrasound.
- The developed system shows significant potential for advancing noninvasive cancer therapies.
