Real-time respiratory motion compensated roadmaps for hepatic arterial interventions

DSC-Recon: Dual-Stage Complementary 4-D Organ Reconstruction From X-Ray Image Sequence for Intraoperative Fusion

Accurately reconstructing 4D critical organs contributes to the visual guidance in X-ray image-guided interventional operation. Current methods estimate intraoperative dynamic meshes by refining a static initial organ mesh from the semantic information in the single-frame X-ray images. However, these methods fall short of reconstructing an accurate and smooth organ sequence due to the distinct respiratory patterns between the initial mesh and X-ray image. To overcome this limitation, we propose a novel dual-stage complementary 4D organ reconstruction (DSC-Recon) model for recovering dynamic organ meshes by utilizing the preoperative and intraoperative data with different respiratory patterns. DSC-Recon is structured as a dual-stage framework: 1) The first stage focuses on addressing a flexible interpolation network applicable to multiple respiratory patterns, which could generate dynamic shape sequences between any pair of preoperative 3D meshes segmented from CT scans. 2) In the second stage, we present a deformation network to take the generated dynamic shape sequence as the initial prior and explore the discriminate feature (i.e., target organ areas and meaningful motion information) in the intraoperative X-ray images, predicting the deformed mesh by introducing a designed feature mapping pipeline integrated into the initialized shape refinement process. Experiments on simulated and clinical datasets demonstrate the superiority of our method over state-of-the-art methods in both quantitative and qualitative aspects.

Augmented Reality Navigation System for Biliary Interventional Procedures With Dynamic Respiratory Motion Correction

OBJECTIVE

Biliary interventional procedures require physicians to track the interventional instrument tip (Tip) precisely with X-ray image. However, Tip positioning relies heavily on the physicians' experience due to the limitations of X-ray imaging and the respiratory interference, which leads to biliary damage, prolonged operation time, and increased X-ray radiation.

METHODS

We construct an augmented reality (AR) navigation system for biliary interventional procedures. It includes system calibration, respiratory motion correction and fusion navigation. Firstly, the magnetic and 3D computed tomography (CT) coordinates are aligned through system calibration. Secondly, a respiratory motion correction method based on manifold regularization is proposed to correct the misalignment of the two coordinates caused by respiratory motion. Thirdly, the virtual biliary, liver and Tip from CT are overlapped to the corresponding position of the patient for dynamic virtual-real fusion.

RESULTS

Our system is respectively evaluated and achieved an average alignment error of 0.75 ± 0.17 mm and 2.79 ± 0.46 mm on phantoms and patients. The navigation experiments conducted on phantoms achieve an average Tip positioning error of 0.98 ± 0.15 mm and an average fusion error of 1.67 ± 0.34 mm after correction.

CONCLUSION

Our system can automatically register the Tip to the corresponding location in CT, and dynamically overlap the 3D virtual model onto patients to provide accurate and intuitive AR navigation.

SIGNIFICANCE

This study demonstrates the clinical potential of our system by assisting physicians during biliary interventional procedures. Our system enables dynamic visualization of virtual model on patients, reducing the reliance on contrast agents and X-ray usage.

Real-time continuous image guidance for endoscopic retrograde cholangiopancreatography based on 3D/2D registration and respiratory compensation

BACKGROUND

It has been confirmed that three-dimensional (3D) imaging allows easier identification of bile duct anatomy and intraoperative guidance of endoscopic retrograde cholangiopancreatography (ERCP), which reduces the radiation dose and procedure time with improved safety. However, current 3D biliary imaging does not have good real-time fusion with intraoperative imaging, a process meant to overcome the influence of intraoperative respiratory motion and guide navigation. The present study explored the feasibility of real-time continuous image-guided ERCP.

AIM

To explore the feasibility of real-time continuous image-guided ERCP.

METHODS

We selected 2 3D-printed abdominal biliary tract models with different structures to simulate different patients. The ERCP environment was simulated for the biliary phantom experiment to create a navigation system, which was further tested in patients. In addition, based on the estimation of the patient's respiratory motion, preoperative 3D biliary imaging from computed tomography of 18 patients with cholelithiasis was registered and fused in real-time with 2D fluoroscopic sequence generated by the C-arm unit during ERCP.

RESULTS

Continuous image-guided ERCP was applied in the biliary phantom with a registration error of 0.46 mm ± 0.13 mm and a tracking error of 0.64 mm ± 0.24 mm. After estimating the respiratory motion, 3D/2D registration accurately transformed preoperative 3D biliary images to each image in the X-ray image sequence in real-time in 18 patients, with an average fusion rate of 88%.

CONCLUSION

Continuous image-guided ERCP may be an effective approach to assist the operator and reduce the use of X-ray and contrast agents.

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.