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Background: Image-guided neurosurgery requires high localization and registration accuracy to enable effective treatment and avoid complications. However, accurate neuronavigation based on preoperative magnetic resonance (MR) or computed tomography (CT) images is challenged by brain deformation occurring during the surgical intervention., Purpose: To facilitate intraoperative visualization of brain tissues and deformable registration with preoperative images, a 3D deep learning (DL) reconstruction framework (termed DL-Recon) was proposed for improved intraoperative cone-beam CT (CBCT) image quality., Methods: The DL-Recon framework combines physics-based models with deep learning CT synthesis and leverages uncertainty information to promote robustness to unseen features. A 3D generative adversarial network (GAN) with a conditional loss function modulated by aleatoric uncertainty was developed for CBCT-to-CT synthesis. Epistemic uncertainty of the synthesis model was estimated via Monte Carlo (MC) dropout. Using spatially varying weights derived from epistemic uncertainty, the DL-Recon image combines the synthetic CT with an artifact-corrected filtered back-projection (FBP) reconstruction. In regions of high epistemic uncertainty, DL-Recon includes greater contribution from the FBP image. Twenty paired real CT and simulated CBCT images of the head were used for network training and validation, and experiments evaluated the performance of DL-Recon on CBCT images containing simulated and real brain lesions not present in the training data. Performance among learning- and physics-based methods was quantified in terms of structural similarity (SSIM) of the resulting image to diagnostic CT and Dice similarity metric (DSC) in lesion segmentation compared to ground truth. A pilot study was conducted involving seven subjects with CBCT images acquired during neurosurgery to assess the feasibility of DL-Recon in clinical data., Results: CBCT images reconstructed via FBP with physics-based corrections exhibited the usual challenges to soft-tissue contrast resolution due to image non-uniformity, noise, and residual artifacts. GAN synthesis improved image uniformity and soft-tissue visibility but was subject to error in the shape and contrast of simulated lesions that were unseen in training. Incorporation of aleatoric uncertainty in synthesis loss improved estimation of epistemic uncertainty, with variable brain structures and unseen lesions exhibiting higher epistemic uncertainty. The DL-Recon approach mitigated synthesis errors while maintaining improvement in image quality, yielding 15%-22% increase in SSIM (image appearance compared to diagnostic CT) and up to 25% increase in DSC in lesion segmentation compared to FBP. Clear gains in visual image quality were also observed in real brain lesions and in clinical CBCT images., Conclusions: DL-Recon leveraged uncertainty estimation to combine the strengths of DL and physics-based reconstruction and demonstrated substantial improvements in the accuracy and quality of intraoperative CBCT. The improved soft-tissue contrast resolution could facilitate visualization of brain structures and support deformable registration with preoperative images, further extending the utility of intraoperative CBCT in image-guided neurosurgery., (© 2023 American Association of Physicists in Medicine.)

Conventional neuro-navigation can be challenged in targeting deep brain structures via transventricular neuroendoscopy due to unresolved geometric error following soft-tissue deformation. Current robot-assisted endoscopy techniques are fairly limited, primarily serving to planned trajectories and provide a stable scope holder. We report the implementation of a robot-assisted ventriculoscopy (RAV) system for 3D reconstruction, registration, and augmentation of the neuroendoscopic scene with intraoperative imaging, enabling guidance even in the presence of tissue deformation and providing visualization of structures beyond the endoscopic field-of-view. Phantom studies were performed to quantitatively evaluate image sampling requirements, registration accuracy, and computational runtime for two reconstruction methods and a variety of clinically relevant ventriculoscope trajectories. A median target registration error of 1.2 mm was achieved with an update rate of 2.34 frames per second, validating the RAV concept and motivating translation to future clinical studies.

Purpose: A method and prototype for a fluoroscopically-guided surgical robot is reported for assisting pelvic fracture fixation. The approach extends the compatibility of existing guidance methods with C-arms that are in mainstream use (without prior geometric calibration) using an online calibration of the C-arm geometry automated via registration to patient anatomy. We report the first preclinical studies of this method in cadaver for evaluation of geometric accuracy., Methods: The robot is placed over the patient within the imaging field-of-view and radiographs are acquired as the robot rotates an attached instrument. The radiographs are then used to perform an online geometric calibration via 3D-2D image registration, which solves for the intrinsic and extrinsic parameters of the C-arm imaging system with respect to the patient. The solved projective geometry is then be used to register the robot to the patient and drive the robot to planned trajectories. This method is applied to a robotic system consisting of a drill guide instrument for guidewire placement and evaluated in experiments using a cadaver specimen., Results: Robotic drill guide alignment to trajectories defined in the cadaver pelvis were accurate within 2 mm and 1° (on average) using the calibration-free approach. Conformance of trajectories within bone corridors was confirmed in cadaver by extrapolating the aligned drill guide trajectory into the cadaver pelvis., Conclusion: This study demonstrates the accuracy of image-guided robotic positioning without prior calibration of the C-arm gantry, facilitating the use of surgical robots with simpler imaging devices that cannot establish or maintain an offline calibration. Future work includes testing of the system in a clinical setting with trained orthopaedic surgeons and residents.

Purpose: Deep brain stimulation is a neurosurgical procedure used in treatment of a growing spectrum of movement disorders. Inaccuracies in electrode placement, however, can result in poor symptom control or adverse effects and confound variability in clinical outcomes. A deformable 3D-2D registration method is presented for high-precision 3D guidance of neuroelectrodes., Methods: The approach employs a model-based, deformable algorithm for 3D-2D image registration. Variations in lead design are captured in a parametric 3D model based on a B-spline curve. The registration is solved through iterative optimization of 16 degrees-of-freedom that maximize image similarity between the 2 acquired radiographs and simulated forward projections of the neuroelectrode model. The approach was evaluated in phantom models with respect to pertinent imaging parameters, including view selection and imaging dose., Results: The results demonstrate an accuracy of (0.2 ± 0.2) mm in 3D localization of individual electrodes. The solution was observed to be robust to changes in pertinent imaging parameters, which demonstrate accurate localization with ≥20° view separation and at 1/10 th the dose of a standard fluoroscopy frame., Conclusions: The presented approach provides the means for guiding neuroelectrode placement from 2 low-dose radiographic images in a manner that accommodates potential deformations at the target anatomical site. Future work will focus on improving runtime though learning-based initialization, application in reducing reconstruction metal artifacts for 3D verification of placement, and extensive evaluation in clinical data from an IRB study underway.

The treatment of malaria is a global health challenge that stands to benefit from the widespread introduction of a vaccine for the disease. A method has been developed to create a live organism vaccine using the sporozoites (SPZ) of the parasite Plasmodium falciparum (Pf), which are concentrated in the salivary glands of infected mosquitoes. Current manual dissection methods to obtain these PfSPZ are not optimally efficient for large-scale vaccine production. We propose an improved dissection procedure and a mechanical fixture that increases the rate of mosquito dissection and helps to deskill this stage of the production process. We further demonstrate the automation of a key step in this production process, the picking and placing of mosquitoes from a staging apparatus into a dissection assembly. This unit test of a robotic mosquito pick-and-place system is performed using a custom-designed micro-gripper attached to a four degree of freedom (4-DOF) robot under the guidance of a computer vision system. Mosquitoes are autonomously grasped and pulled to a pair of notched dissection blades to remove the head of the mosquito, allowing access to the salivary glands. Placement into these blades is adapted based on output from computer vision to accommodate for the unique anatomy and orientation of each grasped mosquito. In this pilot test of the system on 50 mosquitoes, we demonstrate a 100% grasping accuracy and a 90% accuracy in placing the mosquito with its neck within the blade notches such that the head can be removed. This is a promising result for this difficult and non-standard pick-and-place task., Note to Practitioners—: Automated processes could help increase malaria vaccine production to global scale. Currently, production requires technicians to manually dissect mosquitoes, a process that is slow, tedious, and requires a lengthy training regimen. This paper presents an an improved manual fixture and procedure that reduces technician training time. Further, an approach to automate this dissection process is proposed and the critical step of robotic manipulation of the mosquito with the aid of computer vision is demonstrated. Our approach may serve as a useful example of system design and integration for practitioners that seek to perform new and challenging pick-and-place tasks with small, non-uniform, and highly deformable objects.

Purpose: We report the initial development of an image-based solution for robotic assistance of pelvic fracture fixation. The approach uses intraoperative radiographs, preoperative CT, and an end effector of known design to align the robot with target trajectories in CT. The method extends previous work to solve the robot-to-patient registration from a single radiographic view (without C-arm rotation) and addresses the workflow challenges associated with integrating robotic assistance in orthopaedic trauma surgery in a form that could be broadly applicable to isocentric or non-isocentric C-arms., Methods: The proposed method uses 3D-2D known-component registration to localize a robot end effector with respect to the patient by: (1) exploiting the extended size and complex features of pelvic anatomy to register the patient; and (2) capturing multiple end effector poses using precise robotic manipulation. These transformations, along with an offline hand-eye calibration of the end effector, are used to calculate target robot poses that align the end effector with planned trajectories in the patient CT. Geometric accuracy of the registrations was independently evaluated for the patient and the robot in phantom studies., Results: The resulting translational difference between the ground truth and patient registrations of a pelvis phantom using a single (AP) view was 1.3 mm, compared to 0.4 mm using dual (AP+Lat) views. Registration of the robot in air (i.e., no background anatomy) with five unique end effector poses achieved mean translational difference ~1.4 mm for K-wire placement in the pelvis, comparable to tracker-based margins of error (commonly ~2 mm)., Conclusions: The proposed approach is feasible based on the accuracy of the patient and robot registrations and is a preliminary step in developing an image-guided robotic guidance system that more naturally fits the workflow of fluoroscopically guided orthopaedic trauma surgery. Future work will involve end-to-end development of the proposed guidance system and assessment of the system with delivery of K-wires in cadaver studies.

Pelvic trauma surgical procedures rely heavily on guidance with 2D fluoroscopy views for navigation in complex bone corridors. This "fluoro-hunting" paradigm results in extended radiation exposure and possible suboptimal guidewire placement from limited visualization of the fractures site with overlapped anatomy in 2D fluoroscopy. A novel computer vision-based navigation system for freehand guidewire insertion is proposed. The navigation framework is compatible with the rapid workflow in trauma surgery and bridges the gap between intraoperative fluoroscopy and preoperative CT images. The system uses a drill-mounted camera to detect and track poses of simple multimodality (optical/radiographic) markers for registration of the drill axis to fluoroscopy and, in turn, to CT. Surgical navigation is achieved with real-time display of the drill axis position on fluoroscopy views and, optionally, in 3D on the preoperative CT. The camera was corrected for lens distortion effects and calibrated for 3D pose estimation. Custom marker jigs were constructed to calibrate the drill axis and tooltip with respect to the camera frame. A testing platform for evaluation of the navigation system was developed, including a robotic arm for precise, repeatable, placement of the drill. Experiments were conducted for hand-eye calibration between the drill-mounted camera and the robot using the Park and Martin solver. Experiments using checkerboard calibration demonstrated subpixel accuracy [-0.01 ± 0.23 px] for camera distortion correction. The drill axis was calibrated using a cylindrical model and demonstrated sub-mm accuracy [0.14 ± 0.70 mm] and sub-degree angular deviation.