Methods for detecting and localizing surgical instruments in laparoscopic images are an important element of advanced robotic and computer-assisted interventions. Robotic joint encoders and sensors integrated or mounted on the instrument can provide information about the tool's position, but this often has inaccuracy when transferred to the surgeon's point of view. Vision sensors are currently a promising approach for determining the position of instruments in the coordinate frame of the surgical camera. In this study, we propose a vision algorithm for localizing the instrument's pose in 3-D leaving only rotation in the axis of the tool's shaft as an ambiguity. We propose a probabilistic supervised classification method to detect pixels in laparoscopic images that belong to surgical tools. We then use the classifier output to initialize an energy minimization algorithm for estimating the pose of a prior 3-D model of the instrument within a level set framework. We show that the proposed method is robust against noise using simulated data and we perform quantitative validation of the algorithm compared to ground truth obtained using an optical tracker. Finally, we demonstrate the practical application of the technique on in vivo data from minimally invasive surgery with traditional laparoscopic and robotic instruments.

Purpose This manuscript describes the experience of two institutions in commissioning the new HalcyonTM platform. Its purpose is to: (a) validate the pre‐defined beam data, (b) compare relevant commissioning data acquired independently by two separate institutions, and (c) report on any significant differences in commissioning between the Halcyon linear accelerator and other medical linear accelerators. Methods Extensive beam measurements, testing of mechanical and imaging systems, including the multi‐leaf collimator (MLC), were performed at the two institutions independently. The results were compared with published recommendations as well. When changes in standard practice were necessitated by the design of the new system, the efficacy of such changes was evaluated as compared to published approaches (guidelines or vendor documentation). Results Given the proper choice of detectors, good agreement was found between the respective experimental data and the treatment planning system calculations, and between independent measurements by the two institutions. MLC testing, MV imaging, and mechanical system showed unique characteristics that are different from the traditional C‐arm linacs. Although the same methodologies and physics equipment can generally be used for commissioning the Halcyon, some adaptation of previous practices and development of new methods were also necessary. Conclusions We have shown that the vendor pre‐loaded data agree well with the independent measured ones during the commission process. This verifies that a data validation instead of a full‐data commissioning process may be a more efficient approach for the Halcyon. Measurement results could be used as a reference for future Halcyon users.

Lacunes may be caused by therapy-induced vasculopathy in children with brain tumors, with the most significant predictor being age less than 5 years at the time of radiotherapy.

Purpose:To ascertain whether a new delivery system (the Halcyon system) equipped with dual-layer stacked multileaf collimator operating in a mode, which allows independent, fully interdigitating motion of both layers and 6 flattening filter free energy, could generate plans of high clinical quality compared to a well-established delivery system with single layer multileaf collimator.Methods:Twenty patients in each of the 3 groups (advanced head and neck, breast, and high-risk prostate) were selected for an in silico planning study. For each patient, reference plans were developed for volumetric modulated arc therapy technique with 6 MV photon beams from a TrueBeam linear accelerator and compared against the corresponding plans for the Halcyon system. Plan comparison was performed in terms of dose volume histogram quantitative analysis.Results:Concerning the planning target volumes, with identical dose calculation and optimization algorithms and with identical planning techniques, no clinically relevant difference in coverage (D98%), hot spot (D2%), or homogeneity was observed. Similarly, for all the organs at risk, the dosimetric findings showed that (1) all planning constraints were met by the 2 delivery systems and (2) although statistical significant differences were reported for most of the parameters but none of these were judged of potential clinical relevance.Conclusion:The data presented confirmed that the new delivery system can generate treatment plans for volumetric modulated arc therapy with the same dosimetric quality of what is achievable with other systems routinely used in the clinics without significantly changing the current practice. Additional studies which customize the optimization parameters for each delivery device would complement the spectrum of investigations.

We present an analysis of the registration component of a proposed image guidance system for image guided liver surgery, using contrast enhanced CT. The analysis is performed on a visually realistic liver phantom and in-vivo porcine data. A robust registration process that can be deployed clinically is a key component of any image guided surgery system. It is also essential that the accuracy of the registration can be quantified and communicated to the surgeon. We summarise the proposed guidance system and discuss its clinical feasibility. The registration combines an intuitive manual alignment stage, surface reconstruction from a tracked stereo laparoscope and a rigid iterative closest point registration to register the intra-operative liver surface to the liver surface derived from CT. Testing of the system on a liver phantom shows that subsurface landmarks can be localised to an accuracy of 2.9 mm RMS. Testing during five porcine liver surgeries demonstrated that registration can be performed during surgery, with an error of less than 10 mm RMS for multiple surface landmarks.