A visual odometry base-tracking system for intraoperative C-arm guidance

[Mobile C-arm-Radiation exposure and workflow killer? : Potential of an innovative assistance system for intraoperative positioning]

Despite its versatile applicability the intraoperative use of a mobile C‑arm is often problematic and potentially associated with increased radiation exposure for both the patient and the personnel. In particular, the correct positioning for adequate imaging can become a problem as the nonsterile circulating nurse has to coordinate the various maneuvers together with the surgeon without having a good view of the surgical field. The sluggishness of the equipment and the intraoperative setting (sterile borders, additional hardware, etc.) pose further challenges. A light detection and ranging (LIDAR)-based assistance system shows promise to provide accurate and intuitive repositioning support as part of an initial series of experimental trials. For this purpose, the sensors are attached to the C‑arm base unit and enable navigation of the device in the operating room to a stored target position using a simultaneous localization and mapping (SLAM) algorithm. An improvement of the workflow as well as a reduction of radiation exposure represent the possible potential of this system. The advantages over other experimental approaches are the lack of external hardware and the ease of use without isolating the operator from the rest of the operating room environment; however, the suitability for daily use in the presence of additional interfering factors should be verified in further studies.

Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning

OBJECTIVE

Optical coherence tomography (OCT) is an established medical imaging modality that has found widespread use due to its ability to visualize tissue structures at a high resolution. Currently, OCT hand-held imaging probes lack positional information, making it difficult or even impossible to link a specific image to the location it was originally obtained. In this study, we propose a camera-based localization method to track and record the scanner position in real-time, as well as providing a deep learning-based segmentation method.

METHODS

We used camera-based visual odometry (VO) and simultaneous mapping and localization (SLAM) to compute and visualize the location of a hand-held OCT imaging probe. A deep convolutional neural network (CNN) was used for kidney tubule lumens segmentation.

RESULTS

The mean absolute error (MAE) and the standard deviation (STD) for 1D translation were found to be 0.15 mm and 0.26mm respectively. For 2D translation, the MAE and STD were found to be 0.85 mm and 0.50 mm, respectively. The dice coefficient of the segmentation method was 0.7. The t-statistic of the T-test between predicted and actual average densities and predicted and actual average diameters were 7.7547e-13 and 2.2288e-15 respectively. We also experimented on a preserved kidney utilizing our localization method with automatic segmentation. Comparisons of the average density maps and average diameter maps were made between the 3D comprehensive scan and VO system scan.

CONCLUSION

Our results demonstrate that VO can track the probe location at high accuracy, and provides a user-friendly visualization tool to review OCT 2D images in 3D space. It also indicates that deep learning can provide high accuracy and high speed for segmentation.

SIGNIFICANCE

The proposed methods can be potentially used to predict delayed graft function (DGF) in kidney transplantation.

RAY-POS: a LIDAR-based assistance system for intraoperative repositioning of mobile C-arms without external aids

PURPOSE

In current clinical practice, intraoperative repositioning of mobile C-arms is challenging due to a lack of visual cues and efficient guiding tools. This can be detrimental to the surgical workflow and lead to additional radiation burdens for both patient and personnel. To overcome this problem, we present our novel approach Lidar-based X-ray Positioning for Mobile C-arms (RAY-POS) for assisting circulating nurses during intraoperative C-arm repositioning without requiring external aids.

METHODS

RAY-POS consists of a localization module and a graphical user interface for guiding the user back to a previously recorded C-Arm position. We conducted a systematic comparison of simultaneous localization and mapping (SLAM) algorithms using different attachment positions of light detection and ranging (LIDAR) sensors to benchmark localization performance within the operating room (OR). For two promising combinations, we conducted further end-to-end repositioning tests within a realistic OR setup.

RESULTS

SLAM algorithm gmapping with a LIDAR sensor mounted 40 cm above the C-arm's horizontal unit performed best regarding localization accuracy and long-term stability. The distribution of the repositioning error yielded an effective standard deviation of 7.61 mm.

CONCLUSION

We conclude that a proof-of-concept for LIDAR-based C-arm repositioning without external aids has been achieved. In future work, we mainly aim at extending the capabilities of our system and evaluating the usability together with clinicians.

The effect of artificial X-rays on C-arm positioning performance in a simulated orthopaedic surgical setting

PURPOSE

We designed an Artificial X-ray Imaging System (AXIS) that generates simulated fluoroscopic X-ray images on the fly and assessed its utility in improving C-arm positioning performance by C-arm users with little or no C-arm experience.

METHODS

The AXIS system was comprised of an optical tracking system to monitor C-arm movement, a manikin, a reference CT volume registered to the manikin, and a Digitally Reconstructed Radiograph algorithm to generate live simulated fluoroscopic images. A user study was conducted with 30 participants who had little or no C-arm experience. Each participant carried out four tasks using a real C-arm: an introduction session, an AXIS-guided set of pelvic imaging tasks, a non-AXIS guided set of pelvic imaging tasks, and a questionnaire. For each imaging task, the participant replicated a set of three target X-ray images by taking real radiographs of a manikin with a C-arm. The number of X-rays required, task time, and C-arm positioning accuracy were recorded.

RESULTS

We found a significant 53% decrease in the number of X-rays used and a moderate 10-26% improvement in lateral C-arm axis positioning accuracy without requiring more time to complete the tasks when the participants were guided by artificial X-rays. The questionnaires showed that the participants felt significantly more confident in their C-arm positioning ability when they were guided by AXIS. They rated the usefulness of AXIS as very good to excellent, and the realism and accuracy of AXIS as good to very good.

CONCLUSION

Novice users working with a C-arm machine supplemented with the ability to generate simulated X-ray images could successfully accomplish positioning tasks in a simulated surgical setting using markedly fewer X-ray images than when unassisted. In future work, we plan to determine whether such a system can produce similar results in the live operating room without lengthening surgical procedures.