Portable optically tracked ultrasound system for scoliosis measurement

Monitoring spinal curvature in adolescent kyphoscoliosis requires reg-ular radiographic examinations, however, the applied ionizing radiation increases the risk of cancer. Ultrasound imaging is favorable over X-ray because it does not emit ionizing radiation. It has been shown in the past that tracked ultrasound can be used to localize vertebral transverse processes as landmarks along the spine to measure curvature angles. Tests have been performed with spine phan-toms, but scanning protocol, tracking system, data acquisition and processing time has not been considered in human subjects yet. In this paper, a portable op-tically tracked ultrasound system for scoliosis measurement is presented. It pro-vides a simple way to acquire data in the clinical environment with the aim of comparing results to current X-ray-based measurement. The workflow of the pro-cedure was tested on volunteers. The customized open-source software is shared with the community as part of our effort to make a clinically practical system

Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data

Free Point Transformer (FPT) has been proposed as a data-driven, non-rigid point set registration approach using deep neural networks. As FPT does not assume constraints based on point vicinity or correspondence, it may be trained simply and in a flexible manner by minimizing an unsupervised loss based on the Chamfer Distance. This makes FPT amenable to real-world medical imaging applications where ground-truth deformations may be infeasible to obtain, or in scenarios where only a varying degree of completeness in the point sets to be aligned is available. To test the limit of the correspondence finding ability of FPT and its dependency on training data sets, this work explores the generalizability of the FPT from well-curated non-medical data sets to medical imaging data sets. First, we train FPT on the ModelNet40 dataset to demonstrate its effectiveness and the superior registration performance of FPT over iterative and learning-based point set registration methods. Second, we demonstrate superior performance in rigid and non-rigid registration and robustness to missing data. Last, we highlight the interesting generalizability of the ModelNet-trained FPT by registering reconstructed freehand ultrasound scans of the spine and generic spine models without additional training, whereby the average difference to the ground truth curvatures is 1.3 degrees, across 13 patients.Comment: Accepted to ASMUS 2022 Workshop at MICCA

Real-time integration between Microsoft HoloLens 2 and 3D Slicer with demonstration in pedicle screw placement planning

We established a direct communication channel between Microsoft HoloLens 2 and 3D Slicer to exchange transform and image messages between the platforms in real time. This allows us to seamlessly display a CT reslice of a patient in the AR world.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Research supported by projects PI122/00601 and AC20/00102 (Ministerio de Ciencia, Innovación y Universidades, Instituto de Salud Carlos III, Asociación Española Contra el Cáncer and European Regional Development Fund “Una manera de hacer Europa”), project PerPlanRT (ERA Permed), TED2021-129392B-I00 and TED2021-132200B-I00 (MCIN/AEI/10.13039/501100011033 and European Union “NextGenerationEU”/PRTR) and EU Horizon 2020 research and innovation programme Conex plus UC3M (grant agreement 801538). APC funded by Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)

1.5 T augmented reality navigated interventional MRI: paravertebral sympathetic plexus injections

PURPOSE:The high contrast resolution and absent ionizing radiation of interventional magnetic resonance imaging (MRI) can be advantageous for paravertebral sympathetic nerve plexus injections. We assessed the feasibility and technical performance of MRI-guided paravertebral sympathetic injections utilizing augmented reality navigation and 1.5 T MRI scanner.METHODS:A total of 23 bilateral injections of the thoracic (8/23, 35%), lumbar (8/23, 35%), and hypogastric (7/23, 30%) paravertebral sympathetic plexus were prospectively planned in twelve human cadavers using a 1.5 Tesla (T) MRI scanner and augmented reality navigation system. MRI-conditional needles were used. Gadolinium-DTPA-enhanced saline was injected. Outcome variables included the number of control magnetic resonance images, target error of the needle tip, punctures of critical nontarget structures, distribution of the injected fluid, and procedure length.RESULTS: Augmented-reality navigated MRI guidance at 1.5 T provided detailed anatomical visualization for successful targeting of the paravertebral space, needle placement, and perineural paravertebral injections in 46 of 46 targets (100%). A mean of 2 images (range, 1–5 images) were required to control needle placement. Changes of the needle trajectory occurred in 9 of 46 targets (20%) and changes of needle advancement occurred in 6 of 46 targets (13%), which were statistically not related to spinal regions (P = 0.728 and P = 0.86, respectively) and cadaver sizes (P = 0.893 and P = 0.859, respectively). The mean error of the needle tip was 3.9±1.7 mm. There were no punctures of critical nontarget structures. The mean procedure length was 33±12 min.CONCLUSION:1.5 T augmented reality-navigated interventional MRI can provide accurate imaging guidance for perineural injections of the thoracic, lumbar, and hypogastric sympathetic plexus

Freehand ultrasound calibration: phantom versus tracked pointer

ABSTRACT PURPOSE: Ultrasound-guided tracked navigation requires spatial calibration between the ultrasound beam and the tracker. We examined the reproducibility and accuracy of two popular open source calibration methods 1 with a handheld linear ultrasound transducer. METHODS: A total of 10 calibrations were performed using (1) a double N-wire phantom with automatic image segmentation and registration; (2) and registration of landmark points collected with a tracked pointer. Reproducibility and accuracy were characterized by comparing the resulting transformation matrices, and by comparing ground truth landmark points. RESULTS: Transformation matrices calculated with an N-wire phantom showed a variance of X: 0.02 mm (in the direction of sound propagation), Y: 0.03 mm (in the direction of transducer elements) and Z: 0.21 mm (in the elevation direction). Transformation matrices obtained with tracked pointer showed a variance of X: 0.1 mm, Y: 0.10 mm and Z: 0.43 mm. Calibration accuracy was tested with ground truth cross wire points. The N-wire phantom provided a calibration with a distance from ground truth of X: 2.44 ± 1.44 mm, Y: 1.21 ± 0.88 mm, and Z: 1.12 ± 0.82 mm. Tracked pointer calibration had a distance from the ground truth of X: 0.23 ± 0.16 mm, Y: 0.62 ± 0.31 mm, and Z: 0.45 ± 0.33 mm. Distance from ground truth was significantly less (p<0.01) with the tracked pointer method in all directions. CONCLUSION: Calibration using a tracked pointer had a slightly greater variance; however it showed better accuracy over calibrations calculated with N-wire phantoms

Tissue segmentation for workflow recognition in open inguinal hernia repair training

PURPOSE: As medical education adopts a competency-based training method, experts are spending substantial amounts of time instructing and assessing trainees' competence. In this study, we look to develop a computer-assisted training platform that can provide instruction and assessment of open inguinal hernia repairs without needing an expert observer. We recognize workflow tasks based on the tool-tissue interactions, suggesting that we first need a method to identify tissues. This study aims to train a neural network in identifying tissues in a low-cost phantom as we work towards identifying the tool-tissue interactions needed for task recognition. METHODS: Eight simulated tissues were segmented throughout five videos from experienced surgeons who performed open inguinal hernia repairs on phantoms. A U-Net was trained using leave-one-user-out cross validation. The average F-score, false positive rate and false negative rate were calculated for each tissue to evaluate the U-Net's performance. RESULTS: Higher F-scores and lower false negative and positive rates were recorded for the skin, hernia sac, spermatic cord, and nerves, while slightly lower metrics were recorded for the subcutaneous tissue, Scarpa's fascia, external oblique aponeurosis and superficial epigastric vessels. CONCLUSION: The U-Net performed better in recognizing tissues that were relatively larger in size and more prevalent, while struggling to recognize smaller tissues only briefly visible. Since workflow recognition does not require perfect segmentation, we believe our U-Net is sufficient in recognizing the tissues of an inguinal hernia repair phantom. Future studies will explore combining our segmentation U-Net with tool detection as we work towards workflow recognition.</p

Fichtinger G.: Tracked ultrasound snapshots in percutaneous pedicle screw placement navigation: a feasibility study

Abstract 1 Background. Computerized navigation improves the clinical outcome of pedicle screw

Sensor-Based Automated Detection of Electrosurgical Cautery States

In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools&rsquo; location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery&mdash;robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins