Simulation Method for the Physical Deformation of a Three-Dimensional Soft Body in Augmented Reality-Based External Ventricular Drainage

Objectives Intraoperative navigation reduces the risk of major complications and increases the likelihood of optimal surgical outcomes. This paper presents an augmented reality (AR)-based simulation technique for ventriculostomy that visualizes brain deformations caused by the movements of a surgical instrument in a three-dimensional brain model. This is achieved by utilizing a position-based dynamics (PBD) physical deformation method on a preoperative brain image. Methods An infrared camera-based AR surgical environment aligns the real-world space with a virtual space and tracks the surgical instruments. For a realistic representation and reduced simulation computation load, a hybrid geometric model is employed, which combines a high-resolution mesh model and a multiresolution tetrahedron model. Collision handling is executed when a collision between the brain and surgical instrument is detected. Constraints are used to preserve the properties of the soft body and ensure stable deformation. Results The experiment was conducted once in a phantom environment and once in an actual surgical environment. The tasks of inserting the surgical instrument into the ventricle using only the navigation information presented through the smart glasses and verifying the drainage of cerebrospinal fluid were evaluated. These tasks were successfully completed, as indicated by the drainage, and the deformation simulation speed averaged 18.78 fps. Conclusions This experiment confirmed that the AR-based method for external ventricular drain surgery was beneficial to clinicians

Computer-Aided Breast Surgery Framework Using a Markerless Augmented Reality Method

This study proposes a markerless Augmented Reality (AR) surgical framework for breast lesion removal using a depth sensor and 3D breast Computed Tomography (CT) images. A patient mesh in the real coordinate system is acquired through a patient 3D scan using a depth sensor for registration. The patient mesh on the virtual coordinate system is obtained by contrast-based skin segmentation in 3D mesh generated from breast CT scans. Then, the nipple area is detected based on the gradient in the segmented skin area. The region of interest (ROI) is set based on the detection result to select the vertices in the virtual coordinate system. The mesh on the real and virtual coordinate systems is first aligned by matching the center of mass, and the Iterative Closest Point (ICP) method is applied to perform more precise registration. Experimental results of 20 patients’ data showed 98.35 ± 0.71% skin segmentation accuracy in terms of Dice Similarity Coefficient (DSC) value, 2.79 ± 1.54 mm nipple detection error, and 4.69 ± 1.95 mm registration error. Experiments using phantom and patient data also confirmed high accuracy in AR visualization. The proposed method in this study showed that the 3D AR visualization of medical data on the patient’s body is possible by using a single depth sensor without having to use markers

Change of Computed Tomography-Based Body Composition after Adrenalectomy in Patients with Pheochromocytoma

Despite the potential biological importance of the sympathetic nervous system on fat and skeletal muscle metabolism in animal and in vitro studies, its relevance in humans remains undetermined. To clarify the influence of catecholamine excess on human body composition, we performed a retrospective longitudinal cohort study including 313 consecutive patients with histologically confirmed pheochromocytoma who underwent repeat abdominal computed tomography (CT) scans before and after adrenalectomy. Changes in CT-determined visceral fat area (VFA), subcutaneous fat area (SFA), skeletal muscle area (SMA), and skeletal muscle index (SMI) were measured at the level of the third lumbar vertebra. The mean age of all patients was 50.6 ± 13.6 years, and 171/313 (54.6%) were women. The median follow-up duration for repeat CTs was 25.0 months. VFA and SFA were 14.5% and 15.8% higher, respectively (both p p < 0.001); however, the prevalence of sarcopenia was unchanged. This study provides important clinical evidence that sympathetic hyperactivity can contribute to lipolysis in visceral and subcutaneous adipose tissues, but its impact on human skeletal muscle is unclear

Deep Learning Segmentation in 2D X-ray Images and Non-Rigid Registration in Multi-Modality Images of Coronary Arteries

X-ray angiography is commonly used in the diagnosis and treatment of coronary artery disease with the advantage of visualization of the inside of blood vessels in real-time. However, it has several disadvantages that occur in the acquisition process, which causes inconvenience and difficulty. Here, we propose a novel segmentation and nonrigid registration method to provide useful real-time assistive images and information. A convolutional neural network is used for the segmentation of coronary arteries in 2D X-ray angiography acquired from various angles in real-time. To compensate for errors that occur during the 2D X-ray angiography acquisition process, 3D CT angiography is used to analyze the topological structure. A novel energy function-based 3D deformation and optimization is utilized to implement real-time registration. We evaluated the proposed method for 50 series from 38 patients by comparing the ground truth. The proposed segmentation method showed that Precision, Recall, and F1 score were 0.7563, 0.6922, and 0.7176 for all vessels, 0.8542, 0.6003, and 0.7035 for markers, and 0.8897, 0.6389, and 0.7386 for bifurcation points, respectively. In the nonrigid registration method, the average distance of 0.8705, 1.06, and 1. 5706 mm for all vessels, markers, and bifurcation points was achieved. The overall process execution time was 0.179 s