Segmenting lumbar vertebrae in digital video fluoroscopic images through edge enhancement

Video fluoroscopy provides a cost effective way for the diagnosis of low back pain. Backbones or vertebrae are usually segmented manually from fluoroscopic images of low quality during such a diagnosis. In this paper, we try to reduce human workload by performing automatic vertebrae detection and segmentation. Operators need to provide the rough location of landmarks only. The proposed algorithm will perform edge detection, which is based on pattern recognition of texture, along the snake formed from the landmarks. The snake will then attach to the edge detected. Experimental results show that the proposed system can segment vertebrae from video fluoroscopic image automatically and accurately. © 2004 IEEE.published_or_final_versio

3D Shape Reconstruction of Knee Bones from Low Radiation X-ray Images Using Deep Learning

Understanding the bone kinematics of the human knee during dynamic motions is necessary to evaluate the pathological conditions, design knee prosthesis, orthosis and surgical treatments such as knee arthroplasty. Also, knee bone kinematics is essential to assess the biofidelity of the computational models. Kinematics of the human knee has been reported in the literature either using in vitro or in vivo methodologies. In vivo methodology is widely preferred due to biomechanical accuracies. However, it is challenging to obtain the kinematic data in vivo due to limitations in existing methods. One of the several existing methods used in such application is using X-ray fluoroscopy imaging, which allows for the non-invasive quantification of bone kinematics. In the fluoroscopy imaging method, due to procedural simplicity and low radiation exposure, single-plane fluoroscopy (SF) is the preferred tool to study the in vivo kinematics of the knee joint. Evaluation of the three-dimensional (3D) kinematics from the SF imagery is possible only if prior knowledge of the shape of the knee bones is available. The standard technique for acquiring the knee shape is to either segment Magnetic Resonance (MR) images, which is expensive to procure, or Computed Tomography (CT) images, which exposes the subjects to a heavy dose of ionizing radiation. Additionally, both the segmentation procedures are time-consuming and labour-intensive. An alternative technique that is rarely used is to reconstruct the knee shape from the SF images. It is less expensive than MR imaging, exposes the subjects to relatively lower radiation than CT imaging, and since the kinematic study and the shape reconstruction could be carried out using the same device, it could save a considerable amount of time for the researchers and the subjects. However, due to low exposure levels, SF images are often characterized by a low signal-to-noise ratio, making it difficult to extract the required information to reconstruct the shape accurately. In comparison to conventional X-ray images, SF images are of lower quality and have less detail. Additionally, existing methods for reconstructing the shape of the knee remain generally inconvenient since they need a highly controlled system: images must be captured from a calibrated device, care must be taken while positioning the subject's knee in the X-ray field to ensure image consistency, and user intervention and expert knowledge is required for 3D reconstruction. In an attempt to simplify the existing process, this thesis proposes a new methodology to reconstruct the 3D shape of the knee bones from multiple uncalibrated SF images using deep learning. During the image acquisition using the SF, the subjects in this approach can freely rotate their leg (in a fully extended, knee-locked position), resulting in several images captured in arbitrary poses. Relevant features are extracted from these images using a novel feature extraction technique before feeding it to a custom-built Convolutional Neural Network (CNN). The network, without further optimization, directly outputs a meshed 3D surface model of the subject's knee joint. The whole procedure could be completed in a few minutes. The robust feature extraction technique can effectively extract relevant information from a range of image qualities. When tested on eight unseen sets of SF images with known true geometry, the network reconstructed knee shape models with a shape error (RMSE) of 1.91± 0.30 mm for the femur, 2.3± 0.36 mm for the tibia and 3.3± 0.53 mm for the patella. The error was calculated after rigidly aligning (scale, rotation, and translation) each of the reconstructed shape models with the corresponding known true geometry (obtained through MRI segmentation). Based on a previous study that examined the influence of reconstructed shape accuracy on the precision of the evaluation of tibiofemoral kinematics, the shape accuracy of the proposed methodology might be adequate to precisely track the bone kinematics, although further investigation is required

Exploiting Temporal Image Information in Minimally Invasive Surgery

Minimally invasive procedures rely on medical imaging instead of the surgeons direct vision. While preoperative images can be used for surgical planning and navigation, once the surgeon arrives at the target site real-time intraoperative imaging is needed. However, acquiring and interpreting these images can be challenging and much of the rich temporal information present in these images is not visible. The goal of this thesis is to improve image guidance for minimally invasive surgery in two main areas. First, by showing how high-quality ultrasound video can be obtained by integrating an ultrasound transducer directly into delivery devices for beating heart valve surgery. Secondly, by extracting hidden temporal information through video processing methods to help the surgeon localize important anatomical structures. Prototypes of delivery tools, with integrated ultrasound imaging, were developed for both transcatheter aortic valve implantation and mitral valve repair. These tools provided an on-site view that shows the tool-tissue interactions during valve repair. Additionally, augmented reality environments were used to add more anatomical context that aids in navigation and in interpreting the on-site video. Other procedures can be improved by extracting hidden temporal information from the intraoperative video. In ultrasound guided epidural injections, dural pulsation provides a cue in finding a clear trajectory to the epidural space. By processing the video using extended Kalman filtering, subtle pulsations were automatically detected and visualized in real-time. A statistical framework for analyzing periodicity was developed based on dynamic linear modelling. In addition to detecting dural pulsation in lumbar spine ultrasound, this approach was used to image tissue perfusion in natural video and generate ventilation maps from free-breathing magnetic resonance imaging. A second statistical method, based on spectral analysis of pixel intensity values, allowed blood flow to be detected directly from high-frequency B-mode ultrasound video. Finally, pulsatile cues in endoscopic video were enhanced through Eulerian video magnification to help localize critical vasculature. This approach shows particular promise in identifying the basilar artery in endoscopic third ventriculostomy and the prostatic artery in nerve-sparing prostatectomy. A real-time implementation was developed which processed full-resolution stereoscopic video on the da Vinci Surgical System

Biplanar Fluoroscopic Analysis of in vivo Hindfoot Kinematics During Ambulation

The overall goal of this project was to develop and validate a biplanar fluoroscopic system and integrated software to assess hindfoot kinematics. Understanding the motion of the foot and ankle joints may lead to improved treatment methods in persons with foot and ankle pathologies. During gait analysis, skin markers are placed on the lower extremities, which are defined as four rigid-body segments with three joints representing the hip, knee and ankle. This method introduces gross assumptions on the foot and severely limits the analysis of in depth foot mechanics. Multi-segmental models have been developed, but are susceptible to skin motion artifact error. Intra-cortical bone pins studies provide higher accuracy, but are invasive. This dissertation developed and validated a noninvasive biplane fluoroscopy system to overcome the skin motion artifacts and rigid-body assumptions of conventional foot motion analysis. The custom-built biplane fluoroscopy system was constructed from two fluoroscopes separated by 60°, attached to a custom walkway with an embedded force plate. Open source software was incorporated to correct the image distortion and calibrate the capture volume. This study was the first that quantified the cross-scatter contamination in a biplane fluoroscopic system and its effects on the accuracy of marker-based tracking. A cadaver foot study determined the static and dynamic error of the biplane fluoroscopic system using both marker-based and model-based tracking algorithms. The study also developed in vivo 3D kinematic models of the talocrural and subtalar joints during the stance phase of gait. Cross-scatter degradation showed negligible effects in the smallest phantom, suggesting negligible motion tracking error due to cross scatter for distal extremities. Marker-based tracking error had a maximum absolute mean error of 0.21 (± 0.15) in dynamic trials. Model-based tracking results compared to marker-based had an overall dynamic RMS average error of 0.59 mm. Models were developed using custom algorithms to determine talocrural and subtalar joint 3D kinematics. The models offer a viable, noninvasive method suitable for quantifying hindfoot kinematics. Patients with a variety of adult and pediatric conditions which affect foot and ankle dynamics during walking may benefit from this work

Reconstruction 3D personnalisée de la colonne vertébrale à partir d'images radiographiques non-calibrées

Les systèmes de reconstruction stéréo-radiographique 3D -- La colonne vertébrale -- La scoliose idiopathique adolescente -- Évolution des systèmes de reconstruction 3D -- Filtres de rehaussement d'images -- Techniques de segmentation -- Les méthodes de calibrage -- Les méthodes de reconstruction 3D -- Problématique, hypothèses, objectifs et méthode générale -- Three-dimensional reconstruction of the scoliotic spine and pelvis from uncalibrated biplanar X-ray images -- A versatile 3D reconstruction system of the spine and pelvis for clinical assessment of spinal deformities -- Simulation experiments -- Clinical validation -- A three-dimensional retrospective analysis of the evolution of spinal instrumentation for the correction of adolescent idiopathic scoliosis -- Auto-calibrage d'un système à rayons-X à partir de primitives de haut niveau -- Segmentation de la colonne vertébrale -- Approche hiérarchique d'auto-calibrage d'un système d'acquisition à rayons-X -- Personalized 3D reconstruction of the scoliotic spine from hybrid statistical and X-ray image-based models -- Validation protocol

Low Back Pain (LBP)

Low back pain (LBP) is a major public health problem, being the most commonly reported musculoskeletal disorder (MSD) and the leading cause of compromised quality of life and work absenteeism. Indeed, LBP is the leading worldwide cause of years lost to disability, and its burden is growing alongside the increasing and aging population. The etiology, pathogenesis, and occupational risk factors of LBP are still not fully understood. It is crucial to give a stronger focus to reducing the consequences of LBP, as well as preventing its onset. Primary prevention at the occupational level remains important for highly exposed groups. Therefore, it is essential to identify which treatment options and workplace-based intervention strategies are effective in increasing participation at work and encouraging early return-to-work to reduce the consequences of LBP. The present Special Issue offers a unique opportunity to update many of the recent advances and perspectives of this health problem. A number of topics will be covered in order to attract high-quality research papers, including the following major areas: prevalence and epidemiological data, etiology, prevention, assessment and treatment approaches, and health promotion strategies for LBP. We have received a wide range of submissions, including research on the physical, psychosocial, environmental, and occupational perspectives, also focused on workplace interventions

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

AUGMENTED REALITY AND INTRAOPERATIVE C-ARM CONE-BEAM COMPUTED TOMOGRAPHY FOR IMAGE-GUIDED ROBOTIC SURGERY

Minimally-invasive robotic-assisted surgery is a rapidly-growing alternative to traditionally open and laparoscopic procedures; nevertheless, challenges remain. Standard of care derives surgical strategies from preoperative volumetric data (i.e., computed tomography (CT) and magnetic resonance (MR) images) that benefit from the ability of multiple modalities to delineate different anatomical boundaries. However, preoperative images may not reflect a possibly highly deformed perioperative setup or intraoperative deformation. Additionally, in current clinical practice, the correspondence of preoperative plans to the surgical scene is conducted as a mental exercise; thus, the accuracy of this practice is highly dependent on the surgeon’s experience and therefore subject to inconsistencies. In order to address these fundamental limitations in minimally-invasive robotic surgery, this dissertation combines a high-end robotic C-arm imaging system and a modern robotic surgical platform as an integrated intraoperative image-guided system. We performed deformable registration of preoperative plans to a perioperative cone-beam computed tomography (CBCT), acquired after the patient is positioned for intervention. From the registered surgical plans, we overlaid critical information onto the primary intraoperative visual source, the robotic endoscope, by using augmented reality. Guidance afforded by this system not only uses augmented reality to fuse virtual medical information, but also provides tool localization and other dynamic intraoperative updated behavior in order to present enhanced depth feedback and information to the surgeon. These techniques in guided robotic surgery required a streamlined approach to creating intuitive and effective human-machine interferences, especially in visualization. Our software design principles create an inherently information-driven modular architecture incorporating robotics and intraoperative imaging through augmented reality. The system's performance is evaluated using phantoms and preclinical in-vivo experiments for multiple applications, including transoral robotic surgery, robot-assisted thoracic interventions, and cocheostomy for cochlear implantation. The resulting functionality, proposed architecture, and implemented methodologies can be further generalized to other C-arm-based image guidance for additional extensions in robotic surgery