Virtual Bronchoscopy and 3D Spiral CT Reconstructions in the Management of Patient with Bronchial Cancer – Our Experience with Syngo 3D Postprocessing Software

Multislice helical CT generated virtual bronchoscopy (VB) represents one of the most recent developments in three-dimensional computer aided visualisation techniques. VB allows non-invasive and relatively accurate 3D evaluation of tracheobronchal tree. We performed virtual bronchoscopy and in-space 3D volume analysis on CT-data set acquired from sixty-four-year old male with bronchial cancer in order to demonstrate advantages and disadvantages of these methods in diagnostics and preoperative management of metastatic bronchial cancer. Siemens Somatom Emotion 16 helical CT scanner was used for data acquisition. Data post-processing was done with 3D Syngo 2006G software package from Siemens medical systems. CT scanning of the thorax was performed in heavy smoker with an expansive T4N1M1 malignant process in a superior lobe of the right lung accompanied with large metastatic lesion attached on the right lateral chest wall. Metastatic lesions were also found in vertebral column. In-space 3D analysis followed with virtual bronchoscopy had revealed obstruction of apical branch of superior lobe segmental bronchus. External compression done by tumor to the superior segmental and right main bronchus was found. We concluded that multi-slice CT in connection with VB became a possible non-invasive alternative to bronchoscopy, if tissue samples are not required

Image-Based Bronchial Anatomy Codification for Biopsy Guiding in Video Bronchoscopy

Bronchoscopy examinations allow biopsy of pulmonary nodules with minimum risk for the patient. Even for experienced bronchoscopists, it is difficult to guide the bronchoscope to most distal lesions and obtain an accurate diagnosis. This paper presents an image-based codification of the bronchial anatomy for bronchoscopy biopsy guiding. The 3D anatomy of each patient is codified as a binary tree with nodes representing bronchial levels and edges labeled using their position on images projecting the 3D anatomy from a set of branching points. The paths from the root to leaves provide a codification of navigation routes with spatially consistent labels according to the anatomy observes in video bronchoscopy explorations. We evaluate our labeling approach as a guiding system in terms of the number of bronchial levels correctly codified, also in the number of labels-based instructions correctly supplied, using generalized mixed models and computer-generated data. Results obtained for three independent observers prove the consistency and reproducibility of our guiding system. We trust that our codification based on viewer's projection might be used as a foundation for the navigation process in Virtual Bronchoscopy systems

Interventional Bronchoscopy:State-of-the-Art Review

For over 150 years, bronchoscopy, especially flexible bronchoscopy, has been a mainstay for airway inspection, the diagnosis of airway lesions, therapeutic aspiration of airway secretions, and transbronchial biopsy to diagnose parenchymal lung disorders. Its utility for the diagnosis of peripheral pulmonary nodules and therapeutic treatments besides aspiration of airway secretions, however, has been limited. Challenges to the wider use of flexible bronchoscopy have included difficulty in navigating to the lung periphery, the avoidance of vasculature structures when performing diagnostic biopsies, and the ability to biopsy a lesion under direct visualization. The last 10-15 years have seen major advances in thoracic imaging, navigational platforms to direct the bronchoscopist to lung lesions, and the ability to visualize lesions during biopsy. Moreover, multiple new techniques have either become recently available or are currently being investigated to treat a broad range of airway and lung parenchymal diseases, such as asthma, emphysema, and chronic bronchitis, or to alleviate recurrent exacerbations. New bronchoscopic therapies are also being investigated to not only diagnose, but possibly treat, malignant peripheral lung nodules. As a result, flexible bronchoscopy is now able to provide a new and expanding armamentarium of diagnostic and therapeutic tools to treat patients with a variety of lung diseases. This State-of-the-Art review succinctly reviews these techniques and provides clinicians an organized approach to their role in the diagnosis and treatment of a range of lung diseases

Endoscopic Technologies for Peripheral Pulmonary Lesions: From Diagnosis to Therapy

Peripheral pulmonary lesions (PPLs) are frequent incidental findings in subjects when performing chest radiographs or chest computed tomography (CT) scans. When a PPL is identified, it is necessary to proceed with a risk stratification based on the patient profile and the characteristics found on chest CT. In order to proceed with a diagnostic procedure, the first-line examination is often a bronchoscopy with tissue sampling. Many guidance technologies have recently been developed to facilitate PPLs sampling. Through bronchoscopy, it is currently possible to ascertain the PPL’s benign or malignant nature, delaying the therapy’s second phase with radical, supportive, or palliative intent. In this review, we describe all the new tools available: from the innovation of bronchoscopic instrumentation (e.g., ultrathin bronchoscopy and robotic bronchoscopy) to the advances in navigation technology (e.g., radial-probe endobronchial ultrasound, virtual navigation, electromagnetic navigation, shape-sensing navigation, cone-beam computed tomography). In addition, we summarize all the PPLs ablation techniques currently under experimentation. Interventional pulmonology may be a discipline aiming at adopting increasingly innovative and disruptive technologies

BronchoX : bronchoscopy exploration software for biopsy intervention planning

Altres: ACCIO Tecniospring TECSPR17-1-0045Virtual bronchoscopy (VB) is a non-invasive exploration tool for intervention planning and navigation of possible pulmonary lesions (PLs). A VB software involves the location of a PL and the calculation of a route, starting from the trachea, to reach it. The selection of a VB software might be a complex process, and there is no consensus in the community of medical software developers in which is the best-suited system to use or framework to choose. The authors present Bronchoscopy Exploration (BronchoX), a VB software to plan biopsy interventions that generate physician-readable instructions to reach the PLs. The authors' solution is open source, multiplatform, and extensible for future functionalities, designed by their multidisciplinary research and development group. BronchoX is a compound of different algorithms for segmentation, visualisation, and navigation of the respiratory tract. Performed results are a focus on the test the effectiveness of their proposal as an exploration software, also to measure its accuracy as a guiding system to reach PLs. Then, 40 different virtual planning paths were created to guide physicians until distal bronchioles. These results provide a functional software for BronchoX and demonstrate how following simple instructions is possible to reach distal lesions from the trachea

Surgical Subtask Automation for Intraluminal Procedures using Deep Reinforcement Learning

Intraluminal procedures have opened up a new sub-field of minimally invasive surgery that use flexible instruments to navigate through complex luminal structures of the body, resulting in reduced invasiveness and improved patient benefits. One of the major challenges in this field is the accurate and precise control of the instrument inside the human body. Robotics has emerged as a promising solution to this problem. However, to achieve successful robotic intraluminal interventions, the control of the instrument needs to be automated to a large extent. The thesis first examines the state-of-the-art in intraluminal surgical robotics and identifies the key challenges in this field, which include the need for safe and effective tool manipulation, and the ability to adapt to unexpected changes in the luminal environment. To address these challenges, the thesis proposes several levels of autonomy that enable the robotic system to perform individual subtasks autonomously, while still allowing the surgeon to retain overall control of the procedure. The approach facilitates the development of specialized algorithms such as Deep Reinforcement Learning (DRL) for subtasks like navigation and tissue manipulation to produce robust surgical gestures. Additionally, the thesis proposes a safety framework that provides formal guarantees to prevent risky actions. The presented approaches are evaluated through a series of experiments using simulation and robotic platforms. The experiments demonstrate that subtask automation can improve the accuracy and efficiency of tool positioning and tissue manipulation, while also reducing the cognitive load on the surgeon. The results of this research have the potential to improve the reliability and safety of intraluminal surgical interventions, ultimately leading to better outcomes for patients and surgeons

Appearance Modelling and Reconstruction for Navigation in Minimally Invasive Surgery

Minimally invasive surgery is playing an increasingly important role for patient care. Whilst its direct patient benefit in terms of reduced trauma, improved recovery and shortened hospitalisation has been well established, there is a sustained need for improved training of the existing procedures and the development of new smart instruments to tackle the issue of visualisation, ergonomic control, haptic and tactile feedback. For endoscopic intervention, the small field of view in the presence of a complex anatomy can easily introduce disorientation to the operator as the tortuous access pathway is not always easy to predict and control with standard endoscopes. Effective training through simulation devices, based on either virtual reality or mixed-reality simulators, can help to improve the spatial awareness, consistency and safety of these procedures. This thesis examines the use of endoscopic videos for both simulation and navigation purposes. More specifically, it addresses the challenging problem of how to build high-fidelity subject-specific simulation environments for improved training and skills assessment. Issues related to mesh parameterisation and texture blending are investigated. With the maturity of computer vision in terms of both 3D shape reconstruction and localisation and mapping, vision-based techniques have enjoyed significant interest in recent years for surgical navigation. The thesis also tackles the problem of how to use vision-based techniques for providing a detailed 3D map and dynamically expanded field of view to improve spatial awareness and avoid operator disorientation. The key advantage of this approach is that it does not require additional hardware, and thus introduces minimal interference to the existing surgical workflow. The derived 3D map can be effectively integrated with pre-operative data, allowing both global and local 3D navigation by taking into account tissue structural and appearance changes. Both simulation and laboratory-based experiments are conducted throughout this research to assess the practical value of the method proposed