3D echocardiography (3DE) is the standard modality for visualizing heart valves and their surrounding anatomical structures. Commercial cardiovascular ultrasound systems commonly offer a set of parameters that allow clinical users to modify, in real time, visual aspects of the information contained in the echocardiogram. To our knowledge, there is currently no work that demonstrates if the methods currently used by commercial platforms are optimal. In addition, current platforms have limitations in adjusting the visibility of anatomical structures, such as reducing information that obstructs anatomical structures without removing essential clinical information. To overcome this, the present work proposes a new method for 3DE visualization based on “focus + context” (F+C), a concept which aims to present a detailed region of interest while preserving a less detailed overview of the surrounding context. The new method is intended to allow clinical users to modify parameter values differently within a certain region of interest, independently from the adjustment of contextual information. To validate this new method, a user study was conducted amongst clinical experts. As part of the user study, clinical experts adjusted parameters for five echocardiograms of patients with complete atrioventricular canal defect (CAVC) using both the method conventionally used by commercial platforms and the proposed method based on F+C. The results showed relevance for the F+C-based method to visualize 3DE of CAVC patients, where users chose significantly different parameter values with the F+C-based method.
We present our work investigating the feasibility of combining intraoperative ultrasound for brain shift correction and augmented reality (AR) visualization for intraoperative interpretation of patient-specific models in image-guided neurosurgery (IGNS) of brain tumors. We combine two imaging technologies for image-guided brain tumor neurosurgery. Throughout surgical interventions, AR was used to assess different surgical strategies using three-dimensional (3-D) patient-specific models of the patient’s cortex, vasculature, and lesion. Ultrasound imaging was acquired intraoperatively, and preoperative images and models were registered to the intraoperative data. The quality and reliability of the AR views were evaluated with both qualitative and quantitative metrics. A pilot study of eight patients demonstrates the feasible combination of these two technologies and their complementary features. In each case, the AR visualizations enabled the surgeon to accurately visualize the anatomy and pathology of interest for an extended period of the intervention. Inaccuracies associated with misregistration, brain shift, and AR were improved in all cases. These results demonstrate the potential of combining ultrasound-based registration with AR to become a useful tool for neurosurgeons to improve intraoperative patient-specific planning by improving the understanding of complex 3-D medical imaging data and prolonging the reliable use of IGNS.
Cerebral arteriovenous malformations (AVMs) are a type of vascular anomaly consisting of large intertwined
vascular growth (the nidus) that are prone to serious hemorrhaging and can result in patient death if left
untreated. Intervention through surgical clipping of feeding and draining vessels to the nidus is a common
treatment. However, identification of which vessels to clip is challenging even to experienced surgeons aided by
conventional image guidance systems. In this work, we describe our methods for processing static preoperative
angiographic images in order to effectively visualize the feeding and draining vessels of an AVM nidus. Maps from
level-set front propagation processing of the vessel images are used to label the vessels by colour. Furthermore,
images are decluttered using the topological distances between vessels. In order to aid the surgeon in the
vessel clipping decision-making process during surgery, the results are displayed to the surgeon using augmented
virtuality.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.