Fast-track virtual reality software to facilitate 3-dimensional reconstruction in congenital heart disease.

OBJECTIVES: Two limitations of the clinical use of 3-dimensional (3D) reconstruction and virtual reality systems are the relatively high cost and the amount of experience required to use hardware and software to effectively explore medical images. We have tried to simplify the process and validate a new tool developed for this purpose with a novel software package. METHODS: Five patients with right partial anomalous pulmonary venous return with adequate preoperative images acquired with magnetic resonance imaging were enrolled. Five volunteers with no previous experience in the field of 3D reconstruction were instructed to use the software after viewing a short video tutorial. Users were then asked to create a 3D model of each patient's heart using DIVA software. Their results were compared quantitatively and qualitatively with a benchmark reconstruction performed by an experienced user. RESULTS: All our participants recreated 3D models in a relatively short time, maintaining a good overall quality (average quality score ≥ 3 on a scale of 1-5). The overall trend of all the parameters analysed showed a statistical improvement between case 1 and case 5, as users became more and more experienced. CONCLUSIONS: DIVA is a simple software program that allows accurate 3D reconstruction in a relatively short time ("fast-track" virtual reality). In this study, we demonstrated the potential use of DIVA by inexperienced users, with a significant improvement in quality and time after a few cases were performed. Further studies are needed to confirm the potential application of this technology on a larger scale.

Breast Magnetic Resonance Image Analysis for Surgeons Using Virtual Reality: A Comparative Study.

PURPOSE: The treatment of breast cancer, the leading cause of cancer and cancer mortality among women worldwide, is mainly on the basis of surgery. In this study, we describe the use of a medical image visualization tool on the basis of virtual reality (VR), entitled DIVA, in the context of breast cancer tumor localization among surgeons. The aim of this study was to evaluate the speed and accuracy of surgeons using DIVA for medical image analysis of breast magnetic resonance image (MRI) scans relative to standard image slice-based visualization tools. MATERIALS AND METHODS: In our study, residents and practicing surgeons used two breast MRI reading modalities: the common slice-based radiology interface and the DIVA system in its VR mode. Metrics measured were compared in relation to postoperative anatomical-pathologic reports. RESULTS: Eighteen breast surgeons from the Institut Curie performed all the analysis presented. The MRI analysis time was significantly lower with the DIVA system than with the slice-based visualization for residents, practitioners, and subsequently the entire group (P < .001). The accuracy of determination of which breast contained the lesion significantly increased with DIVA for residents (P = .003) and practitioners (P = .04). There was little difference between the DIVA and slice-based visualization for the determination of the number of lesions. The accuracy of quadrant determination was significantly improved by DIVA for practicing surgeons (P = .01) but not significantly for residents (P = .49). CONCLUSION: This study indicates that the VR visualization of medical images systematically improves surgeons' analysis of preoperative breast MRI scans across several different metrics irrespective of surgeon seniority.

Fast-track virtual reality for cardiac imaging in congenital heart disease.

BACKGROUND AND AIM OF THE STUDY: We sought to evaluate the appropriateness of cardiac anatomy renderings by a new virtual reality (VR) technology, entitled DIVA, directly applicable to raw magnetic resonance imaging (MRI) data without intermediate segmentation steps in comparison to standard three-dimensional (3D) rendering techniques (3D PDF and 3D printing). Differences in post-processing times were also evaluated. METHODS: We reconstructed 3D (STL, 3D-PDF, and 3D printed ones) and VR models of three patients with different types of complex congenital heart disease (CHD). We then asked a senior pediatric heart surgeon to compare and grade the results obtained. RESULTS: All anatomical structures were well visualized in both VR and 3D PDF/printed models. Ventricular-arterial connections and their relationship with the great vessels were better visualized with the VR model (Case 2); aortic arch anatomy and details were also better visualized by the VR model (Case 3). The median post-processing time to get VR models using DIVA was 5 min in comparison to 8 h (range 8-12 h including printing time) for 3D models (PDF/printed). CONCLUSIONS: VR directly applied to non-segmented 3D-MRI data set is a promising technique for 3D advanced modeling in CHD. It is systematically more consistent and faster when compared to standard 3D-modeling techniques.

Partial breast resection for multifocal lower quadrant breast tumour using virtual reality.

Oncoplastic surgery allows an increase in the number of indications for conservative breast cancer treatments. However, uncertainty as to whether it can be performed still exists in certain situations such as with multicentric or multifocal lesions, even when the breast volume can accommodate it. With the aid of a virtual reality software, DIVA, allowing the precise visualisation of tumours and breast volumes based entirely on the patient's MRI, we report the ability to rapidly confirm and secure an indication for partial surgery of multiple lesions in a 31-year-old patient. With the described approach, the patient did not have to suffer significant disfigurement from cancerous breast surgery without compromising safety.

New Approach to Accelerated Image Annotation by Leveraging Virtual Reality and Cloud Computing.

Three-dimensional imaging is at the core of medical imaging and is becoming a standard in biological research. As a result, there is an increasing need to visualize, analyze and interact with data in a natural three-dimensional context. By combining stereoscopy and motion tracking, commercial virtual reality (VR) headsets provide a solution to this critical visualization challenge by allowing users to view volumetric image stacks in a highly intuitive fashion. While optimizing the visualization and interaction process in VR remains an active topic, one of the most pressing issue is how to utilize VR for annotation and analysis of data. Annotating data is often a required step for training machine learning algorithms. For example, enhancing the ability to annotate complex three-dimensional data in biological research as newly acquired data may come in limited quantities. Similarly, medical data annotation is often time-consuming and requires expert knowledge to identify structures of interest correctly. Moreover, simultaneous data analysis and visualization in VR is computationally demanding. Here, we introduce a new procedure to visualize, interact, annotate and analyze data by combining VR with cloud computing. VR is leveraged to provide natural interactions with volumetric representations of experimental imaging data. In parallel, cloud computing performs costly computations to accelerate the data annotation with minimal input required from the user. We demonstrate multiple proof-of-concept applications of our approach on volumetric fluorescent microscopy images of mouse neurons and tumor or organ annotations in medical images.

DIVA, a 3D virtual reality platform, improves undergraduate craniofacial trauma education.

Craniofacial fractures management is challenging to teach due to the complex anatomy of the head, even when using three-dimensional CT-scan images. DIVA is a software allowing the straightforward visualization of CT-scans in a user-friendly three-dimensional virtual reality environment. Here, we assess DIVA as an educational tool for craniofacial trauma for undergraduate medical students. Three craniofacial trauma cases (jaw fracture, naso-orbital-ethmoid complex fracture and Le Fort 3 fracture) were submitted to 50 undergraduate medical students, who had to provide diagnoses and treatment plans. Each student then filled an 8-item questionnaire assessing satisfaction, potential benefit, ease of use and tolerance. Additionally, 4 postgraduate students were requested to explore these cases and to place 6 anatomical landmarks on both virtual reality renderings and usual slice-based three-dimensional CT-scan visualizations. High degrees of satisfaction (98%) without specific tolerance issues (86%) were reported. The potential benefit in a better understanding of craniofacial trauma using virtual reality was reported by almost all students (98%). Virtual reality allowed a reliable localization of key anatomical landmarks when compared with standard three-dimensional CT-scan visualization. Virtual reality interfaces such DIVA are beneficial to medical students for a better understanding of craniofacial trauma and allow a reliable rendering of craniofacial anatomy.