Dataset Information

A System for Real-Time, Online Mixed-Reality Visualization of Cardiac Magnetic Resonance Images.

ABSTRACT: Image-guided cardiovascular interventions are rapidly evolving procedures that necessitate imaging systems capable of rapid data acquisition and low-latency image reconstruction and visualization. Compared to alternative modalities, Magnetic Resonance Imaging (MRI) is attractive for guidance in complex interventional settings thanks to excellent soft tissue contrast and large fields-of-view without exposure to ionizing radiation. However, most clinically deployed MRI sequences and visualization pipelines exhibit poor latency characteristics, and spatial integration of complex anatomy and device orientation can be challenging on conventional 2D displays. This work demonstrates a proof-of-concept system linking real-time cardiac MR image acquisition, online low-latency reconstruction, and a stereoscopic display to support further development in real-time MR-guided intervention. Data are acquired using an undersampled, radial trajectory and reconstructed via parallelized through-time radial generalized autocalibrating partially parallel acquisition (GRAPPA) implemented on graphics processing units. Images are rendered for display in a stereoscopic mixed-reality head-mounted display. The system is successfully tested by imaging standard cardiac views in healthy volunteers. Datasets comprised of one slice (46 ms), two slices (92 ms), and three slices (138 ms) are collected, with the acquisition time of each listed in parentheses. Images are displayed with latencies of 42 ms/frame or less for all three conditions. Volumetric data are acquired at one volume per heartbeat with acquisition times of 467 ms and 588 ms when 8 and 12 partitions are acquired, respectively. Volumes are displayed with a latency of 286 ms or less. The faster-than-acquisition latencies for both planar and volumetric display enable real-time 3D visualization of the heart.

SUBMITTER: Franson D

PROVIDER: S-EPMC8709155 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Mixed reality (MR) enables a novel way to visualize virtual objects on real scenarios considering physical constraints. This technology arises with other significant advances in the field of sensors fusion for human-centric 3D capturing. Recent advances for scanning the user environment, real-time visualization and 3D vision using ubiquitous systems like smartphones allow us to capture 3D data from the real world. In this paper, a disruptive application for assessing the status of indoor infrastructures is proposed. The installation and maintenance of hidden facilities such as water pipes, electrical lines and air conditioning tubes, which are usually occluded behind the wall, supposes tedious and inefficient tasks. Most of these infrastructures are digitized but they cannot be visualized onsite. In this research, we focused on the development of a new application (GEUINF) to be launched on smartphones that are capable of capturing 3D data of the real world by depth sensing. This information is relevant to determine the user position and orientation. Although previous approaches used fixed markers for this purpose, our application enables the estimation of both parameters with a centimeter accuracy without them. This novelty is possible since our method is based on a matching process between reconstructed walls of the real world and 3D planes of the replicated world in a virtual environment. Our markerless approach is based on scanning planar surfaces of the user environment and then, these are geometrically aligned with their corresponding virtual 3D entities. In a preprocessing phase, the 2D CAD geometry available from an architectural project is used to generate 3D models of an indoor building structure. In real time, these virtual elements are tracked with the real ones modeled by using ARCore library. Once the alignment between virtual and real worlds is done, the application enables the visualization, navigation and interaction with the virtual facility networks in real-time. Thus, our method may be used by private companies and public institutions responsible of the indoor facilities management and also may be integrated with other applications focused on indoor navigation.

Project description:Background and objectiveMagnetic resonance (MR) imaging is increasingly used in studies of speech as it enables non-invasive visualisation of the vocal tract and articulators, thus providing information about their shape, size, motion and position. Extraction of this information for quantitative analysis is achieved using segmentation. Methods have been developed to segment the vocal tract, however, none of these also fully segment any articulators. The objective of this work was to develop a method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech, thus overcoming the limitations of existing methods.MethodsFive speech MR image sets (392 MR images in total), each of a different healthy adult volunteer, were used in this work. A fully convolutional network with an architecture similar to the original U-Net was developed to segment the following six regions in the image sets: the head, soft palate, jaw, tongue, vocal tract and tooth space. A five-fold cross-validation was performed to investigate the segmentation accuracy and generalisability of the network. The segmentation accuracy was assessed using standard overlap-based metrics (Dice coefficient and general Hausdorff distance) and a novel clinically relevant metric based on velopharyngeal closure.ResultsThe segmentations created by the method had a median Dice coefficient of 0.92 and a median general Hausdorff distance of 5mm. The method segmented the head most accurately (median Dice coefficient of 0.99), and the soft palate and tooth space least accurately (median Dice coefficients of 0.92 and 0.93 respectively). The segmentations created by the method correctly showed 90% (27 out of 30) of the velopharyngeal closures in the MR image sets.ConclusionsAn automatic method to fully segment multiple groups of articulators as well as the vocal tract in two-dimensional MR images of speech was successfully developed. The method is intended for use in clinical and non-clinical speech studies which involve quantitative analysis of the shape, size, motion and position of the vocal tract and articulators. In addition, a novel clinically relevant metric for assessing the accuracy of vocal tract and articulator segmentation methods was developed.

Project description:BackgroundEndomyocardial biopsies (EMB) are an important diagnostic tool for myocarditis and other infiltrative cardiac diseases. Routinely, biopsies are obtained under fluoroscopic guidance with a substantial radiation burden. Despite procedural success, there is a large sampling error caused by missing the affected myocardium. Therefore, multiple (>6) biopsies are taken in the clinical setting. In cardiovascular magnetic resonance (CMR), late gadolinium enhancement (LGE) depicts areas of affected myocardium in myocarditis or in other infiltrative cardiomyopathies. Thus, targeted biopsy under real-time CMR image guidance might reduce the problem of sampling error.MethodsSeven minipigs of the Goettingen strain underwent radiofrequency ablation in the left ventricle. At least two focal lesions were induced on the lateral wall in five and the apex in two animals. Each ablation lesion was created by two consecutive 30 sec ablations (max. 30 W, temperature 60-64 °C). Biopsies were taken immediately after lesion induction using a commercially available 7 F conventional bioptome under fluoroscopic guidance at the ablation site. Afterwards the animals underwent CMR and lesion visualization by LGE at 3T. The lesions were then targeted and biopsied under CMR-guidance using a MR-conditional bioptome guided by a steerable catheter. Interactive real-time (RT) visualization of the intervention on an in-room monitor was based on radial FLASH with nonlinear inverse reconstruction (NLINV) at a temporal resolution of 42 ms. All samples underwent a standard histological evaluation.ResultsRadiofrequency ablation was successful in all animals. Fluoroscopy-guided biopsies were performed with a success rate of 6/6 minipigs - resulting in a nonlethal pericardial effusion in one animal. Visualization of radiofrequency lesions by CMR was successful in 7/7 minipig, i.e. at least one lesion was clearly visible. Localization and tracking of the catheters and the bioptome using interactive control of the imaging plane was achieved in 6/6 MP; however in the animal with a large pericardial effusion after EMB under fluoroscopy no further EMB was attempted for safety reasons. Biopsies under interactive RT-CMR guidance were successfully performed in 5/6 animals, in one animal the bioptome reached the lesion, however the forceps did not cut out a sample. Specimens obtained under CMR guidance contained part of the lesion in 6/15 (40%) myocardial specimens and in 4/5 (80%) animals in which samples were achieved. Conventional biopsies revealed ablation lesions in 4/17 (23.5%) specimens in 3/6 minipigs (50%).ConclusionFocal lesions induced by radiofrequency ablation in a minipig model are a useful tool for CMR-guided biopsy studies. In contrast to fluoroscopy, CMR provides excellent visualization of lesions. Interactive real-time CMR allows excellent passive tracking of the instruments and EMB provides significantly superior sampling accuracy compared to fluoroscopy-guided biopsies. Nonetheless, further improvements of MR-compatible bioptomes and guiding catheters are essential before applying this method in a clinical setting.

Dataset Information

A System for Real-Time, Online Mixed-Reality Visualization of Cardiac Magnetic Resonance Images.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure