Real-Time Non-Invasive Imaging and Detection of Spreading Depolarizations through EEG: An Ultra-Light Explainable Deep Learning Approach

A core aim of neurocritical care is to prevent secondary brain injury. Spreading depolarizations (SDs) have been identified as an important independent cause of secondary brain injury. SDs are usually detected using invasive electrocorticography recorded at high sampling frequency. Recent pilot studies suggest a possible utility of scalp electrodes generated electroencephalogram (EEG) for non-invasive SD detection. However, noise and attenuation of EEG signals makes this detection task extremely challenging. Previous methods focus on detecting temporal power change of EEG over a fixed high-density map of scalp electrodes, which is not always clinically feasible. Having a specialized spectrogram as an input to the automatic SD detection model, this study is the first to transform SD identification problem from a detection task on a 1-D time-series wave to a task on a sequential 2-D rendered imaging. This study presented a novel ultra-light-weight multi-modal deep-learning network to fuse EEG spectrogram imaging and temporal power vectors to enhance SD identification accuracy over each single electrode, allowing flexible EEG map and paving the way for SD detection on ultra-low-density EEG with variable electrode positioning. Our proposed model has an ultra-fast processing speed (<0.3 sec). Compared to the conventional methods (2 hours), this is a huge advancement towards early SD detection and to facilitate instant brain injury prognosis. Seeing SDs with a new dimension - frequency on spectrograms, we demonstrated that such additional dimension could improve SD detection accuracy, providing preliminary evidence to support the hypothesis that SDs may show implicit features over the frequency profile

Cardiac biophysical detailed synergetic modality rendering and visible correlation

The heart is a vital organ in the human body. Research and treatment for the heart have made remarkable progress, and the functional mechanisms of the heart have been simulated and rendered through the construction of relevant models. The current methods for rendering cardiac functional mechanisms only consider one type of modality, which means they cannot show how different types of modality, such as physical and physiological, work together. To realistically represent the three-dimensional synergetic biological modality of the heart, this paper proposes a WebGL-based cardiac synergetic modality rendering framework to visualize the cardiac physical volume data and present synergetic correspondence rendering of the cardiac electrophysiological modality. By constructing the biological detailed interactive histogram, users can implement local details rendering for the heart, which could reveal the cardiac biology details more clearly. We also present cardiac physical-physiological correlation visualization to explore cardiac biological association characteristics. Experimental results show that the proposed framework can provide favorable cardiac biological detailed synergetic modality rendering results in terms of both effectiveness and efficiency. Compared with existing methods, the framework can facilitate the study of the internal mechanism of the heart and subsequently deduce the process of initiation, development, and transformation from a healthy heart to an ill one, and thereby improve the diagnosis and treatment of cardiac disorders

Enhanced Ultrasound Visualization for Procedure Guidance

Intra-cardiac procedures often involve fast-moving anatomic structures with large spatial extent and high geometrical complexity. Real-time visualization of the moving structures and instrument-tissue contact is crucial to the success of these procedures. Real-time 3D ultrasound is a promising modality for procedure guidance as it offers improved spatial orientation information relative to 2D ultrasound. Imaging rates at 30 fps enable good visualization of instrument-tissue interactions, far faster than the volumetric imaging alternatives (MR/CT). Unlike fluoroscopy, 3D ultrasound also allows better contrast of soft tissues, and avoids the use of ionizing radiation.Engineering and Applied Science

Three-dimensional reconstruction and visualization of the cerebral cortex in primates

We present a prototype interactive application for the direct analysis in three dimensions of the cerebral cortex in primates. The paper provides an overview of the current prototype system and presents the techniques used for reconstructing the cortex shape from data derived from histological sections as well as for rendering it at interactive rates. Results are evaluated by discussing the analysis of the right hemisphere of the brain of a macaque monkey used for neuroanatomical tract-tracing experiments.147-15

Towards an Interactive Electromechanical Model of the Heart

International audienceIn this work, we develop an interactive framework for rehearsal and training in the context of cardiac catheter ablation, and for planning in the context of Cardiac Resynchronization Therapy (CRT). To this end, an interactive and real-time electrophysiology model of the heart is developed to fit patient-specific data. The proposed interactive framework relies on two main contributions. An efficient implementation of cardiac electrophysiology is first proposed using latest GPU computing techniques. Second, a mechanical simulation is then coupled to the electrophysiological signals to produce realistic motion of the heart. We demonstrate that pathological mechanical and electrophysiological behaviour can be simulated

Novel system for real-time integration of 3-D echocardiography and fluoroscopy for image-guided cardiac interventions: Preclinical validation and clinical feasibility evaluation

© 2015 IEEE. Real-time imaging is required to guide minimally invasive catheter-based cardiac interventions. While transesophageal echocardiography allows for high-quality visualization of cardiac anatomy, X-ray fluoroscopy provides excellent visualization of devices. We have developed a novel image fusion system that allows real-time integration of 3-D echocardiography and the X-ray fluoroscopy. The system was validated in the following two stages: 1) preclinical to determine function and validate accuracy; and 2) in the clinical setting to assess clinical workflow feasibility and determine overall system accuracy. In the preclinical phase, the system was assessed using both phantom and porcine experimental studies. Median 2-D projection errors of 4.5 and 3.3 mm were found for the phantom and porcine studies, respectively. The clinical phase focused on extending the use of the system to interventions in patients undergoing either atrial fibrillation catheter ablation (CA) or transcatheter aortic valve implantation (TAVI). Eleven patients were studied with nine in the CA group and two in the TAVI group. Successful real-time view synchronization was achieved in all cases with a calculated median distance error of 2.2 mm in the CA group and 3.4 mm in the TAVI group. A standard clinical workflow was established using the image fusion system. These pilot data confirm the technical feasibility of accurate real-time echo-fluoroscopic image overlay in clinical practice, which may be a useful adjunct for real-time guidance during interventional cardiac procedures

Dense 4D nanoscale reconstruction of living brain tissue

Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure–function relationships of the brain’s complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue