FBG-driven simulation for virtual augmentation of fluoroscopic images during endovascular interventions.

Endovascular interventions are procedures designed to diagnose and treat vascular diseases, using catheters to navigate inside arteries and veins. Thanks to their minimal invasiveness, they offer many benefits, such as reduced pain and hospital stays, but also present many challenges for clinicians, as they require specialized training and heavy use of X-rays. This is particularly relevant when accessing (i.

A zero-shot reinforcement learning strategy for autonomous guidewire navigation.

Purpose: The treatment of cardiovascular diseases requires complex and challenging navigation of a guidewire and catheter. This often leads to lengthy interventions during which the patient and clinician are exposed to X-ray radiation. Deep reinforcement learning approaches have shown promise in learning this task and may be the key to automating catheter navigation during robotized interventions.

Transcriptomic Analysis of Alzheimer's Disease Pathways in a Pakistani Population.

Background: Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder that is most prevalent in elderly individuals, especially in developed countries, and its prevalence is now increasing in developing countries like Pakistan.

Objective: Our goal was to characterize key genes and their levels of expression and related molecular transcriptome networks associated with AD pathogenesis in a pilot case-control study in a Pakistani population.

Methods: To obtain the spectrum of molecular networks associated with pathogenesis in AD patients in Pakistan (comparing cases and controls), we used high-throughput qRT-PCR (TaqMan Low-Density Array;  = 33 subjects) coupled with Affymetrix Arrays ( = 8) and Ingenuity Pathway Analysis (IPA) to identify signature genes associated with Amyloid processing and disease pathways.

Augmented reality in liver surgery.

Introduction: During an operation, augmented reality (AR) enables surgeons to enrich their vision of the operating field by means of digital imagery, particularly as regards tumors and anatomical structures. While in some specialties, this type of technology is routinely ustilized, in liver surgery due to the complexity of modeling organ deformities in real time, its applications remain limited. At present, numerous teams are attempting to find a solution applicable to current practice, the objective being to overcome difficulties of intraoperative navigation in an opaque organ.

Insights on the pathogenesis of type 2 diabetes as revealed by signature genomic classifiers in an African American population in the Washington, DC area.

Aims: African Americans (AA) in the United States have a high risk of type 2 diabetes mellitus (T2DM) and suffer from disparities in the prevalence, mortality, and comorbidities of the disease compared to other Americans. The present study aimed to shed light on the molecular mechanisms of disease pathogenesis of T2DM among AA in the Washington, DC region.

Methods: We performed TaqMan Low Density Arrays (TLDA) on 24 genes of interest that belong to three categories: metabolic disease and disorders, cancer-related genes, and neurobehavioural disorders genes.

Unscented Kalman Filtering for Real Time Thermometry During Laser Ablation Interventions.

We present a data-assimilation Bayesian framework in the context of laser ablation for the treatment of cancer. For solving the nonlinear estimation of the tissue temperature evolving during the therapy, the Unscented Kalman Filter (UKF) predicts the next thermal status and controls the ablation process, based on sparse temperature information. The purpose of this paper is to study the outcome of the prediction model based on UKF and to assess the influence of different model settings on the framework performances.

Model-Based Thermometry for Laser Ablation Procedure Using Kalman Filters and Sparse Temperature Measurements.

Objective: We implement a data assimilation Bayesian framework for the reconstruction of the spatiotemporal profile of the tissue temperature during laser irradiation. The predictions of a physical model simulating the heat transfer in the tissue are associated with sparse temperature measurements, using an Unscented Kalman Filter.

Methods: We compare a standard state-estimation filtering procedure with a joint-estimation (state and parameters) approach: whereas in the state-estimation only the temperature is evaluated, in the joint-estimation the filter corrects also uncertain model parameters (i.

Deformation Aware Augmented Reality for Craniotomy using 3D/2D Non-rigid Registration of Cortical Vessels.

Intra-operative brain shift is a well-known phenomenon that describes non-rigid deformation of brain tissues due to gravity and loss of cerebrospinal fluid among other phenomena. This has a negative influence on surgical outcome that is often based on pre-operative planning where the brain shift is not considered. We present a novel brain-shift aware Augmented Reality method to align pre-operative 3D data onto the deformed brain surface viewed through a surgical microscope.

Estimation of boundary conditions for patient-specific liver simulation during augmented surgery.

Purpose: Augmented reality can improve the outcome of hepatic surgeries, assuming an accurate liver model is available to estimate the position of internal structures. While researchers have proposed patient-specific liver simulations, very few have addressed the question of boundary conditions. Resulting mainly from ligaments attached to the liver, they are not visible in preoperative images, yet play a key role in the computation of the deformation.

Constrained stochastic state estimation of deformable 1D objects: Application to single-view 3D reconstruction of catheters with radio-opaque markers.

Minimally invasive fluoroscopy-based procedures are the gold standard for diagnosis and treatment of various pathologies of the cardiovascular system. This kind of procedures imply for the clinicians to infer the 3D shape of the device from 2D images, which is known to be an ill-posed problem. In this paper we present a method to reconstruct the 3D shape of the interventional device, with the aim of improving the navigation.

Augmented Reality during Open Liver Surgery Using a Markerless Non-rigid Registration System.

Introduction: Intraoperative navigation during liver resection remains difficult and requires high radiologic skills because liver anatomy is complex and patient-specific. Augmented reality (AR) during open liver surgery could be helpful to guide hepatectomies and optimize resection margins but faces many challenges when large parenchymal deformations take place. We aimed to experiment a new vision-based AR to assess its clinical feasibility and anatomical accuracy.

Alignment of Cortical Vessels viewed through the Surgical Microscope with Preoperative Imaging to Compensate for Brain Shift.

Brain shift is a non-rigid deformation of brain tissue that is affected by loss of cerebrospinal fluid, tissue manipulation and gravity among other phenomena. This deformation can negatively influence the outcome of a surgical procedure since surgical planning based on pre-operative image becomes less valid. We present a novel method to compensate for brain shift that maps preoperative image data to the deformed brain during intra-operative neurosurgical procedures and thus increases the likelihood of achieving a gross total resection while decreasing the risk to healthy tissue surrounding the tumor.

Simulation of hyperelastic materials in real-time using deep learning.

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: A data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm.

Elastic Registration Based on Compliance Analysis and Biomechanical Graph Matching.

An automatic elastic registration method suited for vascularized organs is proposed. The vasculature in both the preoperative and intra-operative images is represented as a graph. A typical application of this method is the fusion of pre-operative information onto the organ during surgery, to compensate for the limited details provided by the intra-operative imaging modality (e.

Force classification during robotic interventions through simulation-trained neural networks.

Purpose: Intravitreal injection is among the most frequent treatment strategies for chronic ophthalmic diseases. The last decade has seen a serious increase in the number of intravitreal injections, and with it, adverse effects and drawbacks. To tackle these problems, medical assistive devices for robotized injections have been suggested and are projected to enhance delivery mechanisms for a new generation of pharmacological solutions.

An open electromagnetic tracking framework applied to targeted liver tumour ablation.

Purpose: Electromagnetic tracking is a core platform technology in the navigation and visualisation of image-guided procedures. The technology provides high tracking accuracy in non-line-of-sight environments, allowing instrument navigation in locations where optical tracking is not feasible. EMT can be beneficial in applications such as percutaneous radiofrequency ablation for the treatment of hepatic lesions where the needle tip may be obscured due to difficult liver environments (e.

Biomechanics-based graph matching for augmented CT-CBCT.

Purpose: Augmenting intraoperative cone beam computed tomography (CBCT) images with preoperative computed tomography data in the context of image-guided liver therapy is proposed. The expected benefit is an improved visualization of tumor(s), vascular system and other internal structures of interest.

Method: An automatic elastic registration based on matching of vascular trees extracted from both the preoperative and intraoperative images is presented.

Fast elastic registration of soft tissues under large deformations.

A fast and accurate fusion of intra-operative images with a pre-operative data is a key component of computer-aided interventions which aim at improving the outcomes of the intervention while reducing the patient's discomfort. In this paper, we focus on the problematic of the intra-operative navigation during abdominal surgery, which requires an accurate registration of tissues undergoing large deformations. Such a scenario occurs in the case of partial hepatectomy: to facilitate the access to the pathology, e.

Controlling the error on target motion through real-time mesh adaptation: Applications to deep brain stimulation.

An error-controlled mesh refinement procedure for needle insertion simulations is presented. As an example, the procedure is applied for simulations of electrode implantation for deep brain stimulation. We take into account the brain shift phenomena occurring when a craniotomy is performed.

Real-Time Error Control for Surgical Simulation.

Objective: To present the first a posteriori error-driven adaptive finite element approach for real-time simulation, and to demonstrate the method on a needle insertion problem.

Methods: We use corotational elasticity and a frictional needle/tissue interaction model. The problem is solved using finite elements within SOFA.

Handling topological changes during elastic registration : Application to augmented reality in laparoscopic surgery.

Purpose: Locating the internal structures of an organ is a critical aspect of many surgical procedures. Minimally invasive surgery, associated with augmented reality techniques, offers the potential to visualize inner structures, allowing for improved analysis, depth perception or for supporting planning and decision systems.

Methods: Most of the current methods dealing with rigid or non-rigid augmented reality make the assumption that the topology of the organ is not modified.

Robust augmented reality registration method for localization of solid organs' tumors using CT-derived virtual biomechanical model and fluorescent fiducials.

Background: Augmented reality (AR) is the fusion of computer-generated and real-time images. AR can be used in surgery as a navigation tool, by creating a patient-specific virtual model through 3D software manipulation of DICOM imaging (e.g.

Blood vessel modeling for interactive simulation of interventional neuroradiology procedures.

Endovascular interventions can benefit from interactive simulation in their training phase but also during pre-operative and intra-operative phases if simulation scenarios are based on patient data. A key feature in this context is the ability to extract, from patient images, models of blood vessels that impede neither the realism nor the performance of simulation. This paper addresses both the segmentation and reconstruction of the vasculature from 3D Rotational Angiography data, and adapted to simulation: An original tracking algorithm is proposed to segment the vessel tree while filtering points extracted at the vessel surface in the vicinity of each point on the centerline; then an automatic procedure is described to reconstruct each local unstructured point set as a skeleton-based implicit surface (blobby model).