Identification and characterization of two novel KCNH2 mutations contributing to long QT syndrome.

We identified two different inherited mutations in KCNH2 gene, or human ether-a-go-go related gene (hERG), which are linked to Long QT Syndrome. The first mutation was in a 1-day-old infant, whereas the second was in a 14-year-old girl. The two KCNH2 mutations were transiently transfected into either human embryonic kidney (HEK) cells or human induced pluripotent stem-cell derived cardiomyocytes.

ELUCIDATING THE ROLE OF THE HIS-PURKINJE SYSTEM DURING LONG QT MEDIATED ARRHYTHMIAS.

Mutation in the hERG gene leading to partial or complete blockade of the rapid delayed rectifier current causes Long QT Type 2 (LQT2) phenotype, the second most common form of Long QT Syndrome. However, the exact involvement of the His-Purkinje System (HPS) remains elusive. We utilized a finite element model of the rabbit ventricles integrated with a HPS to elucidate the role of HPS during LQT2-mediated arrhythmia.

A comparison of intervertebral ligament properties utilized in a thoracic spine functional unit through kinematic evaluation.

Ligament properties in the literature are variable, yet scarce, but needed to calibrate computational models for spine clinical research applications. A comparison of ligament stiffness properties and their effect on the kinematic behavior of a thoracic functional spinal unit (FSU) is examined in this paper. Six unique ligament property sets were utilized within a volumetric T7-T8 finite element (FE) model developed using computer-aided design (CAD) spinal geometry.

nnUNet-based Multi-modality Breast MRI Segmentation and Tissue-Delineating Phantom for Robotic Tumor Surgery Planning.

Segmentation of the thoracic region and breast tissues is crucial for analyzing and diagnosing the presence of breast masses. This paper introduces a medical image segmentation architecture that aggregates two neural networks based on the state-of-the-art nnU-Net. Additionally, this study proposes a polyvinyl alcohol cryogel (PVA-C) breast phantom, based on its automated segmentation approach, to enable planning and navigation experiments for robotic breast surgery.

Wearable Sensor Gait Analysis of Fall Detection using Attention Network.

The statistical data from the National Council on Aging indicates that a senior adult dies in the US from a fall every 19 minutes. The care of elderly people can be improved by enabling the detection of falling events, especially if it triggers the pneumatic actuation of a protective airbag. This work focuses on detecting impending fall risk of senior subjects within the geriatric population, towards a planned approach to mitigating fall injuries through pneumatic airbag deployment.

Robotically Steered Needles: A Survey of Neurosurgical Applications and Technical Innovations.

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies.

Deformable multisurface segmentation of the spine for orthopedic surgery planning and simulation.

We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results.

Medial axis segmentation of cranial nerves using shape statistics-aware discrete deformable models.

Purpose: We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T MR images.

Methods: We create shape models for ten pairs of cranial nerves. High-resolution T MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model.

TOWARDS MUSCULOSKELETAL SIMULATION-AWARE FALL INJURY MITIGATION: TRANSFER LEARNING WITH DEEP CNN FOR FALL DETECTION.

This paper presents early work on a fall detection method using transfer learning method, in conjunction with a long-term effort to combine efficient machine learning and prior personalized musculoskeletal modeling to deploy fall injury mitigation in geriatric subjects. Inspired by the tremendous progress in image-based object recognition with deep convolutional neural networks (DCNNs), we opt for a pre-trained kinematics-based machine learning approach through existing large-scale annotated accelerometry datasets. The accelerometry datasets are converted to images using time-frequency analysis, based on scalograms, by computing the continuous wavelet transform filter bank.

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.

MRI-Based Medial Axis Extraction and Boundary Segmentation of Cranial Nerves Through Discrete Deformable 3D Contour and Surface Models.

This paper presents a segmentation technique to identify the medial axis and the boundary of cranial nerves. We utilize a 3-D deformable one-simplex discrete contour model to extract the medial axis of each cranial nerve. This contour model represents a collection of two-connected vertices linked by edges, where vertex position is determined by a Newtonian expression for vertex kinematics featuring internal and external forces, the latter of which include attractive forces toward the nerve medial axis.

3D lumbar spine intervertebral disc segmentation and compression simulation from MRI using shape-aware models.

Purpose: More accurate and robust image segmentations are needed for identification of spine pathologies and to assist with spine surgery planning and simulation. A framework for 3D segmentation of healthy and herniated intervertebral discs from T2-weighted magnetic resonance imaging was developed that exploits weak shape priors encoded in simplex mesh active surface models.

Methods: Weak shape priors inherent in simplex mesh deformable models have been exploited to automatically segment intervertebral discs.

Integration of a Multigrid ODE solver into an open medical simulation framework.

In this paper, we present the implementation of a Multigrid ODE solver in SOFA framework. By combining the stability advantage of coarse meshes and the transient detail preserving virtue of fine meshes, Multigrid ODE solver computes more efficiently than classic ODE solvers based on a single level discretization. With the ever wider adoption of the SOFA framework in many surgical simulation projects, introducing this Multigrid ODE solver into SOFA's pool of ODE solvers shall benefit the entire community.

Enabling ITK-based processing and 3D Slicer MRML scene management in ParaView.

This paper documents on-going work to facilitate ITK-based processing and 3D Slicer scene management in ParaView. We believe this will broaden the use of ParaView for high performance computing and visualization in the medical imaging research community. The effort is focused on developing ParaView plug-ins for managing VTK structures from 3D Slicer MRML scenes and encapsulating ITK filters for deployment in ParaView.

Neurosurgery Simulation Using Non-linear Finite Element Modeling and Haptic Interaction.

Real-time surgical simulation is becoming an important component of surgical training. To meet the real-time requirement, however, the accuracy of the biomechancial modeling of soft tissue is often compromised due to computing resource constraints. Furthermore, haptic integration presents an additional challenge with its requirement for a high update rate.

A photothermal model of selective photothermolysis with dynamically changing vaporization temperature.

The theory of selective photothermolysis (SP) is used in many fields of laser surgery and medicine. As several parameters and a number of complicated photothermal interactions are involved in SP, numerical simulations have been providing an important and effective way in SP studies. However, with different photothermal models of SP, simulated results differ considerably.

Approach-specific multi-grid anatomical modeling for neurosurgery simulation with public-domain and open-source software.

We present on-going work on multi-resolution sulcal-separable meshing for approach-specific neurosurgery simulation, in conjunction multi-grid and Total Lagrangian Explicit Dynamics finite elements. Conflicting requirements of interactive nonlinear finite elements and small structures lead to a multi-grid framework. Implications for meshing are explicit control over resolution, and prior knowledge of the intended neurosurgical approach and intended path.

Requirement specification for surgical simulation systems with surgical workflows.

Surgical simulations are normally developed in a cycle of continuous refinement. This leads to high costs in simulator design and as a result to a very limited number of simulators which are used in clinical training scenarios. We propose using Surgical Workflow Analysis for a goal-oriented specification of surgical simulators.

Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery.

The simulation of pituitary gland surgery requires a precise classification of soft tissues, vessels and bones. Bone structures tend to be thin and have diffuse edges in CT data, and thus the common method of thresholding can produce incomplete segmentations. In this paper, we present a novel multi-scale sheet enhancement measure and apply it to paranasal sinus bone segmentation.

Bone enhancement filtering: application to sinus bone segmentation and simulation of pituitary surgery.

We present a novel multi-scale bone enhancement measure that can be used to drive a geometric flow to segment bone structures. This measure has the essential properties to be incorporated in the computation of anatomical models for the simulation of pituitary surgery, enabling it to better account for the presence of sinus bones. We present synthetic examples that validate our approach and show a comparison between existing segmentation techniques of paranasal sinus CT data.