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. We performed associated multiscale computer simulations to elucidate the arrhythmogenic potentials of the KCNH2 mutations. Genetic screening of the first and second index patients revealed a heterozygous missense mutation in KCNH2, resulting in an amino acid change (P632L) in the outer loop of the channel and substitution at position 428 from serine to proline (S428P), respectively. Heterologous expression of P632L and S428P into HEK cells produced no hERG current compared to the wild type (WT). Moreover, the co-transfection of WT and P632L yielded no hERG current; however, the co-transfection of WT and S428P yielded partial hERG current. Action potentials were prolonged in a complete or partial blockade of hERG current from computer simulations which was more severe in Purkinje than ventricular myocytes. Three dimensional simulations revealed a higher susceptibility to reentry in the presence of hERG current blockade. Our experimental findings suggest that both P632L and S428P mutations may impair the KCNH2 gene. The Purkinje cells exhibit a more severe phenotype than ventricular myocytes, and the hERG current blockade renders the ventricles an arrhythmogenic substrate from computer modeling.

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. Following the induction of persistent reentry from an ectopic stimulus, we isolated the HPS at different time points. Moreover, we varied the coupling resistance and the number of myocytes at the Purkinje-Myocardial Junctions (PMJs) to ascertain how the junctional parameters altered reentry dynamics. Reentry was terminated with the earliest termination time for reentry coinciding with the earliest time the HPS was isolated. This observation provides evidence of direct involvement of the HPS during LQT2-mediated ventricular arrhythmia. Increasing the coupling resistance or the number of myocytes at the PMJs reduced the percentage of successful retrograde propagation during reentry. Thus, the HPS alters reentry dynamics. Our multi-scale computer modeling outcomes offer important new understandings of probable arrhythmia mechanisms under LQT2 circumstances.

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. A 7.5 Nm moment was applied along three anatomical planes both with and without costovertebral (CV) joints present. Range of Motion (RoM) was assessed for each property set and compared to published experimental data. Intact and serial ligament removal procedures were implemented in accordance with experimental protocol. The variance in both kinematic behavior and comparability with experimental data among property sets emphasizes the role nonlinear characterization plays in determining proper kinematic behavior in spinal FE models. Additionally, a decrease in RoM variation among property sets was exhibited when the model setup incorporated the CV joint. With proper assessment of the source and size of each ligament, the material properties considered here could be expanded and justified for implementation into thoracic spine clinical studies.

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. The dataset consists of multimodality breast MRI of T2W and STIR images obtained from 10 patients. A statistical analysis of segmentation tasks emphasizes the Dice Similarity Coefficient (DSC), segmentation accuracy, sensitivity, and specificity. We first use a single class labeling to segment the breast region and then exploit it as an input for three-class labeling to segment fatty, fibroglandular (FGT), and tumorous tissues. The first network has a 0.95 DCS, while the second network has a 0.95, 0.83, and 0.41 for fat, FGT, and tumor classes, respectively. Clinical Relevance-This research is relevant to the breast surgery community as it establishes a deep learning-based (DL) algorithmic and phantomic foundation for surgical planning and navigation that will exploit preoperative multimodal MRI and intraoperative ultrasound to achieve highly cosmetic breast surgery. In addition, the planning and navigation will guide a robot that can cut, resect, bag, and grasp a tissue mass that encapsulates breast tumors and positive tissue margins. This image-guided robotic approach promises to potentiate the accuracy of breast surgeons and improve patient outcomes.

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. With the widespread adoption of wearable sensors, there is an increased emphasis on fall prediction models that effectively cope with accelerometry signal data. Fall detection and gait classification are challenging tasks, especially in differentiating falls from near falls. We propose to apply attention to the deep neural network (DNN) analysis of acceleration data where a fall is known to have occurred. We take the maximum value of the sensor signals to define the observation window of the detector. Powered by a transformer DNN with word embedding, attention networks have achieved a state-of-the-art in natural language processing (NLP) tasks. Besides the success of the transformer for efficiently processing long sequences, it supports parallel computing with fast computation. In this paper, we propose a novel transformer attention network for gait analysis of fall detection modeling with Time2Vec positional encoding- founded on a Masked Transformer Network. Using our dataset, we demonstrate that the proposed approach achieves better specificity and sensitivity than the present models.

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. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue shift estimation through imaging scans acquired during the procedure, and simulation-based prediction. Neurosurgical scenarios tend to emphasize straight needles so far, and span deep-brain stimulation (DBS), stereoelectroencephalography (SEEG), intracerebral drug delivery (IDD), stereotactic brain biopsy (SBB), stereotactic needle aspiration for hematoma, cysts and abscesses, and brachytherapy as well as thermal ablation of brain tumors and seizure-generating regions. We emphasize therapeutic considerations and complications that have been documented in conjunction with these applications.

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

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: 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. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation.

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 T2 MR images. METHODS: We create shape models for ten pairs of cranial nerves. High-resolution T2 MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts. RESULTS: The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII-CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21 mm which are sub-voxel accuracy given the input image resolution. CONCLUSION: This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.

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. Subsequently, data augmentation is performed on these scalogram images to increase accuracy, thereby complementing limited labeled fall sensor data, enabling transfer learning from the existing pre-trained model. The experimental results on publicly available URFD datasets demonstrate that transfer learning leads to a better performance than the existing methods in the case of scarce labeled training data.

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. We observe that the error in the computation of the displacement and stress fields is localised around the needle tip and the needle shaft during needle insertion simulation. By suitably and adaptively refining the mesh in this region, our approach enables to control, and thus to reduce, the error whilst maintaining a coarser mesh in other parts of the domain. Through academic and practical examples we demonstrate that our adaptive approach, as compared with a uniform coarse mesh, increases the accuracy of the displacement and stress fields around the needle shaft and, while for a given accuracy, saves computational time with respect to a uniform finer mesh. This facilitates real-time simulations. The proposed methodology has direct implications in increasing the accuracy, and controlling the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anaesthesia, or cryotherapy. Moreover, the proposed approach can be helpful in the development of robotic surgeries because the simulation taking place in the control loop of a robot needs to be accurate, and to occur in real time.

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. We exploit multiscale vesselness filtering and minimal path techniques in the medial axis extraction method, which also computes a radius estimate along the path. Once we have the medial axis and the radius function of a nerve, we identify the nerve surface using a two-simplex deformable model, which expands radially and can accommodate any nerve shape. As a result, the method proposed here combines the benefits of explicit contour and surface models, while also achieving a cornerstone for future work that will emphasize shape statistics, static collision with other critical structures, and tree-shape analysis.

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. An ellipsoidal simplex template mesh was initialized within the disc image boundary through affine landmark-based registration and was allowed to deform according to image gradient forces. Coarse-to-fine multi-resolution approach was adopted in conjunction with decreasing shape memory forces to accurately capture the disc boundary. User intervention is allowed to turn off the shape feature and guide model deformation when the internal simplex shape memory influence hinders detection of pathology. A resulting surface mesh was utilized for disc compression simulation under gravitational and weight loads using Simulation Open Framework Architecture. For testing, 16 healthy discs were automatically segmented, and five pathological discs were segmented with minimal supervision. RESULTS: Segmentation results were validated against expert guided segmentation and demonstrate mean absolute shape distance error of <1 mm. Healthy intervertebral disc compression simulation resulted in a bulging disc under vertical pressure of 100 N/cm(2). CONCLUSION: This study presents the application of a simplex active surface model featuring weak shape priors for 3D segmentation of healthy as well as herniated discs. A framework was developed that enables the application of shape priors in the healthy part of disc anatomy, with user intervention when the priors were inapplicable. The surface-mesh-based segmentation method is part of a processing pipeline for anatomical modelling to support interactive surgery simulation.

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. This contribution potentially has broad ramifications in the surgical simulation research community, given that in a single-resolution system, a constitutively realistic interactive tissue response, which presupposes large elements, is in direct conflict with the need to represent clinically relevant critical tissues in the simulation, which are typically be comprised of small elements.

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. In this paper, we present KWScene, an open source cross-platform library that is being developed to support implementation of these types of plugins. We describe the overall design of the library and provide implementation details and conclude by presenting a concrete example that demonstrates the use of the KWScene library in computational anatomy research at Johns Hopkins Center for Imaging Science.

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. As a result, most real-time surgical simulation systems employ a linear elasticity model, simplified numerical methods such as the boundary element method or spring-particle systems, and coarse volumetric meshes. However, these systems are not clinically realistic. We present here an ongoing work aimed at developing an efficient and physically realistic neurosurgery simulator using a non-linear finite element method (FEM) with haptic interaction. Real-time finite element analysis is achieved by utilizing the total Lagrangian explicit dynamic (TLED) formulation and GPU acceleration of per-node and per-element operations. We employ a virtual coupling method for separating deformable body simulation and collision detection from haptic rendering, which needs to be updated at a much higher rate than the visual simulation. The system provides accurate biomechancial modeling of soft tissue while retaining a real-time performance with haptic interaction. However, our experiments showed that the stability of the simulator depends heavily on the material property of the tissue and the speed of colliding objects. Hence, additional efforts including dynamic relaxation are required to improve the stability of the system.

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. In addition, insufficient attention has been paid to tissue pressure variation during SP in these models, so that vessel rupture and other clinical phenomena cannot be explained. A novel photothermal model of SP was proposed using a Monte Carlo method to simulate the laser transport in the tissue, a heat transfer equation with dynamically changing vaporization temperature to calculate the temperature distribution, and the Arrhenius equation to predict the thermal damage. A factor of trapped vaporized tissue water k was introduced to describe the effects on tissue pressure, temperature, and other related parameters. It was shown that the simulation results are affected significantly by k. Temperature and thermal damage volume are almost identical, respectively, to those obtained with models with vaporization at 100°C and models without vaporization when k = 0 and 1, while thermal damage volume is close to that obtained with models of vaporization at 110°C and 130°C, respectively, when k = 0.022 and k = 0.18. To some extent, the current models without vaporization and models with vaporization at constant temperature can be regarded as special cases at specific situations of this new photothermal model of SP. In addition, more descriptive simulation results, such as temperature, thermal damage, and pressure, are accessible with this model, although the accuracy depends on the value of k, the estimation of which is planned as future work.

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. This information is used to define a subvolume of clinical interest, within some distance of the path and the target pathology. Restricted to this subvolume are a tetrahedralization of finer resolution, the representation of critical tissues, and sulcal separability constraint for all mesh levels.

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. Based on Surgical Workflows, the needed interaction scenarios and properties of a simulator can be derived easily. It is also possible to compare an existing simulator with the real workflow to distinguish whether it behaves realistically. We are currently using this method for the design of a new simulator for transnasal neurosurgery with good success.

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. The measure uses local shape information obtained from an eigenvalue decomposition of the Hessian matrix. It attains a maximum in the middle of a sheet, and also provides local estimates of its width and orientation. These estimates are used to create a vector field orthogonal to bone boundaries, so that a flux maximizing flow algorithm can be applied to recover them. Hence, the sheetness measure has the essential properties to be incorporated into the computation of anatomical models for the simulation of pituitary surgery, enabling it to better account for the presence of sinus bones. We validate the approach quantitatively on synthetic examples, and provide comparisons with existing segmentation techniques on paranasal sinus CT data.

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.