Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

A Neural-Network Framework for the Design of Individualised Hearing-Loss Compensation

Even though sound processing in the human auditory system is complex and highly non-linear, hearing aids (HAs) still rely on simplified descriptions of auditory processing or hearing loss to restore hearing. Standard HA amplification strategies succeed in restoring inaudibility of faint sounds, but fall short of providing targetted treatments for complex sensorineural deficits. To address this challenge, biophysically realistic models of human auditory processing can be adopted in the design of individualised HA strategies, but these are typically non-differentiable and computationally expensive. Therefore, this study proposes a differentiable DNN framework that can be used to train DNN-based HA models based on biophysical auditory-processing differences between normal-hearing and hearing-impaired models. We investigate the restoration capabilities of our DNN-based hearing-loss compensation for different loss functions, to optimally compensate for a mixed outer-hair-cell (OHC) loss and cochlear-synaptopathy (CS) impairment. After evaluating which trained DNN-HA model yields the best restoration outcomes on simulated auditory responses and speech intelligibility, we applied the same training procedure to two milder hearing-loss profiles with OHC loss or CS alone. Our results show that auditory-processing restoration was possible for all considered hearing-loss cases, with OHC loss proving easier to compensate than CS. Several objective metrics were considered to estimate the expected perceptual benefit after processing, and these simulations hold promise in yielding improved understanding of speech-in-noise for hearing-impaired listeners who use our DNN-HA processing. Since our framework can be tuned to the hearing-loss profiles of individual listeners, we enter an era where truly individualised and DNN-based hearing-restoration strategies can be developed and be tested experimentally

Real-time audio processing on a raspberry Pi using deep neural networks

Over the past years, deep neural networks (DNNs) have quickly grown into the state-of-the-art technologyfor various machine learning tasks such as image and speech recognition or natural language processing.However, as DNN-based applications typically require significant amounts of computation, running DNNson resource-constrained devices still constitutes a challenge, especially for real-time applications such aslow-latency audio processing. In this paper, we aimed to perform real-time noise suppression on a low-costembedded platform with limited resources, using a pre-trained DNN-based speech enhancement model. Aportable setup was employed, consisting of a Raspberry Pi 3 Model B+ fitted with a soundcard and head-phones. A (basic) low-latency Python framework was developed to accommodate an audio processing al-gorithm operating in a real-time environment. Various layouts and trainable parameters of the DNN-basedmodel as well as different processing time intervals (from 64 up to 8 ms) were tested and compared usingobjective metrics (e.g. PESQ, segSNR) to achieve the best possible trade-off between noise suppressionperformance and audio latency. We show that 10-layer DNNs with up to 350,000 trainable parameters cansuccessfully be implemented on the Raspberry Pi 3 Model B+ and yield latencies below 16-ms for real-timeaudio applications

Large-scale electrophysiology and deep learning reveal distorted neural signal dynamics after hearing loss

Listeners with hearing loss often struggle to understand speech in noise, even with a hearing aid. To better understand the auditory processing deficits that underlie this problem, we made large-scale brain recordings from gerbils, a common animal model for human hearing, while presenting a large database of speech and noise sounds. We first used manifold learning to identify the neural subspace in which speech is encoded and found that it is low-dimensional and that the dynamics within it are profoundly distorted by hearing loss. We then trained a deep neural network (DNN) to replicate the neural coding of speech with and without hearing loss and analyzed the underlying network dynamics. We found that hearing loss primarily impacts spectral processing, creating nonlinear distortions in cross-frequency interactions that result in a hypersensitivity to background noise that persists even after amplification with a hearing aid. Our results identify a new focus for efforts to design improved hearing aids and demonstrate the power of DNNs as a tool for the study of central brain structures

Controversy and consensus on the management of elevated sperm DNA fragmentation in male infertility: A global survey, current guidelines, and expert recommendations

Purpose Sperm DNA fragmentation (SDF) has been associated with male infertility and poor outcomes of assisted reproductive technology (ART). The purpose of this study was to investigate global practices related to the management of elevated SDF in infertile men, summarize the relevant professional society recommendations, and provide expert recommendations for managing this condition. Materials and Methods An online global survey on clinical practices related to SDF was disseminated to reproductive clinicians, according to the CHERRIES checklist criteria. Management protocols for various conditions associated with SDF were captured and compared to the relevant recommendations in professional society guidelines and the appropriate available evidence. Expert recommendations and consensus on the management of infertile men with elevated SDF were then formulated and adapted using the Delphi method. Results A total of 436 experts from 55 different countries submitted responses. As an initial approach, 79.1% of reproductive experts recommend lifestyle modifications for infertile men with elevated SDF, and 76.9% prescribe empiric antioxidants. Regarding antioxidant duration, 39.3% recommend 4–6 months and 38.1% recommend 3 months. For men with unexplained or idiopathic infertility, and couples experiencing recurrent miscarriages associated with elevated SDF, most respondents refer to ART 6 months after failure of conservative and empiric medical management. Infertile men with clinical varicocele, normal conventional semen parameters, and elevated SDF are offered varicocele repair immediately after diagnosis by 31.4%, and after failure of antioxidants and conservative measures by 40.9%. Sperm selection techniques and testicular sperm extraction are also management options for couples undergoing ART. For most questions, heterogenous practices were demonstrated. Conclusions This paper presents the results of a large global survey on the management of infertile men with elevated SDF and reveals a lack of consensus among clinicians. Furthermore, it demonstrates the scarcity of professional society guidelines in this regard and attempts to highlight the relevant evidence. Expert recommendations are proposed to help guide clinicians

A differentiable optimisation framework for the design of individualised DNN-based hearing-aid strategies

Current hearing aids mostly provide sound amplification fittings based on individual hearing thresholds or perceived loudness, even though it is known that sensorineural hearing damage is functionally complex, and requires different treatment strategies. To meet this demand, we propose an optimisation framework for the design of individualised hearingaid signal processing based on simulated (hearing-impaired) auditory-nerve responses. The framework is fully differentiable, thus the backpropagation algorithm can be used to train DNN-based hearing-aid models that optimally process sound to restore hearing in impaired cochleae. The auditory models within the framework can be tuned to the precise hearing-loss profile of a listener to yield trully individualised restoration strategies. Our simulations show that the trained hearing-aid models were able to enhance the auditory-nerve responses of hearing-impaired cochleae, and this provides a promising outlook for embedding our framework within future hearing aids and augmented-hearing applications