Plasmonic field confinement for separate absorption-multiplication in InGaAs nanopillar avalanche photodiodes

Avalanche photodiodes (APDs) are essential components in quantum key distribution systems and active imaging systems requiring both ultrafast response time to measure photon time of flight and high gain to detect low photon flux. The internal gain of an APD can improve system signal-to-noise ratio (SNR). Excess noise is typically kept low through the selection of material with intrinsically low excess noise, using separate-absorption-multiplication (SAM) heterostructures, or taking advantage of the dead-space effect using thin multiplication regions. In this work we demonstrate the first measurement of excess noise and gain-bandwidth product in III–V nanopillars exhibiting substantially lower excess noise factors compared to bulk and gain-bandwidth products greater than 200 GHz. The nanopillar optical antenna avalanche detector (NOAAD) architecture is utilized for spatially separating the absorption region from the avalanche region via the NOA resulting in single carrier injection without the use of a traditional SAM heterostructure

Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI

Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited positive predictive value. To address this, we present an MRI-based deep learning method for predicting clinically significant prostate cancer applicable to a patient population with subsequent ground truth biopsy results ranging from benign pathology to ISUP grade group~5. Specifically, we demonstrate that mixed supervision via diverse histopathological ground truth improves classification performance despite the cost of reduced concordance with image-based segmentation. That is, where prior approaches have utilized pathology results as ground truth derived from targeted biopsies and whole-mount prostatectomy to strongly supervise the localization of clinically significant cancer, our approach also utilizes weak supervision signals extracted from nontargeted systematic biopsies with regional localization to improve overall performance. Our key innovation is performing regression by distribution rather than simply by value, enabling use of additional pathology findings traditionally ignored by deep learning strategies. We evaluated our model on a dataset of 973 (testing n=160) multi-parametric prostate MRI exams collected at UCSF from 2015-2018 followed by MRI/ultrasound fusion (targeted) biopsy and systematic (nontargeted) biopsy of the prostate gland, demonstrating that deep networks trained with mixed supervision of histopathology can significantly exceed the performance of the Prostate Imaging-Reporting and Data System (PI-RADS) clinical standard for prostate MRI interpretation

High-Dimensional Polynomial Approximation with Applications in Imaging and Recognition

Deep learning has demonstrated unreasonable effectiveness on several high dimensional regression and classification problems, far exceeding theoretical expectations. In this thesis, we analyze this phenomena from the perspective of approximation theory. Utilizing recent theoretical advances, we investigate whether and under what conditions deep networks can escape the curse of dimensionality, providing experimental evidence where the theory falls short. We use these insights to suggest new approaches to network design that is more in accordance with this theory, and give several examples of where such designs succeed

REMOTE HEART MONITORING VIA MEDICAL TELEMETRY

Today, a wide range of heart conditions can be monitored remotely with relatively inexpensive passive sensing technologies, enabling the potential for long-term monitoring and prognosis of patient state under representative environmental stimuli. A medical telemetry system that can incorporate such passive measurements and provide key diagnostic information to medical professionals would provide tremendous value to patients via quantitative and personalized healthcare. This paper presents an overview of passive sensing methods that could be utilized in a medical telemetry system for remote heart monitoring of patients. While active systems are another attractive option, they impose additional constraints on the system that require careful calibration, expert control, and more complex instrumentation. The methods presented here are based on low-cost, sensor technology with the potential to greatly improve long-term non-invasive, heart-health monitoring.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Nonlinear electrocardiographic imaging using polynomial approximation networks

Electrocardiography is a valuable tool to aid in medical understanding and treatment of heart-related ailments, specifically atrial fibrillation (AF) and other irregular cardiac behavior. Although signs of AF will manifest in conventional electrocardiogram (ECG) recordings, interpretation and localization of AF sources require significant clinical expertise. In this vein, electrocardiographic imaging has emerged as an important medical imaging modality that provides reconstructions of the heart's electrical activity from non-invasive multi-lead body-surface ECG and anatomical x-ray computed tomography images. In this paper, we present a nonlinear inversion model for computing this mapping to improve upon the reconstruction performance of current methods. While contemporary techniques typically determine an inverse solution by discretizing and inverting an underdetermined linear system of partial differential equations governing the relationship between voltage potentials of the heart and torso, the presented technique re-casts this problem as a task in function approximation and provides a direct parameterization of the inverse operator using a polynomial neural network. That is, the outlined nonlinear inversion technique is a generalization of contemporary reconstruction techniques which allows geometrical and material parameterizations of the forward-model to be optimized using real experimental data collected from patients suffering from AF, as to better represent the inverse operator with respect to reconstruction metrics applicable to electrophysiology. The accuracy of our model is evaluated against a dataset of real-patient recordings to demonstrate its validity, and mathematical analysis is provided to support the polynomial expansion used in our inversion model

TRACKING INFORMATION IN SAR IMAGE FORMATION AND CLASSIFICATION ALGORITHMS

Traditional target detection pipelines involve two sequential steps: the formation of a range-profile or likely-image, and the classification of likely targets within that image. Although it has been shown that target tracking in the RaDAR image-domain can be unnecessarily noisy, with more accurate and efficient implementations involving a direct analysis of the measured wavefield, image formation remains a desirable output in many applications due to its highly descriptive and interpretable nature. In this paper, we outline a mechanism for formalizing and accelerating this procedure in application-specific use cases. Enabled by recent advances in deep learning, we present a pipeline for automatically selecting an “optimal” filtered back-projection model, forming a likelyimage, and performing target recognition and classification. The architecture allows practitioners to track and optimize the flow of information throughout the pipeline, enabling applications that utilize only intermediate outputs of the algorithm.International Foundation for TelemeteringProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection

Generation of synthetic megavoltage CT for MRI-only radiotherapy treatment planning using a 3D deep convolutional neural network.

BACKGROUND: Megavoltage computed tomography (MVCT) has been implemented on many radiotherapy treatment machines for on-board anatomical visualization, localization, and adaptive dose calculation. Implementing an MR-only workflow by synthesizing MVCT from magnetic resonance imaging (MRI) would offer numerous advantages for treatment planning and online adaptation. PURPOSE: In this work, we sought to synthesize MVCT (sMVCT) datasets from MRI using deep learning to demonstrate the feasibility of MRI-MVCT only treatment planning. METHODS: MVCTs and T1-weighted MRIs for 120 patients treated for head-and-neck cancer were retrospectively acquired and co-registered. A deep neural network based on a fully-convolutional 3D U-Net architecture was implemented to map MRI intensity to MVCT HU. Input to the model were volumetric patches generated from paired MRI and MVCT datasets. The U-Net was initialized with random parameters and trained on a mean absolute error (MAE) objective function. Model accuracy was evaluated on 18 withheld test exams. sMVCTs were compared to respective MVCTs. Intensity-modulated volumetric radiotherapy (IMRT) plans were generated on MVCTs of four different disease sites and compared to plans calculated onto corresponding sMVCTs using the gamma metric and dose-volume-histograms (DVHs). RESULTS: MAE values between sMVCT and MVCT datasets were 93.3 ± 27.5, 78.2 ± 27.5, and 138.0 ± 43.4 HU for whole body, soft tissue, and bone volumes, respectively. Overall, there was good agreement between sMVCT and MVCT, with bone and air posing the greatest challenges. The retrospective dataset introduced additional deviations due to sinus filling or tumor growth/shrinkage between scans, differences in external contours due to variability in patient positioning, or when immobilization devices were absent from diagnostic MRIs. Dose distributions of IMRT plans evaluated for four test cases showed close agreement between sMVCT and MVCT images when evaluated using DVHs and gamma dose metrics, which averaged to 98.9 ± 1.0% and 96.8 ± 2.6% analyzed at 3%/3 mm and 2%/2 mm, respectively. CONCLUSIONS: MVCT datasets can be generated from T1-weighted MRI using a 3D deep convolutional neural network with dose calculation on a sample sMVCT in close agreement with the MVCT. These results demonstrate the feasibility of using MRI-derived sMVCT in an MR-only treatment planning workflow