Learning-based Ensemble Average Propagator Estimation

By capturing the anisotropic water diffusion in tissue, diffusion magnetic resonance imaging (dMRI) provides a unique tool for noninvasively probing the tissue microstructure and orientation in the human brain. The diffusion profile can be described by the ensemble average propagator (EAP), which is inferred from observed diffusion signals. However, accurate EAP estimation using the number of diffusion gradients that is clinically practical can be challenging. In this work, we propose a deep learning algorithm for EAP estimation, which is named learning-based ensemble average propagator estimation (LEAPE). The EAP is commonly represented by a basis and its associated coefficients, and here we choose the SHORE basis and design a deep network to estimate the coefficients. The network comprises two cascaded components. The first component is a multiple layer perceptron (MLP) that simultaneously predicts the unknown coefficients. However, typical training loss functions, such as mean squared errors, may not properly represent the geometry of the possibly non-Euclidean space of the coefficients, which in particular causes problems for the extraction of directional information from the EAP. Therefore, to regularize the training, in the second component we compute an auxiliary output of approximated fiber orientation (FO) errors with the aid of a second MLP that is trained separately. We performed experiments using dMRI data that resemble clinically achievable $q$-space sampling, and observed promising results compared with the conventional EAP estimation method.Comment: Accepted by MICCAI 201

Holistic image reconstruction for diffusion MRI

\u3cp\u3eDiffusion MRI provides unique information on the microarchitecture of biological tissues. One of the major challenges is finding a balance between image resolution, acquisition duration, noise level and image artifacts. Recent methods tackle this challenge by performing super-resolution reconstruction in image space or in diffusion space, regularization of the image data or of postprocessed data (such as the orientation distribution function, ODF) along different dimensions, and/or impose data-consistency in the original acquisition space. Each of these techniques has its own advantages; however, it is rare that even a few of them are combined. Here we present a holistic framework for diffusion MRI reconstruction that allows combining the advantages of all these techniques in a single reconstruction step. In proof-of-concept experiments, we demonstrate super-resolution on HARDI shells and in image space, regularization of the ODF and of the images in spatial and angular dimensions, and data consistency in the original acquisition space. Reconstruction quality is superior to standard reconstruction, demonstrating the feasibility of combining advanced techniques into one step.\u3c/p\u3

Holistic image reconstruction for diffusion MRI

Diffusion MRI provides unique information on the microarchitecture of biological tissues. One of the major challenges is finding a balance between image resolution, acquisition duration, noise level and image artifacts. Recent methods tackle this challenge by performing super-resolution reconstruction in image space or in diffusion space, regularization of the image data or of postprocessed data (such as the orientation distribution function, ODF) along different dimensions, and/or impose data-consistency in the original acquisition space. Each of these techniques has its own advantages; however, it is rare that even a few of them are combined. Here we present a holistic framework for diffusion MRI reconstruction that allows combining the advantages of all these techniques in a single reconstruction step. In proof-of-concept experiments, we demonstrate super-resolution on HARDI shells and in image space, regularization of the ODF and of the images in spatial and angular dimensions, and data consistency in the original acquisition space. Reconstruction quality is superior to standard reconstruction, demonstrating the feasibility of combining advanced techniques into one step

Electronic Digital Systems Signature Comparison And Their Implementation Features On Elliptic Curves Basis

Робота присвячена огляду та аналізу алгоритмів формування електронного цифрового підпису. Порівнюються відомі алгоритми створення електронного цифрового підпису, розглядаються питання реалізації алгоритму електронного цифрового підпису на еліптичних кривих та пропонується схема його програмної реалізації.The paper is devoted to the review and analysis of algorithms for creation electronic digital signature. Known electronic digital signature algorithms are compared, examined the questions of implementation of the elliptic curve digital signature algorithm, proposed scheme of its software implementation

Improving the performance of multiplication of point by the number in digital signature algorithms based on elliptic curves

Робота присвячена підвищенню швидкості постановки та верифікації електронного цифрового підпису на еліптичних кривих за рахунок використання запропонованого удосконаленого алгоритму виконання операцій над точками еліптичних кривих. Проведено огляд та аналіз відомих алгоритмів електронного цифрового підпису, розглядаються питання реалізації алгоритму електронного цифрового підпису на еліптичних кривих.The paper is devoted to the increasing the speed of creation and verification of elliptic curve digital signature by using the proposed improved algorithm of operations on elliptic curve points. Known algorithms of digital signature are reviewed and analyzed, the questions of implementation of elliptic curve digital signature algorithm are examined

Surgical Treatment of Chronic Osteomyelitis

Introduction. In this study, we analysed the results of applying various surgical methods in the combined treatment of inflammatory diseases of bones and joints.Materials and methods. The work was based on data from a multi-dimensional cohort study using non-concurrent (historical) control. A retrospective study included the analysis of medical records covering the period of 2009–2016 (1059 patients). A prospective study consisted in analysing the effectiveness of modern surgical methods in the combined treatment of inflammatory diseases of bones and joints in patients hospitalised to the Septic Surgery Department of the G.G. Kuvatov Republican Clinical Hospital (Ufa, Russia) in 2017–2018 (285 patients).Results and discussion. An analysis of the authors’ own data revealed that injuries (73.21%) and infectious complications after receiving surgery on bones and joints (15.03%) are the most common causes of osteomyelitis. In most cases, the following list of measures is optimal for diagnosing suspected osteomyelitis of various etiologies: X-ray, general clinical tests supplemented by the fistulography or CT of the affected area prior to surgery, as well as the examination of surgical material after surgery. The use of modern methods for surgical debridement and surgical repair of bone defects in the combined treatment of patients with chronic osteomyelitis can significantly reduce the relapse rate. It is recommended that patients with osteomyelitis be treated at large in-patient surgical facilities, which include a specialised department for the treatment of surgical infections and corresponding support services.Conclusion. Apparently, there is no one most optimal method for treating osteomyelitis. The optimal effect in the treatment of osteomyelitis is achieved through a personalised set of therapeutic measures using the following methods: laser vaporisation, negative-pressure wound therapy, ultrasonic cavitation in the focus of inflammation, as well as surgical repair of the post-trepanation bone defect or wound

MODEL-FREE NOVELTY-BASED DIFFUSION MRI

Many limitations of diffusion MRI are due to the instability of the model fitting procedure. Major shortcomings of the model-based approach are a partial information loss due to model simplicity, long scan time requirements due to fitting instability, and the lack of knowledge about how the parameters of a given model would respond to previously unseen microstructural changes, possibly failing to detect certain previously unseen pathologies. Here we show that diffusion MRI pathology detection is feasible without any models and without any prior knowledge of specific pathological changes whatsoever. Instead, raw q-space measurements are used directly without a model, only healthy population data is used for reference, and any deviations in a patient dataset from the healthy reference database are detected using novelty detection methods. This is done in each voxel independently, i.e. without spatial bias

q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans

Numerous scientific fields rely on elaborate but partly suboptimal data processing pipelines. An example is diffusion magnetic resonance imaging (diffusion MRI), a non-invasive microstructure assessment method with a prominent application in neuroimaging. Advanced diffusion models providing accurate microstructural characterization so far have required long acquisition times and thus have been inapplicable for children and adults who are uncooperative, uncomfortable, or unwell. We show that the long scan time requirements are mainly due to disadvantages of classical data processing. We demonstrate how deep learning, a group of algorithms based on recent advances in the field of artificial neural networks, can be applied to reduce diffusion MRI data processing to a single optimized step. This modification allows obtaining scalar measures from advanced models at twelve-fold reduced scan time and detecting abnormalities without using diffusion models. We set a new state of the art by estimating diffusion kurtosis measures from only 12 data points and neurite orientation dispersion and density measures from only 8 data points. This allows unprecedentedly fast and robust protocols facilitating clinical routine and demonstrates how classical data processing can be streamlined by means of deep learning