An Optimal Algorithm for Parallel MRI in Presence of Motion Artifacts

2007 ◽  
Author(s):  
Xin Huang ◽  
Wu-fan Chen
Keyword(s):  
Optimal Algorithm ◽  
Motion Artifacts ◽  
Parallel Mri

DEVELOPMENT OF A DEVICE FOR CONTROL OF THE STATE OF THE ORGANISM BASED ON PHOTOPLETISMOGRAPHY USING TECHNOLOGIES OF DIGITAL ADAPTIVE FILTRATION

2020 ◽  
pp. 100-107
Author(s):  
Алексей Дмитриевич Акишин ◽  
Иван Павлович Семчук ◽  
Александр Петрович Николаев
Keyword(s):  
Heart Rate ◽  
Cardiovascular System ◽  
Adaptive Filtering ◽  
Least Squares Method ◽  
Optimal Algorithm ◽  
Heart Diseases ◽  
Cardiac Activity ◽  
Motion Artifacts ◽  
The Body ◽  
Advantages And Disadvantages

Постоянно растущий интерес к разработке новых неинвазивных и безманжетных методов измерения параметров сердечной деятельности, использование которых давало бы возможность непрерывного и удаленного контроля сердечно-сосудистой системы, обуславливает актуальность данной работы. В многочисленных публикациях продолжаются обсуждения преимуществ и недостатков различных методов ранней диагностики сердечно-сосудистых заболеваний. Однако артефакты движения являются сильной помехой, мешающей точной оценке показателей функционирования сердечно-сосудистой системы. Одним из перспективных методов контроля является метод оценки физиологических параметров с использованием фотоплетизмографии. Данная статья посвящена разработке устройства для фотоплетизмографических исследований и алгоритмических методов обработки регистрируемых сигналов для обеспечения мониторинга сердечного ритма с заданной точностью. В работе используются технологии цифровой адаптивной фильтрации полученных сигналов для мониторинга сердечного ритма в условиях внешних механических и электрических помеховых воздействий, ухудшающих точностные характеристики системы, а также разработана архитектура системы и изготовлен макет устройства, который позволил провести измерения для определения оптимального алгоритма цифровой обработки сигналов. При использовании устройства применялись методы адаптивной фильтрации на основе фильтров Винера, фильтров на основе метода наименьших квадратов и Калмановской фильтрации. Разработанное устройство для фотоплетизмографических исследований обеспечило возможность мониторинга сердечного ритма с заданной точностью, контроля текущего состояния организма и может быть использовано в качестве средства диагностики заболеваний сердца The constantly growing interest in the development of new non-invasive and cuff-free methods for measuring the parameters of cardiac activity, the use of which would give the possibility of continuous and remote monitoring of the cardiovascular system, determines the relevance of this work. Numerous publications continue to discuss the advantages and disadvantages of various methods of early diagnosis of cardiovascular disease. However, motion artifacts are a strong hindrance to the accurate assessment of the performance of the cardiovascular system. One of the promising control methods is the method for assessing physiological parameters using photoplethysmography. This article is devoted to the development of a device for photoplethysmographic studies and algorithmic methods for processing recorded signals to ensure monitoring of the heart rate with a given accuracy. The work uses technologies of digital adaptive filtering of the received signals to monitor the heart rate in conditions of external mechanical and electrical interference, which worsen the accuracy characteristics of the system, as well as the architecture of the system and a prototype of the device, which made it possible to carry out measurements to determine the optimal algorithm for digital signal processing. When using the device, the methods of adaptive filtering based on Wiener filters, filters based on the least squares method and Kalman filtering were used. The developed device for photoplethysmographic studies provided the ability to monitor the heart rate with a given accuracy, control the current state of the body and can be used as a means of diagnosing heart diseases

scholarly journals POCS-enhanced correction of motion artifacts in parallel MRI

2010 ◽  
Vol 63 (4) ◽  
pp. 1104-1110 ◽  
Author(s):  
Alexey A. Samsonov ◽  
Julia Velikina ◽  
Youngkyoo Jung ◽  
Eugene G. Kholmovski ◽  
Chris R. Johnson ◽  
...  
Keyword(s):  
Motion Artifacts ◽  
Parallel Mri

scholarly journals PPG System Development for the Organism Physiological Parameters Monitoring with Artificial Intelligence Technologies

2021 ◽  
Vol 2096 (1) ◽  
pp. 012187
Author(s):  
A D Akishin ◽  
A P Nikolaev ◽  
A V Pisareva
Keyword(s):  
Artificial Intelligence ◽  
Heart Rate ◽  
Signal Processing ◽  
Optimal Algorithm ◽  
Experimental Studies ◽  
Adaptive Algorithms ◽  
Adaptive Signal Processing ◽  
Motion Artifacts ◽  
Physiological Parameters ◽  
The Given

Abstract Monitoring such health parameters as cardiac rate (CR), respiration rate (RR), blood pressure (BP), degree of oxygen in blood (SpO2), body temperature and other requires careful approach to design and development of medical devices. New non-invasive methods introduced in measuring human physiological parameters based on photoplethysmography (PPG) demonstrated their significant potential in monitoring the state of an organism, but their use in wearable devices is largely hampered by exposure to motion artifacts. This article presents a device for photoplethysmographic studies using various adaptive algorithms for processing the registered signals. The work uses artificial intelligence technologies to monitor the heart rate exposed to external mechanical and electrical interference worsening accuracy characteristics of the system. Besides, system architecture was developed, and a device model was manufactured, which made it possible to measure the optimal algorithm for digital signal processing. When using the PPG system, methods of adaptive signal processing based on Wiener filters, filters on the method of least squares (MLS) and Kalman filtering were used. To ensure heart rate monitoring with the given accuracy, studies were performed with participation of volunteers, and analysis was carried out of the results of various signal processing algorithms operation. In the course of experimental studies, a method was proposed to estimate the heart rate calculation accuracy and to analyze the external noise filtering efficiency by adaptive algorithms. PPG designed and developed system made it possible to monitor the heart rate with the given accuracy, control the organism current state and could be used as a means of cardiovascular disease diagnostics.

1/(1-1/k)-Optimal Algorithm for Regular Expression Grouping

2012 ◽  
Vol 23 (9) ◽  
pp. 2261-2272 ◽  
Author(s):  
Ting-Wen LIU ◽  
Yong SUN ◽  
Dong-Bo BU ◽  
Li GUO ◽  
Bin-Xing FANG
Keyword(s):  
Regular Expression ◽  
Optimal Algorithm

Distributed Multicast Routing Optimal Algorithm in Sensor Networks

2014 ◽  
Vol 35 (10) ◽  
pp. 2328-2334
Author(s):  
Jun Liu ◽  
Liang-lun Cheng ◽  
Jian-hua Wang
Keyword(s):  
Sensor Networks ◽  
Multicast Routing ◽  
Optimal Algorithm

Systematic Analysis and Review of Magnetic Resonance Imaging (MRI) Reconstruction Techniques

2021 ◽  
Vol 16 ◽  
Author(s):  
Penta Anil Kumar ◽  
R. Gunasundari ◽  
R. Aarthi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Optimization Technique ◽  
Motion Artifacts ◽  
Reconstruction Technique ◽  
Resonance Imaging ◽  
Research Gaps ◽  
Conventional Mri ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri

Background: Magnetic Resonance Imaging (MRI) plays an important role in the field of medical diagnostic imaging as it poses non-invasive acquisition and high soft-tissue contrast. However, the huge time is needed for the MRI scanning process that results in motion artifacts, degrades image quality, misinterpretation of data, and may cause uncomfortable to the patient. Thus, the main goal of MRI research is to accelerate data acquisition processing without affecting the quality of the image. Introduction: This paper presents a survey based on distinct conventional MRI reconstruction methodologies. In addition, a novel MRI reconstruction strategy is proposed based on weighted Compressive Sensing (CS), Penalty-aided minimization function, and Meta-heuristic optimization technique. Methods: An illustrative analysis is done concerning adapted methods, datasets used, execution tools, performance measures, and values of evaluation metrics. Moreover, the issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to obtain improved contribution for devising significant MRI reconstruction techniques. Results: The proposed method will reduce conventional aliasing artifacts problems, may attain lower Mean Square Error (MSE), higher Peak Signal-to-Noise Ratio (PSNR), and Structural SIMilarity (SSIM) index. Conclusion: The issues of existing methods and the research gaps considering conventional MRI reconstruction schemes are elaborated to devising an improved significant MRI reconstruction technique.

scholarly journals Computed Tomography Imaging in Simulated Ongoing Cardiopulmonary Resuscitation: No Need to Switch Off the Chest Compression Device during Image Acquisition

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1122
Author(s):  
Jessica Graef ◽  
Bernd A. Leidel ◽  
Keno K. Bressem ◽  
Janis L. Vahldiek ◽  
Bernd Hamm ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Cardiopulmonary Resuscitation ◽  
Chest Compression ◽  
Image Acquisition ◽  
Circulatory Arrest ◽  
Motion Artifacts ◽  
Current Standard ◽  
Ct Protocol ◽  
Life Threatening ◽  
Reconstruction Time

Computed tomography (CT) represents the current standard for imaging of patients with acute life-threatening diseases. As some patients present with circulatory arrest, they require cardiopulmonary resuscitation. Automated chest compression devices are used to continue resuscitation during CT examinations, but tend to cause motion artifacts degrading diagnostic evaluation of the chest. The aim was to investigate and evaluate a CT protocol for motion-free imaging of thoracic structures during ongoing mechanical resuscitation. The standard CT trauma protocol and a CT protocol with ECG triggering using a simulated ECG were applied in an experimental setup to examine a compressible thorax phantom during resuscitation with two different compression devices. Twenty-eight phantom examinations were performed, 14 with AutoPulse® and 14 with corpuls cpr®. With each device, seven CT examinations were carried out with ECG triggering and seven without. Image quality improved significantly applying the ECG-triggered protocol (p < 0.001), which allowed almost artifact-free chest evaluation. With the investigated protocol, radiation exposure was 5.09% higher (15.51 mSv vs. 14.76 mSv), and average reconstruction time of CT scans increased from 45 to 76 s. Image acquisition using the proposed CT protocol prevents thoracic motion artifacts and facilitates diagnosis of acute life-threatening conditions during continuous automated chest compression.

scholarly journals Detection of Ventricular Fibrillation Based on Ballistocardiography by Constructing an Effective Feature Set

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3524
Author(s):  
Rongru Wan ◽  
Yanqi Huang ◽  
Xiaomei Wu
Keyword(s):  
Ventricular Fibrillation ◽  
Cross Validation ◽  
Rhythmic Activity ◽  
Detection Algorithm ◽  
Motion Artifacts ◽  
Cardiac Monitoring ◽  
Mechanical Activity ◽  
Time Frequency ◽  
S Transform ◽  
Fatal Arrhythmia

Ventricular fibrillation (VF) is a type of fatal arrhythmia that can cause sudden death within minutes. The study of a VF detection algorithm has important clinical significance. This study aimed to develop an algorithm for the automatic detection of VF based on the acquisition of cardiac mechanical activity-related signals, namely ballistocardiography (BCG), by non-contact sensors. BCG signals, including VF, sinus rhythm, and motion artifacts, were collected through electric defibrillation experiments in pigs. Through autocorrelation and S transform, the time-frequency graph with obvious information of cardiac rhythmic activity was obtained, and a feature set of 13 elements was constructed for each 7 s segment after statistical analysis and hierarchical clustering. Then, the random forest classifier was used to classify VF and non-VF, and two paradigms of intra-patient and inter-patient were used to evaluate the performance. The results showed that the sensitivity and specificity were 0.965 and 0.958 under 10-fold cross-validation, and they were 0.947 and 0.946 under leave-one-subject-out cross-validation. In conclusion, the proposed algorithm combining feature extraction and machine learning can effectively detect VF in BCG, laying a foundation for the development of long-term self-cardiac monitoring at home and a VF real-time detection and alarm system.

Sign in / Sign up

Export Citation Format

Share Document