Motion Artifacts in fMRI: Comparison of 2DFT with PR and Spiral Scan Methods

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

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

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405616666210105125542 ◽

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.

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

Diagnostics ◽

10.3390/diagnostics11061122 ◽

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.

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

Sensors ◽

10.3390/s21103524 ◽

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.

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Evaluation of denoising strategies for task‐based functional connectivity: Equalizing residual motion artifacts between rest and cognitively demanding tasks

Human Brain Mapping ◽

10.1002/hbm.25332 ◽

2021 ◽

Vol 42 (6) ◽

pp. 1805-1828

Author(s):

Daniele Mascali ◽

Marta Moraschi ◽

Mauro DiNuzzo ◽

Silvia Tommasin ◽

Michela Fratini ◽

...

Keyword(s):

Functional Connectivity ◽

Motion Artifacts ◽

Residual Motion ◽

Cognitively Demanding Tasks

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Characterization and correction of cardiovascular motion artifacts in diffusion‐weighted imaging of the pancreas

Magnetic Resonance in Medicine ◽

10.1002/mrm.28846 ◽

2021 ◽

Author(s):

Ruiqi Geng ◽

Yuxin Zhang ◽

Jitka Starekova ◽

David R. Rutkowski ◽

Lloyd Estkowski ◽

...

Keyword(s):

Diffusion Weighted Imaging ◽

Motion Artifacts ◽

Diffusion Weighted

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Recurrent Neural Network for Human Activity Recognition in Embedded Systems Using PPG and Accelerometer Data

Electronics ◽

10.3390/electronics10141715 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1715

Author(s):

Michele Alessandrini ◽

Giorgio Biagetti ◽

Paolo Crippa ◽

Laura Falaschetti ◽

Claudio Turchetti

Keyword(s):

Neural Network ◽

Embedded System ◽

Activity Recognition ◽

Human Activity ◽

Recurrent Neural Network ◽

Low Cost ◽

Low Complexity ◽

Human Activity Recognition ◽

Motion Artifacts ◽

Accelerometer Data

Photoplethysmography (PPG) is a common and practical technique to detect human activity and other physiological parameters and is commonly implemented in wearable devices. However, the PPG signal is often severely corrupted by motion artifacts. The aim of this paper is to address the human activity recognition (HAR) task directly on the device, implementing a recurrent neural network (RNN) in a low cost, low power microcontroller, ensuring the required performance in terms of accuracy and low complexity. To reach this goal, (i) we first develop an RNN, which integrates PPG and tri-axial accelerometer data, where these data can be used to compensate motion artifacts in PPG in order to accurately detect human activity; (ii) then, we port the RNN to an embedded device, Cloud-JAM L4, based on an STM32 microcontroller, optimizing it to maintain an accuracy of over 95% while requiring modest computational power and memory resources. The experimental results show that such a system can be effectively implemented on a constrained-resource system, allowing the design of a fully autonomous wearable embedded system for human activity recognition and logging.

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Image Registration of 18F-FDG PET/CT Using the MotionFree Algorithm and CT Protocols through Phantom Study and Clinical Evaluation

Healthcare ◽

10.3390/healthcare9060669 ◽

2021 ◽

Vol 9 (6) ◽

pp. 669

Author(s):

Deok-Hwan Kim ◽

Eun-Hye Yoo ◽

Ui-Seong Hong ◽

Jun-Hyeok Kim ◽

Young-Heon Ko ◽

...

Keyword(s):

Ct Scan ◽

Ct Scanning ◽

Motion Artifacts ◽

Scanning Time ◽

Pet Ct ◽

Static Images ◽

Type C ◽

Natural Breathing ◽

Fdg Pet Ct ◽

Patient Studies

We evaluated the benefits of the MotionFree algorithm through phantom and patient studies. The various sizes of phantom and vacuum vials were linked to RPM moving with or without MotionFree application. A total of 600 patients were divided into six groups by breathing protocols and CT scanning time. Breathing protocols were applied as follows: (a) patients who underwent scanning without any breathing instructions; (b) patients who were instructed to hold their breath after expiration during CT scan; and (c) patients who were instructed to breathe naturally. The length of PET/CT misregistration was measured and we defined the misregistration when it exceeded 10 mm. In the phantom tests, the images produced by the MotionFree algorithm were observed to have excellent agreement with static images. There were significant differences in PET/CT misregistration according to CT scanning time and each breathing protocol. When applying the type (c) protocol, decreasing the CT scanning time significantly reduced the frequency and length of misregistrations (p < 0.05). The MotionFree application is able to correct respiratory motion artifacts and to accurately quantify lesions. The shorter time of CT scan can reduce the frequency, and the natural breathing protocol also decreases the lengths of misregistrations.

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Dedicated Algorithm for Unobtrusive Fetal Heart Rate Monitoring Using Multiple Dry Electrodes

Sensors ◽

10.3390/s21134298 ◽

2021 ◽

Vol 21 (13) ◽

pp. 4298

Author(s):

Alessandra Galli ◽

Elisabetta Peri ◽

Yijing Zhang ◽

Rik Vullings ◽

Myrthe van der Ven ◽

...

Keyword(s):

Heart Rate ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Processing System ◽

Motion Artifacts ◽

Accurate Estimation ◽

Fetal Heart Rate Monitoring ◽

Dry Electrodes ◽

Textile Electrodes ◽

Dry Electrode

Multi-channel measurements from the maternal abdomen acquired by means of dry electrodes can be employed to promote long-term monitoring of fetal heart rate (fHR). The signals acquired with this type of electrode have a lower signal-to-noise ratio and different artifacts compared to signals acquired with conventional wet electrodes. Therefore, starting from the benchmark algorithm with the best performance for fHR estimation proposed by Varanini et al., we propose a new method specifically designed to remove artifacts typical of dry-electrode recordings. To test the algorithm, experimental textile electrodes were employed that produce artifacts typical of dry and capacitive electrodes. The proposed solution is based on a hybrid (hardware and software) pre-processing step designed specifically to remove the disturbing component typical of signals acquired with these electrodes (triboelectricity artifacts and amplitude modulations). The following main processing steps consist of the removal of the maternal ECG by blind source separation, the enhancement of the fetal ECG and identification of the fetal QRS complexes. Main processing is designed to be robust to the high-amplitude motion artifacts that corrupt the acquisition. The obtained denoising system was compared with the benchmark algorithm both on semi-simulated and on real data. The performance, quantified by means of sensitivity, F1-score and root-mean-square error metrics, outperforms the performance obtained with the original method available in the literature. This result proves that the design of a dedicated processing system based on the signal characteristics is necessary for reliable and accurate estimation of the fHR using dry, textile electrodes.

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