Applying a level set method for resolving physiologic motions in free-breathing and non-gated cardiac MRI

reserved6siIn cardiac MRI, ECG triggering is used or patients are required to hold their breath, to alleviate motion artifacts and deterioration of image quality. However, ECG signal quality is often suboptimal and patients may not be able to adequately hold their breath. Alternative solutions for tracking breathing and cardiac beating can open the way for robust free-breathing and ECG-less cardiac MRI. Herein, we present a novel approach that isolates the effect of breathing, as well as computes both the breathing and cardiac beating waveforms directly from real-time MRI sequences. It turns a challenge into an opportunity to guide the reconstruction of high temporal resolution images. The proposed method is based on a level-set method to segment the left ventricle from a real-time MR sequence collected with free breathing and without ECG triggering. The algorithm extracts an evolving surface area, which captures the heart’s systolic contraction and diastolic expansion in real-time. The computed time series of the heart’s dynamic area is subjected to wavelet analysis, where the breathing and pulsation components are separated. The method was investigated on 12 real-time cardiac MRI acquisitions. We demonstrate that the left ventricular area, as computed by the level set method, produces breathing and cardiac waveforms similar with those extracted by cardiac MR experts (ground-truth). This proof-of-concept work demonstrates the capabilities of the proposed methodology paving the way for incorporation into real-time or retrospective reconstruction of high resolution cardiac MR.mixedUyanik I.; Lindner P.; Tsiamyrtzis P.; Shah D.; Tsekos N.V.; Pavlidis I.T.Uyanik, I.; Lindner, P.; Tsiamyrtzis, Panagiotis; Shah, D.; Tsekos, N. V.; Pavlidis, I. T

Sympathetic Loading in Critical Tasks

In this dissertation I developed or perfected unobtrusive methods to quantify sympathetic arousals. Furthermore, I used these methods to study the sympathetic system's role on critical activities, arriving at intriguing conclusions. Sympathetic arousals occur during states of mental, emotional, and/or sensorimotor strain resulting from adverse or demanding circumstances. They are key elements of human physiology's coping mechanism, shoring up resources to a good effect. When the intensity and duration of these arousals are overwhelming, however, then they may block memory and disrupt rational thought or actions at the moment they are needed the most. Arousals abound in three types of critical activities: high-stakes situations, challenging tasks, and critical multitasking. Accordingly, my research was based on three studies representative of these three activity types: `Subject Screening', `Educational Exam', and `Distracted Driving'. In the first study I investigated the association of sympathetic arousals with deceptive behavior in interrogations. In the second study, I investigated the relationship between sympathetic arousals and exam performance. In the third study, I investigated the interaction between sympathetic arousals and driving performance under cognitive, emotional, and sensorimotor distractions. In the interrogation study, I used for the first time a contact-free electrodermal activity measurement method to quantify arousals. The method detected deceptive behavior based on differential sympathetic responses in well-structured interviews. In the exam study, I documented that sympathetic arousals positively correlate with students' exam performance, dispelling the myth of `easy going' super achievers. Finally, in the driving study, my results revealed that not only apparent sensorimotor stressors (texting while driving) but also hidden stressors (cognitive or emotional) could have a significant effect on driving performance.Computer Science, Department o

Класифікація ішемічної хвороби серця через гармонійні моделі текстури зображення ехокардіографії

Магістерська дисертація за темою «Класифікація ішемічної хвороби серця через гармонійні моделі текстури зображення ехокардіографії» виконана студенткою кафедри біомедичної кібернетики Петруніною Оленою Олександрівною зі спеціальності 122 «Комп’ютерні науки» за освітньо-професійною програмою «Комп’ютерні технології в біології та медицині», та складається зі: вступу; 4 розділів («Аналіз предметної області», «Методи реконструкції зображень», «Моделювання текстури зображення», «Класифікація ішемічної хвороби серця»), розділу зі стартап проєкту, висновків до кожного з цих розділів; загальних висновків; списку використаних джерел, який налічує 99 джерела. Загальний обсяг роботи 105 сторінок. Обсяг роботи: 105 сторінок, 35 ілюстрацій, 40 джерел посилань. Актуальність теми. Ішемічна хвороба серця є доволі поширеною патологією в Україні, тому своєчасне виявлення даного захворювання є надважливою задачею. Мета дослідження. Аналіз та обробка потоків відеоданих ехоКГ. Об’єкт дослідження. Алгоритми реконструкції зображень. Предмет дослідження. Використання алгоритмів реконструкції зображень для розпізнавання ішемічної хвороби серця. Методи дослідження. КОМБІ-ГА (суміш комбінаційного методу групового урахування аргументів і генетичного алгоритму). Інструменти дослідження. Python, Anaconda, Jupyter Notebook.Master's thesis on "Classification of coronary heart disease due to harmonic texture models of echocardiography images" is executed by the student of the department of biomedical cybernetics Petrunina Elena Aleksandrovna in the specialty 122 "Computer science" on the educational and professional program "Computer and technology" consists of: introduction; 4 sections ("Analysis of the subject area", "Image reconstruction methods", "Image texture modeling", "Classification of coronary heart disease"); section with a startup section "The study of the subject area". The total volume of the work is 105 pages. Paper size: 105 pages, 35 illustrations, 40 references. Relevance of the topic. Coronary heart disease is a rather common pathology in Ukraine; therefore, timely detection of the disease is a crucial task. Objective of the study. Analysis and processing of video echoCG streams. Object of study. Image reconstruction algorithms. Subject of study. Image reconstruction algorithms for coronary heart disease recognition. Research methods. COMBI-GA (Combinatorial Argument Group Method and Genetic Algorithm). Research tools. Python, Anaconda, Jupyter Notebook