AM-FM Texture Image Analysis of the Intima and Media Layers of the Carotid Artery

Abstract. The purpose of this paper is to propose the use of amplitude modulation-frequency modulation (AM-FM) features for describing atherosclerotic plaque features that are associated with clinical factors such as intima media thickness and a patient's age. AM-FM analysis reveals the instantaneous amplitude (IA) of the media layer decreases with age. This decrease in IA maybe attributed to the reduction in calcified, stable plaque components and an increase in stroke risk with age. On the other hand, an increase in the median instantaneous frequency (IF) of the media layer suggests the fragmentation of solid, large plaque components, which also lead to an increase in the risk of stroke. The findings suggest that AM-FM features can be used to assess the risk of stroke over a wide range of patient populations. Future work will incorporate a new texture image retrieval system that uses AM-FM features to retrieve intima and intima media layer images that could be associated with the same level of the risk of stroke

Fully automated segmentation and tracking of the intima media thickness in ultrasound video sequences of the common carotid artery

Abstract—The robust identification and measurement of the intima media thickness (IMT) has a high clinical relevance because it represents one of the most precise predictors used in the assessment of potential future cardiovascular events. To facilitate the analysis of arterial wall thickening in serial clinical investigations, in this paper we have developed a novel fully automatic algorithm for the segmentation, measurement, and tracking of the intima media complex (IMC) in B-mode ultrasound video sequences. The proposed algorithm entails a two-stage image analysis process that initially addresses the segmentation of the IMC in the first frame of the ultrasound video sequence using a model-based approach; in the second step, a novel customized tracking procedure is applied to robustly detect the IMC in the subsequent frames. For the video tracking procedure, we introduce a spatially coherent algorithm called adaptive normalized correlation that prevents the tracking process from converging to wrong arterial interfaces. This represents the main contribution of this paper and was developed to deal with inconsistencies in the appearance of the IMC over the cardiac cycle. The quantitative evaluation has been carried out on 40 ultrasound video sequences of the common carotid artery (CCA) by comparing the results returned by the developed algorithm with respect to ground truth data that has been manually annotated by clinical experts. The measured IMTmean ± standard deviation recorded by the proposed algorithm is 0.60 mm ± 0.10, with a mean coefficient of variation (CV) of 2.05%, whereas the corresponding result obtained for the manually annotated ground truth data is 0.60 mm ± 0.11 with a mean CV equal to 5.60%. The numerical results reported in this paper indicate that the proposed algorithm is able to correctly segment and track the IMC in ultrasound CCA video sequences, and we were encouraged by the stability of our technique when applied to data captured under different imaging conditions. Future clinical studies will focus on the evaluation of patients that are affected by advanced cardiovascular conditions such as focal thickening and arterial plaques

Non-invasive Vascular Structure and Pathology Using Very-high Resolution Ultrasound

Very-high resolution ultrasound (VHRU, 25-55MHz) is a recently developed method for non-invasive assessment of vascular structures. With its increased ultrasound frequency, the method allows for noninvasive examination of the vascular wall in vivo with an axial resolution in the range of tens of micrometers. These characteristics make it a feasible method to determine vascular dimensions of superficial arteries and arteries in the pediatric population. The aim of this thesis was the following: 1. To study the application of a semi-automatic border detection software to improve measurement characteristics of the arterial wall layers, 2. To assess accuracy, precision and feasibility of the VHRU method in assessing superficial arterial wall layers in preterm and term neonates, 3. To validate the VHRU method to assess age-related intimal thickening of the arterial wall, and 4. To determine the potential to implement the method as a noninvasive tool in the bedside diagnosis of giant-cell arteritis of the temporal artery. This Thesis shows that there is no significant difference in the technical precision or bias of arterial wall layer dimension measurements using a semi-automated border detection software compared to electronical calipers, but time of analysis is significantly shorter using the automated border detection software. VHRU is feasible, accurate and precise in the measurement of arterial layer thickness (intima-media and intima-media-adventitia thickness) of proximal conduit arteries, such as carotid, brachial and femoral, in preterm and term neonates. The resolution of VHRU is insufficient in the assessment of more peripheral conduit arteries such as the radial artery. VHRU is feasible and able to detect a thickened intimal layer, seen as a four-line pattern of the arterial far wall in the ultrasound image, in superficial peripheral muscular conduit arteries with intima thickness >0.06mm. Measurements leading-to-leading edge of the intimal layer are accurate compared with histological thickness. VHRU is feasible, accurate and precise in assessing transmural inflammation related intimal thickening in patients with giant-cell arteritis of the temporal artery. The method was however not useful in patients with inflammation limited to the adventitia or without inflammation on histology. In conclusion, very-high resolution ultrasound is an emerging method for the assessment of superficial vascular wall layer structures. The harmless and non-invasive method can detect near-microscopical changes in the vascular wall in human subjects from the newborn stage to old age. Very-high resolution ultrasound has a clinical potential in the non-invasive assessment of vascular health and disease related pathology.Kajoamaton korkeataajuusultraääni (VHRU, very-high resolution ultrasound, 25-55MHz) on 2000-luvulla kehitetty ultraäänimenetelmä valtimonseinämän kuvantamiseen. Korkeammilla ultraäänitaajuuksilla kuvan erottelukyky on parempi, lähes mikroskooppitasoa, mutta kuvausalue on rajoittunut lähellä anturia oleviin rakenteisiin. Tässä väitöskirjassa arvioidaan puoliautomaattisen analyysiohjelman käyttöä valtimoseinämän eri kerrosten mittaamisessa. Lisäksi kirjassa selvitetään menetelmän soveltuvuutta vastasyntyneiden lasten valtimoseinämän arvioinnissa, menetelmän käyttöä valtimon sisäkalvon (tunica intima) paksuuden mittauksessa ikääntyneillä, sekä menetelmän hyötyjä jättisoluarteriitin diagnostiikassa. Tutkimme puoliautomaattisen ohjelmankäyttöä kymmenen henkilön eri verisuonista otettujen kuvien arvioinnissa vertailemalla analyysiaikaa ja mittauksien luotettavuutta käsin tehtyihin yksittäismittauksiin. Emme löytäneet eroa menetelmien luotettavuudessa, mutta puoliautomaattisen menetelmän analyysiaika oli merkittävästi lyhyempi. VHRU-menetelmä pystyi luotettavasti ja tarkasti mittaamaan suurten ja keskisuurten valtimoiden seinämän kerrospaksuudet. VHRU-menetelmällä tutkittiin 78 ikääntynyttä potilasta, jotka oli lähetetty ohimovaltimon koepalan ottoon jättisoluarteriittiepäilyn takia. Niiden potilaiden joukossa, joilla ei ollut tulehdusmuutoksia suonen seinämässä, 76 %:lla oli histologisesti paksuuntunut valtimon sisäkalvo joka oli tarkasti ja luotettavasti mitattavissa VHRU-menetelmällä. Jättisoluarteriittipotilailla ohimovaltimon seinämä oli histologiassa selkeästi paksuntunut. VHRU-menetelmällä mitattu yli 0,3 mm:n valtimon sisäkalvo oli tarkka ja herkkä mittari jättisoluarteriitille. Kajoamaton korkeataajuusultraääni on uusi menetelmä, jolla pinnallisten valtimoiden seinämän kerrosten paksuudet voidaan tarkasti ja luotettavasti mitata. Puoliautomaattisella ohjelmalla valtimoseinämän kerroksen paksuuden mittaamista voidaan nopeuttaa. Menetelmän parempi erottelukyky mahdollistaa pienten vastasyntyneiden valtimoseinämän kuvantamisen ja ikääntyvien valtimon sisäkalvon paksuuden mittaamisen. Ohimovaltimon jättisoluarteriitissa suonenseinämä turpoaa tulehduksen seurauksena ja tämä näkyy VHRU-kuvissa paksuuntuneena sisäkalvona. Menetelmä voisi tulevaisuudessa soveltua kliiniseen käyttöön pinnallisten verisuonisairauksien tutkimuksessa ja diagnostiikassa

Human Attention Detection Using AM-FM Representations

Human activity detection from digital videos presents many challenges to the computer vision and image processing communities. Recently, many methods have been developed to detect human activities with varying degree of success. Yet, the general human activity detection problem remains very challenging, especially when the methods need to work “in the wild” (e.g., without having precise control over the imaging geometry). The thesis explores phase-based solutions for (i) detecting faces, (ii) back of the heads, (iii) joint detection of faces and back of the heads, and (iv) whether the head is looking to the left or the right, using standard video cameras without any control on the imaging geometry. The proposed phase-based approach is based on the development of simple and robust methods that relie on the use of Amplitude Modulation - Frequency Modulation (AM-FM) models. The approach is validated using video frames extracted from the Advancing Outof- school Learning in Mathematics and Engineering (AOLME) project. The dataset consisted of 13,265 images from ten students looking at the camera, and 6,122 images from five students looking away from the camera. For the students facing the camera, the method was able to correctly classify 97.1% of them looking to the left and 95.9% of them looking to the right. For the students facing the back of the camera, the method was able to correctly classify 87.6% of them looking to the left and 93.3% of them looking to the right. The results indicate that AM-FM based methods hold great promise for analyzing human activity videos

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID‐19: A Narrative Review

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19

Oxygen Transport in Carotid and Stented Coronary Arteries

Oxygen deficiency, known as hypoxia, in arterial walls has been linked to increased intimal hyperplasia, which is the main adverse biological process causing in-stent restenosis. Stent implantation can have significant effects on the oxygen transport into the arterial wall. Helical flow has been theorised to improve the local haemodynamics and the oxygen transport within stented arteries. In this study an advanced oxygen transport model was developed to assess different stent designs. This advanced oxygen transport model incorporates both the free and bound oxygen contained in blood and includes a shear-dependent dispersion coefficient for red blood cells. In two test cases undertaken the results predicted by the advanced oxygen transport model were compared those predicted by simpler models, and in vivo measurements. Two other test cases analysed the predicted oxygen transport in three different stent designs, and the effects of helical flow on the haemodynamics and oxygen transport in stented coronary arteries. The advanced model showed good agreement with experimental measurements within the mass-transfer boundary layer and at the luminal surface; however, more work is needed for predicting the oxygen transport within the arterial wall. Simplifying the oxygen transport model within the blood produces significant errors in predicting the oxygen transport in arteries. It was found that different stent designs can produce significantly different amounts of hypoxic regions within the stented region. Additionally, helical flow increases the amount of oxygen transferred into the arterial wall, but only in a helical ribbon through the stented region that also experiences high wall shear stress spatial gradients