Sex differences in coronary atherosclerotic plaque activity using 18F-sodium fluoride positron emission tomography.

INTRODUCTION: There are sex differences in the extent, severity, and outcomes of coronary artery disease. We aimed to assess the influence of sex on coronary atherosclerotic plaque activity measured using coronary 18F-sodium fluoride (18F-NaF) positron emission tomography (PET), and to determine whether 18F-NaF PET has prognostic value in both women and men. METHODS: In a post-hoc analysis of observational cohort studies of patients with coronary atherosclerosis who had undergone 18F-NaF PET CT angiography, we compared the coronary microcalcification activity (CMA) in women and men. RESULTS: Baseline 18F-NaF PET CT angiography was available in 999 participants (151 (15%) women) with 4282 patient-years of follow-up. Compared to men, women had lower coronary calcium scores (116 [interquartile range, 27-434] versus 205 [51-571] Agatston units; p = 0.002) and CMA values (0.0 [0.0-1.12] versus 0.53 [0.0-2.54], p = 0.01). Following matching for plaque burden by coronary calcium scores and clinical comorbidities, there was no sex-related difference in CMA values (0.0 [0.0-1.12] versus 0.0 [0.0-1.23], p = 0.21) and similar proportions of women and men had no 18F-NaF uptake (53.0% (n = 80) and 48.3% (n = 73); p = 0.42), or CMA values > 1.56 (21.8% (n = 33) and 21.8% (n = 33); p = 1.00). Over a median follow-up of 4.5 [4.0-6.0] years, myocardial infarction occurred in 6.6% of women (n = 10) and 7.8% of men (n = 66). Coronary microcalcification activity greater than 0 was associated with a similarly increased risk of myocardial infarction in both women (HR: 3.83; 95% CI:1.10-18.49; p = 0.04) and men (HR: 5.29; 95% CI:2.28-12.28; p < 0.001). CONCLUSION: Although men present with more coronary atherosclerotic plaque than women, increased plaque activity is a strong predictor of future myocardial infarction regardless of sex.

Coronary Atherosclerotic Plaque Activity and Risk of Myocardial Infarction.

BACKGROUND: Total coronary atherosclerotic plaque activity across the entire coronary arterial tree is associated with patient-level clinical outcomes. OBJECTIVES: We aimed to investigate whether vessel-level coronary atherosclerotic plaque activity is associated with vessel-level myocardial infarction. METHODS: In this secondary analysis of an international multicenter study of patients with recent myocardial infarction and multivessel coronary artery disease, we assessed vessel-level coronary atherosclerotic plaque activity using coronary 18F-sodium fluoride positron emission tomography to identify vessel-level myocardial infarction. RESULTS: Increased 18F-sodium fluoride uptake was found in 679 of 2,094 coronary arteries and 414 of 691 patients. Myocardial infarction occurred in 24 (4%) vessels with increased coronary atherosclerotic plaque activity and in 25 (2%) vessels without increased coronary atherosclerotic plaque activity (HR: 2.08; 95% CI: 1.16-3.72; P = 0.013). This association was not demonstrable in those treated with coronary revascularization (HR: 1.02; 95% CI: 0.47-2.25) but was notable in untreated vessels (HR: 3.86; 95% CI: 1.63-9.10; Pinteraction = 0.024). Increased coronary atherosclerotic plaque activity in multiple coronary arteries was associated with heightened patient-level risk of cardiac death or myocardial infarction (HR: 2.43; 95% CI: 1.37-4.30; P = 0.002) as well as first (HR: 2.19; 95% CI: 1.18-4.06; P = 0.013) and total (HR: 2.50; 95% CI: 1.42-4.39; P = 0.002) myocardial infarctions. CONCLUSIONS: In patients with recent myocardial infarction and multivessel coronary artery disease, coronary atherosclerotic plaque activity prognosticates individual coronary arteries and patients at risk for myocardial infarction.

Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers.

This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter-zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid-face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid-face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

Self-supervised pseudo multi-class pre-training for unsupervised anomaly detection and segmentation in medical images.

Unsupervised anomaly detection (UAD) methods are trained with normal (or healthy) images only, but during testing, they are able to classify normal and abnormal (or disease) images. UAD is an important medical image analysis (MIA) method to be applied in disease screening problems because the training sets available for those problems usually contain only normal images. However, the exclusive reliance on normal images may result in the learning of ineffective low-dimensional image representations that are not sensitive enough to detect and segment unseen abnormal lesions of varying size, appearance, and shape. Pre-training UAD methods with self-supervised learning, based on computer vision techniques, can mitigate this challenge, but they are sub-optimal because they do not explore domain knowledge for designing the pretext tasks, and their contrastive learning losses do not try to cluster the normal training images, which may result in a sparse distribution of normal images that is ineffective for anomaly detection. In this paper, we propose a new self-supervised pre-training method for MIA UAD applications, named Pseudo Multi-class Strong Augmentation via Contrastive Learning (PMSACL). PMSACL consists of a novel optimisation method that contrasts a normal image class from multiple pseudo classes of synthesised abnormal images, with each class enforced to form a dense cluster in the feature space. In the experiments, we show that our PMSACL pre-training improves the accuracy of SOTA UAD methods on many MIA benchmarks using colonoscopy, fundus screening and Covid-19 Chest X-ray datasets.

3D-Printed Micro Lens-in-Lens for In Vivo Multimodal Microendoscopy.

Multimodal microendoscopes enable co-located structural and molecular measurements in vivo, thus providing useful insights into the pathological changes associated with disease. However, different optical imaging modalities often have conflicting optical requirements for optimal lens design. For example, a high numerical aperture (NA) lens is needed to realize high-sensitivity fluorescence measurements. In contrast, optical coherence tomography (OCT) demands a low NA to achieve a large depth of focus. These competing requirements present a significant challenge in the design and fabrication of miniaturized imaging probes that are capable of supporting high-quality multiple modalities simultaneously. An optical design is demonstrated which uses two-photon 3D printing to create a miniaturized lens that is simultaneously optimized for these conflicting imaging modalities. The lens-in-lens design contains distinct but connected optical surfaces that separately address the needs of both fluorescence and OCT imaging within a lens of 330 µm diameter. This design shows an improvement in fluorescence sensitivity of >10x in contrast to more conventional fiber-optic design approaches. This lens-in-lens is then integrated into an intravascular catheter probe with a diameter of 520 µm. The first simultaneous intravascular OCT and fluorescence imaging of a mouse artery in vivo is reported.

CNN Attention Guidance for Improved Orthopedics Radiographic Fracture Classification.

Convolutional neural networks (CNNs) have gained significant popularity in orthopedic imaging in recent years due to their ability to solve fracture classification problems. A common criticism of CNNs is their opaque learning and reasoning process, making it difficult to trust machine diagnosis and the subsequent adoption of such algorithms in clinical setting. This is especially true when the CNN is trained with limited amount of medical data, which is a common issue as curating sufficiently large amount of annotated medical imaging data is a long and costly process. While interest has been devoted to explaining CNN learnt knowledge by visualizing network attention, the utilization of the visualized attention to improve network learning has been rarely investigated. This paper explores the effectiveness of regularizing CNN network with human-provided attention guidance on where in the image the network should look for answering clues. On two orthopedics radiographic fracture classification datasets, through extensive experiments we demonstrate that explicit human-guided attention indeed can direct correct network attention and consequently significantly improve classification performance. The development code for the proposed attention guidance is publicly available on https://github.com/zhibinliao89/fracture_attention_guidance.

Equivalent Carbon Number and Interclass Retention Time Conversion Enhance Lipid Identification in Untargeted Clinical Lipidomics.

Chromatography is often used as a method for reducing sample complexity prior to analysis by mass spectrometry, and the use of retention time (RT) is becoming increasingly popular to add valuable supporting information in lipid identification. The RT of lipids with the same headgroup in reversed-phase separation can be predicted using the equivalent carbon number (ECN) model. This model describes the effects of acyl chain length and degree of saturation on lipid RT. For the first time, we have found a robust correlation in the chromatographic separation of lipids with different headgroups that share the same fatty acid motive. This relationship can be exploited to perform interclass RT conversion (IC-RTC) by building a model from RT measurements from lipid standards that allows the prediction of RT of one lipid subclass based on another. Here, we utilize ECN modeling and IC-RTC to build a glycerophospholipid RT library with 517 entries based on 136 tandem mass spectrometry-characterized lipid RTs from NIST SRM-1950 plasma and lipid standards. The library was tested on a patient cohort undergoing coronary artery bypass grafting surgery (n = 37). A total of 156 unique circulating glycerophospholipids were identified, of which 52 (1 LPG, 24 PE, 5 PG, 18 PI, and 9 PS) were detected with IC-RTC, thereby demonstrating the utility of this technique for the identification of lipid species not found in commercial standards.

Multimodality Intravascular Imaging of High-Risk Coronary Plaque.

The majority of coronary atherothrombotic events presenting as myocardial infarction (MI) occur as a result of plaque rupture or erosion. Understanding the evolution from a stable plaque into a life-threatening, high-risk plaque is required for advancing clinical approaches to predict atherothrombotic events, and better treat coronary atherosclerosis. Unfortunately, none of the coronary imaging approaches used in clinical practice can reliably predict which plaques will cause an MI. Currently used imaging techniques mostly identify morphological features of plaques, but are not capable of detecting essential molecular characteristics known to be important drivers of future risk. To address this challenge, engineers, scientists, and clinicians have been working hand-in-hand to advance a variety of multimodality intravascular imaging techniques, whereby 2 or more complementary modalities are integrated into the same imaging catheter. Some of these have already been tested in early clinical studies, with other next-generation techniques also in development. This review examines these emerging hybrid intracoronary imaging techniques and discusses their strengths, limitations, and potential for clinical translation from both an engineering and clinical perspective.

Hip osteoarthritis: A novel network analysis of subchondral trabecular bone structures.

Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis. We describe a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone. We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences were discovered, particularly in the proximal third of the femoral head, where HOA networks demonstrated elevated numbers of edges, vertices, and graph components. When further differentiating healthy joint and HOA networks, the latter showed fewer small-world network properties, due to decreased clustering coefficient and increased characteristic path length. Furthermore, we found that HOA networks had reduced length of edges, indicating the formation of compressed trabecular structures. In order to assess our network approach, we developed a deep learning model for classifying HOA and control cases, and we fed it with two separate inputs: (i) micro-CT images of the trabecular bone, and (ii) the network extracted from them. The model with plain micro-CT images achieves 74.6% overall accuracy while the trained model with extracted networks attains 96.5% accuracy. We anticipate our findings to be a starting point for a novel description of bone microstructure in HOA, by considering the phenomenon from a graph theory viewpoint.

The development of tumour vascular networks.

The growth of solid tumours relies on an ever-increasing supply of oxygen and nutrients that are delivered via vascular networks. Tumour vasculature includes endothelial cell lined angiogenesis and the less common cancer cell lined vasculogenic mimicry (VM). To study and compare the development of vascular networks formed during angiogenesis and VM (represented here by breast cancer and pancreatic cancer cell lines) a number of in vitro assays were utilised. From live cell imaging, we performed a large-scale automated extraction of network parameters and identified properties not previously reported. We show that for both angiogenesis and VM, the characteristic network path length reduces over time; however, only endothelial cells increase network clustering coefficients thus maintaining small-world network properties as they develop. When compared to angiogenesis, the VM network efficiency is improved by decreasing the number of edges and vertices, and also by increasing edge length. Furthermore, our results demonstrate that angiogenic and VM networks appear to display similar properties to road traffic networks and are also subject to the well-known Braess paradox. This quantitative measurement framework opens up new avenues to potentially evaluate the impact of anti-cancer drugs and anti-vascular therapies.

A Versatile Big Data Health System for Australia: Driving Improvements in Cardiovascular Health.

Cardiovascular diseases (CVD) are leading causes of death and morbidity in Australia and worldwide. Despite improvements in treatment, there remain large gaps in our understanding to prevent, treat and manage CVD events and associated morbidities. This article lays out a vision for enhancing CVD research in Australia through the development of a Big Data system, bringing together the multitude of rich administrative and health datasets available. The article describes the different types of Big Data available for CVD research in Australia and presents an overview of the potential benefits of a Big Data system for CVD research and some of the major challenges in establishing the system for Australia. The steps for progressing this vision are outlined.

Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.

Machine learning (ML) has been increasingly used within cardiology, particularly in the domain of cardiovascular imaging. Due to the inherent complexity and flexibility of ML algorithms, inconsistencies in the model performance and interpretation may occur. Several review articles have been recently published that introduce the fundamental principles and clinical application of ML for cardiologists. This paper builds on these introductory principles and outlines a more comprehensive list of crucial responsibilities that need to be completed when developing ML models. This paper aims to serve as a scientific foundation to aid investigators, data scientists, authors, editors, and reviewers involved in machine learning research with the intent of uniform reporting of ML investigations. An independent multidisciplinary panel of ML experts, clinicians, and statisticians worked together to review the theoretical rationale underlying 7 sets of requirements that may reduce algorithmic errors and biases. Finally, the paper summarizes a list of reporting items as an itemized checklist that highlights steps for ensuring correct application of ML models and the consistent reporting of model specifications and results. It is expected that the rapid pace of research and development and the increased availability of real-world evidence may require periodic updates to the checklist.

Ultrathin monolithic 3D printed optical coherence tomography endoscopy for preclinical and clinical use.

Preclinical and clinical diagnostics increasingly rely on techniques to visualize internal organs at high resolution via endoscopes. Miniaturized endoscopic probes are necessary for imaging small luminal or delicate organs without causing trauma to tissue. However, current fabrication methods limit the imaging performance of highly miniaturized probes, restricting their widespread application. To overcome this limitation, we developed a novel ultrathin probe fabrication technique that utilizes 3D microprinting to reliably create side-facing freeform micro-optics (<130 µm diameter) on single-mode fibers. Using this technique, we built a fully functional ultrathin aberration-corrected optical coherence tomography probe. This is the smallest freeform 3D imaging probe yet reported, with a diameter of 0.457 mm, including the catheter sheath. We demonstrated image quality and mechanical flexibility by imaging atherosclerotic human and mouse arteries. The ability to provide microstructural information with the smallest optical coherence tomography catheter opens a gateway for novel minimally invasive applications in disease.

Quantitative intravascular biological fluorescence-ultrasound imaging of coronary and peripheral arteries in vivo.

AIMS: (i) to evaluate a novel hybrid near-infrared fluorescence-intravascular ultrasound (NIRF-IVUS) system in coronary and peripheral swine arteries in vivo; (ii) to assess simultaneous quantitative biological and morphological aspects of arterial disease. METHODS AND RESULTS: Two 9F/15MHz peripheral and 4.5F/40MHz coronary near-infrared fluorescence (NIRF)-IVUS catheters were engineered to enable accurate co-registrtation of biological and morphological readings simultaneously in vivo. A correction algorithm utilizing IVUS information was developed to account for the distance-related fluorescence attenuation due to through-blood imaging. Corrected NIRF (cNIRF)-IVUS was applied for in vivo imaging of angioplasty-induced vascular injury in swine peripheral arteries and experimental fibrin deposition on coronary artery stents, and of atheroma in a rabbit aorta, revealing feasibility to intravascularly assay plaque structure and inflammation. The addition of ICG-enhanced NIRF assessment improved the detection of angioplasty-induced endothelial damage compared to standalone IVUS. In addition, NIRF detection of coronary stent fibrin by in vivo cNIRF-IVUS imaging illuminated stent pathobiology that was concealed on standalone IVUS. Fluorescence reflectance imaging and microscopy of resected tissues corroborated the in vivo findings. CONCLUSIONS: Integrated cNIRF-IVUS enables simultaneous co-registered through-blood imaging of disease related morphological and biological alterations in coronary and peripheral arteries in vivo. Clinical translation of cNIRF-IVUS may significantly enhance knowledge of arterial pathobiology, leading to improvements in clinical diagnosis and prognosis, and helps to guide the development of new therapeutic approaches for arterial diseases.

Targeted Near-Infrared Fluorescence Imaging of Atherosclerosis: Clinical and Intracoronary Evaluation of Indocyanine Green.

OBJECTIVES: This study sought to determine whether indocyanine green (ICG)-enhanced near-infrared fluorescence (NIRF) imaging can illuminate high-risk histologic plaque features of human carotid atherosclerosis, and in coronary atheroma of living swine, using intravascular NIRF-optical coherence tomography (OCT) imaging. BACKGROUND: New translatable imaging approaches are needed to identify high-risk biological signatures of atheroma. ICG is a U.S. Food and Drug Administration-approved NIRF imaging agent that experimentally targets plaque macrophages and lipid in areas of enhanced endothelial permeability. However, it is unknown whether ICG can target atheroma in patients. METHODS: Eight patients were enrolled in the BRIGHT-CEA (Indocyanine Green Fluorescence Uptake in Human Carotid Artery Plaque) trial. Five patients were injected intravenously with ICG 99 ± 25 min before clinically indicated carotid endarterectomy. Three saline-injected endarterectomy patients served as control subjects. Excised plaques underwent analysis by intravascular NIRF-OCT, reflectance imaging, microscopy, and histopathology. Next, following ICG intravenous injection, in vivo intracoronary NIRF-OCT and intravascular ultrasound imaged 3 atheroma-bearing coronary arteries of a diabetic, cholesterol-fed swine. RESULTS: ICG was well tolerated; no adverse clinical events occurred up to 30 days post-injection. Multimodal NIRF imaging including intravascular NIRF-OCT revealed that ICG accumulated in all endarterectomy specimens. Plaques from saline-injected control patients exhibited minimal NIRF signal. In the swine experiment, intracoronary NIRF-OCT identified ICG uptake in all intravascular ultrasound-identified plaques in vivo. On detailed microscopic evaluation, ICG localized to plaque areas exhibiting impaired endothelial integrity, including disrupted fibrous caps, and within areas of neovascularization. Within human plaque areas of endothelial abnormality, ICG was spatially related to localized zones of plaque macrophages and lipid, and, notably, intraplaque hemorrhage. CONCLUSIONS: This study demonstrates that ICG targets human plaques exhibiting endothelial abnormalities and provides new insights into its targeting mechanisms in clinical and experimental atheroma. Intracoronary NIRF-OCT of ICG may offer a novel, clinically translatable approach to image pathobiological aspects of coronary atherosclerosis. (Indocyanine Green Fluorescence Uptake in Human Carotid Artery Plaque [BRIGHT-CEA]; NCT01873716).

Biological imaging of atherosclerosis: moving beyond anatomy.

Biological or molecular imaging is now providing exciting new strategies to study atherosclerosis in both animals and humans. These technologies hold the promise to provide disease-specific, molecular information within the context of a systemic or organ-specific disease beyond traditional anatomical-based imaging. By integration of biological, chemical, and anatomical imaging knowledge into diagnostic strategies, a more comprehensive and predictive picture of atherosclerosis is likely to emerge. As such, biological imaging is well positioned to study different stages of atherosclerosis and its treatment, including the sequence of atheroma initiation, progression, and plaque rupture. In this review, we describe the evolving concepts in atherosclerosis imaging with a focus on coronary artery disease, and we provide an overview of recent exciting translational developments in biological imaging. The illuminated examples and discussions will highlight how biological imaging is providing new clinical approaches to identify high-risk plaques, and to streamline the development process of new atherosclerosis therapies.

Molecular imaging of myocardial remodeling after infarction.

Myocardial fibrosis after myocardial infarction plays a major role in cardiac remodeling, development of heart failure, and arrhythmias. Both replacement and interstitial fibrosis are determined by the extent of myofibroblastic proliferation and hence the extent of collagen deposition. It is logical to propose that a molecular imaging strategy that is able to determine the rate of myofibroblastic proliferation should be able to foretell the magnitude of myocardial remodeling and the likelihood of development of heart failure. Of various plausible targets on the proliferating myofibroblasts, receptors for neurohumoral agonists and overexpression of integrin moieties may offer best options for molecular imaging. In this chapter we describe the assessment of angiotensin II receptor and αvß3 integrin upregulation in a mouse model after myocardial infarction using real time in vivo fluorescence imaging and nuclear imaging.