Parametric imaging of attenuation by optical coherence tomography: review of models, methods, and clinical translation

SIGNIFICANCE: Optical coherence tomography (OCT) provides cross-sectional and volumetric images of backscattering from biological tissue that reveal the tissue morphology. The strength of the scattering, characterized by an attenuation coefficient, represents an alternative and complementary tissue optical property, which can be characterized by parametric imaging of the OCT attenuation coefficient. Over the last 15 years, a multitude of studies have been reported seeking to advance methods to determine the OCT attenuation coefficient and developing them toward clinical applications. AIM: Our review provides an overview of the main models and methods, their assumptions and applicability, together with a survey of preclinical and clinical demonstrations and their translation potential. RESULTS: The use of the attenuation coefficient, particularly when presented in the form of parametric en face images, is shown to be applicable in various medical fields. Most studies show the promise of the OCT attenuation coefficient in differentiating between tissues of clinical interest but vary widely in approach. CONCLUSIONS: As a future step, a consensus on the model and method used for the determination of the attenuation coefficient is an important precursor to large-scale studies. With our review, we hope to provide a basis for discussion toward establishing this consensus

Intravascular Ultrasound

Intravascular ultrasound (IVUS) is a cardiovascular imaging technology using a specially designed catheter with a miniaturized ultrasound probe for the assessment of vascular anatomy with detailed visualization of arterial layers. Over the past two decades, this technology has developed into an indispensable tool for research and clinical practice in cardiovascular medicine, offering the opportunity to gather diagnostic information about the process of atherosclerosis in vivo, and to directly observe the effects of various interventions on the plaque and arterial wall. This book aims to give a comprehensive overview of this rapidly evolving technique from basic principles and instrumentation to research and clinical applications with future perspectives

Hyperpolarized 3He Magnetic Resonance Imaging Phenotypes of Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the world. Identifying clinically relevant COPD phenotypes has the potential to reduce the global burden of COPD by helping to alleviate symptoms, slow disease progression and prevent exacerbation by stratifying patient cohorts and forming targeted treatment plans. In this regard, quantitative pulmonary imaging with hyperpolarized 3He magnetic resonance imaging (MRI) and thoracic computed tomography (CT) have emerged as ways to identify and measure biomarkers of lung structure and function. 3He MRI may be used as a tool to probe both functional and structural properties of the lung whereby static-ventilation maps allow the direct visualization of ventilated lung regions and 3He apparent diffusion coefficient maps show the lung microstructure at alveolar scales. At the same time, thoracic CT provides quantitative measurements of lung density and airway wall and lumen dimensions. Together, MRI and CT may be used to characterize the relative contributions of airways disease and emphysema on overall lung function, providing a way to phenotype underlying disease processes in a way that conventional measurements of airflow, taken at the mouth, cannot. Importantly, structure-function measurements obtained from 3He MRI and CT can be extracted from the whole-lung or from individual lung lobes, providing direct information on specific lung regions. In this thesis, my goal was to identify pulmonary imaging phenotypes to provide a better understanding of COPD pathophysiology in ex-smokers with and without airflow limitation. This thesis showed: 1) ex-smokers without airflow limitation had imaging evidence of subclinical lung and vascular disease, 2) pulmonary abnormalities in ex- smokers without airflow limitation were spatially related to airways disease and very mild emphysema, and, 3) in ex-smokers with COPD, there were distinct apical-basal lung phenotypes associated with disease severity. Collectively, these findings provide strong evidence that quantitative pulmonary imaging phenotypes may be used to characterize the underlying pathophysiology of very mild or early COPD and in patients with severe disease

Vascular Segmentation Algorithms for Generating 3D Atherosclerotic Measurements

Atherosclerosis manifests as plaques within large arteries of the body and remains as a leading cause of mortality and morbidity in the world. Major cardiovascular events may occur in patients without known preexisting symptoms, thus it is important to monitor progression and regression of the plaque burden in the arteries for evaluating patient\u27s response to therapy. In this dissertation, our main focus is quantification of plaque burden from the carotid and femoral arteries, which are major sites for plaque formation, and are straight forward to image noninvasively due to their superficial location. Recently, 3D measurements of plaque burden have shown to be more sensitive to the changes of plaque burden than one-/two-dimensional measurements. However, despite the advancements of 3D noninvasive imaging technology with rapid acquisition capabilities, and the high sensitivity of the 3D plaque measurements of plaque burden, they are still not widely used due to the inordinate amount of time and effort required to delineate artery walls plus plaque boundaries to obtain 3D measurements from the images. Therefore, the objective of this dissertation is developing novel semi-automated segmentation methods to alleviate measurement burden from the observer for segmentation of the outer wall and lumen boundaries from: (1) 3D carotid ultrasound (US) images, (2) 3D carotid black-blood magnetic resonance (MR) images, and (3) 3D femoral black-blood MR images. Segmentation of the carotid lumen and outer wall from 3DUS images is a challenging task due to low image contrast, for which no method has been previously reported. Initially, we developed a 2D slice-wise segmentation algorithm based on the level set method, which was then extended to 3D. The 3D algorithm required fewer user interactions than manual delineation and the 2D method. The algorithm reduced user time by ≈79% (1.72 vs. 8.3 min) compared to manual segmentation for generating 3D-based measurements with high accuracy (Dice similarity coefficient (DSC)\u3e90%). Secondly, we developed a novel 3D multi-region segmentation algorithm, which simultaneously delineates both the carotid lumen and outer wall surfaces from MR images by evolving two coupled surfaces using a convex max-flow-based technique. The algorithm required user interaction only on a single transverse slice of the 3D image for generating 3D surfaces of the lumen and outer wall. The algorithm was parallelized using graphics processing units (GPU) to increase computational speed, thus reducing user time by 93% (0.78 vs. 12 min) compared to manual segmentation. Moreover, the algorithm yielded high accuracy (DSC \u3e 90%) and high precision (intra-observer CV \u3c 5.6% and inter-observer CV \u3c 6.6%). Finally, we developed and validated an algorithm based on convex max-flow formulation to segment the femoral arteries that enforces a tubular shape prior and an inter-surface consistency of the outer wall and lumen to maintain a minimum separation distance between the two surfaces. The algorithm required the observer to choose only about 11 points on its medial axis of the artery to yield the 3D surfaces of the lumen and outer wall, which reduced the operator time by 97% (1.8 vs. 70-80 min) compared to manual segmentation. Furthermore, the proposed algorithm reported DSC greater than 85% and small intra-observer variability (CV ≈ 6.69%). In conclusion, the development of robust semi-automated algorithms for generating 3D measurements of plaque burden may accelerate translation of 3D measurements to clinical trials and subsequently to clinical care

Advanced Photothermal Optical Coherence Tomography (PT-OCT) for Quantification of Tissue Composition

Optical coherence tomography (OCT) is an imaging technique that forms 2D or 3D images of tissue structures with micron-level resolution. Today, OCT systems are widely used in medicine, especially in the fields of ophthalmology, interventional cardiology, oncology, and dermatology. Although OCT images provide insightful structural information of tissues, these images are not specific to the chemical composition of the tissue. Yet, chemical tissue composition is frequently relevant to the stage of a disease (e.g., atherosclerosis), leading to poor diagnostic performance of structural OCT images. Photo-thermal optical coherence tomography (PT-OCT) is a functional extension of OCT with the potential to overcome this shortcoming by overlaying the 3D structural images of OCT with depth-resolved light absorption information. Potentially, signal analysis of the light absorption maps can be used to obtain refined insight into the chemical composition of tissue. Such analysis, however, is complex because the underlying physics of PT-OCT is multifactorial. Aside from tissue chemical composition, the optical, thermal, and mechanical properties of tissue affect PT-OCT signals; system/instrumentation parameters also influence PT-OCT signals. As such, obtaining refined insight into tissue chemical composition requires in-depth research aimed at answering several key unknowns and questions about this technique. The goal of this dissertation is to generate in-depth knowledge on sample and system parameters affecting PT-OCT signals, to develop strategies for optimal detection of a molecule of interest (MOI) and potentially for its quantification, and to improve the imaging rate of the system. The following items are major outcomes of this dissertation: 1- Generated comprehensive theory that discovers relations between sample/tissue properties and experimental conditions and their multifactorial effects on PT-OCT signals. 2- Developed system and experimentation strategies for detection of multiple molecules of interest with high specificity. 3- Generated optimized machine learning-powered model, in light of the above two outcomes, for automated depth-resolved interpretation of tissue composition from PT-OCT images. 4- Increased the imaging rate of PT-OCT by orders of magnitude by introducing a new variant of PT-OCT based on pulsed photothermal excitation. 5- Developed algorithms for signal denoising and improving the quality of received signals and the contrast in images which in return enables faster PT-OCT imaging