Real-time, label-free, intraoperative visualization of peripheral nerves and microvasculatures using multimodal optical imaging techniques

Accurate, real-time identification and display of critical anatomic structures, such as the nerve and vasculature structures, are critical for reducing complications and improving surgical outcomes. Human vision is frequently limited in clearly distinguishing and contrasting these structures. We present a novel imaging system, which enables noninvasive visualization of critical anatomic structures during surgical dissection. Peripheral nerves are visualized by a snapshot polarimetry that calculates the anisotropic optical properties. Vascular structures, both venous and arterial, are identified and monitored in real-time using a near-infrared laser-speckle-contrast imaging. We evaluate the system by performing in vivo animal studies with qualitative comparison by contrast-agent-aided fluorescence imaging

Computerized Analysis of Magnetic Resonance Images to Study Cerebral Anatomy in Developing Neonates

The study of cerebral anatomy in developing neonates is of great importance for the understanding of brain development during the early period of life. This dissertation therefore focuses on three challenges in the modelling of cerebral anatomy in neonates during brain development. The methods that have been developed all use Magnetic Resonance Images (MRI) as source data. To facilitate study of vascular development in the neonatal period, a set of image analysis algorithms are developed to automatically extract and model cerebral vessel trees. The whole process consists of cerebral vessel tracking from automatically placed seed points, vessel tree generation, and vasculature registration and matching. These algorithms have been tested on clinical Time-of- Flight (TOF) MR angiographic datasets. To facilitate study of the neonatal cortex a complete cerebral cortex segmentation and reconstruction pipeline has been developed. Segmentation of the neonatal cortex is not effectively done by existing algorithms designed for the adult brain because the contrast between grey and white matter is reversed. This causes pixels containing tissue mixtures to be incorrectly labelled by conventional methods. The neonatal cortical segmentation method that has been developed is based on a novel expectation-maximization (EM) method with explicit correction for mislabelled partial volume voxels. Based on the resulting cortical segmentation, an implicit surface evolution technique is adopted for the reconstruction of the cortex in neonates. The performance of the method is investigated by performing a detailed landmark study. To facilitate study of cortical development, a cortical surface registration algorithm for aligning the cortical surface is developed. The method first inflates extracted cortical surfaces and then performs a non-rigid surface registration using free-form deformations (FFDs) to remove residual alignment. Validation experiments using data labelled by an expert observer demonstrate that the method can capture local changes and follow the growth of specific sulcus

Imaging Sensors and Applications

In past decades, various sensor technologies have been used in all areas of our lives, thus improving our quality of life. In particular, imaging sensors have been widely applied in the development of various imaging approaches such as optical imaging, ultrasound imaging, X-ray imaging, and nuclear imaging, and contributed to achieve high sensitivity, miniaturization, and real-time imaging. These advanced image sensing technologies play an important role not only in the medical field but also in the industrial field. This Special Issue covers broad topics on imaging sensors and applications. The scope range of imaging sensors can be extended to novel imaging sensors and diverse imaging systems, including hardware and software advancements. Additionally, biomedical and nondestructive sensing applications are welcome

Cluster analysis of the signal curves in perfusion DCE-MRI datasets

Pathological studies show that tumors consist of different sub-regions with more homogeneous vascular properties during their growth. In addition, destroying tumor's blood supply is the target of most cancer therapies. Finding the sub-regions in the tissue of interest with similar perfusion patterns provides us with valuable information about tissue structure and angiogenesis. This information on cancer therapy, for example, can be used in monitoring the response of the cancer treatment to the drug. Cluster analysis of perfusion curves assays to find sub-regions with a similar perfusion pattern. The present work focuses on the cluster analysis of perfusion curves, measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The study, besides searching for the proper clustering method, follows two other major topics, the choice of an appropriate similarity measure, and determining the number of clusters. These three subjects are connected to each other in such a way that success in one direction will help solving the other problems. This work introduces a new similarity measure, parallelism measure (PM), for comparing the parallelism in the washout phase of the signal curves. Most of the previous works used the Euclidean distance as the measure of dissimilarity. However, the Euclidean distance does not take the patterns of the signal curves into account and therefore for comparing the signal curves is not sufficient. To combine the advantages of both measures a two-steps clustering is developed. The two-steps clustering uses two different similarity measures, the introduced PM measure and Euclidean distance in two consecutive steps. The results of two-steps clustering are compared with the results of other clustering methods. The two-steps clustering besides good performance has some other advantages. The granularity and the number of clusters are controlled by thresholds defined by considering the noise in signal curves. The method is easy to implement and is robust against noise. The focus of the work is mainly the cluster analysis of breast tumors in DCE-MRI datasets. The possibility to adopt the method for liver datasets is studied as well

Magnetic Resonance Imaging of the Rat Retina

The retina is a thin layer of tissue lining the back of the eye and is primarily responsible for sight in vertebrates. The neural retina has a distinct layered structure with three dense nuclear layers, separated by plexiform layers comprising of axons and dendrites, and a layer of photoreceptor segments. The retinal and choroidal vasculatures nourish the retina from either side, with an avascular layer comprised largely of photoreceptor cells. Diseases that directly affect the neural retina like retinal degeneration as well as those of vascular origin like diabetic retinopathy can lead to partial or total blindness. Early detection of these diseases can potentially pave the way for a timely intervention and improve patient prognosis. Current techniques of retinal imaging rely mainly on optical techniques, which have limited depth resolution and depend mainly on the clarity of visual pathway. Magnetic resonance imaging is a versatile tool that has long been used for anatomical and functional imaging in humans and animals, and can potentially be used for retinal imaging without the limitations of optical methods. The work reported in this thesis involves the development of high resolution magnetic resonance imaging techniques for anatomical and functional imaging of the retina in rats. The rats were anesthetized using isoflurane, mechanically ventilated and paralyzed using pancuronium bromide to reduce eye motion during retinal MRI. The retina was imaged using a small, single-turn surface coil placed directly over the eye. The several physiological parameters, like rectal temperature, fraction of inspired oxygen, end-tidal CO2, were continuously monitored in all rats. MRI parameters like T1, T2, and the apparent diffusion coefficient of water molecules were determined from the rat retina at high spatial resolution and found to be similar to those obtained from the brain at the same field strength. High-resolution MRI of the retina detected the three layers in wild-type rats, which were identified as the retinal vasculature, the avascular layer and the choroidal vasculature. Anatomical MRI performed 24 hours post intravitreal injection of MnCl2, an MRI contrast agent, revealed seven distinct layers within the retina. These layers were identified as the various nuclear and plexiform layers, the photoreceptor segment layer and the choroidal vasculature using Mn54Cl2 emulsion autoradiography. Blood-oxygenlevel dependent (BOLD) functional MRI (fMRI) revealed layer-specific vascular responses to hyperoxic and hypercapnic challenges. Relative blood volume of the retina calculated by using microcrystalline iron oxide nano-colloid, an intravascular contrast agent, revealed high blood-volume in the choroidal vasculature. Fractional changes to blood volume during systemic challenges revealed a higher degree of autoregulation in the retinal vasculature compared to the choroidal vasculature, corroborating the BOLD fMRI data. Finally, the retinal MRI techniques developed were applied to detect structural and vascular changes in a rat model of retinal dystrophy. We conclude that retinal MRI is a powerful investigative tool to resolve layer-specific structure and function in the retina and to probe for changes in retinal diseases. We expect the anatomical and functional retinal MRI techniques developed herein to contribute towards the early detection of diseases and longitudinal evaluation of treatment options without interference from overlying tissue or opacity of the visual pathway

Magnetic Resonance Imaging of the Rat Retina: a Dissertation

The retina is a thin layer of tissue lining the back of the eye and is primarily responsible for sight in vertebrates. The neural retina has a distinct layered structure with three dense nuclear layers, separated by plexiform layers comprising of axons and dendrites, and a layer of photoreceptor segments. The retinal and choroidal vasculatures nourish the retina from either side, with an avascular layer comprised largely of photoreceptor cells. Diseases that directly affect the neural retina like retinal degeneration as well as those of vascular origin like diabetic retinopathy can lead to partial or total blindness. Early detection of these diseases can potentially pave the way for a timely intervention and improve patient prognosis. Current techniques of retinal imaging rely mainly on optical techniques, which have limited depth resolution and depend mainly on the clarity of visual pathway. Magnetic resonance imaging is a versatile tool that has long been used for anatomical and functional imaging in humans and animals, and can potentially be used for retinal imaging without the limitations of optical methods. The work reported in this thesis involves the development of high resolution magnetic resonance imaging techniques for anatomical and functional imaging of the retina in rats. The rats were anesthetized using isoflurane, mechanically ventilated and paralyzed using pancuronium bromide to reduce eye motion during retinal MRI. The retina was imaged using a small, single-turn surface coil placed directly over the eye. The several physiological parameters, like rectal temperature, fraction of inspired oxygen, end-tidal CO2, were continuously monitored in all rats. MRI parameters like T1, T2, and the apparent diffusion coefficient of water molecules were determined from the rat retina at high spatial resolution and found to be similar to those obtained from the brain at the same field strength. High-resolution MRI of the retina detected the three layers in wild-type rats, which were identified as the retinal vasculature, the avascular layer and the choroidal vasculature. Anatomical MRI performed 24 hours post intravitreal injection of MnCl2, an MRI contrast agent, revealed seven distinct layers within the retina. These layers were identified as the various nuclear and plexiform layers, the photoreceptor segment layer and the choroidal vasculature using Mn54Cl2emulsion autoradiography. Blood-oxygenlevel dependent (BOLD) functional MRI (fMRI) revealed layer-specific vascular responses to hyperoxic and hypercapnic challenges. Relative blood volume of the retina calculated by using microcrystalline iron oxide nano-colloid, an intravascular contrast agent, revealed a superfluous choroidal vasculature. Fractional changes to blood volume during systemic challenges revealed a higher degree of autoregulation in the retinal vasculature compared to the choroidal vasculature, corroborating the BOLD fMRI data. Finally, the retinal MRI techniques developed were applied to detect structural and vascular changes in a rat model of retinal dystrophy. We conclude that retinal MRI is a powerful investigative tool to resolve layerspecific structure and function in the retina and to probe for changes in retinal diseases. We expect the anatomical and functional retinal MRI techniques developed herein to contribute towards the early detection of diseases and longitudinal evaluation of treatment options without interference from overlying tissue or opacity of the visual pathway

Machine learning approaches for early prediction of hypertension.

Hypertension afflicts one in every three adults and is a leading cause of mortality in 516, 955 patients in USA. The chronic elevation of cerebral perfusion pressure (CPP) changes the cerebrovasculature of the brain and disrupts its vasoregulation mechanisms. Reported correlations between changes in smaller cerebrovascular vessels and hypertension may be used to diagnose hypertension in its early stages, 10-15 years before the appearance of symptoms such as cognitive impairment and memory loss. Specifically, recent studies hypothesized that changes in the cerebrovasculature and CPP precede the systemic elevation of blood pressure. Currently, sphygmomanometers are used to measure repeated brachial artery pressure to diagnose hypertension after its onset. However, this method cannot detect cerebrovascular alterations that lead to adverse events which may occur prior to the onset of hypertension. The early detection and quantification of these cerebral vascular structural changes could help in predicting patients who are at a high risk of developing hypertension as well as other cerebral adverse events. This may enable early medical intervention prior to the onset of hypertension, potentially mitigating vascular-initiated end-organ damage. The goal of this dissertation is to develop a novel efficient noninvasive computer-aided diagnosis (CAD) system for the early prediction of hypertension. The developed CAD system analyzes magnetic resonance angiography (MRA) data of human brains gathered over years to detect and track cerebral vascular alterations correlated with hypertension development. This CAD system can make decisions based on available data to help physicians on predicting potential hypertensive patients before the onset of the disease