Optical and digital phase conjugation techniques for fiber nonlinearity compensation

We discuss recent progress on the use of optical and digital phase conjugation techniques for nonlinearity compensation in optical fiber links. We compare the achievable performance gain of phase conjugated twin wave applied in two polarization states and time segments with mid-link optical phase conjugation and digital back propagation. For multicarrier transmission scheme such as orthogonal frequency division multiplexing, two recently proposed schemes, namely phase-conjugated pilots and phase-conjugated subcarrier coding are reviewed.

Three-dimensional brain fiber tracking modeling in diffusion tensor imaging

This dissertation establishes the foundation for a new 3-D visual interface integrating Magnetic Resonance Imaging (MRI) to Diffusion Tensor Imaging (DTI). The need for such an interface is critical for understanding brain dynamics, and for providing more accurate diagnosis of key brain dysfunctions in terms of neuronal connectivity. ^ This work involved two research fronts: (1) the development of new image processing and visualization techniques in order to accurately establish relational positioning of neuronal fiber tracts and key landmarks in 3-D brain atlases, and (2) the obligation to address the computational requirements such that the processing time is within the practical bounds of clinical settings. The system was evaluated using data from thirty patients and volunteers with the Brain Institute at Miami Children's Hospital. ^ Innovative visualization mechanisms allow for the first time white matter fiber tracts to be displayed alongside key anatomical structures within accurately registered 3-D semi-transparent images of the brain. ^ The segmentation algorithm is based on the calculation of mathematically-tuned thresholds and region-detection modules. The uniqueness of the algorithm is in its ability to perform fast and accurate segmentation of the ventricles. In contrast to the manual selection of the ventricles, which averaged over 12 minutes, the segmentation algorithm averaged less than 10 seconds in its execution. ^ The registration algorithm established searches and compares MR with DT images of the same subject, where derived correlation measures quantify the resulting accuracy. Overall, the images were 27% more correlated after registration, while an average of 1.5 seconds is all it took to execute the processes of registration, interpolation, and re-slicing of the images all at the same time and in all the given dimensions. ^ This interface was fully embedded into a fiber-tracking software system in order to establish an optimal research environment. This highly integrated 3-D visualization system reached a practical level that makes it ready for clinical deployment. ^

A multi-faceted diagnostic approach to lung infections in patients with cystic fibrosis

One in 3,000 people in the US are born with cystic fibrosis (CF), a genetic disorder affecting the reproductive system, pancreas, and lungs. Lung disease caused by chronic bacterial and fungal infections is the leading cause of morbidity and mortality in CF. Identities of the microbes are traditionally determined by culturing followed by phenotypic and biochemical assays. It was first thought that the bacterial infections were caused by a select handful of bacteria such as S. aureus, H. influenzae, B. cenocepacia, and P. aeruginosa. With the advent of PCR and molecular techniques, the polymicrobial nature of the CF lung became evident. The CF lung contains numerous bacteria and the communities are diverse and unique to each patient. The total complexity of the bacterial infections is still being determined. In addition, only a few members of the fungal communities have been identified. Much of the fungal community composition is still a mystery. This dissertation addresses this gap in knowledge. A snap shot of CF sputa bacterial community was obtained using the length heterogeneity-PCR community profiling technique. The profiles show that south Florida CF patients have a unique, diverse, and dynamic bacterial community which changes over time. The identities of the bacteria and fungi present were determined using the state-of-the-art 454 sequencing. Sequencing results show that the CF lung microbiome contains commonly cultured pathogenic bacteria, organisms considered a part of the healthy core biome, and novel organisms. Understanding the dynamic changes of these identified microbes will ultimately lead to better therapeutical interventions. Early detection is key in reducing the lung damage caused by chronic infections. Thus, there is a need for accurate and sensitive diagnostic tests. This issue was addressed by designing a bacterial diagnostic tool targeted towards CF pathogens using SPR. By identifying the organisms associated with the CF lung and understanding their community interactions, patients can receive better treatment and live longer.

A Multi-Faceted Diagnostic Approach to Lung Infections in Patients with Cystic Fibrosis

One in 3,000 people in the US are born with cystic fibrosis (CF), a genetic disorder affecting the reproductive system, pancreas, and lungs. Lung disease caused by chronic bacterial and fungal infections is the leading cause of morbidity and mortality in CF. Identities of the microbes are traditionally determined by culturing followed by phenotypic and biochemical assays. It was first thought that the bacterial infections were caused by a select handful of bacteria such as S. aureus, H. influenzae, B. cenocepacia, and P. aeruginosa. With the advent of PCR and molecular techniques, the polymicrobial nature of the CF lung became evident. The CF lung contains numerous bacteria and the communities are diverse and unique to each patient. The total complexity of the bacterial infections is still being determined. In addition, only a few members of the fungal communities have been identified. Much of the fungal community composition is still a mystery. This dissertation addresses this gap in knowledge. A snap shot of CF sputa bacterial community was obtained using the length heterogeneity-PCR community profiling technique. The profiles show that south Florida CF patients have a unique, diverse, and dynamic bacterial community which changes over time. The identities of the bacteria and fungi present were determined using the state-of-the-art 454 sequencing. Sequencing results show that the CF lung microbiome contains commonly cultured pathogenic bacteria, organisms considered a part of the healthy core biome, and novel organisms. Understanding the dynamic changes of these identified microbes will ultimately lead to better therapeutical interventions. Early detection is key in reducing the lung damage caused by chronic infections. Thus, there is a need for accurate and sensitive diagnostic tests. This issue was addressed by designing a bacterial diagnostic tool targeted towards CF pathogens using SPR. By identifying the organisms associated with the CF lung and understanding their community interactions, patients can receive better treatment and live longer.

Ensino da ultrassonografia diagnóstica a partir do ciclo básico do curso de medicina utilizando livro digital multimídia

Currently, the diagnostic ultrasound is inserted in various areas of medical action and carried out by many medical professionals, from which militate directly in the image area, such as radiologists and sonographers, but also by gynecologists, pediatricians, neurologists, general practitioners, endocrinologists, angiologists, orthopedists, rheumatologists, urologists, general and vascular surgeons. It is well known that the medical professional, for the exercise of its mission, requires a broad set of skills, competencies and attitudes developed and exercised during their training period. Living with medical students over nearly 20 years in hospital environment, I noticed gaps in the learning process by the students about what is diagnostic ultrasound and its applications, demonstrating failures as understanding the basic acoustic ultrasound, difficulties in identifying of anatomical structures in ultrasound images and inability in requests examinations and interpretations of images and reports. Based on these findings, it was developed in this Professional Masters a multimedia digital book that exposes what the ultrasound as a diagnostic modality imaging, dealing with its historiography and its physical/acoustic concepts, relating the process of formation of the ultrasound image, discussing about the features of sonographic equipments and their embedded technologies and highlighting its diagnostic applications , the latter presented through videos which will be described aspects of captured ultrasound images. This book will be available for access in digital format, serving as a teaching tool in medical education since the beginning of the course, so that can be used in conjunction with the discipline of Gross Anatomy, offered in the basic cycle of the Medicine Undergraduate Course of the Federal University of Rio Grande do Norte (UFRN).

Design of experiments to study the impact of process parameters on droplet size and development of non-invasive imaging techniques in tablet coating

Atomisation of an aqueous solution for tablet film coating is a complex process with multiple factors determining droplet formation and properties. The importance of droplet size for an efficient process and a high quality final product has been noted in the literature, with smaller droplets reported to produce smoother, more homogenous coatings whilst simultaneously avoiding the risk of damage through over-wetting of the tablet core. In this work the effect of droplet size on tablet film coat characteristics was investigated using X-ray microcomputed tomography (XμCT) and confocal laser scanning microscopy (CLSM). A quality by design approach utilising design of experiments (DOE) was used to optimise the conditions necessary for production of droplets at a small (20 μm) and large (70 μm) droplet size. Droplet size distribution was measured using real-time laser diffraction and the volume median diameter taken as a response. DOE yielded information on the relationship three critical process parameters: pump rate, atomisation pressure and coating-polymer concentration, had upon droplet size. The model generated was robust, scoring highly for model fit (R2 = 0.977), predictability (Q2 = 0.837), validity and reproducibility. Modelling confirmed that all parameters had either a linear or quadratic effect on droplet size and revealed an interaction between pump rate and atomisation pressure. Fluidised bed coating of tablet cores was performed with either small or large droplets followed by CLSM and XμCT imaging. Addition of commonly used contrast materials to the coating solution improved visualisation of the coating by XμCT, showing the coat as a discrete section of the overall tablet. Imaging provided qualitative and quantitative evidence revealing that smaller droplets formed thinner, more uniform and less porous film coats.

Advanced Techniques for Image Quality Assessment of Modern X-ray Computed Tomography Systems

X-ray computed tomography (CT) imaging constitutes one of the most widely used diagnostic tools in radiology today with nearly 85 million CT examinations performed in the U.S in 2011. CT imparts a relatively high amount of radiation dose to the patient compared to other x-ray imaging modalities and as a result of this fact, coupled with its popularity, CT is currently the single largest source of medical radiation exposure to the U.S. population. For this reason, there is a critical need to optimize CT examinations such that the dose is minimized while the quality of the CT images is not degraded. This optimization can be difficult to achieve due to the relationship between dose and image quality. All things being held equal, reducing the dose degrades image quality and can impact the diagnostic value of the CT examination.

A recent push from the medical and scientific community towards using lower doses has spawned new dose reduction technologies such as automatic exposure control (i.e., tube current modulation) and iterative reconstruction algorithms. In theory, these technologies could allow for scanning at reduced doses while maintaining the image quality of the exam at an acceptable level. Therefore, there is a scientific need to establish the dose reduction potential of these new technologies in an objective and rigorous manner. Establishing these dose reduction potentials requires precise and clinically relevant metrics of CT image quality, as well as practical and efficient methodologies to measure such metrics on real CT systems. The currently established methodologies for assessing CT image quality are not appropriate to assess modern CT scanners that have implemented those aforementioned dose reduction technologies.

Thus the purpose of this doctoral project was to develop, assess, and implement new phantoms, image quality metrics, analysis techniques, and modeling tools that are appropriate for image quality assessment of modern clinical CT systems. The project developed image quality assessment methods in the context of three distinct paradigms, (a) uniform phantoms, (b) textured phantoms, and (c) clinical images.

The work in this dissertation used the “task-based” definition of image quality. That is, image quality was broadly defined as the effectiveness by which an image can be used for its intended task. Under this definition, any assessment of image quality requires three components: (1) A well defined imaging task (e.g., detection of subtle lesions), (2) an “observer” to perform the task (e.g., a radiologists or a detection algorithm), and (3) a way to measure the observer’s performance in completing the task at hand (e.g., detection sensitivity/specificity).

First, this task-based image quality paradigm was implemented using a novel multi-sized phantom platform (with uniform background) developed specifically to assess modern CT systems (Mercury Phantom, v3.0, Duke University). A comprehensive evaluation was performed on a state-of-the-art CT system (SOMATOM Definition Force, Siemens Healthcare) in terms of noise, resolution, and detectability as a function of patient size, dose, tube energy (i.e., kVp), automatic exposure control, and reconstruction algorithm (i.e., Filtered Back-Projection– FPB vs Advanced Modeled Iterative Reconstruction– ADMIRE). A mathematical observer model (i.e., computer detection algorithm) was implemented and used as the basis of image quality comparisons. It was found that image quality increased with increasing dose and decreasing phantom size. The CT system exhibited nonlinear noise and resolution properties, especially at very low-doses, large phantom sizes, and for low-contrast objects. Objective image quality metrics generally increased with increasing dose and ADMIRE strength, and with decreasing phantom size. The ADMIRE algorithm could offer comparable image quality at reduced doses or improved image quality at the same dose (increase in detectability index by up to 163% depending on iterative strength). The use of automatic exposure control resulted in more consistent image quality with changing phantom size.

Based on those results, the dose reduction potential of ADMIRE was further assessed specifically for the task of detecting small (<=6 mm) low-contrast (<=20 HU) lesions. A new low-contrast detectability phantom (with uniform background) was designed and fabricated using a multi-material 3D printer. The phantom was imaged at multiple dose levels and images were reconstructed with FBP and ADMIRE. Human perception experiments were performed to measure the detection accuracy from FBP and ADMIRE images. It was found that ADMIRE had equivalent performance to FBP at 56% less dose.

Using the same image data as the previous study, a number of different mathematical observer models were implemented to assess which models would result in image quality metrics that best correlated with human detection performance. The models included naïve simple metrics of image quality such as contrast-to-noise ratio (CNR) and more sophisticated observer models such as the non-prewhitening matched filter observer model family and the channelized Hotelling observer model family. It was found that non-prewhitening matched filter observers and the channelized Hotelling observers both correlated strongly with human performance. Conversely, CNR was found to not correlate strongly with human performance, especially when comparing different reconstruction algorithms.

The uniform background phantoms used in the previous studies provided a good first-order approximation of image quality. However, due to their simplicity and due to the complexity of iterative reconstruction algorithms, it is possible that such phantoms are not fully adequate to assess the clinical impact of iterative algorithms because patient images obviously do not have smooth uniform backgrounds. To test this hypothesis, two textured phantoms (classified as gross texture and fine texture) and a uniform phantom of similar size were built and imaged on a SOMATOM Flash scanner (Siemens Healthcare). Images were reconstructed using FBP and a Sinogram Affirmed Iterative Reconstruction (SAFIRE). Using an image subtraction technique, quantum noise was measured in all images of each phantom. It was found that in FBP, the noise was independent of the background (textured vs uniform). However, for SAFIRE, noise increased by up to 44% in the textured phantoms compared to the uniform phantom. As a result, the noise reduction from SAFIRE was found to be up to 66% in the uniform phantom but as low as 29% in the textured phantoms. Based on this result, it clear that further investigation was needed into to understand the impact that background texture has on image quality when iterative reconstruction algorithms are used.

To further investigate this phenomenon with more realistic textures, two anthropomorphic textured phantoms were designed to mimic lung vasculature and fatty soft tissue texture. The phantoms (along with a corresponding uniform phantom) were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Scans were repeated a total of 50 times in order to get ensemble statistics of the noise. A novel method of estimating the noise power spectrum (NPS) from irregularly shaped ROIs was developed. It was found that SAFIRE images had highly locally non-stationary noise patterns with pixels near edges having higher noise than pixels in more uniform regions. Compared to FBP, SAFIRE images had 60% less noise on average in uniform regions for edge pixels, noise was between 20% higher and 40% lower. The noise texture (i.e., NPS) was also highly dependent on the background texture for SAFIRE. Therefore, it was concluded that quantum noise properties in the uniform phantoms are not representative of those in patients for iterative reconstruction algorithms and texture should be considered when assessing image quality of iterative algorithms.

The move beyond just assessing noise properties in textured phantoms towards assessing detectability, a series of new phantoms were designed specifically to measure low-contrast detectability in the presence of background texture. The textures used were optimized to match the texture in the liver regions actual patient CT images using a genetic algorithm. The so called “Clustured Lumpy Background” texture synthesis framework was used to generate the modeled texture. Three textured phantoms and a corresponding uniform phantom were fabricated with a multi-material 3D printer and imaged on the SOMATOM Flash scanner. Images were reconstructed with FBP and SAFIRE and analyzed using a multi-slice channelized Hotelling observer to measure detectability and the dose reduction potential of SAFIRE based on the uniform and textured phantoms. It was found that at the same dose, the improvement in detectability from SAFIRE (compared to FBP) was higher when measured in a uniform phantom compared to textured phantoms.

The final trajectory of this project aimed at developing methods to mathematically model lesions, as a means to help assess image quality directly from patient images. The mathematical modeling framework is first presented. The models describe a lesion’s morphology in terms of size, shape, contrast, and edge profile as an analytical equation. The models can be voxelized and inserted into patient images to create so-called “hybrid” images. These hybrid images can then be used to assess detectability or estimability with the advantage that the ground truth of the lesion morphology and location is known exactly. Based on this framework, a series of liver lesions, lung nodules, and kidney stones were modeled based on images of real lesions. The lesion models were virtually inserted into patient images to create a database of hybrid images to go along with the original database of real lesion images. ROI images from each database were assessed by radiologists in a blinded fashion to determine the realism of the hybrid images. It was found that the radiologists could not readily distinguish between real and virtual lesion images (area under the ROC curve was 0.55). This study provided evidence that the proposed mathematical lesion modeling framework could produce reasonably realistic lesion images.

Based on that result, two studies were conducted which demonstrated the utility of the lesion models. The first study used the modeling framework as a measurement tool to determine how dose and reconstruction algorithm affected the quantitative analysis of liver lesions, lung nodules, and renal stones in terms of their size, shape, attenuation, edge profile, and texture features. The same database of real lesion images used in the previous study was used for this study. That database contained images of the same patient at 2 dose levels (50% and 100%) along with 3 reconstruction algorithms from a GE 750HD CT system (GE Healthcare). The algorithms in question were FBP, Adaptive Statistical Iterative Reconstruction (ASiR), and Model-Based Iterative Reconstruction (MBIR). A total of 23 quantitative features were extracted from the lesions under each condition. It was found that both dose and reconstruction algorithm had a statistically significant effect on the feature measurements. In particular, radiation dose affected five, three, and four of the 23 features (related to lesion size, conspicuity, and pixel-value distribution) for liver lesions, lung nodules, and renal stones, respectively. MBIR significantly affected 9, 11, and 15 of the 23 features (including size, attenuation, and texture features) for liver lesions, lung nodules, and renal stones, respectively. Lesion texture was not significantly affected by radiation dose.

The second study demonstrating the utility of the lesion modeling framework focused on assessing detectability of very low-contrast liver lesions in abdominal imaging. Specifically, detectability was assessed as a function of dose and reconstruction algorithm. As part of a parallel clinical trial, images from 21 patients were collected at 6 dose levels per patient on a SOMATOM Flash scanner. Subtle liver lesion models (contrast = -15 HU) were inserted into the raw projection data from the patient scans. The projections were then reconstructed with FBP and SAFIRE (strength 5). Also, lesion-less images were reconstructed. Noise, contrast, CNR, and detectability index of an observer model (non-prewhitening matched filter) were assessed. It was found that SAFIRE reduced noise by 52%, reduced contrast by 12%, increased CNR by 87%. and increased detectability index by 65% compared to FBP. Further, a 2AFC human perception experiment was performed to assess the dose reduction potential of SAFIRE, which was found to be 22% compared to the standard of care dose.

In conclusion, this dissertation provides to the scientific community a series of new methodologies, phantoms, analysis techniques, and modeling tools that can be used to rigorously assess image quality from modern CT systems. Specifically, methods to properly evaluate iterative reconstruction have been developed and are expected to aid in the safe clinical implementation of dose reduction technologies.

Development of Multi-modal and Super-resolved Retinal Imaging Systems

Advancements in retinal imaging technologies have drastically improved the quality of eye care in the past couple decades. Scanning laser ophthalmoscopy (SLO) and optical coherence tomography (OCT) are two examples of critical imaging modalities for the diagnosis of retinal pathologies. However current-generation SLO and OCT systems have limitations in diagnostic capability due to the following factors: the use of bulky tabletop systems, monochromatic imaging, and resolution degradation due to ocular aberrations and diffraction.

Bulky tabletop SLO and OCT systems are incapable of imaging patients that are supine, under anesthesia, or otherwise unable to maintain the required posture and fixation. Monochromatic SLO and OCT imaging prevents the identification of various color-specific diagnostic markers visible with color fundus photography like those of neovascular age-related macular degeneration. Resolution degradation due to ocular aberrations and diffraction has prevented the imaging of photoreceptors close to the fovea without the use of adaptive optics (AO), which require bulky and expensive components that limit the potential for widespread clinical use.

In this dissertation, techniques for extending the diagnostic capability of SLO and OCT systems are developed. These techniques include design strategies for miniaturizing and combining SLO and OCT to permit multi-modal, lightweight handheld probes to extend high quality retinal imaging to pediatric eye care. In addition, a method for extending true color retinal imaging to SLO to enable high-contrast, depth-resolved, high-fidelity color fundus imaging is demonstrated using a supercontinuum light source. Finally, the development and combination of SLO with a super-resolution confocal microscopy technique known as optical photon reassignment (OPRA) is demonstrated to enable high-resolution imaging of retinal photoreceptors without the use of adaptive optics.

Design, analysis and evaluation of sigma-delta based beamformers for medical ultrasound imaging applications

The inherent analogue nature of medical ultrasound signals in conjunction with the abundant merits provided by digital image acquisition, together with the increasing use of relatively simple front-end circuitries, have created considerable demand for single-bit  beamformers in digital ultrasound imaging systems. Furthermore, the increasing need to design lightweight ultrasound systems with low power consumption and low noise, provide ample justification for development and innovation in the use of single-bit  beamformers in ultrasound imaging systems. The overall aim of this research program is to investigate, establish, develop and confirm through a combination of theoretical analysis and detailed simulations, that utilize raw phantom data sets, suitable techniques for the design of simple-to-implement hardware efficient  digital ultrasound beamformers to address the requirements for 3D scanners with large channel counts, as well as portable and lightweight ultrasound scanners for point-of-care applications and intravascular imaging systems. In addition, the stability boundaries of higher-order High-Pass (HP) and Band-Pass (BP) Σ−Δ modulators for single- and dual- sinusoidal inputs are determined using quasi-linear modeling together with the describing-function method, to more accurately model the  modulator quantizer. The theoretical results are shown to be in good agreement with the simulation results for a variety of input amplitudes, bandwidths, and modulator orders. The proposed mathematical models of the quantizer will immensely help speed up the design of higher order HP and BP Σ−Δ modulators to be applicable for digital ultrasound beamformers. Finally, a user friendly design and performance evaluation tool for LP, BP and HP  modulators is developed. This toolbox, which uses various design methodologies and covers an assortment of  modulators topologies, is intended to accelerate the design process and evaluation of  modulators. This design tool is further developed to enable the design, analysis and evaluation of  beamformer structures including the noise analyses of the final B-scan images. Thus, this tool will allow researchers and practitioners to design and verify different reconstruction filters and analyze the results directly on the B-scan ultrasound images thereby saving considerable time and effort.

Comparison of digital imaging systems for neutron radiography

FAPESP:95/02610

Virtual Clinical Trials in PET Imaging for Improved Diagnosis of and Evaluation of Therapies for Cancer

Thesis (Ph.D.)--University of Washington, 2016-08

Clinical utility of amyloid PET imaging in the differential diagnosis of atypical dementias and its impact on caregivers

La maladie d’Alzheimer (MA) se caractérise pathologiquement par l’accumulation de plaques amyloïde dans le cerveau. La tomographie par émission de positrons (TEP) permet d’imager les plaques amyloïde in vivo. Le but de ce projet est d’évaluer le rôle de la TEP amyloïde dans le processus diagnostique de la MA dans des cas de démences atypiques. Le deuxième but de ce projet est de déterminer l’impact de la révélation d’un diagnostic plus certain chez les proches aidants. 28 patients sans diagnostic malgré une investigation exhaustive ont été sélectionnées et imagées avec le traceur amyloïde 18F-NAV4694 (âge 59,3 ans, é-t. 5,8; MMSE 21.4, é-t 6.0). Les neurologues référents documentaient par la suite tout changement de niveau de certitude, de diagnostic, de traitement et/ou de prise en charge. Les proches aidants consentants ont été rencontrés subséquemment, et un questionnaire avec une échelle de Likert a été utilisé afin de documenter l’impact de l’imagerie leur perception de la maladie. Notre cohorte a été également divisée entre amyloïde positifs (14/28) et négatifs (14/28). Un changement de diagnostic a lieu dans 9/28 cas (32,1% :17.8% ont changé de MA à non-MA, 14,3% de non-MA à MA). Il y avait une augmentation significative (p<0,05) de 44% dans la certitude du neurologue suite à cet examen. Un changement de prise en charge a été obtenu dans 20/28 (71,4%) des cas. Bien que non significatifs statistiquement, un impact favorable sur les proches-aidants a été noté. Cette étude suggère que l’imagerie amyloïde a un rôle bénéfique dans les cas de démences atypiques n’ayant pu être élucidés avec les techniques d’investigations actuellement recommandées. De plus, le processus a été perçu positivement par les proches aidants, notamment en encourageant du temps de qualité avec leurs personnes chères. Ceci illustre un rôle prometteur des biomarqueurs, qui sont de plus en plus explorés.

How new imaging techniques can aid in defining the cardiovascular profile of the high-risk patient

Cardiovascular prevention has been developed in the last eight years producing an ever increasing amount of data requiring frequent updating. Studies using angiography to determine change in coronary obstruction have indicated progression, stabilization, or regression of coronary lesions associated with changes in plasma lipids and lipoproteins. Moreover, the guidelines on arterial hypertension published in 2007 listed the risk factors affecting prognosis but even by 2009 an update modified not only the list of risks, but even the philosophy behind the thought process which introduced as essential element in the prognosis of hypertension the ascertained existence of a damaged organ. Thus, the documentation of atherosclerotic vascular disease (plaques) and the quantification of its extension in the arterial tree became a determinant in the definition of cardiovascular risk. Magnetic Resonance (MRI) and coronary computed tomography (coro CT) applied to the heart and large vessels are the most promising methods.

Tile-Based Two-Dimensional Phase Unwrapping for Digital Holography Using a Modular Framework

A variety of physical and biomedical imaging techniques, such as digital holography, interferometric synthetic aperture radar (InSAR), or magnetic resonance imaging (MRI) enable measurement of the phase of a physical quantity additionally to its amplitude. However, the phase can commonly only be measured modulo 2π, as a so called wrapped phase map. Phase unwrapping is the process of obtaining the underlying physical phase map from the wrapped phase. Tile-based phase unwrapping algorithms operate by first tessellating the phase map, then unwrapping individual tiles, and finally merging them to a continuous phase map. They can be implemented computationally efficiently and are robust to noise. However, they are prone to failure in the presence of phase residues or erroneous unwraps of single tiles. We tried to overcome these shortcomings by creating novel tile unwrapping and merging algorithms as well as creating a framework that allows to combine them in modular fashion. To increase the robustness of the tile unwrapping step, we implemented a model-based algorithm that makes efficient use of linear algebra to unwrap individual tiles. Furthermore, we adapted an established pixel-based unwrapping algorithm to create a quality guided tile merger. These original algorithms as well as previously existing ones were implemented in a modular phase unwrapping C++ framework. By examining different combinations of unwrapping and merging algorithms we compared our method to existing approaches. We could show that the appropriate choice of unwrapping and merging algorithms can significantly improve the unwrapped result in the presence of phase residues and noise. Beyond that, our modular framework allows for efficient design and test of new tile-based phase unwrapping algorithms. The software developed in this study is freely available.