An Enhanced Visualization of DBT Imaging Using Blind Deconvolution and Total Variation Minimization Regularization

Digital Breast Tomosynthesis (DBT) presents out-of-plane artifacts caused by features of high intensity. Given observed data and knowledge about the point spread function (PSF), deconvolution techniques recover data from a blurred version. However, a correct PSF is difficult to achieve and these methods amplify noise. When no information is available about the PSF, blind deconvolution can be used. Additionally, Total Variation (TV) minimization algorithms have achieved great success due to its virtue of preserving edges while reducing image noise. This work presents a novel approach in DBT through the study of out-of-plane artifacts using blind deconvolution and noise regularization based on TV minimization. Gradient information was also included. The methodology was tested using real phantom data and one clinical data set. The results were investigated using conventional 2D slice-by-slice visualization and 3D volume rendering. For the 2D analysis, the artifact spread function (ASF) and Full Width at Half Maximum (FWHMMASF) of the ASF were considered. The 3D quantitative analysis was based on the FWHM of disks profiles at 90°, noise and signal to noise ratio (SNR) at 0° and 90°. A marked visual decrease of the artifact with reductions of FWHMASF (2D) and FWHM90° (volume rendering) of 23.8% and 23.6%, respectively, was observed. Although there was an expected increase in noise level, SNR values were preserved after deconvolution. Regardless of the methodology and visualization approach, the objective of reducing the out-of-plane artifact was accomplished. Both for the phantom and clinical case, the artifact reduction in the z was markedly visible

Enhanced Digital Breast Tomosynthesis diagnosis using 3D visualization and automatic classification of lesions

Breast cancer represents the main cause of cancer-related deaths in women. Nonetheless, the mortality rate of this disease has been decreasing over the last three decades, largely due to the screening programs for early detection. For many years, both screening and clinical diagnosis were mostly done through Digital Mammography (DM). Approved in 2011, Digital Breast Tomosynthesis (DBT) is similar to DM but it allows a 3D reconstruction of the breast tissue, which helps the diagnosis by reducing the tissue overlap. Currently, DBT is firmly established and is approved as a stand-alone modality to replace DM. The main objective of this thesis is to develop computational tools to improve the visualization and interpretation of DBT data. Several methods for an enhanced visualization of DBT data through volume rendering were studied and developed. Firstly, important rendering parameters were considered. A new approach for automatic generation of transfer functions was implemented and two other parameters that highly affect the quality of volume rendered images were explored: voxel size in Z direction and sampling distance. Next, new image processing methods that improve the rendering quality by considering the noise regularization and the reduction of out-of-plane artifacts were developed. The interpretation of DBT data with automatic detection of lesions was approached through artificial intelligence methods. Several deep learning Convolutional Neural Networks (CNNs) were implemented and trained to classify a complete DBT image for the presence or absence of microcalcification clusters (MCs). Then, a faster R-CNN (region-based CNN) was trained to detect and accurately locate the MCs in the DBT images. The detected MCs were rendered with the developed 3D rendering software, which provided an enhanced visualization of the volume of interest. The combination of volume visualization with lesion detection may, in the future, improve both diagnostic accuracy and also reduce analysis time. This thesis promotes the development of new computational imaging methods to increase the diagnostic value of DBT, with the aim of assisting radiologists in their task of analyzing DBT volumes and diagnosing breast cancer

Image reconstruction and processing for stationary digital tomosynthesis systems

Digital tomosynthesis (DTS) is an emerging x-ray imaging technique for disease and cancer screening. DTS takes a small number of x-ray projections to generate pseudo-3D images, it has a lower radiation and a lower cost compared to the Computed Tomography (CT) and an improved diagnostic accuracy compared to the 2D radiography. Our research group has developed a carbon nanotube (CNT) based x-ray source. This technology enables packing multiple x-ray sources into one single x-ray source array. Based on this technology, our group built several stationary digital tomosynthesis (s-DTS) systems, which have a faster scanning time and no source motion blur. One critical step in both tomosynthesis and CT is image reconstruction, which generates a 3D image from the 2D measurement. For tomosynthesis, the conventional reconstruction method runs fast but fails in image quality. A better iterative method exists, however, it is too time-consuming to be used in clinics. The goal of this work is to develop fast iterative image reconstruction algorithm and other image processing techniques for the stationary digital tomosynthesis system, improving the image quality affected by the hardware limitation. Fast iterative reconstruction algorithm, named adapted fan volume reconstruction (AFVR), was developed for the s-DTS. AFVR is shown to be an order of magnitude faster than the current iterative reconstruction algorithms and produces better images over the classical filtered back projection (FBP) method. AFVR was implemented for the stationary digital breast tomosynthesis system (s-DBT), the stationary digital chest tomosynthesis system (s-DCT) and the stationary intraoral dental tomosynthesis system (s-IOT). Next, scatter correction technique for stationary digital tomosynthesis was investigated. A new algorithm for estimating scatter profile was developed, which has been shown to improve the image quality substantially. Finally, the quantitative imaging was investigated, where the s-DCT system was used to assess the coronary artery calcium score.Doctor of Philosoph

Ultrasound guided Diffuse Optical Tomography for Breast Cancer Diagnosis: Algorithm Development

According to National Breast Cancer Society, one in every eight women in United States is diagnosed with breast cancer in her lifetime. American Cancer Society recommends a semi-annual breast-cancer screening for every woman which can be heavily facilitated by the availability of low-cost, non-invasive diagnostic method with good sensitivity and penetration depth. Ultrasound (US) guided Diffuse Optical Tomography (US-guided DOT) has been explored as a breast-cancer diagnostic and screening tool over the past two decades. It has demonstrated a great potential for breast-cancer diagnosis, treatment monitoring and chemotherapy-response prediction. In this imaging method, optical measurements of four different wavelengths are used to reconstruct unknown optical absorption maps which are then used to calculate the hemoglobin concentration of the US-visible lesion. This dissertation focuses on algorithm development for robust data processing, imaging reconstruction and optimal breast cancer diagnostic strategy development in DOT. The inverse problem in DOT is ill-posed, ill-conditioned, and underdetermined. This makes the task of image reconstruction challenging, and thus regularization-based method need to be employed. In this dissertation, a simple two-step reconstruction method that can produce accurate image estimates in DOT is proposed and investigated. In the first step, a truncated Moore-Penrose Pseudoinverse solution is computed to obtain a preliminary estimate of the image. This estimate can be reliably determined from the measured data; subsequently, this preliminary estimate is incorporated into the design of a penalized least squares estimator that is employed to compute the final image estimate. Using physical phantoms, the proposed method was demonstrated to yield more accurate reconstruction compared to other conventional reconstruction methods. The method was also evaluated with clinical data that included 10 benign and 10 malignant cases. The capability of reconstructing high contrast malignant lesions improved by the use of the proposed method.Reconstructed absorption maps are prone to image artifacts from outliers in measurement data from tissue heterogeneity, bad coupling between tissue and light guides, and motion by patient or operator. In this dissertation, a new automated iterative perturbation correction algorithm is proposed to reduce image artifacts based on the structural similarity index (SSIM)) of absorption maps of four optical wavelengths. The SSIM was calculated for each wavelength to assess its similarity with other wavelengths. Absorption map was iteratively reconstructed and projected back into measurement space to quantify projection error. Outlier measurements with highest projection errors were iteratively removed until all wavelength images were structurally similar with SSIM values greater than a threshold. Clinical data demonstrated statistically significant improvement in image artifact reduction.US guidance with DOT helps to reduce false positive rate and hence reduce number of unnecessary biopsies. However, DOT data processing and image reconstruction speed remains slow compared to real-time US. Real-time or near real time diagnosis with DOT is an important step toward the clinical translation of the US-guided DOT. In this dissertation, to address this important need, we present a two-stage diagnostic strategy that is computationally efficient and accurate. In the first stage, benign lesions are identified in near real-time by use of a random forest classifier acting on the DOT measurements and radiologistsՠUS diagnostic scores. The lesions that cannot be reliably classified by the random forest classifier will be passed on to the image reconstruction stage. Functional information from the reconstructed hemoglobin concentrations is used by a Support Vector Machine (SVM) classifier for diagnosis in the second stage. This two-step classification approach that combines both perturbation data and functional features results in improved classification, as quantified using the receiver operating characteristic (ROC) curve. Using this two-step approach, area under the ROC curve (AUC) is 0.937 屠0.009 with sensitivity of 91.4% and specificity of 85.7%. While using functional features and US score, AUC is 0.892 屠0.027 with sensitivity of 90.2% and specificity of 74.5%. The specificity increased by more than 10% due to the implementation of the random forest classifier

On-belt Tomosynthesis: 3D Imaging of Baggage for Security Inspection

This thesis describes the design, testing and evaluation of `On-belt Tomosynthesis' (ObT): a cost-e ective baggage screening system based on limited angle digital x-ray tomosynthesis and close-range photogrammetry. It is designed to be retro tted to existing airport conveyor-belt systems and to overcome the limitations of current systems creating a pseudo-3D imaging system by combining x-ray and optical imaging to form digital tomograms. The ObT design and set-up consists of a con guration of two x-ray sources illuminating 12 strip detectors around a conveyor belt curve forming an 180 arc. Investigating the acquired ObT x-ray images' noise sources and distortions, improvements were demonstrated using developed image correction methods. An increase of 45% in image uniformity was shown as a result, in the postcorrection images. Simulation image reconstruction of objects with lower attenuation coe cients showed the potential of ObT to clearly distinguish between them. Reconstruction of real data showed that objects of bigger attenuation di erences (copper versus perspex, rather than air versus perspex) could be observed better. The main conclusion from the reconstruction results was that the current imaging method needed further re nements, regarding the geometry registration and the image reconstruction. The simulation results con rmed that advancing the experimental method could produce better results than the ones which can currently be achieved. For the current state of ObT, a standard deviation of 2 mm in (a) the source coordinates, and 2 in (b) the detector angles does not a ect the image reconstruction results. Therefore, a low-cost single camera coordination and tracking solution was developed to replace the previously used manual measurements. Results obtained by the developed solution showed that the necessary prerequisites for the ObT image reconstruction could be addressed. The resulting standard deviation was of an average of 0.4 mm and 1 degree for (a) and (b) respectively

Fusion of interventional ultrasound & X-ray

In einer immer älter werdenden Bevölkerung wird die Behandlung von strukturellen Herzkrankheiten zunehmend wichtiger. Verbesserte medizinische Bildgebung und die Einführung neuer Kathetertechnologien führten dazu, dass immer mehr herkömmliche chirurgische Eingriffe am offenen Herzen durch minimal invasive Methoden abgelöst werden. Diese modernen Interventionen müssen durch verschiedenste Bildgebungsverfahren navigiert werden. Hierzu werden hauptsächlich Röntgenfluoroskopie und transösophageale Echokardiografie (TEE) eingesetzt. Röntgen bietet eine gute Visualisierung der eingeführten Katheter, was essentiell für eine gute Navigation ist. TEE hingegen bietet die Möglichkeit der Weichteilgewebedarstellung und kann damit vor allem zur Darstellung von anatomischen Strukturen, wie z.B. Herzklappen, genutzt werden. Beide Modalitäten erzeugen Bilder in Echtzeit und werden für die erfolgreiche Durchführung minimal invasiver Herzchirurgie zwingend benötigt. Üblicherweise sind beide Systeme eigenständig und nicht miteinander verbunden. Es ist anzunehmen, dass eine Bildfusion beider Welten einen großen Vorteil für die behandelnden Operateure erzeugen kann, vor allem eine verbesserte Kommunikation im Behandlungsteam. Ebenso können sich aus der Anwendung heraus neue chirurgische Worfklows ergeben. Eine direkte Fusion beider Systeme scheint nicht möglich, da die Bilddaten eine zu unterschiedliche Charakteristik aufweisen. Daher kommt in dieser Arbeit eine indirekte Registriermethode zum Einsatz. Die TEE-Sonde ist während der Intervention ständig im Fluoroskopiebild sichtbar. Dadurch wird es möglich, die Sonde im Röntgenbild zu registrieren und daraus die 3D Position abzuleiten. Der Zusammenhang zwischen Ultraschallbild und Ultraschallsonde wird durch eine Kalibrierung bestimmt. In dieser Arbeit wurde die Methode der 2D-3D Registrierung gewählt, um die TEE Sonde auf 2D Röntgenbildern zu erkennen. Es werden verschiedene Beiträge präsentiert, welche einen herkömmlichen 2D-3D Registrieralgorithmus verbessern. Nicht nur im Bereich der Ultraschall-Röntgen-Fusion, sondern auch im Hinblick auf allgemeine Registrierprobleme. Eine eingeführte Methode ist die der planaren Parameter. Diese verbessert die Robustheit und die Registriergeschwindigkeit, vor allem während der Registrierung eines Objekts aus zwei nicht-orthogonalen Richtungen. Ein weiterer Ansatz ist der Austausch der herkömmlichen Erzeugung von sogenannten digital reconstructed radiographs. Diese sind zwar ein integraler Bestandteil einer 2D-3D Registrierung aber gleichzeitig sehr zeitaufwendig zu berechnen. Es führt zu einem erheblichen Geschwindigkeitsgewinn die herkömmliche Methode durch schnelles Rendering von Dreiecksnetzen zu ersetzen. Ebenso wird gezeigt, dass eine Kombination von schnellen lernbasierten Detektionsalgorithmen und 2D-3D Registrierung die Genauigkeit und die Registrierreichweite verbessert. Zum Abschluss werden die ersten Ergebnisse eines klinischen Prototypen präsentiert, welcher die zuvor genannten Methoden verwendet.Today, in an elderly community, the treatment of structural heart disease will become more and more important. Constant improvements of medical imaging technologies and the introduction of new catheter devices caused the trend to replace conventional open heart surgery by minimal invasive interventions. These advanced interventions need to be guided by different medical imaging modalities. The two main imaging systems here are X-ray fluoroscopy and Transesophageal  Echocardiography (TEE). While X-ray provides a good visualization of inserted catheters, which is essential for catheter navigation, TEE can display soft tissues, especially anatomical structures like heart valves. Both modalities provide real-time imaging and are necessary to lead minimal invasive heart surgery to success. Usually, the two systems are detached and not connected. It is conceivable that a fusion of both worlds can create a strong benefit for the physicians. It can lead to a better communication within the clinical team and can probably enable new surgical workflows. Because of the completely different characteristics of the image data, a direct fusion seems to be impossible. Therefore, an indirect registration of Ultrasound and X-ray images is used. The TEE probe is usually visible in the X-ray image during the described minimal-invasive interventions. Thereby, it becomes possible to register the TEE probe in the fluoroscopic images and to establish its 3D position. The relationship of the Ultrasound image to the Ultrasound probe is known by calibration. To register the TEE probe on 2D X-ray images, a 2D-3D registration approach is chosen in this thesis. Several contributions are presented, which are improving the common 2D-3D registration algorithm for the task of Ultrasound and X-ray fusion, but also for general 2D-3D registration problems. One presented approach is the introduction of planar parameters that increase robustness and speed during the registration of an object on two non-orthogonal views. Another approach is to replace the conventional generation of digital reconstructedradiographs, which is an integral part of 2D-3D registration but also a performance bottleneck, with fast triangular mesh rendering. This will result in a significant performance speed-up. It is also shown that a combination of fast learning-based detection algorithms with 2D-3D registration will increase the accuracy and the capture range, instead of employing them solely to the  registration/detection of a TEE probe. Finally, a first clinical prototype is presented which employs the presented approaches and first clinical results are shown

Exploring Novel Contrast Agents with Anthropomorphic Mesh Models in MCNP

Breast cancer is the leading cancer in women with an estimated 13% of women in the United States developing a form of invasive breast cancer in her lifetime. The survival rate is estimated to be 85%, but the American Cancer Society estimates that early detection of breast cancer in the localized stage increases the breast cancer survival rate to 99%. However, early detection is dependent on the sensitivity of breast imaging techniques and currently, the sensitivity is suboptimal for women with dense breasts and obscure cancers. Recently, studies have indicated that exploring new contrast agents can provide access to improved sensitivity because of their potential to increase the effective Z of the target tissue. Furthermore, contrast-enhanced tomosynthesis is a viable imaging method that can provide a 3D view of the breast while providing tumor enhancement for improved visibility. This project aims to facilitate the search for practical contrast agents that can improve sensitivity during breast imaging. More specifically, the objective of this project is to find novel ways to improve the differentiation between tumor and glandular tissue by creating a realistic anthropomorphic model that not only considers the geometry of the breast but its physiological components as well. This project aims to combine tomosynthesis breast imaging methods with novel contrast agents to explore their efficacy and limitations. To achieve the goals of this project, several techniques are employed. A realistic tomosynthesis environment is created by constructing a detailed Hologic tomosynthesis breast imaging machine, including the source, flat-panel detector, and support equipment, using MCNP. Realistic breast phantoms that consider geometric and biophysical accuracy are created by incorporating a time dependency into the model. Once the contrast agents are incorporated, their efficacy is calculated by quantifying tumor visibility as a function of breast size, density, tumor location, tumor stage, and tumor type. After running simulations, this project will generate clear and accurate radiographs demonstrating the structural components of the breast and the effects of contrast enhancement on any embedded tumors. The results will provide an indication of the contrast agents that provide promise. The data acquired in this project will provide insight on the process of creating an anthropomorphic breast phantom for tomosynthesis studies, as well as insight on setbacks that are identified with the methods used. Contrast-enhanced tomosynthesis is clinically possible and is a promising technique for improving sensitivity. This project explores this technique and provides insight on possible ways to improve breast imaging sensitivity

EVALUATION OF EARLY TUMOR ANGIOGENESIS USING ULTRASOUND ACOUSTIC ANGIOGRAPHY

Cancer angiogenesis is a feature of tumor growth that produces disorganized and dysfunctional vascular networks. Acoustic angiography is a unique implementation of contrast-enhanced ultrasound that allows us to visualize microvasculature with high resolution and contrast, including blood vessels as small as 100 to 150 micrometers. These angiography images can be analyzed to evaluate the morphology of the blood vessels for the purpose of detecting and diagnosing tumors. This thesis describes the implementation, advantages, and disadvantages of acoustic angiography and evaluates tumor vasculature in a pre-clinical cancer model. Measurements of tortuosity and vascular density in tumor regions were significantly higher than those of control regions, including in the smallest palpable tumors (2-3 mm). Additionally, abnormal tortuosity extended beyond the margin of tumors, as distal tissue separated from the tumor by at least 4 mm exhibited higher tortuosity than healthy individuals. Vascular tortuosity was negatively correlated to distance from the tumor margin using linear regression. Analysis of full images to detect tumors was performed using a reader study approach to assess visual interpretations, and quantitative analysis combined tortuosity with spatial relationships between vessels using a density-based clustering approach. Visual assessment using a reader study design resulted in an area under the receiver operating characteristic (ROC) curve of approximately 0.8, and the ROC curve was significantly correlated with tumor diameter, indicating that larger tumors were detected more accurately using this approach. Quantitative analysis of the same images used a density-based clustering algorithm to combine vessels in an image into clusters based on their tortuosity (using 2 metrics), radius, and proximity to one another. In tumors, highly tortuous vessels were closely packed, forming large clusters in the analysis, while control images lacked such patterns and formed much smaller clusters. Therefore, maximum cluster size was used to detect tumors, achieving an area under the ROC curve of 0.96. Finally, superharmonic molecular imaging was used to image targeted microbubbles with higher contrast to tissue ratios than conventional molecular imaging. These molecular images were combined with vascular acoustic angiography images to begin to relate the expression of endothelial markers of angiogenesis with vascular features such as tortuosity.Doctor of Philosoph

Complexity Reduction in Image-Based Breast Cancer Care

The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device