Image reconstruction and imaging configuration optimization with a novel nanotechnology enabled breast tomosynthesis multi-beam X-ray system

Digital breast tomosynthesis is a new technology that provides three-dimensional information of the breast and makes it possible to distinguish the cancer from overlying breast tissues. We are dedicated to optimizing image reconstruction and imaging configuration for a new multi-beam parallel digital breast tomosynthesis prototype system. Several commonly used algorithms from the typical image reconstruction models which were used for iso-centric tomosynthesis systems were investigated for our multi-beam parallel tomosynthesis imaging system. The representative algorithms, including back-projection (BP), filtered back-projection (FBP), matrix inversion tomosynthesis reconstruction (MITS), maximum likelihood expectation maximization (MLEM), ordered-subset maximum likelihood expectation maximization (OS-MLEM), simultaneous algebraic reconstruction technique (SART), were implemented to fit our system design. An accelerated MLEM algorithm was proposed, which significantly reduced the running time but had the same image quality. Furthermore, two statistical variants of BP reconstruction were validated for our tomosynthesis prototype system. Experiments based on phantoms and computer simulations show that the prototype system combined with our algorithms is capable of providing three-dimensional information of the objects with good image quality and has great potentials to improve digital breast tomosynthesis technology. Four methodologies were employed to optimize the reconstruction algorithms and different imaging configurations for the prototype system. A linear tomosynthesis imaging analysis tool was used to investigate blurring-out reconstruction algorithms. Computer simulations of sphere and wire objects aimed at the performance of out-of-plane artifact removal. A frequency-domain-based methodology, relative NEQ(f) analysis, was investigated to evaluate the overall system performance based on the propagation of signal and noise. Conclusions were made to determine the optimal image reconstruction algorithm and imaging configuration of this new multi-beam parallel digital breast tomosynthesis prototype system for better image quality and system performance

Modeling the Anisotropic Resolution and Noise Properties of Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) is a 3D imaging modality in which a reconstruction of the breast is generated from various x-ray projections. Due to the newness of this technology, the development of an analytical model of image quality has been on-going. In this thesis, a more complete model is developed by addressing the limitations found in the previous linear systems (LS) model [Zhao, Med. Phys. 2008, 35(12): 5219-32]. A central assumption of the LS model is that the angle of x-ray incidence is approximately normal to the detector in each projection. To model the effect of oblique x-ray incidence, this thesis generalizes Swank\u27s calculations of the transfer functions of x-ray fluorescent screens to arbitrary incident angles. In the LS model, it is also assumed that the pixelation in the reconstruction grid is the same as the detector; hence, the highest frequency that can be resolved is the detector alias frequency. This thesis considers reconstruction grids with smaller pixelation to investigate super-resolution, or visibility of higher frequencies. A sine plate is introduced as a conceptual test object to analyze super-resolution. By orienting the long axis of the sine plate at various angles, the feasibility of oblique reconstruction planes is also investigated. This formulation differs from the LS model in which reconstruction planes are parallel to the breast support. It is shown that the transfer functions for arbitrary angles of x-ray incidence can be modeled in closed form. The high frequency modulation transfer function (MTF) and detective quantum efficiency (DQE) are degraded due to oblique x-ray incidence. In addition, using the sine plate, it is demonstrated that a reconstruction can resolve frequencies exceeding the detector alias frequency. Experimental images of bar patterns verified the existence of super-resolution. Anecdotal clinical examples showed that super-resolution improves the visibility of microcalcifications. The feasibility of oblique reconstructions was established theoretically with the sine plate and was validated experimentally with bar patterns. This thesis develops a more complete model of image quality in DBT by addressing the limitations of the LS model. In future studies, this model can be used as a tool for optimizing DBT

Dependency of image quality on system configuration parameters in a stationary digital breast tomosynthesis system

Purpose: In principle, a stationary digital breast tomosynthesis (s-DBT) system has better image quality when compared to continuous motion DBT systems due to zero motion blur of the source. The authors have developed a s-DBT system by using a linear carbon nanotube x-ray source array. The purpose of the current study was to quantitatively evaluate the performance of the s-DBT system;and investigate the dependence of imaging quality on the system configuration parameters. Methods: Physical phantoms were used to assess the image quality of each configuration including inplane resolution as measured by the modulation transfer function (MTF), inplane contrast as measured by the signal difference to noise ratio (SdNR), and depth resolution as measured by the z-axis artifact spread function. Five parameters were varied to create five groups of configurations: (1) total angular span; (2) total number of projection images; (3) distribution of exposure (mAs) across the projection images; (4) entrance dose; (5) detector pixel size. Results: It was found that the z-axis depth resolution increased with the total angular span but was insensitive to the number of projection images, mAs distribution, entrance dose, and detector pixel size. The SdNR was not affected by the angular span or the number of projection images. A decrease in SdNR was observed when the mAs was not evenly distributed across the projection images. As expected, the SdNR increased with entrance dose and when larger pixel sizes were used. For a given detector pixel size, the inplane resolution was found to be insensitive to the total angular span, number of projection images, mAs distribution, and entrance dose. A 25% increase in the MTF was observed when the detector was operating in full resolution mode (70 μm pixel size) compared to 2 × 2 binned mode (140 μm pixel size). Conclusions: The results suggest that the optimal imaging configuration for a s-DBT system is a large angular span, an intermittent number of projection views, and a uniform mAs distribution over all views. With the detector operating at full resolution, a stationary DBT system can achieve an inplane resolution of 5.1 cycles per mm, which is significantly better than continuous motion DBT systems

Camera-based CW diffuse optical tomography for obtaining 3D absorption maps by means of digital tomosynthesis

We present a novel method for obtaining a 3D absorption map of a tissue-like turbid slab in the near-infrared spectral range by tomosynthesis. Transmittance data are obtained for a large number of oblique projection directions by scanning a cw laser source across the surface of the slab and by using a CCD camera for spatially resolved light detection. A perturbation model of light transport is used to convert the intensity maps for the different projections into absorption maps. By applying the tomosynthesis approach to these new maps, 3D absorption information on embedded inclusions has been obtained for the first time. The number and the positions of the lateral offset detectors have been optimized by employing a structural similarity index for comparison of the reconstructed with the true absorption data. We present 3D reconstruction of absorption maps using both Monte Carlo simulations and experiments on phantoms with breast-like optical properties. A comparison with conventional 3D reconstruction by a finite element approach shows the superior location performance of tomosynthesis.Fil: Carbone, Nicolás Abel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Vera, Demián Augusto. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Iriarte, Daniela Ines. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Pomarico, Juan Antonio. Universidad Nacional del Centro de la Provincia de Buenos Aires. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; ArgentinaFil: Macdonald, R.. Physikalisch-Technische Bundesanstalt; AlemaniaFil: Grosenick, D.. Physikalisch-Technische Bundesanstalt; Alemani

Improving Image Reconstruction for Digital Breast Tomosynthesis

Digital breast tomosynthesis (DBT) has been developed to reduce the issue of overlapping tissue in conventional 2-D mammography for breast cancer screening and diagnosis. In the DBT procedure, the patient’s breast is compressed with a paddle and a sequence of x-ray projections is taken within a small angular range. Tomographic reconstruction algorithms are then applied to these projections, generating tomosynthesized image slices of the breast, such that radiologists can read the breast slice by slice. Studies have shown that DBT can reduce both false-negative diagnoses of breast cancer and false-positive recalls compared to mammography alone. This dissertation focuses on improving image quality for DBT reconstruction. Chapter I briefly introduces the concept of DBT and the inspiration of my study. Chapter II covers the background of my research including the concept of image reconstruction, the geometry of our experimental DBT system and figures of merit for image quality. Chapter III introduces our study of the segmented separable footprint (SG) projector. By taking into account the finite size of detector element, the SG projector improves the accuracy of forward projections in iterative image reconstruction. Due to the more efficient access to memory, the SG projector is also faster than the traditional ray-tracing (RT) projector. We applied the SG projector to regular and subpixel reconstructions and demonstrated its effectiveness. Chapter IV introduces a new DBT reconstruction method with detector blur and correlated noise modeling, called the SQS-DBCN algorithm. The SQS-DBCN algorithm is able to significantly enhance microcalcifications (MC) in DBT while preserving the appearance of the soft tissue and mass margin. Comparisons between the SQS-DBCN algorithm and several modified versions of the SQS-DBCN algorithm indicate the importance of modeling different components of the system physics at the same time. Chapter V investigates truncated projection artifact (TPA) removal algorithms. Among the three algorithms we proposed, the pre-reconstruction-based projection view (PV) extrapolation method provides the best performance. Possible improvements of the other two TPA removal algorithms have been discussed. Chapter VI of this dissertation examines the effect of source blur on DBT reconstruction. Our analytical calculation demonstrates that the point spread function (PSF) of source blur is highly shift-variant. We used CatSim to simulate digital phantoms. Analysis on the reconstructed images demonstrates that a typical finite-sized focal spot (~ 0.3 mm) will not affect the image quality if the x-ray tube is stationary during the data acquisition. For DBT systems with continuous-motion data acquisition, the motion of the x-ray tube is the main cause of the effective source blur and will cause loss in the contrast of objects. Therefore modeling the source blur for these DBT systems could potentially improve the reconstructed image quality. The final chapter of this dissertation discusses a few future studies that are inspired by my PhD research.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/144059/1/jiabei_1.pd

Representation learning for breast cancer lesion detection

Breast Cancer (BC) is the second type of cancer with a higher incidence in women, it is responsible for the death of hundreds of thousands of women every year. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical image modalities, such as MG – Mammography (X-Rays), US - Ultrasound, CT - Computer Tomography, MRI - Magnetic Resonance Imaging, and Tomosynthesis have been explored to support radiologists/physicians in clinical decision-making work- flows for the detection and diagnosis of BC. MG is the imaging modality more used at the worldwide level, however, recent research results have demonstrated that breast MRI is more sensitive than mam- mography to find pathological lesions, and it is not limited/affected by breast density issues. Therefore, it is currently a trend to introduce MRI-based breast assessment into clinical workflows (screening and diagnosis), but when compared to MG the workload of radiologists/physicians increases, MRI assess- ment is a more time-consuming task, and its effectiveness is affected not only by the variety of morpho- logical characteristics of each specific tumor phenotype and its origin but also by human fatigue. Com- puter-Aided Detection (CADe) methods have been widely explored primarily in mammography screen- ing tasks, but it remains an unsolved problem in breast MRI settings. This work aims to explore and validate BC detection models using Machine (Deep) Learning algorithms. As the main contribution, we have developed and validated an innovative method that improves the “breast MRI preprocessing phase” to select the patient’s image slices and bounding boxes representing pathological lesions. With this, it is possible to build a more robust training dataset to feed the deep learning models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient images, in which a possible pathological lesion (tumor) has been identified. In experimental settings using a fully annotated (released for public domain) dataset comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%.O cancro da mama (CdM) é o segundo tipo de cancro com maior incidência nas mulheres. É respon- sável pela morte de centenas de milhares de mulheres todos os anos. Contudo, quando detetado nas fases iniciais da doença, os métodos de tratamento provaram ser muito eficazes aumentando a espe- rança de vida e, em muitos casos, os pacientes recuperam totalmente. Têm sido exploradas várias modalidades de imagem médica, tais como MG - Mamografia (Raios-X), US - Ultra-som, CT - Tomo- grafia Computadorizada, MRI - Ressonância Magnética e Tomossíntese, para apoiar radiologistas nos fluxos de trabalho clínico para a deteção e diagnóstico do CdM. A MG é a modalidade de imagem mais utilizada a nível mundial, contudo, resultados de pesquisas recentes demonstraram que o MRI é mais sensível do que a mamografia para encontrar lesões patológicas e, também, não é limitada ou afetada por questões de densidade mamária. Consequentemente, atualmente é uma tendência introduzir a avaliação mamográfica baseada em MRI nos fluxos de trabalho clínico - rastreio e diagnóstico -, mas quando comparada com a MG, a carga de trabalho dos radiologistas aumenta. A avaliação por MRI é uma tarefa mais demorada, e a sua eficácia é afetada não só pela variedade de características morfo- lógicas e origem de cada fenótipo tumoral específico, mas, também pela fadiga humana. Os métodos de deteção assistida por computador (CADe) têm sido amplamente explorados principalmente em ta- refas de rastreio mamográfico, mas continua a ser um problema por resolver em ambientes de resso- nância magnética mamária. Este trabalho visa explorar e validar modelos de deteção de CdM usando algoritmos de Machine (Deep) Learning. Como contributo principal, desenvolvemos e validámos um método inovador que me- lhora a "fase de pré-processamento das imagens de ressonância magnética mamária" para selecionar as fatias de imagem do paciente e as respetivas caixas de contorno que representam as lesões pato- lógicas. Com isto, é possível construir um conjunto de dados de treino mais robusto para alimentar os modelos de deep learning, reduzir o tempo de computação, reduzir a dimensão do conjunto de dados e, mais importante, para identificar com alta precisão as regiões específicas para cada uma das ima- gens do paciente nas quais foi identificada uma possível lesão patológica (tumor). Os resultados expe- rimentais, num conjunto de imagens de ressonância magnética de domínio público totalmente anotado com 922 casos de doentes com CdM, mostram no melhor modelo uma taxa de exatidão de 97.83%. Foi aplicado um método de validação cruzada de 10 folds do qual resultou uma exatidão média de 94,46% com um desvio padrão de 2,43% nos modelos treinados

Near-Field Radar Microwave Imaging as an Add-on Modality to Mammography

According to global statistics, there is a high incidence of cancer in western countries; and, due to the limited resources available in most health care systems, it seems like one of the most feasible options to fight against cancer might be strict prevention policies—such as eliminating carcinogens in people’s daily lives. Nevertheless, early cancer detection and effective treatment are still necessary, and understanding their efficacy and limitations are important issues that need to be addressed in order to ultimately enhance patients’ survival rate. In the case of breast cancer, some of the problems faced by conventional mammography have been addressed in the literature; they include high rate of false-positive and false-negative results, as well as the possibility of overdiagnosis. New technologies, such as digital breast tomosynthesis (DBT), have been able to improve the sensitivity and specificity by using 3D imaging. However, the low contrast (1%) existing between tumors and healthy fibroglandular tissue at X-ray frequencies has been identified as one of the main causes of misdiagnosis in both conventional 2D mammography and DBT. Near-field radar imaging (NRI) provides a unique opportunity to overcome this problem, since the contrast existing between the aforementioned tissues is intrinsically higher (10%) at microwave frequencies. Moreover, the low resolution and highly complex scattering patterns of microwave systems can be enhanced by using prior information from other modalities, such as the DBT. Therefore, a multimodal DBT/NRI imaging system is proposed to exploit their individual strengths while minimizing their weaknesses. In this work, the foundation of this idea is reviewed, and a preliminary design and experimental validation of the NRI system, used as a DBT complement, is introduced