The best way to conquer breast cancer is early detection of the disease. Research studies show that earlier detection results in the increase of life span of the affected person. Traditional two-dimensional mammography is the most prevalent method used in detecting breast cancer. Recently, a three-dimensional digital breast tomosynthesis (DBT) system has been created, which is hopeful to surpass the technology of traditional mammography systems. The DBT system can provide three-dimensional information, allowing physicians to reduce the amount of false negative screening in addition to better monitoring of breast cancer and to catch lesions that may be otherwise cancerous. In this research, the View Angle (VA) and number of projection images (N) were investigated and compared with parallel imaging configurations using two reconstruction algorithms, including Back Projection (BP) and Shift-And-Add (SAA). Modulation Transfer Function (MTF) analyses were conducted with both algorithms, in order to determine which method displayed better image qualities to ultimately improve the detection of breast cancer.

The ability for megavoltage computed tomography patient setup verification using a cobalt-60 (Co–60) gamma ray source has been established in the context of cobalt tomotherapy. However, it would be beneficial to establish improved cobalt imaging that could be used on more conventional units. In terms of safety and efficiency, this imaging technique would provide the patient with less exposure to radiation. Digital tomosynthesis (DT) is an imaging modality that may provide improved depth localization and in-plane visibility compared to conventional portal imaging in modern Co–60 radiation therapy. DT is a practical and efficient method of achieving depth localization from a limited gantry rotation and a limited number of projections. In DT, each plane of the imaging volume can be brought into focus by relatively displacing the composite images and superimposing the shifted dataset according to the acquisition geometry. Digital flat-panel technology has replaced the need for multiple film exposures and therefore the speed of imaging and capabilities for image processing has put DT in the forefront of both clinical and industrial imaging applications. The objective of this work is to develop and evaluate the performance of an experimental system for megavoltage digital tomosynthesis (MVDT) imaging using a Co–60 gamma ray source. Linear and isocentric acquisition geometries are implemented using tomographic angles of 20-60° and 10-60 projections. Reconstruction algorithms are designed for both acquisition geometries. Using the backprojection approach, the data are shifted and added to reconstruct focal planes of interest. Depth localization and its dependence on tomographic angle and projection density are visualized with an anthropomorphic head phantom. High contrast resolution at localized depths is quantified using the modulation transfer function approach. Results show that focal-plane visibility is improved for larger tomographic angles and that focal-plane visibility has negligible dependence on projection density. Lastly, the presence of noise and artifacts in the resulting images are quantified in terms of the signal-to-noise ratio and the artifact spread function. The work presented here is expected to provide the justification required to proceed with a prototype Co–60 MVDT system for patient set-up verification in modern Co–60 radiation therapy. / Thesis (Master, Physics, Engineering Physics and Astronomy) -- Queen's University, 2010-04-28 14:14:07.44

Dissertation

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.

Digital tomosynthesis is a novel three-dimensional imaging technology that utilizes limited number of X-ray projection images to improve the diagnosis and detection of lesions. In recent years, tomosynthesis has been used in a variety of clinical applications such as dental imaging, angiography, chest imaging, bone imaging, and breast imaging. The goal of our research is to develop a framework to enable flexible optimization and comparison of image reconstruction and imaging configurations.

<p>Breast cancer is currently the most common non-skin cancer and the second leading cause of cancer-related death in women here in the United States. X-ray mammography is currently the standard clinical imaging modality for breast cancer screening and diagnosis due to its high sensitivity and resolution at a low patient dose. With the advancement of breast imaging from analog to digital, quantitative measurements rather than qualitative assessments can be made from these images. One such measurement, mammographic breast density (i.e. the percentage of the entire breast volume that is taken up by dense glandular tissue), has been shown to be a biomarker well correlated with cancer risk. However, a digital mammogram still suffers from its projective nature. The resulting overlap of normal breast tissue can obscure lesions, limit quantitative measurement accuracy, and present false alarms leading to unnecessary recall studies. To address this key limitation, several 3D imaging techniques have been developed such as breast magnetic resonance imaging (MRI), dedicated breast computed tomography (CT), and digital breast tomosynthesis (tomo). Perhaps the most recently developed modality is tomo, which is a limited-angle cone-beam CT of the breast compressed in the same geometry as mammography. Because tomo retains all the aforementioned advantages of mammography but adds depth information and can be built based on an existing digital mammography device, measuring breast density and extracting other quantitative features from tomo images was a major focus of this study.</p><p>Before attempting to measure breast density and other features from reconstructed tomo image volumes, the quantitative potential of this imaging modality was assessed. First, we explored a slice-by-slice technique that measures tissue density using only the information from a single slice from the reconstructed tomosynthesis volume with geometrically simple tissue-equivalent phantoms. Once this task has been satisfactorily performed, we studied a probabilistic approach toward quantitation of the entire 3D volume. Some work has been done previously in the realm of 2D hidden Markov random fields (HMRFs) to categorize mammograms according to their Wolf pattern, detect mammographic lesions, and segment satellite and mixed media (text/photograph) images. For this project, a 3D hidden Markov model (HMM) method was developed and applied to tomo images under the simplified assumption that the possible tissue type of each tomo voxel is either adipose (fatty) or glandular (dense). Because adipose and glandular tissue is easily distinguished in MR images, patient breast MRIs were used to train, validate, and finally to assess the accuracy of our HMM segmentation algorithm when applied to tomo images by comparing the volumetric breast density to the MRI breast density for the same patient. Because they are so often studied conjunctively, several image texture features were calculated and compared between MRI and tomo as well.</p><p>Another aim of our study was to investigate whether changes in macroscopic 3D imaging features (texture and density) can accurately predict the chemoprevention response that was measured with Random Periareolar Fine Needle Aspiration (RPFNA) cytology for a uniquely young high-risk cohort of women. This aim to investigate the potential of combining multi-modality macroscopic 3D imaging information with a cytological measure of risk and then investigating how response to tamoxifen and other chemoprevention treatment affects each of these risk biomarkers in young, high-risk women is completely novel in the fields of medical physics and biomedical engineering.</p> / Dissertation

Breast cancer continues to be the most frequently diagnosed cancer in Canadian women. Currently, mammography is the clinically accepted best modality for breast cancer detection and the regular use of screening has been shown to contribute to the reduced mortality. However, mammography suffers from several drawbacks which limit its sensitivity and specificity. As a potential solution, digital breast tomosynthesis (DBT) uses a limited number (typically 10-20) of low-dose x-ray projections to produce a three-dimensional tomographic representation of the breast. The reconstruction of DBT images is challenged by such incomplete sampling. The purpose of this thesis is to evaluate the effect of image acquisition parameters on image quality of DBT for various reconstruction techniques and to optimize these, with three specific goals: A) Develop a better power spectrum estimator for detectability calculation as a task-based image quality index; B) Develop a paired-view algorithm for artifact removal in DBT reconstruction; and C) Increase dose efficiency in DBT by reducing random noise. A better power spectrum estimator was developed using a multitaper technique, which yields reduced bias and variance in estimation compared to the conventional moving average method. This gives us an improved detectability measurement with finer frequency steps. The paired-view scheme in DBT reconstruction provides better image quality than the commonly used sequential method. A simple ordering like the “side-to-side” method can achieve less artifact and higher image quality in reconstructed slices. The new denoising algorithm developed was applied to the projection views acquired in DBT before reconstruction. The random noise was markedly removed while the anatomic details were maintained. With the help of this artifact-removal technique used in reconstruction and the denoising method employed on the projection views, the image quality of DBT is enhanced and lesions should be more readily detectable.

Η ψηφιακή τομοσύνθεση στην τρισδιάστατη απεικόνιση μαστών, είναι μια μέθοδος ανακατασκευής οποιουδήποτε αριθμού τομογραφικών επιπέδων τυχαίου προσανατολισμού, στη βάση ψηφιοποιημένων προβολικών εικόνων της εξεταζόμενης περιοχής, που λαμβάνονται κατά την διάρκεια της περιστροφής του συστήματος ακτινολογική λυχνία-ενισχυτής εικόνας με κέντρο το ισόκεντρο του μηχανήματος και κατά το προεπιλεγμένο τόξο. Δυστυχώς, σε περιπτώσεις αυξημένης πυκνότητας μαστών, η ποιότητα των ανακατασκευασμένων επιπέδων επηρεάζεται από θόρυβο, λόγω των επιπροβαλλόμενων στοιχείων υπερκείμενων ή υποκείμενων επιπέδων. Το αντικείμενο που πραγματεύεται η εργασία, είναι η μελέτη διαφόρων τεχνικών επεξεργασίας των τομών, που θα μπορούσαν να αφαιρέσουν το θόρυβο από τα ανακατασκευασμένα επίπεδα και να βελτιώσουν την ποιότητά τους. Για αυτόν το λόγο, δημιουργήθηκαν τρία πειραματκά phantoms διαφορετικής πολυπλοκότητας, στα οποία προστέθηκαν δυσμορφίες που αντιπροσωπεύουν μικρές ασβεστοποιήσεις και μάζες χαμηλής αντίθεσης. Στη συνέχεια, υποβλήθηκαν στην προσομειωμένη απεικόνιση με ακτίνες Χ, προκειμένου να παραχθεί ένα σύνολο προβολικών εικόνων σε τόξο εύρους από -20° έως +20° κάθε 2°. Οι ακτινοβολημένοι όγκοι ανακατασκευάστηκαν μέσω του αλγόριθμου πολλαπλών προβολών (MPA), γραμμένο σε Matlab. Οι ανακατασκευασμένες φέτες έχουν πλάτος λιγότερο από 1mm, προκειμένου να ανιχνευθούν μικρές ασβεστοποιήσεις. Αναπτύχθηκαν και αξιολογήθηκαν πειραματικά δύο αλγόριθμοι για την περαιτέρω επεξεργασία των λαμβανόμενων τομών. Ο πρώτος είναι βασισμένος στην λογαριθμική μέθοδο αφαίρεσης δομών που βρίσκονται εκτός επιπέδου ενδιαφέροντος, συνδυάζοντας τεχνικές τονισμού της εικόνας. Η δεύτερη τεχνική εκτελεί το φιλτράρισμα της εικόνας, συγκρίνοντας τις τιμές των pixel, σε όλο τoν όγκο προς ανακατασκευή. Η ποιότητα της εικόνας καθορίστηκε μέσω της οπτικής αξιολόγησης των ανακατασκευασμένων επιπέδων καθώς και τον υπολογισμό του CNR. Και οι δύο αξιολογήσεις κατέδειξαν καλή διαχωριστική και αφαίρετική ικανότητα του τομογραφικού θορύβου. Το MPA σε συνδυασμό με τους προτεινόμενους αλγορίθμους αφαίρεσης θορύβου, μείωσε τον τομογραφικό θόρυβο στις ανακατασκευασμένες εικόνες και αύξησε το CNR. Περαιτέρω μελέτες θα περιλαμβάνουν την ανάπτυξη ενός γενικευμένου αλγορίθμου αφαίρεσης θορύβου που θα συνδυάσει τις προτεινόμενες δύο τεχνικές. / Digital tomosynthesis for breast imaging is a method of reconstructing any number of tomographic planes, by using a set of limited angle projections, acquired as the X-ray source moves in an arc around the breast. Unfortunately, in case of dense breasts, the quality of the reconstructed planes is affected by structured noise, due to blur from planes other than the fulcrum plane. The purpose of this project is to study different post-processing techniques that could remove the noise from the reconstructed planes and improve their quality. For this purpose, three software phantoms of different complexity with inserted abnormalities that represent small micro calcifications and low-contrast masses were created. Subsequently, they were subjected to x-ray imaging simulation in order to produce a set of projection images in the gantry arc from -200 to 200 every 20. The irradiated volumes were reconstructed with the Multiple Projections reconstruction Algorithm (MPA) written in Matlab. Reconstructed slices were with width of less than 1mm width, in order to detect the small microcalcifications. Two algorithms for removing unwanted structured noise were explored. The first one is based on the iterative logarithmic subtraction method of unrelated structures combined with image enhancement techniques. The second technique performs a filtering action based on a pixel intensity value comparison, throughout the phantom volume. Image quality was assessed by evaluation of the reconstructed planes in terms of both visual assessment and contrast to noise ratio (CNR). Both evaluations demonstrated good noise discrimination and elimination, followed by an improvement of the contrast and visibility of abnormalities in tomograms. The MPA followed by the proposed noise removal algorithms resulted in less noisy tomosynthesis images, higher CNR and feature contrast for both low- and high contrast details. Further studies include developing of a generalised noise removal algorithm that will combine the proposed two techniques.

Yes / We investigated the potential for digital tomosynthesis (DT) to reduce pediatric x-ray dose while maintaining image quality. We utilized the DT feature (VolumeRadTM) on the GE DefiniumTM 8000 flat panel system installed in the Winnipeg Children’s Hospital. Facial bones, cervical spine, thoracic spine, and knee of children aged 5, 10, and 15 years were represented by acrylic phantoms for DT dose measurements. Effective dose was estimated for DT and for corresponding digital radiography (DR) and computed tomography (CT) patient image sets. Anthropomorphic phantoms of selected body parts were imaged by DR, DT, and CT. Pediatric radiologists rated visualization of selected anatomic features in these images. Dose and image quality comparisons between DR, DT, and CT determined the usefulness of tomosynthesis for pediatric imaging. CT effective dose was highest; total DR effective dose was not always lowest – depending how many projections were in the DR image set. For the cervical spine, DT dose was close to and occasionally lower than DR dose. Expert radiologists rated visibility of the central facial complex in a skull phantom as better than DR and comparable to CT. Digital tomosynthesis has a significantly lower dose than CT. This study has demonstrated DT shows promise to replace CT for some facial bones and spinal diagnoses. Other clinical applications will be evaluated in the future. / Medical Physics Division at CancerCare Manitoba and GE Healthcare (Waukesha, WI).

Currently, mammography is the most common imaging technology used in breast screening. Low dose X-rays are passed through the breast to generate images called mammograms. One type of breast abnormality is a cluster of microcalcifications. Usually, in benign cases, microcalcifications result from the death of fat cells or are due to secretion by the lobules. However, in some cases, clusters of microcalcifications are indicative of early breast cancer, partly because of the secretions by cancer cells or the death of such cells. Due to the different attenuation characteristics of normal breast tissue and microcalcifications, the latter ideally appear as bright white spots and this allows detection and analysis for breast cancer classification. Microcalcification detection is one of the primary foci of screening and has led to the development of computer-aided detection (CAD) systems. However, a fundamental limitation of mammography is that it gives a 2D view of the tightly compressed 3D breast. The depths of entities within the breast are lost after this imaging process, even though the breast tissue is spread out as a result of the compression force applied to the breast. The superimposition of tissues can occlude cancers and this has led to the development of digital breast tomosynthesis (DBT). DBT is a three-dimensional imaging involving an X-ray tube moving in an arc around the breast, over a limited angular range, producing multiple images, which further undergo a reconstruction step to form a three-dimensional volume of breast. However, reconstruction remains the subject of research and small microcalcifications are "smeared" in depth by current algorithms, preventing detailed analysis of the geometry of a cluster. By using the geometry of the DBT acquisition system, we derive the "epipolar" trajectory of a microcalcification. As a first application of the epipolars, we develop a clustering algorithm after using the Hough transform to find corresponding points generated from a microcalcification. Noise points can also be isolated. In addition, we show how microcalcification projections can be detected adaptively. Epipolar analysis has also led to a novel detection algorithm for DBT using a Bayesian method, which estimates a maximum a posterior (MAP) labelling in each individual image and subsequently for all projections iteratively. Not only does this algorithm output the binary decision of whether a pixel is a microcalcification, it can predict the approximate depth of the microcalcification in the breast if it is. Based on the epipolar analysis, reconstruction of just a region of interest (ROI) e.g. microcalcification clusters is possible and it is more straightforward than any existing method using reconstruction slices. This potentially enables future classification of breast cancer when more clinical data becomes available.