Information Fusion of Magnetic Resonance Images and Mammographic Scans for Improved Diagnostic Management of Breast Cancer

Medical imaging is critical to non-invasive diagnosis and treatment of a wide spectrum of medical conditions. However, different modalities of medical imaging employ/apply di erent contrast mechanisms and, consequently, provide different depictions of bodily anatomy. As a result, there is a frequent problem where the same pathology can be detected by one type of medical imaging while being missed by others. This problem brings forward the importance of the development of image processing tools for integrating the information provided by different imaging modalities via the process of information fusion. One particularly important example of clinical application of such tools is in the diagnostic management of breast cancer, which is a prevailing cause of cancer-related mortality in women. Currently, the diagnosis of breast cancer relies mainly on X-ray mammography and Magnetic Resonance Imaging (MRI), which are both important throughout different stages of detection, localization, and treatment of the disease. The sensitivity of mammography, however, is known to be limited in the case of relatively dense breasts, while contrast enhanced MRI tends to yield frequent 'false alarms' due to its high sensitivity. Given this situation, it is critical to find reliable ways of fusing the mammography and MRI scans in order to improve the sensitivity of the former while boosting the specificity of the latter. Unfortunately, fusing the above types of medical images is known to be a difficult computational problem. Indeed, while MRI scans are usually volumetric (i.e., 3-D), digital mammograms are always planar (2-D). Moreover, mammograms are invariably acquired under the force of compression paddles, thus making the breast anatomy undergo sizeable deformations. In the case of MRI, on the other hand, the breast is rarely constrained and imaged in a pendulous state. Finally, X-ray mammography and MRI exploit two completely di erent physical mechanisms, which produce distinct diagnostic contrasts which are related in a non-trivial way. Under such conditions, the success of information fusion depends on one's ability to establish spatial correspondences between mammograms and their related MRI volumes in a cross-modal cross-dimensional (CMCD) setting in the presence of spatial deformations (+SD). Solving the problem of information fusion in the CMCD+SD setting is a very challenging analytical/computational problem, still in need of efficient solutions. In the literature, there is a lack of a generic and consistent solution to the problem of fusing mammograms and breast MRIs and using their complementary information. Most of the existing MRI to mammogram registration techniques are based on a biomechanical approach which builds a speci c model for each patient to simulate the effect of mammographic compression. The biomechanical model is not optimal as it ignores the common characteristics of breast deformation across different cases. Breast deformation is essentially the planarization of a 3-D volume between two paddles, which is common in all patients. Regardless of the size, shape, or internal con guration of the breast tissue, one can predict the major part of the deformation only by considering the geometry of the breast tissue. In contrast with complex standard methods relying on patient-speci c biomechanical modeling, we developed a new and relatively simple approach to estimate the deformation and nd the correspondences. We consider the total deformation to consist of two components: a large-magnitude global deformation due to mammographic compression and a residual deformation of relatively smaller amplitude. We propose a much simpler way of predicting the global deformation which compares favorably to FEM in terms of its accuracy. The residual deformation, on the other hand, is recovered in a variational framework using an elastic transformation model. The proposed algorithm provides us with a computational pipeline that takes breast MRIs and mammograms as inputs and returns the spatial transformation which establishes the correspondences between them. This spatial transformation can be applied in different applications, e.g., producing 'MRI-enhanced' mammograms (which is capable of improving the quality of surgical care) and correlating between different types of mammograms. We investigate the performance of our proposed pipeline on the application of enhancing mammograms by means of MRIs and we have shown improvements over the state of the art

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Mammography Techniques and Review

Mammography remains at the backbone of medical tools to examine the human breast. The early detection of breast cancer typically uses adjunct tests to mammogram such as ultrasound, positron emission mammography, electrical impedance, Computer-aided detection systems and others. In the present digital era it is even more important to use the best new techniques and systems available to improve the correct diagnosis and to prevent mortality from breast cancer. The first part of this book deals with the electrical impedance mammographic scheme, ultrasound axillary imaging, position emission mammography and digital mammogram enhancement. A detailed consideration of CBR CAD System and the availability of mammographs in Brazil forms the second part of this book. With the up-to-date papers from world experts, this book will be invaluable to anyone who studies the field of mammography

Mammography

In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume

Automated Deformable Mapping Methods to Relate Corresponding Lesions in 3D X-ray and 3D Ultrasound Breast Images

Mammography is the current standard imaging method for detecting breast cancer by using x-rays to produce 2D images of the breast. However, with mammography alone there is difficulty determining whether a lesion is benign or malignant and reduced sensitivity to detecting lesions in dense breasts. Ultrasound imaging used in conjunction with mammography has shown valuable contributions for lesion characterization by differentiating between solid and cystic lesions. Conventional breast ultrasound has high false positive rates; however, it has shown improved abilities to detect lesions in dense breasts. Breast ultrasound is typically performed freehand to produce anterior-to-posterior 2D images in a different geometry (supine) than mammography (upright). This difference in geometries is likely responsible for the finding that at least 10% of the time lesions found in the ultrasound images do not correspond with lesions found in mammograms. To solve this problem additional imaging techniques must be investigated to aid a radiologist in identifying corresponding lesions in the two modalities to ensure early detection of a potential cancer. This dissertation describes and validates automated deformable mapping methods to register and relate corresponding lesions between multi-modality images acquired using 3D mammography (Digital Breast Tomosynthesis (DBT) and dedicated breast Computed Tomography (bCT)) and 3D ultrasound (Automated Breast Ultrasound (ABUS)). The methodology involves the use of finite element modeling and analysis to simulate the differences in compression and breast orientation to better align lesions acquired from images from these modalities. Preliminary studies were performed using several multimodality compressible breast phantoms to determine breast lesion registrations between: i) cranio-caudal (CC) and mediolateral oblique (MLO) DBT views and ABUS, ii) simulated bCT and DBT (CC and MLO views), and iii) simulated bCT and ABUS. Distances between the centers of masses, dCOM, of corresponding lesions were used to assess the deformable mapping method. These phantom studies showed the potential to apply this technique for real breast lesions with mean dCOM registration values as low as 4.9 ± 2.4 mm for DBT (CC view) mapped to ABUS, 9.3 ± 2.8 mm for DBT (MLO view) mapped to ABUS, 4.8 ± 2.4 mm for bCT mapped to ABUS, 5.0 ± 2.2 mm for bCT mapped to DBT (CC view), and 4.7 ± 2.5 mm for bCT mapped to DBT (MLO view). All of the phantom studies showed that using external fiducial markers helped improve the registration capability of the deformable mapping algorithm. An IRB-approved proof-of-concept study was performed with patient volunteers to validate the deformable registration method on 5 patient datasets with a total of up to 7 lesions for DBT (CC and MLO views) to ABUS registration. Resulting dCOM’s using the deformable method showed statistically significant improvements over rigid registration techniques with a mean dCOM of 11.6 ± 5.3 mm for DBT (CC view) mapped to ABUS and a mean dCOM of 12.3 ± 4.8 mm for DBT (MLO view) mapped to ABUS. The present work demonstrates the potential for using deformable registration techniques to relate corresponding lesions in 3D x-ray and 3D ultrasound images. This methodology should improve a radiologists’ characterization of breast lesions which can reduce patient callbacks, misdiagnoses, additional patient dose and unnecessary biopsies. Additionally, this technique can save a radiologist time in navigating 3D image volumes and the one-to-one lesion correspondence between modalities can aid in the early detection of breast malignancies.PHDNuclear Engineering & Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/150042/1/canngree_1.pd

Advancing the Clinical Potential of Carbon Nanotube-enabled stationary 3D Mammography

Scope and purpose. 3D imaging has revolutionized medicine. Digital breast tomosynthesis (DBT), also recognized as 3D mammography, is a relatively recent example. stationary DBT (sDBT) is an experimental technology in which the single moving x-ray source of conventional DBT has been replaced by a fixed array of carbon nanotube (CNT)-enabled sources. Given the potential for a higher spatial and temporal resolution compared to commercially-available, moving-source DBT devices, it was hypothesized that sDBT would provide a valuable tool for breast imaging. As such, the purpose of this work was to explore the clinical potential of sDBT. To accomplish this purpose, three broad Aims were set forth: (1) study the challenges of scatter and artifact with sDBT, (2) assess the performance of sDBT relative to standard mammographic screening approaches, and (3) develop a synthetic mammography capability for sDBT. Throughout the work, developing image processing approaches to maximize the diagnostic value of the information presented to readers remained a specific goal. Data sources and methodology. Sitting at the intersection of development and clinical application, this work involved both basic experimentation and human study. Quantitative measures of image quality as well as reader preference and accuracy were used to assess the performance of sDBT. These studies imaged breast-mimicking phantoms, lumpectomy specimens, and human subjects on IRB-approved study protocols, often using standard 2D and conventional 3D mammography for reference. Key findings. Characterizing scatter and artifact allowed the development of new processing approaches to improve image quality. Additionally, comparing the performance of sDBT to standard breast imaging technologies helped identify opportunities for improvement through processing. This line of research culminated in the incorporation of a synthetic mammography capability into sDBT, yielding images that have the potential to improve the diagnostic value of sDBT. Implications. This work advanced the evolution of CNT-enabled sDBT toward a viable clinical tool by incorporating key image processing functionality and characterizing the performance of sDBT relative to standard breast imaging techniques. The findings confirmed the clinical utility of sDBT while also suggesting promising paths for future research and development with this unique approach to breast imaging.Doctor of Philosoph

Computer-aided diagnosis in mammography : correlation of regions in multiple standard mammographic views of the same breast.

Thesis (Ph.D.)-University of KwaZulu-Natal, 2006.Abstract available in PDF file