Mammography

Summary

Mammography (also called mastography) is the process of using low-energy X-rays (usually around 30 kVp) to examine the human breast for diagnosis and screening. The goal of mammography is the early detection of breast cancer, typically through detection of characteristic masses or microcalcifications. As with all X-rays, mammograms use doses of ionizing radiation to create images. These images are then analyzed for abnormal findings. It is usual to employ lower-energy X-rays, typically Mo (K-shell X-ray energies of 17.5 and 19.6 keV) and Rh (20.2 and 22.7 keV) than those used for radiography of bones. Mammography may be 2D or 3D (tomosynthesis), depending on the available equipment and/or purpose of the examination. Ultrasound, ductography, positron emission mammography (PEM), and magnetic resonance imaging (MRI) are adjuncts to mammography. Ultrasound is typically used for further evaluation of masses found on mammography or palpable masses that may or may not be seen on mammograms. Ductograms are still used in some institutions for evaluation of bloody nipple discharge when the mammogram is non-diagnostic. MRI can be useful for the screening of high risk patients, for further evaluation of questionable findings or symptoms, as well as for pre-surgical evaluation of patients with known breast cancer, in order to detect additional lesions that might change the surgical approach (for example, from breast-conserving lumpectomy to mastectomy). For the average woman, the U.S. Preventive Services Task Force recommends (2016) mammography every two years between the ages of 50 and 74, concluding that "the benefit of screening mammography outweighs the harms by at least a moderate amount from age 50 to 74 years and is greatest for women in their 60s". The American College of Radiology and American Cancer Society recommend yearly screening mammography starting at age 40. The Canadian Task Force on Preventive Health Care (2012) and the European Cancer Observatory (2011) recommend mammography every 2 to 3 years between ages 50 and 69.

Official source

https://en.wikipedia.org/wiki/Mammography

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Joint absorption and phase retrieval in grating-based x-ray radiography

Michaël Unser, Emrah Bostan, Masih Nilchian, Marco Stampanoni, Mohammad Reza Nilchiyan

Given the raw absorption and differential phase-contrast images obtained from a grating-based x-ray radiography, we formulate the joint denoising of the absorption image and retrieval of the non-differential phase image as a regularized inverse problem. The choice of the regularizer is driven by the existing correlation between absorption and differential phase; it leads to the linear combination of a total-variation norm with a total-variation nuclear norm. We then develop the corresponding algorithm to efficiently solve this inverse problem. We evaluate our method using different experiments, including mammography data. We conclude that our method provides useful information in the context of mammography screening and diagnosis.

Optical Society of America2016

Breast cancer is the most common, and the number one cause of death by cancer among women. However, when it is sufficiently early detected, heavy treatments can be avoided, and morbidity and mortality can be reduced. This is the reason why randomized clinical trials were started in the 60s, followed in the last decades by screening mammography national programs. The process of breast cancer detection in mammography is complex. Its understanding offers numerous challenges to radiologists and medical physicists. One way to apprehend it is to model this process step-by-step by performing psychophysical experiments with anatomical or synthetic images. In this approach, the images information content is controlled. Since the first experiments with synthetic images created with white noise and simple geometric signals, technical and computational improvements allowed to get ever closer to clinical realism for studying the mechanism of perception of a signal on a radiological image. The present work extends the list of tools that have been used until now in psychophysical experiments in mammography. It proposes a detailed statistical analysis of anatomical images, from which algorithms for breast density classification and realistic breast texture synthesis are developed. In a second phase, psychophysical experiments with simple signals and benign or malignant masses combined with anatomical or synthetic backgrounds are presented. The performance of human observers is analyzed as a function of parameters such as background type, signal type, or uncertainty about the size or the shape of the signal. These results are compared to that of existing or adapted models from the literature, and the different models are evaluated in their ability to predict the performance of human observers for the detection of lesions in such conditions. Each step of this project focused on the objective and reproducible aspect of the image evaluation or of the observer performance. Controlled yet realistic conditions ensure the robustness of the results, as well as their clinical adaptability. Among the main results, synthetic mammographic texture images have been generated and validated. They provide a virtually unlimited database of images with demonstrated visual and statistical realism. Concerning the analysis of the human observers' performance, this work shows that they are sensitive to uncertainty about the signal size but not about its shape, that they use similar detection strategies with real and synthetic images, and that they are mainly sensitive to the anatomical fluctuations in the immediate area around the signal location. These effects, as well as the performance level of human observers for the detection tasks under consideration, could be reproduced by models taking into account human visual system characteristics. The conclusions of this work will be able to guide future studies in the field of detection tasks in mammography or tomosynthesis. This 3D breast imaging technique presents, like mammography, numerous challenges in order to understand and to objectively characterize its clinical potential. Studies with model observers specifically validated for detection tasks in medical imaging provide an excellent alternative in terms of time and costs for answering these questions.

EPFL2009