Multi-modal ultrasound imaging for breast cancer detection

This work describes preliminary results of a two-modality imaging system aimed at the early detection of breast cancer. The first technique is based on compounding conventional echographic images taken at regular angular intervals around the imaged breast. The other modality obtains tomographic images of propagation velocity using the same circular geometry. For this study, a low-cost prototype has been built. It is based on a pair of opposed 128-element, 3.2 MHz array transducers that are mechanically moved around tissue mimicking phantoms. Compounded images around 360 degrees provide improved resolution, clutter reduction, artifact suppression and reinforce the visualization of internal structures. However, refraction at the skin interface must be corrected for an accurate image compounding process. This is achieved by estimation of the interface geometry followed by computing the internal ray paths. On the other hand, sound velocity tomographic images from time of flight projections have been also obtained. Two reconstruction methods, Filtered Back Projection (FBP) and 2D Ordered Subset Expectation Maximization (2D OSEM), were used as a first attempt towards tomographic reconstruction. These methods yield useable images in short computational times that can be considered as initial estimates in subsequent more complex methods of ultrasound image reconstruction. These images may be effective to differentiate malignant and benign masses and are very promising for breast cancer screening. (C) 2015 The Authors. Published by Elsevier B.V.

Simulador radiográfico antropomórfico de mama humana

Mammography is a diagnostic imaging method in which interpretation depends on knowledge of radiological aspects as well as the clinical exam and pathophysiology of breast diseases. In this work a mammography phantom was developed to be used for training in the operation of mammographic x-ray equipment, image quality evaluation, self-examination and clinical examination of palpation. Polyurethane was used for the production of the phantoms for its physical and chemical properties and because it is one of the components normally used in prostheses. According to the range of flexibility of the polyurethane, it was possible to simulate breasts with higher or lower amount of adipose tissue. Pathologies such as areolar necrosis and tissue rejection due to surgery reconstruction after partial mastectomy were also simulated. Calcifications and nodules were simulated using the following materials: polyethylene, poly (methyl methacrylate), polyamide, polyurethane and poly (dimethyl silicone). Among these, polyethylene was able to simulate characteristics of calcification as well as breast nodules. The results from mammographic techniques used in this paper for the evaluation of the phantoms are in agreement with data found in the literature. The image analyses of four phantoms indicated significant similarities with the human skin texture and the female breast parenchyma. It was possible to detect in the radiographic images produced regions of high and low radiographic optical density, which are characteristic of breasts with regions of different amount of adipose tissue. The stiffnesses of breast phantoms were adjusted according to the formulation of the polyurethane which enabled the production of phantoms with distinct radiographic features and texture similar to human female breast parenchyma. Clinical palpation exam of the phantoms developed in this work indicated characteristics similar to human breast in skin texture, areolar region and parenchyma

Fractal descriptors for quantifying brain complexity in MRI

Magnetic Resonance Imaging (MRI) is the in vivo technique most commonly employed to characterize changes in brain structures. The conventional MRI-derived morphological indices are able to capture only partial aspects of brain structural complexity. Fractal geometry and its most popular index, the fractal dimension (FD), can characterize self-similar structures including grey matter (GM) and white matter (WM). Previous literature shows the need for a definition of the so-called fractal scaling window, within which each structure manifests self-similarity. This justifies the existence of fractal properties and confirms Mandelbrot’s assertion that "fractals are not a panacea; they are not everywhere". In this work, we propose a new approach to automatically determine the fractal scaling window, computing two new fractal descriptors, i.e., the minimal and maximal fractal scales (mfs and Mfs). Our method was implemented in a software package, validated on phantoms and applied on large datasets of structural MR images. We demonstrated that the FD is a useful marker of morphological complexity changes that occurred during brain development and aging and, using ultra-high magnetic field (7T) examinations, we showed that the cerebral GM has fractal properties also below the spatial scale of 1 mm. We applied our methodology in two neurological diseases. We observed the reduction of the brain structural complexity in SCA2 patients and, using a machine learning approach, proved that the cerebral WM FD is a consistent feature in predicting cognitive decline in patients with small vessel disease and mild cognitive impairment. Finally, we showed that the FD of the WM skeletons derived from diffusion MRI provides complementary information to those obtained from the FD of the WM general structure in T1-weighted images. In conclusion, the fractal descriptors of structural brain complexity are candidate biomarkers to detect subtle morphological changes during development, aging and in neurological diseases.