Catálogo de publicaciones - libros

Digital breast tomosynthesis promises solutions to many of the problems currently associated with projection mammography, including elimination of artifactual densities from the superposition of normal tissues and increasing the conspicuity of true lesions that would otherwise be masked by superimposed normal tissue. We have investigated the performance of a novel tomosynthesis system in a clinical setup. The novel system uses 48 photon counting, orientation sensitive, linear detectors which are precisely aligned with the focal spot of the x-ray source. The x-ray source and the digital detectors are scanned in a continuous motion across the patient; each linear detector collecting an image at a distinct angle. The results from an assessment of image quality and the initial clinical trial of this device are presented. Initial results provide anecdotal evidence supporting the superiority of tomosynthesis over projection mammography.

This paper presents doctoral thesis of three-dimensional digital breast tomosynthesis in the early diagnosis and detection of breast cancer. The purpose is to prove that digital breast tomosynthesis has the potential to provide clinically important information, which cannot be obtained with conventional breast imaging methods. Three-dimensional digital breast tomosynthesis seeks to (1) determine whether a mammographic finding is the result of a ‘real’ lesion or the superimposition of normal parenchyma structures, (2) detect subtle changes in breast tissue, which might otherwise be missed, and (3) to reduce the number of biopsies performed as well as verify the correct biopsy target if the procedure is needed. This study presents digital breast tomosynthesis in diagnostic mammography by comparing digital breast tomosynthesis with screenfilm and digital mammograms clinical performance, evaluates Tuned Aperture Computed Tomography capability as a 3D breast reconstruction algorithm in the limited angle tomosynthesis system, and demonstrates technical performance of a real-time amorphous-selenium flat-panel detector in full field digital breast tomosynthesis. The results indicate that breast tomosynthesis has the potential to significantly advance diagnostic mammography. Tomosynthesis of the breast will increase specificity. Study also suggests that tomosynthesis might facilitate the detection of cancers at an earlier stage and a smaller size than is possible in two-dimensional mammography [1].

Visibility of lesions in mammography are significantly reduced by the presence of anatomical, or structure, noise. Breast tomosynthesis offers the possibility of reducing this noise. We have compared the detection of low contrast and microcalcification objects with tomosynthesis imaging as a function of dose to full field digital mammography (FFDM) performed at a standard screening dose. The measurements were performed with a variety of phantoms and complex backgrounds. The complex backgrounds greatly reduced object visibility using FFDM; much less so for the tomosynthesis images. In summary, visibility of low contrast objects using tomosynthesis was superior to visibility of these objects in FFDM, even when the tomosynthesis imaging was performed at 1/4 or less of a FFDM dose. Tomosynthesis also showed superior visibility to FFDM for 160-180 micron microcalcifications at 1/2 the FFDM dose.

Tomosynthesis reconstruction that produces high-quality images is a difficult problem, due mainly to the highly incomplete data. In this work we present a motivation for the generalized filtered backprojection (GFBP) approach to tomosynthesis reconstruction. This approach is fast (since non-iterative), flexible, and results in reconstructions with an image quality that is similar or superior to reconstructions that are mathematically optimal. Results based on synthetic data and patient data are presented.

The main limitations of conventional projection mammography consist in tissue overlap and missing depth information. These deficiencies are intended to be reduced by the new technique of digital breast tomosynthesis. From a set of radiographic projections, acquired at different view angles in a linear tomosynthesis research system setup, 3-D slices of the scanned breast region are reconstructed. As the method of choice for the reconstruction we use filtered backprojection. By applying different filters with task-adapted parameters this method allows to control the image quality regarding noise, spatial resolution and artifacts. In order to investigate the basic effects of the various settings in the filtering step the method is first applied to simulated data. The impact of the selected filter functions is then demonstrated with clinical data.

Digital breast tomosynthesis (DBT) is a tomographic technique in which individual slices through the breast are reconstructed from x-ray projection images acquired over a limited angular range. In contrast-enhanced DBT (CE-DBT) functional information is observed by administration of an radiographic contrast agent. The uptake of iodine in the breast is very small and causes changes in x-ray transmission that are smaller than 5%. This presents significant technical challenges if quantitative assessment of contrast agent concentration in tissue is desired. We modeled CE-DBT acquisition by simulating x-ray spectra from 40 to 49 kV. Comparison of attenuation data of our simulated and measured spectra were found to agree well. We investigated the effect of patient motion and scatter on iodine uptake. These parameters were evaluated by means of experiments and theoretical modeling.

We are developing an analytic breast phantom that allows for quantitative comparison of reconstruction algorithms for digital breast tomosynthesis. The phantom consists of simple shapes and aims at capturing the main features of the breast. Projection data can be computed analytically. We present volumes reconstructed from the phantom data using the filtered backprojection, expectation maximization and total variation algorithms. Our results indicate that the TV algorithm achieves highest contrast for mass lesions and best in-depth resolution.

Establishing spatial correspondence between features visible in x-ray mammograms obtained at different times has great potential to aid assessment of change in the breast and facilitate its quantification. The literature contains numerous non-rigid registration algorithms developed for this purpose, but quantitative estimation of registration accuracy is limited. We describe a novel validation method which simulates plausible mammographic compressions of the breast using an MRI derived finite element model. Known 3D displacements are projected into 2D and test images simulated from these same compressed MR volumes. In this way we can generate convincing images with known 2D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for a non-rigid registration algorithm applied to mammograms simulated from three patient MR datasets.

In this paper, we present a new method for simultaneously registering mammograms and detecting abnormalities. We assume that pixels can be divided into two classes: normal tissue and abnormalities (lesions). We define the registration constraints as a mixture of two distributions which describe statistically image gray-level variations for both pixel classes. The two distributions are weighted at each pixel by the probability of abnormality presence. Using the Maximum A Posteriori, we estimate the registration transformation and the probability map of abnormality presence at the same time. We illustrate the properties of our technique with some experiments and compare it with some classical methods.

The detection of architectural distortions and abnormal structures in mammographic images can be based on the analysis of bilateral and temporal cases. This paper presents a novel method for mammographic image registration inspired by existing robust point matching approaches. This novel method is compared with other registration approaches proposed in the literature using both quantitative and qualitative evaluation based on similarity metrics and ROC analysis (ground truth provided by an expert radiologist). Initial evaluation is based on mammographic data of 64 women with malignant masses which indicates the accuracy and robustness of our method.