Digital breast tomosynthesis (DBT) is a new volumetric breast cancer screening modality. It is based on the principles of
computed tomography (CT) and shows promise for improving sensitivity and specificity compared to digital
mammography, which is the current standard protocol. A barrier to critically evaluating any new modality, including
DBT, is the lack of patient data from which statistically significant conclusions can be drawn; such studies require large
numbers of images from both diseased and healthy patients. Since the number of detected lesions is low in relation to the
entire breast cancer screening population, there is a particular need to acquire or otherwise create diseased patient data.
To meet this challenge, we propose a method to insert 3D lesions in the DBT images of healthy patients, such that the
resulting images appear qualitatively faithful to the modality and could be used in future clinical trials or virtual clinical
trials (VCTs). The method facilitates direct control of lesion placement and lesion-to-background contrast and is
agnostic to the DBT reconstruction algorithm employed.
We present performance and quality analysis of GPU accelerated FDK filtered backprojection for cone beam computed tomography (CBCT) reconstruction. Our implementation of the FDK CT reconstruction algorithm does not compromise fidelity at any stage and yields a result that is within 1 HU of a reference C++ implementation. Our streaming implementation is able to perform reconstruction as the images are acquired; it addresses low latency as well as fast throughput, which are key considerations for a "real-time" design. Further, it is scaleable to multiple GPUs for increased performance. The implementation does not place any constraints on image acquisition; it works effectively for arbitrary angular coverage with arbitrary angular spacing. As such, this GPU accelerated CT reconstruction solution may easily be used with scanners that are already deployed. We are able to reconstruct a 512 x 512 x 340 volume from 625 projections, each sized 1024 x 768, in less than 50 seconds. The quoted 50 second timing encompasses the entire reconstruction using bilinear interpolation and includes filtering on the CPU, uploading the filtered projections to the GPU, and also downloading the reconstructed volume from GPU memory to system RAM.
Many medical imaging techniques use mathematical morphology (MM), with discs and spheres being the structuring elements (SE) of choice. Given the non-linear nature of the underlying comparison operations (min, max, AND, OR), MM optimization can be challenging. Many efficient methods have been proposed for various types of SE based on the ability to decompose the SE by way of separability or homotopy. Usually, these methods are only able to approximate disc and sphere SE rather than accomplish MM for the exact SE obtained by discretization of such shapes. We present a method that for efficiently computing MM for binary and gray scale image volumes using digitally convex and X-Y-Z symmetric flat SE, which includes discs and spheres. The computational cost is a function of the diameter of the SE and rather than its volume. Additional memory overhead, if any, is modest. We are able to compute MM on real medical image volumes with greatly reduced running times with increasing gains for larger SE. Our method is also robust to scale: it is applicable to ellipse and ellipsoid SE which may result from discretizing a disc or sphere on an anisotropic grid. In addition, it is easy to implement and can make use of existing image comparison operations. We present performance results on large medical chest CT datasets.
Given the nature of pulmonary embolism (PE), timely and accurate diagnosis is critical. Contrast enhanced high-resolution CT images allow physicians to accurately identify segmental and sub-segmental emboli. However, it is also important to assess the effect of such emboli on the blood flow in the lungs. Expanding upon previous research, we propose a method for 3D visualization of lung perfusion. The proposed method allows users to examine perfusion throughout the entire lung volume at a single glance, with areas of diminished perfusion highlighted so that they are visible independent of the viewing location. This may be particularly valuable for better accuracy in assessing the extent of hemodynamic alterations resulting from pulmonary emboli. The method also facilitates user interaction and may help identify small peripheral sub-segmental emboli otherwise overlooked. 19 patients referred for possible PE were evaluated by CT following the administration of IV contrast media. An experienced thoracic radiologist assessed the 19 datasets with 17 diagnosed as being positive for PE with multiple emboli. Since anomalies in lung perfusion due to PE can alter the distribution of parenchymal densities, we analyzed features collected from histograms of the computed perfusion maps and demonstrate their potential usefulness as a preliminary test to suggest the presence of PE. These histogram features also offer the possibility of distinguishing distinct patterns associated with chronic PE and may even be useful for further characterization of changes in perfusion or overall density resulting from associated conditions such as pneumonia or diffuse lung disease.