This paper describes an initial investigation into means for producing lower-cost CT scanners for resource limited
regions of the world. In regions such as sub-Saharan Africa, intermediate level medical facilities serving millions have
no CT machines, and lack the imaging resources necessary to determine whether certain patients would benefit from
being transferred to a hospital in a larger city for further diagnostic workup or treatment. Low-cost CT scanners would
potentially be of immense help to the healthcare system in such regions. Such scanners would not produce state-of-theart
image quality, but rather would be intended primarily for triaging purposes to determine the patients who would
benefit from transfer to larger hospitals. The lower-cost scanner investigated here consists of a fixed digital radiography
system and a rotating patient stage. This paper describes initial experiments to determine if such a configuration is
feasible. Experiments were conducted using (1) x-ray image acquisition, a physical anthropomorphic chest phantom, and
a flat-panel detector system, and (2) a computer-simulated XCAT chest phantom. Both the physical phantom and
simulated phantom produced excellent image quality reconstructions when the phantom was perfectly aligned during
acquisition, but artifacts were noted when the phantom was displaced to simulate patient motion. An algorithm was
developed to correct for motion of the phantom and demonstrated success in correcting for 5-mm motion during 360-degree acquisition of images. These experiments demonstrated feasibility for this approach, but additional work is required to determine the exact limitations produced by patient motion.
With advances in 3D in vivo imaging technology, non-invasive procedures can be used to characterize tissues to identify
tumors and monitor changes over time. Using a dedicated breast CT system with a quasi-monochromatic cone-beam x-ray source and
flat-panel digital detector, this study was performed in an effort to directly characterize different materials in vivo based on their absolute attenuation coefficients. CT acquisitions were first acquired using a multi-material rod phantom with acrylic, delrin, polyethylene, fat-equivalent, and glandular-equivalent plastic rods, and also with a human cadaver breast. Projections were collected with and without a beam stop array for scatter correction. For each projection, the 2D scatter was estimated with cubic spline interpolation of the average values behind the shadow of each beam stop overlapping the object. Scatter-corrected projections were subsequently calculated by subtracting the scatter images
containing only the region of the object from corresponding projections (consisting of primary and scatter x-rays)
without the beam stop array. Iterative OSTR was used to reconstruct the data and estimate the non-uniform attenuation
distribution. Preliminary results show that with reduced beam hardening from the x-ray beam, scatter correction further reduces the cupping artifact, improves image contrast, and yields attenuation coefficients < 8% of narrow-beam values of the known materials (range 1.2 - 7.8%). Peaks in the histogram showed clear separation between the different material attenuation coefficients. These findings indicate that minimizing beam hardening and applying scatter correction make it practical to directly characterize different tissues in vivo using absolute attenuation coefficients.
KEYWORDS: Breast, Tissues, Data modeling, Image segmentation, Mathematical modeling, 3D modeling, Image processing, Mammography, 3D image processing, Image processing algorithms and systems
We previously proposed a three-dimensional computerized breast phantom that combines empirical data with
the flexibility of mathematical models1. The goal of this project is to enhance the breast phantom to include a
more detailed anatomy than currently visible and create additional phantoms from different breast CT data.
To improve the level of detail in our existing segmentations, the breast CT data is reconstructed at a higher
resolution and additional image processing techniques are used to correct for noise and scatter in the image
data. A refined segmentation algorithm is used that incorporates more detail than previously defined. To
further enhance high-resolution detail, mathematical models, implementing branching algorithms to extend
the glandular tissue throughout the breast and to define Cooper's ligaments, are under investigation. We
perform the simulation of mammography and tomosynthesis using an analytical projection algorithm that can
be applied directly to the mathematical model of the breast without voxelization2. This method speeds up
image acquisition, reduces voxelization artifacts, and produces higher resolution images than the previously
used method. The realistic 3D computerized breast phantom will ultimately be incorporated into the 4DXCAT
phantom3-5 to be used for breast imaging research.
The goal of this work is to create a detailed three-dimensional (3D) digital breast phantom based on
empirical data and to incorporate it into the four-dimensional (4D) NCAT phantom, a computerized
model of the human anatomy widely used in imaging research. Twenty sets of high-resolution breast
CT data were used to create anatomically diverse models. The datasets were segmented using
techniques developed in our laboratory and the breast structures will be defined using a combination of
non-uniform rational b-splines (NURBS) and subdivision surfaces (SD). Imaging data from various
modalities (x-ray and nuclear medicine) were simulated to demonstrate the utility of the new breast
phantoms. As a proof of concept, a simple compression technique was used to deform the breast models
while maintaining a constant volume to simulate modalities (mammography and tomosynthesis) that
involve compression. Initial studies using one CT dataset indicate that the simulated breast phantom is
capable of providing a realistic and flexible representation of breast tissue and can be used with
different acquisition methods to test varying imaging parameters such as dose, resolution, and patient
motion. The final model will have a more accurate depiction of the internal breast structures and will
be scaleable in terms of size and density. Also, more realistic finite-element techniques will be used to
simulate compression. With the ability to simulate realistic, predictive patient imaging data, we believe
the phantom will provide a vital tool to investigate current and emerging breast imaging methods and
techniques.
Digital tomosynthesis is an imaging technique that reconstructs tomographic planes in an object from a set of projection
images taken over a fixed angle1. Preliminary results show that this technique increases the detectability of lung
nodules2. Current settings acquire images with approximately the same exposure as a screen-film lateral. However, due
to the increased detectability of lung nodules from the removal of overlying structures, patient dose may be reduced
while still maintaining increased sensitivity and specificity over conventional chest radiographs. This study describes a
simulation method that provides realistic reduced dose images by adding noise to digital chest tomosynthesis images in
order to simulate lower exposure settings for the purpose of dose optimization. Tomosynthesis projections of human
subjects were taken at dose levels which were specified based on either patient thickness or a photo-timed digital chest
radiograph acquired prior to tomosynthesis acquisition. For the purposes of this study, subtle nodules of varying size
were simulated in the image for demonstration purposes before the noise simulation in order to have a known truth for
nodule location and to evaluate the effect of additive noise on tumor detection. Noise was subsequently added in order to
simulate 3/4, 1/2, and 1/4 of the original exposure in each projection. The projections were then processed with the MITS
algorithm to produce slice images. This method will be applied to a study of dose reduction in the future using human
subject cases.
Digital tomosynthesis is an imaging technique which reconstructs tomographic planes in an object from a set of projection images taken over a fixed angle (1). Results from our initial pilot study show that tomosynthesis increases the detectability of lung nodules; while only 50% of CT confirmed nodules were found on typical chest radiographs, 81% were found on tomosynthesis image sets (2). Temporal subtraction is a method which takes two sequential images and subtracts one from another, emphasizing the appearance of interval change (3-6). As an addition to conventional chest radiography, it has been shown in several studies to significantly increase observer performance in detecting newly developed abnormalities (7-10). Thus the combination of temporal subtraction and tomosynthesis may yield improved sensitivity of detection over either method alone. For this preliminary evaluation into the combination of these techniques, images were taken of an anthropomorphic chest phantom in different orientations and subtle lung nodules were simulated in order to emulate temporal discrepancies in anatomy. An automated method of segmentation, registration, and image warping was employed to align corresponding lung regions of each image set. The visibility of temporal change of simulated nodules was more apparent in the subtraction image. By our subjective analysis, tomosynthesis substantially improved the visibility of nodules relative to conventional chest radiography; and tomosynthesis augmented by temporal subtraction even further enhanced the conspicuity of difficultly placed subtle nodules.
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