We are developing a prototype system for simultaneous digital breast tomosynthesis (DBT) and mechanical imaging (MI). MI maps the local pressure distribution during clinical exams, to distinguish breast abnormalities from the normal tissue. Both DBT alone, and MI when combined with digital mammography, have demonstrated the ability to reduce false positives; however, the benefit of combining DBT with MI has not been investigated. A practical limitation in simultaneous DBT and MI is the presence of the MI sensor in DBT images. Metallic elements of the sensor generate noticeable artifacts, which may interfere with clinical analysis. Previously, we shown that the sensor artifacts can be reduced by flat fielding, which combines projections of the sensor acquired with and without the breast. In this paper we evaluate the flat fielding by assessing artifact reduction and visibility of breast abnormalities. Images of a physical anthropomorphic breast phantom were acquired using a clinical wide-angle DBT system. Visual evaluation was performed by experienced medical physicists. Image quality descriptors were calculated in images with and without flat fielding. To evaluate the visibility of abnormalities we estimated the full width at half maximum (FWHM) for calcifications modeled in the phantom. Our preliminary results suggest a substantial reduction of artifacts by flat fielding (on average 83%). Few noticeable artifacts remain near the breast edge, in the reconstructed image with the sensor in focus. We observed a 17% reduction in the FWHM. Future work would include a detailed assessment, and method optimization using virtual trials as a design aid.
The objective of our study was to evaluate the feasibility of using silver sulfide nanoparticles (Ag2S-NP) as a contrast agent for photon-counting mammography. The efficacies of Ag2S-NP and iodine contrast were evaluated using a contrastembedded gradient phantom. The phantom was constructed using tissue-equivalent materials and varied continuously in composition from 100% glandular tissue to 100% adipose tissue. Each contrast agent was prepared at eight different concentrations: 1, 2, 5, 10, 15, 20, 25, and 30 mg/mL. Tubes of contrast agent were inserted into holes bored through the phantom in the direction of varying glandularity. Various images of the phantom were acquired by altering the acquisition parameters (kV, mAs, and high bin fraction). A range of beam energies from 26 kV to 40 kV was tested in this study. Our results demonstrate that for a given contrast agent, the contrast-to-noise ratio (CNR) is linearly proportional to concentration, and its magnitude is dependent on the kV of the spectrum. At mammographic energies, the Ag2S-NP contrast increases with increasing kV and increasing solution concentration. Comparatively, the iodine signal becomes detectable only when the kV of the image is above iodine’s K-edge (33.2 keV). This indicates that the optimal energy for imaging iodine may exceed the clinical mammographic energy range. In summary, we have demonstrated the feasibility of using Ag2S-NP as a contrast agent for breast imaging. Preliminary results from spectral photon-counting mammography indicate that Ag2S-NP contrast has a significantly higher signal than iodine, especially when imaging at lower energies.
The objective of our study is to optimize the acquisition parameters for imaging Ag2S nanoparticles using contrast-enhanced digital mammography (CE-DM) by varying parameters such as kV, mAs, and filtration. The efficacies of three different contrast materials (Ag2S nanoparticles, silver nanoparticles, and iodine) were assessed using a contrast-embedded gradient phantom. The phantom was constructed using tissue-equivalent materials and varied continuously in composition from 100% glandular tissue to 100% adipose tissue. Each contrast agent was prepared at six different concentrations (1, 2, 5, 10, 15, and 25 mg/mL). Holes were bored through the phantom in the direction of varying glandularity, and tubes of contrast agents were inserted into the holes. Phantoms were imaged at four different energies (26 kV, 32 kV, 45 kV, and 49 kV); 5 energy pairs were considered in this study. Our results demonstrate that for a given contrast agent, the contrast-to-noise ratio is linearly proportional to concentration, and its magnitude is dependent on the energy of the low-energy (LE) image. In our study, it was shown that the LE images at 26 kV are better suited for imaging silver-based nanoparticles, and the LE images at 32 kV are better suited for imaging iodine contrast. Thus, the energy of the LE image should be chosen so that it is as close as possible to the k-edge of the contrast material. Preliminary results from CE-DM imaging indicate that silver contrast has a significantly higher signal than iodine contrast when imaging at lower energies, thus demonstrating the feasibility of using silver-based nanoparticles in breast imaging.
Dual-energy contrast-enhanced digital mammography (DE CE-DM) uses an iodinated contrast agent to image the
perfusion and vasculature of the breast. DE images are obtained by a weighted logarithmic subtraction of the high-energy
(HE) and low-energy (LE) image pairs. We hypothesized that the optimal DE subtraction weighting factor is thickness-dependent,
and developed a method for determining breast tissue composition and thickness in DE CE-DM. Phantoms
were constructed using uniform blocks of 100% glandular-equivalent and 100% adipose-equivalent material. The
thickness of the phantoms ranged from 3 to 8 cm, in 1 cm increments. For a given thickness, the glandular-adipose
composition of the phantom was varied using different combinations of blocks. The logarithmic LE and logarithmic HE
signal intensities were measured; they decrease linearly with increasing glandularity for a given thickness. The signals
decrease with increasing phantom thickness and the x-ray signal decreases linearly with thickness for a given glandularity.
As the thickness increases, the attenuation difference per additional glandular block decreases, indicating beam hardening.
From the calibration mapping, we have demonstrated that we can predict percent glandular tissue and thickness when
given two distinct signal intensities. Our results facilitate the subtraction of tissue at the boundaries of the breast, and aid
in discriminating between contrast agent uptake in glandular tissue and subtraction artifacts.
Dual-energy contrast-enhanced digital breast tomosynthesis (DE CE-DBT) uses an iodinated contrast agent to image the three-dimensional breast vasculature. The University of Pennsylvania has an ongoing DE CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 post-contrast). DE images are obtained by a weighted logarithmic subtraction of the high-energy (HE) and low-energy (LE) image pairs. Temporal subtraction of the post-contrast DE images from the pre-contrast DE image is performed to analyze iodine uptake. Our previous work investigated image registration methods to correct for patient motion, enhancing the evaluation of vascular kinetics. In this project we investigate a segmentation algorithm which identifies blood vessels in the breast from our temporal DE subtraction images. Anisotropic diffusion filtering, Gabor filtering, and morphological filtering are used for the enhancement of vessel features. Vessel labeling methods are then used to distinguish vessel and background features successfully. Statistical and clinical evaluations of segmentation accuracy in DE-CBT images are ongoing.
Routine performance of basic test procedures and dose measurements are essential for assuring high quality of mammograms. International guidelines recommend that breast care providers ascertain that mammography systems produce a constant high quality image, using as low a radiation dose as is reasonably achievable. The main purpose of this research is to develop a framework to monitor radiation dose and image quality in a mixed breast screening and diagnostic imaging environment using an automated tracking system. This study presents a module of this framework, consisting of a computerized system to measure the image quality of the American College of Radiology mammography accreditation phantom. The methods developed combine correlation approaches, matched filters, and data mining techniques. These methods have been used to analyze radiological images of the accreditation phantom. The classification of structures of interest is based upon reports produced by four trained readers. As previously reported, human observers demonstrate great variation in their analysis due to the subjectivity of human visual inspection. The software tool was trained with three sets of 60 phantom images in order to generate decision trees using the software WEKA (Waikato Environment for Knowledge Analysis). When tested with 240 images during the classification step, the tool correctly classified 88%, 99%, and 98%, of fibers, speck groups and masses, respectively. The variation between the computer classification and human reading was comparable to the variation between human readers. This computerized system not only automates the quality control procedure in mammography, but also decreases the subjectivity in the expert evaluation of the phantom images.
KEYWORDS: Breast, Image registration, Image segmentation, Blood vessels, Digital breast tomosynthesis, Tissues, Iodine, 3D image processing, Breast cancer, Visualization
Contrast-enhanced digital breast tomosynthesis (CE-DBT) uses an iodinated contrast agent to image the threedimensional breast vasculature. The University of Pennsylvania is conducting a CE-DBT clinical study in patients with known breast cancers. The breast is compressed continuously and imaged at four time points (1 pre-contrast; 3 postcontrast). A hybrid subtraction scheme is proposed. First, dual-energy (DE) images are obtained by a weighted logarithmic subtraction of the high-energy and low-energy image pairs. Then, post-contrast DE images are subtracted from the pre-contrast DE image. This hybrid temporal subtraction of DE images is performed to analyze iodine uptake, but suffers from motion artifacts. Employing image registration further helps to correct for motion, enhancing the evaluation of vascular kinetics. Registration using ANTS (Advanced Normalization Tools) is performed in an iterative manner. Mutual information optimization first corrects large-scale motions. Normalized cross-correlation optimization then iteratively corrects fine-scale misalignment. Two methods have been evaluated: a 2D method using a slice-by-slice approach, and a 3D method using a volumetric approach to account for out-of-plane breast motion. Our results demonstrate that iterative registration qualitatively improves with each iteration (five iterations total). Motion artifacts near the edge of the breast are corrected effectively and structures within the breast (e.g. blood vessels, surgical clip) are better visualized. Statistical and clinical evaluations of registration accuracy in the CE-DBT images are ongoing.
KEYWORDS: Iodine, Breast, Tomography, Digital breast tomosynthesis, Signal detection, Interference (communication), Signal to noise ratio, X-ray imaging, Visualization, Tissues
Contrast-enhanced (CE) digital breast tomosynthesis (DBT) provides a technique to increase the contrast of radiographic
imaging agents by suppressing soft-tissue signal variation. By reducing the effect of the soft-tissue anatomical noise, it is
then possible to quantify the signal from an iodinated contrast agent. The combination of dual-energy and tomographic
acquisitions allows for both the accurate quantification and localization of an iodinated lesion. Here, we present our
findings demonstrating the relationship that exists between the signal difference to noise ratio (SDNR) and reader
detectability of iodinated lesions in a physical anthropomorphic phantom. The observer study was conducted using the
ViewDEX software platform with a total of nine readers. The readers were asked to score each of the iodinated lesions
on a scale from 1 (entire boundary and area are visible) to 5 (not visible). Both SDNR and lesion detectability were
found to improve as the concentration of the iodine increases, and the thickness of the phantom decreases. Lesion
detectability was better in the tomographic slice that best matches the focal plane of the imaged object. However, SDNR
does not significantly change with focal plane. Our results demonstrated that observer lesion detectability correlated well
with SDNR. Lesions whose SDNR fell below 1 were difficult to distinguish from the background and were in general
not visible. Lesions that were rated entirely visible corresponded to those with SDNR values above 3. Lesions with
intermediate SDNR values were visualized but not confidently from the surrounding background. These threshold SDNR
values can be used to optimize the imaging parameters in CE-DBT.
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