Digital Breast Phantoms Research Articles

TH-A-217BCD-09: Volumetric Breast Density Determination Based On X-Ray Phase-Shifts

Purpose: High breast density is one of the biggest risk factors for breast cancer. Identifying patient having persistent high breast density is important for breast cancer screening and prevention. In this work we propose for the first time an x‐ray phase‐shift based method for measuring the volumetric breast density. Methods: When x‐ray traverses the breast, x‐ray not only gets its intensity attenuated, but also gets its phase shifted. Studying the x‐ray phase‐shifts generated by the breast tissues, we derived a formula for determining the volumetric breast density from the breast phase map. We show that the volumetric breast density can be reconstructed by retrieving the breast phase map from just a single phase‐sensitive projection of the breast. To validate this phase‐shifts method for measuring the volumetric breast density, we performed computer simulations with an anthropomorphic digital breast phantom. In the simulation we simply set the ray path‐lengths inside a breast to its compressed thickness as a practical approximation. Results: Using this phase‐shifts based method, we reconstructed the phantom's volumetric breast density that is differs from the true breast density of the phantom by only 0.22%, achieving a good accuracy for the breast density determination. With the existence of projection noise, the reconstructed volumetric breast density differs from the true breast density of the phantom by only 1.42% or less as long as the breast signal‐noise ratios being larger than 34. Conclusions: We proposed an x‐ray phase‐shift based method for measuring the volumetric breast density. The simulation results validated this novel method. This research was supported in part by the DoD Breast Cancer Research Program grant W81XWH‐08‐1‐0613 and the NIH grant R01CA142587.

A statistically defined anthropomorphic software breast phantom

Digital anthropomorphic breast phantoms have emerged in the past decade because of recent advances in 3D breast x-ray imaging techniques. Computer phantoms in the literature have incorporated power-law noise to represent glandular tissue and branching structures to represent linear components such as ducts. When power-law noise is added to those phantoms in one piece, the simulated fibroglandular tissue is distributed randomly throughout the breast, resulting in dense tissue placement that may not be observed in a real breast. The authors describe a method for enhancing an existing digital anthropomorphic breast phantom by adding binarized power-law noise to a limited area of the breast. Phantoms with (0.5 mm)(3) voxel size were generated using software developed by Bakic et al. Between 0% and 40% of adipose compartments in each phantom were replaced with binarized power-law noise (β = 3.0) ranging from 0.1 to 0.6 volumetric glandular fraction. The phantoms were compressed to 7.5 cm thickness, then blurred using a 3 × 3 boxcar kernel and up-sampled to (0.1 mm)(3) voxel size using trilinear interpolation. Following interpolation, the phantoms were adjusted for volumetric glandular fraction using global thresholding. Monoenergetic phantom projections were created, including quantum noise and simulated detector blur. Texture was quantified in the simulated projections using power-spectrum analysis to estimate the power-law exponent β from 25.6 × 25.6 mm(2) regions of interest. Phantoms were generated with total volumetric glandular fraction ranging from 3% to 24%. Values for β (averaged per projection view) were found to be between 2.67 and 3.73. Thus, the range of textures of the simulated breasts covers the textures observed in clinical images. Using these new techniques, digital anthropomorphic breast phantoms can be generated with a variety of glandular fractions and patterns. β values for this new phantom are comparable with published values for breast tissue in x-ray projection modalities. The combination of conspicuous linear structures and binarized power-law noise added to a limited area of the phantom qualitatively improves its realism.

SU‐E‐I‐149: Digital Breast Tomosynthesis Using 2D Source Scanning Patterns: A Simulation Study

Purpose: Digital tomosynthesis (DTS) imaging with 2D scanning patterns may help reduce the artifacts by more effectively blurring the off‐fulcrum objects. In this study, the effects of various source scanning patterns on the reconstructed images are studied via simulation. Methods: The X‐ray source was assumed to be movable following various scanning patterns. The source‐to‐iso‐center and source‐to‐image distances were assumed to be 500 mm and 600 mm, respectively. Projection images were computed using an improved distance‐driven algorithm. The objects studied included a Shepp‐ Logan phantom and a digital breast phantom constructed from segmented cone beam CT images. The X‐ray absorption coefficients were taken of mono‐energetic X‐ray at 19 keV. The x‐ray source was scanned with 1D (1D DTS) and 2D (2D DTS) patterns. The objects were reconstructed using the iterative expectation‐maximization algorithm. The root‐mean‐squared‐ deviation (RSMD) was calculated to indicate the deviation in terms of CT numbers of the reconstructed image to the original phantom. Results: The RMSD of the reconstructed Shepp‐Logan phantom was 243.3 for 1D DTS and 104.7 for 2D DTS. Visual inspection revealed reduced artifacts and better CT number accuracy with 2D DTS than 1D DTS. For the digital breast phantom, the accuracy of the reconstructed tissue structures was assessed using the CT number uniformity in the dense tissue and adipose tissue regions. The RMSD of the digital breast phantom was 113.1 for 1D DTS and 50.0 for 2D DTS. The comparison showed that the 2D DTS resulted in better accuracy in CT number uniformity. Conclusions: Our results demonstrated that the use of 2D source scanning patterns has the potential advantage of more effectively blurring objects in off‐fulcrum planes, thus reducing the artifacts and resulting in more accurate image reconstruction.This work was supported in part by research grants: CA104759 and CA124585, EB000117 from NIBIB, CA138502A1, and a subcontract from NIST‐ATPs.

SU-GG-I-34: Image-Based Material Decomposition with Energy Resolving Computed Tomography

Purpose: To decompose four materials in the image domain using an energy resolving detector.Methods and Materials: A post‐mortem breast specimen was scanned with cone‐beam CT. One slice of the CTimage was segmented into glandular and fat components and used as a digital phantom for the simulations. Calcium (100, 200, and 300 mg/ml) and iodine (4, 6, and 8 mg/ml) contrast elements were embedded in the glandular region. The x‐ray transport process and CT simulations were performed with a 3‐mm thick CZT detector and an 80 kVp spectrum. The detector resolved the energy spectrum into 5 energy bins (1–30, 31–37, 38–45, 46–56, and 57–80 keV). A four‐material (calcium, iodine, gland and fat) least square fitting procedure was used for decomposition in the image domain. The decomposition technique assumes a dominant material for each voxel. Calibrations were performed with a phantom of 30–70 glandular‐breast ratios with calcium and iodine contrast elements of concentrations of 0.03 to 0.53 in 0.5 mg/ml increments and 0.001 to 0.021 in 0.002 mg/ml increments, respectively. After the calibrations were made, different concentrations of calcium and iodine were embedded in the digital breast phantom and were decomposed. Results: The measured concentrations of calcium and iodine were closely related to their known concentrations. Measured calcium (CM) was related to known calcium (CK) as CM = 1.02CK+0.003 mg/ml. Measured iodine (IM) was related to known iodine (IK) as IM = 0.98IK − 0.00001 mg/ml. Conclusion: Simulations showed that the four‐material decomposition technique could faithfully decompose calcium and iodine on breast computed tomography.

Refraction corrected transmission ultrasound computed tomography for application in breast imaging

We present an iterative framework for CT reconstruction from transmission ultrasound data which accurately and efficiently models the strong refraction effects that occur in our target application: Imaging the female breast. Our refractive ray tracing framework has its foundation in the fast marching method (FNMM) and it allows an accurate as well as efficient modeling of curved rays. We also describe a novel regularization scheme that yields further significant reconstruction quality improvements. A final contribution is the development of a realistic anthropomorphic digital breast phantom based on the NIH Visible Female data set. Our system is able to resolve very fine details even in the presence of significant noise, and it reconstructs both sound speed and attenuation data. Excellent correspondence with a traditional, but significantly more computationally expensive wave equation solver is achieved. Apart from the accurate modeling of curved rays, decisive factors have also been our regularization scheme and the high-quality interpolation filter we have used. An added benefit of our framework is that it accelerates well on GPUs where we have shown that clinical 3D reconstruction speeds on the order of minutes are possible.

Computed tomography with energy-resolved detection: a feasibility study

The feasibility of computed tomography (CT) with energy-resolved x-ray detection has been investigated. A breast CT design with multi slit multi slice (MSMS) data acquisition was used for this study. The MSMS CT includes linear arrays of photon counting detectors separated by gaps. This CT configuration allows for efficient scatter rejection and 3D data acquisition. The energy-resolved CT images were simulated using a digital breast phantom and the design parameters of the proposed MSMS CT. The phantom had 14 cm diameter and 50/50 adipose/glandular composition, and included carcinoma, adipose, blood, iodine and CaCO3 as contrast elements. The x-ray technique was 90 kVp tube voltage with 660 mR skin exposure. Photon counting, charge (energy) integrating and photon energy weighting CT images were generated. The contrast-to-noise (CNR) improvement with photon energy weighting was quantified. The dual energy subtracted images of CaCO3 and iodine were generated using a single CT scan at a fixed x-ray tube voltage. The x-ray spectrum was electronically split into low- and high-energy parts by a photon counting detector. The CNR of the energy weighting CT images of carcinoma, blood, adipose, iodine, and CaCO3 was higher by a factor of 1.16, 1.20, 1.21, 1.36 and 1.35, respectively, as compared to CT with a conventional charge (energy) integrating detector. Photon energy weighting was applied to CT projections prior to dual energy subtraction and reconstruction. Photon energy weighting improved the CNR in dual energy subtracted CT images of CaCO3 and iodine by a factor of 1.35 and 1.33, respectively. The combination of CNR improvements due to scatter rejection and energy weighting was in the range of 1.71–2 depending on the type of the contrast element. The tilted angle CZT detector was considered as the detector of choice. Experiments were performed to test the effect of the tilting angle on the energy spectrum. Using the CZT detector with 20° tilting angle decreased the tailing of the measured x-ray spectrum as compared to a conventional CZT detector. It was concluded that the energy-resolved MSMS CT with tilted angle CZT detector is potentially feasible and could provide a unique combination of photon counting, energy weighting, scatter rejection and single kVp dual energy subtraction CT imaging.