A realistic 3D anthropomorphic software model of microcalcifications may serve as a useful tool to assess the performance of breast imaging applications through simulations. We present a method allowing to simulate visually realistic microcalcifications with large morphological variability. Principal component analysis (PCA) was used to analyze the shape of 281 biopsied microcalcifications imaged with a micro-CT. The PCA analysis requires the same number of shape components for each input microcalcification. Therefore, the voxel-based microcalcifications were converted to a surface mesh with same number of vertices using a marching cube algorithm. The vertices were registered using an iterative closest point algorithm and a simulated annealing algorithm. To evaluate the approach, input microcalcifications were reconstructed by progressively adding principal components. Input and reconstructed microcalcifications were visually and quantitatively compared. New microcalcifications were simulated using randomly sampled principal components determined from the PCA applied to the input microcalcifications, and their realism was appreciated through visual assessment. Preliminary results have shown that input microcalcifications can be reconstructed with high visual fidelity when using 62 principal components, representing 99.5% variance. For that condition, the average L2 norm and dice coefficient were respectively 10.5 μm and 0.93. Newly generated microcalcifications with 62 principal components were found to be visually similar, while not identical, to input microcalcifications. The proposed PCA model of microcalcification shapes allows to successfully reconstruct input microcalcifications and to generate new visually realistic microcalcifications with various morphologies.
Description of purpose Contrast-enhanced spectral mammography can be used to guide needle biopsies. However, in vertical approach the compressed breast is deformed generating a so-called bump in the paddle aperture, which may interfere with the visibility of contrast-uptakes. Local thickness estimation would provide an enhanced image quality of the recombined image, increasing the visibility of the contrast-uptakes to be targeted during the biopsy procedure. In this work we propose a method to estimate the shape of the breast bump in biopsy vertical approach. Materials and Methods Our method consists on two steps: first, we compute a raw thickness which does not take into account the presence of contrast-uptakes; second, we use a physical model to separate the sparse iodine texture from the breast shape. This physical model is composed by a sum of Fourier components, describing the main shape of the bump, a series of low-order polynomials, describing the main compressed thickness, paddle tilt and deflection, and non-linear components describing the translation and rotation of the paddle aperture. A 3D object mimicking a bump was fabricated to test the pertinence of our shape model. Also, clinical images of 21 patients which followed CESM-guided biopsy were visually assessed. Results Comparison between raw and final estimated thickness of our 3D test object shows an error standard deviation of 0.37 mm similar to the noise standard deviation equals to 0.32 mm. The visual assessment of clinical cases showed that the thickness correction removes the superimposed low-frequency pattern due to non-uniform thickness of the bump, improving the identification of the lesion to be targeted. Conclusion The proposed method for thickness estimation is adapted to CESM-guided biopsies in vertical approach and it improves the identification of the contrast-uptakes that need to be targeted during the procedure.
A number of different physical and digital anthropomorphic breast phantoms have been proposed to assess and optimize the performance of breast x-ray imaging systems. All mimic, to some extent, different characteristics of the breast but a systematic realism of phantom realism applied to a number of phantoms using human readers has not been performed, for either full field digital mammography (FFDM), or digital breast tomosynthesis (DBT). We present a reader study in which radiologists performed a subjective evaluation of the visual realism between a selected group of available software phantoms (Stochastic Solid Breast Texture (SSBT) and power law noise texture), physical phantoms (CIRS BR3D breast imaging phantom and the L1 phantom) and clinical mammography images. Regions of interest (ROIs) of 2×2 cm2 and 2×2×3 cm3 , for FFDM and DBT stacks respectively, were scored. The readers were asked to judge how well the ROIs represented real breast texture using a 5-point rating scale. Observer ratings were analysed using the receiver operating characteristic (ROC) methodology and the area under the ROC curve (AUC) was used as the figure-of-merit (FOM). The Mann-Whitney test was used to assess the differences between separate groups. For the question of breast texture realism, the SSBT and power-law noise texture images obtained a high score. For DBT, SSBT was also found to have a high visual realism while the power-law noise texture images were found to have mediocre visual realism.
Anthropomorphic breast phantoms are used to create images that mimic aspects of clinical breast images and are useful in optimization and characterization of breast imaging systems. Here, a full-sized compressed physical breast phantom is designed and manufactured with 100 m resolution, high reproducibility and x-ray properties similar to that of breast tissues. The phantom design is based on a digital model derived from the morphology and distribution of large, medium and small scale fibroglandular and inter-glandular adipose tissue observed in clinical breast computerized tomography (bCT) images. The physical phantom consists of four slabs of a polyamide-12 component that mimics adipose tissue fabricated using selective laser sintering (SLS). The fibroglandular component is a low viscosity resin doped with a small amount of zinc oxide nanoparticles (<110 nm) to increase attenuation. The phantom was imaged on a Senographe Pristina and compared to image simulations of the virtual phantom. The power spectral parameter, β was 3.8±0.2 and 3.9±0.5 for the physical and virtual phantoms in a digital mammogram. The corresponding Laplacian fractional entropy (LFE) averaged 0.22 and 0.14 across the range 0.125–1.29 mm-1. Very good texture cancellation was obtained in contrast-enhanced spectral mammography.
Mammography is currently the primary imaging modality for breast cancer screening and plays an important role in cancer diagnostics. A standard mammographic image acquisition always includes the compression of the breast prior xray exposure. The breast is compressed between two plates (the image receptor and the compression paddle) until a nearly uniform breast thickness is obtained. The breast flattening improves diagnostic image quality1 and reduces the absorbed dose2 . However, this technique can also be a source of discomfort and might deter some women from attending breast screening by mammography3,4. Therefore, the characterization of the pain perceived during breast compression is of potential interest to compare different compression approaches. The aim of this work is to develop simulation tools enabling the characterization of existing breast compression techniques in terms of patient comfort, dose delivered to the patient and resulting image quality. A 3D biomechanical model of the breast was developed providing physics-based predictions of tissue motion and internal stress and strain intensity. The internal stress and strain intensity are assumed to be directly correlated with the patient discomfort. The resulting compressed breast model is integrated in an image simulation framework to assess both image quality and average glandular dose. We present the results of compression simulations on two breast geometries, under different compression paddles (flex or rigid).
Anthropomorphic breast phantoms are useful for development and characterization of breast x-ray imaging systems. Rapid prototyping (RP) opens a new way for generating complex shapes similar to real breast tissue patterns at reasonably high resolution and a high degree of reproducibility. Such a phantom should have x-ray attenuation properties similar to adipose and fibroglandular tissue across a broad x-ray energy range. However material selection is limited to those that are compatible with the printing system, which often requires adding non-organic dopants. Fortunately, there are some off-the-shelf materials that may be suitable for breast phantoms. Here a polyamide-12/water texture phantom is being investigated, which can be used for mammography, tomosynthesis and breast CT. Polyamide-12 (PA-12) is shown to have linear attenuation coefficients across an energy range of 15 – 40 keV matching adipose tissue to within 10% effective breast density. A selective laser sintering (SLS) printer is used for manufacturing the phantom. The phantom was imaged on the Senographe Pristina (GE Healthcare, Chicago, IL), while initial assessment of 3D fidelity with the original design was performed by acquiring volume images of the phantom on a micro-CT system. A root mean distance error of 0.22 mm was seen between the micro-CT volume and the original. The PA-12 structures appeared to be slightly smaller than in the original, possibly due to infiltration of the water into the PA-12 surfaces. Power spectra measurements for mammograms of the simulated and physical phantoms both demonstrated an inverse power-law spectrum shape with exponent β= 3.72 and 3.76, respectively.
KEYWORDS: Digital breast tomosynthesis, Signal attenuation, Breast, Image processing, Clinical trials, X-ray imaging, X-rays, Data modeling, Image acquisition, 3D image processing
The ultimate way to assess the performance of imaging systems is a clinical trial. Due to its limitation by cost and duration, several research groups are investigating the potential to replace clinical trials in part with virtual clinical trials (VCT) as a more efficient alternative. In this paper, we propose a VCT design to compare the microcalcification (μcalc) detection performance in full field digital mammography (FFDM) and digital breast tomosynthesis (DBT). Digital breast phantoms with uniform and breast-texture like backgrounds and digital μcalcs were created. The μcalcs had diameters ranging from 100μm to 600μm and their attenuation properties were varied to be equivalent to 20% to 60% of the attenuation of Aluminum at 22keV. FFDM and DBT image acquisitions according to the nominal topology of a commercial imaging system were simulated with a software x-ray imaging platform. Projection images were processed with commercial image processing and reconstruction algorithms. Microcalcification detection performance was estimated by an objective taskbased assessment using channelized Hotelling observers (CHO) with Laguerre-Gauss channels and by a human observer. For DBT, single-slice (CHO3ss) and a multi-slice CHO (CHO3msa) model observers were considered. Model and human observers performed a lesion-known-statistically and location-known exactly rating-scale detection task. The decision outcomes were used as input to a receiver operating characteristic analysis and the area under the curve was used as the figure-of-merit. Using our VCT set-up, the performance of the CHO and the human observer seems to be fairly well linearly correlated. There is a trend that µcalc detection performance in DBT is higher than in FFDM.
In breast X-ray images, texture has been characterized by a noise power spectrum (NPS) that has an inverse power-law shape described by its slope β in the log-log domain. It has been suggested that the magnitude of the power-law spectrum coefficient β is related to mass lesion detection performance. We assessed β in reconstructed digital breast tomosynthesis (DBT) images to evaluate its sensitivity to different typical reconstruction algorithms including simple back projection (SBP), filtered back projection (FBP) and a simultaneous iterative reconstruction algorithm (SIRT 30 iterations). Results were further compared to the β coefficient estimated from 2D central DBT projections. The calculations were performed on 31 unilateral clinical DBT data sets and simulated DBT images from 31 anthropomorphic software breast phantoms. Our results show that β highly depends on the reconstruction algorithm; the highest β values were found for SBP, followed by reconstruction with FBP, while the lowest β values were found for SIRT. In contrast to previous studies, we found that β is not always lower in reconstructed DBT slices, compared to 2D projections and this depends on the reconstruction algorithm. All β values estimated in DBT slices reconstructed with SBP were larger than β values from 2D central projections. Our study also shows that the reconstruction algorithm affects the symmetry of the breast texture NPS; the NPS of clinical cases reconstructed with SBP exhibit the highest symmetry, while the NPS of cases reconstructed with SIRT exhibit the highest asymmetry.
In breast X-ray imaging, breast texture has been characterized by a radial noise power spectrum (NPS) that has an inverse power-law shape with exponent β. The technique to estimate the radial power-law coefficient β is typically based on averaging 2-dimensional noise power spectra (NPS), calculated from partly overlapping image regions each weighted by a suitable window function. The linear regression applied over a selected frequency range to the logarithm of the 1- dimensional NPS as a function of the logarithm of the radial frequencies, gives β. For each step in this process, several alternative techniques have been proposed. This paper investigates the effect of image region of interest (ROI) size, image data windowing and alternative ways to determine radial frequency in terms of bias, variance and root mean square error (RMSE) in the estimated β. The effects of these three factors were analytically derived and evaluated using synthetic images with known β varying from 1 to 4 to cover the range of textures encountered in 2D and 3D breast X-ray imaging. Our results indicate that the RMSE in estimated β is smallest when the ROIs are multiplied with an appropriate window function and either no radial averaging or radial averaging with small frequency bins is applied. The ROI size yielding the smallest RMSE depends on several factors and needs to be validated with numerical simulations. In clinical practice however, there might be a need to compromise in the choice of the ROI size to balance between the RMSE magnitudes inherent to the applied β estimation technique and encompass the breast texture range so as to obtain an accurate shape of the NPS. When using 2.56 cm x 2.56 cm ROI sizes, applying a 2D Hann window and no radial frequency averaging, the RMSE in the estimated β ranges from 0.04 to 0.1 for true β values equal to 1 and 4. While many subtleties in real images were not modeled to simplify the mathematics in deriving our results, this work is illustrative in demonstrating the limits of commonly used algorithm steps to estimate accurate β values.
We address the detectability of contrast-agent enhancing masses for contrast-agent enhanced spectral mammography (CESM), a dual-energy technique providing functional projection images of breast tissue perfusion and vascularity using simulated CESM images. First, the realism of simulated CESM images from anthropomorphic breast software phantoms generated with a software X-ray imaging platform was validated. Breast texture was characterized by power-law coefficients calculated in data sets of real clinical and simulated images. We also performed a 2-alternative forced choice (2-AFC) psychophysical experiment whereby simulated and real images were presented side-by-side to an experienced radiologist to test if real images could be distinguished from the simulated images. It was found that texture in our simulated CESM images has a fairly realistic appearance. Next, the relative performance of human readers and previously developed mathematical observers was assessed for the detection of iodine-enhancing mass lesions containing different contrast agent concentrations. A four alternative-forced-choice (4 AFC) task was designed; the task for the model and human observer was to detect which one of the four simulated DE recombined images contained an iodineenhancing mass. Our results showed that the NPW and NPWE models largely outperform human performance. After introduction of an internal noise component, both observers approached human performance. The CHO observer performs slightly worse than the average human observer. There is still work to be done in improving model observers as predictors of human-observer performance. Larger trials could also improve our test statistics. We hope that in the future, this framework of software breast phantoms, virtual image acquisition and processing, and mathematical observers can be beneficial to optimize CESM imaging techniques.
The objective is to optimize low-energy (LE) and high-energy (HE) exposure parameters of contrast-enhanced spectral mammography (CESM) examinations in four different clinical applications for which different levels of average glandular dose (AGD) and ratios between LE and total doses are required. The optimization was performed on a Senographe DS with a SenoBright® upgrade. Simulations were performed to find the optima by maximizing the contrast-to-noise ratio (CNR) on the recombined CESM image using different targeted doses and LE image quality. The linearity between iodine concentration and CNR as well as the minimal detectable iodine concentration was assessed. The image quality of the LE image was assessed on the CDMAM contrast-detail phantom. Experiments confirmed the optima found on simulation. The CNR was higher for each clinical indication than for SenoBright®, including the screening indication for which the total AGD was 22% lower. Minimal iodine concentrations detectable in the case of a 3-mm-diameter round tumor were 12.5% lower than those obtained for the same dose in the clinical routine. LE image quality satisfied EUREF acceptable limits for threshold contrast. This newly optimized set of acquisition parameters allows increased contrast detectability compared to parameters currently used without a significant loss in LE image quality.
KEYWORDS: Nonlinear filtering, Image filtering, Digital filtering, Image processing, Image quality, Linear filtering, Denoising, Gaussian filters, Statistical modeling, Signal to noise ratio
Non-linear image processing and reconstruction algorithms that reduced noise while preserving edge detail are currently being evaluated in medical imaging research literature. We have implemented a robust statistics analysis of four widely utilized methods. This work demonstrates consistent trends in filter impact by which such non-linear algorithms can be evaluated. We calculate observer model test statistics and propose metrics based on measured non-Gaussian distributions that can serve as image quality measures analogous to SDNR and detectability. The filter algorithms that vary significantly in their approach to noise reduction include median (MD), bilateral (BL), anisotropic diffusion (AD) and total-variance regularization (TV). It is shown that the detectability of objects limited by Poisson noise is not significantly improved after filtration. There is no benefit to the fraction of correct responses in repeated n-alternate forced choice experiments, for n=2-25. Nonetheless, multi-pixel objects with contrast above the detectability threshold appear visually to benefit from non-linear processing algorithms. In such cases, calculations on highly repeated trials show increased separation of the object-level histogram from the background-level distribution. Increased conspicuity is objectively characterized by robust statistical measures of distribution separation.
This work investigates a dual-energy subtraction technique for cone-beam breast CT combined with an iodinated
contrast agent. Simulations were performed to obtain optimally enhanced iodine-equivalent and morphological images.
The optimal x-ray beam energies and average glandular dose allocation between the LE and HE images were identified.
Cylindrical phantoms were simulated with 10, 14 and 18 cm diameters and composed of 50% fibroglandular breast tissue
equivalent material. They contained spherical lesion inserts composed of 0, 25, 75 and 100% fibroglandular equivalent
tissues, homogeneous mixtures of 50% fibroglandular equivalent tissue and 0.5, 1.0, 2.5 and 5.0 mg/cm3 iodine, as well
as pure calcium hydroxyapatite, emulating calcifications. An acquisition technique with 600 projection images is
proposed. Only primary x-ray photons were simulated and a perfect energy-integrating detector was considered. LE and
HE beams ranging from 20 keV to 80 keV were investigated. The LE and HE projections were reconstructed using a
filtered backprojection algorithm. The LE volume provided the morphological image while the iodine-equivalent volume
was obtained by recombining the LE and HE volumes. Contrast-to-noise ratio (CNR) between the spherical inserts and
background breast tissue normalized to the square root of the total AGD (CNRD) was used as figure-of-merit for lesion
detectability. Based on maximizing CNRD, a 30keV/34keV LE/HE pair and a ~50/50% LE/HE AGD allocation were
found to provide the best possible performance for iodine and morphological imaging for an average size breast.
Dual-energy contrast-enhanced digital breast tomosynthesis (DE
CE-DBT) image quality is affected by a large parameter
space including the tomosynthesis acquisition geometry, imaging technique factors, the choice of reconstruction
algorithm, and the subject breast characteristics. The influence of most of these factors on reconstructed image quality is
well understood for DBT. However, due to the contrast agent uptake kinetics in CE imaging, the subject breast
characteristics change over time, presenting a challenge for optimization . In this work we experimentally evaluate the
sensitivity of the reconstructed image quality to timing of the
low-energy and high-energy images and changes in iodine
concentration during image acquisition. For four contrast uptake patterns, a variety of acquisition protocols were tested
with different timing and geometry. The influence of the choice of reconstruction algorithm (SART or FBP) was also
assessed. Image quality was evaluated in terms of the lesion
signal-difference-to-noise ratio (LSDNR) in the central slice
of DE CE-DBT reconstructions. Results suggest that for maximum image quality, the low- and high-energy image
acquisitions should be made within one x-ray tube sweep, as separate low- and high-energy tube sweeps can degrade
LSDNR. In terms of LSDNR per square-root dose, the image quality is nearly equal between SART reconstructions with
9 and 15 angular views, but using fewer angular views can result in a significant improvement in the quantitative
accuracy of the reconstructions due to the shorter imaging time interval.
Dual-energy contrast-enhanced digital breast tomosynthesis (CE-DBT) using an iodinated contrast agent is an imaging
technique providing 3D functional images of breast lesion vascularity and tissue perfusion. The iodine uptake in the
breast is very small and causes only small changes in x-ray transmission; typically less than 5%. This presents
significant technical challenges on the imaging system performance. The purpose of this paper was to characterize
image lag and scattered radiation and their effects on image quality for dual-energy CE-DBT using a CsI(Tl) phosphor-based
detector. Lag was tested using typical clinical acquisition sequences and exposure parameters and under various
detector read-out modes. The performance of a prototype anti-scatter grid and its potential benefit on the magnitude and
range of the cupping artifact were investigated. Analyses were performed through phantom experiments. Our results
illustrate that the magnitude of image lag is negligible and breast texture cancelation is almost perfect when the detector
is read out several times between x-ray exposures. The anti-scatter grid effectively reduces scatter and the cupping
artifact.
KEYWORDS: Digital breast tomosynthesis, X-rays, Ionization, Sensors, Solid state electronics, Photodiodes, Signal attenuation, Data modeling, Breast, Aluminum
Digital Breast Tomosynthesis (DBT) is an emerging imaging modality that combines tomography with conventional digital mammography. In developing DBT dosimetry, a direct application of mammographic dosimetry has appeal. However, DBT introduces rotation of the x-ray tube relative to the dosimeter, thus raising questions about the angular dependence of mammographic dosimeters. To measure this dependence, two ionization chambers, two solid-stated detectors, and one photodiode were rotated relative to an incident Mo/Mo x-ray beam. In this isocentric DBT simulation, the signal of each dosimeter was studied over an angular range of 180° for tube voltages of 26 to 34 kV. One ionization chamber was then modeled numerically to study the response to various monoenergetic beams. The results show that all dosimeters underestimate dose to varying degrees; solid-state detectors show the greatest angular
dependence while ionization chambers show the least. Correction factors were computed from the data for isocentric
DBT images using projection angles up to ±25°; these factors ranged from 1.0014 to 1.1380. The magnitude of the
angular dependence generally decreased with increasing energy, as shown with both the measured and modeled data. As a result, the error arising in measuring DBT dose with a mammographic dosimeter varies significantly; it cannot always be disregarded. The use of correction factors may be possible but is largely impractical, as they are specific to the dosimeter, x-ray beam, and DBT geometry. Instead, an angle-independent dosimeter may be more suitable for DBT.
Quantitative measures of image quality (IQ) are routinely obtained during the evaluation of imaging systems. These
measures, however, do not necessarily correlate with the IQ of the actual clinical images, which can also be affected by
factors such as patient positioning. No quantitative method currently exists to evaluate clinical IQ. Therefore, we
investigated the potential of using computerized image texture analysis to quantitatively assess IQ. Our hypothesis is that
image texture features can be used to assess IQ as a measure of the image signal-to-noise ratio (SNR). To test feasibility,
the "Rachel" anthropomorphic breast phantom (Model 169, Gammex RMI) was imaged with a Senographe 2000D
FFDM system (GE Healthcare) using 220 unique exposure settings (target/filter, kVs, and mAs combinations). The mAs
were varied from 10%-300% of that required for an average glandular dose (AGD) of 1.8 mGy. A 2.5cm2 retroareolar
region of interest (ROI) was segmented from each image. The SNR was computed from the ROIs segmented from
images linear with dose (i.e., raw images) after flat-field and off-set correction. Image texture features of skewness,
coarseness, contrast, energy, homogeneity, and fractal dimension were computed from the Premium ViewTM postprocessed image ROIs. Multiple linear regression demonstrated a strong association between the computed image texture features and SNR (R2=0.92, p≤0.001). When including kV, target and filter as additional predictor variables, a stronger association with SNR was observed (R2=0.95, p≤0.001). The strong associations indicate that computerized image texture analysis can be used to measure image SNR and potentially aid in automating IQ assessment as a component of the clinical workflow. Further work is underway to validate our findings in larger clinical datasets.
A digital breast tomosynthesis (DBT) reconstruction algorithm has been optimized using an anthropomorphic software
breast phantom. The algorithm was optimized in terms of preserving the x-ray attenuation coefficients of the simulated
tissues. The appearance of the reconstructed images is controlled in the algorithm using three input parameters related
to the reconstruction filter. We varied the input parameters to maximally preserve the attenuation information. The
primary interest was to identify and to distinguish between adipose and non-adipose (dense) tissues. To that end, a
software voxel phantom was used which included two distinct attenuation values of simulated breast tissues. The
phantom allows for great flexibility in simulating breasts of various size, glandularity, and internal composition.
Distinguishing between fatty and dense tissues was treated as a binary decision task quantified using ROC analysis. We
defined the reconstruction geometry to enable voxel-to-voxel comparison between the original and reconstructed
volumes. Separate histograms of the reconstructed pixels corresponding to simulated adipose and non-adipose tissues were computed. ROC curves were generated by varying the reconstructed intensity threshold; pixels above the threshold were classified as dense tissue. The input parameter space was searched to maximize the area under the ROC curve. The reconstructed phantom images optimized in this manner better preserve the tissue x-ray attenuation properties; concordant results are seen in clinical images. Use of the software phantom was successful and practical in this task-based optimization, providing ground truth information about the simulated tissues and providing flexibility in defining anatomical properties.
Analysis of complex imaging tasks requires a phantom that simulates the patient anatomy. We have developed a
technique to fabricate 3D physical anthropomorphic breast phantoms for image quality assessment of 2D and 3D breast
x-ray imaging systems. The phantom design is based on an existing computer model that can generate breast voxel
phantoms of varying size, shape, glandularity, and internal composition. The physical phantom is produced in two
steps. First, the computer model of the glandular tissue, skin and Coopers' ligaments is separated into sections. These
sections are fabricated by high-resolution rapid prototype printing using a single tissue equivalent material. The adipose
tissue regions in the sections are filled using an epoxy-based resin combined with phenolic microspheres. The phantom
sections are then stacked. The phantom is provided with an extra section modified to include iodine-enhanced masses.
We fabricated a prototype phantom corresponding to a 450 ml breast with 45% dense tissue deformed to represent a
5 cm compressed thickness. The rapid prototype and epoxy based resin phantom materials attenuate x rays similar to 50% glandular tissue and 100% adipose tissue, respectively. The iodinated masses are between 4.0 and 9.6 mm thick and contain 2.5 mg/ml and 5 mg/ml iodine. Digital mammography and digital breast tomosynthesis images of the phantom are qualitatively similar in appearance to clinical images. In summary, a method to fabricate a 3D physical anthropomorphic breast phantom has been developed with known ground truth in the form of a companion voxel phantom. This combined system of physical and computational phantoms allows for both qualitative and quantitative image quality assessment.
KEYWORDS: Breast, Digital breast tomosynthesis, Tissues, Image segmentation, Mammography, 3D image processing, 3D image reconstruction, Breast cancer, X-ray imaging, Tomography
Breast density is an independent factor of breast cancer risk. In mammograms breast density is quantitatively measured
as percent density (PD), the percentage of dense (non-fatty) tissue. To date, clinical estimates of PD have varied
significantly, in part due to the projective nature of mammography. Digital breast tomosynthesis (DBT) is a 3D imaging
modality in which cross-sectional images are reconstructed from a small number of projections acquired at different x-ray
tube angles. Preliminary studies suggest that DBT is superior to mammography in tissue visualization, since
superimposed anatomical structures present in mammograms are filtered out. We hypothesize that DBT could also
provide a more accurate breast density estimation. In this paper, we propose to estimate PD from reconstructed DBT
images using a semi-automated thresholding technique. Preprocessing is performed to exclude the image background
and the area of the pectoral muscle. Threshold values are selected manually from a small number of reconstructed slices;
a combination of these thresholds is applied to each slice throughout the entire reconstructed DBT volume. The
proposed method was validated using images of women with recently detected abnormalities or with biopsy-proven
cancers; only contralateral breasts were analyzed. The Pearson correlation and kappa coefficients between the breast
density estimates from DBT and the corresponding digital mammogram indicate moderate agreement between the two
modalities, comparable with our previous results from 2D DBT projections. Percent density appears to be a robust
measure for breast density assessment in both 2D and 3D x-ray breast imaging modalities using thresholding.
The aim of this work is to provide a simulation framework for generation of synthetic tomosynthesis images to
be used for evaluation of future developments in the field of tomosynthesis. An anthropomorphic software tissue
phantom was previously used in a number of applications for evaluation of acquisition modalities and image
post-processing algorithms for mammograms. This software phantom has been extended for similar use with
tomosynthesis. The new features of the simulation framework include a finite element deformation model to
obtain realistic mammographic deformation and projection simulation for a variety of tomosynthesis geometries.
The resulting projections are provided in DICOM format to be applicable for clinically applied reconstruction
algorithms. Examples of simulations using parameters of a currently applied clinical setup are presented. The
overall simulation model is generic, allowing multiple degrees of freedom to cover anatomical variety in the amount
of glandular tissue, degrees of compression, material models for breast tissues, and tomosynthesis geometries.
We have developed a dual-energy subtraction technique for contrast-enhanced breast tomosynthesis. The imaging
system consists of 48 photon-counting linear detectors which are precisely aligned with the focal spot of the x-ray
source. The x-ray source and the digital detectors are translated across the breast in a continuous linear motion; each
linear detector collects an image at a distinct angle. A pre-collimator is positioned above the breast and defines 48 fan-shaped
beams, each aligned with a detector. Low- and high-energy images are acquired in a single scan; half of the
detectors capture a low-energy beam and half capture a high-energy beam, as alternating fan-beams are filtered to
emphasize low and high energies. Imaging was performed with a W-target at 45 and 49 kV. Phantom experiments and
theoretical modeling were conducted. Iodine images were produced with weighted logarithmic subtraction. The
optimal tissue cancellation factor, wt, was determined based on simultaneous preservation of the iodine signal and
suppression of simulated anatomic background. Optimal dose allocation between low- and high-energy images was
investigated. Mean glandular doses were restricted to ensure clinical relevance. Unlike other dual-energy approaches,
both spectra must have the same peak energy in this system design. We have observed that wt is mainly dependent on
filter combination and varies only slightly with kV and breast thickness, thus ensuring a robust clinical implementation.
Optimal performance is obtained when the dose fraction allocated to the high energy images ranges from 0.55 to 0.65.
Using elemental filters, we have been able to effectively suppress the anatomic background.
A database of raw composite mammograms containing simulated microcalcifications was generated. Databases can be used for technology assessment, quality assurance and comparison of different processing algorithms or different visualization modalities in digital mammography. Clinical mammograms were selected and fully documented for this scope. Microcalcifications were simulated in mammography images following a methodology developed and validated in an earlier work of our group. To create microcalcification templates, specimen containing lesions with different morphology types were acquired. From a basic set of (ideal) microcalcification templates, a set of specific templates for the systems under study was generated. The necessary input to do so is the system MTF and attenuation values of aluminum sheets with different thickness. In order to make the whole process less time consuming and applicable on a large scale, dedicated software tools for the creation of composite images have been developed. Automatic analysis of scores from observer performance study, in terms of microcalcification detectability on the composite images, is also implemented. We report on the functionalities foreseen in these new software tools. Simulated microcalcifications were successfully created and inserted in raw images of the Siemens Novation DR, the AGFA DM1000 and the AGFA CR MM2.0.
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 can be observed by administration of an x-ray contrast agent. We have investigated the technical requirements necessary to quantitatively analyze CE-DBT exams. Using a simplified physiological model, a maximum aerial concentration of approximately 2.2 mg iodine/cm2 in a 0.5 cm thick breast lesion is expected when administering 70 ml of 320 mg iodine/ml Visipaque-320®. This corresponds to a small change in x-ray transmission; up to 5% for a 4 cm thick compressed breast. We have modeled CE-DBT acquisition by simulating Rh target 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 scatter, patient motion and temporal stability of the detector on quantifying iodine uptake. These parameters were evaluated by means of experiments and theoretical modeling.
Calculation of the modulation transfer function (MTF) is a multi-step procedure. At each step in the calculation, the algorithms can have intrinsic errors which are independent of the imaging system or physics. We designed a software tool with a graphical user interface to facilitate calculation of MTF and the analysis of accuracy in those calculations. To minimize the source of errors, simulated edge images without any noise or artifacts were used. We first examined the accuracy of a commonly used edge-slope estimation algorithm; namely line-by-line differentiation followed by a linear regression fit. The influence of edge length and edge phase on the linear regression algorithm is demonstrated. Furthermore, the relationship of edge-slope estimation error and MTF error are illustrated. We compared the performance of two kernels, [-1,1] and [-1,0,1], in the computation of the line spread function (LSF) from finite element differentiation of the edge spread function (ESF). We found that there is no practical advantage in choosing the [-1,0,1] kernel, as recommended by IEC. However, a correction for finite element differentiation should be applied; otherwise, there is a measurable error in the MTF. Finally, we added noise into the edge images and compared the performance of two noise reduction methods on the ESF; convolution with a boxcar kernel and a monotonicity constraint. The former method always produces MTF error higher than 4% up to the sampling frequency, while the latter was consistently less than 1%.
A method for calibrating camera geometry is described. This method
has been used to implement synthetic tomography on a commercially
available full-field digital mammography system. The method
utilizes a phantom containing six point-like calibration objects whose positions are approximately known. The image of five calibration objects in a given projection allows an associated projection matrix to be determined up to one free parameter. By using the positions of the shadows of the sixth calibration object in three or more views, one can fit the remaining free parameter associated with each view and the position of the sixth calibration object relative to the first five. Uncertainty in the position or geometry of the phantom does not affect the geometric consistency,
thus tomograms produced by back-projection suffer no blurring
from errors in the determination of camera geometry. Uncertainties
in the position or geometry of the phantom result in proportionate
translations or distortions of the tomograms. For a tomogram
corresponding to a plane containing an object, the positions
of the backprojections of the shadows of the object are consistent
to the same precision as the measurements of the shadows in
each projection, i.e., the positions of the backprojections differ
by about the size of the pixel spacing in the detector.
The purpose of this study is to describe a method that allows the calculation of a contrast-detail curve for a particular
system configuration using simulated micro calcifications into clinical mammograms.
We made use of simulated templates of micro calcifications and adjusted their x-ray transmission coefficients and
resolution to the properties of the mammographic system under consideration (4). We expressed the thickness of the
simulated micro calcifications in terms of Al equivalence.
In a first step we validated that the thickness of very small Al particles with well known size and thickness can be
calculated from their x-ray transmission characteristics at a particular X-ray beam energy.
Then, micro calcifications with equivalent diameters in the plane of the detector ranging from 300 to 800 μm and
thicknesses, expressed in Al equivalent, covering 77 to 800 μm were simulated into the raw data of real clinical images.
The procedure was tested on 2 system configurations: the GE Senographe 2000 D and the Se based Agfa Embrace
DM1000 system. We adapted the X-ray transmissions and spatial characteristics of the simulated micro calcifications
such that the same physical micro calcification could be simulated into images with the specific exposure parameters
(Senographe 2000D: 28 kVp-Rh/Rh, Embrace DM1000: 28 kVp-Mo/Rh), compressed breast thickness (42+/-5mm) and
detector under consideration. After processing and printing, 3 observers scored the visibility of the micro calcifications.
We derived contrast-detail curves. This psychophysical method allows to summarize the performance of a digital
mammography detector including processing and visualization.
We evaluated the visibility of simulated subtle microcalcifications in real digital mammograms acquired with a flat-panel system (GE) and a CR system (Fuji). Ideal templates of microcalcifications were created, based on the attenuation characteristics of subtle microcalcifications from biopsied specimen in magnified images. X-ray transmission coefficients were expressed in Al-equivalent thickness. In this way, the X-ray transmission of a particular lesion could be re-calculated for other X-ray beams, different mammography systems and for different breast thickness. Extra corrections for differences in spatial resolution were based on the pre-sampled MTF. Zero to 10 simulated microcalcifications were randomly distributed in square frames. These software phantoms were then inserted in sets of raw mammograms of the modalities under study. The composed images were compressed, processed and printed as in clinical routine. Two experienced radiologists indicated the locations of the microcalcifications and rated their detection confidence. It is possible to assess the visibility of 'well controlled’ microcalcifications in digital clinical mammograms. Microcalcifications were better visible in the CR images than in the flat panel images. This psychophysical method comes close to the radiologists’ practice. It allows fpr including processing and visualization in the analysis and was well appreciated by our radiologists.
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