A semi-empirical model for scatter field reduction in digital mammography.

X-ray mammography is the gold standard technique in breast cancer screening programmes. One of the main challenges that mammography is still facing is scattered radiation, which degrades the quality of the image and complicates the diagnosis process. Anti-scatter grids, the main standard physical scattering reduction technique, have some unresolved challenges as they increase the dose delivered to the patient, do not remove all the scattered radiation and increase the cost of the equipment. Alternative scattering reduction methods based on post-processing algorithms, have lately been under investigation. This study is concerned with the use of image post-processing to reduce the scatter contribution in the image, by convolving the primary plus scatter image with kernels obtained from simplified Monte Carlo (MC) simulations. The proposed semi-empirical approach uses up to five thickness-dependant symmetric kernels to accurately estimate the scatter contribution of different areas of the image. Single breast thickness-dependant kernels can over-estimate the scatter signal up to 60%, while kernels adapting to local variations have to be modified for each specific case adding high computational costs. The proposed method reduces the uncertainty to a 4%-10% range for a 35-70 mm breast thickness range, making it a very efficient, case-independent scatter modelling technique. To test the robustness of the method, the scattered corrected image has been successfully compared against full MC simulations for a range of breast thicknesses. In addition, clinical images of the 010A CIRS phantom were acquired with a mammography system with and without the presence of the anti-scatter grid. The grid-less images were post-processed and their quality was compared against the grid images, by evaluating the contrast-to-noise ratio and variance ratio using several test objects, which simulate calcifications and tumour masses. The results obtained show that the method reduces the scatter to similar levels than the anti-scatter grids.

A Bethesda-like system for breast cytopathology: A retrospective assessment two decades on.

Fine-needle aspiration biopsy (FNAB) has been used for many decades in the investigation of breast lesions. Originally, cases were signed out using the categories benign and malignant. The benign category contained specimens showing fibrocystic change as well as benign neoplasms such as fibroadenoma. The malignant category contained carcinomas, lymphomas, and phyllodes tumors with specific diagnoses often given in place of the term malignant. Categorization was less clear when the cytopathologists could not definitively separate benign from malignant. This led to the use of terms, such as atypical, suspicious for malignancy, and atypical suspicious with variable definitions and utilization among cytopathologists. In 1997, a uniform approach to breast FNAB was proposed with well-defined diagnostic categories and criteria. This system foreshadowed the recent International Academy of Cytology Standardized Reporting System for Breast Fine-Needle Aspiration Biopsy. These two systems are compared and contrasted.

Pathologic assessment of tumor-associated macrophages and their histologic localization in invasive breast carcinoma.

BACKGROUND: Tumor-associated macrophages (TAMs) are important in regulating cross-talk between tumor cells and tumor microenvironment. TAMs are involved in multiple steps of tumor progression and invasion. This study aimed to compare CD163 expression with the widely used CD68 pan-macrophage marker in invasive breast carcinoma. Furthermore, it focused on assessing the significance of TAMs localization in relation to clinicopathological parameters. RESULTS: CD68 and CD163 immunohistochemical expressions within TAMs infiltrating both tumor nest (TN) and tumor stroma (TS) were evaluated in 60 specimens with invasive breast carcinoma. High CD68-positive stromal TAMs was significantly related to larger tumor, nodal metastasis and vascular invasion (p = 0.003, 0.037, 0.032, respectively), whereas high CD163-positive stromal TAMs was significantly related to larger tumors, nodal metastasis, stage III tumors, vascular invasion, estrogen receptor (ER) negativity, and triple-negative subtype (p = 0.023, < 0.001, 0.001, 0.022, 0.002, 0.017, respectively). On multivariate analysis, high CD68-positive TAMs infiltrating TS was significantly associated with larger tumor and positive nodal metastasis (p = 0.006 and 0.016, respectively), whereas high CD163 TAMs density within TS was significantly associated with positive vascular invasion, nodal metastasis, and molecular subtypes (p = 0.003, 0.001, and 0.009, respectively). CONCLUSION: TAMs within tumor stroma and tumor nest have different levels of association with poor prognostic parameters. So, it is of great importance to consider the histologic localization of TAMs in addition to the degree of TAMs infiltration.

Normalized glandular dose coefficients in mammography, digital breast tomosynthesis and dedicated breast CT.

PURPOSE: To provide mean glandular dose (MGD) estimates via Monte Carlo (MC) simulations as a function of the breast models and scan parameters in mammography, digital breast tomosynthesis (DBT) and dedicated breast CT (BCT). METHODS: The MC code was based on GEANT4 toolkit. The simulated compressed breast was either a cylinder with a semi-circular section or ad hoc shaped for oblique view (MLO). In DBT we studied the influence of breast models and exam parameters on the T-factors (i.e. the conversion factor for the calculation of the MGD in DBT from that for a 0-degree projection), and in BCT we investigated the influence on the MGD estimates of the ion chamber volume used for the air kerma measurements. RESULTS: In mammography, a model representative of a breast undergoing an MLO view exam did not produce substantial differences (0.4%) in MGD estimates, when compared to a conventional cranio-caudal (CC) view breast model. The beam half value layer did not present a significant influence on T-factors in DBT (<0.8%), while the skin model presented significant influence on MGD estimates (up to 3.3% at 30 degrees scan angle), increasing for larger scan angles. We derived a correction factor for taking into account the different ion chamber volume used in MGD estimates in BCT. CONCLUSIONS: A series of MC code modules for MGD estimates in 2D and 3D breast imaging have been developed in order to take into account the most recent advances in breast models.

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive. METHODS: In this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation. RESULTS: The four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%. CONCLUSION: Our results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

Skin models and their impact on mean glandular dose in mammography.

PURPOSE: To quantify the influence of different skin models on mammographic breast dosimetry, based on dosimetric protocols and recent breast skin thickness findings. METHODS: By using an adapted PENELOPE (v. 2014) + PenEasy (v. 2015) Monte Carlo (MC) code, simulations were performed in order to obtain the mean glandular dose (MGD), the normalized MGD by incident air Kerma (DgN), and the glandular depth dose (GDD(z)). The geometry was based on a cranio-caudal mammographic examination. Monoenergetic and polyenergetic beams were implemented, for a breast thickness from 2 cm to 9 cm, with different compositions. Seven skin models were used: a 5 mm adipose layer; a skin layer ranging from 5 mm to 1.45 mm, a 1.45 mm skin thickness with a subcutaneous adipose layer of 2 mm and 3.55 mm. RESULTS: The differences, for monoenergetic beams, are higher (up to 200%) for lower energies (8 keV), thicker and low glandular content breasts, decreasing to less than 5% at 40 keV. Without a skin layer, the differences reach a maximum of 1240%. The relative difference in DgN values for 1.45 mm skin and 5 mm adipose layers and polyenergetic beams varies from -14% to 12%. CONCLUSIONS: The implemented MC code is suitable for mammography dosimetry calculations. The skin models have major impacts on MGD values, and the results complement previous literature findings. The current protocols should be updated to include a more realistic skin model, which provides a reliable breast dose estimation.

Homogeneous vs. patient specific breast models for Monte Carlo evaluation of mean glandular dose in mammography.

PURPOSE: To compare, via Monte Carlo simulations, homogeneous and non-homogenous breast models adopted for mean glandular dose (MGD) estimates in mammography vs. patient specific digital breast phantoms. METHODS: We developed a GEANT4 Monte Carlo code simulating four homogenous cylindrical breast models featured as follows: (1) semi-cylindrical section enveloped in a 5-mm adipose layer; (2) semi-elliptical section with a 4-mm thick skin; (3) semi-cylindrical section with a 1.45-mm skin layer; (4) semi-cylindrical section in a 1.45-mm skin layer and 2-mm subcutaneous adipose layer. Twenty patient specific digital breast phantoms produced from a dedicated CT scanner were assumed as reference in the comparison. We simulated two spectra produced from two anode/filter combinations. An additional digital breast phantom was produced via BreastSimulator software. RESULTS: With reference to the results for patient-specific breast phantoms and for W/Al spectra, models #1 and #3 showed higher MGD values by about 1% (ranges [-33%; +28%] and [-31%; +30%], respectively), while for model #4 it was 2% lower (range [-34%; +26%]) and for model #2 -11% (range [-39%; +14%]), on average. On the other hand, for W/Rh spectra, models #1 and #4 showed lower MGD values by 2% and 1%, while for model #2 and #3 it was 14% and 8% lower, respectively (ranges [-43%; +13%] and [-41%; +21%]). The simulation with the digital breast phantom produced with BreastSimulator showed a MGD overestimation of +33%. CONCLUSIONS: The homogeneous breast models led to maximum MGD underestimation and overestimation of 43% and 28%, respectively, when compared to patient specific breast phantoms derived from clinical CT scans.

Protein-based nanotoxicology assessment strategy.

The nanomaterial community calls for standardized in vitro assays to determine nanoparticle toxicity in the effort to reduce the number of in vivo validation experiments. We demonstrate that chip-based protein detection is suitable for assessing toxicity and may complement traditional assays to improve selection of primary hits for subsequent analysis. As nanodrug mimics, we analyzed the effect of transiently transfected siRNAs in MCF7 breast cancer cells and normal MCF12A breast cells, resembling a differential screen. As a measure of cytotoxicity, we determined cell viability as well as protein expression of glyceraldehyde-3-phosphate dehydrogenase, transferrin receptor, and the proliferation marker Ki67. The evaluation of cell lethality and protein expression unraveled cellular effects overseen by one method alone.

A Monte Carlo study of monoenergetic and polyenergetic normalized glandular dose (DgN) coefficients in mammography.

We investigated the influence of model assumptions in GEANT4 Monte Carlo (MC) simulations for the calculation of monoenergetic and polyenergetic normalized glandular dose coefficients (DgN) in mammography, focussing on the effect of the skin thickness and composition, of the role of compression paddles and of the bremsstrahlung processes. We showed that selecting a skin thickness of 4 mm instead of 1.45 mm produced DgN values with deviations from 9% to 32% for x-ray spectra routinely adopted in mammography. Consideration of the bremsstrahlung radiation had a weak influence on monoenergetic DgN. Simulations (in the range 8-40 kVp) which included consideration of bremsstrahlung radiation, a skin thickness of 1.45 mm and a 2 mm thick compression paddles produced polyenergetic DgN coefficients up to 19% higher than corresponding literature data. Adding a 2 mm thick adipose layer between the skin layer and the radiosensitive portion of the breast produces polyenergetic DgN values up to 15% higher than those routinely adopted. These findings provide a quantitative estimate of the influence of model parameters on the calculation of the mean glandular dose in mammography.

Quantitative Assessment of Breast Parenchymal Uptake on 18F-FDG PET/CT: Correlation with Age, Background Parenchymal Enhancement, and Amount of Fibroglandular Tissue on MRI.

Background parenchymal enhancement (BPE), and the amount of fibroglandular tissue (FGT) assessed with MRI have been implicated as sensitive imaging biomarkers for breast cancer. The purpose of this study was to quantitatively assess breast parenchymal uptake (BPU) on 18F-FDG PET/CT as another valuable imaging biomarker and examine its correlation with BPE, FGT, and age. METHODS: This study included 129 patients with suspected breast cancer and normal imaging findings in one breast (BI-RADS 1), whose cases were retrospectively analyzed. All patients underwent prone 18F-FDG PET/CT and 3-T contrast-enhanced MRI of the breast. In all patients, interpreter 1 assessed BPU quantitatively using SUVmax Interpreters 1 and 2 assessed amount of FGT and BPE in the normal contralateral breast by subjective visual estimation, as recommended by BI-RADS. Interpreter 1 reassessed all cases and repeated the BPU measurements. Statistical tests were used to assess correlations between BPU, BPE, FGT, and age, as well as inter- and intrainterpreter agreement. RESULTS: BPU on 18F-FDG PET/CT varied among patients. The mean BPU SUVmax ± SD was 1.57 ± 0.6 for patients with minimal BPE, 1.93 ± 0.6 for mild BPE, 2.42 ± 0.5 for moderate BPE, and 1.45 ± 0.3 for marked BPE. There were significant (P < 0.001) moderate to strong correlations among BPU, BPE, and FGT. BPU directly correlated with both BPE and FGT on MRI. Patient age showed a moderate to strong indirect correlation with all 3 imaging-derived tissue biomarkers. The coefficient of variation for quantitative BPU measurements with SUVmax was 5.6%, indicating a high reproducibility. Interinterpreter and intrainterpreter agreement for BPE and FGT was almost perfect, with a κ-value of 0.860 and 0.822, respectively. CONCLUSION: The results of our study demonstrate that BPU varied among patients. BPU directly correlated with both BPE and FGT on MRI, and BPU measurements were highly reproducible. Patient age showed a strong inverse correlation with all 3 imaging-derived tissue biomarkers. These findings indicate that BPU may serve as a sensitive imaging biomarker for breast cancer prediction, prognosis, and risk assessment.

Ultrasound viscoelasticity assessment using an adaptive torsional shear wave propagation method.

PURPOSE: Different approaches have been used in dynamic elastography to assess mechanical properties of biological tissues. Most techniques are based on a simple inversion based on the measurement of the shear wave speed to assess elasticity, whereas some recent strategies use more elaborated analytical or finite element method (FEM) models. In this study, a new method is proposed for the quantification of both shear storage and loss moduli of confined lesions, in the context of breast imaging, using adaptive torsional shear waves (ATSWs) generated remotely with radiation pressure. METHODS: A FEM model was developed to solve the inverse wave propagation problem and obtain viscoelastic properties of interrogated media. The inverse problem was formulated and solved in the frequency domain and its robustness to noise and geometric constraints was evaluated. The proposed model was validated in vitro with two independent rheology methods on several homogeneous and heterogeneous breast tissue-mimicking phantoms over a broad range of frequencies (up to 400 Hz). RESULTS: Viscoelastic properties matched benchmark rheology methods with discrepancies of 8%-38% for the shear modulus G' and 9%-67% for the loss modulus G″. The robustness study indicated good estimations of storage and loss moduli (maximum mean errors of 19% on G' and 32% on G″) for signal-to-noise ratios between 19.5 and 8.5 dB. Larger errors were noticed in the case of biases in lesion dimension and position. CONCLUSIONS: The ATSW method revealed that it is possible to estimate the viscoelasticity of biological tissues with torsional shear waves when small biases in lesion geometry exist.

A Global View of Breast Tissue Banking.

The importance of accessing high quality clinical samples for translational research is now firmly recognised. Traditionally these samples were collected and curated by individuals with an interest in a particular disease type. In recent years the idea of centralising and storing tissue collections in the form of tissue banks or biobanks has developed. As a result a number of biobanks have been established in many different countries. These can be either single centres or multi centre collaborations, often in the form of a federated network. This chapter outlines the development of breast tissue banking in a global context and discusses some of the challenges that lie ahead for the field, in particular how to meet the growing needs of researchers, how to make the best use of donated samples and how to increase the visibility of samples residing in biobanks to researchers.

Characterization and assessment of hyperelastic and elastic properties of decellularized human adipose tissues.

Decellularized adipose tissue (DAT) has shown potential as a regenerative scaffold for plastic and reconstructive surgery to augment or replace damaged or missing adipose tissue (e.g. following lumpectomy or mastectomy). The mechanical properties of soft tissue substitutes are of paramount importance in restoring the natural shape and appearance of the affected tissues, and mechanical mismatching can lead to unpredictable scar tissue formation and poor implant integration. The goal of this work was to assess the linear elastic and hyperelastic properties of decellularized human adipose tissue and compare them to those of normal breast adipose tissue. To assess the influence of the adipose depot source on the mechanical properties of the resultant decellularized scaffolds, we performed indentation tests on DAT samples sourced from adipose tissue isolated from the breast, subcutaneous abdominal region, omentum, pericardial depot and thymic remnant, and their corresponding force-displacement data were acquired. Elastic and hyperelastic parameters were estimated using inverse finite element algorithms. Subsequently, a simulation was conducted in which the estimated hyperelastic parameters were tested in a real human breast model under gravity loading in order to assess the suitability of the scaffolds for implantation. Results of these tests showed that in the human breast, the DAT would show similar deformability to that of native normal tissue. Using the measured hyperelastic parameters, we were able to assess whether DAT derived from different depots exhibited different intrinsic nonlinearities. Results showed that DAT sourced from varying regions of the body exhibited little intrinsic nonlinearity, with no statistically significant differences between the groups.

Characterization of scatter in digital mammography from use of Monte Carlo simulations and comparison to physical measurements.

PURPOSE: Monte Carlo simulations were performed with the goal of verifying previously published physical measurements characterizing scatter as a function of apparent thickness. A secondary goal was to provide a way of determining what effect tissue glandularity might have on the scatter characteristics of breast tissue. The overall reason for characterizing mammography scatter in this research is the application of these data to an image processing-based scatter-correction program. METHODS: mcnpx was used to simulate scatter from an infinitesimal pencil beam using typical mammography geometries and techniques. The spreading of the pencil beam was characterized by two parameters: mean radial extent (MRE) and scatter fraction (SF). The SF and MRE were found as functions of target, filter, tube potential, phantom thickness, and the presence or absence of a grid. The SF was determined by separating scatter and primary by the angle of incidence on the detector, then finding the ratio of the measured scatter to the total number of detected events. The accuracy of the MRE was determined by placing ring-shaped tallies around the impulse and fitting those data to the point-spread function (PSF) equation using the value for MRE derived from the physical measurements. The goodness-of-fit was determined for each data set as a means of assessing the accuracy of the physical MRE data. The effect of breast glandularity on the SF, MRE, and apparent tissue thickness was also considered for a limited number of techniques. RESULTS: The agreement between the physical measurements and the results of the Monte Carlo simulations was assessed. With a grid, the SFs ranged from 0.065 to 0.089, with absolute differences between the measured and simulated SFs averaging 0.02. Without a grid, the range was 0.28-0.51, with absolute differences averaging -0.01. The goodness-of-fit values comparing the Monte Carlo data to the PSF from the physical measurements ranged from 0.96 to 1.00 with a grid and 0.65 to 0.86 without a grid. Analysis of the data suggested that the nongrid data could be better described by a biexponential function than the single exponential used here. The simulations assessing the effect of breast composition on SF and MRE showed only a slight impact on these quantities. When compared to a mix of 50% glandular/50% adipose tissue, the impact of substituting adipose or glandular breast compositions on the apparent thickness of the tissue was about 5%. CONCLUSIONS: The findings show agreement between the physical measurements published previously and the Monte Carlo simulations presented here; the resulting data can therefore be used more confidently for an application such as image processing-based scatter correction. The findings also suggest that breast composition does not have a major impact on the scatter characteristics of breast tissue. Application of the scatter data to the development of a scatter-correction software program can be simplified by ignoring the variations in density among breast tissues.

Breast tomosynthesis with monochromatic beams: a feasibility study using Monte Carlo simulations.

The aim of this study is to investigate the impact on image quality of using monochromatic beams for lower dose breast tomosynthesis (BT). For this purpose, modeling and simulation of BT and mammography imaging processes have been performed using two x-ray beams: one at 28 kVp and a monochromatic one at 19 keV at different entrance surface air kerma ranging between 0.16 and 5.5 mGy. Two 4 cm thick computational breast models, in a compressed state, were used: one simple homogeneous and one heterogeneous based on CT breast images, with compositions of 50% glandular-50% adipose and 40% glandular-60% adipose tissues by weight, respectively. Modeled lesions, representing masses and calcifications, were inserted within these breast phantoms. X-ray transport in the breast models was simulated with previously developed and validated Monte Carlo application. Results showed that, for the same incident photon fluence, the use of the monochromatic beam in BT resulted in higher image quality compared to the one using polychromatic acquisition, especially in terms of contrast. For the homogenous phantom, the improvement ranged between 15% and 22% for calcifications and masses, respectively, while for the heterogeneous one this improvement was in the order of 33% for the masses and 17% for the calcifications. For different exposures, comparable image quality in terms of signal-difference-to-noise ratio and higher contrast for all features was obtained when using a monochromatic 19 keV beam at a lower mean glandular dose, compared to the polychromatic one. Monochromatic images also provide better detail and, in combination with BT, can lead to substantial improvement in visualization of features, and particularly better edge detection of low-contrast masses.

Radiation dose in breast CT imaging with monochromatic x-rays: simulation study of the influence of energy, composition and thickness.

Recent developments have shown that high resolution phase contrast x-ray computed tomography (CT) of the breast can be performed at clinically compatible doses. Results have yet been obtained in vitro on full breasts, and the clinical translation of the technique seems more and more possible. This work presents a method to quickly estimate the average dose in the organ using the software GATE. The influence of different parameters on the dose distribution, like breast composition and thickness, and for preclinical test, the presence of a skin/PMMA external layer, has been investigated. Several correction factors, to be applied to the given dose database, are also introduced to allow the use of these results in geometries different from those studied here. An energy optimization study is presented that considers also the influence on the energy choice of x-ray detector. A simple analytical method to estimate the best energy that minimizes the dose-transmittance ratio in CT imaging is presented and compared with the results of simulations.

Estimation of mean glandular dose for contrast enhanced digital mammography: factors for use with the UK, European and IAEA breast dosimetry protocols.

The UK, European and IAEA protocols for breast dosimetry in mammography use tabulations of conversion factors, which relate measurements of incident air kerma to the mean glandular dose to the breast. To supplement the existing tabulations, a Monte Carlo computer program has been used to calculate conversion factors for the high-energy spectra used for contrast enhanced digital mammography. The calculations were made for the x-ray spectra from a tungsten target (tube voltage range 40-50 kV) filtered by 0.28, 0.30 and 0.32 mm of copper, and from molybdenum and rhodium targets (tube voltage range 40-49 kV), each filtered by 0.30 mm of copper. The g-factors for all of these spectra were plotted for each breast thickness as a function of half value layer (HVL) and were found to lie on smooth curves within 0.3%. These reflect the fact that the characteristic x-rays present in the spectra from molybdenum and rhodium are heavily filtered and all the spectra are essentially Bremsstrahlung. As a consequence, the s-factor previously used in the dosimetry protocols to adjust for different target/filter combinations can be taken as unity for all of the spectra considered. Tables of g-factors and c-factors are provided for breast thicknesses in the range 20-110 mm and HVLs in the range 2.4-3.6 mm of aluminium. The tables of c-factors are given for breast glandularities in the range 0.1%-100% and for typical glandularities for women in the age bands 40-49 and 50-64 attending the UK national breast screening programme.

A Monte Carlo study of the influence of focal spot size, intensity distribution, breast thickness and magnification on spatial resolution of an a-Se digital mammography system using the generalized MTF.

In this study the generalized Modulation Transfer Function (GMTF) and the geometric sharpness (Sgeo) were used (i) to study the effects of various focal spot sizes (0.04 mm-0.3 mm), x-ray intensity distributions (Gaussian and double Gaussian), breast thicknesses (2-7 cm) and magnifications M (1.0-2.0) on the spatial resolution of an a-Se digital mammography system, (ii) to identify suitable focal spots for magnification mammography and (iii) derive optimum magnifications. For the calculation of GMTF the required components were: focal spot MTF, obtained from theory, detector MTF, scatter MTF and scatter fraction obtained from Monte Carlo simulations. The results showed that focal spots with sizes up to 0.18 mm are suitable for magnification mammography offering a GMTF which is >50% and >20% at the respective object frequencies of 6.5 mm(-1) and 9 mm(-1). Focal spots with sizes < 0.16 mm and Gaussian. intensity distribution, or sizes ≤ 0.1 mm and double Gaussian, offer a system resolution which improves or does not deteriorate with magnification for most object frequencies. For larger focal spots, i.e. 0.16-0.18 mm for a Gaussian and 0.12-0.18 mm for a double Gaussian. intensity distribution, optimum magnifications exist which depend on the object frequency and breast thickness. System resolution (in terms of Sgeo) is maximized at M = 1.8-2.0 (all breast thicknesses) for Gaussian intensity distribution, and at M = 1.4-1.6 (breast thicknesses ≤ 4 cm) and M = 1.6-1.8 (thicker breasts) for double Gaussian.

Depth discrimination in diffuse optical transmission imaging by planar scanning off-axis fibers: initial applications to optical mammography.

We present a method for depth discrimination in parallel-plate, transmission mode, diffuse optical imaging. The method is based on scanning a set of detector pairs, where the two detectors in each pair are separated by a distance δDi along direction δ D i within the x-y scanning plane. A given optical inhomogeneity appears shifted by αi δ D i (with 0≤ αi ≤1) in the images collected with the two detection fibers of the i-th pair. Such a spatial shift can be translated into a measurement of the depth z of the inhomogeneity, and the depth measurements based on each detector pair are combined into a specially designed weighted average. This depth assessment is demonstrated on tissue-like phantoms for simple inhomogeneities such as straight rods in single-rod or multiple-rod configurations, and for more complex curved structures which mimic blood vessels in the female breast. In these phantom tests, the method has recovered the depth of single inhomogeneities in the central position of the phantom to within 4 mm of their actual value, and within 7 mm for more superficial inhomogeneities, where the thickness of the phantom was 65 mm. The application of this method to more complex images, such as optical mammograms, requires a robust approach to identify corresponding structures in the images collected with the two detectors of a given pair. To this aim, we propose an approach based on the inner product of the skeleton images collected with the two detectors of each pair, and we present an application of this approach to optical in vivo images of the female breast. This depth discrimination method can enhance the spatial information content of 2D projection images of the breast by assessing the depth of detected structures, and by allowing for 3D localization of breast tumors.

Technical note: Skin thickness measurements using high-resolution flat-panel cone-beam dedicated breast CT.

PURPOSE: To determine the mean and range of location-averaged breast skin thickness using high-resolution dedicated breast CT for use in Monte Carlo-based estimation of normalized glandular dose coefficients. METHODS: This study retrospectively analyzed image data from a clinical study investigating dedicated breast CT. An algorithm similar to that described by Huang et al. ["The effect of skin thickness determined using breast CT on mammographic dosimetry," Med. Phys. 35(4), 1199-1206 (2008)] was used to determine the skin thickness in 137 dedicated breast CT volumes from 136 women. The location-averaged mean breast skin thickness for each breast was estimated and the study population mean and range were determined. Pathology results were available for 132 women, and were used to investigate if the distribution of location-averaged mean breast skin thickness varied with pathology. The effect of surface fitting to account for breast curvature was also studied. RESULTS: The study mean (± interbreast SD) for breast skin thickness was 1.44 ± 0.25 mm (range: 0.87-2.34 mm), which was in excellent agreement with Huang et al. Based on pathology, pair-wise statistical analysis (Mann-Whitney test) indicated that at the 0.05 significance level, there were no significant difference in the location-averaged mean breast skin thickness distributions between the groups: benign vs malignant (p = 0.223), benign vs hyperplasia (p = 0.651), hyperplasia vs malignant (p = 0.229), and malignant vs nonmalignant (p = 0.172). CONCLUSIONS: Considering this study used a different clinical prototype system, and the study participants were from a different geographical location, the observed agreement between the two studies suggests that the choice of 1.45 mm thick skin layer comprising the epidermis and the dermis for breast dosimetry is appropriate. While some benign and malignant conditions could cause skin thickening, in this study cohort the location-averaged mean breast skin thickness distributions did not differ significantly with pathology. The study also underscored the importance of considering breast curvature in estimating breast skin thickness.