Translating contrast enhanced computed tomography images to liver radioembolization dose distribution for more comprehensively indicating patients.

Objective.Contrast-enhanced computed tomography (CECT) is commonly used in the pre-treatment evaluation of liver Y-90 radioembolization feasibility. CECT provides detailed imaging of the liver and surrounding structures, allowing healthcare providers to assess the size, location, and characteristics of liver tumors prior to the treatment. Here we propose a method for translating CECT images to an expected dose distribution for tumor(s) and normal liver tissue.Approach.A pre-procedure CECT is used to obtain an iodine arterial-phase distribution by subtracting the non-contrast CT from the late arterial phase. The liver segments surrounding the targeted tumor are selected using Couinaud's method. The resolution of the resulting images is then degraded to match the resolution of the positron emission tomography (PET) images, which can image the Y-90 activity distribution post-treatment. The resulting images are then used in the same way as PET images to compute doses using the local deposition method. CECT images from three patients were used to test this method retrospectively and were compared with Y-90 PET-based dose distributions through dose volume histograms.Main results.Results show a concordance between predicted and delivered Y-90 dose distributions with less than 10% difference in terms of mean dose, for doses greater than 10% of the 98th percentile (D2%).Significance.CECT-derived predictions of Y-90 radioembolization dose distributions seem promising as a supplementary tool for physicians when assessing treatment feasibility. This dosimetry prediction method could provide a more comprehensive pre-treatment evaluation-offering greater insights than a basic assessment of tumor opacification on CT images.

Joint AAPM Task Group 282/EFOMP Working Group Report: Breast dosimetry for standard and contrast-enhanced mammography and breast tomosynthesis.

Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system. Many of these simplifications were necessary, for the most part, due to the need to limit the computational cost of obtaining the required dose conversion coefficients decades ago, when these models were first implemented. With the advancements in computational power, and to address most of the known limitations of previous breast dosimetry methods, a new breast dosimetry method, based on new breast models, has been developed, implemented, and tested. This model, developed jointly by the American Association of Physicists in Medicine and the European Federation for Organizations of Medical Physics, is applicable to standard mammography, digital breast tomosynthesis, and their contrast-enhanced variants. In addition, it includes models of the breast in both the cranio-caudal and the medio-lateral oblique views. Special emphasis was placed on the breast and system models used being based on evidence, either by analysis of large sets of patient data or by performing measurements on imaging devices from a range of manufacturers. Due to the vast number of dose conversion coefficients resulting from the developed model, and the relative complexity of the calculations needed to apply it, a software program has been made available for download or online use, free of charge, to apply the developed breast dosimetry method. The program is available for download or it can be used directly online. A separate User's Guide is provided with the software.

Microcalcification detectability in breast CT images using CNN observers.

BACKGROUND: Breast computed tomography (CT) is an emerging breast imaging modality, and ongoing developments aim to improve breast CT's ability to detect microcalcifications. To understand the effects of different parameters on microcalcification detectability, a virtual clinical trial study was conducted using hybrid images and convolutional neural network (CNN)-based model observers. Mathematically generated microcalcifications were embedded into breast CT data sets acquired at our institution, and parameters related to calcification size, calcification contrast, cluster diameter, cluster density, and image display method (i.e., single slices, slice averaging, and maximum-intensity projections) were evaluated for their influence on microcalcification detectability. PURPOSE: To investigate the individual effects and the interplay of parameters affecting microcalcification detectability in breast CT. METHODS: Spherical microcalcifications of varying diameters (0.04, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40 mm) and native intensities were computer simulated to portray the partial volume effects of the imaging system. Calcifications were mathematically embedded into 109 patient breast CT volume data sets as individual calcifications or as clusters of calcifications. Six numbers of calcifications (1, 3, 5, 7, 10, 15) distributed within six cluster diameters (1, 3, 5, 6, 8, 10 mm) were simulated to study the effect of cluster density. To study the role of image display method, 2D regions of interest (ROIs) and 3D volumes of interest (VOIs) were generated using single slice extraction, slice averaging, and maximum-intensity projection (MIP). 2D and 3D CNNs were trained on the ROIs and VOIs, and receiver operating characteristic (ROC) curve analysis was used to evaluate detection performance. The area under the ROC curve (AUC) was used as the primary performance metric. RESULTS: Detection performance decreased with increasing section thickness, and peak detection performance occurred using the native section thickness (0.2 mm) and MIP display. The MIP display method, despite using a single slice, yielded comparable performance to the native section thickness, which employed 50 slices. Reduction in slices did not sacrifice detection accuracy and provided significant computational advantages over multi-slice image volumes. Larger cluster diameters resulted in reduced overall detectability, while smaller cluster diameters led to increased detectability. Additionally, we observed that the presence of more calcifications within a cluster improved the overall detectability, while fewer calcifications decreased it. CONCLUSIONS: As breast CT is still a relatively new breast imaging modality, there is an ongoing need to identify optimal imaging protocols. This work demonstrated the utility of MIP presentation for displaying image volumes containing microcalcification clusters. It is likely that human observers may also benefit from viewing MIPs compared to individual slices. The results of this investigation begin to elucidate how model observers interact with microcalcification clusters in a 3D volume, and will be useful for future studies investigating a broader set of parameters related to breast CT.

Model observer performance in contrast-enhanced lesions in breast CT: The influence of contrast concentration on detectability.

BACKGROUND: The use of iodine-based contrast agent for better delineation of tumors in breast CT (bCT) has been shown to be compelling, similar to the tumor enhancement in contrast-enhanced breast MRI. Contrast-enhanced bCT (CE-bCT) is a relatively new tool, and a structured evaluation of different imaging parameters at play has yet to be conducted. In this investigation, data sets of acquired bCT images from 253 patients imaged at our institution were used in concert with simulated mathematically inserted spherical contrast-enhanced lesions to study the role of contrast enhancement on detectability. PURPOSE: To quantitatively evaluate the improvement in lesion detectability due to contrast enhancement across lesion diameter, section thickness, view plane, and breast density using a pre-whitened matched filter (PWMF) model observer. METHODS: The relationship between iodine concentration and Hounsfield units (HU) was measured using spectral modeling. The lesion enhancement from clinical CE-bCT images in 22 patients was evaluated, and the average contrast enhancement (ΔHU) was determined. Mathematically generated spherical mass lesions of varying diameters (1, 3, 5, 9, 11, 15 mm) and contrast enhancement levels (0, 0.25, 0.50, 0.75, 1) were inserted at random locations in 253 actual patient bCT datasets. Images with varying thicknesses (0.4-19.8 mm) were generated by slice averaging, and the role of view plane (coronal and axial planes) was studied. A PWMF was used to generate receiver operating characteristic (ROC) curves across parameters of lesion diameter, contrast enhancement, section thickness, view plane, and breast density. The area under the ROC curve (AUC) was used as the primary performance metric, generated from over 90,000 simulated lesions. RESULTS: An average 20% improvement (ΔAUC = 0.1) in lesion detectability due to contrast enhancement was observed across lesion diameter, section thickness, breast density, and view plane. A larger improvement was observed when stratifying patients based on breast density. For patients with VGF ≤ 40%, detection performance improved up to 20% (until AUC →1), and for patients with denser breasts (VGF > 40%), detection performance improved more drastically, ranging from 20% to 80% for 1- and 5-mm lesions. For the 1 mm lesion, detection performance raised slightly at the 1.2 mm section thickness before falling off as thickness increased. For larger lesions, detection performance was generally unaffected as section thickness increased up until it reached 5.8 mm, where performance began to decline. Detection performance was higher in the axial plane compared to the coronal plane for smaller lesions and thicker sections. CONCLUSIONS: For emerging diagnostic tools like CE-bCT, it is important to optimize imaging protocols for lesion detection. In this study, we found that intravenous contrast can be used to detect small lesions in dense breasts. Optimal section thickness for detectability has dependencies on breast density and lesion size, therefore, display thickness should be adjusted in real-time using display software. These findings may be useful for the development of CE-bCT as well as other x-ray-based breast imaging modalities.

Comparisons of glandular breast dose between digital mammography, tomosynthesis and breast CT based on anthropomorphic patient-derived breast phantoms.

PURPOSE: To evaluate the bias to the mean glandular dose (MGD) estimates introduced by the homogeneous breast models in digital breast tomosynthesis (DBT) and to have an insight into the glandular dose distributions in 2D (digital mammography, DM) and 3D (DBT and breast dedicated CT, BCT) x-ray breast imaging by employing breast models with realistic glandular tissue distribution and organ silhouette. METHODS: A Monte Carlo software for DM, DBT and BCT simulations was adopted for the evaluation of glandular dose distribution in 60 computational anthropomorphic phantoms. These computational phantoms were derived from 3D breast images acquired via a clinical BCT scanner. RESULTS: g·c·s·T conversion coefficients based on homogeneous breast model led to a MGD overestimate of 18% in DBT when compared to MGD estimated via anthropomorphic phantoms; this overestimate increased up to 21% for recently computed DgNDBT conversion coefficients. The standard deviation of the glandular dose distribution in BCT resulted 60% lower than in DM and 55% lower than in DBT. The glandular dose peak - evaluated as the average value over the 5% of the gland receiving the highest dose - is 2.8 times the MGD in DM, this factor reducing to 2.6 and 1.6 in DBT and BCT, respectively. CONCLUSIONS: Conventional conversion coefficients for MGD estimates based on homogeneous breast models overestimate MGD by 18%, when compared to MGD estimated via anthropomorphic phantoms. The ratio between the peak glandular dose and the MGD is 2.8 in DM. This ratio is 8% and 75% higher than in DBT and BCT, respectively.

High-resolution µ CT imaging for characterizing microcalcification detection performance in breast CT.

Purpose: To demonstrate the utility of high-resolution micro-computed tomography ( µ CT ) for determining ground-truth size and shape properties of calcium grains for evaluation of detection performance in breast CT (bCT). Approach: Calcium carbonate grains ( ∼ 200 µ m ) were suspended in 1% agar solution to emulate microcalcifications ( µ Calcs ) within a fibroglandular tissue background. Ground-truth imaging was performed on a commercial µ CT scanner and was used for assessing calcium-grain size and shape, and for generating µ Calc signal profiles. Calcium grains were placed within a realistic breast-shaped phantom and imaged on a prototype bCT system at 3- and 6-mGy mean glandular dose (MGD) levels, and the non-prewhitening detectability was assessed. Additionally, the µ CT -derived signal profiles were used in conjunction with the bCT system characterization (MTF and NPS) to obtain predictions of bCT detectability. Results: Estimated detectability of the calcium grains on the bCT system ranged from 2.5 to 10.6 for 3 mGy and from 3.8 to 15.3 for 6 mGy with large fractions of the grains meeting the Rose criterion for visibility. Segmentation of µ CT images based on morphological operations produced accurate results in terms of segmentation boundaries and segmented region size. A regression model linking bCT detectability to µ Calc parameters indicated significant effects of µ Calc size and vertical position within the breast phantom. Detectability using µ CT -derived detection templates and bCT statistical properties (MTF and NPS) were in good correspondence with those measured directly from bCT ( R 2 > 0.88 ). Conclusions: Parameters derived from µ CT ground-truth data were shown to produce useful characterizations of detectability when compared to estimates derived directly from bCT. Signal profiles derived from µ CT imaging can be used in conjunction with measured or hypothesized statistical properties to evaluate the performance of a system, or system component, that may not currently be available.

Computer-aided diagnosis of masses in breast computed tomography imaging: deep learning model with combined handcrafted and convolutional radiomic features.

Purpose: A computer-aided diagnosis (CADx) system for breast masses is proposed, which incorporates both handcrafted and convolutional radiomic features embedded into a single deep learning model. Approach: The model combines handcrafted and convolutional radiomic signatures into a multi-view architecture, which retrieves three-dimensional (3D) image information by simultaneously processing multiple two-dimensional mass patches extracted along different planes through the 3D mass volume. Each patch is processed by a stream composed of two concatenated parallel branches: a multi-layer perceptron fed with automatically extracted handcrafted radiomic features, and a convolutional neural network, for which discriminant features are learned from the input patches. All streams are then concatenated together into a final architecture, where all network weights are shared and the learning occurs simultaneously for each stream and branch. The CADx system was developed and tested for diagnosis of breast masses ( N = 284 ) using image datasets acquired with independent dedicated breast computed tomography systems from two different institutions. The diagnostic classification performance of the CADx system was compared against other machine and deep learning architectures adopting handcrafted and convolutional approaches, and three board-certified breast radiologists. Results: On a test set of 82 masses (45 benign, 37 malignant), the proposed CADx system performed better than all other model architectures evaluated, with an increase in the area under the receiver operating characteristics curve (AUC) of 0.05 ± 0.02 , and achieving a final AUC of 0.947, outperforming the three radiologists ( AUC = 0.814 - 0.902 ). Conclusions: In conclusion, the system demonstrated its potential usefulness in breast cancer diagnosis by improving mass malignancy assessment.

Dataset of patient-derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography.

PURPOSE: To present a dataset of computational digital breast phantoms derived from high-resolution three-dimensional (3D) clinical breast images for the use in virtual clinical trials in two-dimensional (2D) and 3D x-ray breast imaging. ACQUISITION AND VALIDATION METHODS: Uncompressed computational breast phantoms for investigations in dedicated breast CT (BCT) were derived from 150 clinical 3D breast images acquired via a BCT scanner at UC Davis (California, USA). Each image voxel was classified in one out of the four main materials presented in the field of view: fibroglandular tissue, adipose tissue, skin tissue, and air. For the image classification, a semi-automatic software was developed. The semi-automatic classification was compared via manual glandular classification performed by two researchers. A total of 60 compressed computational phantoms for virtual clinical trials in digital mammography (DM) and digital breast tomosynthesis (DBT) were obtained from the corresponding uncompressed phantoms via a software algorithm simulating the compression and the elastic deformation of the breast, using the tissue's elastic coefficient. This process was evaluated in terms of glandular fraction modification introduced by the compression procedure. The generated cohort of 150 uncompressed computational breast phantoms presented a mean value of the glandular fraction by mass of 12.3%; the average diameter of the breast evaluated at the center of mass was 105 mm. Despite the slight differences between the two manual segmentations, the resulting glandular tissue segmentation did not consistently differ from that obtained via the semi-automatic classification. The difference between the glandular fraction by mass before and after the compression was 2.1% on average. The 60 compressed phantoms presented an average glandular fraction by mass of 12.1% and an average compressed thickness of 61 mm. DATA FORMAT AND ACCESS: The generated digital breast phantoms are stored in DICOM files. Image voxels can present one out of four values representing the different classified materials: 0 for the air, 1 for the adipose tissue, 2 for the glandular tissue, and 3 for the skin tissue. The generated computational phantoms datasets were stored in the Zenodo public repository for research purposes (http://doi.org/10.5281/zenodo.4529852, http://doi.org/10.5281/zenodo.4515360). POTENTIAL APPLICATIONS: The dataset developed within the INFN AGATA project will be used for developing a platform for virtual clinical trials in x-ray breast imaging and dosimetry. In addition, they will represent a valid support for introducing new breast models for dose estimates in 2D and 3D x-ray breast imaging and as models for manufacturing anthropomorphic physical phantoms.

Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging.

PURPOSE: To develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images. METHODS: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). RESULTS: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96). CONCLUSIONS: The proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.

A prototype Multi-X-ray-source array (MXA) for digital breast tomosynthesis.

The design and testing of a prototype Multi-X-ray-source Array (MXA) for digital breast tomosynthesis is reported. The MXA is comprised of an array of tungsten filament cathodes with focus cup grid-controlled modulation and a common rotating anode housed in a single vacuum envelope. Prototypes consisting of arrays of three-source elements and eleven-source-elements were fabricated and evaluated. The prototype sources demonstrated focal spot sizes of 0.3 mm at 45 kV with 50 mA. Measured x-ray spectra were consistent with the molybdenum anode employed, and the tube output (air kerma) was between 0.6 mGy/100 mAs at 20 kV and 17 mGy/100 mAs at 45 kV with a distance of 100 cm. HVL measurements ranged from 0.5 mm Al at 30 kV to 0.8 mm Al at 45 kV, and x-ray pulse widths were varied from 20 ms to 110 ms at operating frequencies ultimately to be limited by source turn-on/off times of ∼1 ms. Initial results of reconstructed tomographic data are presented.

High resolution microcalcification signal profiles for dedicated breast CT.

This study introduces a methodology for generating high resolution signal profiles of microcalcification (MC) grains for validating breast CT (bCT) systems. A physical MC phantom was constructed by suspending calcium carbonate grains in an agar solution emulating MCs in a fibroglandular tissue background. Additionally, small Teflon spheres (2.4 mm diameter) were embedded in the agar solution for the purpose of fiducial marking and assessment of segmentation accuracy. The MC phantom was imaged on a high resolution (34 µm) commercial small-bore µCT scanner at high dose, and the images were used as the gold-standard for assessing MC size and for generating high resolution signal profiles of each MC. High-dose bCT scans of the MC phantom suspended in-air were acquired using 1 × 1 binning mode (75 µm dexel pitch) by averaging three repeat scans to produce a single low-noise reconstruction of the MC phantom. The high resolution µCT volume data set was then registered with the corresponding bCT data set after correcting for the bCT system spatial resolution. Microcalcification signal profiles constructed using low-noise bCT images were found to be in good agreement with those generated using the µCT scanner with all differences < 10% within the VOI surrounding each MC. The MC signal profiles were used as detection templates for a non-prewhitening-matched-filter model observer for scans acquired in a realistic breast phantom at 3, 6, and 9 mGy mean glandular dose. MC detectability using signal templates derived from bCT were shown to be in good agreement with those generated using µCT.

Conspicuity of suspicious breast lesions on contrast enhanced breast CT compared to digital breast tomosynthesis and mammography.

OBJECTIVE: Compare conspicuity of suspicious breast lesions on contrast-enhanced dedicated breast CT (CEbCT), tomosynthesis (DBT) and digital mammography (DM). METHODS: 100 females with BI-RADS 4/5 lesions underwent CEbCT and/or DBT prior to biopsy in this IRB approved, HIPAA compliant study. Two breast radiologists adjudicated lesion conspicuity scores (CS) for each modality independently. Data are shown as mean CS ±standard deviation. Two-sided t-test was used to determine significance between two modalities within each subgroup. Multiple comparisons were controlled by the false-discovery rate set to 5%. RESULTS: 50% of studied lesions were biopsy-confirmed malignancies. Malignant masses were more conspicuous on CEbCT than on DBT or DM (9.7 ±0.5, n = 25; 6.8 ± 3.1, n = 15; 6.7 ± 3.0, n = 27; p < 0.05). Malignant calcifications were equally conspicuous on all three modalities (CEbCT 8.7 ± 0.8, n = 18; DBT 8.5 ± 0.6, n = 15; DM 8.8 ± 0.7, n = 23; p = NS). Benign masses were equally conspicuous on CEbCT (6.6 ± 4.1, n = 22); DBT (6.4 ± 3.8, n = 17); DM (5.9 ± 3.6, n = 24; p = NS). Benign calcifications CS were similar between DBT (8.5 ± 1.0, n = 17) and DM (8.8 ± 0.8, n = 26; p = NS) but less conspicuous on CEbCT (4.0 ± 2.9, n = 25, p < 0.001). 55 females were imaged with all modalities. Results paralleled the entire cohort. 69%(n = 62) of females imaged by CEbCT had dense breasts. Benign/malignant lesion CSs in dense/non-dense categories were 4.8 ± 3.7, n = 33, vs 6.0 ± 3.9, n = 14, p = 0.35; 9.2 ± 0.9, n = 29 vs. 9.4 ± 0.7, n = 14; p = 0.29, respectively. CONCLUSION: Malignant masses are more conspicuous on CEbCT than DM or DBT. Malignant microcalcifications are equally conspicuous on all three modalities. Benign calcifications remain better visualized by DM and DBT than with CEbCT. We observed no differences in benign masses on all modalities. CS of both benign and malignant lesions were independent of breast density. ADVANCES IN KNOWLEDGE: CEbCT is a promising diagnostic imaging modality for suspicious breast lesions.

Updated breast CT dose coefficients (DgNCT ) using patient-derived breast shapes and heterogeneous fibroglandular distributions.

PURPOSE: The purpose of this work was to generate uncompressed heterogeneous breast phantom models using size-dependent fibroglandular distributions derived from a large cohort of breast CT (bCT) datasets, and to compare differences in normalized glandular dose coefficients for bCT "DgNCT " when the realistic heterogeneous model is considered relative to the simple, homogeneous model used in the past. METHODS: A cohort of 274 segmented bCT datasets were used to quantify the fibroglandular tissue distribution within the breast parenchyma. Each dataset was interpolated to an isotropic voxel size of 0.25 mm and the breast center-of-mass was aligned for all coronal slices. Each aligned dataset was converted into two binarized volumes representing voxels containing only glandular tissue "G(x,y,z)" and voxels containing glandular or adipose tissue "AG(x,y,z)". The datasets were classified by volume in accordance with previously reported size-dependent, breast-shaped phantoms. Within the five groups - each containing on average 55 datasets, all G(x,y,z) and AG(x,y,z) volumes were summed separately representing the cumulative distribution of glandular tissue or breast parenchyma (glandular + adipose), respectively. G(x,y,z) was divided by AG(x,y,z) on a voxel-by-voxel basis resulting in a glandular fraction "GF(x,y,z)" distribution for each phantom size. The GF(x,y,z) distributions were used to construct heterogeneous mathematical phantoms for the small, median, and large breast sizes with a 1.5 mm skin thickness - based on previously reported measurements from bCT, and a 5 mm skin thickness for comparison with outdated assumptions of skin thickness. A subset of 15 bCT datasets from the cohort (five for each breast size) were used to construct voxelized patient models for validation of the heterogeneous phantom models. Monte Carlo techniques were used to estimate monoenergetic DgN(E)C T values for photon energies from 9 to 70 keV (in 1 keV intervals) using the mathematical phantoms composed of either heterogeneous or homogeneous breast parenchyma. Polyenergetic (pDgNCT ) coefficients were determined by weighting the DgN(E)C T values by x-ray spectra tuned to the beam characteristic of breast CT. Dose coefficients were compared between the two breast compositions for each volume class, breast density, and skin thickness. RESULTS: For photon energies ≲45 keV, the homogeneous model overestimates DgN(E) values relative to the realistic heterogeneous model sorted into five volume classes. The 5 mm skin thickness underestimates DgN(E) values relative to the realistic 1.5 mm thickness for lower energies and the differences diminish up to 70 keV. Averaged across all phantom sizes the homogeneous model overestimates pDgNCT by 5.7% and 23.3% for the 60 kV W/Cu and 49 kV W/Al spectra, respectively. The heterogeneous model was also found to be in agreement with the voxelized bCT patient models with pDgNCT differences less than 2.3% and 5.2% for the 60 kV W/Cu and 49 kV W/Al spectra, respectively, across all phantom sizes. CONCLUSION: Anatomically accurate heterogeneous phantom models were developed using bCT image-derived fibroglandular tissue distributions. These new models improve the accuracy of bCT dosimetry, and in conjunction with previous models for mammography, may help in providing a more universally accepted breast dosimetry model.

Estimating a size-specific dose for helical head CT examinations using Monte Carlo simulation methods.

PURPOSE: Size-specific dose estimates (SSDE) conversion factors have been determined by AAPM Report 204 to adjust CTDIvol to account for patient size but were limited to body CT examinations. The purpose of this work was to determine conversion factors that could be used for an SSDE for helical, head CT examinations for patients of different sizes. METHODS: Validated Monte Carlo (MC) simulation methods were used to estimate dose to the center of the scan volume from a routine, helical head examination for a group of patient models representing a range of ages and sizes. Ten GSF/ICRP voxelized phantom models and five pediatric voxelized patient models created from CT image data were used in this study. CT scans were simulated using a Siemens multidetector row CT equivalent source model. Scan parameters were taken from the AAPM Routine Head protocols for a fixed tube current (FTC), helical protocol, and scan lengths were adapted to the anatomy of each patient model. MC simulations were performed using mesh tallies to produce voxelized dose distributions for the entire scan volume of each model. Three tally regions were investigated: (1) a small 0.6 cc volume at the center of the scan volume, (2) 0.8-1.0 cm axial slab at the center of the scan volume, and (3) the entire scan volume. Mean dose to brain parenchyma for all three regions was calculated. Mean bone dose and a mass-weighted average dose, consisting of brain parenchyma and bone, were also calculated for the slab in the central plane and the entire scan volume. All dose measures were then normalized by CTDIvol for the 16 cm phantom (CTDIvol,16 ). Conversion factors were determined by calculating the relationship between normalized doses and water equivalent diameter (Dw ). RESULTS: CTDIvol,16 -normalized mean brain parenchyma dose values within the 0.6 cc volume, 0.8-1.0 cm central axial slab, and the entire scan volume, when parameterized by Dw , had an exponential relationship with a coefficient of determination (R2 ) of 0.86, 0.84, and 0.88, respectively. There was no statistically significant difference between the conversion factors resulting from these three different tally regions. Exponential relationships between CTDIvol,16 -normalized mean bone doses had R2 values of 0.83 and 0.87 for the central slab and for the entire scan volume, respectively. CTDIvol,16 -normalized mass-weighted average doses had R2 values of 0.39 and 0.51 for the central slab and for the entire scan volume, respectively. CONCLUSIONS: Conversion factors that describe the exponential relationship between CTDIvol,16 -normalized mean brain dose and a size metric (Dw ) for helical head CT examinations have been reported for two different interpretations of the center of the scan volume. These dose descriptors have been extended to describe the dose to bone in the center of the scan volume as well as a mass-weighted average dose to brain and bone. These may be used, when combined with other efforts, to develop an SSDE dose coefficients for routine, helical head CT examinations.

Average glandular dose coefficients for pendant-geometry breast CT using realistic breast phantoms.

PURPOSE: To design volume-specific breast phantoms from breast CT (bCT) data sets and estimate the associated normalized mean glandular dose coefficients for breast CT using Monte Carlo methods. METHODS: A large cohort of bCT data sets (N = 215) was used to evaluate breast volume into quintiles (plus the top 5%). The average radius profile was then determined for each of the six volume-specific groups and used to both fabricate physical phantoms and generate mathematical phantoms (V1-V6; "V" denotes classification by volume). The MCNP6 Monte Carlo code was used to model a prototype bCT system fabricated at our institution; and this model was validated against physical measurements in the fabricated phantoms. The mathematical phantoms were used to simulate normalized mean glandular dose coefficients for both monoenergetic source photons "DgNCT (E)" (8-70 keV in 1 keV intervals) and polyenergetic x-ray beams "pDgNCT " (35-70 kV in 1 kV intervals). The Monte Carlo code was used to study the influence of breast size (V1 vs. V5) and glandular fraction (6.4% vs. 45.8%) on glandular dose. The pDgNCT coefficients estimated for the V1, V3, and V5 phantoms were also compared to those generated using simple, cylindrical phantoms with equivalent volume and two geometrical constraints including; (a) cylinder radius determined at the breast phantom chest wall "Rcw "; and (b) cylinder radius determined at the breast phantom center-of-mass "RCOM ". RESULTS: Satisfactory agreement was observed for dose estimations using MCNP6 compared with both physical measurements in the V1, V3, and V5 phantoms (R2 = 0.995) and reference bCT dose coefficients using simple phantoms (R2 = 0.999). For a 49 kV spectrum with 1.5 mm Al filtration, differences in glandular fraction [6.5% (5th percentile) vs. 45.8% (95th percentile)] had a 13.2% influence on pDgNCT for the V3 phantom, and differences in breast size (V1 vs. V5) had a 16.6% influence on pDgNCT for a breast composed of 17% (median) fibroglandular tissue. For cylindrical phantoms with a radius of RCOM , the differences were 1.5%, 0.1%, and 2.1% compared with the V1, V3, and V5 phantoms, respectively. CONCLUSION: Breast phantoms were designed using a large cohort of bCT data sets across a range of six breast sizes. These phantoms were then fabricated and used for the estimation of glandular dose in breast CT. The mathematical phantoms and associated glandular dose coefficients for a range of breast sizes (V1-V6) and glandular fractions (5th to 95th percentiles) are available for interested users.

Two-dimensional breast dosimetry improved using three-dimensional breast image data.

Conventional mammographic dosimetry has been developed over the past 40 years. Prior to the availability of high-resolution three-dimensional breast images, certain assumptions about breast anatomy were required. These assumptions were based on the information evident on two-dimensional mammograms; they included assumptions of thick skin, a uniform mixture of glandular and adipose tissue, and a median breast density of 50%. Recently, the availability of high-resolution breast CT studies has provided more accurate data about breast anatomy, and this, in turn, has provided the opportunity to update mammographic dosimetry. Based on hundreds of data sets on breast CT volume, a number of studies were performed and reported which have shed light on the basic breast anatomy specific to dosimetry in mammography. It was shown that the average skin thickness of the breast was approximately 1.5 mm, instead of the 4 or 5 mm in the past. In another study, 3-D breast CT data sets were used for validation of the 2-D algorithm developed at the University of Toronto, leading to data suggesting that the overall average breast density is of the order of 16-20%, rather than the previously assumed 50%. Both of these assumptions led to normalized glandular dose (DgN) coefficients which are higher than those of the past. However, a comprehensive study on hundreds of breast CT data sets confirmed the findings of other investigators that there is a more centralized average location of glandular tissue within the breast. Combined with Monte Carlo studies for dosimetry, when accurate models of the distribution of glandular tissue were used, a 30% reduction in the radiation dose (as determined by the DgN coefficient) was found as an average across typical molybdenum and tungsten spectra used clinically. The 30% average reduction was found even when the thinner skin and the lower average breast density were considered. The article reviews three specific anatomic observations made possible based on high-resolution breast CT data by several different research groups. It is noted that, periodically, previous assumptions pertaining to dosimetry can be updated when new information becomes available, so that more accurate dosimetry is achieved. Dogmatic practices typically change slowly, but it is hoped that the medical physics community will continue to evaluate changes in DgN coefficients such that they become more accurate.

Generation and analysis of clinically relevant breast imaging x-ray spectra.

PURPOSE: The purpose of this work was to develop and make available x-ray spectra for some of the most widely used digital mammography (DM), breast tomosynthesis (BT), and breast CT (bCT) systems in North America. METHODS: The Monte Carlo code MCNP6 was used to simulate minimally filtered (only beryllium) x-ray spectra at 8 tube potentials from 20 to 49 kV for DM/BT, and 9 tube potentials from 35 to 70 kV for bCT. Vendor-specific anode compositions, effective anode angles, focal spot sizes, source-to-detector distances, and beryllium filtration were simulated. For each 0.5 keV energy bin in all simulated spectra, the fluence was interpolated using cubic splines across the range of simulated tube potentials to produce spectra in 1 kV increments from 20 to 49 kV for DM/BT and from 35 to 70 kV for bCT. The HVL of simulated spectra with conventional filtration (at 35 kV for DM/BT and 49 kV for bCT) was used to assess spectral differences resulting from variations in: (a) focal spot size (0.1 and 0.3 mm IEC), (b) solid angle at the detector (i.e., small and large FOV size), and (c) geometrical specifications for vendors that employ the same anode composition. RESULTS: Averaged across all DM/BT vendors, variations in focal spot and FOV size resulted in HVL differences of 2.2% and 0.9%, respectively. Comparing anode compositions separately, the HVL differences for Mo (GE, Siemens) and W (Hologic, Philips, and Siemens) spectra were 0.3% and 0.6%, respectively. Both the commercial Koning and prototype "Doheny" (UC Davis) bCT systems utilize W anodes with a 0.3 mm focal spot. Averaged across both bCT systems, variations in FOV size resulted in a 2.2% difference in HVL. In addition, the Koning spectrum was slightly harder than Doheny with a 4.2% difference in HVL. Therefore to reduce redundancy, a generic DM/BT system and a generic bCT system were used to generate the new spectra reported herein. The spectral models for application to DM/BT were dubbed the Molybdenum, Rhodium, and Tungsten Anode Spectral Models using Interpolating Cubic Splines (MASMICSM-T , RASMICSM-T , and TASMICSM-T ; subscript "M-T" indicating mammography and tomosynthesis). When compared against reference models (MASMIPM , RASMIPM , and TASMIPM ; subscript "M" indicating mammography), the new spectral models were in close agreement with mean differences of 1.3%, -1.3%, and -3.3%, respectively, across tube potential comparisons of 20, 30, and 40 kV with conventional filtration. TASMICSbCT -generated bCT spectra were also in close agreement with the reference TASMIP model with a mean difference of -0.8%, across tube potential comparisons of 35, 49, and 70 kV with 1.5 mm Al filtration. CONCLUSIONS: The Mo, Rh, and W anode spectra for application in DM and BT (MASMICSM-T , RASMICSM-T , and TASMICSM-T ) and the W anode spectra for bCT (TASMICSbCT ) as described in this study should be useful for individuals interested in modeling the performance of modern breast x-ray imaging systems including dual-energy mammography which extends to 49 kV. These new spectra are tabulated in spreadsheet form and are made available to any interested party.

Breast dose in mammography is about 30% lower when realistic heterogeneous glandular distributions are considered.

PURPOSE: Current dosimetry methods in mammography assume that the breast is comprised of a homogeneous mixture of glandular and adipose tissues. Three-dimensional (3D) dedicated breast CT (bCT) data sets were used previously to assess the complex anatomical structure within the breast, characterizing the statistical distribution of glandular tissue in the breast. The purpose of this work was to investigate the effect of bCT-derived heterogeneous glandular distributions on dosimetry in mammography. METHODS: bCT-derived breast diameters, volumes, and 3D fibroglandular distributions were used to design realistic compressed breast models comprised of heterogeneous distributions of glandular tissue. The bCT-derived glandular distributions were fit to biGaussian functions and used as probability density maps to assign the density distributions within compressed breast models. The MCNPX 2.6.0 Monte Carlo code was used to estimate monoenergetic normalized mean glandular dose "DgN(E)" values in mammography geometry. The DgN(E) values were then weighted by typical mammography x-ray spectra to determine polyenergetic DgN (pDgN) coefficients for heterogeneous (pDgNhetero) and homogeneous (pDgNhomo) cases. The dependence of estimated pDgN values on phantom size, volumetric glandular fraction (VGF), x-ray technique factors, and location of the heterogeneous glandular distributions was investigated. RESULTS: The pDgNhetero coefficients were on average 35.3% (SD, 4.1) and 24.2% (SD, 3.0) lower than the pDgNhomo coefficients for the Mo-Mo and W-Rh x-ray spectra, respectively, across all phantom sizes and VGFs when the glandular distributions were centered within the breast phantom in the coronal plane. At constant breast size, increasing VGF from 7.3% to 19.1% lead to a reduction in pDgNhetero relative to pDgNhomo of 23.6%-27.4% for a W-Rh spectrum. Displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width in the superior and inferior directions, resulted in a 37.3% and a -26.6% change in the pDgNhetero coefficient, respectively, relative to the centered distribution for the Mo-Mo spectrum. Lateral displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width, resulted in a 1.5% change in the pDgNhetero coefficient relative to the centered distribution for the W-Rh spectrum. CONCLUSIONS: Introducing bCT-derived heterogeneous glandular distributions into mammography phantom design resulted in decreased glandular dose relative to the widely used homogeneous assumption. A homogeneous distribution overestimates the amount of glandular tissue near the entrant surface of the breast, where dose deposition is exponentially higher. While these findings are based on clinically measured distributions of glandular tissue using a large cohort of women, future work is required to improve the classification of glandular distributions based on breast size and overall glandular fraction.