The aim of this study is to evaluate the accuracy of one of the commercial breast density quantization software. The estimation is performed by means of mammograms of five different 3D printed breast phantoms obtained from clinical patient images. Mammograms of these phantoms were acquired and analyzed using the density software. In addition, the spectra used by the automatic exposure control for each mammogram was accurately characterized and modeled using a previously published spectral model. The result is that the amount of estimated dense tissue is accurate to within an intra-phantom mean of 10% (std. dev. <4.4%), with negligible bias.
This work compares estimates of the radiation dose in mammography obtained using three different fibroglandular tissue distributions. Ninety volumetric images of patient breasts were acquired with a dedicated breast CT system and the voxels automatically classified as containing skin, adipose, or glandular tissue. The classified images underwent simulated mechanical compression to mimic the mammographic cranio-caudal acquisition. The voxels containing fibroglandular and adipose tissue were then distributed in the breast phantoms following three different methods: patient-based (i.e., maintaining the original distribution), homogeneous (i.e., each voxel is a homogeneous mixture of adipose and glandular tissue) and newly-proposed continuous (i.e., the glandular tissue is distributed according to a general model, derived from the patient breast CT data). All breast phantoms were used in Monte Carlo simulations to estimate the radiation dose. The results show that the doses estimated using the continuous fibroglandular tissue distribution agree within 3% of the doses estimated using the heterogeneous patient-based distribution, and that it leads to a dose reduction of 27% compared to the homogeneous distribution.
KEYWORDS: Breast, Tissues, Image compression, Digital breast tomosynthesis, 3D modeling, Medical physics, Image segmentation, X-rays, Mammography, Skin
Anthropomorphic digital breast phantoms are an essential part in the development, simulation, and optimisation of x-ray breast imaging systems. They could be used in many applications, such as running virtual clinical trials or developing dosimetry methods. 3D image modalities, such as breast computed tomography (BCT), provide high resolution images to help produce breast models with realistic internal tissue distribution. However, in order to mimic X-ray imaging procedures such as mammography or digital breast tomosynthesis, the breast model needs to be compressed. In this work, we describe a method to generate compressed breast phantoms using a biomechanical finite element (FE) model from BCT volumes, by simulating physically realistic tissue deformation. Unlike prior literature, we propose a new tissue interpolation methodology which avoids interpolating the deformation fields, resulting in the preservation of the breast tissue amount during the compression process and therefore increasing the accuracy of the deformation. In this study, a total of 88 BCT images were compressed in order to obtain a set of realistic phantoms. The information associated with the phantom (i.e. amount of glandular tissue and adipose tissue and total breast volume) is compared before and after compression (showing a correlation R of 0.99). Also, the same metrics were evaluated between compressed phantoms and VolparaTM measurements from breast tomosynthesis images (R=0.81 − 0.85). Furthermore, we include a 3D surface analysis and describe several medical physics applications in which our phantoms have been used: x-ray dosimetry, scattered radiation estimation or glandular tissue assessment.
Purpose: To evaluate whether combining a polychromatic reconstruction algorithm for breast CT with projection data acquired using alternating high and low energy spectra allows a significant dose reduction while maintaining image quality. Materials and Methods: A breast phantom was scanned on a clinical breast CT scanner using the automatic exposure control selected exposure at the regular spectrum with a tube voltage of 49 kV and a 1.576 mm aluminum filter and with a second, higher energy spectrum created by adding a 0.254 mm copper filter. An acquisition with spectrum switching was simulated by interleaving projections from the standard and high energy datasets, and a previously developed polychromatic reconstruction algorithm was modified to reconstruct the breast CT images. Image quality was assessed using the signal difference-to-noise ratio (SDNR) of a high and a low contrast target present in the phantom. A Monte Carlo simulation was performed to determine the mean glandular dose (MGD) of each scan. Results: Acquisition of the simulated scan with spectrum switching would result in an MGD of 6.57 mGy, compared to the standard acquisition MGD of 10.4 mGy, a reduction of 37%. At the same time, the measured SDNR of the mixed spectrum reconstructions was slightly higher than that of the standard acquisition, with an increase in SDNR of 6.6% (p < 0.01) for the high contrast target and 5.3% (p = 0.12) for the low contrast target. Conclusion: Our approach combining a polychromatic reconstruction algorithm for breast CT with an advanced acquisition protocol using alternating high and low energy spectra can lower dose by at least a third without loss of target SDNR
An accurate measurement of the breast glandular fraction, or glandularity, is important for many research and clinical applications, such as breast cancer risk assessment. We propose a method to estimate the loss of glandular tissue detail due to the limited voxel size in tomographic images of the breast. CT images of a breast tissue specimen were acquired using a CdTe single photon counting detector (nominal pixel size of 60 μm) and using a monochromatic synchrotron radiation x-ray beam. Images were reconstructed using a filtered backprojection algorithm at seven different voxel sizes (range 60-420 μm, with a 60 μm step) and twelve groups of Regions of Interest (ROIs) with different percentage and patterns of glandular tissue were extracted. All ROIs within each group contained the same portion of the image (and therefore the same glandular fraction) reconstructed at a different voxel size. The glandular tissue was segmented and the glandularity calculated for all ROIs. A machine learning algorithm was trained on the glandularity values as a function of reconstruction voxel size. After the training was completed, the algorithm could estimate, given a tomographic breast image reconstructed at a given voxel size with a certain glandularity, the increase (or decrease) of glandularity if the same image were reconstructed with a smaller (or larger) voxel dimension. The algorithm was tested on six additional groups of ROIs, resulting in an average relative standard error between the calculated and estimated glandularity of 0.02 ± 0.016.
Two dosimetric quantities [mean glandular dose (MGD) and entrance surface air kerma (ESAK)] and the diagnostic performance of phase-contrast mammography with synchrotron radiation (MSR) are compared to conventional digital mammography (DM). Seventy-one patients (age range, 41 to 82 years) underwent MSR after a DM examination if questionable or suspicious breast abnormalities were not clarified by ultrasonography. The MGD and the ESAK delivered in both examinations were evaluated and compared. Two on-site radiologists rated the images in consensus according to the Breast Imaging Reporting and Data System assessment categories, which were then correlated with the final diagnoses by means of statistical generalized linear models (GLMs). Receiver operating characteristic curves were also used to assess the diagnostic performance by comparing the area under the curve (AUC). An important MGD and ESAK reduction was observed in MSR due to the monoenergetic beam. In particular, an average 43% reduction was observed for the MGD and a reduction of more than 50% for the ESAK. GLM showed higher diagnostic accuracy, especially in terms of specificity, for MSR, confirmed by AUC analysis (p<0.001). The study design implied that the population was characterized by a high prevalence of disease and that the radiologists, who read the DM images before referring the patient to MSR, could have been influenced in their assessments. Within these limitations, the use of synchrotron radiation with the phase-contrast technique applied to mammography showed an important dose reduction and a higher diagnostic accuracy compared with DM. These results could further encourage research on the translation of x-ray phase-contrast imaging into the clinics.
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