Average absorbed breast dose (2ABD): an easy radiation dose index for digital breast tomosynthesis

Background: To propose a practical and simple method to individually evaluate the average absorbed dose for digital breast tomosynthesis. Methods: The method is based on the estimate of incident air kerma (ka,i) on the breast surface. An analytical model was developed to calculate the ka,i from the tube voltage, tube load, breast thickness, x-ray tube yield, and anode-filter combination. A homogeneous phantom was employed to simulate the breast in experimental measurements and to assess the dose-depth relationship. The ka,i values were employed to calculate the “average absorbed breast dose” (2ABD) index. Four mammographic units were used to develop and test our method under many conditions close to clinical settings. The average glandular dose (AGD) calculated following the method described by Dance et al., and the 2ABD computed through our method (i.e., from the exposure parameters) were compared in a number of conditions. Results: A good agreement was obtained between the ka,i computed through our model and that measured under different clinical conditions: discrepancies < 6% were found in all conditions. 2ABD matches with a good accuracy the AGD for a 100% glandular-breast: the minimum, maximum, and mean differences were < 0.1%, 7%, and 2.4%, respectively; the discrepancies increase with decreasing breast glandularity. Conclusions: The proposed model, based on only few exposure parameters, represents a simple way to individually calculate an index, 2ABD, which can be interpreted as the average absorbed dose in a homogeneous phantom, approximating a 100% glandular breast. The method could be easily implemented in any mammographic device performing DBT

History of mammography: analysis of breast imaging diagnostic achievements over the last century

Breast cancer is the most common forms of cancer and a leading cause of mortality in women. Early and correct diagnosis is, therefore, essential to save lives. The development of diagnostic imaging applied to the breast has been impressive in recent years and the most used diagnostic test in the world is mammography, a low-dose X-ray technique used for imaging the breast. In the first half of the 20th century, the diagnosis was in practice only clinical, with consequent diagnostic delay and an unfavorable prognosis in the short term. The rise of organized mammography screening has led to a remarkable reduction in mortality through the early detection of breast malignancies. This historical review aims to offer a complete panorama of the development of mammography and breast imaging during the last century. Through this study, we want to understand the foundations of the pillar of radiology applied to the breast through to the most modern applications such as contrast-enhanced mammography (CEM), artificial intelligence, and radiomics. Understanding the history of the development of diagnostic imaging applied to the breast can help us understand how to better direct our efforts toward an increasingly personalized and effective diagnostic approach. The ultimate goal of imaging applied to the detection of breast malignancies should be to reduce mortality from this type of disease as much as possible. With this paper, we want to provide detailed documentation of the main steps in the evolution of breast imaging for the diagnosis of breast neoplasms; we also want to open up new scenarios where the possible current and future applications of imaging are aimed at being more precise and personalized

Comparison of different image reconstruction algorithms for Digital Breast Tomosynthesis and assessment of their potential to reduce radiation dose

Tese de mestrado, Engenharia Física, 2022, Universidade de Lisboa, Faculdade de CiênciasDigital Breast Tomosynthesis is a three-dimensional medical imaging technique that allows the view of sectional parts of the breast. Obtaining multiple slices of the breast constitutes an advantage in contrast to conventional mammography examination in view of the increased potential in breast cancer detectability. Conventional mammography, despite being a screening success, has undesirable specificity, sensitivity, and high recall rates owing to the overlapping of tissues. Although this new technique promises better diagnostic results, the acquisition methods and image reconstruction algorithms are still under research. Several articles suggest the use of analytic algorithms. However, more recent articles highlight the iterative algorithm’s potential for increasing image quality when compared to the former. The scope of this dissertation was to test the hypothesis of achieving higher quality images using iterative algorithms acquired with lower doses than those using analytic algorithms. In a first stage, the open-source Tomographic Iterative GPU-based Reconstruction (TIGRE) Toolbox for fast and accurate 3D x-ray image reconstruction was used to reconstruct the images acquired using an acrylic phantom. The algorithms used from the toolbox were the Feldkamp, Davis, and Kress, the Simultaneous Algebraic Reconstruction Technique, and the Maximum Likelihood Expectation Maximization algorithm. In a second and final state, the possibility of further reducing the radiation dose using image postprocessing tools was evaluated. A Total Variation Minimization filter was applied to the images reconstructed with the TIGRE toolbox algorithm that provided the best image quality. These were then compared to the images of the commercial unit used for the image acquisitions. With the use of image quality parameters, it was found that the Maximum Likelihood Expectation Maximization algorithm performance was the best of the three for lower radiation doses, especially with the filter. In sum, the result showed the potential of the algorithm in obtaining images with quality for low doses

Breast Tomosynthesis: Aspects on detection and perception of simulated lesions

The aim of this thesis was to investigate aspects on detectability of simulated lesions (microcalcifications and masses) in digital mammography (DM) and breast tomosynthesis (BT). Perception in BT image volumes were also investigated by evaluating certain reading conditions. The first study concerned the effect of system noise on the detection of masses and microcalcification clusters in DM images using a free-response task. System noise has an impact on image quality and is related to the dose level. It was found to have a substantial impact on the detection of microcalcification clusters, whereas masses were relatively unaffected. The effect of superimposed tissue in DM is the major limitation hampering the detection of masses. BT is a three-dimensional technique that reduces the effect of superimposed tissue. In the following two studies visibility was quantified for both imaging modalities in terms of the required contrast at a fixed detection performance (92% correct decisions). Contrast detail plots for lesions with sizes 0.2, 1, 3, 8 and 25 mm were generated. The first study involved only an in-plane BT slice, where the lesion centre appeared. The second study repeated the same procedure in BT image volumes for 3D distributed microcalcification clusters and 8 mm masses at two dose levels. Both studies showed that BT needs substantially less contrast than DM for lesions above 1 mm. Furthermore, the contrast threshold increased as the lesion size increased for both modalities. This is in accordance with the reduced effect of superimposed tissue in BT. For 0.2 mm lesions, substantially more contrast was needed. At equal dose, DM was better than BT for 0.2 mm lesions and microcalcification clusters. Doubling the dose substantially improved the detection in BT. Thus, system noise has a substantial impact on detection. The final study evaluated reading conditions for BT image volumes. Four viewing procedures were assessed: free scroll browsing only or combined with initial cine loops at frame rates of 9, 14 and 25 fps. They were viewed on a wide screen monitor placed in vertical or horizontal positions. A free-response task and eye tracking were utilized to record the detection performance, analysis time, visual attention and search strategies. Improved reading conditions were found for horizontally aligned BT image volumes when using free scroll browsing only or combined with a cine loop at the fastest frame rate

Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation

The two-dimensional nature of mammography makes estimation of the overall breast density challenging, and estimation of the true patient-specific radiation dose impossible. Digital breast tomosynthesis (DBT), a pseudo-3D technique, is now commonly used in breast cancer screening and diagnostics. Still, the severely limited 3rd dimension information in DBT has not been used, until now, to estimate the true breast density or the patient-specific dose. This study proposes a reconstruction algorithm for DBT based on deep learning specifically optimized for these tasks. The algorithm, which we name DBToR, is based on unrolling a proximal-dual optimization method. The proximal operators are replaced with convolutional neural networks and prior knowledge is included in the model. This extends previous work on a deep learning-based reconstruction model by providing both the primal and the dual blocks with breast thickness information, which is available in DBT. Training and testing of the model were performed using virtual patient phantoms from two different sources. Reconstruction performance, and accuracy in estimation of breast density and radiation dose, were estimated, showing high accuracy (density <+/-3%; dose <+/-20%) without bias, significantly improving on the current state-of-the-art. This work also lays the groundwork for developing a deep learning-based reconstruction algorithm for the task of image interpretation by radiologists.Comment: Accepted in Medical Image Analysi

Large area CMOS active pixel sensor x‐ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134791/1/mp2368.pd

Average Absorbed Breast Dose (2ABD) to Mean Glandular Dose (MGD) Conversion Function for Digital Breast Tomosynthesis: A New Approach

Background: In this work a new method for the Mean Glandular Dose evaluation in digital breast tomosynthesis (DBT) is presented. Methods: Starting from the experimental-based dosimetric index, 2ABD, which represents the average absorbed breast dose, the mean glandular dose MGD2ABD was calculated using a conversion function of glandularity f(G), obtained through the use of Monte Carlo simulations.Results: f(G) was computed for a 4.5 cm thick breast: from its value MGD2ABD for different compressed breast thicknesses and glandularities was obtained. The comparison between MGD2ABD estimates and the dosimetric index provided in the current dosimetry protocols, following the Dance's approach, MGDDance, showed a good agreement (<10%) for all the analyzed breast thicknesses and glandularities. Conclusion: The strength of the proposed method can be considered an accurate mean glandular dose assessment starting from few and accessible parameters, reported in the header DICOM of each DBT exam

Measurement of Tumor Extent and Effects of Breast Compression in Digital Mammography and Breast Tomosynthesis

Breast cancer is the most common form of cancer affecting women in the western countries. Today x-ray digital mammography (DM) of the breast is commonly used for early detection of breast cancer. However, the sensitivity of mammography is limited, mainly due to the fact that a 3D volume is projected down to a 2D image. This problem can be partially solved by a tomographic technique. Breast tomosynthesis (BT) reduces the detrimental effect of the projected anatomy. Tumor size is an important predictor of prognosis and treatment effect. We hypothesized that the tumor outline would be better defined in BT and therefore tumor measurement in BT would be more accurate compared with DM. The results showed that breast tumor size measured on BT correlated better with the size measured by the pathologists on the surgical specimens compared with measurement on DM. Breast compression is important in mammography both to improve image quality and to reduce the radiation dose to the breast, but it also has a negative consequence as some women refrain from mammography due to the pain associated with the examination. Since BT is a 3D technique, it was hypothesized that less breast compression force can be applied. The results indicated that less compression force is possible without significantly compromising the diagnostic quality of the image and that the patient comfort was improved. An applied breast compression force as used in mammography results in a pressure distribution over the breast. The pressure distribution was assessed using thin pressure sensors attached to the compression plate. The results showed that the pressure distribution was heterogeneous in appearance and varied widely between different breasts. In almost half of the subjects most of the pressure was over the juxtathoracic part of the breast and the pectoral muscle with little or no pressure over the rest of the breast. Another concern regarding breast compression is the question whether the resulting pressure might damage tumors, causing a shedding of malignant cells into the blood system. Peripheral venous blood samples were drawn before and after breast compression and analyzed for circulating tumor cells. The study found no elevated number of circulating cancer cells in peripheral blood after breast compression. Future analysis of samples from veins draining the breast are needed to study if circulating tumor cells are being trapped in the lung capillaries

Amorphous In–Ga–Zn–O thin‐film transistor active pixel sensor x‐ray imager for digital breast tomosynthesis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134962/1/mp2382.pd