A combined analytical and Monte Carlo method for detailed simulations of antiscatter grids in x-ray medical imaging: implementing scatter within the grid.

Objective. To implement a hybrid method, which combines analytical tracking and interaction simulation using Monte Carlo (MC) techniques, in order to model photon transport inside antiscatter grids (ASG) for x-ray imaging.Approach. A new tally was developed for PENELOPE (v.2018) and penEasy (v. 2020) MC code to simulate photon transmission through ASGs. Two established analytical algorithms from the literature were implemented in this tally. In addition, a new hybrid method was introduced by extending one of the analytical algorithms to include photon-interactions inside the grid, while preserving the imaged grid structure. Calculations of primary(TP),scatter(TS),and total(TT)grid transmissions in addition to theQfactor (Q=TP2/TT) were performed. The new tally was validated for a quadric geometry ASG, and experimental measurements with a PMMA phantom of several thicknesses. In addition, the contribution of the scatter inside the grid was studied for three interspace materials, and a high resolution image of the grid was simulated.Main results. An excellent agreement was found between the two analytical models compared with the quadric grid without scatter, and the hybrid method with the geometrical grid with scatter. Average deviations of 0.2% and 1.4% were found betweenTPandTSfor the hybrid method and quadric grid, while for the hybrid method and experimental measurements these values were 1% and 20%. Antiscatter grids with aluminium as interspace material had the highest amount of scatter from inside the grid to the final image, followed up by paper fibre and air. The high resolution image of the grid was equivalent using the quadric geometry or the hybrid mode.Significance. The hybrid method provides a means of studying scattered radiation from the antiscatter grid with the advantage of higher performance, with results that are consistent with a full quadric geometry simulation of the ASG.

Multiscale Monte Carlo simulations for dosimetry in x-ray breast imaging: Part II - Microscopic scales.

BACKGROUND: Although the benefits of breast screening and early diagnosis are known for reducing breast cancer mortality rates, the effects and risks of low radiation doses to the cells in the breast are still ongoing topics of study. PURPOSE: To study specific energy distributions ( f ( z , D g ) $f(z,D_{g})$ ) in cytoplasm and nuclei of cells corresponding to glandular tissue for different x-ray breast imaging modalities. METHODS: A cubic lattice (500 µm length side) containing 4064 spherical cells was irradiated with photons loaded from phase space files with varying glandular voxel doses ( D g $D_{g}$ ). Specific energy distributions were scored for nucleus and cytoplasm compartments using the PENELOPE (v. 2018) + penEasy (v. 2020) Monte Carlo (MC) code. The phase space files, generated in part I of this work, were obtained from MC simulations in a voxelized anthropomorphic phantom corresponding to glandular voxels for different breast imaging modalities, including digital mammography (DM), digital breast tomosynthesis (DBT), contrast enhanced digital mammography (CEDM) and breast CT (BCT). RESULTS: In general, the average specific energy in nuclei is higher than the respective glandular dose scored in the same region, by up to 10%. The specific energy distributions for nucleus and cytoplasm are directly related to the magnitude of the glandular dose in the voxel ( D g $D_{g}$ ), with little dependence on the spatial location. For similar D g $D_{g}$ values, f ( z , D g ) $f(z,D_{g})$ for nuclei is different between DM/DBT and CEDM/BCT, indicating that distinct x-ray spectra play significant roles in f ( z , D g ) $f(z,D_{g})$ . In addition, this behavior is also present when the specific energy distribution ( F g ( z ) $F_{g}(z)$ ) is considered taking into account the GDD in the breast. CONCLUSIONS: Microdosimetry studies are complementary to the traditional macroscopic breast dosimetry based on the mean glandular dose (MGD). For the same MGD, the specific energy distribution in glandular tissue varies between breast imaging modalities, indicating that this effect could be considered for studying the risks of exposing the breast to ionizing radiation.

Multiscale Monte Carlo simulations for dosimetry in x-ray breast imaging: Part I - Macroscopic scales.

BACKGROUND: X-ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used. PURPOSE: To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters). METHODS: Nine anthropomorphic breast phantoms with a voxel resolution of 0.5 mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast-enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC-GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4 mGy (to facilitate comparisons). RESULTS: The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniform GDD. CONCLUSIONS: For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breast dosimetry.

Impact of fibroglandular tissue distribution and breast shape in voxelized breast models for dosimetry in mammography.

Objective.This work proposes to study the impact of different voxelized heterogeneous breast models (gaussian centered - GaussC; gaussian lower - GaussL; and fitted equation patient-based on 3D realistic distribution (Fedonet al2021) - FitPB) for dosimetry in mammography compared to a well-established homogeneous approximation. Influence of breast outer shape also was investigated by comparing semicylindric and anthropomorphic breasts.Approach.By using the PENELOPE (v. 2018) + penEasy (v. 2020) MC code, simulations were performed to evaluate the normalized glandular dose (DgN) and the glandular depth dose (GDD(z)) for different breast characteristics and x-ray beam spectra.Main results.The averageDgNoverestimation caused by homogeneous tissue approximation was 33.0%, with the highest values attributed to GaussLand FitPBmodels, where fibroglandular tissue is concentrated deeper in the breast. The observed variation between anthropomorphic and semicylindrical breast shapes was, on average, 5.6%, legitimizing the latter approximation for breast dosimetry. Thicker breasts and lower energy beams resulted in larger overestimation caused by the homogeneous approach, while variations inDgNvalues among different heterogeneous models were higher for thinner breast and lower energy beams. Moreover, the depth where differences betweenGDD(z) for different breast models became maximum depends on the axial variation of fibroglandular tissue concentration between each model. TheGDD(z) dependence results in a significant variation of the contribution of each breast depth to mean glandular dose (MGD) among the breast models studied.Significance.Intercomparison between different breast models for dosimetry can be useful for estimating more accurateMGDvalues for population-based dosimetry, for exploring the use of 1D gaussian distribution for breast dosimetry, and for understanding the dose distributions inside the fibroglandular tissues, which could be a novel source of information for risk estimations.

Technical note: MC-GPU breast dosimetry validations with other Monte Carlo codes and phase space file implementation.

PURPOSE: To validate the MC-GPU Monte Carlo (MC) code for dosimetric studies in X-ray breast imaging modalities: mammography, digital breast tomosynthesis, contrast enhanced digital mammography, and breast-CT. Moreover, to implement and validate a phase space file generation routine. METHODS: The MC-GPU code (v. 1.5 DBT) was modified in order to generate phase space files and to be compatible with PENELOPE v. 2018 derived cross-section database. Simulations were performed with homogeneous and anthropomorphic breast phantoms for different breast imaging techniques. The glandular dose was computed for each case and compared with results from the PENELOPE (v. 2014) + penEasy (v. 2015) and egs _ brachy (EGSnrc) MC codes. Afterward, several phase space files were generated with MC-GPU and the scored photon spectra were compared between the codes. The phase space files generated in MC-GPU were used in PENELOPE and EGSnrc to calculate the glandular dose, and compared with the original dose scored in MC-GPU. RESULTS: MC-GPU showed good agreement with the other codes when calculating the glandular dose distribution for mammography, mean glandular dose for digital breast tomosynthesis, and normalized glandular dose for breast-CT. The latter case showed average/maximum relative differences of 2.3%/27%, respectively, compared to other literature works, with the larger differences observed at low energies (around 10 keV). The recorded photon spectra entering a voxel were similar (within statistical uncertainties) between the three MC codes. Finally, the reconstructed glandular dose in a voxel from a phase space file differs by less than 0.65%, with an average of 0.18%-0.22% between the different MC codes, agreement within approximately 2 σ statistical uncertainties. In some scenarios, the simulations performed in MC-GPU were from 20 up to 40 times faster than those performed by PENELOPE. CONCLUSIONS: The results indicate that MC-GPU code is suitable for breast dosimetric studies for different X-ray breast imaging modalities, with the advantage of a high performance derived from GPUs. The phase space file implementation was validated and is compatible with the IAEA standard, allowing multiscale MC simulations with a combination of CPU and GPU codes.

Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study.

PURPOSE: Breast dosimetry in mammography is an important aspect of radioprotection since women are exposed periodically to ionizing radiation due to breast cancer screening programs. Mean glandular dose (MGD) is the standard quantity employed for the establishment of dose reference levels in retrospective population studies. However, MGD calculations requires breast glandularity estimation. This work proposes a deep learning framework for volume glandular fraction (VGF) estimations based on mammography images, which in turn are converted to glandularity values for MGD calculations. METHODS: 208 virtual breast phantoms were generated and compressed computationally. The mammography images were obtained with Monte Carlo simulations (MC-GPU code) and a ray-tracing algorithm was employed for labeling the training data. The architectures of the neural networks are based on the XNet and multilayer perceptron, adapted for each task. The network predictions were compared with the ground truth using the coefficient of determination (r2). RESULTS: The results have shown a good agreement for inner breast segmentation (r2 = 0.999), breast volume prediction (r2 = 0.982) and VGF prediction (r2 = 0.935). Moreover, the DgN coefficients using the predicted VGF for the virtual population differ on average 1.3% from the ground truth values. Afterwards with the obtained DgN coefficients, the MGD values were estimated from exposure factors extracted from the DICOM header of a clinical cohort, with median(75 percentile) values of 1.91(2.45) mGy. CONCLUSION: We successfully implemented a deep learning framework for VGF and MGD calculations for virtual breast phantoms.

Impact of photoelectric cross section data on systematic uncertainties for Monte Carlo breast dosimetry in mammography.

Monte Carlo (MC) simulations are employed extensively in breast dosimetry studies. In the energy interval of interest in mammography energy deposition is predominantly caused by the photoelectric effect, and the corresponding cross sections used by the MC codes to model this interaction process have a direct influence on the simulation results. The present work compares two photoelectric cross section databases in order to estimate the systematic uncertainty, related to breast dosimetry, introduced by the choice of cross sections for photoabsorption. The databases with and without the so-called normalization screening correction are denoted as 'renormalized' or 'un-normalized', respectively. The simulations were performed with the PENELOPE/penEasy code system, for a geometry resembling a mammography examination. The mean glandular dose (MGD), incident air kerma (Kair), normalized glandular dose (DgN) and glandular depth-dose (GDD(z)) were scored, for homogeneous breast phantoms, using both databases. The AAPM Report TG-195 case 3 was replicated, and the results were included. Moreover, cases with heterogeneous and anthropomorphic breast phantoms were also addressed. The results simulated with the un-normalized cross sections are in better overall agreement with the TG-195 data than those from the renormalized cross sections; for MGD the largest discrepancies are 0.13(6)% and 0.74(5)%, respectively. The MGD,Kairand DgN values simulated with the two databases show differences that diminish from approximately 10%/3%/6.8% at 8.25 keV down to 1.5%/1.7%/0.4% at 48.75 keV, respectively. For polyenergetic spectra, deviations up to 2.5% were observed. The disagreement between the GDDs simulated with the analyzed databases increases with depth, ranging from -1% near the breast entrance to 4% near the bottom. Thus, the choice of photoelectric cross section database affects the MC simulation results of breast dosimetry and adds a non-negligible systematic uncertainty to the dosimetric quantities used in mammography.