Monte Carlo evaluation of glandular dose in cone-beam X-ray computed tomography dedicated to the breast: Homogeneous and heterogeneous breast models

Dedicated cone-beam breast CT: Data acquisition strategies based on projection angle-dependent normalized glandular dose coefficients

BACKGROUND

Dedicated cone-beam breast computed tomography (CBBCT) using short-scan acquisition is being actively investigated to potentially reduce the radiation dose to the breast. This would require determining the optimal x-ray source trajectory for such short-scan acquisition.

PURPOSE

To quantify the projection angle-dependent normalized glandular dose coefficient (D g N C T $Dg{N^{CT}}$ ) in CBBCT, referred to as angularD g N C T $Dg{N^{CT}}$ , so that the x-ray ray source trajectory that minimizes the radiation dose to the breast for short-scan acquisition can be determined.

MATERIALS AND METHODS

A cohort of 75 CBBCT clinical datasets was segmented and used to generate three breast models - (I) patient-specific breast with heterogeneous fibroglandular tissue distribution and real breast shape, (II) patient-specific breast shape with homogeneous tissue distribution and matched fibroglandular weight fraction, and (III) homogeneous semi-ellipsoidal breast with patient-specific breast dimensions and matched fibroglandular weight fraction, which corresponds to the breast model used in current radiation dosimetry protocols. For each clinical dataset, the angularD g N C T $Dg{N^{CT}}$ was obtained at 10 discrete angles, spaced 36° apart, for full-scan, circular, x-ray source trajectory from Monte Carlo simulations. Model III is used for validating the Monte Carlo simulation results. Models II and III are used to determine if breast shape contributes to the observed trends in angularD g N C T $Dg{N^{CT}}$ . A geometry-based theory in conjunction with center-of-mass (C O M $COM$ ) based distribution analysis is used to explain the projection angle-dependent variation in angularD g N C T $Dg{N^{CT}}$ .

RESULTS

The theoretical model predicted that the angularD g N C T $Dg{N^{CT}}$ will follow a sinusoidal pattern and the amplitude of the sinusoid increases when the center-of-mass of fibroglandular tissue (C O M f $CO{M_f}$ ) is farther from the center-of-mass of the breast (C O M b $CO{M_b}$ ). It also predicted that the angularD g N C T $Dg{N^{CT}}$ will be minimized at x-ray source positions complementary to theC O M f $CO{M_f}$ . TheC O M f $CO{M_f}$ was superior to theC O M b $CO{M_b}$ in 80% (60/75) of the breasts. From Monte Carlo simulations and for homogeneous breasts (models II and III), the deviation in breast shape from a semi-ellipsoid had minimal effect on angularD g N C T $Dg{N^{CT}}$ and showed less than 4% variation. From Monte Carlo simulations and for model I, as predicted by our theory, the angularD g N C T $Dg{N^{CT}}$ followed a sinusoidal pattern with maxima and minima at x-ray source positions superior and inferior to the breast, respectively. For model I, the projection angle-dependent variation in angularD g N C T $Dg{N^{CT}}$ was 16.4%.

CONCLUSION

The heterogeneous tissue distribution affected the angularD g N C T $Dg{N^{CT}}$ more than the breast shape. For model I, the angularD g N C T $Dg{N^{CT}}$ was lowest when the x-ray source was inferior to the breast. Hence, for short-scan CBBCT acquisition withC O M b $CO{M_b}$ aligned with axis-of-rotation, an x-ray source trajectory inferior to the breast is preferable and such an acquisition spanning 205° can potentially reduce the mean glandular dose by up to 52%.

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 DgN_DBT 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.

Virtual Clinical Trials in 2D and 3D X-ray Breast Imaging and Dosimetry: Comparison of CPU-Based and GPU-Based Monte Carlo Codes

Simple Summary

Virtual clinical trials in X-ray breast imaging may permit substantial reduction of the costs, times, and exposure risk to patient of clinical trials. Monte Carlo simulation techniques are increasingly adopted for VCT in breast imaging and dosimetry studies. This work aims to compare three different platforms for breast VCT studies, to develop real-time virtual DM, DBT and BCT examinations, where the in-silico image acquisition process takes a computational time comparable to that typical of a corresponding real clinical examination.

Abstract

Computational reproductions of medical imaging tests, a form of virtual clinical trials (VCTs), are increasingly being used, particularly in breast imaging research. The accuracy of the computational platform that is used for the imaging and dosimetry simulation processes is a fundamental requirement. Moreover, for practical usage, the imaging simulation computation time should be compatible with the clinical workflow. We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. All the codes showed compatible results in terms of simulated dose maps and imaging values within a maximum discrepancy of 3%. The GPU-based code produced a reduction of the computation time up to factor 10⁴, and so permits real-time VCT studies for X-ray breast imaging.

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.

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.

Normalized glandular dose coefficients for digital breast tomosynthesis systems with a homogeneous breast model

This work aims at calculating and releasing tabulated values of dose conversion coefficients, DgN_DBT, for mean glandular dose (MGD) estimates in digital breast tomosynthesis (DBT). The DgN_DBT coefficients are proposed as unique conversion coefficients for MGD estimates, in place of dose conversion coefficients in mammography (DgN_DM or c, g, s triad as proposed in worldwide quality assurance protocols) used together with the T correction factor. DgN_DBT is the MGD per unit incident air kerma measured at the breast surface for a 0° projection and the entire tube load used for the scan. The dataset of polyenergetic DgN_DBT coefficients was derived via a Monte Carlo software based on the Geant4 toolkit. Dose coefficients were calculated for a grid of values of breast characteristics (breast thickness in the range 20-90 mm and glandular fraction by mass of 1%, 25%, 50%, 75%, 100%) and the simulated geometries, scan protocols, irradiation geometries and typical spectral qualities replicated those of six commercial DBT systems (GE SenoClaire, Hologic Selenia Dimensions, GE Senographe Pristina, Fujifilm Amulet Innovality, Siemens Mammomat Inspiration and IMS Giotto Class). For given breast characteristics, target/filter combination, tube voltage and half value layer (HVL), two spectra with two HVL values have been simulated in order to permit MGD estimates from experimental HVL values via mathematical interpolation from tabulated values. The adopted breast model assumes homogenous composition of glandular and adipose tissues; it includes a 1.45 mm thick skin envelope in place of the 4-5 mm envelope commonly adopted in dosimetry protocols. The simulation code was validated versus AAPM Task group 195 Monte Carlo reference data sets (absolute differences not higher than 1.1%) and by comparison to relative dosimetry measurements with radiochromic film in a PMMA test object (differences within the maximum experimental uncertainty of 11%). The calculated coefficients show maximum relative deviations of -17.6% and +6.1% from those provided by the DBT dose coefficients adopted in the EUREF protocol and of 1.5%, on average, from data in the AAPM TG223 report. A spreadsheet is provided for interpolating the tabulated DgN_DBT coefficients for arbitrary values of HVL, compressed breast thickness and glandular fraction, in the corresponding investigated ranges, for each DBT unit modeled in this work.

Radiation dosimetry of a clinical prototype dedicated cone-beam breast CT system with offset detector

PURPOSE

A clinical-prototype, dedicated, cone-beam breast computed tomography (CBBCT) system with offset detector is undergoing clinical evaluation at our institution. This study is to estimate the normalized glandular dose coefficients ( DgN CT ) that provide air kerma-to-mean glandular dose conversion factors using Monte Carlo simulations.

MATERIALS AND METHODS

The clinical prototype CBBCT system uses 49 kV x-ray spectrum with 1.39 mm 1st half-value layer thickness. Monte Carlo simulations (GATE, version 8) were performed with semi-ellipsoidal, homogeneous breasts of various fibroglandular weight fractions ( f g = 0.01 , 0.15 , 0.5 , 1 ) , chest wall diameters ( d = 8 , 10 , 14 , 18 , 20  cm), and chest wall to nipple length ( l = 0.75 d ), aligned with the axis of rotation (AOR) located at 65 cm from the focal spot to determine the DgN CT . Three geometries were considered - 40 × 30 -cm detector with no offset that served as reference and corresponds to a clinical CBBCT system, 30 × 30 -cm detector with 5 cm offset, and a 30 × 30 -cm detector with 10 cm offset.

RESULTS

For 5 cm lateral offset, the DgN CT ranged 0.177 - 0.574  mGy/mGy and reduction in DgN CT with respect to reference geometry was observed only for 18 cm ( 6.4 % ± 0.23 % ) and 20 cm ( 9.6 % ± 0.22 % ) diameter breasts. For the 10 cm lateral offset, the DgN CT ranged 0.221 - 0.581  mGy/mGy and reduction in DgN CT was observed for all breast diameters. The reduction in DgN CT was 1.4 % ± 0.48 % , 7.1 % ± 0.13 % , 17.5 % ± 0.19 % , 25.1 % ± 0.15 % , and 27.7 % ± 0.08 % for 8, 10, 14, 18, and 20 cm diameter breasts, respectively. For a given breast diameter, the reduction in DgN CT with offset-detector geometries was not dependent on f g . Numerical fits of DgN CT d , l , f g were generated for each geometry.

CONCLUSION

The DgN CT and the numerical fit, D g N CT d , l , f g would be of benefit for current CBBCT systems using the reference geometry and for future generations using offset-detector geometry. There exists a potential for radiation dose reduction with offset-detector geometry, provided the same technique factors as the reference geometry are used, and the image quality is clinically acceptable.

Development of breast lesions models database

PURPOSE

We present the development and the current state of the MaXIMA Breast Lesions Models Database, which is intended to provide researchers with both segmented and mathematical computer-based breast lesion models with realistic shape.

METHODS

The database contains various 3D images of breast lesions of irregular shapes, collected from routine patient examinations or dedicated scientific experiments. It also contains images of simulated tumour models. In order to extract the 3D shapes of the breast cancers from patient images, an in-house segmentation algorithm was developed for the analysis of 50 tomosynthesis sets from patients diagnosed with malignant and benign lesions. In addition, computed tomography (CT) scans of three breast mastectomy cases were added, as well as five whole-body CT scans. The segmentation algorithm includes a series of image processing operations and region-growing techniques with minimal interaction from the user, with the purpose of finding and segmenting the areas of the lesion. Mathematically modelled computational breast lesions, also stored in the database, are based on the 3D random walk approach.

RESULTS

The MaXIMA Imaging Database currently contains 50 breast cancer models obtained by segmentation of 3D patient breast tomosynthesis images; 8 models obtained by segmentation of whole body and breast cadavers CT images; and 80 models based on a mathematical algorithm. Each record in the database is supported with relevant information. Two applications of the database are highlighted: inserting the lesions into computationally generated breast phantoms and an approach in generating mammography images with variously shaped breast lesion models from the database for evaluation purposes. Both cases demonstrate the implementation of multiple scenarios and of an unlimited number of cases, which can be used for further software modelling and investigation of breast imaging techniques. The created database interface is web-based, user friendly and is intended to be made freely accessible through internet after the completion of the MaXIMA project.

CONCLUSIONS

The developed database will serve as an imaging data source for researchers, working on breast diagnostic imaging and on improving early breast cancer detection techniques, using existing or newly developed imaging modalities.