Biological effects induced by doses of mammographic screening

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 similarD 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 inf ( 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.

The relationship between age of digital mammmogrpahy systems and number of reported faults and downtime

PURPOSE

To investigate the relationship between age of mammographic x-ray equipment, and number of reported faults and related consequences.

METHODS

A centralised online fault reporting database is used by all UK breast screening programmes to collate faults with mammography equipment. Data on faults occurring in 2018 and 2019 for digital x-ray imaging systems were analysed. The effect of the age of mammography systems on the number of equipment faults, and the consequences of these faults was examined. The number of days downtime, number of cancelled appointments, number of repeated images, and number of recalled participants were used to quantify the severity of faults.

RESULTS

This analysis covers a two year period and includes 4271 faults and 522 individual x-ray sets. On average, an x-ray set was 6.1 years old at the time when a fault occurred. 77% of x-ray sets experienced five of fewer annual faults. X-ray sets of nine years old had the highest average number of annual faults. Systems of ten years old had the highest average number of days downtime per year, and the highest average number of cancellations per year. The indicated primary use of 48% of the x-ray sets included in this analysis was screening, but a disproportionate 87% of cancelled appointments occurred due to faults on these units compared to those used primarily for assessment, or for a mixture of assessment and screening.

CONCLUSIONS

Information from this unique dataset can be used to support guidance on equipment replacement programmes for mammographic x-ray sets.

Regression-based neural network for improving image reconstruction in diffuse optical tomography

Diffuse optical tomography (DOT) is a non-invasive imaging technique utilizing multi-scattered light at visible and infrared wavelengths to detect anomalies in tissues. However, the DOT image reconstruction is based on solving the inverse problem, which requires massive calculations and time. In this article, for the first time, to the best of our knowledge, a simple, regression-based cascaded feed-forward deep learning neural network is derived to solve the inverse problem of DOT in compressed breast geometry. The predicted data is subsequently utilized to visualize the breast tissues and their anomalies. The dataset in this study is created using a Monte-Carlo algorithm, which simulates the light propagation in the compressed breast placed inside a parallel plate source-detector geometry (forward process). The simulated DL-DOT system's performance is evaluated using the Pearson correlation coefficient (R) and the Mean squared error (MSE) metrics. Although a comparatively smaller dataset (50 nos.) is used, our simulation results show that the developed feed-forward network algorithm to solve the inverse problem delivers an increment of ∼30% over the analytical solution approach, in terms of R. Furthermore, the proposed network's MSE outperforms that of the analytical solution's MSE by a large margin revealing the robustness of the network and the adaptability of the system for potential applications in medical settings.