Breast CT Research Articles

In-line spectroscopy for iodine quantification in dynamic contrast-enhanced dedicated breast CT.

In 4D dynamic contrast-enhanced dedicated breast computed tomography (4D DCE-bCT), the functional properties of the breast will be characterized by monitoring the uptake and washout of iodine-based contrast agents over time. This information could be valuable in breast cancer treatment. However, prior to clinical implementation, it is crucial to validate the quantitative estimates of iodine concentrations at each time point during acquisition. To develop an in-line spectroscopy system capable of measuring iodine concentrations in a dynamic x-ray breast phantom in real-time. Potassium iodide served as the contrast agent. The system was set-up at both the entrance and exit of the phantom. It comprises a fiber-coupled green LED and collimator, which together ensure that a parallel beam passes through the sample holder. Transmitted light is captured by a collimator on the opposite side and directed through a fiber optic cable to a photodetector for intensity measurement. The relationship between 13 iodine concentrations (0-6mg I/mL) and light transmission was tested, and the system's repeatability and accuracy were determined. The system exhibited a strong correlation between iodine concentration and transmission values, achieving a root-mean-square error of 0.007. The repeated measurements had relative standard deviations of 0.04% and 0.1% for repeated water measurements at the phantom's entrance and exit, respectively. Furthermore, the accuracy measurements gave a mean error of fitting of 0.008(±0.07) mg I/mL. The in-line spectroscopy system can effectively monitor iodine concentrations in a dynamic breast phantom, providing a reliable method for quantitative validation of 4D DCE-bCT.

Grating-based phase-contrast computed tomography for breast tissue at an inverse compton source

The introduction of mammography screening programs has significantly reduced breast cancer mortality rates. Nevertheless, some lesions remain undetected, especially in dense breast tissue. Studies have shown that phase-contrast imaging can improve breast cancer diagnosis by increasing soft tissue contrast. Furthermore, grating-based phase-contrast imaging enables the simultaneous acquisition of absorption, phase-contrast, and scattering, so-called dark-field images. The latter allows the classification of microcalcifications. In addition, breast computed tomography (BCT) systems can identify and discriminate overlapping but clinically relevant structures. This study investigates the benefit of combining grating-based phase-contrast with BCT. We explore the potential of grating-based phase-contrast breast computed tomography (gbpc-BCT) with a breast phantom and a freshly dissected fibroadenoma. Improved image contrast could be achieved with radiation doses comparable to those used in clinical BCT.

A Case of CUP with Malignant Pleural Effusion: Overcoming Diagnostic and Therapeutic Hurdles with Chemotherapy

Introduction: Cancers of unknown primary (CUPs) present a diagnostic challenge as their origin is unidentified at diagnosis. Massive pleural effusion (MPE), indicative of lung metastasis in CUP, categorizes the condition into an unfavorable subset with a poor prognosis. Patients in this subset may exhibit a lower response to specific therapies. Case Presentation: A 62-year-old woman presented with cough, severe dyspnea, and MPE in the left lung. Thoracocentesis was performed, extracting 1,200 mL of hemorrhagic fluid, followed by the placement of an indwelling pleural catheter. The cytological examination of the pleural effusion indicated an adenocarcinoma, with immunohistochemistry revealing positive CK7 and negative CK20, Napsin A, and TTF-1. Additionally, elevated levels of Ca-125 (1,605 U/mL) and Ca 15-3 (242 U/mL) raised suspicion of gynecological malignancy. Thorax and abdominal CT scans, breast and thyroid ultrasounds showed no signs of malignancy, leading to the diagnosis of CUP. The patient’s performance status according to the Eastern Cooperative Oncology Group (ECOG) score was 4. Initial carboplatin 5 AUC and paclitaxel 175 mg/m2 administration resulted in improvement in performance status with ECOG score of 1, alleviation of dyspnea, reduction in pleural effusion 1 week after chemotherapy, with minimal effusion observed at 3 weeks, and Ca-125 levels decreased to 33.6 U/mL thereafter. Discussion: Empiric chemotherapy using carboplatin and paclitaxel is a feasible option for managing CUP with MPE mimicking gynecological malignancies with elevated Ca-125 and Ca 15-3 markers; initiating chemotherapy in poor performance status patients is beneficial with proper clinical judgment.

Dedicated Breast CT: Getting Ready for Prime Time.

Dedicated breast CT is an imaging modality that provides true 3D imaging of the breast with many advantages over current conventional breast imaging modalities. The addition of intravascular contrast increases the sensitivity of breast CT substantially. As such, there are immediate potential applications in the clinical workflow. These include using breast CT to replace much of the traditional diagnostic workup when faced with indeterminate breast lesions. Contrast-enhanced breast CT may be appropriate as a supplemental screening tool for women at high risk of breast cancer, similar to breast MRI. In addition, emerging studies are demonstrating the utility of breast CT in neoadjuvant chemotherapy tumor response monitoring as well as planning for surgical treatment options. While short exam times and fully 3D imaging in a noncompressed position are advantages of this modality, limited coverage of chest wall/axilla due to prone positioning and use of ionizing radiation are drawbacks. To date, several studies have reported on the performance characteristics of this promising modality.

IDMap: Leveraging AI and Data Technologies for Early Cancer Detection

Cancer screening is vital in cutting mortality rates, and containing the impact of cancer in a worldwide basis. The current conventional detection techniques including imaging and biopsy though efficient are also characterized with drawbacks like; invasive, expensive, and inaccurate. This abstract will describe the new AI and data solution in the fight against early cancer detection, which presents a massive opportunity to improve accuracy, cut down the time that it takes to deliver a diagnosis, and bring quality health care to possibly millions of patients. ML and, in particular, DL are prospective in terms of decision making upon medical data including imaging, genomic sequences, and electronic health records to detect biomarkers of cancer in early stages. The statistics show that the AI-driven systems are capable to provide better diagnostic outcomes than conventional methods in some fields including mammography for breast cancer and CT for lung. Moreover, AI’s integration in genomic studies helps in determining Cancer related genes and biomarkers hence supporting precision medicine that adapts treatment to the specific genetic information of the patient. Apart from having outlets in AI, big data analytics, cloud computing, and IoT are equally important in early cancer detection as well. Big data analysis enables the analysis of large and complicated data sets with the aid of which one may identify inklings that may point towards possible early development of cancer. The use of cloud computing in health care mainly provides meaningful platforms for the storage and management of the large volumes of medical data in a way that allows improved efficiency and high levels of security. Wearable sensors collect data on different biomarkers throughout a patient’s body, and convey real-time information regarding whether the biomarkers’ levels are approaching cancerous state. Despite this great promise, there are various issues that have to be solved: data protection, privacy, and security, problems with the algorithms’ biases, and integration into practice. Ethical questions are generally important to tackle the uncertainty surrounding data and decision-making in clinical care using A I systems. The future trends in early cancer diagnostics will involve deeper integration of the approaches as AI and big data technology, which will enable more precise prevention and treatment. The applicability of this approach can also extend to the early identification of cancer, but also the prevention of its occurrence through proper intervention. In conclusion, conversing AI and data technologies will be useful for enhancing the efficiency of the early cancer diagnosis, and that is why the perspectives for the patients’ recovery and the further decrease in the mortality rates connected with cancer are rather promising. The approaches in this area remain informative developing technologies that are likely to be integrated into clinical work as the leading organizational models for the future of oncology and preventive health.

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach

Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100 μm, a thickness of 500 μm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.

Detectability of Breast Cancer in Dedicated Breast CT Compared With Mammography Dependent on Breast Density.

To evaluate the detectability of non-contrast-enhanced and contrast-enhanced spiral breast computed tomography ([non]-CE-SBCT) compared with mammography. Secondary objectives are to determine detectability depending on breast density and to evaluate appearance of breast malignancies according to BI-RADS descriptors. This retrospective institutional review board-approved study included 90 women with 105 biopsy-proven malignant breast lesions. Breast density, BI-RADS descriptors, and detectability were evaluated by 2 independent readers. Diagnostic confidence was rated on a 4-point Likert scale. For readers 1 and 2, detectability was 83.8% and 80.0% for mammography, 99.1% and 99.1% for CE-SBCT ( P < 0.05), and 66.7% and 61.9% for non-CE-SBCT ( P < 0.05). With both readers, detectability in CE-SBCT was high for density A/B/C/D (both 100%/100%/100%/87.5%). Detectability of readers declined with increasing density for mammography (density A = 100%, B = 89.1% and 95.1%, C = 73.1%, D = 50.0% and 71.4%; P < 0.05) and for non-CE-SBCT (density A = 87.5% and 90.7%, B = 65.5% and 69.1%, C = 54.8% and 60.0%, D = 37.5%; P < 0.05). Mass lesions were detected with CT as often as with mammography, whereas architectural distortions and microcalcifications were detected less often with SBCT. Diagnostic confidence was very high or high in 97.2% for CE-SBCT, in 74.1% for non-CE-SBCT, and in 81.4% for mammography. Detectability and diagnostic confidence were very high in CE-SBCT, regardless of breast density. The detectability of non-CE-SBCT was lower than that of mammography and declined with increasing breast density.

Dedicated Photon-Counting CT for Detection and Classification of Microcalcifications: An Intraindividual Comparison With Digital Breast Tomosynthesis.

Clinical experience regarding the use of dedicated photon-counting breast CT (PC-BCT) for diagnosis of breast microcalcifications is scarce. This study systematically compares the detection and classification of breast microcalcifications using a dedicated breast photon-counting CT, especially designed for examining the breast, in comparison with digital breast tomosynthesis (DBT). This is a prospective intraindividual study on women with DBT screening-detected BI-RADS-4/-5 microcalcifications who underwent PC-BCT before biopsy. PC-BCT images were reconstructed with a noninterpolated spatial resolution of 0.15 × 0.15 × 0.15 mm (reconstruction mode 1 [RM-1]) and with 0.3 × 0.3 × 0.3 mm (reconstruction mode 2 [RM-2]), plus thin-slab maximum intensity projection (MIP) reconstructions. Two radiologists independently rated the detection of microcalcifications in direct comparison with DBT on a 5-point scale. The distribution and morphology of microcalcifications were then rated according to BI-RADS. The size of the smallest discernible microcalcification particle was measured. For PC-BCT, the average glandular dose was determined by Monte Carlo simulations; for DBT, the information provided by the DBT system was used. Between September 2022 and July 2023, 22 participants (mean age, 61; range, 42-85 years) with microcalcifications (16 malignant; 6 benign) were included. In 2/22 with microcalcifications in the posterior region, microcalcifications were not detectable on PC-BCT, likely because they were not included in the PC-BCT volume. In the remaining 20 participants, microcalcifications were detectable. With high between-reader agreement (κ > 0.8), conspicuity of microcalcifications was rated similar for DBT and MIPs of RM-1 (mean, 4.83 ± 0.38 vs 4.86 ± 0.35) (P = 0.66), but was significantly lower (P < 0.05) for the remaining PC-BCT reconstructions: 2.11 ± 0.92 (RM-2), 2.64 ± 0.80 (MIPs of RM-2), and 3.50 ± 1.23 (RM-1). Identical distribution qualifiers were assigned for PC-BCT and DBT in 18/20 participants, with excellent agreement (κ = 0.91), whereas identical morphologic qualifiers were assigned in only 5/20, with poor agreement (κ = 0.44). The median size of smallest discernible microcalcification particle was 0.2 versus 0.6 versus 1.1 mm in DBT versus RM-1 versus RM-2 (P < 0.001), likely due to blooming effects. Average glandular dose was 7.04 mGy (PC-BCT) versus 6.88 mGy (DBT) (P = 0.67). PC-BCT allows reliable detection of in-breast microcalcifications as long as they are not located in the posterior part of the breast and allows assessment of their distribution, but not of their individual morphology.

Intensity-based iterative reconstruction with cross-channel regularization for grating interferometry breast CT

This work demonstrates the successful reconstruction of phase contrast images under challenging acquisition conditions in grating interferometry breast CT (GI-BCT) with an algorithm that adds a novel regularization functional to the existing iterative-based intensity reconstruction (IBIR) algorithm. The addition of a cross-channel regularizer allows to leverage the absorption channel’s convergence to promote that of the phase channel, which otherwise struggles to converge. We demonstrate convergence of phase contrast images on both simulations and real data. This work sets a step towards a clinically compatible reconstruction procedure using cross-channel regularization for the generation of standalone phase-contrast images of breasts.

Maximizing microcalcification detectability in low-dose dedicated cone-beam breast CT: parallel cascades-based theoretical analysis.

We aim to determine the combination of X-ray spectrum and detector scintillator thickness that maximizes the detectability of microcalcification clusters in dedicated cone-beam breast CT. A cascaded linear system analysis was implemented in the spatial frequency domain and was used to determine the detectability index using numerical observers for the imaging task of detecting a microcalcification cluster with 0.17mm diameter calcium carbonate spheres. The analysis considered a thallium-doped cesium iodide scintillator coupled to a complementary metal-oxide semiconductor detector and an analytical filtered-back-projection reconstruction algorithm. Independent system parameters considered were the scintillator thickness, applied X-ray tube voltage, and X-ray beam filtration. The combination of these parameters that maximized the detectability index was considered optimal. Prewhitening, nonprewhitening, and nonprewhitening with eye filter numerical observers indicate that the combination of 0.525 to 0.6mm thick scintillator, 70kV, and 0.25 to 0.4mm added copper filtration maximized the detectability index at a mean glandular dose (MGD) of 4.5mGy. Using parallel cascade systems' analysis, the combination of parameters that could maximize the detection of microcalcifications was identified. The analysis indicates that a harder beam than that used in current practice may be beneficial for the task of detecting microcalcifications at an MGD suitable for breast cancer screening.

Investigating Radiotherapy Effects on PD-L1 Expression in Circulating Tumor Cells: An Exploratory Study.

Circulating tumor cells (CTCs) and Programmed death-ligand 1 (PD-L1) play pivotal roles in cancer biology and therapy response. This exploratory study aimed to elucidate the influence of neoadjuvant radiotherapy on PD-L1 expression in tumor tissues and CTCs of patients with inoperable locally advanced breast cancer. We conducted a prospective cohort study at Universitas Andalas Hospital Padang from January to December 2022 with 27 patients. Biopsies and blood draws were executed before and after the tenth fractions of neoadjuvant radiotherapy. Following radiotherapy, CTCs were isolated using magnetic beads enrichment, followed by an RT-PCR analysis for PD-L1 expression. Correlations between PD-L1 expression and tumor response, evaluated via local response and RECIST criteria before and after radiotherapy breast CT scan, were examined using Fisher's exact and chi-square tests. Our data revealed no significant alterations in PD-L1 expression in either tumor tissues or CTCs during radiotherapy (p=0.848 for tissue, p=0.548 for CTCs). Notably, PD-L1 expression in tumor tissue before treatment was significantly associated with RECIST (p=0.021), while other correlations with local response and RECIST were not statistically significant. The study implies radiotherapy may not significantly influence PD-L1 expression in tumor tissue and CTCs. However, pre-treatment PD-L1 expression in tumor tissue correlates with RECIST criteria. These findings highlight the need for additional, comprehensive studies to elucidate further the interplay between PD-L1, CTCs, and radiotherapy response.

Characterisation of Portuguese radiotherapy departments: Organisation, occupational exposure values and diagnostic reference levels for breast and prostate computed tomography planning

IntroductionPortugal currently hosts 24 active radiotherapy departments, 8 public and 16 privates, presenting potential radiation exposure risks to multidisciplinary teams. Patients in these treatments also face ionising radiation during treatment planning and verification. MethodsAuthorisation and ethical approval were secured for a national online survey, disseminated to radiotherapy departments across Portugal. The survey encompassed three sections: equipment, staff, and radiographer role characterisation; occupational exposure values for one month; and exposure parameters, including computed tomography (CT) dose values [CT dose index (CTDIvol) and dose length product (DLP)] for breast and prostate cancer CT planning. Local Diagnostic Reference Levels (DRLs) derived were based on the 75th percentile of median dose values. ResultsThe study garnered a 50% response rate from public institutions, 12,5% from private and 25% from all active radiotherapy institutions in Portugal. All departments employ Three-Dimensional Conformal Radiation Therapy (3D–CRT) and incorporate Intensity Modulated Radiation Therapy (IMRT) and/or Volumetric Modulated Arc Therapy (VMAT) irradiation techniques. Additionally, half of the departments also perform Brachytherapy (BT). Radiographers demonstrated an occupational dose of zero mSv. CT planning dose values were 13 mGy and 512 mGy cm for breast CT and 16 mGy and 689 mGy cm for prostate CT, pertaining to CTDIvol and DLP, respectively. ConclusionMost aspects of national radiotherapy characterisation align with the established literature. Occupational exposure values exhibited consistency across radiotherapy modalities. An approach to national DRLs was formulated for breast and prostate CT planning, yielding values congruent with recent European studies. Implications for practiceThis study offers vital insights for analysing occupational contexts and risk prevention, serving as the initial characterisation of the national radiotherapy landscape. It also pioneers the calculation of DRLs for CT planning in radiotherapy to optimise procedures.

Technical note: Characterization, validation, and spectral optimization of a dedicated breast CT system for contrast-enhanced imaging.

The development of a new imaging modality, such as 4D dynamic contrast-enhanced dedicated breast CT (4D DCE-bCT), requires optimization of the acquisition technique, particularly within the 2D contrast-enhanced imaging modality. Given the extensive parameter space, cascade-systems analysis is commonly used for such optimization. To implement and validate a parallel-cascaded model for bCT, focusing on optimizing and characterizing system performance in the projection domain to enhance the quality of input data for image reconstruction. A parallel-cascaded system model of a state-of-the-art bCT system was developed and model predictions of the presampled modulation transfer function (MTF) and the normalized noise power spectrum (NNPS) were compared with empirical data collected in the projection domain. Validation was performed using the default settings of 49kV with 1.5mm aluminum filter and at 65kV and 0.257mm copper filter. A 10mm aluminum plate was added to replicate the breast attenuation. Air kerma at the isocenter was measured at different tube current levels. Discrepancies between the measured projection domain metrics and model-predicted values were quantified using percentage error and coefficient of variation (CoV) for MTF and NNPS, respectively. The optimal filtration was for a 5mm iodine disk detection task at 49, 55, 60, and 65kV. The detectability index was calculated for the default aluminum filtration and for copper thicknesses ranging from 0.05 to 0.4mm. At 49kV, MTF errors were +5.1% and -5.1% at 1 and 2 cycles/mm, respectively; NNPS CoV was 5.3% (min=3.7%; max=8.5%). At 65kV, MTF errors were -0.8% and -3.2%; NNPS CoV was 13.1% (min=11.4%; max=16.9%). Air kerma output was linear, with 11.67 µGy/mA (R2=0.993) and 19.14µGy/mA (R2=0.996) at 49 and 65kV, respectively. For iodine detection, a 0.25mm-thick copper filter at 65kV was found optimal, outperforming the default technique by 90%. The model accurately predicts bCT system performance, specifically in the projection domain, under varied imaging conditions, potentially contributing to the enhancement of 2D contrast-enhanced imaging in 4D DCE-bCT.

Virtual dosimetry study with three cone-beam breast computed tomography scanners using a fast GPU-based Monte Carlo code

Objective. To compare the dosimetric performance of three cone-beam breast computed tomography (BCT) scanners, using real-time Monte Carlo-based dose estimates obtained with the virtual clinical trials (VCT)-BREAST graphical processing unit (GPU)-accelerated platform dedicated to VCT in breast imaging. Approach. A GPU-based Monte Carlo (MC) code was developed for replicating in silico the geometric, x-ray spectra and detector setups adopted, respectively, in two research scanners and one commercial BCT scanner, adopting 80 kV, 60 kV and 49 kV tube voltage, respectively. Our cohort of virtual breasts included 16 anthropomorphic voxelized breast phantoms from a publicly available dataset. For each virtual patient, we simulated exams on the three scanners, up to a nominal simulated mean glandular dose of 5 mGy (primary photons launched, in the order of 1011–1012 per scan). Simulated 3D dose maps (recorded for skin, adipose and glandular tissues) were compared for the same phantom, on the three scanners. MC simulations were implemented on a single NVIDIA GeForce RTX 3090 graphics card. Main results. Using the spread of the dose distribution as a figure of merit, we showed that, in the investigated phantoms, the glandular dose is more uniform within less dense breasts, and it is more uniformly distributed for scans at 80 kV and 60 kV, than at 49 kV. A realistic virtual study of each breast phantom was completed in about 3.0 h with less than 1% statistical uncertainty, with 109 primary photons processed in 3.6 s computing time. Significance. We reported the first dosimetric study of the VCT-BREAST platform, a fast MC simulation tool for real-time virtual dosimetry and imaging trials in BCT, investigating the dose delivery performance of three clinical BCT scanners. This tool can be adopted to investigate also the effects on the 3D dose distribution produced by changes in the geometrical and spectrum characteristics of a cone-beam BCT scanner.

An attenuation field network for dedicated cone beam breast CT with short scan and offset detector geometry

The feasibility of full-scan, offset-detector geometry cone-beam CT has been demonstrated for several clinical applications. For full-scan acquisition with offset-detector geometry, data redundancy from complementary views can be exploited during image reconstruction. Envisioning an upright breast CT system, we propose to acquire short-scan data in conjunction with offset-detector geometry. To tackle the resulting incomplete data, we have developed a self-supervised attenuation field network (AFN). AFN leverages the inherent redundancy of cone-beam CT data through coordinate-based representation and known imaging physics. A trained AFN can query attenuation coefficients using their respective coordinates or synthesize projection data including the missing projections. The AFN was evaluated using clinical cone-beam breast CT datasets (n = 50). While conventional analytical and iterative reconstruction methods failed to reconstruct the incomplete data, AFN reconstruction was not statistically different from the reference reconstruction obtained using full-scan, full-detector data in terms of image noise, image contrast, and the full width at half maximum of calcifications. This study indicates the feasibility of a simultaneous short-scan and offset-detector geometry for dedicated breast CT imaging. The proposed AFN technique can potentially be expanded to other cone-beam CT applications.

Assessing Accurate Placement of Non-Wire Localization Markers in Non- Palpable Breast Tumors Using Breast CT: A Technical Note

Assessing Accurate Placement of Non-Wire Localization Markers in Non- Palpable Breast Tumors Using Breast CT: A Technical Note

Data-Driven Gradient Regularization for Quasi-Newton Optimization in Iterative Grating Interferometry CT Reconstruction.

Grating interferometry CT (GI-CT) is a promising technology that could play an important role in future breast cancer imaging. Thanks to its sensitivity to refraction and small-angle scattering, GI-CT could augment the diagnostic content of conventional absorption-based CT. However, reconstructing GI-CT tomographies is a complex task because of ill problem conditioning and high noise amplitudes. It has previously been shown that combining data-driven regularization with iterative reconstruction is promising for tackling challenging inverse problems in medical imaging. In this work, we present an algorithm that allows seamless combination of data-driven regularization with quasi-Newton solvers, which can better deal with ill-conditioned problems compared to gradient descent-based optimization algorithms. Contrary to most available algorithms, our method applies regularization in the gradient domain rather than in the image domain. This comes with a crucial advantage when applied in conjunction with quasi-Newton solvers: the Hessian is approximated solely based on denoised data. We apply the proposed method, which we call GradReg, to both conventional breast CT and GI-CT and show that both significantly benefit from our approach in terms of dose efficiency. Moreover, our results suggest that thanks to its sharper gradients that carry more high spatial-frequency content, GI-CT can benefit more from GradReg compared to conventional breast CT. Crucially, GradReg can be applied to any image reconstruction task which relies on gradient-based updates.

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

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. 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). Nine anthropomorphic breast phantoms with a voxel resolution of 0.5mm 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 4mGy (to facilitate comparisons). 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 uniformGDD. 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 breastdosimetry.

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

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 ofstudy. To study specific energy distributions ( ) in cytoplasm and nuclei of cells corresponding to glandular tissue for different x-ray breast imagingmodalities. 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 ( ). 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). 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 ( ), with little dependence on the spatial location. For similar values, for nuclei is different between DM/DBT and CEDM/BCT, indicating that distinct x-ray spectra play significant roles in . In addition, this behavior is also present when the specific energy distribution ( ) is considered taking into account the GDD in thebreast. 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 ionizingradiation.

Dedicated Cone-Beam Breast CT: Reproducibility of Volumetric Glandular Fraction with Advanced Image Reconstruction Methods.

Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp-Davis-Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm3). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods (p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction (p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation.