X-ray Mammography Research Articles

Mastalgia and Why It Should Be Evaluated With Imaging in Areas Where Use of Breast Cancer Screening Services are Unsatisfactory.

Mastalgia or breast pain is a very common symptom in women attending breast clinic. The aim of this study was to evaluate whether imaging for mastalgia leads to cancer detection in an area where routine breast cancer screening services are underutilized. This prospective study was performed between 1st March 2021 to 31st January 2023 at a tertiary care academic institution of central India. All patients underwent through clinical examination by a surgeon. Then patients were referred for ultrasound and/or X-ray mammography (MMG) depending on age. Cancer detection rate was calculated. The final cohort consisted of 176 patients with mastalgia and without any abnormality on clinical breast examination. Sixteen patients had mass lesion on radiology and core needle biopsy resulted as infiltrating duct carcinoma in 7 patients and benign phylloides tumor in one patient. Overall case detection rate for cancer was 4%. The breast cancer detection rate in patients presenting with mastalgia was low. However, in the absence of routine mammographic screening in the Indian general population, these would have been missed. Hence, diagnostic assessment for mastalgia is an appropriate strategy in countries where routine screening MMG is lacking.

Uncertainty Estimation for Dual View X-ray Mammographic Image Registration Using Deep Ensembles.

Techniques are developed for generating uncertainty estimates for convolutional neural network (CNN)-based methods for registering the locations of lesions between the craniocaudal (CC) and mediolateral oblique (MLO) mammographic X-ray image views. Multi-view lesion correspondence is an important task that clinicians perform for characterizing lesions during routine mammographic exams. Automated registration tools can aid in this task, yet if the tools also provide confidence estimates, they can be of greater value to clinicians, especially in cases involving dense tissue where lesions may be difficult to see. A set of deep ensemble-based techniques, which leverage a negative log-likelihood (NLL)-based cost function, are implemented for estimating uncertainties. The ensemble architectures involve significant modifications to an existing CNN dual-view lesion registration algorithm. Three architectural designs are evaluated, and different ensemble sizes are compared using various performance metrics. The techniques are tested on synthetic X-ray data, real 2D X-ray data, and slices from real 3D X-ray data. The ensembles generate covariance-based uncertainty ellipses that are correlated with registration accuracy, such that the ellipse sizes can give a clinician an indication of confidence in the mapping between the CC and MLO views. The results also show that the ellipse sizes can aid in improving computer-aided detection (CAD) results by matching CC/MLO lesion detects and reducing false alarms from both views, adding to clinical utility. The uncertainty estimation techniques show promise as a means for aiding clinicians in confidently establishing multi-view lesion correspondence, thereby improving diagnostic capability.

Analysing Student Feedback on the Integration of 3D Printing in the Teaching of Mammography for Radiologic Technologists

The objective of this study is to assess the suitability of using printed breast models as educational tools for instructing radiographer students on positioning and compression techniques in X-ray mammography. A physical anthropomorphic breast phantom, created with a stereolithography 3D printer based on a computational breast model, served as the main tool in the teaching approach. The practical exercise, conducted in three sessions for first, second, and third-year radiographer students, involved a structured approach in front of the mammography device. A questionnaire assessed participants' opinions on the 3D breast phantom and the exercise's impact on positioning, compression, and mammography device teachings. Among the 83 students surveyed, 52 provided responses, demonstrating a success rate of 71.2% in mastering the exercise utilizing the physical breast phantom. A significant portion of students express the importance of integrating contemporary digital technologies into their academic curriculum, based on the practical experience. The instructor demonstrated thorough preparation and competence in guiding the practical session. The positive experience in this teaching approach strongly encourages future professionals to integrate these new technologies into their daily practice, especially for acquiring skills in compression techniques and breast positioning.

Establishment of Indonesian Diagnostic Reference Levels for Mammography and Dental Radiography for Use as Medical Exposure Optimization Tools

Diagnostic Reference Level (DRL) is a dose value that will be an indicator in optimizing patient radiation protection. This study aims to determine the national DRL value in Indonesia when examining patients using mammography and dental radiography. The method used in this study combines quantitative research and descriptive analysis. The patient radiation dose data used comes from the 2019 to 2024 dose survey from health facilities in Indonesia for each type of mammography and dental radiography examination. Based on data analysis, it has been proposed to be adopted as an Indonesian DRL value for dental radiography for the types of examinations Panoramic - Standard for pediatric patients and Cephalo - LAT, Panoramic - Standard, Panoramic - Front, Panoramic - Bitewing, Intraoral - Periapical Anterior, and Intraoral - Periapical molars in adult patients. The Indonesian DRL value for mammography for Cranial-Caudal and Mediolateral-Oblique examination types is also proposed. The proposed value to become Indonesia's DRL will be agreed upon and determined to be Indonesia's DRL value and will be applied for five years. After five years, a review was done to update Indonesia's DRL value. BAPETEN needs to guide the form of more intensive socialization and assistance to increase awareness and compliance in carrying out optimization through the use of DRL as well as compliance in recording and reporting patient radiation dose data to the National Patient Radiation Dose Data Information System (Si-INTAN). Keywords: Diagnostic Reference Level (DRL), optimization of radiation protection, radiation protection of patients, X-ray mammography, X-ray dental radiography

The Effectiveness of Semi-Supervised Learning Techniques in Identifying Calcifications in X-ray Mammography and the Impact of Different Classification Probabilities

Identifying calcifications in mammograms is crucial for early breast cancer detection, and semi-supervised learning, which utilizes a small dataset for supervised learning combined with deep learning, is anticipated to be an effective approach for automating this identification process. This study explored the impact of semi-supervised learning on identifying mammographic calcifications by including 712 mammographic images from 252 patients in public datasets. Initially, 212 mammogram images were segmented into patches and classified visually for calcification presence. A subset of these patches, derived from 169 mammogram images, was used to train a ResNet50-based classifier. The classifier was evaluated using patches generated from 43 mammograms as a test data set. Additionally, 500 more mammogram images were processed into patches and analyzed using the trained ResNet50 model, with semi-supervised learning applied to patches exceeding certain classification probabilities. This process aimed to enhance the classifier’s accuracy and achieve improvements over the initial model. The findings indicated that semi-supervised learning significantly benefits the accuracy of calcification detection in mammography, underscoring its utility in enhancing diagnostic methodologies.

Multimodal breast cancer risk classifier based on biomaterial impedance analysis

Purpose of the research. Breast cancer is the most common malignant tumor among women in Europe and its early detection plays a leading role in reducing mortality rates. Currently, X-ray mammography is the standard screening method for detecting breast cancer. However, due to the morphological similarities between benign and malignant lesions, many of the positive screening mammograms are false positive (up to 40%). Therefore, automation and intellectualization of this process is an urgent task.Methods. The presented studies examine the problems of finding new, highly sensitive, prompt and non-invasive methods for detecting malignant tumors, based on the use of modern computer and telecommunication technologies, which make it possible not only to identify early manifestations of a pathological focus, but also to monitor the process of the effectiveness of therapy without significant harm to the patient’s health.Results. The presented model of a multi-channel classifier integrates the capabilities of multi-frequency bioimpedance measurements and matrix acquisition of information from the surface of human skin through multi-electrode matrix systems. To do this, based on a matrix of electrodes, 3D mapping of the skin surface in problem areas is carried out. Through multi-frequency scanning, we obtain a three-dimensional bioimpedance image, which is analyzed by a convolutional neural network and/or by a decision maker. The proposed solution allows simultaneous analysis of data by an expert (bioimpedance image) and a convolutional neural network (trained classifier), which leads to a reduction in false positive results.Conclusion. The possibilities of multichannel monitoring open up prospects for constructing impedance multidimensional "portraits" of malignant tumors. To classify “portraits” (diagnostics and preclinical diagnostics), methods and algorithms for image recognition and classification are used.

A survey on mean glandular dose in mammography examination and the factors affecting it in Shahid Sadoughi Hospital, Yazd, Iran.

mammography is the gold standard in the early detection of breast cancer. Due to the increase in the rate of women suffering from this malignancy all over the world, this imaging modality has been widely used. Considering the side effects caused by ionizing radiation to measure the carcinogenic risk of mammography X-rays, mean glandular dose (MGD) is the best parameter to evaluate the dose received by patients undergoing mammography. The aims of this study were to measure MGD in mammography in mammographic craniocaudal (CC) and mediolateral oblique (MLO) projections and investigate the relationship of MGD with compressed breast thickness (CBT), body mass index, age of the patient, and device exposure conditions. this cross-sectional analytical study was conducted on women aged 30-70 referring to the mammography unit of Shahid Sadoughi Hospital in Yazd city from May to August 2022. The TLD-GR 200 (thermoluminescence dosimeter) was placed on the breast of the patients for CC and MLO projections, and then the MGD was obtained by multiplying the entrance surface dose and the normalized glandular dose. Analysis of data (such as demographic information of patients, CBT, kVp, and mAs) was done by SPSS 23 software. The normality of the data was checked using Shapiro-Wilk tests. It was found that except for age, other variables did not have a normal distribution; therefore, equivalent parametric and nonparametric tests were used. In this regard, Spearman's correlation was used to assess the correlation between variables. P-value < .05 was considered as level of significance. The mean±standard deviation (SD) age of patients was 47.3±7.1years. The median±IQR (the interquartile range (IQR) is a measure of statistical dispersion, which is the spread of the data. It is defined as the difference between the 75th and 25th percentiles of the data.) (mean±SD) value of MGD per woman was 1.2±0.4mGy (1.3±0.3mGy). The median±IQR MGD in the MLO and CC projections was 1.6±0.6mGy and 0.9±0.4mGy, respectively. Significant relationships (P-value ≤ .001) were observed between MGD with CBT (R=0.62) and age (R=-0.85). The results showed that the mammography unit at Shahid Sadoughi Hospital in Yazd is functioning properly. The calculated median±IQR MGD per woman referring to this unit (1.2±0.4mGy) was clearly below the dose limit recommended by American College of Radiology and International Commission on Radiological Protection (3mGy). Moreover, among the factors affecting MGD, the highest correlation was seen between MGD and age (R=-0.85).

Diagnosis of some rare forms of breast cancer

Background. Breast cancer (BC) is a heterogeneous disease, in which it is crucial not only to detect the tumor, but also to determine its biological characteristics to choose an optimal treatment strategy. Herein, we review existing literature on the subject and analyze own data.Aim. To analyze clinical and morphological characteristics of rare histological types of BC.Materials and methods. In this retrospective study, we analyzed the results of instrumental examination of 180 patients with mucinous BC treated in Sverdlovsk Regional Oncology Dispensary between 2020 and 2022. The mean age of the participants was 62.5 years. All patients underwent comprehensive examination, including digital mammography using the Fujifilm Amulet Innovality system, multiparametric ultrasound breast examination using the EPIQ 5 system (Phillips, Netherlands), and 1.5 T dynamic contrast-enhanced magnetic resonance imaging with a 16-channel – dedicated breast coil (GE Healthcare, USA).Results and conclusion. The incidence of mucinous BC was 1.8 %. Other rare types of BC, such as occult, medullary, papillary, tubular, and cribriform cancer were observed in less than 1 % of cases, which is consistent with the available literature data. Mucinous tumors were primarily represented by luminal B histology (73.3 %); the Ki-67 proliferation index was usually 35 %. The most common radiological manifestation was a hyperdense formation with clear contours (85 %). Ultrasound examination usually revealed a hypoechoic formation with clear and irregular contours (92 %); however, 8 % of patients presented with isoechoic formations that can hardly be distinguished from the background. Magnetic resonance imaging showed the following patterns: mass (87.5 %) with a persistent type of contrast accumulation (56.2 %) (type I curve) or curve exhibiting a plateau (43.8 %) (type II curve). Contrast agent accumulation started from the first minutes and was &gt;100 % in all patients. Limitation of diffusion was observed in 25 % of patients. Patients with mucinous BC were usually characterized by late manifestation; 3 patients additionally had other cancers, including colorectal cancer, mucoepidermal tumor of the salivary gland, pancreatic cancer. None of these patients had family history of cancer. Isolated findings of X-ray mammography, ultrasound, and magnetic resonance imaging suggested benign breast tumors and no cancer-specific features.A multimodal approach enabled early detection of non-specific signs of rare forms of BC characterized by a specific clinical pattern with symptoms typical of this particular cancer from.

Abstract PO3-07-08: Breast density estimation with a microwave-frequency imaging system

Abstract Background: Microwave imaging has been proposed as an alternative method of breast imaging that is low-cost and comfortable for women as it avoids excessive compression. Microwave properties of tissues relate to water content and behavior (Gabriel et al, 1996); specifically, fatty tissues have lower properties and glandular tissues have greater properties (Lazebnik et al, 2007). These differences in microwave signatures of fatty and glandular tissues provide the opportunity to map the composition of the breast and create a density score without a mammogram. This density score may find utility in risk stratification or monitoring interventions aimed at decreasing breast density (Salazar et a, 2020). Purpose: We examine the feasibility of developing a density score based on microwave images that correlates to mammographic breast density (VOLPARA score) in a pilot study with healthy volunteers. Imaging System: We have developed a microwave imaging system that facilitates scanning of large groups of patients, as well as comparison to x-ray mammography (Mojabi et al, 2023). The system consists of two plates which are placed in contact with the breast. Microwave transmitters and receivers are embedded in the plates; signals transmitted through the breast are used to estimate microwave frequency properties of tissues, and maps of these estimates form a 2D image. Methods: 50 patients provided informed consent (study approved by Health Ethics Research Board of Alberta CC-21-0082). Both breasts of each volunteer were scanned. Previously performed mammograms were available for 21 of the volunteers. The number of volunteers with VOLPARA scores A, B, C, and D is 2, 8, 6, and 5, respectively. The percent density reported with the VOLPARA score is also available for these volunteers. Microwave images were formed for each scan and analyzed to predict density with three approaches: (1) average permittivity, (2) segmented regions, and (3) pixel-based intensities. The results demonstrate that the average permittivity of the breast typically increases from VOLPARA A to D, with some overlap in average values observed between the density categories. A correlation between average permittivity and percent breast density was observed. Regions representing glandular tissues are segmented from microwave images; the average values of these regions clearly differentiate between VOLPARA A and D, however do not show consistent ranges for VOLPARA scores B and C. Finally, the microwave breast density estimated using pixel-based intensities shows good correlation with the percentage density calculations from mammograms. Conclusions: Microwave images contain features related to the glandular tissues embedded in fat. By analyzing the composition of the breast in these images, density scores are created. While average permittivity appears to correlate to percent density calculated from mammograms, area or pixel-based approaches appear to have greater potential for categorizing into density classes. Expanding the number of participants, identifying biomarkers, and exploring deep learning techniques for density prediction are considered for future work Citation Format: Elise Fear, Jeremie Bourqui, Pedram Mojabi, Bobbie-Jo Docktor, Anita Garland, Danielle Deutscher, Zahra Lasemiimeni, Kathleen McMahon, Brendon Besler, Roger Tsang. Breast density estimation with a microwave-frequency imaging system [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-08.

Investigating the detection of breast cancer with deep transfer learning using ResNet18 and ResNet34

A lot of underdeveloped nations particularly in Africa struggle with cancer-related, deadly diseases. Particularly in women, the incidence of breast cancer is rising daily because of ignorance and delayed diagnosis. Only by correctly identifying and diagnosing cancer in its very early stages of development can be effectively treated. The classification of cancer can be accelerated and automated with the aid of computer-aided diagnosis and medical image analysis techniques. This research provides the use of transfer learning from a Residual Network 18 (ResNet18) and Residual Network 34 (ResNet34) architectures to detect breast cancer. The study examined how breast cancer can be identified in breast mammography pictures using transfer learning from ResNet18 and ResNet34, and developed a demo app for radiologists using the trained models with the best validation accuracy. 1, 200 datasets of breast x-ray mammography images from the National Radiological Society’s (NRS) archives were employed in the study. The dataset was categorised as implant cancer negative, implant cancer positive, cancer negative and cancer positive in order to increase the consistency of x-ray mammography images classification and produce better features. For the multi-class classification of the images, the study gave an average accuracy for binary classification of benign or malignant cancer cases of 86.7% validation accuracy for ResNet34 and 92% validation accuracy for ResNet18. A prototype web application showcasing ResNet18 performance has been created. The acquired results show how transfer learning can improve the accuracy of breast cancer detection, providing invaluable assistance to medical professionals, particularly in an African scenario.

Influence of anode/filtration setup on X-ray multimeter energy response in mammography applications

Quality control and assurance of mammography X-ray generators include the use of solid-state detectors and/or ionization chambers calibrated in standard reference radiation fields. The IEC 61267:2005 standard defines reference mammography radiation fields for Mo/Mo anode/filtration, while various anode/filtration combinations are encountered in clinical mammography X-ray units. Not all Secondary Standard Dosimetry Laboratories have an X-ray generator with Mo/Mo radiation setup. Thus, traceability can be established only for the available anode/filter combinations, commonly limited to W/Al. In this study, W/Al radiation fields were established under laboratory conditions by performing half-value layer measurements. Then, four solid-state detectors were characterized. X-ray multimeter performance was evaluated in terms of energy response in the W/Al radiation fields. In the laboratory conditions, the energy response of the dosimeters had a large deviation from unity for dosimeters without appropriate anode/filter combination selected in the software settings, even though most of the dosimeters had a uniform relative response. The discrepancy in the response was further investigated by examining its variation induced by available detector software settings, and it was determined that the dosimeter response can vary up to 20 %. In a clinical setup, the half-value layer was determined, and detector performance was examined. Dosimeters were tested in clinical fields with Mo/Mo, Mo/Rh, W/Rh, W/Ag anode/filter combinations in the X-ray tube voltage range from 25 kV to 35 kV. For most clinical radiation fields, multi-element detectors had energy response deviation within ±5 %. The single-element detector had one software setting available and exhibited strong energy dependence. Extensive testing of detector response, such as presented in this paper. allows for correction factor interpolation based on the half-value layer.

Machine learning and new insights for breast cancer diagnosis.

Breast cancer (BC) is the most prominent form of cancer among females all over the world. The current methods of BC detection include X-ray mammography, ultrasound, computed tomography, magnetic resonance imaging, positron emission tomography and breast thermographic techniques. More recently, machine learning (ML) tools have been increasingly employed in diagnostic medicine for its high efficiency in detection and intervention. The subsequent imaging features and mathematical analyses can then be used to generate ML models, which stratify, differentiate and detect benign and malignant breast lesions. Given its marked advantages, radiomics is a frequently used tool in recent research and clinics. Artificial neural networks and deep learning (DL) are novel forms of ML that evaluate data using computer simulation of the human brain. DL directly processes unstructured information, such as images, sounds and language, and performs precise clinical image stratification, medical record analyses and tumour diagnosis. Herein, this review thoroughly summarizes prior investigations on the application of medical images for the detection and intervention of BC using radiomics, namely DL and ML. The aim was to provide guidance to scientists regarding the use of artificial intelligence and ML in research and the clinic.

Geant4 Simulation of Scatter Radiation Removal: Comparison and Validation of Anti-scatter Grid and Air Gap for X-ray Mammography

Abstract: X-ray mammography modality provides excellent low-contrast resolution images with low scatter radiation, making it the gold standard in diagnosing breast cancer. Anti-scatter grid and air gap techniques are typically used to further minimize the scatter radiation and improve image quality. Thus, Geant4 simulation was used to investigate the effectiveness of these techniques in removing scatter radiation in X-ray mammography. The effectiveness of an anti-scatter grid was evaluated using the Bucky factor, where it linearly increased with increasing the anti-scatter grid ratio. It was found that increasing the grid frequency affects the Bucky factor depending on the design of the grid ratio. This research proved that designing an anti-scatter grid with high grid frequency (80 lp/mm), low grid ratio (2:1), and proper orientation minimized common anti-scatter grid artifacts. The effectiveness of the air gap technique was also evaluated using the air gap dose factor. It increased non-linearly with increasing magnification. This research validated that using smaller pixel sizes and small focal spot sizes improved spatial resolution with magnification. Our simulation validated that the anti-scatter grid and air gap were effective techniques in removing scatter radiation. By comparing these techniques, the anti-scatter grid was more effective in removing scatter radiation at the expense of increasing the radiation absorbed dose with the exception of 2.0 magnification. It’s recommended to be extremely cautious when using 2.0 magnification or a grid ratio higher or equal to 8:1. These parameters may cause the radiation absorbed dose to be increased by several folds. Keywords: Geant4, Gate, X-ray mammography, Scatter removal, Anti-scatter grid, Air gap.

Breast cancer classification method based on improved VGG16 using mammography images

Background and objectiveBreast cancer has become the leading global cancer, and early detection and diagnosis of breast cancer are of paramount importance for treatment. MethodologyThis paper proposes a breast X-ray mammography image classification model based on Convolutional Neural Network (CNN). The model categorizes breast X-ray mammography images into benign and malignant classes. Built upon the VGG network, the model adjusts the network structure and conducts experiments on the dataset collected and organized by the Medical Imaging Department of Ganzhou People's Hospital and The Sixth Affiliated Hospital of Jinan University. To address the issue of imbalanced data in the dataset, the model employs a focal loss function for optimization and combines transfer learning and data augmentation strategies during network training. ResultsExperimental results demonstrate that the model achieves an average recognition rate of 96.945% across four different magnification levels. Notably, recognition rates exceed 95.5% for the 50X, 100X, and 200× magnification levels, demonstrating excellent classification capabilities. ConclusionThis model ignificantly improving classification accuracy compared to previous models, which provides meaningful insights into the classification of breast X-ray mammography images.

Follow-up of breast cancer: why is it necessary to start a Consensus in 2024?

In Italy, breast cancer is the most frequently diagnosed cancer in women, with 55,900 new cases in 2023 (over 90% in the early stages). Due to the screening, early diagnosis and adjuvant treatment, these patients have a 5-years survival rate of 87% after the diagnosis. There are 834,154 women in Italy with a previous diagnosis of breast cancer: most of these women require a follow-up. The AIOM, ESMO and ASCO Guidelines recommend for early breast cancer (EBC) a clinical follow-up with only physical examination (and eliciting of symptoms) and an annual X-ray mammography, on the basis of the results of two randomized trials published in 1994 that showed no benefit in overall survival with intensive follow-up. However, an Italian survey reported the application by 80% of oncologists of an intensive follow-up based on the individual patient’s risk of recurrence. In fact, the oncologists believe that an early diagnosis of locoregional or distant recurrence may allow an early start of very effective therapies. In this lack of up-to-date scientific data, many questions about follow-up remain unanswered and the few ongoing studies will provide results in several years. Non-compliance with guideline recommendations leads to increased costs for the healthcare system. Furthermore, management varies widely from centre to centre with regard to guideline recommendations, resulting in inequalities between patients. For these reasons, the follow-up of breast cancer should be reconsidered. In the absence of recent scientific evidence, a multidisciplinary group of breast cancer experts has initiated a Consensus on the follow-up of EBC according to the mini-Delphi methodology. The project will be completed by the end of 2024.

Mammography with deep learning for breast cancer detection.

X-ray mammography is currently considered the golden standard method for breast cancer screening, however, it has limitations in terms of sensitivity and specificity. With the rapid advancements in deep learning techniques, it is possible to customize mammography for each patient, providing more accurate information for risk assessment, prognosis, and treatment planning. This paper aims to study the recent achievements of deep learning-based mammography for breast cancer detection and classification. This review paper highlights the potential of deep learning-assisted X-ray mammography in improving the accuracy of breast cancer screening. While the potential benefits are clear, it is essential to address the challenges associated with implementing this technology in clinical settings. Future research should focus on refining deep learning algorithms, ensuring data privacy, improving model interpretability, and establishing generalizability to successfully integrate deep learning-assisted mammography into routine breast cancer screening programs. It is hoped that the research findings will assist investigators, engineers, and clinicians in developing more effective breast imaging tools that provide accurate diagnosis, sensitivity, and specificity for breast cancer.

Stepped frequency ultrasound computed tomography with waveform inversion

Routine US (ultrasound) scans for breast imaging run on a conventional console suffer from machine and operator dependence and are subject to personal interpretation. Recently, the framework of USCT (ultrasound computed tomography) has emerged as a safe, powerful and operator independent alternative to diagnostic US scans and x-rays mammography. The most known systems employ one circular array or a combination of transmitters and receivers by exploiting reflection, diffraction and transmission data. These systems are based on a pulsed transmission. Following propagation in tissue, the signals are usually recorded with a direct RF sampling scheme and stored as digital time-series. Image reconstruction is performed in the frequency domain in the 400 kHz–1 MHz bandwidth over a limited number of discrete frequencies. In this paper, we propose a new architecture based on the stepped frequency continuous waveform (SFCW) principle. In this scheme, the transmission is a continuous one and the received waveforms undergo a homo-dyne stage. By sequentially transmitting single tones at different frequencies, data can be collected directly in the frequency domain at specific frequencies, with programmable frequency steps and with any desired SNR. We describe in detail the transmitter and the receiver paths and compare with a conventional pulsed USCT architecture. Finally, we highlight the benefits of a SFCW-USCT device and comment on SNR, absorbed power, data fidelity and data storage.

Evaluation of radiation attenuation properties of breast equivalent phantom material made of gelatin material in mammographic X-ray energies

The work discusses the development of a novel breast phantom material using local gelatin for medical imaging and patient-specific quality assurance. Various gelatin samples were fabricated, differing in hardness (200, 240, and 250 Bloom values) and gelatin-to-water ratios (15%, 20%, and 25%). These samples were evaluated for density, which ranged from 1.04 to 1.08 g/cm³, and for mechanical properties, revealing that higher hardness and gelatin-to-water ratios led to increased stress-strain values. The study also measured the linear and mass attenuation coefficients (μ, μm) of these samples under different X-ray energies (20–40 kV), finding that samples S7, S8, and S9 exhibited the highest μ values across all energies. The properties of these gelatin samples were compared with data on human breast tissue of different ages from the XCOM database. Samples S1 to S6 closely matched the breast tissue of the young age group, with a minimal difference of less than 0.33. Fitness tests (χ2) indicated that samples with 20% and 25% gelatin content were particularly effective in mimicking young breast tissue. This research suggests the potential of using gelatin samples with at least 20% gelatin powder as a promising material for mimicking young breast tissue in medical phantoms.

Key comparison BIPM.RI(I)-K7 of the air-kerma standards of the BFKH, Hungary, and the BIPM in mammography x-rays

Main textA key comparison has been made between the air-kerma standards of the BFKH, Hungary, and the BIPM in mammography x-ray beams. The results show the standards to agree at the level of the expanded uncertainty of the comparison of 9.2 parts in 103. The results are analysed and presented in terms of degrees of equivalence, suitable for entry in the BIPM key comparison database.To reach the main text of this paper, click on Final Report. Note that this text is that which appears in Appendix B of the BIPM key comparison database https://www.bipm.org/kcdb/.The final report has been peer-reviewed and approved for publication by the CCRI, according to the provisions of the CIPM Mutual Recognition Arrangement (CIPM MRA).

A Study on the Uncertainty of the Average Glandular Dose in Quality Control of Mammographic X-ray Units

【Purpose】 Accurate control of X-ray units and dosimeters and analysis of the uncertainties associated with the accurate measurement of radiation doses are essential for the effective establishment and application of diagnostic reference levels. In this study, the uncertainty of the average glandular dose (AGD) in the quality control of mammography equipment was evaluated in detail, and recommendations were provided to improve the accuracy and safety of radiological practice. 【Methods】 In the uncertainty analysis of the AGD, the relative standard uncertainties in the measurements of the half-value layer, the incident air kerma, and the conversion factor were considered and finally expressed as expanded uncertainties, the intervals of which were clearly defined. 【Results】 From the AGD measurements using two types of dosimeters, it was found that the primary sources of uncertainty are the uncertainty of the calibration factors of the dosimeters and the uncertainty of the conversion factors.【Conclusion】 To reduce uncertainty, the use of regularly calibrated dosimeters is effective and reliable. Two types of dosimeters are commonly used; the results of this study may serve as a reference value for the uncertainty of AGD in quality control in medical facilities.