scispace - formally typeset
Search or ask a question

What factors effect on dose in digital mammography or digital breast tomosynthesis? 


Best insight from top research papers

Various factors influence the dose in digital mammography (DM) and digital breast tomosynthesis (DBT). For DM and DBT, the breast size plays a crucial role in dose assessment, with smaller breast sizes resulting in a greater difference in absorbed dose between DBT and MMG . Additionally, the angle of X-ray incidence affects dose distribution, with lower X-ray energy and deviations from the standard angle leading to variations in absorbed dose in the examined breast and other organs . Moreover, the presence of additional imaging modalities, such as mechanical imaging (MI) during DBT, can impact radiation dose, potentially increasing the average glandular dose (AGD) by up to 13% on average . These factors collectively contribute to the complexity of dose evaluation and optimization in digital breast imaging techniques.

Answers from top 5 papers

More filters
Papers (5)Insight
In digital breast tomosynthesis, the angular deviation of the tube significantly impacts the absorbed dose in the breast and other organs, with higher doses compared to digital mammography.
Factors affecting dose in digital mammography and digital breast tomosynthesis include the presence of a mechanical imaging sensor, automatic exposure control, and imaging mode, leading to an average 13% increase in dose.
In Digital Breast Tomosynthesis, factors affecting dose include image quality enhancement methods like 2D TV minimization, allowing for lower doses without compromising image quality or lesion detectability.
Factors affecting dose in digital mammography or digital breast tomosynthesis include X-ray energy, breast size, and depth, with smaller breasts and lower energies showing greater dose differences between modalities.
Factors affecting dose in digital mammography or digital breast tomosynthesis include breast size, X-ray energy, and depth, influencing absorbed dose distribution and differences between modalities.

Related Questions

What are the factors that affect the dose reference values for CT?4 answersThe factors that affect the dose reference values for CT include the size-specific dose estimates (SSDE) based on the volume CT dose index (CTDIvol). Changes in technology and the use of different computational phantoms also influence the dose coefficients. The concept of diagnostic reference range (DRR) and diagnostic reference levels (DRLs) are used to establish the balance between radiation dose and image quality necessary for accurate diagnosis. In pediatric brain CT, the radiation dose indices (volume CT dose index and dose length product) increase with age and weight, and equations can be derived to estimate age- and weight-dependent standard dose indices. Additionally, the water equivalent diameter (WED) of the examination volume affects the reference dose parameter, which is indirectly proportional to the power of the noise level in the CT image dataset.
What are the differences in radiation doses in mGy.cm² between multislice tomography and cone-beam tomography for use in dentistry?4 answersCone beam computed tomography (CBCT) in dentistry has shown improvements in image quality and reduced radiation dose compared to multislice tomography. CBCT devices have better detectors and extraction algorithms, allowing for enhanced image quality while minimizing radiation exposure. The optimization of reconstruction algorithms and the use of pulsed beams have further reduced the grading dose. In a study measuring patient radiation exposure, CBCT scans using a CS9300 Carestream system resulted in mean Kerma Area Product (KAP) values ranging from 314 to 399 mGycm2, depending on voxel size. Comparatively, literature reports KAP values for CBCT ranging from 210 to 2140 mGycm2. Therefore, CBCT offers reduced radiation doses compared to multislice tomography in dentistry.
What are the radiation safety towards occupational dose in radiologic technology?5 answersRadiologic technologists and ancillary staff who work with or near ionizing radiation face possible short- and long-term effects of occupational radiation exposure. Protection techniques such as time, distance, and shielding can help avoid dangerous exposure levels. Occupational dose limits, dose calculation, and devices used to measure exposure are important for keeping radiation exposure as low as reasonably achievable. Proper radiation protection, increased radiation knowledge, and adherence to safety practices can help prevent overexposure to radiation and its adverse effects. Radiation safety standards and the use of protective accessories like lead aprons and goggles are important for individual protection. Implementing periodic radiation safety training for occupational workers is beneficial for practicing a radiation safety culture.
What are the limitations of x-ray mammography in detecting breast cancer?5 answersX-ray mammography has limitations in detecting breast cancer. The difficult interpretation of mammograms can lead to an increase in missed cancers and misinterpreted non-cancerous lesions, which can affect the accuracy of detection. Additionally, the unequal focus on mass boxes during analysis can result in the network focusing too much on larger masses at the expense of smaller ones, leading to a decrease in accuracy. Furthermore, mammography screening can also lead to overdiagnosis of breast cancer, where some women may be diagnosed with breast cancer that would not have caused harm or required treatment. These limitations highlight the need for improved methods, such as computer-aided diagnosis systems and content-based mammogram retrieval, to assist radiologists in interpreting mammograms and reducing the morbidity and mortality of breast cancer.
What are the challenges of digital breast tomosynthesis reconstruction?2 answersDigital breast tomosynthesis (DBT) reconstruction faces several challenges. One challenge is enhancing the quality of the recovered images, which is still an ongoing research topic. Another challenge is the spatial variation of distances between voxels in the volumetric image, which can affect the accuracy of the reconstruction. Additionally, the rapid adoption of DBT into clinical practice has outpaced the evidence of its clinical and cost-effectiveness, highlighting the need for more research and evidence-based policies. Furthermore, the reconstruction process in DBT involves solving a large-scale inverse problem, which requires efficient computational methods and regularization techniques. Overlapping breast tissue can also be a challenge in DBT, as it can mimic or obscure lesions, leading to unnecessary recalls and missed cancers.
What are the disadvantages of digital breast tomosynthesis?5 answersDigital breast tomosynthesis (DBT) has several disadvantages. One major disadvantage is the increased radiation dose compared to standard 2D mammography. DBT requires the acquisition of multiple low-dose exposures, which results in a higher radiation dose to the breast. Another disadvantage is the lack of defined indications for DBT in breast cancer screening and its role in the diagnosis and staging of breast cancer. Further studies are needed to assess the combined reconstruction of the 2D view using 3D tomosynthesis data, which could potentially reduce patient exposure to radiation. Additionally, DBT is still being implemented in routine clinical breast imaging practice, indicating that it may not be widely available. Overall, while DBT offers advantages in detecting and characterizing breast lesions, it is important to consider these disadvantages when evaluating its use in clinical practice.

See what other people are reading

What is numerical reasoning?
4 answers
Numerical reasoning involves utilizing arithmetic operations like addition, subtraction, sorting, and counting within machine reading comprehension tasks. It is a complex subtask that requires models to perform calculations and statistics to reason out answers effectively. In the financial domain, numerical reasoning plays a crucial role in analyzing quantitative data from financial reports, enhancing business efficiency, and reducing costs significantly. Various model architectures have been developed to tackle numerical reasoning tasks, with some achieving near-human performance on benchmarks like the DROP dataset. These models often involve components like retriever modules, generator modules, and ensemble modules to process and answer numerical reasoning questions accurately.
Dose calculation algorithms used in treatment planning system for 3D radiotherapy and VMAT
5 answers
Various dose calculation algorithms are utilized in treatment planning systems for 3D radiotherapy and VMAT. These algorithms play a crucial role in accurately determining radiation doses for optimal treatment outcomes. Studies have compared algorithms like the Anisotropic Analytical Algorithm (AAA), Monte Carlo (MC), and others. Research has shown that algorithms such as AAA and Acuros XB (AXB) demonstrate variances in accuracy and dosimetric impact. Additionally, advancements in treatment planning systems have led to the development of fully automated systems for VMAT planning, enhancing plan quality and efficiency. Furthermore, the integration of deep learning techniques has shown promise in rapidly generating machine delivery parameters for VMAT plans based on predicted doses, showcasing potential for improved treatment planning processes.
Domain general Auditory Processing by Kazuya Saito and Adam tierney
5 answers
Domain-general auditory processing, as studied by Kazuya Saito and Adam Tierney, plays a crucial role in second language (L2) speech acquisition. Research indicates that individuals with enhanced auditory processing abilities show improved L2 vowel proficiency. Specifically, auditory acuity to key acoustic cues, such as F2 frequencies, promotes the acquisition of knowledge about speech categories like English vowels. Moreover, auditory processing contributes uniquely to L2 speech learning, even after controlling for variables like biographical backgrounds and memory abilities. This highlights the significance of auditory processing in representing and integrating sound dimensions implicitly, aiding in long-term memory formation for L2 speech acquisition. Such findings emphasize the importance of domain-general auditory processing in enhancing L2 speech learning outcomes.
What is the role of model calibration in accurately diagnosing cancer through histopathological analysis?
5 answers
Model calibration plays a crucial role in accurately diagnosing cancer through histopathological analysis.Calibration ensures that AI systems are reliable and consistent across different laboratories, standardizing whole slide image appearance for robust performance in cancer diagnosis. By incorporating inductive biases about example difficulty and utilizing per-image annotator agreement, model calibration can significantly improve the accuracy and reliability of histopathology image classifiers. Additionally, fine-tuning deep learning models with techniques like regularization, batch normalization, and hyperparameter optimization can enhance the performance of deep networks in diagnosing various cancers, such as colon and lung cancers, leading to high precision, recall, and accuracy rates. Moreover, in cytopathology, calibration techniques like focal loss, multiple outputs, and temperature scaling can provide well-calibrated models for cancer detection from urinary cytopathology screening images, improving accuracy and confidence levels aligned with ground truth probabilities.
How does meteorological parameters effects on air pollution?
5 answers
Meteorological parameters significantly influence air pollution levels. Various studies highlight the correlation between meteorological factors and air quality. Factors like wind speed, air temperature, and atmospheric pressure show reliable statistical relationships with pollutants like CO, CO2, O3, and PM10. In regions like Sichuan-Chongqing, low atmospheric layer height, slow wind speeds, and temperature inversions contribute to severe pollution episodes. Additionally, in areas like DKI Jakarta, PM10 exhibits strong correlations with temperature, relative humidity, and solar radiation. Studies in Lucknow emphasize the impact of meteorological parameters on pollutants like PM2.5, NO2, O3, and NH3 during different seasons, with variables like temperature, wind speed, and relative humidity playing crucial roles. These findings collectively demonstrate the significant role of meteorological parameters in influencing air pollution levels.
How effective are alternative frameworks in comparison to the results-process-context framework in performance assessment?
5 answers
Alternative frameworks in performance assessment have shown promising effectiveness compared to traditional approaches like the results-process-context framework. For instance, a study by Lévesque and Sutherlandhighlights the evolution towards a more comprehensive system-functioning approach in healthcare performance assessment, incorporating 12 derived constructs to gauge performance across various dimensions. Additionally, El Maazouz et al.introduce a DSL-based framework for performance assessment, enhancing experiment setups' explicit documentation and facilitating result analysis and reproducibility. Moreover, George et al.propose a network-based metric generation framework for contextual productivity assessment, addressing biases in existing methods. These alternative frameworks offer improved clarity, coverage, and adaptability in assessing performance across different domains, showcasing their effectiveness in enhancing assessment practices.
What method should i use to remove unrealistic values from a high variance time series?
5 answers
To effectively remove unrealistic values from a high variance time series, a suitable method would be to utilize a high-dimensional time series cleaning approach based on generative adversarial networks and spatial correlation. Additionally, employing a method that involves the application of the Discrete Fourier Transform (DFT) can be beneficial, as it allows for time series completion by managing various gap sizes and replacing missing values, resulting in low error percentages (1% average) and reflecting the likely behavior pattern of the time series in the absence of misleading outliers and missing data. Furthermore, a novel data cleaning and truth discovery method named RWTD based on random walk sampling has shown effectiveness in estimating true data from multiple uncertain sensors' observations in time series data.
What is Dijkstra's Algorithm?
5 answers
Dijkstra's Algorithm is a classic greedy algorithm used for solving the shortest path problem in networks. It is particularly effective for finding the optimal path in a single-source scenario within a loop network without negative weights. However, due to the algorithm's inefficiency caused by traversing a large number of nodes, enhancements have been proposed. For instance, the Targeted Multiobjective Dijkstra Algorithm (T-MDA) improves upon the traditional Dijkstra's Algorithm by incorporating A*-like techniques and efficient path management, resulting in better performance and negligible memory consumption. Additionally, researchers have explored optimizing Dijkstra's Algorithm by introducing pheromone calculations to reduce redundant inflection points and enhance path planning efficiency, especially for mobile robot navigation.
How does exercise help with weight management?
4 answers
Exercise plays a crucial role in weight management by influencing energy balance, metabolic rate, and body composition. Regular physical activity contributes to weight loss by increasing energy expenditure and promoting a negative energy balance. While exercise alone may not always lead to significant weight loss, it is essential for maintaining a healthy weight in the long term. Different types of exercises, such as endurance training, resistance training, and high-intensity interval training, offer distinct benefits in managing obesity by supporting fat loss, metabolic fitness, and weight loss maintenance. Combining exercise with dietary modifications enhances weight loss success by preserving lean mass, improving insulin sensitivity, and increasing cardiorespiratory fitness. Therefore, exercise is a fundamental component of comprehensive weight management strategies, promoting overall health and well-being.
What are the current trends and advancements in the field of lip synchronization for foreign languages?
5 answers
Current trends in lip synchronization for foreign languages include advancements in multilingual lip reading and synchronized lip-to-speech (SLTS) models. Multilingual lip reading leverages shared patterns in lip movements across languages, proposing synchronous bidirectional learning (SBL) for effective synergy. On the other hand, SLTS models address data asynchrony issues in training and evaluation, introducing automatic synchronization mechanisms (ASM) and improved evaluation metrics sensitive to time alignment. Additionally, research focuses on enhancing the viewing experience of foreign language videos through automated cross-language lip synchronization, aiming to generate superior photorealistic lip-synchronization over original videos. These advancements aim to improve the accuracy and naturalness of lip synchronization for diverse linguistic contexts.
What is common spatial pattern algorithm?
5 answers
The Common Spatial Patterns (CSP) algorithm is a widely utilized method in Brain-Computer Interface (BCI) systems for feature extraction from electroencephalography (EEG) data. CSP aims to enhance the signal-to-noise ratio by deriving spatial filters that maximize the variance of EEG signals for one class while minimizing it for another, making it effective for binary classification tasks. However, CSP is prone to overfitting and poor generalization, especially with limited calibration data. To address these limitations, a novel algorithm called Spectrally Adaptive Common Spatial Patterns (SACSP) has been proposed, which enhances CSP by learning temporal/spectral filters for each spatial filter, leading to improved generalizability and higher classification accuracy.