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Summary: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally and is responsible for an estimated one‐third of deaths as well as significant morbidity and health care utilisation.Technological and bioinformatic advances have facilitated the discovery of pathogenic germline variants for some specific CVDs, including familial hypercholesterolaemia, cardiomyopathies and arrhythmic syndromes. Use of these genetic tests for earlier disease identification is increasing due, in part, to decreasing costs, Medicare rebates, and consumer comfort with genetic testing.However, CVDs that occur more commonly, including coronary artery disease and atrial fibrillation, do not display monogenic inheritance patterns. Genetically, these diseases have generally been associated with many genetic variants each with a small effect size. This complexity can be expressed mathematically as a polygenic risk score. Genetic testing kits that provide polygenic risk scoring are becoming increasingly available directly to private‐paying consumers outside the traditional clinical setting.An improved understanding of the evidence of genetics in CVD will offer clinicians new opportunities for individualised risk prediction and preventive therapy. [ABSTRACT FROM AUTHOR]

Background: There is increasing interest in using ultrasound for thermal ablation, histotripsy, and thermal or cavitational enhancement of drug delivery for the treatment of pancreatic cancer. Ultrasonic and thermal modelling conducted as part of the treatment planning process requires acoustic property values for all constituent tissues, but the literature contains no data for the human pancreas. Purpose: This study presents the first acoustic property measurements of human pancreatic samples and provides examples of how these properties impact a broad range of ultrasound therapies. Methods: Data were collected on human pancreatic tissue samples at physiological temperature from 23 consented patients in cooperation with a hospital pathology laboratory. Propagation of ultrasound over the 2.1–4.5 MHz frequency range through samples of various thicknesses and pathologies was measured using a set of custom‐built ultrasonic calipers, with the data processed to estimate sound speed and attenuation. The results were used in acoustic and thermal simulations to illustrate the impacts on extracorporeal ultrasound therapies for mild hyperthermia, thermal ablation, and histotripsy implemented with a CE‐marked clinical system operating at 0.96 MHz. Results: The mean sound speed and attenuation coefficient values for human samples were well below the range of values in the literature for non‐human pancreata, while the human attenuation power law exponents were substantially higher. The simulated impacts on ultrasound mediated therapies for the pancreas indicated that when using the human data instead of the literature average, there was a 30% reduction in median temperature elevation in the treatment volume for mild hyperthermia and 43% smaller volume within a 60°C contour for thermal ablation, all driven by attenuation. By comparison, impacts on boiling and intrinsic threshold histotripsy were minor, with peak pressures changing by less than 15% (positive) and 1% (negative) as a consequence of the counteracting effects of attenuation and sound speed. Conclusion: This study provides the most complete set of speed of sound and attenuation data available for the human pancreas, and it reiterates the importance of acoustic material properties in the planning and conduct of ultrasound‐mediated procedures, particularly thermal therapies. [ABSTRACT FROM AUTHOR]

Background: A classic approach in medical image registration is to formulate an optimization problem based on the image pair of interest, and seek a deformation vector field (DVF) to minimize the corresponding objective, often iteratively. It has a clear focus on the targeted pair, but is typically slow. In contrast, more recent deep‐learning‐based registration offers a much faster alternative and can benefit from data‐driven regularization. However, learning is a process to "fit" the training cohort, whose image or motion characteristics or both may differ from the pair of images to be tested, which is the ultimate goal of registration. Therefore, generalization gap poses a high risk with direct inference alone. Purpose: In this study, we propose an individualized adaptation to improve test sample targeting, to achieve a synergy of efficiency and performance in registration. Methods: Using a previously developed network with an integrated motion representation prior module as the implementation backbone, we propose to adapt the trained registration network further for image pairs at test time to optimize the individualized performance. The adaptation method was tested against various characteristics shifts caused by cross‐protocol, cross‐platform, and cross‐modality, with test evaluation performed on lung CBCT, cardiac MRI, and lung MRI, respectively. Results: Landmark‐based registration errors and motion‐compensated image enhancement results demonstrated significantly improved test registration performance from our method, compared to tuned classic B‐spline registration and network solutions without adaptation. Conclusions: We have developed a method to synergistically combine the effectiveness of pre‐trained deep network and the target‐centric perspective of optimization‐based registration to improve performance on individual test data. [ABSTRACT FROM AUTHOR]

Mobility using wheelchair is a crucial feature for disabled people. Between a manual wheelchair and an electric wheelchair, there is a very interesting and promising solution consisting of an electrical kit that converts a manual wheelchair to an electric wheelchair. Autonomad‐Mobility is a company selling such kits, where the wheels are equipped with a brushless motor. It presents new possibilities for mobility, especially for crossing obstacles and running on different types of soil. Several methods have been tested, from linear to nonlinear ones including the linear parameter‐varying control framework. Nevertheless, the most dangerous and critical issue is to be able to produce a so‐called safe and robust swing‐up: the transition from standard mode (four wheels on the ground) to gyroscopic mode (the wheelchair balancing on the rear push‐wheels). The gyroscopic mode is a unique feature improving mobility by providing a way to overcome obstacles that would normally be unsurpassable due to the caster wheels. Whereas some results exist, the solution must be compatible with any wheelchair and any disabled person whatever is her/his height and weight. Robustness of this unstable feature is therefore the key point of the work knowing that "exact" modeling is unrealistic. With the model in descriptor form and using the quasi‐linear parameter‐varying framework new stabilization conditions formulated as linear matrix inequality constraints are developed for attaining the goal. Simulations and real‐time experiments demonstrate the interest of this approach. [ABSTRACT FROM AUTHOR]

The pseudohypohalous acid hypothiocyanite/hypothiocyanous acid (OSCN−/HOSCN) has been known to play an antimicrobial role in mammalian immunity for decades. It is a potent oxidant that kills bacteria but is non‐toxic to human cells. Produced from thiocyanate (SCN−) and hydrogen peroxide (H2O2) in a variety of body sites by peroxidase enzymes, HOSCN has been explored as an agent of food preservation, pathogen killing, and even improved toothpaste. However, despite the well‐recognized antibacterial role HOSCN plays in host–pathogen interactions, little is known about how bacteria sense and respond to this oxidant. In this work, we will summarize what is known and unknown about HOSCN in innate immunity and recent advances in understanding the responses that both pathogenic and non‐pathogenic bacteria mount against this antimicrobial agent, highlighting studies done with three model organisms, Escherichia coli, Streptococcus spp., and Pseudomonas aeruginosa. [ABSTRACT FROM AUTHOR]

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability. Purpose: The purpose of this work is to develop a deep learning-based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter-observer variability. Methods: Our dataset contains CT scans from 349 IPF patients and 529 non-IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two-stage model:at stage one, we built a multi-scale, domain knowledge-guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high- and medium-resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient-level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics. Results: During the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high- or medium-resolution attention, the learned attentionmaps are not satisfactory;when including both high- and medium-resolution attention, under certain hyperparameter settings, the model reaches the highest AUC among all experiments (AUC±SD = 0.99±0.01) and the estimated attention maps concentrate on the regions of interests for this task. Three bestperforming hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set. Conclusions: Our results suggest that, for a task with only scan-level labels available, MSGA+RF can utilize the population-level domain knowledge to guide the training of the network, which increases both model accuracy and explainability. [ABSTRACT FROM AUTHOR]

Purpose: Size‐specific dose estimate (SSDE) is a metric that adjusts CTDIvol to account for patient size. While not intended to be an estimate of organ dose, AAPM Report 204 notes the difference between the patient organ dose and SSDE is expected to be 10–20%. The purpose of this work was therefore to evaluate SSDE against estimates of organ dose obtained using Monte Carlo (MC) simulation techniques applied to routine exams across a wide range of patient sizes. Materials and Methods: Size‐specific dose estimate was evaluated with respect to organ dose based on three routine protocols taken from Siemens scanners: (a) brain parenchyma dose in routine head exams, (b) lung and breast dose in routine chest exams, and (c) liver, kidney, and spleen dose in routine abdomen/pelvis exams. For each exam, voxelized phantom models were created from existing models or derived from clinical patient scans. For routine head exams, 15 patient models were used which consisted of 10 GSF/ICRP voxelized phantom models and five pediatric voxelized patient models created from CT image data. For all exams, the size metric used was water equivalent diameter (Dw). For the routine chest exams, data from 161 patients were collected with a Dw range of ~16–44 cm. For the routine abdomen/pelvis exams, data from 107 patients were collected with a range of Dw from ~16 to 44 cm. Image data from these patients were segmented to generate voxelized patient models. For routine head exams, fixed tube current (FTC) was used while tube current modulation (TCM) data for body exams were extracted from raw projection data. The voxelized patient models and tube current information were used in detailed MC simulations for organ dose estimation. Organ doses from MC simulation were normalized by CTDIvol and parameterized as a function of Dw. For each patient scan, the SSDE was obtained using Dw and CTDIvol values of each scan, according to AAPM Report 220 for body scans and Report 293 for head scans. For each protocol and each patient, normalized organ doses were compared with SSDE. A one‐sided tolerance limit covering 95% (P = 0.95) of the population with 95% confidence (α = 0.05) was used to assess the upper tolerance limit (TU) between SSDE and normalized organ dose. Results: For head exams, the TU between SSDE and brain parenchyma dose was observed to be 12.5%. For routine chest exams, the TU between SSDE and lung and breast dose was observed to be 35.6% and 68.3%, respectively. For routine abdomen/pelvis exams, the TU between SSDE and liver, spleen, and kidney dose was observed to be 30.7%, 33.2%, and 33.0%, respectively. Conclusions: The TU of 20% between SSDE and organ dose was found to be insufficient to cover 95% of the sampled population with 95% confidence for all of the organs and protocols investigated, except for brain parenchyma dose. For the routine body exams, excluding the breasts, a wider threshold difference of ~30–36% would be needed. These results are, however, specific to Siemens scanners. [ABSTRACT FROM AUTHOR]

Purpose: Recent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel. Method: A cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer‐aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well‐known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA. Results: All included radiomic features lacked intercondition reproducibility (OCCC < 0.9) among all the 36 conditions. Out of 226 radiomic features analyzed, only 17 and 18 features were considered reproducible (OCCC ≥ 0.9) to dose and kernel variation, respectively, within the corresponding condition subsets. Slice thickness demonstrated the largest impact on radiomic feature values where only one to five features were reproducible at a few condition subsets. ANOVA revealed significant interactions (P < 0.05) between CT parameters affecting the variability of >50% of radiomic features. Conclusion: We systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies. [ABSTRACT FROM AUTHOR]

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiology requires specific training, skills, and techniques, as described in each document. Reproduction or modification of the published practice guidelines and technical standards by those entities not providing these services is not authorized. The following terms are used in the AAPM practice guidelines: (a) Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. (b) Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work was to estimate and compare breast and lung doses of chest CT scans using organ‐based tube current modulation (OBTCM) to those from conventional, attenuation‐based automatic tube current modulation (ATCM) across a range of patient sizes. Methods: Thirty‐four patients (17 females, 17 males) who underwent clinically indicated CT chest/abdomen/pelvis (CAP) examinations employing OBTCM were collected from two multi‐detector row CT scanners. Patient size metric was assessed as water equivalent diameter (Dw) taken at the center of the scan volume. Breast and lung tissues were segmented from patient image data to create voxelized models for use in a Monte Carlo transport code. The OBTCM schemes for the chest portion were extracted from the raw projection data. ATCM schemes were estimated using a recently developed method. Breast and lung doses for each TCM scenario were estimated for each patient model. CTDIvol‐normalized breast (nDbreast) and lung (nDlung) doses were subsequently calculated. The differences between OBTCM and ATCM normalized organ dose estimates were tested using linear regression models that included CT scanner and Dw as covariates. Results: Mean dose reduction from OBTCM in nDbreast was significant after adjusting for the scanner models and patient size (P = 0.047). When pooled with females and male patient, mean dose reduction from OBTCM in nDlung was observed to be trending after adjusting for the scanner model and patient size (P = 0.085). Conclusions: One specific manufacturer's OBTCM was analyzed. OBTCM was observed to significantly decrease normalized breast relative to a modeled version of that same manufacturer's ATCM scheme. However, significant dose savings were not observed in lung dose over all. Results from this study support the use of OBTCM chest protocols for females only. [ABSTRACT FROM AUTHOR]

Purpose: Task Group Report 195 of the American Association of Physicists in Medicine contains reference datasets for the direct comparison of results among different Monte Carlo (MC) simulation tools for various aspects of imaging research that employs ionizing radiation. While useful for comparing and validating MC codes, that effort did not provide the information needed to compare absolute dose estimates from CT exams. Therefore, the purpose of this work is to extend those efforts by providing a reference dataset for benchmarking fetal dose derived from MC simulations of clinical CT exams. Acquisition and validation methods: The reference dataset contains the four necessary elements for validating MC engines for CT dosimetry: (a) physical characteristics of the CT scanner, (b) patient information, (c) exam specifications, and (d) fetal dose results from previously validated and published MC simulations methods in tabular form. Scanner characteristics include non‐proprietary descriptions of equivalent source cumulative distribution function (CDF) spectra and bowtie filtration profiles, as well as scanner geometry information. Additionally, for the MCNPX MC engine, normalization factors are provided to convert raw simulation results to absolute dose in mGy. The patient information is based on a set of publicly available fetal dose models and includes de‐identified image data; voxelized MC input files with fetus, uterus, and gestational sac identified; and patient size metrics in the form of water equivalent diameter (Dw) z‐axis distributions from a simulated topogram (Dw,topo) and from the image data (Dw,image). Exam characteristics include CT scan start and stop angles and table and patient locations, helical pitch, nominal collimation and measured beam width, and gantry rotation time for each simulation. For simulations involving estimating doses from exams using tube current modulation (TCM), a realistic TCM scheme is presented that is estimated based upon a validated method. (d) Absolute and CTDIvol‐normalized fetal dose results for both TCM and FTC simulations are given for each patient model under each scan scenario. Data format and usage notes: Equivalent source CDFs and bowtie filtration profiles are available in text files. Image data are available in DICOM format. Voxelized models are represented by a header followed by a list of integers in a text file representing a three‐dimensional model of the patient. Size distribution metrics are also given in text files. Results of absolute and normalized fetal dose with associated MC error estimates are presented in tabular form in an Excel spreadsheet. All data are stored on Zenodo and are publicly accessible using the following link: https://zenodo.org/record/3959512. Potential applications: Similar to the work of AAPM Report 195, this work provides a set of reference data for benchmarking fetal dose estimates from clinical CT exams. This provides researchers with an opportunity to compare MC simulation results to a set of published reference data as part of their efforts to validate absolute and normalized fetal dose estimates. This could also be used as a basis for comparison to other non‐MC approaches, such as deterministic approaches, or to commercial packages that provide estimates of fetal doses from clinical CT exams. [ABSTRACT FROM AUTHOR]

Terrestrial ecosystems are often thought to be effective sinks of anthropogenic CO2 emissions with biosphere greening considered unequivocal evidence of this process. Increasing atmospheric concentrations of CO2 along with other greenhouse gases are however, responsible for global warming. As temperature increases, the rate at which biomes sequester CO2 may decline as the optimum temperature for photosynthetic production is exceeded, thereby reducing their potential to sequester CO2. Here we present evidence from 3 years of direct measurements of CO2 exchanges over subtropical coastal ecosystems in eastern Australia that the optimum temperature range for photosynthesis of 24.1°C to 27.4°C is routinely exceeded. This causes a rapid decline in photosynthetic production made worse when soil water content decreases. As climate change continues, both rising temperatures and predicted decline in rainfall will see these coastal ecosystems ability to sequester CO2 decrease further rapidly. We suggest similar research is needed urgently over other terrestrial ecosystems. Plain Language Summary: Emissions of CO2, primarily from the combustion of fossil fuels, have long been considered to result in an enhanced greening of terrestrial ecosystems through CO2 fertilization. However, increases in atmospheric CO2 along with other greenhouse gases have caused global warming. As air temperatures increase, the optimum temperature at which ecosystems sequester CO2 from the atmosphere may be exceeded and CO2 uptake by ecosystems declines. This results in more CO2 including from anthropogenic emissions remaining in the atmosphere where it may cause further warming and climate change. Here we present the first direct measurements of CO2 exchange over three subtropical coastal ecosystems in Australia. We find that the optimum temperature range for photosynthetic production (CO2 sequestration from the atmosphere) of 24.1°C to 27.4°C is often exceeded and that predicted future warming will result in a rapid decline in the ability of the coastal ecosystems to sequester CO2. Key Points: The first study to identify optimum temperatures for photosynthetic production in subtropical coastal ecosystems is presentedThe optimum temperature range for photosynthesis of 24.1°C to 27.4°C is often exceeded under the current climatePredicted future global warming will result in rapid decline net ecosystem exchange in Australian subtropical coastal ecosystems [ABSTRACT FROM AUTHOR]

Purpose: An important challenge for deep learning models is generalizing to new datasets that may be acquired with acquisition protocols different from the training set. It is not always feasible to expand training data to the range encountered in clinical practice. We introduce a new technique, physics‐based data augmentation (PBDA), that can emulate new computed tomography (CT) data acquisition protocols. We demonstrate two forms of PBDA, emulating increases in slice thickness and reductions of dose, on the specific problem of false‐positive reduction in the automatic detection of lung nodules. Methods: We worked with CT images from the lung image database consortium (LIDC) collection. We employed a hybrid ensemble convolutional neural network (CNN), which consists of multiple CNN modules (VGG, DenseNet, ResNet), for a classification task of determining whether an image patch was a suspicious nodule or a false positive. To emulate a reduction in tube current, we injected noise by simulating forward projection, noise addition, and backprojection corresponding to 1.5 mAs (a "chest x‐ray" dose). To simulate thick slice CT scans from thin slice CT scans, we grouped and averaged spatially contiguous CT within thin slice data. The neural network was trained with 10% of the LIDC dataset that was selected to have either the highest tube current or the thinnest slices. The network was tested on the remaining data. We compared PBDA to a baseline with standard geometric augmentations (such as shifts and rotations) and Gaussian noise addition. Results: PBDA improved the performance of the networks when generalizing to the test dataset in a limited number of cases. We found that the best performance was obtained by applying augmentation at very low doses (1.5 mAs), about an order of magnitude less than most screening protocols. In the baseline augmentation, a comparable level of Gaussian noise was injected. For dose reduction PBDA, the average sensitivity of 0.931 for the hybrid ensemble network was not statistically different from the average sensitivity of 0.935 without PBDA. Similarly for slice thickness PBDA, the average sensitivity of 0.900 when augmenting with doubled simulated slice thicknesses was not statistically different from the average sensitivity of 0.895 without PBDA. While there were cases detailed in this paper in which we observed improvements, the overall picture was one that suggests PBDA may not be an effective data enrichment tool. Conclusions: PBDA is a newly proposed strategy for mitigating the performance loss of neural networks related to the variation of acquisition protocol between the training dataset and the data that is encountered in deployment or testing. We found that PBDA does not provide robust improvements with the four neural networks (three modules and the ensemble) tested and for the specific task of false‐positive reduction in nodule detection. [ABSTRACT FROM AUTHOR]

Purpose: Reducing dose level to achieve ALARA is an important task in diagnostic and therapeutic applications of computed tomography (CT) imaging. Effective image quality enhancement strategies are crucial to compensate for the degradation caused by dose reduction. In the past few years, deep learning approaches have demonstrated promising denoising performance on natural/synthetic images. This study tailors a neural network model for (ultra‐)low‐dose CT denoising, and assesses its performance in enhancing CT image quality and emphysema quantification. Methods: The noise statistics in low‐dose CT images has its unique characteristics and differs from that used in general denoising models. In this study, we first simulate the paired ultra‐low‐dose and targeted high‐quality image of reference, with a well‐validated pipeline. These paired images are used to train a denoising convolutional neural network (DnCNN) with residual mapping. The performance of the DnCNN tailored to CT denoising (DnCNN‐CT) is assessed over various dose reduction levels, with respect to both image quality and emphysema scoring quantification. The possible over‐smoothing behavior of DnCNN and its impact on different subcohort of patients are also investigated. Results: Performance evaluation results showed that DnCNN‐CT provided significant image quality enhancement, especially for very‐low‐dose level. With DnCNN‐CT denoising on 3%‐dose cases, the peak signal‐to‐noise ratio improved by 8 dB and the structure similarity index increased by 0.15. This outperformed the original DnCNN and the state‐of‐the‐art nonlocal‐mean‐type denoising scheme. Emphysema mask was also investigated, where lung voxels of abnormally low attenuation coefficient were marked as potential emphysema. Emphysema mask generated after DnCNN‐CT denoising on 3%‐dose image was demonstrated to agree well with that from the full‐dose reference. Despite over‐smoothing in DnCNN denoising, which contributed to slight underestimation of emphysema score compared to the reference, such minor overcorrection did not affect clinical conclusions. The proposed method provided effective detection for cases with appreciable emphysema while serving as a reasonable correction for normal cases without emphysema. Conclusions: This work provides a tailored DnCNN for (ultra‐)low‐dose CT denoising, and demonstrates significant improvement on both the image quality and the clinical emphysema quantification accuracy over various dose levels. The clinical conclusion of emphysema obtained from the denoised low‐dose images agrees well with that from the full‐dose ones. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work was to estimate scanner‐independent CTDIvol‐to‐fetal‐dose coefficients for tube current‐modulated (TCM) and fixed tube current (FTC) computed tomography (CT) examinations of pregnant patients of various gestational ages undergoing abdominal/pelvic CT examinations. Methods: For 24 pregnant patients of gestational age from <5 to 36 weeks who underwent clinically indicated CT examinations, voxelized models of maternal and fetal (or embryo) anatomy were created from abdominal/pelvic image data. Absolute fetal dose (Dfetus) was estimated using Monte Carlo (MC) simulations of helical scans covering the abdomen and pelvis for TCM and FTC scans. Estimated TCM schemes were generated for each patient model using a validated method that accounts for patient attenuation and scanner output limits for one scanner model and were incorporated into MC simulations. FTC scans were also simulated for each patient model with multidetector row CT scanners from four manufacturers. Normalized fetal dose estimates, nDfetus, was obtained by dividing Dfetus from the MC simulations by CTDIvol. Patient size was described using water equivalent diameter (Dw) measured at the three‐dimensional geometric centroid of the fetus. Fetal depth (DEf) was measured from the anterior skin surface to the anterior part of the fetus. nDfetus and Dw were correlated using an exponential model to develop equations for fetal dose conversion coefficients for TCM and FTC abdominal/pelvic CT examinations. Additionally, bivariate linear regression was performed to analyze the correlation of nDfetus with Dw and fetal depth (DEf). For one scanner model, nDfetus from TCM was compared to FTC and the size‐specific dose estimate (SSDE) conversion coefficients (f‐factors) from American Association of Physicists in Medicine (AAPM) Report 204. nDfetus from FTC simulations was averaged across all scanners for each patient (nDfetus¯). nDfetus¯ was then compared with SSDE f‐factors and correlated with Dw using an exponential model and with Dw and DEf using a bivariate linear model. Results: For TCM, the coefficient of determination (R2) of nDfetus and Dw was observed to be 0.73 using an exponential model. Using the bivariate linear model with Dw and DEf, an R2 of 0.78 was observed. For the TCM technology modeled, TCM yielded nDfetus values that were on average 6% and 17% higher relative to FTC and SSDE f‐factors, respectively. For FTC, the R2 of nDfetus¯ with respect to Dw was observed to be 0.64 using an exponential model. Using the bivariate linear model, an R2 of 0.75 was observed for nDfetus¯ with respect to Dw and DEf. A mean difference of 0.4% was observed between nDfetus¯ and SSDE f‐factors. Conclusion: Good correlations were observed for nDfetus from TCM and FTC scans using either an exponential model with Dw or a bivariate linear model with both Dw and DEf. These results indicate that fetal dose from abdomen/pelvis CT examinations of pregnant patients of various gestational ages may be reasonably estimated with models that include (a) scanner‐reported CTDIvol and (b) Dw as a patient size metric, in addition to (c) DEf if available. These results also suggest that SSDE f‐factors may provide a reasonable (within ±25%) estimate of nDfetus for TCM and FTC abdomen/pelvis CT exams. [ABSTRACT FROM AUTHOR]

Purpose: With recent substantial improvements in modern computing, interest in quantitative imaging with CT has seen a dramatic increase. As a result, the need to both create and analyze large, high‐quality datasets of clinical studies has increased as well. At present, no efficient, widely available method exists to accomplish this. The purpose of this technical note is to describe an open‐source high‐throughput computational pipeline framework for the reconstruction and analysis of diagnostic CT imaging data to conduct large‐scale quantitative imaging studies and to accelerate and improve quantitative imaging research. Methods: The pipeline consists of two, primary "blocks": reconstruction and analysis. Reconstruction is carried out via a graphics processing unit (GPU) queuing framework developed specifically for the pipeline that allows a dataset to be reconstructed using a variety of different parameter configurations such as slice thickness, reconstruction kernel, and simulated acquisition dose. The analysis portion then automatically analyzes the output of the reconstruction using "modules" that can be combined in various ways to conduct different experiments. Acceleration of analysis is achieved using cluster processing. Efficiency and performance of the pipeline are demonstrated using an example 142 subject lung screening cohort reconstructed 36 different ways and analyzed using quantitative emphysema scoring techniques. Results: The pipeline reconstructed and analyzed the 5112 reconstructed datasets in approximately 10 days, a roughly 72× speedup over previous efforts using the scanner for reconstructions. Tightly coupled pipeline quality assurance software ensured proper performance of analysis modules with regard to segmentation and emphysema scoring. Conclusions: The pipeline greatly reduced the time from experiment conception to quantitative results. The modular design of the pipeline allows the high‐throughput framework to be utilized for other future experiments into different quantitative imaging techniques. Future applications of the pipeline being explored are robustness testing of quantitative imaging metrics, data generation for deep learning, and use as a test platform for image‐processing techniques to improve clinical quantitative imaging. [ABSTRACT FROM AUTHOR]

Purpose: Size‐specific dose estimates (SSDE) conversion factors have been determined by AAPM Report 204 to adjust CTDIvol to account for patient size but were limited to body CT examinations. The purpose of this work was to determine conversion factors that could be used for an SSDE for helical, head CT examinations for patients of different sizes. Methods: Validated Monte Carlo (MC) simulation methods were used to estimate dose to the center of the scan volume from a routine, helical head examination for a group of patient models representing a range of ages and sizes. Ten GSF/ICRP voxelized phantom models and five pediatric voxelized patient models created from CT image data were used in this study. CT scans were simulated using a Siemens multidetector row CT equivalent source model. Scan parameters were taken from the AAPM Routine Head protocols for a fixed tube current (FTC), helical protocol, and scan lengths were adapted to the anatomy of each patient model. MC simulations were performed using mesh tallies to produce voxelized dose distributions for the entire scan volume of each model. Three tally regions were investigated: (1) a small 0.6 cc volume at the center of the scan volume, (2) 0.8–1.0 cm axial slab at the center of the scan volume, and (3) the entire scan volume. Mean dose to brain parenchyma for all three regions was calculated. Mean bone dose and a mass‐weighted average dose, consisting of brain parenchyma and bone, were also calculated for the slab in the central plane and the entire scan volume. All dose measures were then normalized by CTDIvol for the 16 cm phantom (CTDIvol,16). Conversion factors were determined by calculating the relationship between normalized doses and water equivalent diameter (Dw). Results: CTDIvol,16‐normalized mean brain parenchyma dose values within the 0.6 cc volume, 0.8–1.0 cm central axial slab, and the entire scan volume, when parameterized by Dw, had an exponential relationship with a coefficient of determination (R2) of 0.86, 0.84, and 0.88, respectively. There was no statistically significant difference between the conversion factors resulting from these three different tally regions. Exponential relationships between CTDIvol,16‐normalized mean bone doses had R2 values of 0.83 and 0.87 for the central slab and for the entire scan volume, respectively. CTDIvol,16‐normalized mass‐weighted average doses had R2 values of 0.39 and 0.51 for the central slab and for the entire scan volume, respectively. Conclusions: Conversion factors that describe the exponential relationship between CTDIvol,16‐normalized mean brain dose and a size metric (Dw) for helical head CT examinations have been reported for two different interpretations of the center of the scan volume. These dose descriptors have been extended to describe the dose to bone in the center of the scan volume as well as a mass‐weighted average dose to brain and bone. These may be used, when combined with other efforts, to develop an SSDE dose coefficients for routine, helical head CT examinations. [ABSTRACT FROM AUTHOR]

Purpose: It is important to enhance image quality for low‐dose CT acquisitions to push the ALARA boundary. Current state‐of‐the‐art block‐matching three‐dimensional (BM3D) denoising scheme assumes white Gaussian noise (WGN) model. This study proposes a novel filtering module to be incorporated into the BM3D framework for ultra‐low‐dose CT denoising, by accounting for its specific power spectral properties. Methods: In the current BM3D algorithm, the Wiener filtering is applied in the transform domain to a post‐thresholding signal for enhanced denoising. However, unlike most natural/synthetic images, low‐dose CTs do not obey the ideal Gaussian noise model. Based on the specific noise properties of ultra‐low‐dose CT, we derive the optimal transform‐domain coefficients of Wiener filter based on the minimum mean‐square‐error (MMSE) criterion, taking the noise spectrum and the signal/noise cross spectrum into consideration. In the absence of ground‐truth signal, the hard‐thresholding denoising module in the previous stage is used as a plug‐in estimator. We evaluate the denoising performance on thoracic CT image datasets containing paired full‐dose and ultra‐low‐dose images simulated by a well‐validated clinical engine (or pipeline). We also assess its clinical implication by applying the denoising methods to the emphysema quantification task. Our modified BM3D method is compared with the current one, using peak signal‐to‐noise ratio (PSNR) and emphysema scoring results as evaluation metrics. Results: The noise in ultra‐low‐dose CT presented distinct non‐Gaussian characteristics and was correlated with image intensity. Performance evaluation showed that the current Wiener filter in basic BM3D algorithm yielded little denoising enhancement on ultra‐low‐dose CT images. In contrast, the proposed Wiener filter achieved (1.46, 1.91) dB performance gain in mean and median peak signal‐to‐noise ratio (PSNR) for 5%‐dose image denoising and (0.93, 0.95) dB improvement for 10% dose. A paired t‐test of the PSNRs between denoising using the current and the proposed Wiener filters demonstrated statistically significant improvement, yielding P‐values of 1.45E‐12 and 1.34E‐7 on 5% and 10%‐dose images, respectively. In addition, emphysema quantification on the denoised images using the modified BM3D method also had statistically significant advantage over that using the current BM3D scheme, resulting in a P‐value of 6.30E‐5 with the commonly used measure. Conclusions: This work tailors the Wiener filter in BM3D algorithm to data statistics and demonstrates statistically significant performance improvement on ultra‐low‐dose CT image denoising and a subsequent emphysema quantification task. Such performance gain is more pronounced with a lower dose level. The development and rationale are generally enough for other image denoising tasks when the WGN assumption is violated. [ABSTRACT FROM AUTHOR]

Aluminum oxide based optically stimulated luminescent dosimeters (OSLD) have been recognized as a useful dosimeter for measuring CT dose, particularly for patient dose measurements. Despite the increasing use of this dosimeter, appropriate dosimeter calibration techniques have not been established in the literature; while the manufacturer offers a calibration procedure, it is known to have relatively large uncertainties. The purpose of this work was to evaluate two clinical approaches for calibrating these dosimeters for CT applications, and to determine the uncertainty associated with measurements using these techniques. Three unique calibration procedures were used to calculate dose for a range of CT conditions using a commercially available OSLD and reader. The three calibration procedures included calibration (a) using the vendor‐provided method, (b) relative to a 120 kVp CT spectrum in air, and (c) relative to a megavoltage beam (implemented with 60Co). The dose measured using each of these approaches was compared to dose measured using a calibrated farmer‐type ion chamber. Finally, the uncertainty in the dose measured using each approach was determined. For the CT and megavoltage calibration methods, the dose measured using the OSLD nanoDot was within 5% of the dose measured using an ion chamber for a wide range of different CT scan parameters (80–140 kVp, and with measurements at a range of positions). When calibrated using the vendor‐recommended protocol, the OSLD measured doses were on average 15.5% lower than ion chamber doses. Two clinical calibration techniques have been evaluated and are presented in this work as alternatives to the vendor‐provided calibration approach. These techniques provide high precision for OSLD‐based measurements in a CT environment. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on topics including evolution of the eukaryotic cell called eukaryogenesis; mitochondrial DNA (mtDNA); and mitochondrial genes.

Purpose: The purpose of this study was to estimate the radiation dose to the lung and breast as well as the effective dose from tube current modulated (TCM) lung cancer screening (LCS) scans across a range of patient sizes. Methods: Monte Carlo (MC) methods were used to calculate lung, breast, and effective doses from a low‐dose LCS protocol for a 64‐slice CT that used TCM. Scanning parameters were from the protocols published by AAPM's Alliance for Quality CT. To determine lung, breast, and effective doses from lung cancer screening, eight GSF/ICRP voxelized phantom models with all radiosensitive organs identified were used to estimate lung, breast, and effective doses. Additionally, to extend the limited size range provided by the GSF/ICRP phantom models, 30 voxelized patient models of thoracic anatomy were generated from LCS patient data. For these patient models, lung and breast were semi‐automatically segmented. TCM schemes for each of the GSF/ICRP phantom models were generated using a validated method wherein tissue attenuation and scanner limitations were used to determine the TCM output as a function of table position and source angle. TCM schemes for voxelized patient models were extracted from the raw projection data. The water equivalent diameter, Dw, was used as the patient size descriptor. Dw was estimated for the GSF/ICRP models. For the thoracic patient models, Dw was extracted from the DICOM header of the CT localizer radiograph. MC simulations were performed using the TCM scheme for each model. Absolute organ doses were tallied and effective doses were calculated using ICRP 103 tissue weighting factors for the GSF/ICRP models. Metrics of scanner radiation output were determined based on each model's TCM scheme, including CTDIvol, dose length product (DLP), and CTDIvol, Low Att, a previously described regional metric of scanner output covering most of the lungs and breast. All lung and breast doses values were normalized by scan‐specific CTDIvol and CTDIvol, Low Att. Effective doses were normalized by scan‐specific CTDIvol and DLP. Absolute and normalized doses were reported as a function of Dw. Results: Lung doses normalized by CTDIvol, Low Att were modeled as an exponential relationship with respect to Dw with coefficients of determination (R2) of 0.80. Breast dose normalized by CTDIvol, Low Att was modeled with an exponential relationship to Dw with an R2 of 0.23. For all eight GSF/ICRP phantom models, the effective dose using TCM protocols was below 1.6 mSv. Effective doses showed some size dependence but when normalized by DLP demonstrated a constant behavior. Conclusion: Lung, breast, and effective doses from LCS CT exams with TCM were estimated with respect to patient size. Normalized lung dose can be reasonably estimated with a measure of a patient size such as Dw and regional metric of CTDIvol covering the thorax such as CTDIvol, Low Att, while normalized breast dose can also be estimated with a regional metric of CTDIvol but with a larger degree of variability than observed for lung. Effective dose normalized by DLP can be estimated with a constant multiplier. [ABSTRACT FROM AUTHOR]

Purpose: To facilitate investigations into the impacts of acquisition and reconstruction parameters on quantitative imaging, radiomics and CAD using CT imaging, we previously released an open‐source implementation of a conventional weighted filtered backprojection reconstruction called FreeCT_wFBP. Our purpose was to extend that work by providing an open‐source implementation of a model‐based iterative reconstruction method using coordinate descent optimization, called FreeCT_ICD. Methods: Model‐based iterative reconstruction offers the potential for substantial radiation dose reduction, but can impose substantial computational processing and storage requirements. FreeCT_ICD is an open‐source implementation of a model‐based iterative reconstruction method that provides a reasonable tradeoff between these requirements. This was accomplished by adapting a previously proposed method that allows the system matrix to be stored with a reasonable memory requirement. The method amounts to describing the attenuation coefficient using rotating slices that follow the helical geometry. In the initially proposed version, the rotating slices are themselves described using blobs. We have replaced this description by a unique model that relies on trilinear interpolation together with the principles of Joseph's method. This model offers an improvement in memory requirement while still allowing highly accurate reconstruction for conventional CT geometries. The system matrix is stored column‐wise and combined with an iterative coordinate descent (ICD) optimization. The result is FreeCT_ICD, which is a reconstruction program developed on the Linux platform using C++ libraries and released under the open‐source GNU GPL v2.0 license. The software is capable of reconstructing raw projection data of helical CT scans. In this work, the software has been described and evaluated by reconstructing datasets exported from a clinical scanner which consisted of an ACR accreditation phantom dataset and a clinical pediatric thoracic scan. Results: For the ACR phantom, image quality was comparable to clinical reconstructions as well as reconstructions using open‐source FreeCT_wFBP software. The pediatric thoracic scan also yielded acceptable results. In addition, we did not observe any deleterious impact in image quality associated with the utilization of rotating slices. These evaluations also demonstrated reasonable tradeoffs in storage requirements and computational demands. Conclusion: FreeCT_ICD is an open‐source implementation of a model‐based iterative reconstruction method that extends the capabilities of previously released open‐source reconstruction software and provides the ability to perform vendor‐independent reconstructions of clinically acquired raw projection data. This implementation represents a reasonable tradeoff between storage and computational requirements and has demonstrated acceptable image quality in both simulated and clinical image datasets. [ABSTRACT FROM AUTHOR]

Abstract: Acoustically‐responsive microbubbles have been widely researched as agents for both diagnostic and therapeutic applications of ultrasound. There has also been considerable interest in magnetically‐functionalised microbubbles as multi‐modality imaging agents and carriers for targeted drug delivery. In this paper, we present a design for an integrated device capable of generating co‐aligned magnetic and acoustic fields in order to accumulate microbubbles at a specific location and to activate them acoustically. For this proof‐of‐concept study, the device was designed to concentrate microbubbles at a distance of 10 mm from the probe's surface, commensurate with relevant tissue depths in preclinical small animal models. Previous studies have indicated that both microbubble concentration and duration of cavitation activity are positively correlated with therapeutic effect. The utility of the device was assessed in vitro tests in a tissue‐mimicking phantom containing a single vessel (1.2 mm diameter). At a peak fluid velocity of 4.2 mm s−1 microbubble accumulation was observed under B‐mode ultrasound imaging and the corresponding cavitation activity was sustained for a period more than 4 times longer than that achieved with an identical acoustic field but in the absence of a magnet. The feasibility of developing a larger scale device for human applications is discussed. [ABSTRACT FROM AUTHOR]

Purpose: To study the variability in volume change estimates of pulmonary nodules due to segmentation approaches used across several algorithms and to evaluate these effects on the ability to predict nodule malignancy. Methods: We obtained 100 patient image datasets from the National Lung Screening Trial (NLST) that had a nodule detected on each of two consecutive low dose computed tomography (LDCT) scans, with an equal proportion of malignant and benign cases (50 malignant, 50 benign). Information about the nodule location for the cases was provided by a screen capture with a bounding box and its axial location was indicated. Five participating quantitative imaging network (QIN) institutions performed nodule segmentation using their preferred semi‐automated algorithms with no manual correction; teams were allowed to provide additional manually corrected segmentations (analyzed separately). The teams were asked to provide segmentation masks for each nodule at both time points. From these masks, the volume was estimated for the nodule at each time point; the change in volume (absolute and percent change) across time points was estimated as well. We used the concordance correlation coefficient (CCC) to compare the similarity of computed nodule volumes (absolute and percent change) across algorithms. We used Logistic regression model on the change in volume (absolute change and percent change) of the nodules to predict the malignancy status, the area under the receiver operating characteristic curve (AUROC) and confidence intervals were reported. Because the size of nodules was expected to have a substantial effect on segmentation variability, analysis of change in volumes was stratified by lesion size, where lesions were grouped into those with a longest diameter of <8 mm and those with longest diameter ≥ 8 mm. Results: We find that segmentation of the nodules shows substantial variability across algorithms, with the CCC ranging from 0.56 to 0.95 for change in volume (percent change in volume range was [0.15 to 0.86]) across the nodules. When examining nodules based on their longest diameter, we find the CCC had higher values for large nodules with a range of [0.54 to 0.93] among the algorithms, while percent change in volume was [0.3 to 0.95]. Compared to that of smaller nodules which had a range of [−0.0038 to 0.69] and percent change in volume was [−0.039 to 0.92]. The malignancy prediction results showed fairly consistent results across the institutions, the AUC using change in volume ranged from 0.65 to 0.89 (Percent change in volume was 0.64 to 0.86) for entire nodule range. Prediction improves for large nodule range (≥ 8 mm) with AUC range 0.75 to 0.90 (percent change in volume was 0.74 to 0.92). Compared to smaller nodule range (<8 mm) with AUC range 0.57 to 0.78 (percent change in volume was 0.59 to 0.77). Conclusions: We find there is a fairly high concordance in the size measurements for larger nodules (≥8 mm) than the lower sizes (<8 mm) across algorithms. We find the change in nodule volume (absolute and percent change) were consistent predictors of malignancy across institutions, despite using different segmentation algorithms. Using volume change estimates without corrections shows slightly lower predictability (for two teams). [ABSTRACT FROM AUTHOR]

A thorough and complete understanding of the structural geology and evolution of the Cooper-Eromanga Basin has been hampered by low-resolution seismic data that becomes particularly difficult to interpret below the thick Permian coal measures. As a result, researchers are tentative to interpret the basement fault architecture within the basin, which is largely undefined. To provide a better understanding of the basement fault geometry, all available two-dimensional seismic lines together with 12 three-dimensional seismic surveys were structurally interpreted with assistance from seismic attribute analysis. The Upper Cretaceous Cadna-owie Formation and top Permian reflectors were analysed using a common seismic attribute technique (incoherency) that was used to infer the presence of faults that may have otherwise been overlooked. Detailed basement fault maps for each seismic survey were constructed and used in conjunction with two-dimensional seismic data interpretation to produce a regional basement fault map. Large north-northeast-south-southwest-striking sinistral strike-slip faults were identified within the Patchawarra Trough appearing to splay from the main northeast-southwest-striking ridge. These sinistral north-northeast-south-southwest-striking faults, together with field-scale southeast-northwest-striking dextral strike-slip faults, are optimally oriented to have potentially developed as a conjugated fault set under a south-southeast-north-northwest-oriented strike-slip stress regime. Geomechanical modelling for a regionally extensive system of Cretaceous polygonal faults was performed to calculate the Leakage Factor and Dilation Tendency of individual faults. Faults that extend into Lower Cretaceous oil-rich reservoirs with strikes of between 060>N and 140>N and a high to near-vertical dip angle were identified to most likely be acting as conduits for the tertiary migration of hydrocarbons from known Lower Cretaceous hydrocarbon reservoirs into shallow Cretaceous sediments. This research provides valuable information on the regional basement fault architecture and a more detailed exploration target for the Cooper-Eromanga Basin, which were previously not available in literature. [ABSTRACT FROM AUTHOR]

Studies involving Monte Carlo simulations are common in both diagnostic and therapy medical physics research, as well as other fields of basic and applied science. As with all experimental studies, the conditions and parameters used for Monte Carlo simulations impact their scope, validity, limitations, and generalizability. Unfortunately, many published peer‐reviewed articles involving Monte Carlo simulations do not provide the level of detail needed for the reader to be able to properly assess the quality of the simulations. The American Association of Physicists in Medicine Task Group #268 developed guidelines to improve reporting of Monte Carlo studies in medical physics research. By following these guidelines, manuscripts submitted for peer‐review will include a level of relevant detail that will increase the transparency, the ability to reproduce results, and the overall scientific value of these studies. The guidelines include a checklist of the items that should be included in the Methods, Results, and Discussion sections of manuscripts submitted for peer‐review. These guidelines do not attempt to replace the journal reviewer, but rather to be a tool during the writing and review process. Given the varied nature of Monte Carlo studies, it is up to the authors and the reviewers to use this checklist appropriately, being conscious of how the different items apply to each particular scenario. It is envisioned that this list will be useful both for authors and for reviewers, to help ensure the adequate description of Monte Carlo studies in the medical physics literature. [ABSTRACT FROM AUTHOR]

Purpose: CT Perfusion (CTP) is a widely used clinical imaging modality. However, CTP typically involves the use of substantial radiation dose (CTDIvol ≥~200 mGy). The purpose of this study is to present a low‐dose CTP technique using a projection view‐sharing reconstruction algorithm originally developed for dynamic MRI — “K‐space Weighted Image Contrast” (KWIC). Methods: The KWIC reconstruction is based on an angle‐bisection scheme. In KWIC, a Fourier transform was performed along each projection to form a “k‐space”‐like CT data space, based on the central‐slice theorem. As a projection view‐sharing technique, KWIC preserves the spatiotemporal resolution of undersampled CTP data by progressively increasing the number of projection views shared for more distant regions of “k‐space”. KWIC reconstruction was evaluated on a digital FORBILD head phantom with numerically simulated time‐varying objects. The numerically simulated scans were undersampled using the angle‐bisection scheme to achieve 50%, 25%, and 12.5% of the original dose (288, 144, and 72 projections, respectively). The area‐under‐the‐curve (AUC), time‐to‐peak (TTP), and full width half maximum (FWHM) were measured in KWIC recons and compared to fully sampled filtered back projection (FBP) reconstructions. KWIC reconstruction and dose reduction was also implemented for three clinical CTP cases (45 s, 1156 projections per turn, 1 s/turn, CTDIvol 217 mGy). Quantitative perfusion metrics were computed and compared between KWIC reconstructed CTP data and those of standard FBP reconstruction. Results: The AUC, TTP, and FWHM in the numerical simzulations were unaffected by the undersampling/dose reduction (down to 12.5% dose) with KWIC reconstruction compared to the fully sampled FBP reconstruction. The normalized root‐mean‐square‐error (NRMSE) of the AUC in the FORBILD head phantom is 0.04, 0.05, and 0.07 for 50%, 25%, and 12.5% KWIC, respectively, as compared to FBP reconstruction. The cerebral blood flow (CBF) and cerebral blood volume had no significant difference between FBP and 50%, 25%, and 12.5% KWIC reconstructions (P > 0.05). Conclusions: This study demonstrates that KWIC preserves perfusion metrics for CTP with substantially reduced dose. Clinical implementation will require further investigation into methods of rapid switching of a CT x‐ray source. [ABSTRACT FROM AUTHOR]

Purpose The vast majority of body CT exams are performed with automatic exposure control ( AEC), which adapts the mean tube current to the patient size and modulates the tube current either angularly, longitudinally or both. However, most radiation dose estimation tools are based on fixed tube current scans. Accurate estimates of patient dose from AEC scans require knowledge of the tube current values, which is usually unavailable. The purpose of this work was to develop and validate methods to accurately estimate the tube current values prescribed by one manufacturer's AEC system to enable accurate estimates of patient dose. Methods Methods were developed that took into account available patient attenuation information, user selected image quality reference parameters and x-ray system limits to estimate tube current values for patient scans. Methods consistent with AAPM Report 220 were developed that used patient attenuation data that were: (a) supplied by the manufacturer in the CT localizer radiograph and (b) based on a simulated CT localizer radiograph derived from image data. For comparison, actual tube current values were extracted from the projection data of each patient. Validation of each approach was based on data collected from 40 pediatric and adult patients who received clinically indicated chest (n = 20) and abdomen/pelvis (n = 20) scans on a 64 slice multidetector row CT (Sensation 64, Siemens Healthcare, Forchheim, Germany). For each patient dataset, the following were collected with Institutional Review Board ( IRB) approval: (a) projection data containing actual tube current values at each projection view, (b) CT localizer radiograph (topogram) and (c) reconstructed image data. Tube current values were estimated based on the actual topogram (actual-topo) as well as the simulated topogram based on image data (sim-topo). Each of these was compared to the actual tube current values from the patient scan. In addition, to assess the accuracy of each method in estimating patient organ doses, Monte Carlo simulations were performed by creating voxelized models of each patient, identifying key organs and incorporating tube current values into the simulations to estimate dose to the lungs and breasts (females only) for chest scans and the liver, kidney, and spleen for abdomen/pelvis scans. Organ doses from simulations using the actual tube current values were compared to those using each of the estimated tube current values (actual-topo and sim-topo). Results When compared to the actual tube current values, the average error for tube current values estimated from the actual topogram (actual-topo) and simulated topogram (sim-topo) was 3.9% and 5.8% respectively. For Monte Carlo simulations of chest CT exams using the actual tube current values and estimated tube current values (based on the actual-topo and sim-topo methods), the average differences for lung and breast doses ranged from 3.4% to 6.6%. For abdomen/pelvis exams, the average differences for liver, kidney, and spleen doses ranged from 4.2% to 5.3%. Conclusions Strong agreement between organ doses estimated using actual and estimated tube current values provides validation of both methods for estimating tube current values based on data provided in the topogram or simulated from image data. [ABSTRACT FROM AUTHOR]

Purpose Currently, available Computed Tomography dose metrics are mostly based on fixed tube current Monte Carlo ( MC) simulations and/or physical measurements such as the size specific dose estimate ( SSDE). In addition to not being able to account for Tube Current Modulation ( TCM), these dose metrics do not represent actual patient dose. The purpose of this study was to generate and evaluate a dose estimation model based on the Generalized Linear Model ( GLM), which extends the ability to estimate organ dose from tube current modulated examinations by incorporating regional descriptors of patient size, scanner output, and other scan-specific variables as needed. Methods The collection of a total of 332 patient CT scans at four different institutions was approved by each institution's IRB and used to generate and test organ dose estimation models. The patient population consisted of pediatric and adult patients and included thoracic and abdomen/pelvis scans. The scans were performed on three different CT scanner systems. Manual segmentation of organs, depending on the examined anatomy, was performed on each patient's image series. In addition to the collected images, detailed TCM data were collected for all patients scanned on Siemens CT scanners, while for all GE and Toshiba patients, data representing z-axis-only TCM, extracted from the DICOM header of the images, were used for TCM simulations. A validated MC dosimetry package was used to perform detailed simulation of CT examinations on all 332 patient models to estimate dose to each segmented organ (lungs, breasts, liver, spleen, and kidneys), denoted as reference organ dose values. Approximately 60% of the data were used to train a dose estimation model, while the remaining 40% was used to evaluate performance. Two different methodologies were explored using GLM to generate a dose estimation model: (a) using the conventional exponential relationship between normalized organ dose and size with regional water equivalent diameter ( WED) and regional CTDIvol as variables and (b) using the same exponential relationship with the addition of categorical variables such as scanner model and organ to provide a more complete estimate of factors that may affect organ dose. Finally, estimates from generated models were compared to those obtained from SSDE and Im PACT. Results The Generalized Linear Model yielded organ dose estimates that were significantly closer to the MC reference organ dose values than were organ doses estimated via SSDE or Im PACT. Moreover, the GLM estimates were better than those of SSDE or Im PACT irrespective of whether or not categorical variables were used in the model. While the improvement associated with a categorical variable was substantial in estimating breast dose, the improvement was minor for other organs. Conclusions The GLM approach extends the current CT dose estimation methods by allowing the use of additional variables to more accurately estimate organ dose from TCM scans. Thus, this approach may be able to overcome the limitations of current CT dose metrics to provide more accurate estimates of patient dose, in particular, dose to organs with considerable variability across the population. [ABSTRACT FROM AUTHOR]

Purpose Lung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection ( CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population. Methods The raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial ( NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the Lung RADS assessment categories, and sub-analyses were performed. Results From the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to Lung RADS category 4 based on size and composition, while 18 (15%) corresponded to Lung RADS category 3 and 73 (61%) corresponded to Lung RADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case. Conclusions CAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams. [ABSTRACT FROM AUTHOR]

Key points Raised arterial blood CO2 (hypercapnia) is a feature of many lung diseases., CO2 has been shown to act as a cell signalling molecule in human cells, notably by influencing the levels of cell signalling second messengers: cAMP and Ca2+., Hypercapnia reduced cAMP-stimulated cystic fibrosis transmembrane conductance regulator-dependent anion and fluid transport in Calu-3 cells and primary human airway epithelia but did not affect cAMP-regulated HCO3− transport via pendrin or Na+/HCO3− cotransporters., These results further support the role of CO2 as a cell signalling molecule and suggests CO2-induced reductions in airway anion and fluid transport may impair innate defence mechanisms of the lungs., Abstract Hypercapnia is clinically defined as an arterial blood partial pressure of CO2 of above 40 mmHg and is a feature of chronic lung disease. In previous studies we have demonstrated that hypercapnia modulates agonist-stimulated cAMP levels through effects on transmembrane adenylyl cyclase activity. In the airways, cAMP is known to regulate cystic fibrosis transmembrane conductance regulator (CFTR)-mediated anion and fluid secretion, which contributes to airway surface liquid homeostasis. The aim of the current work was to investigate if hypercapnia could modulate cAMP-regulated ion and fluid transport in human airway epithelial cells. We found that acute exposure to hypercapnia significantly reduced forskolin-stimulated elevations in intracellular cAMP as well as both adenosine- and forskolin-stimulated increases in CFTR-dependent transepithelial short-circuit current, in polarised cultures of Calu-3 human airway cells. This CO2-induced reduction in anion secretion was not due to a decrease in HCO3− transport given that neither a change in CFTR-dependent HCO3− efflux nor Na+/HCO3− cotransporter-dependent HCO3− influx were CO2-sensitive. Hypercapnia also reduced the volume of forskolin-stimulated fluid secretion over 24 h, yet had no effect on the HCO3− content of the secreted fluid. Our data reveal that hypercapnia reduces CFTR-dependent, electrogenic Cl− and fluid secretion, but not CFTR-dependent HCO3− secretion, which highlights a differential sensitivity of Cl− and HCO3− transporters to raised CO2 in Calu-3 cells. Hypercapnia also reduced forskolin-stimulated CFTR-dependent anion secretion in primary human airway epithelia. Based on current models of airways biology, a reduction in fluid secretion, associated with hypercapnia, would be predicted to have important consequences for airways hydration and the innate defence mechanisms of the lungs. [ABSTRACT FROM AUTHOR]

Purpose: With growing interest in quantitative imaging, radiomics, and CAD using CT imaging, the need to explore the impacts of acquisition and reconstruction parameters has grown. This usually requires extensive access to the scanner on which the data were acquired and its workflow is not designed for large-scale reconstruction projects. Therefore, the authors have developed a freely available, open-source software package implementing a common reconstruction method, weighted filtered backprojection (wFBP), for helical fan-beam CT applications. Methods: FreeCT_wFBP is a low-dependency, GPU-based reconstruction program utilizing c for the host code and Nvidia CUDA C for GPU code. The software is capable of reconstructing helical scans acquired with arbitrary pitch-values, and sampling techniques such as flying focal spots and a quarter-detector offset. In this work, the software has been described and evaluated for reconstruction speed, image quality, and accuracy. Speed was evaluated based on acquisitions of the ACR CT accreditation phantom under four different flying focal spot configurations. Image quality was assessed using the same phantom by evaluating CT number accuracy, uniformity, and contrast to noise ratio (CNR). Finally, reconstructed mass-attenuation coefficient accuracy was evaluated using a simulated scan of a FORBILD thorax phantom and comparing reconstructed values to the known phantom values. Results: The average reconstruction time evaluated under all flying focal spot configurations was found to be 17.4±}1.0 s for a 512 row × 512 column × 32 slice volume. Reconstructions of the ACR phantom were found to meet all CT Accreditation Program criteria including CT number, CNR, and uniformity tests. Finally, reconstructed mass-attenuation coefficient values of water within the FORBILD thorax phantom agreed with original phantom values to within 0.0001 mm2/g (0.01%). Conclusions: FreeCT_wFBP is a fast, highly configurable reconstruction package for third-generation CT available under the GNU GPL. It shows good performance with both clinical and simulated data. [ABSTRACT FROM AUTHOR]

The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x-ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self-teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs. [ABSTRACT FROM AUTHOR]

Purpose: Measuring the size of nodules on chest CT is important for lung cancer staging and measuring therapy response. 3D volumetry has been proposed as a more robust alternative to 1D and 2D sizing methods. There have also been substantial advances in methods to reduce radiation dose in CT. The purpose of this work was to investigate the effect of dose reduction and reconstruction methods on variability in 3D lung-nodule volumetry. Methods: Reduced-dose CT scans were simulated by applying a noise-addition tool to the raw (sinogram) data from clinically indicated patient scans acquired on a multidetector-row CT scanner (Definition Flash, Siemens Healthcare). Scans were simulated at 25%, 10%, and 3% of the dose of their clinical protocol (CTDIvol of 20.9 mGy), corresponding to CTDIvol values of 5.2, 2.1, and 0.6 mGy. Simulated reduced-dose data were reconstructed with both conventional filtered backprojection (B45 kernel) and iterative reconstruction methods (SAFIRE: I44 strength 3 and I50 strength 3). Three lab technologist readers contoured "measurable" nodules in 33 patients under each of the different acquisition/reconstruction conditions in a blinded study design. Of the 33 measurable nodules, 17 were used to estimate repeatability with their clinical reference protocol, as well as interdose and inter-reconstruction-method reproducibilities. The authors compared the resulting distributions of proportional differences across dose and reconstruction methods by analyzing their means, standard deviations (SDs), and t-test and F-test results. Results: The clinical-dose repeatability experiment yielded a mean proportional difference of 1.1% and SD of 5.5%. The interdose reproducibility experiments gave mean differences ranging from -5.6% to -1.7% and SDs ranging from 6.3% to 9.9%. The inter-reconstruction-method reproducibility experiments gave mean differences of 2.0% (I44 strength 3) and -0.3% (I50 strength 3), and SDs were identical at 7.3%. For the subset of repeatability cases, inter-reconstruction-method mean/SD pairs were (1.4%, 6.3%) and (-0.7%, 7.2%) for I44 strength 3 and I50 strength 3, respectively. Analysis of representative nodules confirmed that reader variability appeared unaffected by dose or reconstruction method. Conclusions: Lung-nodule volumetry was extremely robust to the radiation-dose level, down to the minimum scanner-supported dose settings. In addition, volumetry was robust to the reconstruction methods used in this study, which included both conventional filtered backprojection and iterative methods. [ABSTRACT FROM AUTHOR]

Purpose: The extensive use of computed tomography (CT) in diagnostic procedures is accompanied by a growing need for more accurate and patient-specific dosimetry techniques. Optically stimulated luminescent dosimeters (OSLDs) offer a potential solution for patient-specific CT point-based surface dosimetry by measuring air kerma. The purpose of this work was to characterize the OSLD nanoDot for CT dosimetry, quantifying necessary correction factors, and evaluating the uncertainty of these factors. Methods: A characterization of the Landauer OSL nanoDot (Landauer, Inc., Greenwood, IL) was conducted using both measurements and theoretical approaches in a CT environment. The effects of signal depletion, signal fading, dose linearity, and angular dependence were characterized through direct measurement for CT energies (80-140 kV) and delivered doses ranging from ~5 to >1000 mGy. Energy dependence as a function of scan parameters was evaluated using two independent approaches: direct measurement and a theoretical approach based on Burlin cavity theory and Monte Carlo simulated spectra. This beam-quality dependence was evaluated for a range of CT scanning parameters. Results: Correction factors for the dosimeter response in terms of signal fading, dose linearity, and angular dependence were found to be small for most measurement conditions (<3%). The relative uncertainty was determined for each factor and reported at the two-sigma level. Differences in irradiation geometry (rotational versus static) resulted in a difference in dosimeter signal of 3% on average. Beam quality varied with scan parameters and necessitated the largest correction factor, ranging from 0.80 to 1.15 relative to a calibration performed in air using a 120 kV beam. Good agreement was found between the theoretical and measurement approaches. Conclusions: Correction factors for the measurement of air kerma were generally small for CT dosimetry, although angular effects, and particularly effects due to changes in beam quality, could be more substantial. In particular, it would likely be necessary to account for variations in CT scan parameters and measurement location when performing CT dosimetry using OSLD. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to assess the accuracy of a Monte Carlo simulation-based method for estimating radiation dose from multidetector computed tomography (MDCT) by comparing simulated doses in ten patients to in-vivo dose measurements. Methods: MD Anderson Cancer Center Institutional Review Board approved the acquisition of in-vivo rectal dose measurements in a pilot study of ten patients undergoing virtual colonoscopy. The dose measurements were obtained by affixing TLD capsules to the inner lumen of rectal catheters. Voxelized patient models were generated from the MDCT images of the ten patients, and the dose to the TLD for all exposures was estimated using Monte Carlo based simulations. The Monte Carlo simulation results were compared to the in-vivo dose measurements to determine accuracy. Results: The calculated mean percent difference between TLD measurements and Monte Carlo simulations was -4.9% with standard deviation of 8.7% and a range of -22.7% to 5.7%. Conclusions: The results of this study demonstrate very good agreement between simulated and measured doses in-vivo. Taken together with previous validation efforts, this work demonstrates that the Monte Carlo simulation methods can provide accurate estimates of radiation dose in patients undergoing CT examinations. [ABSTRACT FROM AUTHOR]

Purpose: Task Group 204 introduced effective diameter (ED) as the patient size metric used to correlate size-specific-dose-estimates. However, this size metric fails to account for patient attenuation properties and has been suggested to be replaced by an attenuation-based size metric, water equivalent diameter (DW). The purpose of this study is to investigate different size metrics, effective diameter, and water equivalent diameter, in combination with regional descriptions of scanner output to establish the most appropriate size metric to be used as a predictor for organ dose in tube current modulated CT exams. Methods: 101 thoracic and 82 abdomen/pelvis scans from clinically indicated CT exams were collected retrospectively from a multidetector row CT (Sensation 64, Siemens Healthcare) with Institutional Review Board approval to generate voxelized patient models. Fully irradiated organs (lung and breasts in thoracic scans and liver, kidneys, and spleen in abdominal scans) were segmented and used as tally regions in Monte Carlo simulations for reporting organ dose. Along with image data, raw projection data were collected to obtain tube current information for simulating tube current modulation scans using Monte Carlo methods. Additionally, previously described patient size metrics [ED, DW, and approximated water equivalent diameter (DWa)] were calculated for each patient and reported in three different ways: a single value averaged over the entire scan, a single value averaged over the region of interest, and a single value from a location in the middle of the scan volume. Organ doses were normalized by an appropriate mAs weighted CTDIvoi to reflect regional variation of tube current. Linear regression analysis was used to evaluate the correlations between normalized organ doses and each size metric. Results: For the abdominal organs, the correlations between normalized organ dose and size metric were overall slightly higher for all three differently (global, regional, and middle slice) reported DW and DWa than they were for ED, but the differences were not statistically significant. However, for lung dose, computed correlations using water equivalent diameter calculated in the middle of the image data (DW,middle) and averaged over the low attenuating region of lung (DW,regional) were statistically significantly higher than correlations of normalized lung dose with ED. Conclusions: To conclude, effective diameter and water equivalent diameter are very similar in abdominal regions; however, their difference becomes noticeable in lungs. Water equivalent diameter, specifically reported as a regional average and middle of scan volume, was shown to be better predictors of lung dose. Therefore, an attenuation-based size metric (water equivalent diameter) is recommended because it is more robust across different anatomic regions. Additionally, it was observed that the regional size metric reported as a single value averaged over a region of interest and the size metric calculated from a single slice/image chosen from the middle of the scan volume are highly correlated for these specific patient models and scan types. [ABSTRACT FROM AUTHOR]

Purpose: AAPM Task Group 204 introduced size-specific dose estimates for pediatric and adult patients undergoing body CT examinations. This investigation extends that work to head CT exams by using Monte Carlo simulations to develop size-specific, scanner-independent CTDIvol-to-organ-dose conversion coefficients. Methods: Using eight patient models from the GSF family of voxelized phantoms, dose to the brain and lens of the eye was estimated using Monte Carlo simulations of contiguous axial and helical scans for 64-slice multidetector CT scanners from four major manufacturers. For each patient model and scan mode, scanner-independent CTDIvol-to-organ-dose conversion coefficients were calculated by normalizing organ dose by scanner-specific 16 cm CTDIvoi values and averaging across all scanners. Head size was measured using both geometric and attenuation-based size metrics. Head perimeter and effective diameter (ED), both geometric size metrics, were measured directly from the GSF data at the first slice superior to the eyes. Because the GSF models' pixel data are provided in terms of organ identification numbers instead of CT numbers, an indirect estimate of water equivalent diameter (WED), an attenuation-based size metric, was determined based on the relationships between WED and both ED and perimeter for a sample of patient data. Correlations between CTDIvol-to-organ-dose conversion coefficients and the various patient size metrics were then explored. Results: The analysis of the patient data revealed a best-fit linear relationship (R² of 0.87) between ED and WED across a wide variety of patient sizes. Using this relationship along with ED determined from the GSF data, WED was estimated for each GSF model. An exponential relationship between CTDIvol normalized organ dose and WED was observed for both contiguous axial and helical scan-ning. For head perimeter and ED measured directly from the GSF data, an exponential relationship between CTDIvol normalized organ dose and patient size was also observed for each scan mode. For all patient size metrics and scan modes, R² of the exponential fits ranged from 0.92 to 0.93 and 0.73 to 0.85 for the brain and lens of the eye, respectively. Conclusions: For all scan modes, strong correlation exists between CTDIvol normalized brain dose and both geometric and attenuation-based patient size metrics. A slightly lower correlation between CTDIvol normalized organ dose and patient size was observed for the lens of the eye. This may be due to the combination of the eye lens being a small peripheral organ and the presence of surface dose variation in both contiguous axial and helical scanning. Results indicate that robust estimates of patient-specific head CT dose may be provided using the size-specific, scanner-independent CTDIvol-to-organ-dose conversion coefficients described in this work. [ABSTRACT FROM AUTHOR]

Purpose: Monte Carlo (MC) simulation methods have been widely used in patient dosimetry in computed tomography (CT), including estimating patient organ doses. However, most simulation methods have undergone a limited set of validations, often using homogeneous phantoms with simple geometries. As clinical scanning has become more complex and the use of tube current modulation (TCM) has become pervasive in the clinic, MC simulations should include these techniques in their methodologies and therefore should also be validated using a variety of phantoms with different shapes and material compositions to result in a variety of differently modulated tube current profiles. The purpose of this work is to perform the measurements and simulations to validate a Monte Carlo model under a variety of test conditions where fixed tube current (FTC) and TCM were used. Methods: A previously developed MC model for estimating dose from CT scans that models TCM, built using the platform of MCNPX, was used for CT dose quantification. In order to validate the suitability of this model to accurately simulate patient dose from FTC and TCM CT scan, measurements and simulations were compared over a wide range of conditions. Phantoms used for testing range from simple geometries with homogeneous composition (16 and 32 cm computed tomography dose index phantoms) to more complex phantoms including a rectangular homogeneous water equivalent phantom, an elliptical shaped phantom with three sections (where each section was a homogeneous, but different material), and a heterogeneous, complex geometry anthropomorphic phantom. Each phantom requires varying levels of x-, y - and z-modulation. Each phantom was scanned on a multidetector row CT (Sensation 64) scanner under the conditions of both FTC and TCM. Dose measurements were made at various surface and depth positions within each phantom. Simulations using each phantom were performed for FTC, detailed x-y-z TCM, and z-axis-only TCM to obtain dose estimates. This allowed direct comparisons between measured and simulated dose values under each condition of phantom, location, and scan to be made. Results: For FTC scans, the percent root mean square (RMS) difference between measurements and simulations was within 5% across all phantoms. For TCM scans, the percent RMS of the difference between measured and simulated values when using detailed TCM and z-axis-only TCM simulations was 4.5% and 13.2%, respectively. For the anthropomorphic phantom, the difference between TCM measurements and detailed TCM and z-axis-only TCM simulations was 1.2% and 8.9%, respectively. For FTC measurements and simulations, the percent RMS of the difference was 5.0%. Conclusions: This work demonstrated that the Monte Carlo model developed provided good agreement between measured and simulated values under both simple and complex geometries including an anthropomorphic phantom. This work also showed the increased dose differences for z-axis-only TCM simulations, where considerable modulation in the x-y plane was present due to the shape of the rectangular water phantom. Results from this investigation highlight details that need to be included in Monte Carlo simulations of TCM CT scans in order to yield accurate, clinically viable assessments of patient dosimetry. [ABSTRACT FROM AUTHOR]

BACKGROUND Emergence delay and female-skewed sex ratios among adults of Diabrotica virgifera virgifera LeConte (Coleoptera: Chrysomelidae) from Bt corn have been reported in field studies. The authors used a simulation model to study the effect of a maturation delay and a female-skewed sex ratio for D. v. virgifera emerging from Bt corn on the evolution of Bt resistance. RESULTS The effect of skewed toxin mortality in one sex on evolution of Bt resistance was insignificant. An emergence delay among resistant beetles from Bt corn slowed resistance evolution. A shift in the time of emergence for homozygous susceptible beetles from Bt corn did not have a significant effect on the evolution of Bt resistance in D. v. virgifera. CONCLUSION This simulation study suggested that skewed toxin mortality in one sex and an emergence delay for beetles in Bt corn are not major concerns for managing resistance by D. v. virgifera to single-toxin or pyramided Bt corn. © 2013 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

R4224 --> 655.11 --> Many bacterial species, including Escherichia coli (E. coli) utilize the enzyme polyphosphate kinase (PPK) to synthesize polyphosphate (polyP) in response to biological stress. Multiple studies have shown that impairing PPK activity impairs bacterial pathogenicity and survival, which has made it a target for therapeutic development. Unfortunately, PPK regulation is poorly understood. To address this, we previously described a series of mutations in E. coli ppk, termed ppk*, that result in high polyP accumulation in vivo. However, the specific activity of PPK* enzyme in vitro, when purified using a C‐term 6X His‐tag (PPK*‐HT), was comparable to purified WT enzyme (PPK‐HT). PPK activity is tied to its oligomeric state, with the dimeric form synthesizing polyP. To determine if the ppk* mutation altered oligomerization, we tested PPK‐HT and PPK*‐HT enzymes in an analytical ultracentrifuge. We found that the PPK‐HT enzyme was mostly dimeric but had a small monomeric population. Conversely, the PPK*‐HT enzyme was exclusively dimeric. This suggested that the ppk* mutation may affect oligomerization, but the His‐tag masked any effect on activity. We next hypothesized that the C‐term His tag alters the in vitro activity. To test this, we compared the specific activities of PPK‐HT and PPK*‐HT to that of PPK purified using a smaller C‐term C tag (PPK‐CT) and PPK enzyme purified with no tags (PPK‐NT). PPK*‐HT and PPK‐HT had comparable activities in vitro but were significantly more active than the untagged enzyme. PPK‐CT and PPK‐NT had comparable activities. This suggests that the C‐tag does not alter PPK activity while the His‐tag does. We next tested the purified enzymes' sensitivity to substrate by testing PPK activity in 6mM and 20mM ATP. We found that the His‐tag increased the enzyme's activity in the higher ATP concentrations. Comparing the 20mM and 6mM samples, the His‐tag enzymes showed an increase in activity of ~3.5 fold compared to their 6mM conditions. Conversely, the PPK‐CT and PPK‐UT only showed a 1.4‐ and 1.9‐ fold increase in activity, respectively. PolyP synthesis impairs bacterial growth and replication. We hypothesized that a more active PPK would impair recovery following a prolonged stationary phase. We developed strains carrying one of four expression vectors: an empty vector control (EV), PPK‐HT, PPK‐CT, or native PPK. Using these four strains, we developed a bacterial growth recovery curve assay. We found that while the PPK‐CT and untagged PPK strains had comparable recovery and growth rates, the PPK‐HT strain had a slower recovery rate resulting in a later shift into log phase. Taken together, our results have significant implications for polyP biology. We have shown that the standard purification method produces an enzyme with altered substrate sensitivities, making it a poorer candidate for drug development studies. C‐tag enzyme is comparable to untagged enzyme, making it a reliable candidate for future studies. Finally, the in vitro differences produce an in vivo phenotype, making it easy to test the effect of the tags in living systems. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to adapt an equivalent source model originally developed for conventional CT Monte Carlo dose quantification to the radiation oncology context and validate its application for evaluating concomitant dose incurred by a kilovoltage (kV) cone-beam CT (CBCT) system integrated into a linear accelerator. Methods: In order to properly characterize beams from the integrated kV CBCT system, the authors have adapted a previously developed equivalent source model consisting of an equivalent spectrum module that takes into account intrinsic filtration and an equivalent filter module characterizing the added bowtie filtration. An equivalent spectrum was generated for an 80, 100, and 125 kVp beam with beam energy characterized by half-value layer measurements. An equivalent filter description was generated from bowtie profile measurements for both the full- and half-bowtie. Equivalent source models for each combination of equivalent spectrum and filter were incorporated into the Monte Carlo software package MCNPX. Monte Carlo simulations were then validated against in-phantom measurements for both the radiographic and CBCT mode of operation of the kV CBCT system. Radiographic and CBCT imaging dose was measured for a variety of protocols at various locations within a body (32 cm in diameter) and head (16 cm in diameter) CTDI phantom. The in-phantom radiographic and CBCT dose was simulated at all measurement locations and converted to absolute dose using normalization factors calculated from air scan measurements and corresponding simulations. The simulated results were compared with the physical measurements and their discrepancies were assessed quantitatively. Results: Strong agreement was observed between in-phantom simulations and measurements. For the radiographic protocols, simulations uniformly underestimated measurements by 0.54%-5.14% (mean difference = -3.07%, SD = 1.60%). For the CBCT protocols, simulations uniformly underestimated measurements by 1.35%-5.31% (mean difference = -3.42%, SD = 1.09%). Conclusions: This work demonstrates the feasibility of using a measurement-based kV CBCT source model to facilitate dose calculations with Monte Carlo methods for both the radiographic and CBCT mode of operation. While this initial work validates simulations against measurements for simple geometries, future work will involve utilizing the source model to investigate kV CBCT dosimetry with more complex anthropomorphic phantoms and patient specific models. [ABSTRACT FROM AUTHOR]

Complex organisms rely on experience to optimize the function of perceptual and motor systems in situations relevant to survival. It is well established that visual cues reliably paired with danger are processed more efficiently than neutral cues, and that such facilitated sensory processing extends to low levels of the visual system. The neurophysiological mechanisms mediating biased sensory processing, however, are not well understood. Here we used grating stimuli specifically designed to engage luminance or chromatic pathways of the human visual system in a differential classical conditioning paradigm. Behavioral ratings and visual electroencephalographic steady-state potentials were recorded in healthy human participants. Our findings indicate that the visuocortical response to high-spatial-frequency isoluminant (red-green) grating stimuli was not modulated by fear conditioning, but low-contrast, low-spatial-frequency reversal of grayscale gratings resulted in pronounced conditioning effects. We conclude that sensory input conducted via the chromatic pathways into retinotopic visual cortex has limited access to the bi-directional connectivity with brain networks mediating the acquisition and expression of fear, such as the amygdaloid complex. Conversely, luminance information is necessary to establish amplification of learned danger signals in hierarchically early regions of the visual system. [ABSTRACT FROM AUTHOR]

Purpose: CT neuroperfusion examinations are capable of delivering high radiation dose to the skin or lens of the eyes of a patient and can possibly cause deterministic radiation injury. The purpose of this study is to: (a) estimate peak skin dose and eye lens dose from CT neuroperfusion examinations based on several voxelized adult patient models of different head size and (b) investigate how well those doses can be approximated by some commonly used CT dose metrics or tools, such as CTDIvol, American Association of Physicists in Medicine (AAPM) Report No. 111 style peak dose measurements, and the ImPACT organ dose calculator spreadsheet. Methods: Monte Carlo simulation methods were used to estimate peak skin and eye lens dose on voxelized patient models, including GSF's Irene, Frank, Donna, and Golem, on four scanners from the major manufacturers at the widest collimation under all available tube potentials. Doses were reported on a per 100 mAs basis. CTDIvol measurements for a 16 cm CTDI phantom, AAPM Report No. 111 style peak dose measurements, and ImPACT calculations were performed for available scanners at all tube potentials. These were then compared with results from Monte Carlo simulations. Results: The dose variations across the different voxelized patient models were small. Dependent on the tube potential and scanner and patient model, CTDIvol values overestimated peak skin dose by 26%-65%, and overestimated eye lens dose by 33%-106%, when compared to Monte Carlo simulations. AAPM Report No. 111 style measurements were much closer to peak skin estimates ranging from a 14% underestimate to a 33% overestimate, and with eye lens dose estimates ranging from a 9% underestimate to a 66% overestimate. The ImPACT spreadsheet overestimated eye lens dose by 2%-82% relative to voxelized model simulations. Conclusions: CTDIvol consistently overestimates dose to eye lens and skin. The ImPACT tool also overestimated dose to eye lenses. As such they are still useful as a conservative predictor of dose for CT neuroperfusion studies. AAPM Report No. 111 style measurements are a better predictor of both peak skin and eye lens dose than CTDIvol and ImPACT for the patient models used in this study. It should be remembered that both the AAPM Report No. 111 peak dose metric and CTDIvol dose metric are dose indices and were not intended to represent actual organ doses. [ABSTRACT FROM AUTHOR]