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PURPOSE: The Medical Imaging and Data Resource Center (MIDRC) was created to facilitate medical imaging machine learning (ML) research for tasks including early detection, diagnosis, prognosis, and assessment of treatment response related to the coronavirus disease 2019 pandemic and beyond. The purpose of this work was to create a publicly available metrology resource to assist researchers in evaluating the performance of their medical image analysis ML algorithms. APPROACH: An interactive decision tree, called MIDRC-MetricTree, has been developed, organized by the type of task that the ML algorithm was trained to perform. The criteria for this decision tree were that (1) users can select information such as the type of task, the nature of the reference standard, and the type of the algorithm output and (2) based on the user input, recommendations are provided regarding appropriate performance evaluation approaches and metrics, including literature references and, when possible, links to publicly available software/code as well as short tutorial videos. RESULTS: Five types of tasks were identified for the decision tree: (a) classification, (b) detection/localization, (c) segmentation, (d) time-to-event (TTE) analysis, and (e) estimation. As an example, the classification branch of the decision tree includes two-class (binary) and multiclass classification tasks and provides suggestions for methods, metrics, software/code recommendations, and literature references for situations where the algorithm produces either binary or non-binary (e.g., continuous) output and for reference standards with negligible or non-negligible variability and unreliability. CONCLUSIONS: The publicly available decision tree is a resource to assist researchers in conducting task-specific performance evaluations, including classification, detection/localization, segmentation, TTE, and estimation tasks.

Coronavirus disease 2019 (COVID-19), is an ongoing issue in certain populations, presenting rapidly worsening pneumonia and persistent symptoms. This study aimed to test the predictability of rapid progression using radiographic scores and laboratory markers and present longitudinal changes. This retrospective study included 218 COVID-19 pneumonia patients admitted at the Chungnam National University Hospital. Rapid progression was defined as respiratory failure requiring mechanical ventilation within one week of hospitalization. Quantitative COVID (QCOVID) scores were derived from high-resolution computed tomography (CT) analyses: (1) ground glass opacity (QGGO), (2) mixed diseases (QMD), and (3) consolidation (QCON), and the sum, quantitative total lung diseases (QTLD). Laboratory data, including inflammatory markers, were obtained from electronic medical records. Rapid progression was observed in 9.6% of patients. All QCOVID scores predicted rapid progression, with QMD showing the best predictability (AUC = 0.813). In multivariate analyses, the QMD score and interleukin(IL)-6 level were important predictors for rapid progression (AUC = 0.864). With >2 months follow-up CT, remained lung lesions were observed in 21 subjects, even after several weeks of negative reverse transcription polymerase chain reaction test. AI-driven quantitative CT scores in conjugation with laboratory markers can be useful in predicting the rapid progression and monitoring of COVID-19.

BackgroundIdiopathic 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.PurposeThe 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.MethodsOur 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.ResultsDuring 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 attention maps 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 best-performing hyperparameter selections according to MSGA were applied to the holdout test set and reached comparable model performance to that of the validation set.ConclusionsOur 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.

PurposeRecent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity.MethodsWe retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe.ResultsThe mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups.ConclusionWe observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.

Patients with systemic sclerosis are at high risk of developing systemic sclerosis-associated interstitial lung disease. Symptoms and outcomes of systemic sclerosis-associated interstitial lung disease range from subclinical lung involvement to respiratory failure and death. Early and accurate diagnosis of systemic sclerosis-associated interstitial lung disease is therefore important to enable appropriate intervention. The most sensitive and specific way to diagnose systemic sclerosis-associated interstitial lung disease is by high-resolution computed tomography, and experts recommend that high-resolution computed tomography should be performed in all patients with systemic sclerosis at the time of initial diagnosis. In addition to being an important screening and diagnostic tool, high-resolution computed tomography can be used to evaluate disease extent in systemic sclerosis-associated interstitial lung disease and may be helpful in assessing prognosis in some patients. Currently, there is no consensus with regards to frequency and scanning intervals in patients at risk of interstitial lung disease development and/or progression. However, expert guidance does suggest that frequency of screening using high-resolution computed tomography should be guided by risk of developing interstitial lung disease. Most experienced clinicians would not repeat high-resolution computed tomography more than once a year or every other year for the first few years unless symptoms arose. Several computed tomography techniques have been developed in recent years that are suitable for regular monitoring, including low-radiation protocols, which, together with other technologies, such as lung ultrasound and magnetic resonance imaging, may further assist in the evaluation and monitoring of patients with systemic sclerosis-associated interstitial lung disease. A video abstract to accompany this article is available at: https://www.globalmedcomms.com/respiratory/Khanna/HRCTinSScILD.

The rapid development of deep-learning methods in medical imaging has called for an analysis method suitable for non-linear and data-dependent algorithms. In this work, we investigate a local linearity analysis where a complex neural network can be represented as piecewise linear systems. We recognize that a large number of neural networks consists of alternating linear layers and rectified linear unit (ReLU) activations, and are therefore strictly piecewise linear. We investigated the extent of these locally linear regions by gradually adding perturbations to an operating point. For this work, we explored perturbations based on image features of interest, including lesion contrast, background, and additive noise. We then developed strategies to extend these strictly locally linear regions to include neighboring linear regions with similar gradients. Using these approximately linear regions, we applied singular value decomposition (SVD) analysis to each local linear system to investigate and explain the overall nonlinear and data-dependent behaviors of neural networks. The analysis was applied to an example CT denoising algorithm trained on thorax CT scans. We observed that the strictly local linear regions are highly sensitive to small signal perturbations. Over a range of lesion contrast from 0.007 to 0.04 mm-1, there is a total of 33992 linear regions. The Jacobians are also shift-variant. However, the Jacobians of neighboring linear regions are very similar. By combining linear regions with similar Jacobians, we narrowed down the number of approximately linear regions to four over lesion contrast from 0.001 to 0.08 mm-1. The SVD analysis to different linear regions revealed denoising behavior that is highly dependent on the background intensity. Analysis further identified greater amount of noise reduction in uniform regions compared to lesion edges. In summary, the local linearity analysis framework we proposed has the potential for us to better characterize and interpret the non-linear and data-dependent behaviors of neural networks.

Lung cancer is by far the leading cause of cancer death in the US. Recent studies have demonstrated the effectiveness of screening using low dose CT (LDCT) in reducing lung cancer related mortality. While lung nodules are detected with a high rate of sensitivity, this exam has a low specificity rate and it is still difficult to separate benign and malignant lesions. The ISBI 2018 Lung Nodule Malignancy Prediction Challenge, developed by a team from the Quantitative Imaging Network of the National Cancer Institute, was focused on the prediction of lung nodule malignancy from two sequential LDCT screening exams using automated (non-manual) algorithms. We curated a cohort of 100 subjects who participated in the National Lung Screening Trial and had established pathological diagnoses. Data from 30 subjects were randomly selected for training and the remaining was used for testing. Participants were evaluated based on the area under the receiver operating characteristic curve (AUC) of nodule-wise malignancy scores generated by their algorithms on the test set. The challenge had 17 participants, with 11 teams submitting reports with method description, mandated by the challenge rules. Participants used quantitative methods, resulting in a reporting test AUC ranging from 0.698 to 0.913. The top five contestants used deep learning approaches, reporting an AUC between 0.87 - 0.91. The team's predictor did not achieve significant differences from each other nor from a volume change estimate (p =.05 with Bonferroni-Holm's correction).

PurposeRecent 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.MethodA 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.ResultsAll included radiomic features lacked intercondition reproducibility (OCCC 50% of radiomic features.ConclusionWe 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.

BackgroundThe flipped-classroom involves watching prerecorded lectures at home followed by group learning exercises within the classroom. This study compares the flipped classroom approach with the traditional classroom for teaching horizontal strabismus didactics in ophthalmology residency.MethodsIn this multicenter, randomized controlled survey study from October 2017 to July 2018, 110 ophthalmology residents were taught esotropia and exotropia sequentially, randomized by order and classroom style. Flipped classroom participants were assigned a preclass video lecture prior to the in-class case-based activity. The traditional classroom included a preparatory reading assignment and an in-person lecture. Residents completed three identical 5-question assessments (pretest, post-test, and 3-month retention) and surveys for each classroom. The primary outcome measured residents' preferences for classroom styles; the secondary outcome compared knowledge acquisition.ResultsIn our study cohort, the flipped classroom resulted in greater at-home preparation than the traditional classroom (P = 0.001) and was preferred by 33 of 53 residents (62%); 45 of 53 (85%) wished to see the flipped classroom used at least 25% of the time. The exotropia flipped classroom scored higher than traditional classroom on the pretest (3.71/5 [74%] vs 2.87/5 [57%]; P

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.

PurposeThe 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.MethodsThirty-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.ResultsMean 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).ConclusionsOne 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.

PurposeTask 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 methodsThe 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 notesEquivalent 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 applicationsSimilar 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.

Several institutions have developed image feature extraction software to compute quantitative descriptors of medical images for radiomics analyses. With radiomics increasingly proposed for use in research and clinical contexts, new techniques are necessary for standardizing and replicating radiomics findings across software implementations. We have developed a software toolkit for the creation of 3D digital reference objects with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. Here, we present the definition of these objects, parameterized derivations of a subset of their radiomics values, computer code for object generation, example use cases, and a user-downloadable sample collection used for the examples cited in this paper.

PurposeThe 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.MethodsFor 24 pregnant patients of gestational age from

PurposeWith 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.MethodsThe 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.ResultsThe 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.ConclusionsThe 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.

PurposeSize-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.MethodsValidated 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 ).ResultsCTDIvol,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.ConclusionsConversion 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.

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 60 Co). 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.

PurposeThe 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.MethodsMonte 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.ResultsLung 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.ConclusionLung, 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.

Inorganic polyphosphate (polyP) constitutes one of the most conserved and ubiquitous molecules in biology. Recent work in bacteria demonstrated that polyP increases oxidative stress resistance by preventing stress-induced protein aggregation and promotes biofilm formation by stimulating functional amyloid formation. To gain insights into these two seemingly contradictory functions of polyP, we investigated the effects of polyP on the folding model lactate dehydrogenase. We discovered that the presence of polyP during the thermal unfolding process stabilizes folding intermediates of lactate dehydrogenase as soluble micro-β-aggregates with amyloid-like properties. Size and heterogeneity of the oligomers formed in this process were dependent on the polyP chain length, with longer chains forming smaller, more homogenous complexes. This ability of polyP to stabilize thermally unfolded proteins even upon exposure to extreme temperatures appears to contribute to the observed resistance of uropathogenic Escherichia coli toward severe heat shock treatment. These results suggest that the working mechanism of polyP is the same for both soluble and amyloidogenic proteins, with the ultimate outcome likely being determined by a combination of polyP chain length and the client protein itself. They help to explain how polyP can simultaneously function as general stress-protective chaperone and instigator of amyloidogenic processes in vivo.

PurposeTo 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.MethodsModel-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.ResultsFor 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.ConclusionFreeCT_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.

PurposeTo 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.MethodsWe 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

ObjectiveA recently proposed "5DCT" protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artefacts, which arise when tissue motion is greater than scan speed.MethodsUsing a unique set of digital phantoms based on patient data and verified with a motion phantom, this work identifies the minimum scanner parameters required to successfully generate free-breathing artefact-free fast-helical scans. A motion phantom and 5 patients were imaged 25 times under free-breathing conditions in alternating directions with a 64-slice CT scanner employing a low-dose fast-helical protocol. A series of high temporal resolution (0.1 s) 5DCT scan data sets was generated in each case. A simulated CT scanner was used to "image" each free-breathing data set. Various CT scanner detector widths and rotation times were simulated, and verified using the motion phantom results. Motion-induced artefacts were quantified in patient images using structural similarity maps to determine the similarity between axial slices.ResultsIncreasing amounts of motion-induced artefacts were observed with increasing rotation times >0.2 s for 16 mm detector configuration.ConclusionThe current generation of 16-slice CT scanners, which are present in the majority of Radiation Oncology departments, are not capable of generating free-breathing sorting artefact-free images required for 5DCT. Advances in knowledge: A recently proposed "5DCT" protocol uses deformable registration of free-breathing fast-helical CT scans to generate a breathing motion model. In order to allow accurate registration, free-breathing images are required to be free of doubling-artefacts, which arise when tissue motion is greater than scan speed. The results suggest that the current generation of 16-slice CT scanners, present in the majority of Radiation Oncology departments, are not capable of generating the free-breathing images required for 5DCT.

PurposeCT 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).MethodsThe 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.ResultsThe 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).ConclusionsThis 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.

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 manufacturers 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.

PurposeCurrently, 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.MethodsThe 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 ImPACT.ResultsThe 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 ImPACT. Moreover, the GLM estimates were better than those of SSDE or ImPACT 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.ConclusionsThe 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.

PurposeLung 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.MethodsThe 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 LungRADS assessment categories, and sub-analyses were performed.ResultsFrom 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 LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS 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.ConclusionsCAD 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.

ObjectiveThe U.S. Centers for Medicare & Medicaid Services (CMS) recently approved the use of low-dose CT for lung cancer screening and described volumetric CT dose index (CTDIvol) requirements. These were based on the National Lung Screening Trial, which used only fixed-tube-current techniques. The aim of this study was to evaluate dose index data from a lung cancer screening program using automatic exposure control (AEC) techniques to ensure compliance with requirements and to correlate dose index values with patient size.Materials and methodsCTDIvol, dose-length product (DLP), and body mass index (BMI) data were collected for 563 lung cancer screening examinations performed with AEC between January 1, 2014, through August 31, 2015. CTDIvol and DLP were analyzed according to the patient's BMI classification. Results were compared with the CMS requirement that the CTDIvol for a standard-sized patient (height, 170 cm; weight, 70 kg) be 3.0 mGy or less, with adjustments for patients of different sizes. For a subset of patients, the average water-equivalent diameter and size-specific dose estimate were estimated.ResultsThe average CTDIvol for a standard-sized patient was 1.8 mGy, which meets CMS requirements. CTDIvol values were lower for smaller patients and higher for larger patients. Overall, the mean CTDIvol and DLP were 2.1 mGy and 74 mGy⋅cm, respectively. The size-specific dose estimate for the average water-equivalent diameter (27.5 cm) of the patient subset was 2.6 mGy.ConclusionThe screening protocols using AEC resulted in CTDIvol values that were compliant with CMS requirements. CTDIvol values greater than 3.0 mGy were only observed for overweight or obese patients.

Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features. The purpose of this study is to investigate the sensitivity of quantitative descriptors of pulmonary nodules to segmentations and to illustrate comparisons across different feature types and features computed by different implementations of feature extraction algorithms. We calculated the concordance correlation coefficients of the features as a measure of their stability with the underlying segmentation; 68% of the 830 features in this study had a concordance CC of ≥0.75. Pairwise correlation coefficients between pairs of features were used to uncover associations between features, particularly as measured by different participants. A graphical model approach was used to enumerate the number of uncorrelated feature groups at given thresholds of correlation. At a threshold of 0.75 and 0.95, there were 75 and 246 subgroups, respectively, providing a measure for the features' redundancy.

BackgroundThe purpose of this study was to investigate the potential of antibody-directed immunotherapy targeting the aminophospholipid phosphatidylserine, which promotes immunosuppression when exposed in the tumor microenvironment, alone and in combination with antibody treatment towards the T-cell checkpoint inhibitor PD-1 in breast carcinomas, including triple-negative breast cancers.MethodsImmune-competent mice bearing syngeneic EMT-6 or E0771 tumors were subjected to treatments comprising of a phosphatidylserine-targeting and an anti-PD-1 antibody either as single or combinational treatments. Anti-tumor effects were determined by tumor growth inhibition and changes in overall survival accompanying each treatment. The generation of a tumor-specific immune response in animals undergoing complete tumor regression was assessed by secondary tumor cell challenge and splenocyte-produced IFNγ in the presence or absence of irradiated tumor cells. Changes in the presence of tumor-infiltrating lymphocytes were assessed by flow cytometry, while mRNA-based immune profiling was determined using NanoString PanCancer Immune Profiling Panel analysis.ResultsTreatment by a phosphatidylserine-targeting antibody inhibits in-vivo growth and significantly enhances the anti-tumor activity of antibody-mediated PD-1 therapy, including providing a distinct survival advantage over treatment by either single agent. Animals in which complete tumor regression occurred with combination treatments were resistant to secondary tumor challenge and presented heightened expression levels of splenocyte-produced IFNγ. Combinational treatment by a phosphatidylserine-targeting antibody with anti-PD-1 therapy increased the number of tumor-infiltrating lymphocytes more than that observed with single-arm therapies. Finally, immunoprofiling analysis revealed that the combination of anti-phosphatidylserine targeting antibody and anti-PD-1 therapy enhanced tumor-infiltrating lymphocytes, and increased expression of pro-immunosurveillance-associated cytokines while significantly decreasing expression of pro-tumorigenic cytokines that were induced by single anti-PD-1 therapy.ConclusionsOur data suggest that antibody therapy targeting phosphatidylserine-associated immunosuppression, which has activity as a single agent, can significantly enhance immunotherapies targeting the PD-1 pathway in murine breast neoplasms, including triple-negative breast cancers.

In tumor-bearing animals, the membrane phospholipid phosphatidylserine (PS) suppresses immune responses, suggesting that PS signaling could counteract the antitumor effect of antibody-driven immune checkpoint blockade. Here, we show that treating melanoma-bearing mice with a PS-targeting antibody enhances the antitumor activity of downstream checkpoint inhibition. Combining PS-targeting antibodies with CTLA-4 or PD-1 blockade resulted in significantly greater inhibition of tumor growth than did single-agent therapy. Moreover, combination therapy enhanced CD4(+) and CD8(+) tumor-infiltrating lymphocyte numbers; elevated the fraction of cells expressing the proinflammatory cytokines IL2, IFNγ, and TNFα; and increased the ratio of CD8 T cells to myeloid-derived suppressor cells and regulatory T cells in tumors. Similar changes in immune cell profiles were observed in splenocytes. Taken together, these data show that antibody-mediated PS blockade enhances the antitumor efficacy of immune checkpoint inhibition. Cancer Immunol Res; 4(6); 531-40. ©2016 AACR.

PurposeWith 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.MethodsFreeCT_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.ResultsThe 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 mm(2)/g (0.01%).ConclusionsFreeCT_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.

Lack of classifier robustness is a barrier to widespread adoption of computer-aided diagnosis systems for computed tomography (CT). We propose a novel Robustness-Driven Feature Selection (RDFS) algorithm that preferentially selects features robust to variations in CT technical factors. We evaluated RDFS in CT classification of fibrotic interstitial lung disease using 3D texture features. CTs were collected for 99 adult subjects separated into three datasets: training, multi-reconstruction, testing. Two thoracic radiologists provided cubic volumes of interest corresponding to six classes: pulmonary fibrosis, ground-glass opacity, honeycombing, normal lung parenchyma, airway, vessel. The multi-reconstruction dataset consisted of CT raw sinogram data reconstructed by systematically varying slice thickness, reconstruction kernel, and tube current (using a synthetic reduced-tube-current algorithm). Two support vector machine classifiers were created, one using RDFS ("with-RDFS") and one not ("without-RDFS"). Classifier robustness was compared on the multi-reconstruction dataset, using Cohen's kappa to assess classification agreement against a reference reconstruction. Classifier performance was compared on the testing dataset using the extended g-mean (EGM) measure. With-RDFS exhibited superior robustness (kappa 0.899-0.989) compared to without-RDFS (kappa 0.827-0.968). Both classifiers demonstrated similar performance on the testing dataset (EGM 0.778 for with-RDFS; 0.785 for without-RDFS), indicating that RDFS does not compromise classifier performance when discarding nonrobust features. RDFS is highly effective at improving classifier robustness against slice thickness, reconstruction kernel, and tube current without sacrificing performance, a result that has implications for multicenter clinical trials that rely on accurate and reproducible quantitative analysis of CT images collected under varied conditions across multiple sites, scanners, and timepoints.

Rationale and objectivesTumor volume change has potential as a biomarker for diagnosis, therapy planning, and treatment response. Precision was evaluated and compared among semiautomated lung tumor volume measurement algorithms from clinical thoracic computed tomography data sets. The results inform approaches and testing requirements for establishing conformance with the Quantitative Imaging Biomarker Alliance (QIBA) Computed Tomography Volumetry Profile.Materials and methodsIndustry and academic groups participated in a challenge study. Intra-algorithm repeatability and inter-algorithm reproducibility were estimated. Relative magnitudes of various sources of variability were estimated using a linear mixed effects model. Segmentation boundaries were compared to provide a basis on which to optimize algorithm performance for developers.ResultsIntra-algorithm repeatability ranged from 13% (best performing) to 100% (least performing), with most algorithms demonstrating improved repeatability as the tumor size increased. Inter-algorithm reproducibility was determined in three partitions and was found to be 58% for the four best performing groups, 70% for the set of groups meeting repeatability requirements, and 84% when all groups but the least performer were included. The best performing partition performed markedly better on tumors with equivalent diameters greater than 40 mm. Larger tumors benefitted by human editing but smaller tumors did not. One-fifth to one-half of the total variability came from sources independent of the algorithms. Segmentation boundaries differed substantially, not ony in overall volume but also in detail.ConclusionsNine of the 12 participating algorithms pass precision requirements similar to what is indicated in the QIBA Profile, with the caveat that the present study was not designed to explicitly evaluate algorithm profile conformance. Change in tumor volume can be measured with confidence to within ±14% using any of these nine algorithms on tumor sizes greater than 10 mm. No partition of the algorithms was able to meet the QIBA requirements for interchangeability down to 10 mm, although the partition comprising best performing algorithms did meet this requirement for a tumor size of greater than approximately 40 mm.

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.

PurposeTo summarize data on computed tomographic (CT) radiation doses collected from consecutive CT examinations performed at 12 facilities that can contribute to the creation of reference levels.Materials and methodsThe study was approved by the institutional review boards of the collaborating institutions and was compliant with HIPAA. Radiation dose metrics were prospectively and electronically collected from 199 656 consecutive CT examinations in 83 181 adults and 3871 consecutive CT examinations in 2609 children at the five University of California medical centers during 2013. The median volume CT dose index (CTDIvol), dose-length product (DLP), and effective dose, along with the interquartile range (IQR), were calculated separately for adults and children and stratified according to anatomic region. Distributions for DLP and effective dose are reported for single-phase examinations, multiphase examinations, and all examinations.ResultsFor adults, the median CTDIvol was 50 mGy (IQR, 37-62 mGy) for the head, 12 mGy (IQR, 7-17 mGy) for the chest, and 12 mGy (IQR, 8-17 mGy) for the abdomen. The median DLPs for single-phase, multiphase, and all examinations, respectively, were as follows: head, 880 mGy · cm (IQR, 640-1120 mGy · cm), 1550 mGy · cm (IQR, 1150-2130 mGy · cm), and 960 mGy · cm (IQR, 690-1300 mGy · cm); chest, 420 mGy · cm (IQR, 260-610 mGy · cm), 880 mGy · cm (IQR, 570-1430 mGy · cm), and 550 mGy · cm (IQR 320-830 mGy · cm); and abdomen, 580 mGy · cm (IQR, 360-860 mGy · cm), 1220 mGy · cm (IQR, 850-1790 mGy · cm), and 960 mGy · cm (IQR, 600-1460 mGy · cm). Median effective doses for single-phase, multiphase, and all examinations, respectively, were as follows: head, 2 mSv (IQR, 1-3 mSv), 4 mSv (IQR, 3-8 mSv), and 2 mSv (IQR, 2-3 mSv); chest, 9 mSv (IQR, 5-13 mSv), 18 mSv (IQR, 12-29 mSv), and 11 mSv (IQR, 6-18 mSv); and abdomen, 10 mSv (IQR, 6-16 mSv), 22 mSv (IQR, 15-32 mSv), and 17 mSv (IQR, 11-26 mSv). In general, values for children were approximately 50% those for adults in the head and 25% those for adults in the chest and abdomen.ConclusionThese summary dose data provide a starting point for institutional evaluation of CT radiation doses.

Although there has been extensive research done on the biological response to doses of ionizing radiation relevant to radiodiagnostic procedures, very few studies have examined radiation schemes similar to those frequently utilized in CT exams. Instead of a single exposure, CT exams are often made up of a series of scans separated on the order of minutes. DNA damage dose-response kinetics after radiation doses and schemes similar to CT protocols were established in both cultured (ESW-WT3) and whole blood lymphocytes and compared to higher dose exposures. Both the kinetics and extent of H2AX phosphorylation were found to be dose dependent. Damage induction and detection showed a clear dose response, albeit different, at all time points and differences in the DNA repair kinetics of ESW-WT3 and whole blood lymphocytes were characterized. Moreover, using a modified split-dose in vitro experiment, we show that phosphorylation of H2AX is significantly reduced after exposure to CT doses fractionated over a few minutes compared to the same total dose delivered as a single exposure. Because the split-dose exposures investigated here are more similar to those experienced during a CT examination, it is essential to understand why and how these differences occur. This work provides compelling evidence supporting differential biological responses not only between high and low doses, but also between single and multiple exposures to low doses of ionizing radiation.

PurposeMeasuring 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.MethodsReduced-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.ResultsThe 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.ConclusionsLung-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.

The Quantitative Imaging Biomarker Alliance (QIBA) is a multidisciplinary consortium sponsored by the RSNA to define processes that enable the implementation and advancement of quantitative imaging methods described in a QIBA profile document that outlines the process to reliably and accurately measure imaging features. A QIBA profile includes factors such as technical (product-specific) standards, user activities, and relationship to a clinically meaningful metric, such as with nodule measurement in the course of CT screening for lung cancer. In this report, the authors describe how the QIBA approach is being applied to the measurement of small pulmonary nodules such as those found during low-dose CT-based lung cancer screening. All sources of variance with imaging measurement were defined for this process. Through a process of experimentation, literature review, and assembly of expert opinion, the strongest evidence was used to define how to best implement each step in the imaging acquisition and evaluation process. This systematic approach to implementing a quantitative imaging biomarker with standardized specifications for image acquisition and postprocessing for a specific quantitative measurement of a pulmonary nodule results in consistent performance characteristics of the measurement (eg, bias and variance). Implementation of the QIBA small nodule profile may allow more efficient and effective clinical management of the diagnostic workup of individuals found to have suspicious pulmonary nodules in the course of lung cancer screening evaluation.

PurposeThe 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.MethodsA 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.ResultsCorrection factors for the dosimeter response in terms of signal fading, dose linearity, and angular dependence were found to be small for most measurement conditions (

PurposeThe 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.MethodsMD 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.ResultsThe 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%.ConclusionsThe 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.

PurposeTask 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.Methods101 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 CTDIvol to reflect regional variation of tube current. Linear regression analysis was used to evaluate the correlations between normalized organ doses and each size metric.ResultsFor 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.ConclusionsTo 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.

PurposeTo determine the variability of lesion size measurements in computed tomography data sets of patients imaged under a "no change" ("coffee break") condition and to determine the impact of two reading paradigms on measurement variability.Method and materialsUsing data sets from 32 non-small cell lung cancer patients scanned twice within 15 minutes ("no change"), measurements were performed by five radiologists in two phases: (1) independent reading of each computed tomography dataset (timepoint): (2) a locked, sequential reading of datasets. Readers performed measurements using several sizing methods, including one-dimensional (1D) longest in-slice dimension and 3D semi-automated segmented volume. Change in size was estimated by comparing measurements performed on both timepoints for the same lesion, for each reader and each measurement method. For each reading paradigm, results were pooled across lesions, across readers, and across both readers and lesions, for each measurement method.ResultsThe mean percent difference (±SD) when pooled across both readers and lesions for 1D and 3D measurements extracted from contours was 2.8 ± 22.2% and 23.4 ± 105.0%, respectively, for the independent reads. For the locked, sequential reads, the mean percent differences (±SD) reduced to 2.52 ± 14.2% and 7.4 ± 44.2% for the 1D and 3D measurements, respectively.ConclusionEven under a "no change" condition between scans, there is variation in lesion size measurements due to repeat scans and variations in reader, lesion, and measurement method. This variation is reduced when using a locked, sequential reading paradigm compared to an independent reading paradigm.

The ACR recognizes that low-dose CT for lung cancer screening has the potential to significantly reduce mortality from lung cancer in the appropriate high-risk population. The ACR supports the recommendations of the US Preventive Services Task Force and the National Comprehensive Cancer Network for screening patients. To be effective, lung cancer screening should be performed at sites providing high-quality low-dose CT examinations overseen and interpreted by qualified physicians using a structured reporting and management system. The ACR has developed a set of tools necessary for radiologists to take the lead on the front lines of lung cancer screening. The ACR Lung Cancer Screening Center designation is built upon the ACR CT accreditation program and requires use of Lung-RADS or a similar structured reporting and management system. This designation provides patients and referring providers with the assurance that they will receive high-quality screening with appropriate follow-up care.

PurposeAAPM 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.MethodsUsing 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 CTDIvol 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.ResultsThe analysis of the patient data revealed a best-fit linear relationship (R(2) 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 scanning. 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, R(2) 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.ConclusionsFor 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.

PurposeMonte 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.MethodsA 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.ResultsFor 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%.ConclusionsThis 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.

Objective To evaluate the success rate of a low-dose (50 % mAs reduction) computed tomography (CT) biopsy technique. This protocol was adopted based on other successful reduced-CT radiation dose protocols in our department, which were implemented in conjunction with quality improvement projects. Materials and methods The technique included a scout view and initial localizing scan with standard dose. Additional scans obtained for further guidance or needle adjustment were acquired by reducing the tube current-time product (mAs) by 50 %. The radiology billing data were searched for CT-guided musculoskeletal procedures performed over a period of 8 months following the initial implementation of the protocol. These were reviewed for the type of procedure and compliance with the implemented protocol. The compliant CT-guided biopsy cases were then retrospectively reviewed for patient demographics, tumor pathology, and lesion size. Pathology results were compared to the ultimate diagnoses and were categorized as diagnostic, accurate, or successful. Results Of 92 CT-guided procedures performed during this period, two were excluded as they were not biopsies (one joint injection and one drainage), 19 were excluded due to non-compliance (operators neglected to follow the protocol), and four were excluded due to lack of available follow-up in our electronic medical records. A total of 67 compliant biopsies were performed in 63 patients (two had two biopsies, and one had three biopsies). There were 32 males and 31 females with an average age of 50 (range, 15-84 years). Of the 67 biopsies, five were non-diagnostic and inaccurate and thus unsuccessful (7 %); five were diagnostic but inaccurate and thus unsuccessful (7 %); 57 were diagnostic and accurate thus successful (85 %). These results were comparable with results published in the radiology literature. Conclusions The success rate of CT-guided biopsies using a low-dose protocol is comparable to published rates for conventional dose biopsies. The implemented low-dose protocol did not change the success rate of CT-guided musculoskeletal biopsies. © 2014 ISS.

ObjectivesThe purpose of this study was to define clinically appropriate, computer-aided lung nodule detection (CAD) requirements and protocols based on recent screening trials. In the following paper, we describe a CAD evaluation methodology based on a publically available, annotated computed tomography (CT) image data set, and demonstrate the evaluation of a new CAD system with the functionality and performance required for adoption in clinical practice.MethodsA new automated lung nodule detection and measurement system was developed that incorporates intensity thresholding, a Euclidean Distance Transformation, and segmentation based on watersheds. System performance was evaluated against the Lung Imaging Database Consortium (LIDC) CT reference data set.ResultsThe test set comprised thin-section CT scans from 108 LIDC subjects. The median (±IQR) sensitivity per subject was 100 (±37.5) for nodules ≥ 4 mm and 100 (±8.33) for nodules ≥ 8 mm. The corresponding false positive rates were 0 (±2.0) and 0 (±1.0), respectively. The concordance correlation coefficient between the CAD nodule diameter and the LIDC reference was 0.91, and for volume it was 0.90.ConclusionsThe new CAD system shows high nodule sensitivity with a low false positive rate. Automated volume measurements have strong agreement with the reference standard. Thus, it provides comprehensive, clinically-usable lung nodule detection and assessment functionality.Key points• CAD requirements can be based on lung cancer screening trial results. • CAD systems can be evaluated using publically available annotated CT image databases. • A new CAD system was developed with a low false positive rate. • The CAD system has reliable measurement tools needed for clinical use.

The American College of Radiology, with more than 30,000 members, is the principal organization of radiologists, radiation oncologists,and clinical medical physicists in the United States. The College is a nonprofit professional society whose primary purposes are to advance thescience of radiology, improve radiologic services to the patient, study the socioeconomic aspects of the practice of radiology, and encouragecontinuing education for radiologists, radiation oncologists, medical physicists, and persons practicing in allied professional fields.The American College of Radiology will periodically define new practice parameters and technical standards for radiologic practice to helpadvance the science of radiology and to improve the quality of service to patients throughout the United States. Existing practice parameters andtechnical standards will be reviewed for revision or renewal, as appropriate, on their fifth anniversary or sooner, if indicated.Each practice parameter and technical standard, representing a policy statement by the College, has undergone a thorough consensusprocess in which it has been subjected to extensive review and approval. The practice parameters and technical standards recognize that the safeand 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 parameter and technical standard by those entities not providing these services is notauthorized.?