Assessing variability in non-contrast CT for the evaluation of stroke: The effect of CT image reconstruction conditions on AI-based CAD measurements of ASPECTS value and hypodense volume.

Purpose: To rule out hemorrhage, non-contrast CT (NCCT) scans are used for early evaluation of patients with suspected stroke. Recently, artificial intelligence tools have been developed to assist with determining eligibility for reperfusion therapies by automating measurement of the Alberta Stroke Program Early CT Score (ASPECTS), a 10-point scale with > 7 or ≤ 7 being a threshold for change in functional outcome prediction and higher chance of symptomatic hemorrhage, and hypodense volume. The purpose of this work was to investigate the effects of CT reconstruction kernel and slice thickness on ASPECTS and hypodense volume. Methods: The NCCT series image data of 87 patients imaged with a CT stroke protocol at our institution were reconstructed with 3 kernels (H10s-smooth, H40s-medium, H70h-sharp) and 2 slice thicknesses (1.5mm and 5mm) to create a reference condition (H40s/5mm) and 5 non-reference conditions. Each reconstruction for each patient was analyzed with the Brainomix e-Stroke software (Brainomix, Oxford, England) which yields an ASPECTS value and measure of total hypodense volume (mL). Results: An ASPECTS value was returned for 74 of 87 cases in the reference condition (13 failures). ASPECTS in non-reference conditions changed from that measured in the reference condition for 59 cases, 7 of which changed above or below the clinical threshold of 7 for 3 non-reference conditions. ANOVA tests were performed to compare the differences in protocols, Dunnett's post-hoc tests were performed after ANOVA, and a significance level of p < 0.05 was defined. There was no significant effect of kernel (p = 0.91), a significant effect of slice thickness (p < 0.01) and no significant interaction between these factors (p = 0.91). Post-hoc tests indicated no significant difference between ASPECTS estimated in the reference and any non-reference conditions. There was a significant effect of kernel (p < 0.01) and slice thickness (p < 0.01) on hypodense volume, however there was no significant interaction between these factors (p = 0.79). Post-hoc tests indicated significantly different hypodense volume measurements for H10s/1.5mm (p = 0.03), H40s/1.5mm (p < 0.01), H70h/5mm (p < 0.01). No significant difference was found in hypodense volume measured in the H10s/5mm condition (p = 0.96). Conclusion: Automated ASPECTS and hypodense volume measurements can be significantly impacted by reconstruction kernel and slice thickness.

Quantitative Computed Tomography Lung COVID Scores with Laboratory Markers: Utilization to Predict Rapid Progression and Monitor Longitudinal Changes in Patients with Coronavirus 2019 (COVID-19) Pneumonia.

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.

Multi-scale, domain knowledge-guided attention + random forest: a two-stage deep learning-based multi-scale guided attention models to diagnose idiopathic pulmonary fibrosis from computed tomography images.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and usually fatal lung disease of unknown reasons, generally affecting the elderly population. Early diagnosis of IPF is crucial for triaging patients' treatment planning into anti-fibrotic treatment or treatments for other causes of pulmonary fibrosis. However, current IPF diagnosis workflow is complicated and time-consuming, which involves collaborative efforts from radiologists, pathologists, and clinicians and it is largely subject to inter-observer variability. PURPOSE: The purpose of this work is to develop a deep learning-based automated system that can diagnose subjects with IPF among subjects with interstitial lung disease (ILD) using an axial chest computed tomography (CT) scan. This work can potentially enable timely diagnosis decisions and reduce inter-observer variability. METHODS: Our dataset contains CT scans from 349 IPF patients and 529 non-IPF ILD patients. We used 80% of the dataset for training and validation purposes and 20% as the holdout test set. We proposed a two-stage model: at stage one, we built a multi-scale, domain knowledge-guided attention model (MSGA) that encouraged the model to focus on specific areas of interest to enhance model explainability, including both high- and medium-resolution attentions; at stage two, we collected the output from MSGA and constructed a random forest (RF) classifier for patient-level diagnosis, to further boost model accuracy. RF classifier is utilized as a final decision stage since it is interpretable, computationally fast, and can handle correlated variables. Model utility was examined by (1) accuracy, represented by the area under the receiver operating characteristic curve (AUC) with standard deviation (SD), and (2) explainability, illustrated by the visual examination of the estimated attention maps which showed the important areas for model diagnostics. RESULTS: During the training and validation stage, we observe that when we provide no guidance from domain knowledge, the IPF diagnosis model reaches acceptable performance (AUC±SD = 0.93±0.07), but lacks explainability; when including only guided high- or medium-resolution attention, the learned 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. CONCLUSIONS: Our results suggest that, for a task with only scan-level labels available, MSGA+RF can utilize the population-level domain knowledge to guide the training of the network, which increases both model accuracy and explainability.

AAPM Medical Physics Practice Guideline 1.b: CT protocol management and review practice guideline.

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.

Reference dataset for benchmarking fetal doses derived from Monte Carlo simulations of CT exams.

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

Correction to: Toward clinically usable CAD for lung cancer screening with computed tomography.

The original version of this article, published on 24 July 2014, unfortunately contained a mistake. In section "Discussion," a sentence was worded incorrectly.

RECORDS: improved Reporting of montE CarlO RaDiation transport Studies: Report of the AAPM Research Committee Task Group 268.

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

Estimating patient dose from CT exams that use automatic exposure control: Development and validation of methods to accurately estimate tube current values.

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

Estimating organ doses from tube current modulated CT examinations using a generalized linear model.

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

The impact of x-ray tube stabilization on localized radiation dose in axial CT scans: initial results in CTDI phantoms.

Rise, fall, and stabilization of the x-ray tube output occur immediately before and after data acquisition on some computed tomography (CT) scanners and are believed to contribute additional dose to anatomy facing the x-ray tube when it powers on or off. In this study, we characterized the dose penalty caused by additional radiation exposure during the rise, stabilization, and/or fall time (referred to as overscanning). A 32 cm CT dose-index (CTDI) phantom was scanned on three CT scanners: GE Healthcare LightSpeed VCT, GE Healthcare Discovery CT750 HD, and Siemens Somatom Definition Flash. Radiation exposure was detected for various x-ray tube start acquisition angles using a 10 cm pencil ionization chamber placed in the peripheral chamber hole at the phantom's 12 o'clock position. Scan rotation time, ionization chamber location, phantom diameter, and phantom centering were varied to quantify their effects on the dose penalty caused by overscanning. For 1 s single, axial rotations, CTDI at the 12 o'clock chamber position (CTDI100, 12:00) was 6.1%, 4.0%, and 4.4% higher when the start angle of the x-ray tube was aligned at the top of the gantry (12 o'clock) versus when the start angle was aligned at 9 o'clock for the Siemens Flash, GE CT750 HD, and GE VCT scanner, respectively. For the scanners' fastest rotation times (0.285 s for the Siemens and 0.4 s for both GE scanners), the dose penalties increased to 22.3%, 10.7%, and 10.5%, respectively, suggesting a trade-off between rotation speed and the dose penalty from overscanning. In general, overscanning was shown to have a greater radiation dose impact for larger diameter phantoms, shorter rotation times, and to peripheral phantom locations. Future research is necessary to determine an appropriate method for incorporating the localized dose penalty from overscanning into standard dose metrics, as well as to assess the impact on organ dose.

ACR CT Accreditation Program and the Lung Cancer Screening Program Designation.

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.

Robustness-Driven Feature Selection in Classification of Fibrotic Interstitial Lung Disease Patterns in Computed Tomography Using 3D Texture Features.

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.

Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195.

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.

Variability in CT lung-nodule volumetry: Effects of dose reduction and reconstruction methods.

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

Role of the Quantitative Imaging Biomarker Alliance in optimizing CT for the evaluation of lung cancer screen-detected nodules.

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.

Determining the Variability of Lesion Size Measurements from CT Patient Data Sets Acquired under "No Change" Conditions.

PURPOSE: To 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 MATERIALS: Using 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. RESULTS: The 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. CONCLUSION: Even 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.

Attenuation-based size metric for estimating organ dose to patients undergoing tube current modulated CT exams.

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