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.

A comparison of breast and lung doses from chest CT scans using organ-based tube current modulation (OBTCM) vs. Automatic tube current modulation (ATCM).

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

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.

Calibration strategies for use of the nanoDot OSLD in CT applications.

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.

Patient Size-Specific Analysis of Dose Indexes From CT Lung Cancer Screening.

OBJECTIVE: The 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 METHODS: CTDIvol, 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. RESULTS: The 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. CONCLUSION: The 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.

Radiation Doses in Consecutive CT Examinations from Five University of California Medical Centers.

PURPOSE: To 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 METHODS: The 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. RESULTS: For 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. CONCLUSION: These summary dose data provide a starting point for institutional evaluation of CT radiation doses.

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

OBJECTIVES: The 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. METHODS: A 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. RESULTS: The 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. CONCLUSIONS: The 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.

Success rates for computed tomography-guided musculoskeletal biopsies performed using a low-dose technique.

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.

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.

MIDRC-MetricTree: a decision tree-based tool for recommending performance metrics in artificial intelligence-assisted medical image analysis.

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.

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.

Quantifying lung fissure integrity using a three-dimensional patch-based convolutional neural network on CT images for emphysema treatment planning.

Purpose: Evaluation of lung fissure integrity is required to determine whether emphysema patients have complete fissures and are candidates for endobronchial valve (EBV) therapy. We propose a deep learning (DL) approach to segment fissures using a three-dimensional patch-based convolutional neural network (CNN) and quantitatively assess fissure integrity on CT to evaluate it in subjects with severe emphysema. Approach: From an anonymized image database of patients with severe emphysema, 129 CT scans were used. Lung lobe segmentations were performed to identify lobar regions, and the boundaries among these regions were used to construct approximate interlobar regions of interest (ROIs). The interlobar ROIs were annotated by expert image analysts to identify voxels where the fissure was present and create a reference ROI that excluded non-fissure voxels (where the fissure is incomplete). A CNN configured by nnU-Net was trained using 86 CT scans and their corresponding reference ROIs to segment the ROIs of left oblique fissure (LOF), right oblique fissure (ROF), and right horizontal fissure (RHF). For an independent test set of 43 cases, fissure integrity was quantified by mapping the segmented fissure ROI along the interlobar ROI. A fissure integrity score (FIS) was then calculated as the percentage of labeled fissure voxels divided by total voxels in the interlobar ROI. Predicted FIS (p-FIS) was quantified from the CNN output, and statistical analyses were performed comparing p-FIS and reference FIS (r-FIS). Results: The absolute percent error mean (±SD) between r-FIS and p-FIS for the test set was 4.0% (±4.1%), 6.0% (±9.3%), and 12.2% (±12.5%) for the LOF, ROF, and RHF, respectively. Conclusions: A DL approach was developed to segment lung fissures on CT images and accurately quantify FIS. It has potential to assist in the identification of emphysema patients who would benefit from EBV treatment.

Target-oriented deep learning-based image registration with individualized test-time adaptation.

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

Sequestration of imaging studies in MIDRC: stratified sampling to balance demographic characteristics of patients in a multi-institutional data commons.

Purpose: The Medical Imaging and Data Resource Center (MIDRC) is a multi-institutional effort to accelerate medical imaging machine intelligence research and create a publicly available image repository/commons as well as a sequestered commons for performance evaluation and benchmarking of algorithms. After de-identification, approximately 80% of the medical images and associated metadata become part of the open commons and 20% are sequestered from the open commons. To ensure that both commons are representative of the population available, we introduced a stratified sampling method to balance the demographic characteristics across the two datasets. Approach: Our method uses multi-dimensional stratified sampling where several demographic variables of interest are sequentially used to separate the data into individual strata, each representing a unique combination of variables. Within each resulting stratum, patients are assigned to the open or sequestered commons. This algorithm was used on an example dataset containing 5000 patients using the variables of race, age, sex at birth, ethnicity, COVID-19 status, and image modality and compared resulting demographic distributions to naïve random sampling of the dataset over 2000 independent trials. Results: Resulting prevalence of each demographic variable matched the prevalence from the input dataset within one standard deviation. Mann-Whitney U test results supported the hypothesis that sequestration by stratified sampling provided more balanced subsets than naïve randomization, except for demographic subcategories with very low prevalence. Conclusions: The developed multi-dimensional stratified sampling algorithm can partition a large dataset while maintaining balance across several variables, superior to the balance achieved from naïve randomization.

An analysis of the regional heterogeneity in tissue elasticity in lung cancer patients with COPD.

Purpose: Recent 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. Methods: We 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. Results: The 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. Conclusion: We 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.

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.

Radiation exposure from CT scans: how to close our knowledge gaps, monitor and safeguard exposure--proceedings and recommendations of the Radiation Dose Summit, sponsored by NIBIB, February 24-25, 2011.

This article summarizes the proceedings of a portion of the Radiation Dose Summit, which was organized by the National Institute of Biomedical Imaging and Bioengineering and held in Bethesda, Maryland, in February 2011. The current understandings of ways to optimize the benefit-risk ratio of computed tomography (CT) examinations are summarized and recommendations are made for priority areas of research to close existing gaps in our knowledge. The prospects of achieving a submillisievert effective dose CT examination routinely are assessed.

American Thoracic Society documents: an official American Thoracic Society/Society of Thoracic Radiology Clinical Practice Guideline--Evaluation of Suspected Pulmonary Embolism in Pregnancy.

BACKGROUND: Pulmonary embolism (PE) is a leading cause of maternal mortality in the developed world. Along with appropriate prophylaxis and therapy, prevention of death from PE in pregnancy requires a high index of clinical suspicion followed by a timely and accurate diagnostic approach. METHODS: To provide guidance on this important health issue, a multidisciplinary panel of major medical stakeholders was convened to develop evidence-based guidelines for evaluation of suspected pulmonary embolism in pregnancy using the Grades of Recommendation, Assessment, Development, and Evaluation (GRADE) system. In formulation of the recommended diagnostic algorithm, the important outcomes were defined to be diagnostic accuracy and diagnostic yield; the panel placed a high value on minimizing cumulative radiation dose when determining the recommended sequence of tests. RESULTS: Overall, the quality of the underlying evidence for all recommendations was rated as very low or low with some of the evidence considered for recommendations extrapolated from studies of the general population. Despite the low quality evidence, strong recommendations were made for three specific scenarios: performance of chest radiography (CXR) as the first radiation-associated procedure; use of lung scintigraphy as the preferred test in the setting of a normal CXR; and performance of computed-tomographic pulmonary angiography (CTPA) rather than digital subtraction angiography (DSA) in a pregnant woman with a nondiagnostic ventilation-perfusion (V/Q) result. DISCUSSION: The recommendations presented in this guideline are based upon the currently available evidence; availability of new clinical research data and development and dissemination of new technologies will necessitate a revision and update.

Reproducibility of volume and densitometric measures of emphysema on repeat computed tomography with an interval of 1 week.

OBJECTIVES: The reproducibilities of CT lung volume and densitometric measures of emphysema were assessed over 1 week. The influence of breathhold on reproducibility was assessed. METHODS: HRCT was performed on 44 subjects at inspiration on two visits with a 7-day interval. CT lung volume, relative area below -950HU (RA950-raw), and 15th percentile density (PD15-raw) were computed. Volume correction was used to obtain RA950-adj and PD15-adj. Reproducibilities between visits were assessed using concordance correlation coefficient (CCC) and repeatability coefficient (RC). Reproducibilities were compared between raw and adjusted measures. Differences between visits were computed for volume and density measures. Correlations were computed for density differences versus volume difference. Subgroup analysis was performed using a 0.25 L volume difference threshold. RESULTS: High CCC were observed for all measures in full group (CCC > 0.97). Reproducibilities of volume (RC = 0.67 L), RA950-raw (RC = 2.3%), and PD15-raw (RC = 10.6HU) were observed. Volume correction significantly improved PD15 (RC = 3.6HU) but not RA950 (RC = 1.7%). RA950-raw and PD15-raw had significantly better RC in <0.25 L subgroup than ≥0.25 L. Significant correlations with volume were observed for RA950-raw and PD15-raw (R (2) > 0.71), but not RA950-adj or PD15-adj (R (2) < 0.11). CONCLUSIONS: Good breathhold and RA950 reproducibilities were achieved. PD15 was less reproducible but improved with volume correction or superior breathhold reproduction. KEY POINTS: • Good breath-hold reproducibility is achievable between multiple CT examinations. • Reproducibility of densitometric measures may be improved by statistical volume correction. • Volume correction may result in decreased signal. • Densitometric reproducibility may also be improved by achieving good breath-hold reproduction. • Careful consideration of signal and noise is necessary in reproducibility assessment.

Emphysema lung lobe volume reduction: effects on the ipsilateral and contralateral lobes.

OBJECTIVES: To investigate volumetric and density changes in the ipsilateral and contralateral lobes following volume reduction of an emphysematous target lobe. METHODS: The study included 289 subjects with heterogeneous emphysema, who underwent bronchoscopic volume reduction of the most diseased lobe with endobronchial valves and 132 untreated controls. Lobar volume and low-attenuation relative area (RA) changes post-procedure were measured from computed tomography images. Regression analysis (Spearman's rho) was performed to test the association between change in the target lobe volume and changes in volume and density variables in the other lobes. RESULTS: The target lobe volume at full inspiration in the treatment group had a mean reduction of -0.45 L (SE = 0.034, P < 0.0001), and was associated with volume increases in the ipsilateral lobe (rho = -0.68, P < 0.0001) and contralateral lung (rho = -0.16, P = 0.006), and overall reductions in expiratory RA (rho = 0.31, P < 0.0001) and residual volume (RV)/total lung capacity (TLC) (rho = 0.13, P = 0.03). CONCLUSIONS: When the volume of an emphysematous target lobe is reduced, the volume is redistributed primarily to the ipsilateral lobe, with an overall reduction. Image-based changes in lobar volumes and densities indicate that target lobe volume reduction is associated with statistically significant overall reductions in air trapping, consistent with expansion of the healthier lung. KEY POINTS: Computed tomography allows assessment of the treatment of emphysema with endobronchial valves. • Endobronchial valves can reduce the volume of an emphysematous lung lobe. • Compensatory expansion is greater in ipsilateral lobes than in the contralateral lung. • Reduced air trapping is measurable by RV/TLC and smaller low attenuation area.