Setting up regional diagnostic reference levels for pediatric computed tomography in Latin America: preliminary results, challenges and the work ahead.

We established a framework for collecting radiation doses for head, chest and abdomen-pelvis computed tomography (CT) in children scanned at multiple imaging sites across Latin America with an aim towards establishing diagnostic reference levels (DRLs) and achievable doses (ADs) in pediatric CT in Latin America. Our study included 12 Latin American sites (in Argentina, Bolivia, Brazil, Chile, Colombia, Ecuador, Honduras and Panama) contributing data on the four most common pediatric CT examinations (non-contrast head, non-contrast chest, post-contrast chest and post-contrast abdomen-pelvis). Sites contributed data on patients' age, sex and weight, scan factors (tube current and potential), volume CT dose index (CTDIvol) and dose length product (DLP). Data were verified, leading to the exclusion of two sites with missing or incorrect data entries. We estimated overall and site-specific 50th (AD) and 75th (diagnostic reference level [DRL]) percentile CTDIvol and DLP for each CT protocol. Non-normal data were compared using the Kruskal-Wallis test. Sites contributed data from 3,934 children (1,834 females) for different CT exams (head CT 1,568/3,934, 40%; non-contrast chest CT 945/3,934, 24%; post-contrast chest CT 581/3,934, 15%; abdomen-pelvis CT 840/3,934, 21%). There were significant statistical differences in 50th and 75th percentile CTDIvol and DLP values across the participating sites (P<0.001). The 50th and 75th percentile doses for most CT protocols were substantially higher than the corresponding doses reported from the United States of America. Our study demonstrates substantial disparities and variations in pediatric CT examinations performed in multiple sites in Latin America. We will use the collected data to improve scan protocols and perform a follow-up CT study to establish DRLs and ADs based on clinical indications.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

Low concordance between CTPA and echocardiography in identification of right ventricular strain in PERT patients with acute pulmonary embolism.

PURPOSE: Right ventricular strain (RVS) is used to risk stratify patients with acute pulmonary embolism (PE) and influence treatment decisions. Guidelines suggest that either computed tomography pulmonary angiography (CTPA) or transthoracic echocardiography (TTE) can be used to assess RVS. We sought to determine how often CTPA and TTE yield discordant results and to assess the test characteristics of CTPA compared to TTE. METHODS: We analyzed data from a single-center registry of PE cases severe enough to warrant activation of the hospital's Pulmonary Embolism Response Team (PERT). We defined RVS as a right ventricular to left ventricular ratio (RV/LV) ≥ 1 or radiologist's interpretation of RVS on CTPA or as the presence of either RV dilation, hypokinesis, or septal bowing on TTE. RESULTS: We included 554 patients in our analysis, of whom 333 (60%) had concordant RVS findings on CTPA and TTE. Using TTE as the reference standard, CTPA had a sensitivity of 95% (95% CI 92-97%) and a specificity of 4% (95% CI 2-8%) for identifying RVS. CONCLUSIONS: In a selected population of patients with acute PE for which PERT was activated, CTPA is highly sensitive but not specific for the detection of RVS when compared to TTE.

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 Countries in the IAEA Study.

Background There is lack of guidance on specific CT protocols for imaging patients with coronavirus disease 2019 (COVID-19) pneumonia. Purpose To assess international variations in CT utilization, protocols, and radiation doses in patients with COVID-19 pneumonia. Materials and Methods In this retrospective data collection study, the International Atomic Energy Agency coordinated a survey between May and July 2020 regarding CT utilization, protocols, and radiation doses from 62 health care sites in 34 countries across five continents for CT examinations performed in patients with COVID-19 pneumonia. The questionnaire obtained information on local prevalence, method of diagnosis, most frequent imaging, indications for CT, and specific policies on use of CT in COVID-19 pneumonia. Collected data included general information (patient age, weight, clinical indication), CT equipment (CT make and model, year of installation, number of detector rows), scan protocols (body region, scan phases, tube current and potential), and radiation dose descriptors (CT dose index and dose length product). Descriptive statistics and generalized estimating equations were performed. Results Data from 782 patients (median age, 59 years [interquartile range, 15 years]) from 54 health care sites in 28 countries were evaluated. Less than one-half of the health care sites used CT for initial diagnosis of COVID-19 pneumonia and three-fourths used CT for assessing disease severity. CT dose index varied based on CT vendors (7-11 mGy; P < .001), number of detector rows (8-9 mGy; P < .001), year of CT installation (7-10 mGy; P = .006), and reconstruction techniques (7-10 mGy; P = .03). Multiphase chest CT examinations performed at 20% of sites (11 of 54) were associated with higher dose length product compared with single-phase chest CT examinations performed in 80% of sites (43 of 54) (P = .008). Conclusion CT use, scan protocols, and radiation doses in patients with coronavirus disease 2019 pneumonia showed wide variation across health care sites within the same and between different countries. Many patients were imaged multiple times and/or with multiphase CT scan protocols. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Lee in this issue.

Five-star rating system for acceptable quality and dose in CT.

KEY POINTS: • Recent papers have shown examples of the methodology involved in integrating image quality with radiation dose and assessing acceptable quality dose (AQD).• As a further step in that direction, translating a 5-point score into a 5-star rating shall be helpful for wider and uniform application and shall be in line with the popular use of the 5-star rating.

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset.

OBJECTIVE: Assess if deep learning-based artificial intelligence (AI) algorithm improves reader performance for lung cancer detection on chest X-rays (CXRs). METHODS: This reader study included 173 images from cancer-positive patients (n = 98) and 346 images from cancer-negative patients (n = 196) selected from National Lung Screening Trial (NLST). Eight readers, including three radiology residents, and five board-certified radiologists, participated in the observer performance test. AI algorithm provided image-level probability of pulmonary nodule or mass on CXRs and a heatmap of detected lesions. Reader performance was compared with AUC, sensitivity, specificity, false-positives per image (FPPI), and rates of chest CT recommendations. RESULTS: With AI, the average sensitivity of readers for the detection of visible lung cancer increased for residents, but was similar for radiologists compared to that without AI (0.61 [95% CI, 0.55-0.67] vs. 0.72 [95% CI, 0.66-0.77], p = 0.016 for residents, and 0.76 [95% CI, 0.72-0.81] vs. 0.76 [95% CI, 0.72-0.81, p = 1.00 for radiologists), while false-positive findings per image (FPPI) was similar for residents, but decreased for radiologists (0.15 [95% CI, 0.11-0.18] vs. 0.12 [95% CI, 0.09-0.16], p = 0.13 for residents, and 0.24 [95% CI, 0.20-0.29] vs. 0.17 [95% CI, 0.13-0.20], p < 0.001 for radiologists). With AI, the average rate of chest CT recommendation in patients positive for visible cancer increased for residents, but was similar for radiologists (54.7% [95% CI, 48.2-61.2%] vs. 70.2% [95% CI, 64.2-76.2%], p < 0.001 for residents and 72.5% [95% CI, 68.0-77.1%] vs. 73.9% [95% CI, 69.4-78.3%], p = 0.68 for radiologists), while that in cancer-negative patients was similar for residents, but decreased for radiologists (11.2% [95% CI, 9.6-13.1%] vs. 9.8% [95% CI, 8.0-11.6%], p = 0.32 for residents and 16.4% [95% CI, 14.7-18.2%] vs. 11.7% [95% CI, 10.2-13.3%], p < 0.001 for radiologists). CONCLUSIONS: AI algorithm can enhance the performance of readers for the detection of lung cancers on chest radiographs when used as second reader. KEY POINTS: • Reader study in the NLST dataset shows that AI algorithm had sensitivity benefit for residents and specificity benefit for radiologists for the detection of visible lung cancer. • With AI, radiology residents were able to recommend more chest CT examinations (54.7% vs 70.2%, p < 0.001) for patients with visible lung cancer. • With AI, radiologists recommended significantly less proportion of unnecessary chest CT examinations (16.4% vs. 11.7%, p < 0.001) in cancer-negative patients.

Multicenter Assessment of CT Pneumonia Analysis Prototype for Predicting Disease Severity and Patient Outcome.

To perform a multicenter assessment of the CT Pneumonia Analysis prototype for predicting disease severity and patient outcome in COVID-19 pneumonia both without and with integration of clinical information. Our IRB-approved observational study included consecutive 241 adult patients (> 18 years; 105 females; 136 males) with RT-PCR-positive COVID-19 pneumonia who underwent non-contrast chest CT at one of the two tertiary care hospitals (site A: Massachusetts General Hospital, USA; site B: Firoozgar Hospital Iran). We recorded patient age, gender, comorbid conditions, laboratory values, intensive care unit (ICU) admission, mechanical ventilation, and final outcome (recovery or death). Two thoracic radiologists reviewed all chest CTs to record type, extent of pulmonary opacities based on the percentage of lobe involved, and severity of respiratory motion artifacts. Thin-section CT images were processed with the prototype (Siemens Healthineers) to obtain quantitative features including lung volumes, volume and percentage of all-type and high-attenuation opacities (≥ -200 HU), and mean HU and standard deviation of opacities within a given lung region. These values are estimated for the total combined lung volume, and separately for each lung and each lung lobe. Multivariable analyses of variance (MANOVA) and multiple logistic regression were performed for data analyses. About 26% of chest CTs (62/241) had moderate to severe motion artifacts. There were no significant differences in the AUCs of quantitative features for predicting disease severity with and without motion artifacts (AUC 0.94-0.97) as well as for predicting patient outcome (AUC 0.7-0.77) (p > 0.5). Combination of the volume of all-attenuation opacities and the percentage of high-attenuation opacities (AUC 0.76-0.82, 95% confidence interval (CI) 0.73-0.82) had higher AUC for predicting ICU admission than the subjective severity scores (AUC 0.69-0.77, 95% CI 0.69-0.81). Despite a high frequency of motion artifacts, quantitative features of pulmonary opacities from chest CT can help differentiate patients with favorable and adverse outcomes.

Multiplatform, Non-Breath-Hold Fast Scanning Protocols: Should We Stop Giving Breath-Hold Instructions for Routine Chest CT? [Formula: see text].

OBJECTIVE: We assessed if non-breath-hold (NBH) fast scanning protocol can provide respiratory motion-free images for interpretation of chest computed tomography (CT). MATERIALS AND METHODS: In our 2-phase project, we first collected baseline data on frequency of respiratory motion artifacts on breath-hold chest CT in 826 adult patients. The second phase included 62 patients (mean age 66 ± 15 years; 21 females, 41 males) who underwent an NBH chest CT on either single-source (n = 32) or dual-source (n = 30) multidetector-row CT scanners. Clinical indications for chest CT, reason for using NBH CT, scanner type, scan duration, and radiation dose (CT dose index volume, dose length product) were recorded. Two thoracic radiologists (R1 and R2) independently graded respiratory motion artifacts (1 = no respiratory motion artifacts with unrestricted evaluation; 2 = minor motion artifacts limited to one lung lobe or less with good diagnostic quality; 3 = moderate motion artifacts limited to 2 to 3 lung lobes but adequate for clinical diagnosis; 4 = poor evaluability or unevaluable from severe motion artifacts; and 5 = limited quality due to other causes like high noise, beam hardening, or metallic artifacts), and recorded pulmonary and mediastinal findings. Descriptive analyses, Cohen κ test for interobserver agreement, and Student t test were performed for statistical analysis. RESULTS: No NBH chest CT were deemed uninterpretable by either radiologist; most NBH CT (R1-59 of 62, 95%; R2-62 of 62, 100%) had no or minimal motion artifacts. Only 3 of 62 (R1) NBH chest CT had motion artifacts limiting diagnostic evaluation for lungs but not in the mediastinum. CONCLUSION: Non-breath-hold fast protocol enables acquisition of diagnostic quality chest CT free of respiratory motion artifacts in patients who cannot hold their breath.

Comparison of Baseline, Bone-Subtracted, and Enhanced Chest Radiographs for Detection of Pneumothorax.

PURPOSE: To assess and compare detectability of pneumothorax on unprocessed baseline, single-energy, bone-subtracted, and enhanced frontal chest radiographs (chest X-ray, CXR). METHOD AND MATERIALS: Our retrospective institutional review board-approved study included 202 patients (mean age 53 ± 24 years; 132 men, 70 women) who underwent frontal CXR and had trace, moderate, large, or tension pneumothorax. All patients (except those with tension pneumothorax) had concurrent chest computed tomography (CT). Two radiologists reviewed the CXR and chest CT for pneumothorax on baseline CXR (ground truth). All baseline CXR were processed to generate bone-subtracted and enhanced images (ClearRead X-ray). Four radiologists (R1-R4) assessed the baseline, bone-subtracted, and enhanced images and recorded the presence of pneumothorax (side, size, and confidence for detection) for each image type. Area under the curve (AUC) was calculated with receiver operating characteristic analyses to determine the accuracy of pneumothorax detection. RESULTS: Bone-subtracted images (AUC: 0.89-0.97) had the lowest accuracy for detection of pneumothorax compared to the baseline (AUC: 0.94-0.97) and enhanced (AUC: 0.96-0.99) radiographs (P < .01). Most false-positive and false-negative pneumothoraces were detected on the bone-subtracted images and the least numbers on the enhanced radiographs. Highest detection rates and confidence were noted for the enhanced images (empiric AUC for R1-R4 0.96-0.99). CONCLUSION: Enhanced CXRs are superior to bone-subtracted and unprocessed radiographs for detection of pneumothorax. CLINICAL RELEVANCE/APPLICATION: Enhanced CXRs improve detection of pneumothorax over unprocessed images; bone-subtracted images must be cautiously reviewed to avoid false negatives.

Use of Multiphase CT Protocols in 18 Countries: Appropriateness and Radiation Doses.

PURPOSE: To assess the frequency, appropriateness, and radiation doses associated with multiphase computed tomography (CT) protocols for routine chest and abdomen-pelvis examinations in 18 countries. MATERIALS AND METHODS: In collaboration with the International Atomic Energy Agency, multi-institutional data on clinical indications, number of scan phases, scan parameters, and radiation dose descriptors (CT dose-index volume; dose-length product [DLP]) were collected for routine chest (n = 1706 patients) and abdomen-pelvis (n = 426 patients) CT from 18 institutions in Asia, Africa, and Europe. Two radiologists scored the need for each phase based on clinical indications (1 = not indicated, 2 = probably indicated, 3 = indicated). We surveyed 11 institutions for their practice regarding single-phase and multiphase CT examinations. Data were analyzed with the Student t test. RESULTS: Most institutions use multiphase protocols for routine chest (10/18 institutions) and routine abdomen-pelvis (10/11 institutions that supplied data for abdomen-pelvis) CT examinations. Most institutions (10/11) do not modify scan parameters between different scan phases. Respective total DLP for 1-, 2-, and 3-phase routine chest CT was 272, 518, and 820 mGy·cm, respectively. Corresponding values for 1- to 5-phase routine abdomen-pelvis CT were 400, 726, 1218, 1214, and 1458 mGy cm, respectively. For multiphase CT protocols, there were no differences in scan parameters and radiation doses between different phases for either chest or abdomen-pelvis CT (P = 0.40-0.99). Multiphase CT examinations were unnecessary in 100% of routine chest CT and in 63% of routine abdomen-pelvis CT examinations. CONCLUSIONS: Multiphase scan protocols for the routine chest and abdomen-pelvis CT examinations are unnecessary, and their use increases radiation dose.

Chest CT practice and protocols for COVID-19 from radiation dose management perspective.

The global pandemic of coronavirus disease 2019 (COVID-19) has upended the world with over 6.6 million infections and over 391,000 deaths worldwide. Reverse-transcription polymerase chain reaction (RT-PCR) assay is the preferred method of diagnosis of COVID-19 infection. Yet, chest CT is often used in patients with known or suspected COVID-19 due to regional preferences, lack of availability of PCR assays, and false-negative PCR assays, as well as for monitoring of disease progression, complications, and treatment response. The International Atomic Energy Agency (IAEA) organized a webinar to discuss CT practice and protocol optimization from a radiation protection perspective on April 9, 2020, and surveyed participants from five continents. We review important aspects of CT in COVID-19 infection from the justification of its use to specific scan protocols for optimizing radiation dose and diagnostic information.Key Points• Chest CT provides useful information in patients with moderate to severe COVID-19 pneumonia.• When indicated, chest CT in most patients with COVID-19 pneumonia must be performed with non-contrast, low-dose protocol.• Although chest CT has high sensitivity for diagnosis of COVID-19 pneumonia, CT findings are non-specific and overlap with other viral infections including influenza and H1N1.

Quantitative lobar pulmonary perfusion assessment on dual-energy CT pulmonary angiography: applications in pulmonary embolism.

PURPOSE: To assess quantitative lobar pulmonary perfusion on DECT-PA in patients with and without pulmonary embolism (PE). MATERIALS AND METHODS: Our retrospective study included 88 adult patients (mean age 56 ± 19 years; 38 men, 50 women) who underwent DECT-PA (40 PE present; 48 PE absent) on a 384-slice, third-generation, dual-source CT. All DECT-PA examinations were reviewed to record the presence and location of occlusive and non-occlusive PE. Transverse thin (1 mm) DECT images (80/150 kV) were de-identified and exported offline for processing on a stand-alone deep learning-based prototype for automatic lung lobe segmentation and to obtain the mean attenuation numbers (in HU), contrast amount (in mg), and normalized iodine concentration per lung and lobe. The zonal volumes and mean enhancement were obtained from the Lung Analysis™ application. Data were analyzed with receiver operating characteristics (ROC) and analysis of variance (ANOVA). RESULTS: The automatic lung lobe segmentation was accurate in all DECT-PA (88; 100%). Both lobar and zonal perfusions were significantly lower in patients with PE compared with those without PE (p < 0.0001). The mean attenuation numbers, contrast amounts, and normalized iodine concentrations in different lobes were significantly lower in the patients with PE compared with those in the patients without PE (AUC 0.70-0.78; p < 0.0001). Patients with occlusive PE had significantly lower quantitative perfusion compared with those without occlusive PE (p < 0.0001). CONCLUSION: The deep learning-based prototype enables accurate lung lobe segmentation and assessment of quantitative lobar perfusion from DECT-PA. KEY POINTS: • Deep learning-based prototype enables accurate lung lobe segmentation and assessment of quantitative lobar perfusion from DECT-PA. • Quantitative lobar perfusion parameters (AUC up to 0.78) have a higher predicting presence of PE on DECT-PA examinations compared with the zonal perfusion parameters (AUC up to 0.72). • The lobar-normalized iodine concentration has the highest AUC for both presence of PE and for differentiating occlusive and non-occlusive PE.

Semiautomatic Segmentation and Radiomics for Dual-Energy CT: A Pilot Study to Differentiate Benign and Malignant Hepatic Lesions.

OBJECTIVE. This study assessed a machine learning-based dual-energy CT (DECT) tumor analysis prototype for semiautomatic segmentation and radiomic analysis of benign and malignant liver lesions seen on contrast-enhanced DECT. MATERIALS AND METHODS. This institutional review board-approved study included 103 adult patients (mean age, 65 ± 15 [SD] years; 53 men, 50 women) with benign (60/103) or malignant (43/103) hepatic lesions on contrast-enhanced dual-source DECT. Most malignant lesions were histologically proven; benign lesions were either stable on follow-up CT or had characteristic benign features on MRI. Low- and high-kilovoltage datasets were deidentified, exported offline, and processed with the DECT tumor analysis for semiautomatic segmentation of the volume and rim of each liver lesion. For each segmentation, contrast enhancement and iodine concentrations as well as radiomic features were derived for different DECT image series. Statistical analyses were performed to determine if DECT tumor analysis and radiomics can differentiate benign from malignant liver lesions. RESULTS. Normalized iodine concentration and mean iodine concentration in the benign and malignant lesions were significantly different (p < 0.0001-0.0084; AUC, 0.695-0.856). Iodine quantification and radiomic features from lesion rims (AUC, ≤ 0.877) had higher accuracy for differentiating liver lesions compared with the values from lesion volumes (AUC, ≤ 0.856). There was no difference in the accuracies of DECT iodine quantification (AUC, 0.91) and radiomics (AUC, 0.90) for characterizing liver lesions. CONCLUSION. DECT radiomics were more accurate than iodine quantification for differentiating solid benign and malignant hepatic lesions.

Reducing Radiation Dose and Contrast Medium Volume With Application of Dual-Energy CT in Children and Young Adults.

OBJECTIVE. The purpose of this study was to assess if dual-source dual-energy CT (DS-DECT) can be used with lower radiation doses and contrast material volumes than single-energy CT (SECT) in children and young adults. MATERIALS AND METHODS. This retrospective study included 85 consecutive children and young adults (age range, 1 month old to 19 years old; 81 male, 70 female) who underwent contrast-enhanced DS-DECT of the chest (n = 41) or the abdomen and pelvis (n = 44) on second- or third-generation dual-source CT scanners (Somatom Flash or Force, Siemens Healthineers) for clinically indicated reasons. We included 66 age-, sex-, body region-, and weight-matched patients who underwent SECT on the same scanner. Patients were scanned with either SECT (with automatic exposure control using both CARE kV [Siemens Healthineers] and CARE Dose 4D [Siemens Healthineers]) or DS-DECT (with CARE Dose 4D). Two pediatric radiologists assessed clinical indications, radiologic findings, image quality, and any study limitations (noise or artifacts). Patient demographics (age, sex, weight), scan parameters (tube voltage, tube current-time product, pitch, section thickness), CT dose descriptors (volume CT dose index, dose-length product, size-specific dose estimate [SSDE]), and contrast material volume were recorded. Descriptive statistics, paired t test, and Cohen kappa test were performed. RESULTS. Mean patient ages and weights ± SD in DS-DECT (10 ± 6 years old, 38 ± 23 kg) and SECT (11 ± 7 years old, 43 ± 29 kg) groups were not significantly different (p > 0.05). Respective SSDEs for chest DS-DECT (4.0 ± 2.1 mGy), chest SECT (6.1 ± 4.4 mGy), abdomen-pelvis DS-DECT (5.0 ± 5.0 mGy), and abdomen-pelvis SECT (8.3 ± 4.0 mGy) were significantly different (p = 0.003-0.005). Contrast material volume for DS-DECT examinations was 19-22% lower compared with the weight- and body region-matched scans obtained with SECT. Image quality of DECT was acceptable in all patients. CONCLUSION. In children and young adults, chest and abdomen-pelvis DS-DECT enables substantial radiation dose and contrast volume reductions compared with weight- and region-matched SECT.

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

OBJECTIVE. The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. MATERIALS AND METHODS. Our prospective multiinstitutional study included 59 adult patients (33 women, 26 men; mean age ± SD, 65 ± 12 years old; mean body mass index [weight in kilograms divided by the square of height in meters] = 27 ± 5) who underwent routine chest (n = 22; 16 women, six men) and abdominopelvic (n = 37; 17 women, 20 men) CT on a 640-MDCT scanner (Aquilion ONE, Canon Medical Systems). All patients gave written informed consent for the acquisition of low-dose (LD) CT (LDCT) after a clinically indicated standard-dose (SD) CT (SDCT). The SDCT series (120 kVp, 164-644 mA) were reconstructed with interactive reconstruction (IR) (adaptive iterative dose reduction [AIDR] 3D, Canon Medical Systems), and the LDCT (100 kVp, 120 kVp; 30-50 mA) were reconstructed with filtered back-projection (FBP), IR (AIDR 3D and forward-projected model-based iterative reconstruction solution [FIRST], Canon Medical Systems), and deep learning reconstruction (DLR) (Advanced Intelligent Clear-IQ Engine [AiCE], Canon Medical Systems). Four subspecialty-trained radiologists first read all LD image sets and then compared them side-by-side with SD AIDR 3D images in an independent, randomized, and blinded fashion. Subspecialty radiologists assessed image quality of LDCT images on a 3-point scale (1 = unacceptable, 2 = suboptimal, 3 = optimal). Descriptive statistics were obtained, and the Wilcoxon sign rank test was performed. RESULTS. Mean volume CT dose index and dose-length product for LDCT (2.1 ± 0.8 mGy, 49 ± 13mGy·cm) were lower than those for SDCT (13 ± 4.4 mGy, 567 ± 249 mGy·cm) (p < 0.0001). All 31 clinically significant abdominal lesions were seen on SD AIDR 3D and LD DLR images. Twenty-five, 18, and seven lesions were detected on LD AIDR 3D, LD FIRST, and LD FBP images, respectively. All 39 pulmonary nodules detected on SD AIDR 3D images were also noted on LD DLR images. LD DLR images were deemed acceptable for interpretation in 97% (35/37) of abdominal and 95-100% (21-22/22) of chest LDCT studies (p = 0.2-0.99). The LD FIRST, LD AIDR 3D, and LD FBP images had inferior image quality compared with SD AIDR 3D images (p < 0.0001). CONCLUSION. At submillisievert chest and abdominopelvic CT doses, DLR enables image quality and lesion detection superior to commercial IR and FBP images.

Can Dual-Energy Computed Tomography Quantitative Analysis and Radiomics Differentiate Normal Liver From Hepatic Steatosis and Cirrhosis?

OBJECTIVES: This study aimed to assess if dual-energy computed tomography (DECT) quantitative analysis and radiomics can differentiate normal liver, hepatic steatosis, and cirrhosis. MATERIALS AND METHODS: Our retrospective study included 75 adult patients (mean age, 54 ± 16 years) who underwent contrast-enhanced, dual-source DECT of the abdomen. We used Dual-Energy Tumor Analysis prototype for semiautomatic liver segmentation and DECT and radiomic features. The data were analyzed with multiple logistic regression and random forest classifier to determine area under the curve (AUC). RESULTS: Iodine quantification (AUC, 0.95) and radiomic features (AUC, 0.97) differentiate between healthy and abnormal liver. Combined fat ratio percent and mean mixed CT values (AUC, 0.99) were the strongest differentiators of healthy and steatotic liver. The most accurate differentiating parameters of normal liver and cirrhosis were a combination of first-order statistics (90th percentile), gray-level run length matrix (short-run low gray-level emphasis), and gray-level size zone matrix (gray-level nonuniformity normalized; AUC, 0.99). CONCLUSION: Dual-energy computed tomography iodine quantification and radiomics accurately differentiate normal liver from steatosis and cirrhosis from single-section analyses.

Computed Tomography Radiomics Can Predict Disease Severity and Outcome in Coronavirus Disease 2019 Pneumonia.

PURPOSE: This study aimed to assess if computed tomography (CT) radiomics can predict the severity and outcome of patients with coronavirus disease 2019 (COVID-19) pneumonia. METHODS: This institutional ethical board-approved study included 92 patients (mean age, 59 ± 17 years; 57 men, 35 women) with positive reverse transcription polymerase chain reaction assay for COVID-19 infection who underwent noncontrast chest CT. Two radiologists evaluated all chest CT examinations and recorded opacity type, distribution, and extent of lobar involvement. Information on symptom duration before hospital admission, the period of hospital admission, presence of comorbid conditions, laboratory data, and outcomes (recovery or death) was obtained from the medical records. The entire lung volume was segmented on thin-section Digital Imaging and Communication in Medicine images to derive whole-lung radiomics. Data were analyzed using multiple logistic regression with receiver operator characteristic area under the curve (AUC) as the output. RESULTS: Computed tomography radiomics (AUC, 0.99) outperformed clinical variables (AUC, 0.89) for prediction of the extent of pulmonary opacities related to COVID-19 pneumonia. Type of pulmonary opacities could be predicted with CT radiomics (AUC, 0.77) but not with clinical or laboratory data (AUC, <0.56; P > 0.05). Prediction of patient outcome with radiomics (AUC, 0.85) improved to an AUC of 0.90 with the addition of clinical variables (patient age and duration of presenting symptoms before admission). Among clinical variables, the combination of peripheral capillary oxygen saturation on hospital admission, duration of symptoms, platelet counts, and patient age provided an AUC of 0.81 for predicting patient outcomes. CONCLUSIONS: Radiomics from noncontrast CT reliably predict disease severity (AUC, 0.99) and outcome (AUC, 0.85) in patients with COVID-19 pneumonia.

Viewing Imaging Studies: How Patient Location and Imaging Site Affect Referring Physicians.

The purpose of this study was to assess if clinical indications, patient location, and imaging sites predict the viewing pattern of referring physicians for CT and MR of the head, chest, and abdomen. Our study included 166,953 CT/MR images of head/chest/abdomen in 2016-2017 in the outpatient (OP, n = 83,981 CT/MR), inpatient (IP, n = 51,052), and emergency (ED, n = 31,920) settings. There were 125,329 CT/MR performed in the hospital setting and 41,624 in one of the nine off-campus locations. We extracted information regarding body region (head/chest/abdomen), patient location, and imaging site from the electronic medical records (EPIC). We recorded clinical indications and the number of times referring physicians viewed CT/MR (defined as the number of separate views of imaging in the EPIC). Data were analyzed with the Microsoft SQL and SPSS statistical software. About 33% of IP CT and MR studies are viewed > 6 times compared to 7% for OP and 19% of ED studies (p < 0.001). Conversely, most OP studies (55%) were viewed 1-2 times only, compared to 21% for IP and 38% for ED studies (p < 0.001). In-hospital exams are viewed (≥ 6 views; 39% studies) more frequently than off-campus imaging (≥ 6 views; 17% studies) (p < 0.001). For head CT/MR, certain clinical indications (i.e., stroke) had higher viewing rates compared to other clinical indications such as malignancy, headache, and dizziness. Conversely, for chest CT, dyspnea-hypoxia had much higher viewing rates (> 6 times) in IP (55%) and ED (46%) than in OP settings (22%). Patient location and imaging site regardless of clinical indications have a profound effect on viewing patterns of referring physicians. Understanding viewing patterns of the referring physicians can help guide interpretation priorities and finding communication for imaging exams based on patient location, imaging site, and clinical indications. The information can help in the efficient delivery of patient care.

Radiation Dose for Multiregion CT Protocols: Challenges and Limitations.

OBJECTIVE. The purpose of this study was to devise a method for classification of individual chest and abdomen-pelvis CT doses for multiregion CT. MATERIALS AND METHODS. A retrospective analysis of volume CT dose index (CTDIvol) and dose-length product (DLP) associated with chest (150 adult patients), abdomen-pelvis (150 patients), and multiregion combined chest-abdomen-pelvis CT (210 patients; 60 single-run chest-abdomen-pelvis CT; 150 split-run with separate chest and abdomen-pelvis CT). All 510 CT examinations were performed with one of four MDCT scanners (64-, 64-, 128-, 256-MDCT). CTDIvol, DLP, and scan length were recorded. Scan lengths were obtained for these 510 CT examinations and for an additional 7745 examinations of patients at another institution. Data were analyzed by ANOVA and ROC analysis. RESULTS. The respective DLPs (chest, 258-381 mGy · cm; abdomen-pelvis, 360-433 mGy · cm; single-run chest-abdomen-pelvis, 595-636 mGy · cm) and scan lengths (chest, 31-33 cm; abdomen-pelvis, 45-46 cm; single-run chest-abdomen-pelvis, 63-65 cm) for chest, abdomen-pelvis, and multiregion combined chest-abdomen-pelvis CT were significantly different (p < 0.0001). For split-run, chest-abdomen-pelvis CT, scan lengths and dose indexes for individual body regions were not different from those of single-body-region CT (p > 0.05). ROC analysis of chest and abdomen examinations showed an ideal scan length threshold of 38 cm to differentiate abdomen-pelvis CT from chest CT with accuracy of 97.39% and an AUC of 0.9764. CONCLUSION. Despite interscanner variabilities in CT radiation doses, shorter scan length for chest than for abdomen-pelvis CT enables accurate binning of radiation doses for split-run combined chest-abdomen-pelvis CT.