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PURPOSE Early detection is significant to reduce lung cancer-related death. Computer-aided detection system (CADs) can help radiologists to make an early diagnosis. In this paper, we propose a novel 3D gray density coding feature (3D GDC) and fuse it with extracted geometric features. The fusion feature and random forest are used for benign-malignant pulmonary nodule classification on Chest CT. METHODS First, a dictionary model is created to acquire codebook. It is used to obtain feature descriptors and includes 3D block database (BD) and distance matrix clustering centers. 3D BD is balanced and randomly selecting from benign and malignant pulmonary nodules of training data. Clustering centers is got by clustering the distance matrix, which is the distance between every two blocks in 3D BD. Then, feature descriptor is obtained by coding the pulmonary nodule with codebook, and 3D GDC feature is the result of histogram statistics on feature descriptor. Second, geometric features are extracted for fusion feature. Finally, random forest is performed for benign-malignant pulmonary nodule classification with fusion feature of the 3D gray density coding feature and the geometric features. RESULTS We verify the effectiveness of our method on the public LIDC-IDRI dataset and the private ZSHD dataset. For LIDC-IDRI dataset, compared with other state-of-the-art methods, we achieve more satisfactory results with 93.17 ± 1.94% for accuracy and 97.53 ± 1.62% for AUC. As for private ZSHD dataset, it contains a total of 238 lung nodules from 203 patients. The accuracy and AUC achieved by our method are 90.0% and 93.15%. CONCLUSIONS The results show that our method can provide doctors with more accurate results of benign-malignant pulmonary nodule classification for auxiliary diagnosis, and our method is more interpretable than 3D CNN methods, which can provide doctors with more auxiliary information.

This study was to explore the application value of chest computed tomography (CT) images processed by artificial intelligence (AI) algorithms in the diagnosis of neonatal bronchial pneumonia (NBP). The AI adaptive statistical iterative reconstruction (ASiR) algorithm was adopted to reconstruct the chest CT image to compare and analyze the effect of the reconstruction of CT image under the ASiR algorithm under different preweight and postweight values based on the objective measurement and subjective evaluation. 85 neonates with pneumonia treated in hospital from September 1, 2015, to July 1, 2020, were selected as the research objects to analyze their CT imaging characteristics. Subsequently, the peripheral blood of healthy neonates during the same period was collected, and the levels of C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) were detected. The efficiency of CT examination, CRP, ESR, and combined examination in the diagnosis of NBP was analyzed. The results showed that the subjective quality score, lung window subjective score, and mediastinal window subjective score were the highest after CT image reconstruction when the preweight value of the ASiR algorithm was 50%. After treatment, 79 NBP cases (92.9%) showed ground-glass features in CT images. Compared with the healthy neonates, the levels of CRP and ESR in the peripheral blood of neonates with bronchial pneumonia were much lower ( P < 0.05 ). The accuracy rates of CT examination, CRP examination, ESR examination, CRP + ESR examination, and CRP + ESR + CT examination for the diagnosis of NBP were 80.7%, 75.3%, 75.1%, 80.3%, and 98.6%, respectively. CT technology based on AI algorithm showed high clinical application value in the feature analysis of NBP.

The purpose of the present work was to evaluate performance in pulmonary nodule detection, reading times and patient doses for ultra-low dose computed tomography (ULD-CT), standard dose chest CT (SD-CT), and digital radiography (DR). Pulmonary nodules were simulated in an anthropomorphic lung phantom. Thirty cases, 18 with lesions (45 total lesions of 3–12 mm) and 12 without lesions were acquired for each imaging modality. Three radiologists interpreted the cases in a free-response study. Performance was assessed using the JAFROC figure-of-merit (FOM). Performance was not significantly different between ULD-CT and SD-CT (FOMs: 0.787 vs 0.814; ΔFOM: 0.03), but both CT techniques were superior to DR (FOM: 0.541; ΔFOM: 0.31 and 0.28). Overall, the CT modalities took longer time to interpret than DR. ULD chest CT may serve as an alternative to both SD-CT and conventional radiography, considerably reducing dose in the first case and improving diagnostic accuracy in the second.

Background Radiomics analysis is a newly emerging quantitative image analysis technique. The aim of this study was to extract a radiomics signature from the computed tomography (CT) imaging to determine the infarction onset time in patients with acute middle cerebral artery occlusion (MCAO). Methods A total of 123 patients with acute MCAO in the M1 segment (85 patients in the development cohort and 38 patients in the validation cohort) were enrolled in the present study. Clinicoradiological profiles, including head CT without contrast enhancement and computed tomographic angiography (CTA), were collected. The time from stroke onset (TFS) was classified into two subcategories: ≤ 4.5 h, and > 4.5 h. The middle cerebral artery (MCA) territory on CT images was segmented to extract and score the radiomics features associated with the TFS. In addition, the clinicoradiological factors related to the TFS were identified. Subsequently, a combined model of the radiomics signature and clinicoradiological factors was constructed to distinguish the TFS ≤ 4.5 h. Finally, we evaluated the overall performance of our constructed model in an external validation sample of ischemic stroke patients with acute MCAO in the M1 segment. Results The area under the curve (AUC) of the radiomics signature for discriminating the TFS in the development and validation cohorts was 0.770 (95% confidence interval (CI): 0.665–0.875) and 0.792 (95% CI: 0.633–0.950), respectively. The AUC of the combined model comprised of the radiomics signature, age and ASPECTS on CT in the development and validation cohorts was 0.808 (95% CI: 0.701–0.916) and 0.833 (95% CI: 0.702–0.965), respectively. In the external validation cohort, the AUC of the radiomics signature was 0.755 (95% CI: 0.614–0.897), and the AUC of the combined model was 0.820 (95% CI: 0.712–0.928). Conclusions The CT-based radiomics signature is a valuable tool for discriminating the TFS in patients with acute MCAO in the M1 segment, which may guide the use of thrombolysis therapy in patients with indeterminate stroke onset time.

Background We investigated the performance improvement of physicians with varying levels of chest radiology experience when using a commercially available artificial intelligence (AI)-based computer-assisted detection (CAD) software to detect lung cancer nodules on chest radiographs from multiple vendors. Methods Chest radiographs and their corresponding chest CT were retrospectively collected from one institution between July 2017 and June 2018. Two author radiologists annotated pathologically proven lung cancer nodules on the chest radiographs while referencing CT. Eighteen readers (nine general physicians and nine radiologists) from nine institutions interpreted the chest radiographs. The readers interpreted the radiographs alone and then reinterpreted them referencing the CAD output. Suspected nodules were enclosed with a bounding box. These bounding boxes were judged correct if there was significant overlap with the ground truth, specifically, if the intersection over union was 0.3 or higher. The sensitivity, specificity, accuracy, PPV, and NPV of the readers’ assessments were calculated. Results In total, 312 chest radiographs were collected as a test dataset, including 59 malignant images (59 nodules of lung cancer) and 253 normal images. The model provided a modest boost to the reader’s sensitivity, particularly helping general physicians. The performance of general physicians was improved from 0.47 to 0.60 for sensitivity, from 0.96 to 0.97 for specificity, from 0.87 to 0.90 for accuracy, from 0.75 to 0.82 for PPV, and from 0.89 to 0.91 for NPV while the performance of radiologists was improved from 0.51 to 0.60 for sensitivity, from 0.96 to 0.96 for specificity, from 0.87 to 0.90 for accuracy, from 0.76 to 0.80 for PPV, and from 0.89 to 0.91 for NPV. The overall increase in the ratios of sensitivity, specificity, accuracy, PPV, and NPV were 1.22 (1.14–1.30), 1.00 (1.00–1.01), 1.03 (1.02–1.04), 1.07 (1.03–1.11), and 1.02 (1.01–1.03) by using the CAD, respectively. Conclusion The AI-based CAD was able to improve the ability of physicians to detect nodules of lung cancer in chest radiographs. The use of a CAD model can indicate regions physicians may have overlooked during their initial assessment.

Background Motion artifacts affect the images of coronary calcified plaques. This study utilized convolutional neural networks (CNNs) to classify the motion-contaminated images of moving coronary calcified plaques and to determine the influential factors for the classification performance. Methods Two artificial coronary arteries containing four artificial plaques of different densities were placed on a robotic arm in an anthropomorphic thorax phantom. Each artery moved linearly at velocities ranging from 0 to 60 mm/s. CT examinations were performed with four state-of-the-art CT systems. All images were reconstructed with filtered back projection and at least three levels of iterative reconstruction. Each examination was performed at 100%, 80% and 40% radiation dose. Three deep CNN architectures were used for training the classification models. A five-fold cross-validation procedure was applied to validate the models. Results The accuracy of the CNN classification was 90.2 ± 3.1%, 90.6 ± 3.5%, and 90.1 ± 3.2% for the artificial plaques using Inception v3, ResNet101 and DenseNet201 CNN architectures, respectively. In the multivariate analysis, higher density and increasing velocity were significantly associated with higher classification accuracy (all P P > 0.05). Conclusions The CNN achieved a high accuracy of 90% when classifying the motion-contaminated images into the actual category, regardless of different vendors, velocities, radiation doses, and reconstruction algorithms, which indicates the potential value of using a CNN to correct calcium scores.

BACKGROUND: Low-tube voltage scanning improves CT attenuation value of contrast medium (CM). Thus, we hypothesized that 70 kVp in pediatric abdominal CT angiography (CTA) could be used to reduce both radiation and CM dose and improve patient comfort at the same time. OBJECTIVE: To evaluate the feasibility of using 70 kVp in pediatric abdominal CTA to reduce radiation dose and CM dose and improve patient care for children. MATERIALS AND METHODS: Forty-six children needing abdominal CTA were enrolled in the study group using low-dose scanning protocol with 70 kVp and 0.7–1.1 ml/kg contrast dose, and reconstructed with 50%ASIR-V. They were compared with other 46 children in control group with matching body weight and underwent conventional CT scans with 100 kVp, 1.2–1.8 ml/kg contrast dose and reconstructed using 50%ASIR. Image quality of large vessels was evaluated using a 5-point scale. CT value and standard deviation of descending aorta (Ao) was measured, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. Radiation dose, contrast dose, the maximum injection pressure between the two groups were also compared. RESULTS: Score for displaying large vessels by 70 kVp images was 3.91±0.28, lower than that (4.17±0.38) of the control group (p

Abdominal aortic calcification (AAC) is correlated with cardiovascular outcomes independent of traditional risk factors. Quantification of AAC on computed tomography (CT) has not been standardized. Reconstruction parameters have been shown to impact coronary calcium scores. The aim of our study was to assess the impact of abdominal CT reconstruction parameters, slice thickness (ST), and display field of view (DFOV) on AAC quantitative scoring on abdominal CT examinations.We retrospectively measured AAC on noncontrast CT of 46 patients (mean age, 64.1 years; 35 males) using 5 different reconstruction protocols with a range of ST and DFOV: protocol A, 2.5 mm ST, 35 cm DFOV; protocol B, 2.5 mm ST, 50 cm DFOV; protocol C, 2.5 mm ST, 25 cm DFOV; protocol D, 5 mm ST, 35 cm DFOV; and protocol E: 0.625 mm ST, 35 cm DFOV. The AAC scores from each protocol were compared using concordance correlation coefficient and Bland-Altman agreement analyses.The AAC mean (SD) scores for each protocol were as follows: A, 2022 (2418); B, 2022 (2412); C, 1939 (2310); D, 2220 (2695); and E, 1862 (2234). The AAC mean score differences between protocols and reference protocol A were -0.47, 82.01, -198.94, and 160 for protocols B, C, D, and E, respectively, with differences between protocols C to E statistically significantly different (P0.05). The different protocols showed overall excellent correlation (concordance correlation coefficient,0.9) between AAC scores.Slice thickness and DFOV can impact AAC score measurement. A description of reconstruction parameters is important to allow comparisons across different cohorts.

Objective To quantitatively evaluate computed tomography (CT) parameters of coronavirus disease 2019 (COVID-19) pneumonia an artificial intelligence (AI)-based software in different clinical severity groups during the disease course. Methods From March 11 to April 15, 2020, 51 patients (age, 18-84 years; 28 men) diagnosed and hospitalized with COVID-19 pneumonia with a total of 116 CT scans were enrolled in the study. Patients were divided into mild (n = 12), moderate (n = 31), and severe (n = 8) groups based on clinical severity. An AI-based quantitative CT analysis, including lung volume, opacity score, opacity volume, percentage of opacity, and mean lung density, was performed in initial and follow-up CTs obtained at different time points. Receiver operating characteristic analysis was performed to find the diagnostic ability of quantitative CT parameters for discriminating severe from nonsevere pneumonia. Results In baseline assessment, the severe group had significantly higher opacity score, opacity volume, higher percentage of opacity, and higher mean lung density than the moderate group (all P ≤ 0.001). Through consecutive time points, the severe group had a significant decrease in lung volume (P = 0.006), a significant increase in total opacity score (P = 0.003), and percentage of opacity (P = 0.007). A significant increase in total opacity score was also observed for the mild group (P = 0.011). Residual opacities were observed in all groups. The involvement of more than 4 lobes (sensitivity, 100%; specificity, 65.26%), total opacity score greater than 4 (sensitivity, 100%; specificity, 64.21), total opacity volume greater than 337.4 mL (sensitivity, 80.95%; specificity, 84.21%), percentage of opacity greater than 11% (sensitivity, 80.95%; specificity, 88.42%), total high opacity volume greater than 10.5 mL (sensitivity, 95.24%; specificity, 66.32%), percentage of high opacity greater than 0.8% (sensitivity, 85.71%; specificity, 80.00%) and mean lung density HU greater than -705 HU (sensitivity, 57.14%; specificity, 90.53%) were related to severe pneumonia. Conclusions An AI-based quantitative CT analysis is an objective tool in demonstrating disease severity and can also assist the clinician in follow-up by providing information about the disease course and prognosis according to different clinical severity groups.

There have been remarkable changes in our lives and the way we perceive the world with advances in computing technology. Healthcare sector is evolving with the intervention of the latest computer-driven technology and has made a remarkable change in the diagnosis and treatment of various diseases. Due to many governing factors including air pollution, there is a rapid rise in chest-related diseases and the number of such patients is rising at an alarming rate. In this research work, we have employed machine learning approach for the detecting various chest-related problems using convolutional neural networks (CNN) on an open dataset of chest X-rays. The method has an edge over the traditional approaches for image segmentation including thresholding, k -means clustering, and edge detection. The CNN cannot scan and process the whole image at an instant; it needs to recursively scan small pixel spots until it has scanned the whole image. Spatial transformation layers and VGG19 have been used for the purpose of feature extraction, and ReLU activation function has been employed due to its inherent low complexity and high computation efficiency; finally, stochastic gradient descent has been used as an optimizer. The main advantage of the current method is that it retains the essential features of the image for prediction along with incorporating a considerable dimensional reduction. The model delivered substantial improvement over existing research in terms of precision, f -score, and accuracy of prediction. This model if used precisely can be very effective for healthcare practitioners in determining the thoracic or pneumonic symptoms in the patient at an early stage thus guiding the practitioner to start the treatment immediately leading to fast improvement in the health status of the patient.

Purpose Detecting bladder cancer (BC) in routine CT images is important but is sometimes difficult when cancer is small. We evaluated the ability of 40-keV advanced monoenergetic images to depict BC. Materials and methods Fifty-two patients with a median age of 74 years (range 45–92) who were diagnosed as BC with transurethral resection or cystectomy, were included. They were examined with contrast-enhanced dual-energy CT (DE-CT) and advanced virtual monoenergetic images (40 keV) were reconstructed. For evaluating depictability of BC on 40-keV or virtual-120-kVp images, the difference in CT number between the cancer and bladder wall (BC–BW value) were calculated. We also subjectively assessed depictability of BC in virtual-120-kVp and 40-keV images using a 4-grade Likert scale (3: clear, 0: not visualized). Results In 42 of 52 patients, BC–BW values could be calculated because BC was detected on CT images. The mean BC–BW value at 40 keV was significantly higher than that of virtual 120 kVp [80.5 ± 54 (SD) vs. 11.4 ± 12.5 HU, P P Conclusion The advanced monoenergetic reconstruction technique reconstructed using DE-CT image is useful to depict BC.

The purpose of this study is to evaluate the radiation dose, image quality, and diagnostic accuracy of prospective ECG-gated cardiac CT at 70 kV and adaptive statistical iterative reconstruction (ASIR), with a single source, 512 slice MDCT in the diagnosis of complex congenital heart disease in infants. We retrospectively evaluated 47 infants (ages 1 day to 353 days) with prospective ECG-gated cardiac CT that was performed on a single source 512 slice CT at low tube voltage (70 kV) using a wide detector aperture, adaptive statistical iterative reconstruction algorithm (ASIR), and specific reconstruction software reducing coronary motion artifacts (SnapShot Freeze). All cardiac images were obtained during the first pass of contrast material through the anatomic structures of interest and the targets for the center of the acquisition window were set 45% of the R-R interval during one cardiac cycle without sedation and breath-hold. The median effective dose measured in our study was 0.64 ± 0.16 mSv. The average subjective overall image quality score was 4.34 ± 0.31 (range 3-5). For the determination of objective image quality, Mean Noise (HU), SNR, and CNR values ​​emerged as 20.8, 28.7(for pulmonary artery), and 27.1, respectively. Diagnostic accuracy was 100% for the main purposes for the main clinical indication. During cardiac CT examination, pathologies in addition to cardiac anomalies were found in 9/47 of cases (7 severe airway obstructions,1 posterior diaphragmatic hernia, 1 vertebral anomaly). Prospective ECG-gated cardiac CT scan at 70 kV and ASIR in infants with complex CHD provides low radiation dose (submillisievert) in a single heartbeat with a good objective and subjective image quality. It also provides important benefits in the diagnosis of additional pathology.

Objectives To evaluate the effects of anatomical phantom structure on task-based image quality assessment compared with a uniform phantom background. Methods Two neck phantom types of identical shape were investigated: a uniform type containing 10-mm lesions with 4, 9, 18, 30, and 38 HU contrast to the surrounding area and an anatomically realistic type containing lesions of the same size and location with 10, 18, 30, and 38 HU contrast. Phantom images were acquired at two dose levels (CTDIvol of 1.4 and 5.6 mGy) and reconstructed using filtered back projection (FBP) and adaptive iterative dose reduction 3D (AIDR 3D). Detection accuracy was evaluated by seven radiologists in a 4-alternative forced choice experiment. Results Anatomical phantom structure impaired lesion detection at all lesion contrasts (p p p = 0.375) and was superior to FBP (p p = 0.027 for FBP and 81.1% vs. 73%, p = 0.018 for AIDR 3D). Conclusions A lesion contrast increase from 9 to 30 HU is necessary for similar detectability in anatomical and uniform neck phantom images. Anatomical phantom structure influences task-based assessment of iterative reconstruction and dose effects. Key Points • A lesion contrast increase from 9 to 30 HU is necessary for similar low-contrast detectability in anatomical and uniform neck phantom images. • Phantom background structure influences task-based assessment of iterative reconstruction and dose effects. • Transferability of CT assessment to clinical imaging can be expected to improve as the realism of the test environment increases.

Objective This study investigated the role of radiomics in evaluating the alterations of oncogenic signaling pathways in head and neck cancer. Methods Radiomics features were extracted from 106 enhanced computed tomography images with head and neck squamous cell carcinoma. Support vector machine-recursive feature elimination was used for feature selection. Support vector machine algorithm was used to develop radiomics scores to predict genetic alterations in oncogenic signaling pathways. The performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve. Results The alterations of the Cell Cycle, HIPPO, NOTCH, PI3K, RTK RAS, and TP53 signaling pathways were predicted by radiomics scores. The AUC values of the training cohort were 0.94, 0.91, 0.94, 0.93, 0.87, and 0.93, respectively. The AUC values of the validation cohort were all greater than 0.7. Conclusions Radiogenomics is a new method for noninvasive acquisition of tumor molecular information at the genetic level.

Introduction Pre‐operative templating using digital radiography is an effective method of planning for total hip arthroplasty (THA) and requires a generalised fixed magnification factor (MF) or external calibration markers (ECM). The effect on image magnification when changing source‐to‐image distance (SID), object‐to‐image distance (OID) and different imaging conditions is not well described. This study aims to quantify the range of effects manipulation of radiographic parameters can have on image magnification across different body habitus and imaging conditions. Methods A simple phantom study was performed. A 25 mm ECM was placed at eight different OID values along the anterior–posterior phantom plane at three different SID values and imaging conditions, and X‐rays were obtained. On each radiograph, the ECM was measured using a line calliper tool by three radiographers and recorded. The MF was calculated and recorded. Results The smallest observed image MF was 1.16, for an 8 cm OID, 120 cm SID with the ECM placed within the central ray and the X‐ray detector in bucky underneath the X‐ray table. The largest image MF was 1.40 for a 15 cm OID, 100 cm SID with the X‐ray detector placed underneath an emergency department imaging trolley. Conclusions Digital pre‐operative templating for THA relies on accurate radiographic positioning and is dependent of the patient body habitus, radiographic parameters and imaging conditions selected by the radiographer. The use of appropriately positioned ECMs – placed medially between the patient’s internally rotated legs at the level of the greater trochanter, lowers the potential for magnification inaccuracies., Digital preoperative templating for total hip arthroplasty (THA) relies on accurate radiographic positioning, and is dependent of the patient body habitus, radiographic parameters and imaging conditions selected by the performing radiographer. Based on the findings of this study, we believe that the use of a generalised magnification factor (MF) does not account for deviation of these factors, and hence can be inaccurate. The use of appropriately positioned external calibration markers (ECM) – placed medially between the patient’s internally rotated legs at the level of the greater trochanter, lowers the potential for magnification inaccuracies.

In this study, the optimal monochromatic energy level in dual-energy spectral CT required for imaging coronary stents after percutaneous coronary intervention (PCI) was explored. Thirty-five consecutive patients after PCI were examined using the dual-energy spectral CT imaging mode. The original images were reconstructed at 40–140 keV (10-keV interval) monochromatic levels. The in-stent and out-stent CT values at each monochromatic level were measured to calculate the signal-to-noise ratio(SNR) and contrast-to-noise ratio (CNR) for the vessel and the CT value difference between the in-stent and out-stent lumen (dCT (in–out)), which reflects the artificial CT number increase due to the beam hardening effect caused by the stents. The subjective image quality of the stent and in-stent vessel was evaluated by two radiologists using a 5-point scale. With the increase in energy level, the CT value, SNR, CNR, and dCT (in–out) all decreased. At 80 keV, the mean CT value in-stent reached (345.24 ± 93.43) HU and dCT (in–out) started plateauing. In addition, the subjective image quality of the stents and vessels peaked at 80 keV. The 80 keV monochromatic images are optimal for imaging cardiac patients with stents after PCI, balancing the enhancement and SNR and CNR in the vessels while minimizing the beam hardening artifacts caused by the stents.

Background Angiography-derived physiological assessment of coronary lesions has emerged as an alternative to wire based assessment aiming at less-invasiveness and shorter procedural time as well as cost effectiveness in physiology-guided decision making. However, current available image-derived physiology software have limitations including the requirement of multiple projections and are time consuming.Methods/Design The ReVEAL iFR (Radiographic imaging Validation and EvALuation for Angio-iFR) trial is a multicenter, multicontinental, validation study which aims to validate the diagnostic accuracy of the Angio-iFR medical software device (Philips, San Diego, US) in patients undergoing angiography for Chronic Coronary Syndrome (CCS). The Angio-iFR will enable operators to predict both the iFR and FFR value within a few seconds from a single projection of cine angiography by using a lumped parameter fluid dynamics model. Approximately 440 patients with at least one de-novo 40% to 90% stenosis by visual angiographic assessment will be enrolled in the study. The primary endpoint is the sensitivity and specificity of the iFR and FFR for a given lesion compared to the corresponding invasive measures. The enrollment started in August 2019, and was completed in March 2021.Summary The Angio-iFR system has the potential of simplifying physiological evaluation of coronary stenosis compared with available systems, providing estimates of both FFR and iFR. The ReVEAL iFR study will investigate the predictive performance of the novel Angio-iFR software in CCS patients. Ultimately, based on its unique characteristics, the Angio-iFR system may contribute to improve adoption of functional coronary assessment and the workflow in the catheter laboratory.

To compare image quality and diagnostic accuracy of arterial stenosis in low-dose lower-extremity CT angiography (CTA) between adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) algorithms.46 patients undergoing low-dose lower-extremity CTA were enrolled. Images were reconstructed using ASIR-V (blending factor of 50% (AV-50) and 100% (AV-100)) and DLIR (medium (DL-M), and high (DL-H)). CT values and standard deviation of the aorta, psoas, popliteal artery, popliteal and ankle muscles were measured. The edge-rise distance and edge-rise slope were calculated. The degrees of granularity and edge blurring were assessed using a 5-point scale. The stenosis degrees were measured on the four reconstructions, and their mean square errors against that of digital subtraction angiography were calculated and compared.For both ASIR-V and DLIR, higher reconstruction intensity generated lower noise and higher signal-to-noise ratio and contrast-to-noise ratio values. The standard deviation values in AV-100 images were significantly lower than other reconstructions. The two DLIR image groups had higher edge-rise slope and lower edge-rise distance (DL-M:1.79 ± 0.37 mm and DL-H:1.82 ± 0.38 mmDLIR images had higher image quality ratings with lower image noise and sharper vessel walls in low-dose lower-extremity CTA, and DL-H provides the best overall image quality and highest accuracy in diagnosing artery stenoses.DLIR provides high-quality images with sharper edges compared to ASIR-V during low-dose CTA of lower extremity arteries, and DLIR (high) provides the best overall image quality and highest accuracy in diagnosing artery stenoses among all reconstruction algorithms (ASIR-V and DLIR). ASIR-V with blending factor of 100% has the strongest noise reduction ability among all reconstruction algorithms (ASIR-V and DLIR); however, it generates the most blurred images.

Multi-energy computed tomography is a technology that is being increasingly used in the emergency room (ER) setting and has many applications that can impact patient care, including virtual monoenergetic imaging and material-specific imaging. It is important for radiologists to understand this technology, and how it can be optimally used in the ER setting.

Lung CAD system can provide auxiliary third-party opinions for doctors, improve the accuracy of lung nodule recognition. The selection and fusion of nodule features and the advancement of recognition algorithms are crucial improving lung CAD systems. Based on the HDL model, this paper mainly focuses on the three key algorithms of feature extraction, feature fusion and nodule recognition of lung CAD system. First, CBAM is embedded into VGG16 and VGG19, and feature extraction models AE-VGG16 and AE-VGG19 are constructed, so that the network can pay more attention to the key feature information in nodule description. Then, feature dimensionality reduction based on PCA and feature fusion based on CCA are sequentially performed on the extracted depth features to obtain low-dimensional fusion features. Finally, the fusion features are input into the proposed MKL-SVM-IPSO model based on the improved Particle Swarm Optimization algorithm to speed up the training speed, get the global optimal parameter group. The public dataset LUNA16 was selected for the experiment. The results show that the accuracy of lung nodule recognition of the proposed lung CAD system can reach 99.56%, and the sensitivity and F1-score can reach 99.3% and 0.9965, respectively, which can reduce the possibility of false detection and missed detection of nodules.

This study aimed at implementing practice to build a standardized protocol to test the performance of computer-aided detection (CAD) algorithms for pulmonary nodules. A test dataset was established according to a standardized procedure, including data collection, curation and annotation. Six types of pulmonary nodules were manually annotated as reference standard. Three specific rules to match algorithm output with reference standard were applied and compared. These rules included: (1) “center hit” [whether the center of algorithm highlighted region of interest (ROI) hit the ROI of reference standard]; (2) “center distance” (whether the distance between algorithm highlighted ROI center and reference standard center was below a certain threshold); (3) “area overlap” (whether the overlap between algorithm highlighted ROI and reference standard was above a certain threshold). Performance metrics were calculated and the results were compared among ten algorithms under test (AUTs). The test set currently consisted of CT sequences from 593 patients. Under “center hit” rule, the average recall rate, average precision, and average F1 score of ten algorithms under test were 54.68, 38.19, and 42.39%, respectively. Correspondingly, the results under “center distance” rule were 55.43, 38.69, and 42.96%, and the results under “area overlap” rule were 40.35, 27.75, and 31.13%. Among the six types of pulmonary nodules, the AUTs showed the highest miss rate for pure ground-glass nodules, with an average of 59.32%, followed by pleural nodules and solid nodules, with an average of 49.80 and 42.21%, respectively. The algorithm testing results changed along with specific matching methods adopted in the testing process. The AUTs showed uneven performance on different types of pulmonary nodules. This centralized testing protocol supports the comparison between algorithms with similar intended use, and helps evaluate algorithm performance.

To assess whether the joint application of hybrid iterative reconstruction (HIR) and an adaptive filter (AF) could reduce streak artifacts and improve image quality of neck-and-shoulder computed tomography (CT).This study included 96 patients with suspicious neck lesions who underwent a routine nonenhanced scan on a 64-slice CT scanner. The raw data were reconstructed using four different settings: filtered back projection (FBP), HIR, FBP + AF, and HIR + AF. Regions of interest were manually drawn in erector spine, axillary fat, latissimus dorsi, and dorsal cervical fat. Mean and standard deviation (SD) of the CT number, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were obtained and compared using Wilcoxon signed-rank tests. The qualitative assessments of five factors were compared by two independent investigators.Compared to the other three settings, HIR + AF reduced noise in the area where the streak artifact of the lower neck were most serious (SD; all p ≤ 0.001). The SNR and CNR were improved significantly (all p ≤ 0.001). Compared to the other three settings, HIR + AF showed a significant improvement in CT image quality regarding the visibility of suspicious lesions, the extent of streaking artifacts, noise, soft-tissue contrast, and visualization of small structures (all p ≤ 0.02).The combination of HIR and AF can significantly reduce streaking artifacts and improve image quality in neck-and-shoulder CT imaging.

OBJECTIVES: To compare image quality, radiation dose, and iodine intake of coronary computed tomography angiography (CCTA) acquired by wide-detector using different tube voltages and different concentrations of contrast medium (CM) for overweight patients. MATERIALS AND METHODS: A total of 150 overweight patients (body mass index≥25 kg/m2) who underwent CCTA are enrolled and divided into three groups according to scan protocols namely, group A (120 kVp, 370 mgI/ml CM); group B (100 kVp, 350 mgI/ml CM); and group C (80 kVp, 320 mgI/ml CM). The CT values, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and figure-of-merit (FOM) of all images are calculated. Images are subjectively assessed using a 5-point scale. In addition, the CT dose index volume (CTDIvol) and dose length product (DLP) of each patient are recorded. The effective radiation dose (ED) is also calculated. Above data are then statistically analyzed. RESULTS: The mean CT values, SNR, CNR, and subjective image quality of group A are significantly lower than those of groups B and C (P 0.05). FOMs show a significantly increase trend from group A to C (P

To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low-dose chest CT in comparison with 40% adaptive statistical iterative reconstruction-Veo (ASiR-V40%) algorithm.This retrospective study included 86 patients who underwent low-dose CT for lung cancer screening. Images were reconstructed with ASiR-V40% and DLIR at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) levels. CT value and standard deviation of lung tissue, erector spinae muscles, aorta, and fat were measured and compared across the four reconstructions. Subjective image quality was evaluated by two blind readers from three aspects: image noise, artifact, and visualization of small structures.The effective dose was 1.03 ± 0.36 mSv. There was no significant difference in CT values of erector spinae muscles and aorta, whereas the maximum difference for lung tissue and fat was less than 5 HU among the four reconstructions. Compared with ASiR-V40%, the DLIR-L, DLIR-M, and DLIR-H reconstructions reduced the noise in aorta by 11.44%, 33.03%, and 56.1%, respectively, and had significantly higher subjective quality scores in image artifacts (all p 0.001). ASiR-V40%, DLIR-L, and DLIR-M had equivalent score in visualizing small structures (all p 0.05), whereas DLIR-H had slightly lower score.Compared with ASiR-V40%, DLIR significantly reduces image noise in low-dose chest CT. DLIR strength is important and should be adjusted for different diagnostic needs in clinical application.

Objectives: To investigate the improvement of two denoising models with different learning targets (Dir and Res) of generative adversarial network (GAN) on image quality and lung nodule detectability in chest low-dose CT (LDCT). Methods: In training phase, by using LDCT images simulated from standard dose CT (SDCT) of 200 participants, Dir model was trained targeting SDCT images, while Res model targeting the residual between SDCT and LDCT images. In testing phase, a phantom and 95 chest LDCT, exclusively with training data, were included for evaluation of imaging quality and pulmonary nodules detectability. Results: For phantom images, structural similarity, peak signal-to-noise ratio of both Res and Dir models were higher than that of LDCT. Standard deviation of Res model was the lowest. For patient images, image noise and quality of both two models, were better than that of LDCT. Artifacts of Res model was less than that of LDCT. The diagnostic sensitivity of lung nodule by two readers for LDCT, Res and Dir model, were 72/77%, 79/83% and 72/79% respectively. Conclusion: Two GAN denoising models, including Res and Dir trained with different targets, could effectively reduce image noise of chest LDCT. The image quality evaluation scoring and nodule detectability of Res denoising model was better than that of Dir denoising model and that of hybrid IR images. Advances in knowledge: The GAN-trained model, which learned the residual between SDCT and LDCT images, reduced image noise and increased the lung nodule detectability by radiologists on chest LDCT. This demonstrates the potential for clinical benefit.

In this study, we compared the image quality of deep learning reconstruction (DLR) with that of conventional image reconstruction methods under the same conditions of reconstruction FOV and acquisition dose assuming abdomen computed tomography (CT) in children. Standard deviation (SD) of the CT value, noise power spectrum (NPS), and task-based modulation transfer function (TTF) were evaluated. DLR reduced image noise while maintaining sharpness, and the noise reduction effect showed a different characteristic depending on the size of reconstruction FOV from the conventional image reconstruction methods. The SD of CT value increased gradually in the range from 320 mm to 240 mm, but there was almost no change from 240 mm to 200 mm. The NPS showed completely different characteristics. The low-frequency component increased, and the high-frequency component decreased at 240 mm. However, the frequency component below 0.5 cycle/mm decreased at 200 mm and the peak frequency moved to the lower side at 320 mm. DLR showed the highest TTF value compared to the conventional reconstruction methods.

Objective To evaluate the effect of a deep learning reconstruction (DLR) method on the visibility of contrast-enhanced CT images of the biliary system by comparing it with different iterative reconstruction algorithms including the adaptive iterative dose reduction 3D (AIDR 3D) algorithm,forward projected model based iterative reconstruction solution (FIRST),and filtered back projection (FBP) algorithm. Methods A total of 30 patients subjected to abdominal contrast-enhanced CT and diagnosed with dilatation of common bile duct or extrahepatic bile duct were retrospectively included in this study.The images of the portal phase were reconstructed via four different algorithms (FBP,AIDR 3D,FIRST,and DLR).Signal to noise ratio (SNR) and contrast to noise ratio (CNR) of the dilated bile duct,liver parenchyma,measurable bile duct lesions,and image noise were compared between the four datasets.In subjective analyses,two radiologists independently scored the image quality (best:4 points,second:3 points;third:2 points;fourth:1 point) of the four datasets based on the noise and image visual quality of the biliary system.The Friedman and the Bonferroni-Dunn post-hoc tests were performed for comparison. Results The DLR images (bile duct:4.42±0.87;liver parenchyma:3.78±1.47) yielded higher CNR than the FBP (bile duct:2.21±1.02

Considering that false-positive and true pulmonary nodules are highly similar in shapes and sizes between lung computed tomography scans, we develop and evaluate a false-positive nodules reduction method applied to the computer-aided diagnosis system. To improve the pulmonary nodule diagnosis quality, a 3D convolutional neural networks (CNN) model is constructed to effectively extract spatial information of candidate nodule features through the hierarchical architecture. Furthermore, three paths corresponding to three receptive field sizes are adopted and concatenated in the network model, so that the feature information is fully extracted and fused to actively adapting to the changes in shapes, sizes, and contextual information between pulmonary nodules. In this way, the false-positive reduction is well implemented in pulmonary nodule detection. Multi-path 3D CNN is performed on LUNA16 dataset, which achieves an average competitive performance metric score of 0.881, and excellent sensitivity of 0.952 and 0.962 occurs to 4, 8 FP/Scans. By constructing a multi-path 3D CNN to fully extract candidate target features, it accurately identifies pulmonary nodules with different sizes, shapes, and background information. In addition, the proposed general framework is also suitable for similar 3D medical image classification tasks.

The X-ray radiation from computed tomography (CT) brought us the potential risk. Simply decreasing the dose makes the CT images noisy and diagnostic performance compromised. Here, we develop a novel denoising low-dose CT image method. Our framework is based on an improved generative adversarial network coupling with the hybrid loss function, including the adversarial loss, perceptual loss, sharpness loss, and structural similarity loss. Among the loss function terms, perceptual loss and structural similarity loss are made use of to preserve textural details, and sharpness loss can make reconstruction images clear. The adversarial loss can sharp the boundary regions. The results of experiments show the proposed method can effectively remove noise and artifacts better than the state-of-the-art methods in the aspects of the visual effect, the quantitative measurements, and the texture details.

Bone age is one of biological indicators of maturity used in clinical practice and it is a very important parameter of a child’s assessment, especially in paediatric endocrinology. The most widely used method of bone age assessment is by performing a hand and wrist radiograph and its analysis with Greulich-Pyle or Tanner-Whitehouse atlases, although it has been about 60 years since they were published. Due to the progress in the area of Computer-Aided Diagnosis and application of artificial intelligence in medicine, lately, numerous programs for automatic bone age assessment have been created. Most of them have been verified in clinical studies in comparison to traditional methods, showing good precision while eliminating inter- and intra-rater variability and significantly reducing the time of assessment. Additionally, there are available methods for assessment of bone age which avoid X-ray exposure, using modalities such as ultrasound or magnetic resonance imaging.

Background Patients with urolithiasis undergo radiation overexposure from computed tomography (CT) scans. Improvement of image reconstruction is necessary for radiation dose reduction. Purpose To evaluate a deep learning-based reconstruction algorithm for CT (DLIR) in the detection of urolithiasis at low-dose non-enhanced abdominopelvic CT. Material and Methods A total of 75 patients who underwent low-dose abdominopelvic CT for urolithiasis were retrospectively included. Each examination included three reconstructions: DLIR; filtered back projection (FBP); and hybrid iterative reconstruction (IR; ASiR-V 70%). Image quality was subjectively and objectively assessed using attenuation and noise measurements in order to calculate the signal-to-noise ratio (SNR), absolute contrast, and contrast-to-noise ratio (CNR). Attenuation of the largest stones were also compared. Detectability of urinary stones was assessed by two observers. Results Image noise was significantly reduced with DLIR: 7.2 versus 17 and 22 for ASiR-V 70% and FBP, respectively. Similarly, SNR and CNR were also higher compared to the standard reconstructions. When the structures had close attenuation values, contrast was lower with DLIR compared to ASiR-V. Attenuation of stones was also lowered in the DLIR series. Subjective image quality was significantly higher with DLIR. The detectability of all stones and stones >3 mm was excellent with DLIR for the two observers (intraclass correlation [ICC] = 0.93 vs. 0.96 and 0.95 vs. 0.99). For smaller stones (Conclusion For low-dose abdominopelvic CT, DLIR reconstruction exhibited image quality superior to ASiR-V and FBP as well as an excellent detection of urinary stones.

OBJECTIVE To investigate reader performance as a function of patient size for the detection of hepatic metastases when an automatic exposure control (AEC) system is used, which varies image noise as a function of patient size. METHODS Abdominal computed tomograhy examinations across 100, 120, 160, and 200 quality reference tube current-time product were collected, involving a cohort of 83 patients. Three radiologists identified hepatic metastases across all dose levels. Partial Spearman rank correlation and multivariate logistic regression were used to evaluate correlations between reader performance and patient size and lesion size/contrast while accounting for potential confounding effects. Analyses were repeated on an emulated less-variable noise AEC. RESULTS No statistically significant correlation was observed between patient size and radiologist performance (for variable-noise AEC: range of partial Spearman ρ, -0.157 to -0.035]; range of adjusted odds ratios, 0.987, 1.006). CONCLUSIONS Reader performance was independent of patient size, suggesting that variable-noise AEC provides better modulation for larger patients than constant-noise AEC.

Introduction The utility of evaluating a sagittal view of CT of the spine is well-known. In many clinical cases, the sagittal view includes noise generated from surrounding objects and may degrade the image quality. Iterative reconstruction (IR) techniques are useful for noise reduction; however, they can reduce spatial resolution. The aim of this study was to evaluate the effectiveness of the adaptive statistical iterative reconstruction (ASiR) for generating sagittal CT images of the spine when compared to filtered back projection (FBP). Methods The image quality of clinical images from 25 patients were subjectively assessed. Three radiologists rated spatial resolution, image noise, and overall image quality using a five-point scale. For objective assessment, z-direction modulation transfer function (z-MTF) was measured using a custom-made phantom. Additionally, z-axis noise power spectrum (z-NPS) was measured using a water phantom. An improved adaptive statistical iterative reconstruction algorithm called ASiR-V was used in this study. Blending levels were 50%, and 100% (ASiR-V50, ASiR-V100, respectively). Results For subjective assessments, images using ASiR-V100 were determined to have the best overall image quality, despite having received the worst score in the assessment of spatial resolution. For objective assessments, the image using ASiR-V50 and ASiR-V100 curves were slightly degraded in terms of low contrast z-MTF when compared to FBP. Conclusion ASiR-V was effective to improve the image quality when compared with FBP when reviewing sagittal reformats of the spine. Implications for practice This study suggests that high resolution is not the only thing that is key when reviewing sagittal CT spinal reformats. Such images should be provided as part of routine CT spine protocols, where available.

Anti-melanoma differentiation-associated gene 5-positive dermatomyositis-associated interstitial lung disease (MDA5+ DM-ILD) is a life-threatening disease. This study aimed to develop a novel pulmonary CT visual scoring method for assessing the prognosis of the disease, and an artificial intelligence (AI) algorithm-based analysis and an idiopathic pulmonary fibrosis (IPF)-based scoring were conducted as comparators. A retrospective cohort of hospitalized patients with MDA5+ DM-ILD was analyzed. Since most fatalities occur within the first half year of the disease course, the primary outcome was the six-month all-cause mortality since the time of admission. A ground glass opacity (GGO) and consolidation-weighted CT visual scoring model for MDA5+ DM-ILD, namely ‘MDA5 score’, was then developed with C-index values of 0.80 (95%CI 0.75–0.86) in the derivation dataset (n = 116) and 0.84 (95%CI 0.71–0.97) in the validation dataset (n = 57), respectively. While, the AI algorithm-based analysis, namely ‘AI score’, yielded C-index 0.78 (95%CI 0.72–0.84) for the derivation dataset and 0.77 (95%CI 0.64–0.90) for the validation dataset. These findings suggest that the newly derived ‘MDA5 score’ may serve as an applicable prognostic predictor for MDA5+ DM-ILD and facilitate further clinical trial design. The AI based CT quantitative analysis provided a promising solution for ILD evaluation.

OBJECTIVES: The objective of this study was to quantitatively assess the image quality of Advanced Modeled Iterative Reconstruction (ADMIRE) and the PixelShine (PS) deep learning algorithm for the optimization of low-dose computed tomography protocols in midfacial trauma.STUDY DESIGN: Six fresh frozen human cadaver head specimens were scanned by computed tomography using both standard and low-dose scan protocols. Three iterative reconstruction strengths were applied to reconstruct bone and soft tissue data sets and these were subsequently applied to the PS algorithm. Signal-to-noise ratios (SNRs) and contrast-to-noise ratios (CNRs) were calculated for each data set by using the image noise measurements of 10 consecutive image slices from a standardized region of interest template.RESULTS: The low-dose scan protocol resulted in a 61.7% decrease in the radiation dose. Radiation dose reduction significantly reduced, and iterative reconstruction and the deep learning algorithm significantly improved, the CNR for bone and soft tissue data sets. The algorithms improved image quality after substantial dose reduction. The greatest improvement in SNRs and CNRs was found using the iterative reconstruction algorithm.CONCLUSION: Both the ADMIRE and PS algorithms significantly improved image quality after substantial radiation dose reduction.

To test whether higher iodine concentration together with higher noise level could lead to a further dose reduction in an already low dose coronary CT angiography (CCTA) protocol without comprising image quality.One hundred eighty patients with suspected coronary artery disease (CAD) were randomly assigned into three groups: (a) conventional dose (CD) group, 100 kV with a noise index (NI) of 25 and iohexol (350 mg I/ml); (b) low dose (LD) group, 80 kV with a NI of 25 and iohexol (350 mg I/ml); (c) further low dose (FLD) group, 80 kV with a NI of 30 and iomeprol (400 mg I/ml). The volume and injection rate of contrast medium were fixed at 60 ml and 5 ml/s. The radiation dose (volume CT dose index [CTDIvol], dose length product [DLP], and effective dose [ED]) were recorded. For image quality, both quantitative (enhancement, noise, signal-to-noise ratio [SNR], and contrast-to-noise ratio [CNR]) and qualitative indices were assessed.Compared to the CD group, ED was reduced by 16% and 42% in the LD and FLD groups, respectively (p0.05). Qualitative analysis showed no significant difference among the 3 groups (p0.05), while quantitative analysis revealed significantly higher attenuation in the LD and FLD groups. Signal-to-noise ratios and CNRs of the LD and FLD groups were significantly higher except for the CNR at the left circumflex branch of the FLD group (p0.05).Increasing iodine concentration and noise level may further reduce the radiation dose by 26% on top of a 16% reduction from 100 kV to 80 kV without image quality compromise.

BACKGROUND. Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. OBJECTIVE. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard. METHODS. A fully automated volumetric hepatosplenic segmentation algorithm using 3D convolutional neural networks was applied to unenhanced and contrast-enhanced series from a sample of 1204 healthy adults (mean age, 45.2 years; 726 women, 478 men) undergoing CT evaluation for renal donation. The mean volumetric attenuation was computed from all designated liver and spleen voxels. PDFF was estimated from unenhanced CT attenuation and served as the reference standard. Contrast-enhanced attenuations were evaluated for detecting PDFF thresholds of 5% (mild steatosis, 10% and 15% (moderate steatosis); PDFF less than 5% was considered normal. RESULTS. Using unenhanced CT as reference, estimated PDFF was ≥ 5% (mild steatosis), ≥ 10%, and ≥ 15% (moderate steatosis) in 50.1% (n = 603), 12.5% (n = 151) and 4.8% (n = 58) of patients, respectively. ROC AUC values for predicting PDFF thresholds of 5%, 10%, and 15% using contrast-enhanced liver attenuation were 0.669, 0.854, and 0.962, respectively, and using contrast-enhanced liver-spleen attenuation difference were 0.662, 0.866, and 0.986, respectively. A total of 96.8% (90/93) of patients with contrast-enhanced liver attenuation less than 90 HU had steatosis (PDFF ≥ 5%); this threshold of less than 90 HU achieved sensitivity of 75.9% and specificity of 95.7% for moderate steatosis (PDFF ≥ 15%). Liver attenuation less than 100 HU achieved sensitivity of 34.0% and specificity of 94.2% for any steatosis (PDFF ≥ 5%). A total of 93.8% (30/32) of patients with contrast-enhanced liver-spleen attenuation difference 10 HU or less had moderate steatosis (PDFF ≥ 15%); a liver-spleen difference less than 5 HU achieved sensitivity of 91.4% and specificity of 95.0% for moderate steatosis. Liver-spleen difference less than 10 HU achieved sensitivity of 29.5% and specificity of 95.5% for any steatosis (PDFF ≥ 5%). CONCLUSION. Contrast-enhanced volumetric hepatosplenic attenuation derived using a fully automated deep learning CT tool may allow objective categoric assessment of hepatic steatosis. Accuracy was better for moderate than mild steatosis. Further confirmation using different scanning protocols and vendors is warranted. CLINICAL IMPACT. If these results are confirmed in independent patient samples, this automated approach could prove useful for both individualized and population-based steatosis assessment.

Summary Background Chest x-rays are widely used in clinical practice; however, interpretation can be hindered by human error and a lack of experienced thoracic radiologists. Deep learning has the potential to improve the accuracy of chest x-ray interpretation. We therefore aimed to assess the accuracy of radiologists with and without the assistance of a deep-learning model. Methods In this retrospective study, a deep-learning model was trained on 821 681 images (284 649 patients) from five data sets from Australia, Europe, and the USA. 2568 enriched chest x-ray cases from adult patients (≥16 years) who had at least one frontal chest x-ray were included in the test dataset; cases were representative of inpatient, outpatient, and emergency settings. 20 radiologists reviewed cases with and without the assistance of the deep-learning model with a 3-month washout period. We assessed the change in accuracy of chest x-ray interpretation across 127 clinical findings when the deep-learning model was used as a decision support by calculating area under the receiver operating characteristic curve (AUC) for each radiologist with and without the deep-learning model. We also compared AUCs for the model alone with those of unassisted radiologists. If the lower bound of the adjusted 95% CI of the difference in AUC between the model and the unassisted radiologists was more than −0·05, the model was considered to be non-inferior for that finding. If the lower bound exceeded 0, the model was considered to be superior. Findings Unassisted radiologists had a macroaveraged AUC of 0·713 (95% CI 0·645–0·785) across the 127 clinical findings, compared with 0·808 (0·763–0·839) when assisted by the model. The deep-learning model statistically significantly improved the classification accuracy of radiologists for 102 (80%) of 127 clinical findings, was statistically non-inferior for 19 (15%) findings, and no findings showed a decrease in accuracy when radiologists used the deep-learning model. Unassisted radiologists had a macroaveraged mean AUC of 0·713 (0·645–0·785) across all findings, compared with 0·957 (0·954–0·959) for the model alone. Model classification alone was significantly more accurate than unassisted radiologists for 117 (94%) of 124 clinical findings predicted by the model and was non-inferior to unassisted radiologists for all other clinical findings. Interpretation This study shows the potential of a comprehensive deep-learning model to improve chest x-ray interpretation across a large breadth of clinical practice. Funding Annalise.ai.

PURPOSE To determine the suitable kVp pair for optimal image quality of the virtual monochromatic images (VMIs) and iodine quantification accuracy at low concentration, using a third generation dual-source CT (DSCT). MATERIALS AND METHODS Multi-energy CT phantoms with and without body rings were scanned with a DSCT using four kVp pairs (tube "A"/"B" voltage): 100/Sn150, 90/Sn150, 80/Sn150 and 70/Sn150 kVp. The reference mAs was adjusted to obtain a CTDIvol close to 11 mGy. HU values accuracy (RMSDHU), noise (SD) and contrast-to-noise ratio (CNR) of iodine inserts of 0.5, 1, 2 and 5 mg/mL concentrations were assessed on VMIs at 40/50/60/70 keV. Iodine quantification accuracy was assessed using the RMSDiodine and iodine bias (IBiodine). RESULTS The RMSDHU decreased when the tube "A" voltage increased. The mean noise value increased significantly with tube "A" voltage (p

Objectives The aim of this study was to evaluate the sensitivity of CT-based thermometry for clinical applications regarding a three-component tissue phantom of fat, muscle and bone. Virtual monoenergetic images (VMI) by dual-energy measurements and conventional polychromatic 120-kVp images with modern reconstruction algorithms adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning image reconstruction (DLIR) were compared. Methods A temperature-regulating water circuit system was developed for the systematic evaluation of the correlation between temperature and Hounsfield units (HU). The measurements were performed on a Revolution CT with gemstone spectral imaging technology (GSI). Complementary measurements were performed without GSI (voltage 120 kVp, current 130–545 mA). The measured object was a tissue equivalent phantom in a temperature range of 18 to 50°C. The evaluation was carried out for VMI at 40 to 140 keV and polychromatic 120-kVp images. Results The regression analysis showed a significant inverse linear dependency between temperature and average HU regardless of ASIR-V and DLIR. VMI show a higher temperature sensitivity compared to polychromatic images. The temperature sensitivities were 1.25 HU/°C (120 kVp) and 1.35 HU/°C (VMI at 140 keV) for fat, 0.38 HU/°C (120 kVp) and 0.47 HU/°C (VMI at 40 keV) for muscle and 1.15 HU/°C (120 kVp) and 3.58 HU/°C (VMI at 50 keV) for bone. Conclusions Dual-energy with VMI enables a higher temperature sensitivity for fat, muscle and bone. The reconstruction with ASIR-V and DLIR has no significant influence on CT-based thermometry, which opens up the potential of drastic dose reductions. Key Points • Virtual monoenergetic images (VMI) enable a higher temperature sensitivity for fat (8%), muscle (24%) and bone (211%) compared to conventional polychromatic 120-kVp images. • With VMI, there are parameters, e.g. monoenergy and reconstruction kernel, to modulate the temperature sensitivity. In contrast, there are no parameters to influence the temperature sensitivity for conventional polychromatic 120-kVp images. • The application of adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning–based image reconstruction (DLIR) has no effect on CT-based thermometry, opening up the potential of drastic dose reductions in clinical applications.

OBJECTIVE: To investigate feasibility of applying deep learning image reconstruction (DLIR) algorithm in a low-kilovolt enhanced scan of the upper abdomen. METHODS: A total of 64 patients (BMI0.05). The subjective image quality scores in the DL-H and B groups are higher than those in the other groups, and the FBP group has significantly lower image quality than the remaining groups (P