Three-dimensional aortic geometry mapping via registration of non-gated contrast-enhanced or gated and respiratory-navigated MR angiographies.

BACKGROUND: The measurement of aortic dimensions and their evolution are key in the management of patients with aortic diseases. Manual assessment, the current guideline-recommended method and clinical standard, is subjective, poorly reproducible, and time-consuming, limiting the capacity to track aortic growth in everyday practice. Aortic geometry mapping (AGM) via image registration of serial computed tomography angiograms outperforms manual assessment, providing accurate and reproducible 3D maps of aortic diameter and growth rate. This observational study aimed to evaluate the accuracy and reproducibility of AGM on non-gated contrast-enhanced (CE-) and cardiac- and respiratory-gated (GN-) magnetic resonance angiographies (MRA). METHODS: Patients with thoracic aortic disease followed with serial CE-MRA (n = 30) or GN-MRA (n = 15) acquired at least 1 year apart were retrospectively and consecutively identified. Two independent observers measured aortic diameters and growth rates (GR) manually at several thoracic aorta reference levels and with AGM. Agreement between manual and AGM measurements and their inter-observer reproducibility were compared. Reproducibility for aortic diameter and GR maps assessed with AGM was obtained. RESULTS: Mean follow-up was 3.8 ± 2.3 years for CE- and 2.7 ± 1.6 years for GN-MRA. AGM was feasible in the 93% of CE-MRA pairs and in the 100% of GN-MRA pairs. Manual and AGM diameters showed excellent agreement and inter-observer reproducibility (ICC>0.9) at all anatomical levels. Agreement between manual and AGM GR was more limited, both in the aortic root by GN-MRA (ICC=0.47) and in the thoracic aorta, where higher accuracy was obtained with GN- than with CE-MRA (ICC=0.55 vs 0.43). The inter-observer reproducibility of GR by AGM was superior compared to manual assessment, both with CE- (thoracic: ICC= 0.91 vs 0.51) and GN-MRA (root: ICC=0.84 vs 0.52; thoracic: ICC=0.93 vs 0.60). AGM-based 3D aortic size and growth maps were highly reproducible (median ICC >0.9 for diameters and >0.80 for GR). CONCLUSION: Mapping aortic diameter and growth on MRA via 3D image registration is feasible, accurate and outperforms the current manual clinical standard. This technique could broaden the possibilities of clinical and research evaluation of patients with aortic thoracic diseases.

Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.

In pediatric radiology, balancing diagnostic accuracy with reduced radiation exposure is paramount due to the heightened vulnerability of younger patients to radiation. Technological advancements in computed tomography (CT) reconstruction techniques, especially model-based iterative reconstruction and deep learning image reconstruction, have enabled significant reductions in radiation doses without compromising image quality. Deep learning image reconstruction, powered by deep learning algorithms, has demonstrated superiority over traditional techniques like filtered back projection, providing enhanced image quality, especially in pediatric head and cardiac CT scans. Photon-counting detector CT has emerged as another groundbreaking technology, allowing for high-resolution images while substantially reducing radiation doses, proving highly beneficial for pediatric patients requiring frequent imaging. Furthermore, cloud-based dose tracking software focuses on monitoring radiation exposure, ensuring adherence to safety standards. However, the deployment of these technologies presents challenges, including the need for large datasets, computational demands, and potential data privacy issues. This article provides a comprehensive exploration of these technological advancements, their clinical implications, and the ongoing efforts to enhance pediatric radiology's safety and effectiveness.

Complimentary Cardiac Computed Tomography Ventricular Volumetry-Derived Metrics of Severity in Patients with Ebstein Anomaly: Comparison with Echocardiography-Based Severity Indices.

Detailed three-dimensional cardiac segmentations using cardiac computed tomography (CT) data is technically feasible in patients with Ebstein anomaly, but its complementary role has not been evaluated. This single-center, retrospective study was aimed to evaluate the complementary role of cardiac CT ventricular volumetry in evaluating the severity of Ebstein anomaly. Preoperative cardiac CT ventricular volumetry was performed in 21 children with Ebstein anomaly. CT-based ventricular functional measures were compared between Carpentier types, and between definitive surgical repair types. The Celermajer severity index measured with echocardiography was correlated with CT-based functional parameters. Total right ventricle (RV) and functional RV (fRV) volumes, fRV fraction, fRV/left ventricle (LV) volume ratio, and end-diastolic CT severity index demonstrated statistically significant differences between Carpentier type A/B and Carpentier type C/D (p < 0.05). The Celermajer severity index measured with echocardiography showed a high positive correlation with the end-diastolic CT severity index (R = 0.720, p < 0.002). There were no statistically significant differences in both echocardiography- and CT-based functional measures between patients with biventricular repair and patients with one-and-a-half or univentricular repair (p > 0.05). Compared with echocardiography, cardiac CT ventricular volumetry can provide the severity of Ebstein anomaly objectively and may be used in select patients when echocardiographic results are inconclusive or inconsistent.

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography.

OBJECTIVES: Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition. METHODS: For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated. RESULTS: Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002). CONCLUSIONS: Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases. KEY POINTS: • Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

Partial voxel interpolation to reduce partial volume error of cardiac computed tomography ventricular volumetry in patients with congenital heart disease.

BACKGROUND: Varying degrees of partial volume error depending on the complexity of the endocardial borders are inevitable in threshold-based cardiac computed tomography (CT) ventricular volumetry. These errors can potentially be reduced by using a partial voxel interpolation (PVI) method, but this has not been tested for cardiac CT ventricular volumetry. OBJECTIVE: To evaluate the partial volume error-reducing effects of the PVI method in cardiac CT ventricular volumetry among patients with congenital heart disease (CHD). MATERIALS AND METHODS: The cardiac CT ventricular volumetry data were obtained from 55 patients (median age 12.0 years) with CHD. The ventricular and myocardial volumes, ejection fraction and ventricular mass-volume ratio were quantified and compared before and after the PVI method. The correlation between the myocardial volumes in the end-systolic and end-diastolic phases was tested. The effect of the PVI method on the classification of ventricular hypertrophy was evaluated. RESULTS: The indexed ventricular volumes after PVI were significantly smaller (7.4-11.5%) than those before PVI (P<0.001). In contrast, the indexed myocardial volumes were significantly larger (6.2-27.7%) after PVI (P<0.001). The ejection fractions and mass-volume ratios were significantly larger (1.6-2.2% and 19.7-42.5%, respectively) after PVI (P<0.001 and P<0.001, respectively). The indexed myocardial masses showed prominently high correlation between the end-systolic and end-diastolic phases (R, 0.961-0.990; P<0.001). The proportions of no and severe hypertrophy were significantly decreased (P<0.002) and increased (P<0.032), respectively, after the application of the PVI method. CONCLUSION: The PVI method can reduce partial volume error in cardiac CT ventricular volumetry among patients with CHD.

[Preliminary evaluation of the trueness of 5 chairside 3D facial scanning techniques].

OBJECTIVE: To quantitatively evaluate the trueness of five chairside three-dimensional facial scanning techniques, and to provide reference for the application of oral clinical diagnosis and treatment. METHODS: The three-dimensional facial data of the subjects were collected by the traditional professional three-dimensional facial scanner Face Scan, which was used as the reference data of this study. Four kinds of portable three-dimensional facial scanners (including Space Spider, LEO, EVA and DS-FScan) and iPhone Ⅹ mobile phone (Bellus3D facial scanning APP) were used to collect three-dimensional facial data from the subjects. In Geomagic Studio 2013 software, through data registration, deviation analysis and other functions, the overall three-dimensional deviation and facial partition three-dimensional deviation of the above five chairside three-dimensional facial scanning technologies were calculated, and their trueness performance evaluated. Scanning time was recorded during the scanning process, and the subject's comfort was scored by visual analogue scale(VAS). The scanning efficiency and patient acceptance of the five three-dimensional facial scanning techniques were evaluated. RESULTS: DS-FScan had the smallest mean overall and mean partition three-dimensional deviation between the test data and the reference data, which were 0.334 mm and 0.329 mm, respectively. The iPhone Ⅹ mobile phone had the largest mean overall and mean partition three-dimensional deviation between the test data and the reference data, which were 0.483 mm and 0.497 mm, respectively. The detailed features of the three-dimensional facial data obtained by Space Spider were the best. The iPhone Ⅹ mobile phone had the highest scanning efficiency and the highest acceptance by the subject. The average scanning time of the iPhone Ⅹ mobile phone was 14 s, and the VAS score of the subjects' scanning comfort was 9 points. CONCLUSION: Among the five chairside three-dimensional face scanning technologies, the trueness of the scan data of the four portable devices had no significant difference, and they were all better than the iPhone Ⅹ mobile phone scan. The subject with the iPhone Ⅹ scanning technology had the best expe-rience.

Assessment of first-trimester utero-placental vascular morphology by 3D power Doppler ultrasound image analysis using a skeletonization algorithm: the Rotterdam Periconception Cohort.

STUDY QUESTION: Can three-dimensional (3D) Power Doppler (PD) ultrasound and a skeletonization algorithm be used to assess first-trimester development of the utero-placental vascular morphology? SUMMARY ANSWER: The application of 3D PD ultrasonography and a skeletonization algorithm facilitates morphologic assessment of utero-placental vascular development in the first trimester and reveals less advanced vascular morphologic development in pregnancies with placenta-related complications than in pregnancies without placenta-related complications. WHAT IS KNOWN ALREADY: Suboptimal development of the utero-placental vasculature is one of the main contributors to the periconceptional origin of placenta-related complications. The nature and attribution of aberrant vascular structure and branching patterns remain unclear, as validated markers monitoring first-trimester utero-placental vascular morphologic development are lacking. STUDY DESIGN, SIZE, DURATION: In this prospective observational cohort, 214 ongoing pregnancies were included before 10 weeks gestational age (GA) at a tertiary hospital between January 2017 and July 2018, as a subcohort of the ongoing Rotterdam Periconception Cohort study. PARTICIPANTS/MATERIALS, SETTING, METHODS: By combining 3D PD ultrasonography and virtual reality, utero-placental vascular volume (uPVV) measurements were obtained at 7, 9 and 11 weeks GA. A skeletonization algorithm was applied to the uPVV measurements to generate the utero-placental vascular skeleton (uPVS), a network-like structure containing morphologic characteristics of the vasculature. Quantification of vascular morphology was performed by assigning a morphologic characteristic to each voxel in the uPVS (end-, vessel-, bifurcation- or crossing-point) and calculating total vascular network length. A Mann-Whitney U test was performed to investigate differences in morphologic development of the first-trimester utero-placental vasculature between pregnancies with and without placenta-related complications. Linear mixed models were used to estimate trajectories of the morphologic characteristics in the first trimester. MAIN RESULTS AND THE ROLE OF CHANCE: All morphologic characteristics of the utero-placental vasculature increased significantly in the first trimester (P < 0.005). In pregnancies with placenta-related complications (n = 54), utero-placental vascular branching was significantly less advanced at 9 weeks GA (vessel points P = 0.040, bifurcation points P = 0.050, crossing points P = 0.020, total network length P = 0.023). Morphologic growth trajectories remained similar after adjustment for parity, conception mode, foetal sex and occurrence of placenta-related complications. LIMITATIONS, REASONS FOR CAUTION: The tertiary setting of this prospective observational study provides high internal, but possibly limited external, validity. Extrapolation of the study's findings should therefore be addressed with caution. WIDER IMPLICATIONS OF THE FINDINGS: The uPVS enables assessment of morphologic development of the first-trimester utero-placental vasculature. Further investigation of this innovative methodology needs to determine its added value for the assessment of (patho-) physiological utero-placental vascular development. STUDY FUNDING/COMPETING INTEREST(S): This research was funded by the Department of Obstetrics and Gynecology of the Erasmus MC, University Medical Centre, Rotterdam, The Netherlands. There are no conflicts of interest. TRIAL REGISTRATION NUMBER: Registered at the Dutch Trial Register (NTR6854).

Registration-based semi-automatic assessment of aortic diameter growth rate from contrast-enhanced computed tomography outperforms manual quantification.

OBJECTIVES: Manual assessment of aortic diameters on double-oblique reformatted computed tomography angiograms (CTA) is considered the current standard, although the reproducibility for growth rates has not been reported. Deformable registration of CTA has been proposed to provide 3D aortic diameters and growth maps, but validation is lacking. This study aimed to quantify accuracy and inter-observer reproducibility of registration-based and manual assessment of aortic diameters and growth rates. METHODS: Forty patients with ≥ 2 CTA acquired at least 6 months apart were included. Aortic diameters and growth rate were obtained in the aortic root and the entire thoracic aorta using deformable image registration by two independent observers, and compared with the current standard at typical anatomical landmarks. RESULTS: Compared with manual assessment, the registration-based technique presented low bias (0.46 mm), excellent agreement (ICC = 0.99), and similar inter-observer reproducibility (ICC = 0.99 for both) for aortic diameters; and low bias (0.10 mm/year), good agreement (ICC = 0.82), and much higher inter-observer reproducibility for growth rates (root: ICC = 0.96 vs 0.68; thoracic aorta: ICC = 0.96 vs 0.80). Registration-based growth rate reproducibility over a 6-month-long follow-up was similar to that obtained by manual assessment after 2.7 years (LoA = [- 0.01, 0.33] vs [- 0.13, 0.21] mm/year, respectively). Mapping of diameter and growth rate was highly reproducible (ICC > 0.9) in the whole thoracic aorta. CONCLUSIONS: Registration-based assessment of aortic dilation on CTA is accurate and substantially more reproducible than the current standard, even at follow-up as short as 6 months, and provides robust 3D mapping of aortic diameters and growth rates beyond the pre-established anatomic landmarks. KEY POINTS: • Registration-based semi-automatic assessment of progressive aortic dilation on CTA is accurate and substantially more reproducible than the current standard. • The registration-based technique allows robust growth rate assessment at follow-up as short as 6 months, with a similar reproducibility to that obtained by manual assessment at around 3 years. • The use of image registration provides robust 3D mapping of aortic diameters and growth rates beyond the pre-established anatomic landmarks.

Automatic quantification of myocardium and pericardial fat from coronary computed tomography angiography: a multicenter study.

OBJECTIVES: To develop a deep learning-based method for simultaneous myocardium and pericardial fat quantification from coronary computed tomography angiography (CCTA) for the diagnosis and treatment of cardiovascular disease (CVD). METHODS: We retrospectively identified CCTA data obtained between May 2008 and July 2018 in a multicenter (six centers) CVD study. The proposed method was evaluated on 422 patients' data by two studies. The first overall study involves training model on CVD patients and testing on non-CVD patients, as well as training on non-CVD patients and testing on CVD patients. The second study was performed using the leave-center-out approach. The method performance was evaluated using Dice similarity coefficient (DSC), Jaccard index (JAC), 95% Hausdorff distance (HD95), mean surface distance (MSD), residual mean square distance (RMSD), and the center of mass distance (CMD). The robustness of the proposed method was tested using the nonparametric Kruskal-Wallis test and post hoc test to assess the equality of distribution of DSC values among different tests. RESULTS: The automatic segmentation achieved a strong correlation with contour (ICC and R > 0.97, p value < 0.001 throughout all tests). The accuracy of the proposed method remained high through all the tests, with the median DSC higher than 0.88 for pericardial fat and 0.96 for myocardium. The proposed method also resulted in mean MSD, RMSD, HD95, and CMD of less than 1.36 mm for pericardial fat and 1.00 mm for myocardium. CONCLUSIONS: The proposed deep learning-based segmentation method enables accurate simultaneous quantification of myocardium and pericardial fat in a multicenter study. KEY POINTS: • Deep learning-based myocardium and pericardial fat segmentation method tested on 422 patients' coronary computed tomography angiography in a multicenter study. • The proposed method provides segmentations with high volumetric accuracy (ICC and R > 0.97, p value < 0.001) and similar shape as manual annotation by experienced radiologists (median Dice similarity coefficient ≥ 0.88 for pericardial fat and 0.96 for myocardium).

Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction.

OBJECTIVE: To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen. METHODS: One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDIvol: chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected: chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets for each case. RESULTS: The average measured noise for DLIR-CHT, ASiR-CHT, and ASiR-ABD was 8.01 ± 2.81, 14.8 ± 2.56, and 12.3 ± 2.28, respectively (p < .001). Deep learning-based image reconstruction (DLIR) also showed the best SNR and CNR (p < .001). However, in the subjective analysis, ASiR-ABD showed less subjective noise than DLIR (2.94 ± 0.23 vs. 2.87 ± 0.26; p < .001), while DLIR showed better spatial resolution (2.60 ± 0.34 vs. 2.44 ± 0.31; p = .02). ASiR-ABD showed a better overall image quality (p = .001), but two of the three readers preferred DLIR more frequently. CONCLUSION: With < 50% of the radiation dose, DLIR chest CT showed comparable image quality in the upper abdomen to that of dedicated abdominal CT and was preferred by most readers. KEY POINTS: • With < 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.

Novel Three-Dimensional Bladder Reconstruction Model from B-Mode Ultrasound Image to Improve the Accuracy of Bladder Volume Measurement.

Traditional bladder volume measurement from B-mode (two-dimensional) ultrasound has been found to produce inaccurate results, and thus in this work we aim to improve the accuracy of measurement from B-mode ultrasound. A total of 75 electronic medical records including ultrasonic images were reviewed retrospectively from 64 patients. We put forward a novel bladder volume measurement method, in which a three-dimensional (3D) reconstruction model was established from conventional two-dimensional (2D) ultrasonic images to estimate the bladder volume. The differences and relationships were analyzed among the actual volume, the traditional estimated volume, and the new reconstruction model estimated volume. We also compared the data in different volume groups from small volume to high volume. The mean actual volume is 531.8 mL and the standard deviation is 268.7 mL; the mean percentage error of traditional estimation is -28%. In our new bladder measurement method, the mean percentage error is -10.18% (N = 2), -4.72% (N = 3), -0.33% (N = 4), and 2.58% (N = 5). There is no significant difference between the actual volume and our new bladder measurement method (N = 4) in all data or the divided four groups. The estimated volumes from the traditional method or our new method are highly correlated with the actual volume. Our data show that the three-dimensional bladder reconstruction model provides an accurate measurement from conventional B-mode ultrasonic images compared with the traditional method. The accuracy is seen across different groups of volume, and thus we can conclude that this is a reliable and economical volume measurement model that can be applied in general software or in apps on mobile devices.

The precision of gingival recession measurements is increased by an automated curvature analysis method.

BACKGROUND: The extent of gingival recession represents one of the most important measures determining outcome of periodontal plastic surgery. The accurate measurements are, thus, critical for optimal treatment planning and outcome evaluation. Present study aimed to introduce automated curvature-based digital gingival recession measurements, evaluate the agreement and reliability of manual measurements, and identify sources of manual variability. METHODS: Measurement of gingival recessions was performed manually by three examiners and automatically using curvature analysis on representative cross-sections (n = 60). Cemento-enamel junction (CEJ) and gingival margin (GM) measurement points selection was the only variable. Agreement and reliability of measurements were analysed using intra- and inter-examiner correlations and Bland-Altman plots. Measurement point selection variability was evaluated with manual point distance deviation from an automatic point. The effect of curvature on manual point selection was evaluated with scatter plots. RESULTS: Bland-Altman plots revealed a high variability of examiner's recession measurements indicated by high 95% limits of agreement range of approximately 1 mm and several outliers beyond the limits of agreement. CEJ point selection was the main source of examiner's variability due to smaller curvature values than GM, i.e., median values of - 0.98 mm- 1 and - 4.39 mm- 1, respectively, indicating straighter profile for CEJ point. Scatter plots revealed inverse relationship between curvature and examiner deviation for CEJ point, indicating a threshold curvature value around 1 mm- 1. CONCLUSIONS: Automated curvature-based approach increases the precision of recession measurements by reproducible measurement point selection. Proposed approach allows evaluation of teeth with indistinguishable CEJ that could be not be included in the previous studies.

Automated detection of pulmonary embolism in CT pulmonary angiograms using an AI-powered algorithm.

OBJECTIVES: To evaluate the performance of an AI-powered algorithm for the automatic detection of pulmonary embolism (PE) on chest computed tomography pulmonary angiograms (CTPAs) on a large dataset. METHODS: We retrospectively identified all CTPAs conducted at our institution in 2017 (n = 1499). Exams with clinical questions other than PE were excluded from the analysis (n = 34). The remaining exams were classified into positive (n = 232) and negative (n = 1233) for PE based on the final written reports, which defined the reference standard. The fully anonymized 1-mm series in soft tissue reconstruction served as input for the PE detection prototype algorithm that was based on a deep convolutional neural network comprising a Resnet architecture. It was trained and validated on 28,000 CTPAs acquired at other institutions. The result series were reviewed using a web-based feedback platform. Measures of diagnostic performance were calculated on a per patient and a per finding level. RESULTS: The algorithm correctly identified 215 of 232 exams positive for pulmonary embolism (sensitivity 92.7%; 95% confidence interval [CI] 88.3-95.5%) and 1178 of 1233 exams negative for pulmonary embolism (specificity 95.5%; 95% CI 94.2-96.6%). On a per finding level, 1174 of 1352 findings marked as embolus by the algorithm were true emboli. Most of the false positive findings were due to contrast agent-related flow artifacts, pulmonary veins, and lymph nodes. CONCLUSION: The AI prototype algorithm we tested has a high degree of diagnostic accuracy for the detection of PE on CTPAs. Sensitivity and specificity are balanced, which is a prerequisite for its clinical usefulness. KEY POINTS: • An AI-based prototype algorithm showed a high degree of diagnostic accuracy for the detection of pulmonary embolism on CTPAs. • It can therefore help clinicians to automatically prioritize exams with a high suspection of pulmonary embolism and serve as secondary reading tool. • By complementing traditional ways of worklist prioritization in radiology departments, this can speed up the diagnostic and therapeutic workup of patients with pulmonary embolism and help to avoid false negative calls.

Feasibility of use of medical dual energy scanner for forensic detection and characterization of explosives, a phantom study.

OBJECTIVE: Detection of explosives is a challenge due to the use of improvised and concealed bombs. Post-bomb strike bodies are handled by emergency and forensic teams. We aimed to determine whether medical dual-energy computed tomography (DECT) algorithm and prediction model can readily detect and distinguish a range of explosives on the human body during disaster victim identification (DVI) processes of bombings. MATERIALS AND METHODS: A medical DECT of 8 explosives (Semtex, Pastex, Hexamethylene triperoxide diamine, Acetone peroxide, Nitrocellulose, Pentrite, Ammonium Nitrate, and classified explosive) was conducted ex-vivo and on an anthropomorphic phantom. Hounsfield unit (HU), electron density (ED), effective atomic number (Zeff), and dual energy index (DEI),were compared by Wilcoxon signed rank test. Intra-class (ICC) and Pearson correlation coefficients (r) were computed. Explosives classification was performed through a prediction model with test-retest samples. RESULTS: Except for DEI (p = 0.036), means of HU, ED, and Zeff were not statistically different (p > 0.05) between explosives ex-vivo and on the phantom (r > 0.80). Intra- and inter-reader ICC were good to excellent: 0.806 to 0.997 and 0.890, respectively. Except for the phantom DEI, all measurements from each individual explosive differed significantly. HU, ED, Zeff, and DEI differed depending on the type of explosive. Our decision tree provided Zeff and ED for explosives classification with high accuracy (83.7%) and excellent reliability (100%). CONCLUSION: Our medical DECT algorithm and prediction model can readily detect and distinguish our range of explosives on the human body. This would avoid possible endangering of DVI staff.

Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.

OBJECTIVE. The objective of our study was to assess the effect of the combination of deep learning-based denoising (DLD) and iterative reconstruction (IR) on image quality and Lung Imaging Reporting and Data System (Lung-RADS) evaluation on chest ultra-low-dose CT (ULDCT). MATERIALS AND METHODS. Forty-one patients with 252 nodules were evaluated retrospectively. All patients underwent ULDCT (mean ± SD, 0.19 ± 0.01 mSv) and standard-dose CT (SDCT) (6.46 ± 2.28 mSv). ULDCT images were reconstructed using hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR), and they were postprocessed using DLD (i.e., HIR-DLD and MBIR-DLD). SDCT images were reconstructed using filtered back projection. Three independent radiologists subjectively evaluated HIR, HIR-DLD, MBIR, and MBIR-DLD images on a 5-point scale in terms of noise, streak artifact, nodule edge, clarity of small vessels, homogeneity of the normal lung parenchyma, and overall image quality. Two radiologists independently evaluated the nodules according to Lung-RADS using HIR, MBIR, HIR-DLD, and MBIR-DLD ULDCT images and SDCT images. The median scores for subjective analysis were analyzed using Wilcoxon signed rank test with Bonferroni correction. Intraobserver agreement for Lung-RADS category between ULDCT and SDCT was evaluated using the weighted kappa coefficient. RESULTS. In the subjective analysis, ULDCT with DLD showed significantly better scores than did ULDCT without DLD (p < 0.001), and MBIR-DLD showed the best scores among the ULDCT images (p < 0.001) for all items. In the Lung-RADS evaluation, HIR showed fair or moderate agreement (reader 1 and reader 2: κw = 0.46 and 0.32, respectively); MBIR, moderate or good agreement (κw = 0.68 and 0.57); HIR-DLD, moderate agreement (κw = 0.53 and 0.48); and MBIR-DLD, good agreement (κw = 0.70 and 0.72). CONCLUSION. DLD improved the image quality of both HIR and MBIR on ULDCT. MBIR-DLD was superior to HIR_DLD for image quality and for Lung-RADS evaluation.

Quantitative CT Evidence of Airway Inflammation in WTC Workers and Volunteers with Low FVC Spirometric Pattern.

BACKGROUND: The most common abnormal spirometric pattern reported in WTC worker and volunteer cohorts has consistently been that of a nonobstructive reduced forced vital capacity (low FVC). Low FVC is associated with obesity, which is highly prevalent in these cohorts. We used quantitative CT (QCT) to investigate proximal and distal airway inflammation and emphysema in participants with stable low FVC pattern. METHODS: We selected study participants with at least two available longitudinal surveillance spirometries, and a chest CT with QCT measurements of proximal airway inflammation (wall area percent, WAP), end-expiratory air trapping, suggestive of distal airway obstruction (expiratory to inspiratory mean lung attenuation ratio, MLAEI), and emphysema (percentage of lung volume with attenuation below - 950 HU, LAV%). The comparison groups in multinomial logistic regression models were participants with consistently normal spirometries, and participants with stable fixed obstruction (COPD). RESULTS: Compared to normal spirometry participants, and after adjusting for age, sex, race/ethnicity, BMI, smoking, and early arrival at the WTC disaster site, low FVC participants had higher WAP (ORadj 1.24, 95% CI 1.06, 1.45, per 5% unit), suggestive of proximal airway inflammation, but did not differ in MLAEI, or LAV%. COPD participants did not differ in WAP with the low FVC ones and were more likely to have higher MLAEI or LAV% than the other two subgroups. DISCUSSION: WTC workers with spirometric low FVC have higher QCT-measured WAP compared to those with normal spirometries, but did not differ in distal airway and emphysema measurements, independently of obesity, smoking, and other covariates.

[Artificial intelligence and machine learning in oncologic imaging]. / Künstliche Intelligenz und maschinelles Lernen in der onkologischen Bildgebung.

Machine learning (ML) is entering many areas of society, including medicine. This transformation has the potential to drastically change medicine and medical practice. These aspects become particularly clear when considering the different stages of oncologic patient care and the involved interdisciplinary and intermodality interactions. In recent publications, computers-in collaboration with humans or alone-have been outperforming humans regarding tumor identification, tumor classification, estimating prognoses, and evaluation of treatments. In addition, ML algorithms, e.g., artificial neural networks (ANNs), which constitute the drivers behind many of the latest achievements in ML, can deliver this level of performance in a reproducible, fast, and inexpensive manner. In the future, artificial intelligence applications will become an integral part of the medical profession and offer advantages for oncologic diagnostics and treatment.

Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review.

Breast cancer is not preventable. To reduce the death rate and improve the survival chances of breast cancer patients, early and accurate detection is the only panacea. Delay in diagnosis of this disease causes 60% of deaths. Thermal imaging is a low-risk modality for early breast cancer decision making without injecting any form of energy into the human body. Thermography as a screening tool was first introduced and well accepted in 1956. However, a study in 1977 found that it lagged behind other screening tools and is subjective. Soon after, its use was discontinued. This review discusses various screening tools used to detect breast cancer with a focus on thermography along with their advantages and shortcomings. With the maturation of thermography equipment and technological advances, this technique is emerging and has become the refocus of many biomedical researchers across the globe in the past decade. This study dispenses an exhaustive review of the work done related to interpretation of breast thermal variations and confers the discipline, frameworks, and methodologies used by different authors to diagnose breast cancer. Different performance metrics like accuracy, specificity, and sensitivity have also been examined. This paper outlines the most pressing research gaps for future work to improvise the accuracy of results for diagnosis of breast abnormalities using image processing tools, mathematical modelling and artificial intelligence. However, supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively. Altogether, our findings inform that it is a promising research problem and a potential solution for early detection of breast cancer in younger women.

Thickness change of masseter muscles and the surrounding soft tissues in female patients during orthodontic treatment: a retrospective study.

BACKGROUND: Facial esthetics is a major concern of orthodontic patients. This study aims to evaluate orthodontic treatment-related thickness changes of the masseter muscles and surrounding soft tissues and the potential factors that would influence these changes during orthodontic treatment in female adults. METHODS: Forty-two female adult patients were included in this retrospective study and were divided into extraction (n = 22) and nonextraction (n = 20) groups. Pretreatment and posttreatment cone-beam computed tomography (CBCT) images were superimposed and reconstructed. The thickness changes of the masseter area of facial soft tissue (MAS), masseter muscles (MM) and surrounding fat tissue (FT) were measured. Pretreatment age, treatment duration, sagittal relationship (ANB), and vertical relationship (Frankfort-mandibular plane angle, FMA)-related MAS, MM and FT changes were compared between extraction and nonextraction groups. Spearman's correlation coefficient was calculated between the above variables. Regression analysis was conducted to confirm the causal relations of the variables. RESULTS: The thickness of MAS and MM significantly decreased in both groups, with larger decreases (> 1 mm) in the extraction group. There were strong correlations (r > 0.7) between the thickness decrease in MAS and MM in both groups and moderate correlations (r > 0.4) between MAS and FT in the nonextraction group. A significantly greater decrease of MAS and MM were found to be moderately correlated with a smaller FMA (r > 0.4) in the extraction group. Scatter plots and regression analysis confirmed these correlations. CONCLUSIONS: Masseter muscles and the surrounding soft tissue exhibited a significant decrease in thickness during orthodontic treatment in female adults. Low-angle patients experienced a greater decrease in soft tissue thickness in the masseter area in the extraction case. But the thickness changes were clinically very small in most patients.

Diffusion-Weighted MRI of Breast Cancer: Improved Lesion Visibility and Image Quality Using Synthetic b-Values.

BACKGROUND: Diffusion-weighted imaging (DWI) is an MRI technique with the potential to serve as an unenhanced breast cancer detection tool. Synthetic b-values produce images with high diffusion weighting to suppress residual background signal, while avoiding additional measurement times and reducing artifacts. PURPOSE: To compare acquired DWI images (at b = 850 s/mm2 ) and different synthetic b-values (at b = 1000-2000 s/mm2 ) in terms of lesion visibility, image quality, and tumor-to-tissue contrast in patients with malignant breast tumors. STUDY TYPE: Retrospective. POPULATION: Fifty-three females with malignant breast lesions. FIELD STRENGTH/SEQUENCE: T2 w, DWI EPI with STIR fat-suppression, and dynamic contrast-enhanced T1 w at 3T. ASSESSMENT: From acquired images using b-values of 50 and 850 s/mm2 , synthetic images were calculated at b = 1000, 1200, 1400, 1600, 1800, and 2000 s/mm2 . Four readers independently rated image quality, lesion visibility, preferred b-value, as well as the lowest and highest b-value, over the range of b-values tested, to provide a diagnostic image. STATISTICAL TESTS: Medians and mean ranks were calculated and compared using the Friedman test and Wilcoxon signed-rank test. Reproducibility was analyzed by intraclass correlation (ICC), Fleiss, and Cohen's κ. RESULTS: Relative signal-to-noise and contrast-to-noise ratios decreased with increasing b-values, while the signal-intensity ratio between tumor and tissue increased significantly (P < 0.001). Intermediate b-values (1200-1800 s/mm2 ) were rated best concerning image quality and lesion visibility; the preferred b-value mostly lay at 1200-1600 s/mm2 . Lowest and highest acceptable b-values were 850 s/mm2 and 2000 s/mm2 . Interreader agreement was moderate to high concerning image quality (ICC: 0.50-0.67) and lesion visibility (0.70-0.93), but poor concerning preferred and acceptable b-values (κ = 0.032-0.446). DATA CONCLUSION: Synthetically increased b-values may be a way to improve tumor-to-tissue contrast, lesion visibility, and image quality of breast DWI, while avoiding the disadvantages of performing DWI at very high b-values. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1754-1761.