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Purpose: To compare real-time compressed sensing (CS) and standard balanced steady-state free precession (bSSFP) cardiac cine imaging in children., Materials and Methods: Twenty children (mean age, 15 years ± 5 [SD], range, 7-21 years; 10 male participants) with biventricular congenital heart disease ( n = 11) or cardiomyopathy ( n = 9) were prospectively included. Examinations were performed with 1.5-T imagers by using both bSSFP and CS sequences in all participants. Quantification of ventricular volumes and function was performed for all images by two readers blinded to patient diagnosis and type of sequence. Values were correlated with phase-contrast flow measurements by one reader. Intra- and interreader agreement were analyzed., Results: There were no significant differences between ventricular parameters measured on CS compared with those of bSSFP ( P > .05) for reader 1. Only ejection fraction showed a significant difference ( P = .02) for reader 2. Intrareader agreement was considerable for both sequences (bSSFP: mean difference range, +1 to -2.6; maximum CI, +7.9, -13; bias range, 0.1%-4.1%; intraclass correlation coefficient [ICC] range, 0.931-0.997. CS: mean difference range, +7.4 to -5.6; maximum CI, +37.2, -48.8; bias range, 0.5%-7.5%; ICC range, 0.717-0.997). Interreader agreement was acceptable but less robust, especially for CS (bSSFP: mean difference range, +2.6 to -5.6; maximum CI, +60.7, -65.3; bias range, 1.6%-6.2%; ICC range, 0.726-0.951. CS: mean difference range, +10.7 to -9.1; maximum CI, +87.5, -84.6; bias range, 1.1%-17.3%; ICC range, 0.509-0.849). The mean acquisition time was shorter for CS (20 seconds; range, 17-25 seconds) compared with that for bSSFP (160 seconds; range, 130-190 seconds) ( P < .001)., Conclusion: CS cardiac cine imaging provided equivalent ventricular volume and function measurements with shorter acquisition times compared with those of bSSFP and may prove suitable for the pediatric population. Keywords: Compressed Sensing, Balanced Steady-State Free Precession, Cine Imaging, Cardiovascular MRI, Pediatrics, Cardiac, Heart, Cardiomyopathies, Congenital, Segmentation© RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: D. Curione No relevant relationships. P. Ciliberti No relevant relationships. C.B.M. No relevant relationships. D. Capra No relevant relationships. V.B. No relevant relationships. P. Ciancarella No relevant relationships. T.P.S. No relevant relationships. C.N. No relevant relationships. D.F. No relevant relationships. M.A.P. No relevant relationships. F.S. No relevant relationships. A.S. No relevant relationships., (© 2022 by the Radiological Society of North America, Inc.)

Mammographic breast density (BD) is commonly visually assessed using the Breast Imaging Reporting and Data System (BI-RADS) four-category scale. To overcome inter- and intraobserver variability of visual assessment, the authors retrospectively developed and externally validated a software for BD classification based on convolutional neural networks from mammograms obtained between 2017 and 2020. The tool was trained using the majority BD category determined by seven board-certified radiologists who independently visually assessed 760 mediolateral oblique (MLO) images in 380 women (mean age, 57 years ± 6 [SD]) from center 1; this process mimicked training from a consensus of several human readers. External validation of the model was performed by the three radiologists whose BD assessment was closest to the majority (consensus) of the initial seven on a dataset of 384 MLO images in 197 women (mean age, 56 years ± 13) obtained from center 2. The model achieved an accuracy of 89.3% in distinguishing BI-RADS a or b (nondense breasts) versus c or d (dense breasts) categories, with an agreement of 90.4% (178 of 197 mammograms) and a reliability of 0.807 (Cohen κ) compared with the mode of the three readers. This study demonstrates accuracy and reliability of a fully automated software for BD classification. Keywords: Mammography, Breast, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms Supplemental material is available for this article. © RSNA, 2022., Competing Interests: Disclosures of conflicts of interest: V.M. No relevant relationships. M.I. CTO and employee of DeepTrace Technologies. DeepTrace Technologies is a spin-off of Scuola Universitaria Superiore IUSS, Pavia, Italy; shareholder in DeepTrace Technologies. A.C. No relevant relationships. M.A. Scientific advisor for Bracco Imaging. C.S. CEO of DeepTrace Technologies. DeepTrace Technologies is a spin-off of Scuola Universitaria Superiore IUSS, Pavia, Italy; shareholder in DeepTrace Technologies. A.A.A. No relevant relationships. D.C. No relevant relationships. S.C. No relevant relationships. G.D.P. No relevant relationships. D.F. No relevant relationships. G.G. No relevant relationships. C.B.M. No relevant relationships. G.M. No relevant relationships. G.P. No relevant relationships. S.S. Honoraria for lectures from GE Healthcare; support for attending meetings/travel from GE Healthcare. I.C. Shareholder in DeepTrace Technologies. S.P. No relevant relationships. F.S. Member of Scientific Advisory Board for DeepTrace Technologies., (2022 by the Radiological Society of North America, Inc.)

The purpose of this work was to assess the performance of a convolutional neural network (CNN) for automatic thoracic aortic measurements in a heterogeneous population. From June 2018 to May 2019, this study retrospectively analyzed 250 chest CT scans with or without contrast enhancement and electrocardiographic gating from a heterogeneous population with or without aortic pathologic findings. Aortic diameters at nine locations and maximum aortic diameter were measured manually and with an algorithm (Artificial Intelligence Rad Companion Chest CT prototype, Siemens Healthineers) by using a CNN. A total of 233 examinations performed with 15 scanners from three vendors in 233 patients (median age, 65 years [IQR, 54-72 years]; 144 men) were analyzed: 68 (29%) without pathologic findings, 72 (31%) with aneurysm, 51 (22%) with dissection, and 42 (18%) with repair. No evidence of a difference was observed in maximum aortic diameter between manual and automatic measurements ( P = .48). Overall measurements displayed a bias of -1.5 mm and a coefficient of repeatability of 8.0 mm at Bland-Altman analyses. Contrast enhancement, location, pathologic finding, and positioning inaccuracy negatively influenced reproducibility ( P < .003). Sites with dissection or repair showed lower agreement than did sites without. The CNN performed well in measuring thoracic aortic diameters in a heterogeneous multivendor CT dataset. Keywords: CT, Vascular, Aorta © RSNA, 2022., Competing Interests: Disclosures of Conflicts of Interest: C.B.M. No relevant relationships. M.v.A. AI algorithms were provided by Siemens. A.E.S. Editorial board member of Radiology: Cardiothoracic Imaging. S.J.L. No relevant relationships. P.H. Employee of Siemens Healthineers; stock shares in Siemens Healthineers. G.S.K.F. Employee of Siemens Healthineers; stock/stock options in Siemens Healthineers. F. Secchi No relevant relationships. F. Sardanelli Grants to institution from Bayer and General Electric; member of speakers’ bureau and of advisory group for General Electric, Bayer, and Bracco (honoraria paid to author); support for attending meetings/travel from Bayer, Bracco, and General Electric; equipment to hospital from General Electric. C.N.D.C. Institutional research support from Siemens., (2022 by the Radiological Society of North America, Inc.)