Here are links to cited references to further explore the topics proposed in our newsletters.
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Oderich GS, Kärkkäinen JM, Reed NR et al. Penetrating aortic ulcer and intramural hematoma. Cardiovasc Intervent Radiol. 2019 Mar;42(3):321-334.
LinkPanagiotopoulos N, Drüschler F, Simon M et al. Significance of an additional unenhanced scan in computed tomography angiography of patients with suspected acute aortic syndrome. World J Radiol. 2018 Nov 28;10(11):150-161.
LinkHerrán FL, Bang TJ, Restauri N et al. CT imaging of complications of aortic intramural hematoma: a pictorial essay. Diagn Interv Radiol. 2018 Nov;24(6):342-347.
LinkFukui T. Management of acute aortic dissection and thoracic aortic rupture. J Intensive Care. 2018 Mar 1;6:15.
LinkYasaka K, Akai H, Kunimatsu Aesserli M et al. Deep learning for staging liver fibrosis on CT: a pilot study. Eur Radiol. 2018 May 14. doi: 10.1007/s00330-018-5499-7. [Epub ahead of print]
LinkPesapane F, Codari M, Sardanelli F. Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp. 2018 Oct 24;2(1):35
LinkSelvarajah A, Bennamoun M, Playford D et al. Application of artificial intelligence in coronary computed tomography angiography. Curr Cardiovasc Imaging Rep. 2018;11:12
LinkNakajima J. Advances in techniques for identifying small pulmonary nodules. Surg Today. 2018 Nov 24. doi: 10.1007/s00595-018-1742-8. [Epub ahead of print]
LinkSnoeckx A, Reyntiens P, Desbuquoit D et al. Evaluation of the solitary pulmonary nodule: size matters, but do not ignore the power of morphology. Insights Imaging. 2018 Feb;9(1):73-86
LinkZhao X, Liu L, Qi S et al. Agile convolutional neural network for pulmonary nodule classification using CT images. Int J Comput Assist Radiol Surg. 2018 Apr;13(4):585-595
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