The impact of iterative reconstruction algorithms on machine learning-based coronary CT angiography-derived fractional flow reserve (CT-FFRML) values
Author:
Publisher
Springer Science and Business Media LLC
Subject
Cardiology and Cardiovascular Medicine,Radiology, Nuclear Medicine and imaging
Link
http://link.springer.com/content/pdf/10.1007/s10554-020-01807-7.pdf
Reference32 articles.
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4. Itu L, Rapaka S, Passerini T, Georgescu B, Schwemmer C, Schoebinger M, Flohr T, Sharma P (1985) Comaniciu D (2016) A machine-learning approach for computation of fractional flow reserve from coronary computed tomography. J Appl Physiol 121(1):42–52. https://doi.org/10.1152/japplphysiol.00752.2015
5. Tesche C, De Cecco CN, Baumann S, Renker M, McLaurin TW, Duguay TM, Bayer RR 2nd, Steinberg DH, Grant KL, Canstein C, Schwemmer C, Schoebinger M, Itu LM, Rapaka S, Sharma P, Schoepf UJ (2018) Coronary CT angiography-derived fractional flow reserve: machine learning algorithm versus computational fluid dynamics modeling. Radiology 288(1):64–72. https://doi.org/10.1148/radiol.2018171291
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