@article {Choi2020,
	title = {3D Modeling of Surgically Implanted Stent-Based Valves in the Mitral Position in Children},
	journal = {The Annals of Thoracic Surgery},
	year = {2020},
	doi = {10.1016/j.athoracsur.2020.02.020},
	url = {https://doi.org/10.1016/j.athoracsur.2020.02.020},
	author = {Perry S. Choi and HannahH Nam and Andras Lasso and Christian Herz and Simon Drouin and David M. Harrild and Michael Quartermain and Gabor Fichtinger and Christopher E. Mascio and Sitaram Emani and MatthewA Jolley}
}
@article {Fedorov2020,
	title = {Quantitative Imaging Informatics for Cancer Research},
	journal = {JCO Clinical Cancer Informatics},
	volume = {4},
	year = {2020},
	pages = {444-453.},
	doi = {https://doi.org/10. 1200/CCI.19.00165},
	author = {Andriy Fedorov and Reinhard Beichel and Jayashree Kalpathy-Cramer and David Clunie and Michael Onken and J{\"o}rg Riesmeier and Christian Herz and Christian Bauer and Andrew Beers and Jean-ChristopheC. Fillion-Robin and Andras Lasso and Csaba Pinter and Steve Pieper and Marco Nolden and Klaus Maier-Hein and Markus D. Herrmann and Joel Saltz and Fred Prior and Fiona M. Fennessy and John Buatti and Ron Kikinis}
}
@article {Herz2017,
	title = {dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM},
	journal = {Cancer Research},
	volume = {77},
	number = {21},
	year = {2017},
	month = {11/2017},
	pages = {e87{\textendash}e90},
	abstract = {<p>Quantitative analysis of clinical image data is an active area of research that holds promise for precision medicine, early assessment of treatment response, and objective characterization of the disease. Interoperability, data sharing, and the ability to mine the resulting data are of increasing importance, given the explosive growth in the number of quantitative analysis methods being proposed. The Digital Imaging and Communications in Medicine (DICOM) standard is widely adopted for image and metadata in radiology. dcmqi (DICOM for Quantitative Imaging) is a free, open source library that implements conversion of the data stored in commonly used research formats into the standard DICOM representation. dcmqi source code is distributed under BSD-style license. It is freely available as a precompiled binary package for every major operating system, as a Docker image, and as an extension to 3D Slicer. Installation and usage instructions are provided in the GitHub repository at https://github.com/qiicr/dcmqi. Cancer Res; 77(21); e87{\textendash}90. {\textcopyright}2017 AACR.</p>
},
	issn = {0008-5472},
	doi = {10.1158/0008-5472.CAN-17-0336},
	url = {http://cancerres.aacrjournals.org/content/77/21/e87},
	author = {Christian Herz and Jean-ChristopheC. Fillion-Robin and Michael Onken and J{\"o}rg Riesmeier and Andras Lasso and Csaba Pinter and Gabor Fichtinger and Steve Pieper and Clunie, David and Ron Kikinis and Andriy Fedorov}
}
