Hell MM - Search Results

1

Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study.

Commandeur F, Goeller M, Razipour A, Cadet S, Hell MM, Kwiecinski J, Chen X, Chang HJ, Marwan M, Achenbach S, Berman DS, Slomka PJ, Tamarappoo BK, Dey D. Commandeur F, et al. Among authors: hell mm. Radiol Artif Intell. 2019 Nov 27;1(6):e190045. doi: 10.1148/ryai.2019190045. Radiol Artif Intell. 2019. PMID: 32090206 Free PMC article.

2

Prospectively ECG-triggered high-pitch coronary angiography with third-generation dual-source CT at 70 kVp tube voltage: feasibility, image quality, radiation dose, and effect of iterative reconstruction.

Hell MM, Bittner D, Schuhbaeck A, Muschiol G, Brand M, Lell M, Uder M, Achenbach S, Marwan M. Hell MM, et al. J Cardiovasc Comput Tomogr. 2014 Nov-Dec;8(6):418-25. doi: 10.1016/j.jcct.2014.09.003. Epub 2014 Sep 16. J Cardiovasc Comput Tomogr. 2014. PMID: 25439789

3

[Aortic valve stenosis: computed tomography prior to transcatheter aortic valve implantation (TAVI). How can the outcome be improved?].

Hell M, Achenbach S, Arnold M. Hell M, et al. Herz. 2015 Jun;40(4):576-82. doi: 10.1007/s00059-015-4232-4. Herz. 2015. PMID: 25963033 Review. German.

4

Relationship of epicardial fat volume from noncontrast CT with impaired myocardial flow reserve by positron emission tomography.

Otaki Y, Hell M, Slomka PJ, Schuhbaeck A, Gransar H, Huber B, Nakazato R, Germano G, Hayes SW, Thomson LE, Friedman JD, Achenbach S, Berman DS, Dey D. Otaki Y, et al. J Cardiovasc Comput Tomogr. 2015 Jul-Aug;9(4):303-9. doi: 10.1016/j.jcct.2015.03.005. Epub 2015 Mar 25. J Cardiovasc Comput Tomogr. 2015. PMID: 25977114

5

Non-invasive prediction of hemodynamically significant coronary artery stenoses by contrast density difference in coronary CT angiography.

Hell MM, Dey D, Marwan M, Achenbach S, Schmid J, Schuhbaeck A. Hell MM, et al. Eur J Radiol. 2015 Aug;84(8):1502-1508. doi: 10.1016/j.ejrad.2015.04.024. Epub 2015 May 11. Eur J Radiol. 2015. PMID: 26001435

6

CT-based analysis of pericoronary adipose tissue density: Relation to cardiovascular risk factors and epicardial adipose tissue volume.

Hell MM, Achenbach S, Schuhbaeck A, Klinghammer L, May MS, Marwan M. Hell MM, et al. J Cardiovasc Comput Tomogr. 2016 Jan-Feb;10(1):52-60. doi: 10.1016/j.jcct.2015.07.011. Epub 2015 Jul 29. J Cardiovasc Comput Tomogr. 2016. PMID: 26256553

7

Epicardial adipose tissue volume but not density is an independent predictor for myocardial ischemia.

Hell MM, Ding X, Rubeaux M, Slomka P, Gransar H, Terzopoulos D, Hayes S, Marwan M, Achenbach S, Berman DS, Dey D. Hell MM, et al. J Cardiovasc Comput Tomogr. 2016 Mar-Apr;10(2):141-9. doi: 10.1016/j.jcct.2016.01.009. Epub 2016 Jan 13. J Cardiovasc Comput Tomogr. 2016. PMID: 26817413

8

Contrast volume reduction using third generation dual source computed tomography for the evaluation of patients prior to transcatheter aortic valve implantation.

Bittner DO, Arnold M, Klinghammer L, Schuhbaeck A, Hell MM, Muschiol G, Gauss S, Lell M, Uder M, Hoffmann U, Achenbach S, Marwan M. Bittner DO, et al. Among authors: hell mm. Eur Radiol. 2016 Dec;26(12):4497-4504. doi: 10.1007/s00330-016-4320-8. Epub 2016 Mar 19. Eur Radiol. 2016. PMID: 26995206

9

Software innovations in computed tomography for structural heart disease interventions.

Hell M, Marwan M, Gaede L, Achenbach S. Hell M, et al. EuroIntervention. 2016 May 17;12 Suppl X:X68-X74. doi: 10.4244/EIJV12SXA13. EuroIntervention. 2016. PMID: 27174116 Free article.

10

Prediction of fluoroscopic angulations for transcatheter aortic valve implantation by CT angiography: influence on procedural parameters.

Hell MM, Biburger L, Marwan M, Schuhbaeck A, Achenbach S, Lell M, Uder M, Arnold M. Hell MM, et al. Eur Heart J Cardiovasc Imaging. 2017 May 1;18(8):906-914. doi: 10.1093/ehjci/jew144. Eur Heart J Cardiovasc Imaging. 2017. PMID: 27461209

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