Machine Learning in the Cathlab

Congress:

ECR 2022

Poster Number:

C-11045

Type:

Scientific Exhibit

Keywords:

Artificial Intelligence, Computer applications, Interventional vascular, Image manipulation / Reconstruction, Neural networks, Computer Applications-Detection, diagnosis, Computer Applications-General, Technology assessment, Image registration, Image verification

Authors:

D. Ruijters

DOI:

10.26044/ecr2022/C-11045

DOI-Link:

https://dx.doi.org/10.26044/ecr2022/C-11045

References

P. Ambrosini, I. Smal, D. Ruijters, W. Niessen, A. Moelker, and T. van Walsum, “A Hidden Markov Model for 3D Catheter Tip Tracking with 2D X-ray Catheterization Sequence and 3D Rotational Angiography”, IEEE Transactions on Medical Imaging, vol. 36(3), pp. 757-768, March 2017. https://doi.org/10.1109/TMI.2016.2625811
L. Spelle, D. Ruijters, D. Babic, R. Homan, P. Mielekamp, J. Guillermic, and J. Moret, “First clinical experience in applying XperGuide in embolization of jugular paragangliomas by direct intratumoral puncture”, International Journal of Computer Assisted Radiology and Surgery, vol. 4(6), pp. 527-533, November 2009. https://doi.org/10.1007/s11548-009-0370-6
E. Kang, J. Min, and J.C. Ye. "A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction." Medical physics 44(10): e360-e375, 2017. https://doi.org/10.1002/mp.12344
D. Ruijters, B.M. ter Haar Romeny, and P. Suetens, “Efficient GPU-Accelerated Elastic Image Registration”, in Proceedings of the Sixth International Conference on BIOMEDICAL ENGINEERING (BioMed), February 13-15, 2008, Innsbruck (Austria), pp. 419-424.
B. Fan, W. Fan, and C. Smith. “Adverse drug event detection and extraction from open data: A deep learning approach”. Information Processing & Management, 57(1):102131, 2020. https://doi.org/10.1016/j.ipm.2019.102131
C. Garrow, K.-F. Kowalewski, L. Li, M. Wagner, M. Schmidt, S. Engelhardt, D.A. Hashimoto, H.G. Kenngott, S. Bodenstedt, S. Speidel, B.P. Müller-Stich, and F. Nickel. “Machine Learning for Surgical Phase Recognition: A Systematic Review”, Annals of Surgery 273(4): 684-693, 2021. https://doi.org/10.1097/SLA.0000000000004425
Y. Hong, F. Commandeur, S. Cadet, M. Goeller, M.K. Doris, X. Chen, J. Kwiecinski, D.S. Berman, P.J. Slomka, H.J. Chang, and D. Dey. “Deep learning-based stenosis quantification from coronary CT Angiography”. Proc SPIE Int Soc Opt Eng.: 109492I, 2019. https://doi.org/10.1117/12.2512168
J.M. Kwon, K.H. Jeon, H.M. Kim, M.J. Kim, S. Lim, K.H. Kim, P.S. Song, J. Park, R.K. Choi, and B.H. Oh. “Deep-learning-based risk stratification for mortality of patients with acute myocardial infarction”, PloS one, 14(10), e0224502, 2019. https://doi.org/10.1371/journal.pone.0224502
O. Brina, R. Ouared, O. Bonnefous, F. van Nijnatten, P. Bouillot, P. Bijlenga, K. Schaller, K.-O. Lovblad, T. Grünhagen, D. Ruijters, and V.M. Pereira, “Intra-Aneurysmal Flow Patterns: Illustrative Comparison among Digital Subtraction Angiography, Optical Flow, and Computational Fluid Dynamics”, AJNR American Journal of Neuroradiology, vol. 35(12):2348-2353, 2014. https://doi.org/10.3174/ajnr.A4063
Litjens, F. Ciompi, J.M. Wolterink, B.D. de Vos, T. Leiner, J. Teuwen, and I. Išgum, “State-of-the-art deep learning in cardiovascular image analysis”, JACC: Cardiovascular Imaging, vol. 12(8 Part 1), pp. 1549-1565, 2019. https://doi.org/10.1016/j.jcmg.2019.06.009
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A. Garcia, S. Bhakta, J. Kay, K.-C. Chan, O. Wink, D. Ruijters, and J.D. Carroll, “On-line multi-slice computed tomography interactive overlay with conventional X-ray: A new and advanced imaging fusion concept”, International Journal of Cardiology, vol. 133(3), pp. e101-e105, April 17, 2009. https://doi.org/10.1016/j.ijcard.2007.11.049
Balzer, T. Zeus, K. Hellhammer, V. Veulemans, S. Eschenhagen, E. Kehmeier, C. Meyer, T. Rassaf, and M. Kelm, „Initial clinical experience using the EchoNavigator-system during structural heart disease interventions”, World journal of cardiology, vol. 7(9), pp. 562, 2015. https://dx.doi.org/10.4330%2Fwjc.v7.i9.562
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D. de Vos, F.F. Berendsen, M.A. Viergever, H. Sokooti, M. Staring, and I. Išgum, “A deep learning framework for unsupervised affine and deformable image registration”, Medical image analysis, vol. 52, pp. 128-143, 2019. https://doi.org/10.1016/j.media.2018.11.010
F. Mut, D. Ruijters, D. Babic, C. Bleise, P. Lylyk, and J.R. Cebral, “Effects of changing physiologic conditions on the in vivo quantification of hemodynamic variables in cerebral aneurysms treated with flow diverting devices”, International Journal for Numerical Methods in Biomedical Engineering, vol. 30(1), pp. 135-142, January 2014. https://doi.org/10.1002/cnm.2594

Fig 1: Automatic device detection, such as catheter segmentation and catheter tip...

Fig 2: Device recognition (valve, pigtail, temp lead), including deployment status....

Fig 3: The presence of stitches does not prohibit the machine learning driven stent...

Fig 4: Example of spatially registered multi-modal datasets, used for percutaneous...

Fig 5: Example of intra-vascular flow imaging using digitally subtracted angiography...