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Page 10 of 12 Torabinia et al. Mini-invasive Surg 2021;5:32 https://dx.doi.org/10.20517/2574-1225.2021.63

be trivial and therefore methods that utilize the spine as an intrinsic fiducial maker should be used during
[58]
acquisition of clinical images, as described in our previous work .
Due to the above listed limitations, this work will have the most immediate impact for performing
quantitative analysis of training procedure on 3D printed heart models. We expect that more sophisticated
heart models that include motion and match disease states will be created, along with specific criteria for
success for each model/intervention to provide feedback in the form of quantitative metrics. Furthermore,
the ability to process images in real-time and display the catheter in MR renderings will improve training by
providing assistance during the training session, as described in our previous work that adopted EM sensors
for tracking . We believe this tracking system will serve to lower the learning curve for new fellows and
[35]
refine the procedural techniques of attendings.

DECLARATIONS
Acknowledgments
We thank the Dalio Institute of Cardiovascular Imaging for their support and funding.

Authors’ contributions
Conceived of the presented work: Mosadegh B, Torabinia M
Took the lead in writing the manuscript: Torabinia M, with support from Mosadegh B
Carried out the experiment: Caprio A, Torabinia M
Involved in processing and analyzing the datasets: Jang S, Ma T, Tran H, Mekki L, Chen I, led by
Torabinia M
Supervised the theoretical and deep learning framework: Sabuncu M, Mosadegh B
Supervised the image acquisition: Wong S, Mosadegh B
All authors discussed the results and commented on the manuscript.

Availability of data and materials
Not applicable.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2021.

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