Corresponding author:
PD Dr. med. Simon H. Sündermann, MD
Charité- Universitaetsmedizin Berlin
Department of Cardiovascular Surgey
Augustenburger Platz 1
13353 Berlin
Phone: +49 30 450 565841
Email:
simon.suendermann@charite.de
Web: https://herzchirurgie.charite.de/
https://tavi.charite.de/
In the study by Kenichi and colleagues published in this issue of the
Journal of Cardiac Surgery, a pig’s heart was filled with silicone,
subjected to a CT scan and the images imported into special software to
display it as a virtual 3D model and measure the relevant anatomical
structures. What sounds a little strange at first glance makes perfect
sense for a few reasons: Cardiac imaging has become an important part of
procedure planning in cardiovascular medicine, especially for structural
heart but also coronary artery
disease1,2.
Image modalities that play a crucial role in this context are cardiac
computed tomography
(CCT)3 ,
echocardiography4–6and cardiac magnetic resonance imaging
(CMRI)7.
Each modality has its own advantages and disadvantages. While CCT has a
very high spatial resolution, echocardiography and CMR can be used to
perform functional examinations of the heart. To take advantage of
different modalities simultaneously, fusion of imaging modalities has
been proposed, e.g. fusion of echocardiography and fluoroscopy for
mitral
interventions8or fusion of invasive coronary angiography and myocardial perfusion
imaging9and others. With the increasing computing power of computers, the use of
medical images is now reaching new possibilities: The available data is
used to segment cardiac structures or even whole
hearts10–12.
These geometric models can then be used for further applications, such
as simulating cardiac
interventions13–15or 3D printing to better understand complex anatomies. The latter became
popular before interventions involving congenital heart defects16, as
these are highly individualised from patient to patient. Using a
3D-printed model, surgical planning can be done ”hands-on”. But the use
of 3D models can also be very helpful in adult cardiac surgery, e.g.
mitral valve repair, where the anatomy on the arrested heart can be very
complex to
assess17.
In addition to sophisticated modelling of the anatomical structures of
the heart and therapy simulation, a third application of the use of
high-quality cardiac images can be seen in education and training. Here,
too, virtual visualisations of three-dimensional images, but also
3D-printed models can be used. For example, training ventricular septal
defect repair on a silicone model of a heart is described as a useful
method for
training18.
In adult cardiac procedures, 3D-printed models can help in planning
access, for example in surgical minimally invasive aortic valve
replacement19 or to
avoid complications in transcatheter aortic valve
implantation20.
The very recent trend is to integrate 3D models into virtual reality
applications, as described in the paper by Kenichi and colleagues
presented in this issue. Advantages of such tools are the possibilities
to interact with 3D images with little effort. Models can be rotated in
all directions, zoomed in and out and measurements can be taken. In
addition, 3D models can be used in augmented reality scenarios, e.g. for
decision support in complex surgical
procedures21.
Despite numerous achievements in this field, there are still some
limitations to this advanced technology: First of all, the availability
of such tools is limited. Cost can play a role. However, another
important factor is the ”unfriendly” user interface of many applications
and the complicated integration into the clinical environment.
Development is sometimes done without sufficient communication with the
clinician, who is the end user. However, this problem arises on both
sides of the street. Clinicians often have limited interest in new tools
that could help facilitate procedures in the future, but are not yet
finally developed. The applications are at an early stage and still have
many flaws and drawbacks. Very few are available as ready-to-use tools.
Paradoxically, much input from clinicians would be important on the way
to a ready-to-use solution, but this is often lacking. Another fact that
could lead to mistrust of the new tools is the very small number of
patients that are often included in studies to validate the tools. A
very important feature of applications used in a medical context is of
course the accuracy of the models and measurements. In many studies, the
validation of the models is not convincing precisely because of the
small number of patients and images that are used to develop and
validate the models which may be enough to produce nice pictures and
prove the feasibility of certain applications or when the model is used
for training purposes. However, as soon as it becomes clinically
relevant, the accuracy of the models must be close to one hundred
percent. The method described in this paper is an interesting concept
for an alternative method to validate the accuracy of a particular tool,
in this case virtual reality modelling. The model was compared with
other standard measures like 3D-MPR assessment and with manual
measurements made possible by using the silicon cast of the original
anatomy. Unfortunately, only the feasibility on one model was shown and
the measurements were not repeated with multiple hearts or with multiple
investigators performing the measurements. However, such a method has
the potential to be useful for validation purposes, as the silicon model
is naturally more available and consistent than patient data. The
development of methods that do not require patient involvement and
produce the same results is highly desirable. A big step has already
been taken with the advent of machine learning algorithms used in
cardiovascular modelling. Several entities are currently being studied,
for example, aortic aneurysm. Liang et al. used datasets from
twenty-five patients to sample 729 representative aortic shapes from all
shape distributions described by a statistical shape model. Rupture risk
was calculated using finite element analysis and the backward
displacement
method22.
They could show that their approach is much faster than other methods
and had a high risk classification accuracy. Currently, modelling heart
structures and simulating therapies is already possible. Many concepts
are on the way and developing rapidly. To be convincing and relevant in
daily clinical routine, comprehensible validation to prove accuracy is
essential. Otherwise, it is dangerous for clinicians to rely on virtual
models. The path is already paved, but currently cardiac modelling,
simulation and virtual reality are still in development and need to be
considered as additional tools for planning and decision-making in
cardiac interventions. In the near future, the incredible opportunities
presented by such techniques are likely to play a major role in
cardiovascular care. Small steps and studies like the one by Kenichi and
colleagues are very important to continue on this path, so that virtual
reality arrives in medicine and is not reserved for video game lovers.
Literature
1. Di Carli MF, Geva T,
Davidoff R. The Future of Cardiovascular Imaging. Circulation2016;133:2640–2661.
2. Edvardsen T, Asch FM,
Davidson B, Delgado V, DeMaria A, Dilsizian V, Gaemperli O, Garcia MJ,
Kamp O, Lee DC, Neglia D, Neskovic AN, Pellikka PA, Plein S, Sechtem U,
Shea E, Sicari R, Villines TC, Lindner JR, Popescu BA. Non-Invasive
Imaging in Coronary Syndromes: Recommendations of The European
Association of Cardiovascular Imaging and the American Society of
Echocardiography, in Collaboration with The American Society of Nuclear
Cardiology, Society of Cardiovascular Computed Tomography, and Society
for Cardiovascular Magnetic Resonance. J Am Soc Echocardiogr2022;35:329–354.
3. Pontone G, Rossi A,
Guglielmo M, Dweck MR, Gaemperli O, Nieman K, Pugliese F,
Maurovich-Horvat P, Gimelli A, Cosyns B, Achenbach S. Clinical
applications of cardiac computed tomography: a consensus paper of the
European Association of Cardiovascular Imaging—part II. Eur
Heart J Cardiovasc Imaging 2022;23:e136–e161.
4. Agricola E, Meucci F,
Ancona F, Pardo Sanz A, Zamorano JL. Echocardiographic guidance in
transcatheter structural cardiac interventions. EuroIntervention2022;17:1205–1226.
5. Barreiro-Perez M,
Caneiro-Queija B, Puga L, Gonzalez-Ferreiro R, Alarcon R, Parada JA,
Iñiguez-Romo A, Estevez-Loureiro R. Imaging in Transcatheter Mitral
Valve Replacement: State-of-Art Review. J Clin Med Res2021;10.
6. Sengupta A, Alexis SL, Zaid
S, Tang GHL, Lerakis S, Martin RP. Imaging the mitral valve: a primer
for the interventional surgeon. Ann Cardiothorac Surg2021;10:28–42.
7. Ismail TF, Strugnell W,
Coletti C, Božić-Iven M, Weingärtner S, Hammernik K, Correia T, Küstner
T. Cardiac MR: From Theory to Practice. Front Cardiovasc Med2022;9:826283.
8. Sündermann SH, Biaggi P,
Grünenfelder J, Gessat M, Felix C, Bettex D, Falk V, Corti R. Safety and
feasibility of novel technology fusing echocardiography and fluoroscopy
images during MitraClip interventions. EuroIntervention2014;9:1210–1216.
9. Xu Z, Tang H, Malhotra S,
Dong M, Zhao C, Ye Z, Zhou Y, Xu S, Li D, Wang C, Zhou W.
Three-dimensional Fusion of Myocardial Perfusion SPECT and Invasive
Coronary Angiography Guides Coronary Revascularization. J Nucl
Cardiol 2022.
10. Tautz L, Neugebauer M,
Hüllebrand M, Vellguth K, Degener F, Sündermann S, Wamala I, Goubergrits
L, Kuehne T, Falk V, Hennemuth A. Extraction of open-state mitral valve
geometry from CT volumes. Int J Comput Assist Radiol Surg2018;13:1741–1754.
11. Baillargeon B, Rebelo N,
Fox DD, Taylor RL, Kuhl E. The Living Heart Project: A robust and
integrative simulator for human heart function. Eur J Mech A:
Solids 2014;48:38–47.
12. Jafari A, Pszczolkowski E,
Krishnamurthy A. A framework for biomechanics simulations using
four-chamber cardiac models. J Biomech 2019;91:92–101.
13. Finotello A, Gorla R,
Brambilla N, Bedogni F, Auricchio F, Morganti S. Finite element analysis
of transcatheter aortic valve implantation: Insights on the modelling of
self-expandable devices. J Mech Behav Biomed Mater2021;123:104772.
14. Walczak L, Tautz L,
Neugebauer M, Georgii J, Wamala I, Sündermann S, Falk V, Hennemuth A.
Interactive editing of virtual chordae tendineae for the simulation of
the mitral valve in a decision support system. Int J Comput Assist
Radiol Surg 2020.
15. Choi A, Rim Y, Mun JS, Kim
H. A novel finite element-based patient-specific mitral valve repair:
virtual ring annuloplasty. Biomed Mater Eng2014;24:341–347.
16. Kiraly L, Shah NC,
Abdullah O, Al-Ketan O, Rowshan R. Three-Dimensional Virtual and Printed
Prototypes in Complex Congenital and Pediatric Cardiac Surgery-A
Multidisciplinary Team-Learning Experience. Biomolecules2021;11.
17. Yang Y, Wang H, Song H, Hu
X, Hu R, Cao S, Guo J, Zhou Q. A soft functional mitral valve model
prepared by three-dimensional printing as an aid for an advanced mitral
valve operation. Eur J Cardiothorac Surg2022;61:877–885.
18. Mattus MS, Ralph TB,
Keller SMP, Waltz AL, Bramlet MT. Creation of Patient-Specific Silicone
Cardiac Models with Applications in Pre-surgical Plans and Hands-on
Training. J Vis Exp 2022.
19. Wamala I, Brüning J,
Dittmann J, Jerichow S, Weinhold J, Goubergritis L, Hennemuth A, Falk V,
Kempfert J. Simulation of a Right Anterior Thoracotomy Access for Aortic
Valve Replacement Using a 3D Printed Model. Innovations2019;14:428–435.
20. Yang C, Song G, Niu G, Wu
Y. Coronary protection for the small left coronary sinus during
transcatheter aortic valve replacement: a case report. Eur Heart J
Case Rep 2022;6:ytac011.
21. Chen L, Tang W, John NW.
Real-time geometry-aware augmented reality in minimally invasive
surgery. Healthc Technol Lett 2017;4:163–167.
22. Liang L, Liu M, Martin C,
Elefteriades JA, Sun W. A machine learning approach to investigate the
relationship between shape features and numerically predicted risk of
ascending aortic aneurysm. Biomech Model Mechanobiol2017;16:1519–1533.