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Page 2 of 12 Torabinia et al. Mini-invasive Surg 2021;5:32 https://dx.doi.org/10.20517/2574-1225.2021.63

Keywords: Catheter tracking, image guidance, deep learning, 3D printing, minimally invasive surgery, 3D trajectory,
percutaneous interventions, patient-specific

INTRODUCTION
Since minimally invasive surgery (MIS) emerged in the 1980s, surgical skills and minimally invasive
[1-3]
equipment have achieved significant advancements . The minimally invasive approach holds a unique
place for various surgical specialties, such as general surgery, urology , thoracic surgery , plastic surgery ,
[5]
[6]
[4]
[7]
and cardiac surgery . MIS has not only improved the recovery time of patient’s from specific procedures,
but is also enabled to provide improved outcomes . These benefits to patients, hospitals and physicians
[8,9]
have attributed to the rapid development of new MIS procedures, including cardiovascular diseases. The
success of cardiac interventions over the last three decades has significantly reduce the mortality and
morbidity of coronary, valvular, and various congenital diseases [10,11] . However, expansion of MIS to more
[12]
complex surgeries demand further development of new technologies, including robotic surgical systems ,
navigation , guidance , and visualizations , dexterity enhancement , and 3D printing technology .
[13]
[14]
[15]
[17]
[16]
In recent years, 3D printing technology has been attractive in diverse areas of medicine, including
cardiovascular disease . Increasing interest in anatomical modeling and the growing need for pre-operative
[18]
planning using personalized anatomical models to test for device fit and practicing catheter positioning
have encouraged the creation and evolution of 3D printed patient-specific models . Recently, there are
[19]
several studies showing various implementations of 3D printed heart models for different stages of
structural heart interventions, such as pre-operative planning [20-23] , intra-operative models for enhanced
structural orientation [24-26] , and evaluations of novel procedural pathways [27,28] . Garekar et al. utilized a 3D
[29]
printed model for a double outlet right ventricle. The study showed the 3D printed model provided better
intuition to decide on an operative approach than conventional imaging (i.e., echocardiography) .
[29]
[23]
Chaowu et al. demonstrated a 3D printed model for transcatheter closure of secundum atrial septal
defect, where their findings suggested that 3D printing has the potential to screen for appropriate
candidates. Other examples include tetralogy of Fallot [22,30] , hypoplastic left heart syndrome [31,32] , and
ventricular septal defect [33,34] . Despite the successful implementation from prior work, the existing surgical
planning from 3D printed models does not have methods to analyze how a catheter had actually
maneuvered in the 3D printed model.

Our group recently reported a novel training system that provides catheter navigation in mixed reality
(MR), with real-time visual feedback of a physical catheter’s position within a patient-specific 3D heart
model . This method used electromagnetic (EM) sensors to track the catheter position. Although this
[35]
method is advantageous for portability, it has a low accuracy (up to ~5 mm), requires manual integration of
sensors into a catheter, and the hardware not readily available in catheterization labs.

To address these limitations, we propose a novel deep learning-driven method for tracking a catheter in a
3D printed model from bi-plane fluoroscopic images acquired during the procedure. The catheter and heart
position are co-registered in a single coordinate system using affine transformations based on four fiducial
radiopaque markers, which are located on the 3D printed model. Additionally, the 3D trajectory of the
catheter is produced, visualizing the path taken during the mock procedures. Our proposed method has the
potential to provide quantitative analysis for training exercises of percutaneous procedures guided by bi-
plane fluoroscopy.