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Guidance of transseptal punctures for
left heart interventions using personalized
biomechamical models and volumetric
ultrasound imaging
Thesis Plan
Doctoral Program in Biomedical Engineering
Student:
Pedro André Gonçalves Morais Student nº UP201400020
Supervision team:
João Manuel R. S. Tavares (DEMec/FEUP, Portugal)
João Luís Araújo Martins Vilaça (ICVS/3B’s, Portugal)
Jan D’hooge (KULeuven, Belgium)
June of 2015
Summary
Access to the left atrium (LA) of the heart is required for several minimally
invasive cardiac interventions of the left heart, such as mitral valve replacement,
catheter ablation for atrial fibrillation or left atria appendage closure. Hereto, the atrial
septum is punctured using a catheter inserted in the right atrium (RA) via the venous
system under bidimensional fluoroscopic guidance. Although this approach has been
used for many years, complications and procedural failure are common. Moreover, the
exact location at which the septum needs to be traversed, in order to avoid these
complications and/or to enable reaching a given LA target site, is currently entirely
based on the physician’s experience.
The aim of this project is therefore to develop technologies to assist the
physician in performing transseptal punctures (TSPs). Hereto, subject-specific
biomechanical models based on finite element methods that allow optimizing the TSP
location pre-operatively will be developed. In addition, this interventional plan will be
fused with peri-interventional transesophageal volumetric echocardiography, for TSP
guidance. In this way, intra-procedural radiation exposure will be reduced,
complications will be avoided, and surgery time will be reduced.
i
Contents
1. Introduction ................................................................................................................... 2
1.1. Percutaneous Cardiac Interventions ...................................................................... 2
1.2. Transcatheter left-atria procedures ........................................................................ 2
1.2.1. Atrial anatomy ................................................................................................ 3
1.2.2. Left atrium access route .................................................................................. 5
1.2.3. Transseptal puncture technique ...................................................................... 5
1.3. Motivation .............................................................................................................. 8
1.4. Aims and Contributions ......................................................................................... 9
1.5. Document outline ................................................................................................. 10
2. State of the art ............................................................................................................. 12
2.1 Patient-specific anatomical cardiac models .......................................................... 12
2.2 Biomechanical simulation .................................................................................... 16
2.3 Image-fusion and catheter tracking techniques .................................................... 18
3. Methods….. ................................................................................................................ 26
3.1 Work package 1 – Development and validation of an intra-procedural guidance
framework … .............................................................................................................. 26
3.2 Work package 2 – Building a patient-specific anatomical model ........................ 28
3.3 Work package 3 – Creating a software environment for interventional planning
and simulation ............................................................................................................ 29
3.4 Work package 4 – Real-time image fusion for interventional guidance ................. -
……………………………………………………………………………………….30
3.5 Timetable .............................................................................................................. 32
4. Final Remarks ............................................................................................................. 34
5. References …………………………………………………………………………..36
ii
iii
Abbreviations
2D Two-dimensional
3D Three-dimensional
ASM Active shape model
BEAS B-spline Explicit Active Surface
BRK Brockenbrough
CS Coronary Sinus
CT Computed Tomography
CTA Computed Tomography Angiography
DOFs Degrees of freedom
DRRs Digitally reconstructed radiographs
ECG Electrocardiography
EM Electromagnetic sensor
FO Fossa ovalis
FOV Field of view
GHT Generalized Hough Transform
GPU Graphics processing unit
IAS Interatrial septum
iASD Iatrogenic Atrial septal defects
ICP Iterative closest point
IVC Inferior vena cava
LA Left atrium
LAA Left atrial appendage
LAO Left anterior oblique
LV Left ventricle
MV Mitral valve
MRI Magnetic resonance imaging
PCI Percutaneous cardiac interventions
PFO Patent Foramen ovale
RA Right atrium
RAO Right anterior oblique
RV Right ventricle
SVC Superior vena cava
SURF Speed up robust features
TA Transaortic
TEE Transesophageal echocardiography
TSP Transseptal puncture
US Ultrasound
iv
v
Figures list
Figure 1 - Heart anatomy [15]. ......................................................................................... 4
Figure 2 - Schematic of (a) transaortic route and (b) transseptal puncture technique
(adapted from [24]). .......................................................................................................... 6
Figure 3 - Transseptal puncture technique. (a) A dilator and sheath are placed into the
SVC using a guidewire; (b) a needle is inserted into the dilator until c) the SVC; the
needle is pull-down and two movements are detected, namely: (d) entrance into the RA
and (e) entrance into FO; (f) after FO identification the puncture is performed (adapted
from: [35]). ....................................................................................................................... 7
Figure 4 - Ecabert et al. methodology [57]. a) A rough contour is initially estimated
through a generalized hough transform approach. In (b) and (c) a global and local
adaptation of the model is performed, respectively. Finally, in (d) a deformable model
strategy is used to refine the contour. ............................................................................. 12
Figure 5 - Overview of the technique presented by Zheng et al. [63]. ........................... 14
Figure 6 - Schematic used by atlas-based approaches to segment MR images. This
method uses an affine registration (1) to roughly align the unseen image with the atlas.
The obtained transformations are posteriorly used to map the label images (2) into the
unseen image and generate a region-of-interest through a majority vote strategy (3).
Using only this region, a non-rigid registration (4) is used to align the different datasets.
The resulting deformation fields are used to transform the labels and generate the final
contour (5) [65]. .............................................................................................................. 15
Figure 7 - (a) Calibration cage used to align US world with probe position and (b)
radiopaque markers to fuse X-ray and US world [87]. ................................................... 20
Figure 8 - Workflow used to align MRI/CT, X-ray and US images. During the pre-
intervention stage, surface model of the target structure is generated. Additionally, the
esophagus centerline was manually segmented and included into the model. Regarding
intra-procedural stage, an initial alignment between the ultrasound and X-ray image is
performed via the US probe position. The abovementioned model is then manually
positioned in the X-ray image before being automatically registered via the ultrasound
image [88]. ...................................................................................................................... 21
Figure 9 - Overview of the proposed project. During the pre-procedural stage an
anatomical atrial model will be constructed from CT images. Moreover, biomechanical
vi
simulation will be used to estimate the optimal puncture position and to estimate the
optimal trajectory. During the intra-procedural stage, the high-resolution model will be
fused with real-time image acquisition techniques (3D TEE), transferring consequently
the planning data to the intra-procedural for the real procedure. Finally, during the intra-
procedural stage an augmented reality framework will be developed to guide the expert
for the optimal transseptal puncture location. ................................................................ 26
vii
Tables list
Table 1 – Overview of the main anatomic variation of the atria. ..................................... 8
viii
Introduction
1
Introduction
Introduction
2
1. Introduction
1.1. Percutaneous Cardiac Interventions
Percutaneous cardiac interventions (PCI) cover the minimally invasive
procedures where access to the heart is performed via blood circulation system. Instead
of the traditional open-chest surgery that requires a long surgical cut in the chest wall to
access the target organ, minimally invasive interventions use only a small hole through
the skin to access the vascular system and, consequently, to access the cardiac structure.
Since vascular access is used throughout PCI, a direct visualization of the target
is not possible and surgical instruments manipulation is cumbersome. As such, several
kind of equipment are required to guarantee a safe medical procedure, namely imaging
modalities (e.g. fluoroscopy or ultrasound imaging) to guide the expert throughout the
entire procedure; and transcatheter surgical tools (e.g. electrocautery needles, guidewire,
dilators, sheaths, contrast-injection tools) to ease the instruments manipulation.
Several advantages are associated with these minimally invasive techniques,
namely: less postoperative pain, reduced procedure time, improved cosmetics, less
blood loss, fewer procedural complications, lower costs and shorter hospital stay [1].
However, these techniques require experienced experts/operators and high-level
equipment in the surgical room (e.g. fluoroscopy-image acquisition, electrocautery
system). Furthermore, interventional complications can result in open-chest surgery and
interventional failures are possible.
Regarding the medical application of PCI, a high number of cardiac procedures
are currently performed using this approach, such as: cardiac valves replacement or
repair [2, 3], atrial or ventricular septal defect closure [4, 5], ablation for atrial
fibrillation [6], ablation for control of ventricular tachycardia [7], coronary interventions
[8] and cardiogenic shock [9].
1.2. Transcatheter left-atria procedures
A huge number of PCI are performed in left atrium (LA) chamber, being usually
applied in interventions, such as: catheter ablation for atrial fibrillation, pulmonary vein
isolation and left atrial appendage (LAA) closure. The importance of atrial fibrillation
was addressed in a projection study published in 2013 [10], reporting a total of 8.8
Introduction
3
million of patients with atrial fibrillation per year in the Europe Union, with predictions
indicating a 2-fold increase by 2060.
However, LA is the most difficult cardiac chamber to access percutaneously
[11]. Direct physical access to this structure is not possible, consequently hampering the
entire transcatheter procedure. As such, in the last years a high number of LA access
techniques were described in literature. These strategies reach the LA chamber via one
of the remaining cardiac chambers, which reduces the catheter dexterity and increases
the number of risks throughout the intervention.
During the next sub-sections these techniques are described and the advantages
and main complications of each approach are pointed out. Furthermore, some
considerations about atrial anatomy will be made.
1.2.1. Atrial anatomy
The atria are divided in two chambers (right and left atrium) by a muscular
septal wall.
The right atrium (RA) shows larger volume and thinner walls (approximately 2
mm) than LA. Anatomically, the superior RA is composed by the superior vena cava
(SVC) and the right atrial appendage (Figure 1) [12]. The inferior RA is constituted by
the inferior vena cava (IVC) and the tricuspid valve (Figure 1). The SVC receives the
blood from the superior part of the body, while the IVC returns the blood from the
inferior one. The tricuspid valve controls the blood circulation between the RA and the
right ventricle (RV). Furthermore, particular attention with the coronary sinus (CS)
position is required. The CS is a set of vessels that collect blood from the myocardium
draining into the RA. This structure is positioned between the orifice of the IVC and the
tricuspid valve (Figure 1) [12, 13].
On the other hand, LA is smaller with thicker walls (approximately 3 mm). LA
presents a cuboidal shape, being limited superiorly by four pulmonary veins and the left
atrial appendage (Figure 1) [12]. Inferiorly, the mitral valve (MV) is responsible by the
control of the blood circulation on the left heart [13]. As a final remark, it should be
noticed that: 1) aorta artery and pulmonary artery cover externally the LA [14]; and 2)
LA is separated from the esophagus by a thin fibrous pericardium [14].
A muscular structure, termed interatrial septal wall (IAS), is found between the
two atria. IAS is formed from the fusion of the septum primum (LA septum) and the
Introduction
4
septum secundum (RA septum) [1]. The fusion region is termed limbus (or “true
septum”) presenting a larger thickness. However, a depression can be detected in the
middle of the limbus, which is called fossa ovalis (FO) [9]. The FO is the thinnest
region of the IAS and it is composed by thin fibrous tissue [9]. Additionally, it has an
oval or circular shape and can only be detected from the RA [1]. Anatomically, FO
presents an expected average area between 1.5-2.4 cm2 and it is situated at the lower
part of the septum, between the IVC and the CS [1]. Since lowest thickness is found at
the FO, transseptal access of the LA is traditionally performed through this structure.
[12]. Beyond the FO, His Bundle can be also detected at the inferior IAS wall and it is
composed by myocardial cells that propagate the electric pulse from the atrioventricular
node until the ventricles [9].
Figure 1 - Heart anatomy [15].
Introduction
5
1.2.2. Left atrium access route
Two techniques are commonly used to access LA chamber, namely: transaortic
(TA) access and transseptal puncture (TSP). Recent studies indicated that both
strategies show similar success rates, procedure time and complication rate [11, 16].
In TA access, a catheter inserted over the femoral artery is retrogradely
advanced through the aortic valve towards the left-ventricle (LV). Posteriorly, the
catheter is rotated 180° and advanced throughout the MV until the LA chamber
(Figure 2a). Regarding the TSP, a catheter is inserted into the RA via the venous
system, through which a needle can be moved forward, in order to puncture the IAS
wall and consequently to access the LA (Figure 2b). Note that TSP technique
establishes a “more direct” access route, when compared with TA approach.
Nonetheless, in complex situations TSP technique can perforate a large vessel (e.g.
aorta) resulting in serious complications for the patient.
Despite the similar performance and safety between the two techniques, TA
route requires a 180° rotation of the catheter, complicating the manipulation of the
catheter and hampering, therefore, the procedure. As such, in the last years a superior
number of procedures based on TSP were registered [11].
1.2.3. Transseptal puncture technique
In the current section, a briefly description of the TSP procedure is presented.
Moreover, an overview of the traditional procedural complications is stated. TSP
procedure has been widely explained in literature [11, 17, 18], reporting the guidance
equipment and catheters used to safe puncture the IAS wall. The technique is guided
using the bidimensional fluoroscopy imaging and it is performed using a mechanical
Brockenbrough needle (BRK, St. Jude Medical, Minneapolis, MN, USA). Furthermore,
several auxiliary catheters are used to prevent puncture of vital structures. For instance,
catheters at the aorta, CS and His Bundle are commonly used. Regarding procedural
time, frequently 1 to 15 minutes are required to perform this task. [19-23].
Introduction
6
Figure 2 - Schematic of (a) transaortic route and (b) transseptal puncture technique (adapted from
[24]).
The procedure starts with the insertion of a guidewire (0.81-0.89 mm) into the
SVC using the right femoral vein access. This step is guided by anterioposterior
fluoroscopy view. The guidewire is used to define a safe route between the femoral vein
and the SVC. A dilator and sheath are also positioned into the SVC using the guidewire.
At this stage, the guidewire is replaced by the BRK needle with the needle being
maintained inside the sheath to prevent inadvertent punctures. Then, the assembly
(needle, sheath and dilator) is positioned on the FO region. The assembly is rotated until
4-5 clock position and posteriorly pull-down using left anterior oblique (LAO)
fluoroscopy view to control the assembly rotation. At this point, two movements will be
detected: the first indicates the entrance of the assembly into the RA; and the second,
which is less perceptible, occurs when the assembly is inside the FO region. At this
point, two movements will be detected: the first indicates the entrance of the assembly
into the RA; and the second, which is less perceptible, occurs when the assembly is
inside the FO region. A confirmation of the assembly position is achieved using the
right anterior oblique (RAO) direction of the fluoroscopy. Since puncture outside of the
FO region increases the risk of vital structures perforation and limit the maneuverability
of the catheter in the LA, exhaustive confirmation of the needle position should be
Introduction
7
performed [14]. Additionally, a confirmation of the actual position of the aorta, CS and
His Bundle is required to ensure a safe route for the puncture.
Finally, the puncture can be performed and the surgical tool can be introduced
into the LA, being used left atria pressure variation or contrast agents to confirm the
needle position. It should be noticed that a repetition of this entire procedure is required
when the assembly is not aligned with the FO or when the expert has doubts about the
assembly position.
Regarding the number of complications and failures, a low rate (approximately
1% of the procedures [11]) is commonly associated with the TSP technique. However,
the physician should be aware that: aortic root puncture, arterial air embolism,
pericardial tamponade, right or left atrial wall puncture, transient ST-segment elevation,
pleuritic chest pain, persistence of atrial septal defect and death are complications that
can be caused by this intervention [11, 20, 22, 25-29]. Furthermore, since TSP creates a
hole in the IAS, post-procedural complications are reported, such as persistent
iatrogenic atrial septal defects (iASD). iASD can originate serious complications (e.g.
mitral valve calcification, lower cardiac output, increased rate of paradoxical
embolism), consequently requiring a second procedure [24, 30-33].
As a final remark, it should be noticed that a different LA access site is required
when an abnormal anatomy is identified. These modifications are crucial to ensure the
maximum safety of the procedure and reduce the number of complications [34]. Table 1
presents an overview of the most common anatomical variations of the atria region.
Figure 3 - Transseptal puncture technique. (a) A dilator and sheath are placed into the SVC using a
guidewire; (b) a needle is inserted into the dilator until c) the SVC; the needle is pull-down and two
movements are detected, namely: (d) entrance into the RA and (e) entrance into FO; (f) after FO
identification the puncture is performed (adapted from: [35]).
Introduction
8
1.3. Motivation
Although TSP appears as a safe technique (complication rate lower than 1%)
with large application around the world, complications and failures are still a reality.
The optimal location at which the septum needs to be punctured in order to access the
LA is currently based on experience [11], which it is a suboptimal strategy that can
result in serious procedural complications, such as aortic root puncture, damage of the
atrial wall, atrial septal defect and ultimately in death [17].
Despite the high-success rate normally achieved by experienced surgeons the
same is not observed with non-experienced physicians. Yao et al. and Bayrak et al.
proved that significant higher failure rate (20% higher) and larger procedural time (two
times slower) are achieved by unexperienced interventionists, raising question about the
real safety of the TSP procedure and claiming for novel solutions in order to trainee the
physicians [38, 39].
This scenario is aggravated when a second TSP procedure is performed, due to
the difficulty to identify the scarred FO position. Hu et al. presented a comparison study
between one TSP and repeated TSP procedure, and the results proved that a higher
Table 1 – Overview of the main anatomic variation of the atria.
Anatomic
variations Description Difficulties Solution
Patent
foramen
ovale (PFO)
- Direct route between the
RA and the LA [36].
- LA access without any
puncture [36].
Since the PFO is located at the
anterior and superior part of the
IAS wall, pulmonary vein
procedures is not
straightforward [34, 36].
TSP should be
used, even in the
presence of PFO
[34].
Left atrium
dilation
- LA dilation results in a
posterior position of the FO
[37].
Higher risk of puncture an
undesirable structure [37].
Different TSP
site should be
used [37].
Abnormal
mechanical
properties
of the IAS
- Heart diseases can result
in elastic or thickened IAS
wall [21].
- Patients with previous
TSP procedure, present a
thickened IAS wall [20].
TSP can result in serious
complication for the patient,
such as, atria roof puncture or
aortic route puncture.
Moreover, TSP can fail [21].
Radio-frequency
(RF) needles
should be used
[21].
Abnormal
position of
the FO
- Superior position of the
FO is detected [14].
Superior LA access reduces the
maneuverability of catheter in
pulmonary veins procedures
[14].
Puncture the
inferior part of
the FO [14].
Introduction
9
number of procedural complications/failures (three times higher) were achieved with
repeated TSP [40].
Moreover, in the presence of abnormal atrial anatomy, different puncture
orientation and position should be used. As such, the physician needs to identify the
anatomical variation, assess the surrounding structures and correct the needle
orientation, using a simple bidimensional X-ray image. Obviously, this task is not
straightforward and novel equipment was included into the surgical room in order to
simplify the puncture procedure. RF needles [20, 26, 41], pre-planning based on high-
resolution imaging [42, 43], real-image acquisition through fiber optics systems [44],
electroanatomic mapping [45-47], intra-procedural 3D image acquisition through
transesophageal [48-50] and intracardiac echocardiography were proposed [22, 51, 52].
Nonetheless, each expert uses a different strategy with different equipment, missing a
consensus about the optimal strategy to perform this task. Furthermore, augmented-
reality frameworks that combine information obtained from multiple modalities and
guide the physician for the optimal puncture position are still missing [53, 54].
Finally, determining the optimal puncturing site is not straightforward, as not
only the ease of puncturing depends on the location but, also, the target site to be
reached in the LA has to be taken into account, as crossing the septum limits the
maneuverability and dexterity of the catheters and, thus, their ability to reach LA target
sites [34, 39, 55]. To date, this decision is entirely based on the experience of the
physician and secondary punctures are required regularly (15% of the procedures) as the
first one turns out inadequate. Should be noticed that secondary puncture is time-
consuming, frustrating, require large radiation dose and can originate serious
complications [56].
1.4. Aims and Contributions
The aim of this PhD project is to develop an integrated interventional planning
and guidance framework to assist the physician in successfully performing TSPs.
Hereto, patient-specific anatomical models will be combined with biomechanical
simulations and real-time image fusion. Specifically, the following algorithms and tools
will be developed, implemented and tested:
An algorithm for the automatic segmentation of the LA, RA and the
proximal parts of the inferior and superior vena cava on pre-interventional
Introduction
10
computed-tomography (CT) data, in order to build patient-specific
anatomical models.
Computational models for the most used LA catheters, in order to simulate
their maneuverability during the TSP intervention.
A patient-specific biomechanical (based on the finite element method) model
of the inter-atrial septum in order to simulate the puncturing at a given
location considering the local tissue properties, and estimate the trajectory of
the catheter and the target locations inside the LA.
Real-time image fusion of the anatomical model and the TSP planning with
intra-interventional transesophageal volumetric ultrasound images for TSP
guidance.
1.5. Document outline
The current document is divided into three chapters, namely: introduction, state-
of-the methods and methods.
In the next chapter, an overview of pre-procedural and intra-procedural
strategies is presented, indicating the techniques currently available to generate patient-
specific anatomical models, biomechanical simulation and image fusion approaches.
The third chapter presents a detailed explanation about the methods that we
intend to develop during the PhD project, in order to accurately guide the physician
throughout the TSP procedure.
Finally, the fourth chapter presents the final remarks of this work, indicating the
expected results and advantages of this novel framework.
State of the art
11
State of the art
State of the art
12
2. State of the art
Reaching the LA using a TSP technique was initially proposed in 1959 and has
become the standard approach for several left heart interventions [17, 48]. Since a
transcatheter approach is used, non-invasive image acquisition of the patient anatomy is
crucial. Moreover, pre-procedural planning based on high-resolution imaging
techniques is required to recognize complex procedures, to identify safe puncture site
and to define the optimal puncture route.
As such, in this chapter we present an overview of pre-planning cardiac
procedures techniques, namely: generation of patient-specific anatomical models and
biomechanical simulation techniques. Finally, an overview of intra-procedural strategies
is given.
2.1 Patient-specific anatomical cardiac models
In order to aid the physician throughout the pre-procedural planning, several
authors have proposed automatic and semi-automatic strategies to generate patient-
specific anatomical models from: CT, magnetic resonance (MRI) and ultrasound (US)
imaging. During this sub-section a particular interest on patient-specific anatomical
models of the atrial region is given, due to the importance of these chambers for the
current work.
Ecabert et al. (Philips Research Institute) presented a novel strategy for fully
automatic segmentation of the whole heart in 3D CT [57]. The proposed method relies
in two main-stages, namely: 1) heart localization (Figure 4a); and 2) segmentation
refinement using a deformable model (Figure 4b-d).
Figure 4 - Ecabert et al. methodology [57]. a) A rough contour is initially estimated through a
generalized hough transform approach. In (b) and (c) a global and local adaptation of the model is
performed, respectively. Finally, in (d) a deformable model strategy is used to refine the contour.
State of the art
13
The heart localization method was performed through an adapted 3D generalized
Hough transform (GHT), due to the high robustness and versatility to detect any
arbitrary shape in the target image presented by this strategy (Figure 4a). During the
second phase, a shape-constrained deformable model was applied to refine the whole-
heart mesh. This refinement was performed iteratively using two alternating steps,
combining therefore, parametric and deformable adaptation. Parametric adaptation starts
with a global resize (Figure 4b) of the mesh followed by a local adaptation of each
contour (Figure 4c). Finally, deformable adaptation was applied to guarantee optimal fit
between the resulting mesh and the patient anatomy (Figure 4d). Note that shape
knowledge prior was used to constrain the model evolution.
The current solution was tested on 37 3D-CT datasets with surface-to-surface
errors lower than 1 mm in all the cardiac structures. Regarding the computational time,
a total of 22 seconds per dataset was reported.
Daoudi et al. presented a deformable model strategy to segment the LA in 2D-
CT [58]. This method starts with contrast enhancement based on adaptive histogram
equalization, followed by morphological operators and a region growing technique to
create a coarse contour of the LA chamber. A refinement step is, finally, performed
using gradient vector flow technique.
The strategy was tested on 20 CT datasets, however only visual assessment was
performed. Furthermore, since bidimensional segmentation is performed, relevant
clinical indicators were not extracted from these images.
Almeida et al. suggested a semi-automatic strategy to delineate the LA chamber
in US image [59]. This strategy focused on the B-spline Explicit Active Surface
(BEAS) framework, which was previously proposed for left-ventricle segmentation in
US and MRI data [60, 61]. In both situations, high accuracy and lower computational
time were achieved. However, since LA presents a less regular shape than LV, an
adaptation of the BEAS parameters was presented. The current methodology was tested
on 20 volumetric sequences, proving that LA functional parameters can be derived from
the semi-automatic contours. Nevertheless, since the current work is only a preliminary
report, exhaustive validation of the method is missing.
Haak et al. proposed a novel strategy based on active shape models (ASM) for
segment multiple heart chambers in 3D ultrasound imaging [62]. Since ultrasound
shows a limited field-of-view (FOV), wide-view ultrasound images were manually
generated using several individual US records. The wide-view image was posteriorly
State of the art
14
semi-automatically segmented using a three-stage approach: 1) heart pose estimation; 2)
heart pose and shape estimation; and 3) refinement of the contour obtained in each
chamber. In each stage, a gamma mixture model was used to generate a blood-tissue
probability map, which it was subsequently fitted with an ASM model of the heart
chambers, generating consequently the optimal contour. Regarding the validation step,
single US image and wide-view US images were segmented using the abovementioned
techniques, showing a considerable improvement of Dice coefficient for the fused data.
Instead of deformable models approaches, Zheng et al. (Siemens Corporate
Research) proposed a novel machine learning strategy to automatically segment the
whole-heart in 3D CT volumes [63]. The proposed technique, termed marginal space
learning, identifies the optimal heart pose through a classification approach. A 9-
dimensional vector with position, orientation and scaling was used to train the classifier
method and generate, consequently, a full parameter space with a large number of
hypotheses (Figure 5). During the test step, a multi-stage strategy was used to identify
the optimal pose for each chamber. The method starts with a restricted number of
possibilities and increases the dimensionality of the problem in each stage. Finally, a
mean shape model of the heart model was deformed until the estimated optimal heart
pose, creating the final segmentation (Figure 5).
A total of 323 3D-CT volumes were used throughout the experimental
validation, presenting surface-to-surface errors lower than 1.6 mm for all the structures.
Regarding the computational time, a total of 2 seconds per volume were required [63].
Figure 5 - Overview of the technique presented by Zheng et al. [63].
State of the art
15
Based on machine learning techniques, Margeta et al. also presented a
supervised learning method for fully automatic left atrium segmentation from 3D
cardiac MR datasets [64]. The method starts with a blood-pool region extraction
through a simple threshold. However, since intensity homogeneity was observed
between the different chambers, multiple cardiac structures were detected. As such, the
authors applied a learning technique based on decision forest to identify the LA in the
resulting refined region. The current strategy was tested on 10 different datasets through
a leave-one-out approach. Regarding the results, unsatisfactory dice coefficients lower
than 70% was achieved, proving that the proposed strategy was not able to delineate the
entire cavity.
Zuluaga et al. focused on a multi-atlas approach to accurately segment the
whole heart from 3D MR and 3D computed tomography angiography (CTA) sequences
[65].
Figure 6 - Schematic used by atlas-based approaches to segment MR images. This method uses an
affine registration (1) to roughly align the unseen image with the atlas. The obtained
transformations are posteriorly used to map the label images (2) into the unseen image and
generate a region-of-interest through a majority vote strategy (3). Using only this region, a non-
rigid registration (4) is used to align the different datasets. The resulting deformation fields are
used to transform the labels and generate the final contour (5) [65].
State of the art
16
The method proposed by Zuluaga et al. relies on two steps: 1) region of interest
(ROI) localization through affine alignment between the unseen image and the atlas (see
(1) and (2) in Figure 6 and 2) whole-heart segmentation based on non-rigid registration
between the resulting ROI and the atlas (see (3-5) in Figure 6). The resulting
deformation fields were posteriorly combined to transform the labels from the atlas to
the unknown image, and consequently generate the final contour. Since a set of non-
rigid alignments were required to delineate the final contour, high computational time
was required.
This strategy was tested on 23 and 8 MRI and CTA datasets, respectively.
Interesting dice scores of 90.8% and 89% for the whole-heart chambers were reported
for each situation, validating this methodology. Regarding the computational time, a
total of 30 and 60 minutes were required in MR and CTA datasets, respectively.
Similarly, Kirisili et al. proposed a multi-atlas-based approach to segment the
whole heart from CTA data. The current strategy was validated into a large-scale,
multicenter and multivendor study with a total of 1380 datasets [66]. Note that 8 labeled
CTA datasets were used to generate the atlas. The experts evaluated the result obtained
in the large database and they indicated that 49% of the cases were very accurately
segmented (errors below 1 mm) and 29% of the database results were accurately
segmented (error between 2 and 3 mm), demonstrating the accuracy and robustness of
the atlas-based technique. Furthermore, eight fully segmented datasets were used to
estimate the surface-to-surface error, showing an error lower than 1.5 mm for the
different chambers. Regarding the computational time, approximately 20 minutes per
volume was required.
2.2 Biomechanical simulation
The recent advances in non-invasive high-resolution image acquisition
techniques have made them feasible to generate accurate patient-specific anatomical
models and consequently create novel computational/biomechanical models relevant to
the clinical practice.
Initially, several authors used these novel image techniques to develop generic
simulation models capable to provide useful information about the atrial conduction
system [67-69], motion pattern characterization [70, 71], hemodynamics of the heart
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[72], influence and function of the LAA [73] and study of different pathologies [74].
However, since generic models were used, the individual information of each patient
was neglected limiting the application of this solution in the clinical practice. As such,
several authors suggested complex methodologies in order to develop accurate patient-
specific models [75-78]. This novel model combines the abovementioned generic
models with patient-specific data obtained through multiple image acquisition or
multiple clinical measurements, such as multiple acquisition of the electrocardiography
(ECG) signal [75, 76].
Regarding the minimal invasive cardiac procedures, Mansi et al. (Siemens
Corporate Research) proposed a novel framework to simulate the procedure used to
treat MV regurgitation, namely MitralClip [79]. As such, they generated a patient-
specific model of the MV from ultrasound imaging, being posteriorly used a finite
element method to simulate the valve closure and the entire correction procedure. The
generated patient-specific MV model was tested on 11 patients with an average point-
to-mesh error of 1.47 ± 0.27 mm. Moreover, the entire simulation framework was
applied/tested on one patient with results qualitatively similar to the real surgical
outcome [79].
Similarly, Stevanella et al. proposed a finite element model of the mitral valve in
order to predict the outcome of mitral annuloplasty procedures [80]. In this case,
patient-specific models were obtained from MR data. Furthermore, hyperelastic
anisotropic mechanical properties were assigned to the MV tissues. The current strategy
was tested on one healthy and one unhealthy patient, obtaining similar results to the real
procedure [80].
Instead of mitral valve procedures, Wang et al. developed a patient-specific
model to quantify and characterize the interaction between the transcatheter stent and
the stenotic aortic valve [81]. In this case, finite element models of the patient-specific
anatomy and the transcatheter stent were generated. The anatomic model was created
from CT data, being applied anisotropic hyperelastic materials to simulate the tissue
mechanical properties. Regarding the transcatheter stent, an eight-node hexahedral was
used to generate the solid element and a four-node quadrilateral element was selected to
model the balloon. The proposed methodology was tested on one patient, simulating
with success the entire valve-replacement procedure. As such, the authors proved that
this approach can be used to extract relevant pre-planning information such as optimal
State of the art
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stent position, procedural risks and possible post-intervention complication (e.g.
paravalvular leakage).
Similarly, Morganti et al. proposed a novel framework to simulate the
transcatheter aortic valve implantation [82]. The aortic model was obtained from CT
images, including the native structure of this valve, namely: leaflets and calcific
plaques. Isotropic and homogeneous materials were used to define the mechanical
properties of this valve. Regarding the prosthesis model, the authors have focused on
the commercially available Edwards SAPIEN valve, creating it structure from a micro-
CT image. Moreover, the Von Mises plasticity model with isotropic hardening was used
to represent the mechanical parameters of the prosthesis virtually modulated.
This novel framework was tested on two patients. The results obtained for each
patient indicated that the proposed simulator can be used to realistic simulation of the
minimal invasive procedure, presenting the patient-specific stress distribution of the
aortic wall and possible risk of post-procedural complications, namely paravalvular
leakage [82].
Finally, Jayender et al. proposed an approach to estimate the optimal puncture
location by combining these pre-interventional models with a mechanical model of the
catheter to be used for the LA intervention [83]. The pre-interventional models were
obtained from CT datasets through a semi-automatic strategy. Regarding the catheter
model, it was considered being made up infinitesimal rigid links along a backbone
curve. As such, the optimal puncturing site could be estimated based on the thickness of
the septal wall and the mechanical maneuverability of the catheter at all the positions of
the LA. The current system was only tested in one offline dataset, missing exhaustive
validation, mainly in abnormal septal wall situations. Moreover, since different
catheters can be used to puncture the septal wall, namely mechanical-based and RF-
based, different catheter models should be used.
2.3 Image-fusion and catheter tracking techniques
During the previous sub-sections, pre-interventional planning methods were
presented, where accurate and robust planning of the entire procedure is performed.
Nonetheless, the pre-procedural planning data need to be combined with intra-
procedural information (e.g. image acquisition) in order to guide the physician during
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the entire procedure. As such, in this sub-section an overview of image-fusion and
catheter tracking techniques are described.
External tracking hardware, such as electromagnetic sensor (EM, e.g Aurora
NDI) or optical infrared systems (e.g. Polaris, NDI, Canada), were suggested to guide
image-based procedures [84]. These systems are coupled with the surgical catheter,
being consequently used to combine pre-procedural and intra-procedural data through a
set of markers. Nonetheless, these systems can suffer of electromagnetic interference
caused by the remaining surgical equipment. Moreover, a complex initial setup is
required.
Based on this technique, Jeevan et al. suggested EM sensors integrated on the
catheter tip to guide the transseptal puncture procedure [85]. In this case, the catheter
was rigidly aligned with a patient-specific atria geometry, which was obtained from a
pre-interventional MRI. This method was only validated in one phantom model, proving
that this system reduces the procedure time, has no learning curve and can reduce the
number of complications. However, this system was only tested in static models without
any real-time image acquisition (e.g. X-ray or US imaging), being far from being
applied in real situations.
Hatt et al. focused on an EM-tracking framework to fuse X-ray, MRI and US
image [86]. Note that US and X-ray are real-time image acquisition modalities that are
required throughout the intervention, while the MRI is obtained during the planning
stage. The method requires two pre-intervention calibrations, namely: 1) between the
EM and US probe and 2) between EM world and X-ray image. During the first
calibration step, two EM sensors were coupled with the US probe and a surgical needle,
respectively. Posteriorly, ultrasound image acquisition of the surgical needle was
performed. The differences between tool position into US image and physical distance
measured through EM sensor were used to calibrate the system. Regarding the second
stage, two-custom-built phantoms with metal beads were used to calibrate the
fluoroscopy image with the EM sensor. The difference between the metal beads
measured through fluoroscopy and EM sensor were used to define the optimal
transformation between these two systems. Finally, an intra-operative calibration of
MRI and EM system was performed, based on fiducial markers. As such, a set of
markers was externally positioned on the patient, being the markers easily detected in
the MRI image. Moreover, the EM sensor was used to generate a 3D-world with the
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different position of these markers. The position differences measured through MRI and
EM sensor were used to align the different worlds.
The current system was tested on phantom and animal model, obtaining an
accuracy error lower than 5 mm for the worst situation [86].
Lang et al. suggested a novel image-strategy to register US, CT and fluoroscopy
imaging without any tracking device [87]. The novel method requires two calibration
steps, namely: 1) alignment between probe position and transesophageal
echocardiography (TEE/US) world; and 2) alignment between the probe position and
fluoroscopy world. The first stage is performed through a calibration cage (Figure 7a),
while the second require a set of tracking beads coupled with US probe (Figure 7b).
Thus, a transformation that maps the US world into the fluoroscopy world was obtained.
Then, a semi-automatic segmentation is applied to delineate the relevant
structure from 3D CT data. The obtained mesh is registered with a set of manual
landmarks extracted from US data through an iterative closest point (ICP) approach.
Moreover, since a transformation between US and fluoroscopy worlds were obtained
during the calibration phase, the patient-specific data obtained from CT images can be
also transferred for the fluoroscopy world [87].
The algorithm was tested on excised porcine hearts datasets, with an acceptable
accuracy of 2.6 mm for tracked US to CT. Nonetheless, since the required landmarks
are difficult to be detected in US imaging, high registration errors between CT and US
can be obtained, showing therefore that the proposed method is dependent of the user
input.
Figure 7 - (a) Calibration cage used to align US world with probe position and (b) radiopaque
markers to fuse X-ray and US world [87].
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Similarly, Housden et al. (Philips Healthcare) proposed a novel approach to
align CT, US and fluoroscopy images [88]. The method requires an initial calibration
between the probe and TEE data through a calibration cage (see (1) in Figure 8). Then, a
semi-automatic strategy was proposed to align the fluoroscopy with the US worlds,
through a rigid alignment between a virtual model of the probe and a projection of US
probe in bidimensional X-ray (see (1) in Figure 8). For that, the initial manual
identification of the probe position into fluoroscopy image was required. After the
initial alignment, a high-resolution surface model of the target structure was generated
(Figure 8). Furthermore, a manual segmentation of esophagus centerline is created and
included into the abovementioned high-resolution model.
Finally, a simple downhill iterative optimization algorithm was used to align the
high-resolution model with the US data (see (2) in Figure 8). Note that, the esophagus
position was used to constrain the optimization.
The method was only tested on phantom model, missing its application in real
situations. Moreover, the current methodology is static being not able to guarantee
optimal fit between the pre-procedural image (3D CT) and the real-time intra-
procedural image throughout the cardiac cycle.
Figure 8 - Workflow used to align MRI/CT, X-ray and US images. During the pre-intervention
stage, surface model of the target structure is generated. Additionally, the esophagus centerline was
manually segmented and included into the model. Regarding intra-procedural stage, an initial
alignment between the ultrasound and X-ray image is performed via the US probe position. The
abovementioned model is then manually positioned in the X-ray image before being automatically
registered via the ultrasound image [88].
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Huang et al. proposed a different solution to align pre-operative data, 3D CT or
3D MR, with 2D ultrasound imaging [89]. In this formulation, both datasets were
spatially registered using a rigid transformation, while their time alignment/consistency
was obtained using a simultaneously recorded ECG. However, since only rigid
transformations were considered, a periodic heart motion is assumed, neglecting the
heart deformation caused by the respiration and surgical procedures. The current
strategy was tested on beating heart phantom and animal models, with an accuracy of
1.7 ± 0.4 mm.
Gao et al. suggested a strategy to align 3D TEE data with fluoroscopy imaging
[90]. The method starts with an automatic US probe identification on X-ray imaging,
through graphics processing unit (GPU)-based image-registration between a 3D virtual
model of the probe and the 2D fluoroscopy image. Then, using a pre-interventional
calibration of the US probe and US image, a transformation map between the
fluoroscopy world and US world can be achieved. The method was tested on a realistic
heart phantom, obtaining a target registration error lower than 2 mm. Furthermore, an
offline patient dataset was also used, resulting in mean registration errors between 1.5-
4.2 mm. However, it should be noticed that the probe estimation is a time-consuming
(2-15 seconds) method, hampering their application in real-time procedures and failing
to follow the cardiac structure throughout the respiratory cycle. In addition, exhaustive
offline and online validation of the proposed technique is missing [90].
Recently, Lang et al. presented a novel strategy to align US and CT/MR data. In
order to perform this alignment, two different registration techniques were described
and compared, namely: 1) surface-based registration and 2) image registration [91].
The first approach relies on a segmentation technique based on continuous max-flow
algorithm to delineate the relevant structure in CT/MR and US, followed by a mesh-
alignment based on ICP strategy. Regarding the second technique, an image-alignment
was performed based on non-rigid registration with mutual information metric. Note
that any feature/contour extraction was required in this technique.
Moreover, a GPU-implementation of the tracking method was used. Both
methods were tested on 18 datasets. Registrations errors lower than 2.5 mm, dice
coefficients higher than 80% and low computational time were obtained with surface
and image registration approaches, proving that both approaches have potential to be
used in image-guidance procedures. Nonetheless, these approaches were only tested in
offline aortic replacement procedures, being required further clinical validation.
State of the art
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Furthermore, since fluoroscopy imaging is not used, catheter tracking approaches are
required to identify the surgical equipment’s into the ultrasound imaging, which was not
addressed in the current study [91].
Grbic et al. (Siemens Healthcare) proposed a different strategy to align pre-
operative 3D-CT and intra-operative rotational-angiography based on surrogate
anatomical structures position [92]. The selected surrogate anatomical structure relies
on the trachea bifurcation, which it is visible in both modalities without contrast. As
such, in each image modality a probabilistic boosting classifier is used to estimate the
global position of the trachea bifurcation. Then, a rigid-registration between the two
meshes is performed in order to align the CT and rotational-angiography worlds. The
method was tested on 28 patient datasets obtaining with an accuracy of 7.57 ± 3.22 mm.
Instead of multi-machines image acquisitions, the novel C-Arm technologies can
be used to acquire 3D CT volumes and 2D fluoroscopy images that are intrinsically
registered. However, since 3D CT is only acquired in one temporal moment, patient
anatomy variations caused by the catheter insertion are not considered. As such, Liao et
al. (Siemens Corporation) proposed a fully-automatic solution to align the
bidimensional X-ray images with the 3D dataset, compensating the motion variations
expected throughout the procedure [93]. Note that the proposed strategy is only used
when agent contrast is emitted. Thus, the method automatically detects the contrast
injection on X-ray images based on histogram analysis and a likelihood ratio test. Then,
an optimized alignment based on rigid transformations is performed between the
contrast-based fluoroscopy image under study and a set of bidimensional digitally
reconstructed radiographs (DRRs). Should be noticed that these 2D-DRRs were
extracted from 3D CT volumes using various plane orientations. The current solution
was tested on 34 datasets, presenting a mean registration error of 0.66±0.47 mm and a
computational time of 2.5 seconds per alignment. Although high accuracy was
achieved, the current solution is far from being applicable in real-time. Moreover, since
a static 3D volume is rigidly aligned, the dynamics of the heart (e.g. aortic valve closure
and opening) are ignored.
Although the previous approaches focused on solutions to combine pre-
interventional with intra-interventional data, surgical instruments are not considered on
these models, hampering their application in real image-guidance procedures.
As such, Brost et al. (Siemens Corporation) focused on solutions to
identify/track the surgical catheter in fluoroscopy imaging, which can be used to
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accurately guide the expert in several minimal invasive interventions, such as:
transseptal puncture or catheter ablation [94]. In this formulation, the catheter structure
is segmented through machine learning technique. The authors presented a real-time
strategy to identify the catheter through a cascade of boosted classifier combined with
haar-features. Regarding the tracking approach, a consecutive segmentation approach
was used. This technique was tested in 12 offline datasets, showing a tracking error
lower than 0.7 mm [94].
Moreover, Buck et al. also proposed a novel solution to track the target catheter
from X-ray imaging [95]. The method relies on template-matching combined with
Kalman filters to estimate the catheter tip and reduce the search space, respectively.
Note that this template is a bidimensional projection of a virtual 3D cylindrical model
with rounded tip. The current solution was tested on 14 fluoroscopy sequences
presenting a maximum tracking error of 1.7 mm. Furthermore, clinical validation of this
technique was performed, being well accepted by the experts. Nonetheless, the presence
of multiple catheters caused some tracking errors.
Contrarily, some authors focused on catheter tracking and catheter identification
from US imaging. Since US image shows a small FOV with several artifacts, automatic
identification of the catheter structure is challenging. Thus, Wu et al. solved the
abovementioned problem through a four-stage technique: 1) automatic or semi-
automatic catheter identification in X-ray imaging based on speeded up robust features
(SURF) and Frangi vesselness filter [96]; 2) catheter tracking in X-ray imaging using
Kalman filters; 3) fast registration of the X-ray and US imaging based on US probe
position into X-ray images; and 4) catheter segmentation/tracking in US images using
the displacement field estimated throughout stage (2).
The current methodology was tested on 5 porcine models and 4 patient datasets.
The catheter tracking presented an error lower than 2 mm. Regarding the computational
time, a total of 1.3 seconds per frame were required, hampering its application on real-
time procedures [96].
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Methods
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3. Methods
The PhD project consists of four main work packages (WPs) executed in two
stages. In the first stage (WP1) a preliminary augmented reality framework will be
developed to guide the TSP procedure, presenting therefore a proof-of concept about the
main-topic addressed throughout this project. During the final stage, novel modules will
be developed to automate the abovementioned framework and to improve the efficacy
of TSP procedure (Figure 9).
3.1 Work package 1 – Development and validation of an intra-
procedural guidance framework
In this WP, a preliminary augmented-reality framework will be developed to
guide the surgeon throughout the transseptal puncture procedure. This novel system will
fuse different imaging modalities to ease the recognition of the optimal puncture site.
Moreover, the fluoroscopy will be removed from the intervention, being only used
volumetric ultrasound as intra-procedural image acquisition.
Figure 9 - Overview of the proposed project. During the pre-procedural stage an anatomical atrial
model will be constructed from CT images. Moreover, biomechanical simulation will be used to
estimate the optimal puncture position and to estimate the optimal trajectory. During the intra-
procedural stage, the high-resolution model will be fused with real-time image acquisition
techniques (3D TEE), transferring consequently the planning data to the intra-procedural for the
real procedure. Finally, during the intra-procedural stage an augmented reality framework will be
developed to guide the expert for the optimal transseptal puncture location.
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Task 1.1 – Building an augmented-reality framework: Within this task, a novel
framework will be developed to combine pre-CT operative data with intra-operative
TEE data, transferring the pre-procedural planning, namely fossa ovalis position, for the
real procedure. This initial alignment will be performed through a set of landmarks
placed manually at specific position of the atria region in CT and TEE images. Then, an
ICP strategy will be used to minimize the distance between landmarks and align both
datasets.
Regarding the catheter/needle position, a commercially available
electromagnetic tracking device (Aurora NDI) will be placed on the tip of catheter,
guiding the surgeon for the optimal puncture location using the developed augmented
reality framework.
Task 1.2 – Development of patient-specific phantom models: The entire
framework will be, initially, validated using a mock model of the atria region. Hereto,
an elastic model will be created using stereolithography and polyvinyl alcohol. This
model will be extracted from clinical CT datasets, which were previously manually
segmented, resulting in an identical experimental and in-silico geometry. The
contraction of the heart will be simulated through passive inflation via a pumping
system similar to what was done by the co-supervising lab for the left ventricle.
Task 1.3 – Validation of the augmented reality framework: The framework
developed throughout Task 1.1 will be tested in silico models. A total of 10 different
dynamic phantoms will be created, including normal and non-normal atrial anatomy.
With this validation, we intend to present a proof-of-concept about the aim of this
project, proving that quick and safe puncture can be achieved with this novel strategy,
even with non-experienced surgeons. Moreover, we will present a novel solution where
the intra-procedural radiation was completely removed.
As a final remark, should be noticed that this initial WP is dependent of the user
input, which can result in procedural failures and complication, mainly when
manipulated by unexperienced physicians. As such, during the remaining task of this
project, we intend to develop solutions/modules to automate the proposed framework.
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3.2 Work package 2 – Building a patient-specific anatomical model
In this WP, a patient-specific anatomical model will be built based on pre-
interventional CT. In order to avoid time consuming manual segmentation and its
associated inter-operator variability, this WP will focus on automation of the
segmentation.
Task 2.1 Automatic segmentation of the RA, LA and venae cava from CT: In
order to build patient-specific anatomical models, an accurate segmentation of the
anatomical structures from CT is required, namely: LA, RA and venae cava. The co-
supervising lab has already developed a robust, accurate and real-time segmentation
framework using a level-set like formulation, termed BEAS framework [60]. It was
designed and validated for segmentation of the left ventricle from ultrasound [60] and
magnetic resonance imaging [97]. However, the left-ventricle shows a more regular
shape when compared with the atria region, being required several modifications in the
current segmentation framework in order to segment the abovementioned cardiac
structure. As such, within this WP an adaptation of the BEAS framework will be
proposed, namely: 1) adaption of the segmentation energies to cope with the specific
CT challenges; 2) reformulation of the parametric space in order to allow coupled
segmentation of both atria and to be able to cope with the more complex geometric of
the atria; 3) competitive contours will be implemented to delineate with maximum
accuracy the atrial septal wall, which show low thickness and low contrast; and 4) a
strategy to delineate the epicardium at the atrial region will be proposed.
Finally, strategies will be derived to allow for fully automatic/automated
initialization of the segmentation process, based on mean shape models of the heart or
template-matching strategies.
Task 2.2 Validation of the segmentation approach: The developed automatic
segmentation method will be validated against manual contouring by experts.
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3.3 Work package 3 – Creating a software environment for
interventional planning and simulation
In order to identify the optimal puncture location, software tools will be
developed and implemented to simulate the intervention, in this WP. Hereto, a catheter
specific mechanical model will be created and combined with the anatomical model
derived from WP2.
Task 3.1 Building a mechanical catheter model: Within this WT, a Computer
Aided Design (CAD) model will be generated that completely defines the mechanical
properties of the catheter most used for TSP (NRG®
RF Transseptal Needle and BRKTM
Transseptal Needle). Since these catheters normally have a flexible region with few
degrees of freedom (DOF) in the proximal region and a higher number of DOF on the
distal region, it will be described as a multi-body system with rigid links and flexible
joints, which is a common approach for modeling catheters [98, 99].
Task 3.2: Experimental validation of the catheter model: The mechanical
catheter model will be validated experimentally by comparing the effective catheter
behavior against the simulated one. In order to quantify the 3D trajectory of the real
catheter, it will be integrated with an electromagnetic tracking system (Aurora NDI).
Task 3.3: Simulation of the intervention: In this task, the anatomical model
(WP1) will used to generate a finite element mesh within a finite element analysis
package (namely, Abaqus), in order to obtain patient-specific biomechanical models of
the atria and in particular the inter-atrial septum. In the same virtual environment, the
mechanical catheter model will be inserted. In this way, virtual manipulations can be
performed allowing testing different possible trajectories of the catheters from the vena
cave to the septum. Based on mechanical properties of the atrial tissue available in
literature [100, 101], the interaction of the catheter with the septal wall as well as the
force required for puncturing a given septal site can be mimicked. Finally, the trajectory
through the LA (after TSP) towards a predefined target site can be simulated.
Task 3.4 Trajectory optimization: This above virtual intervention will be used in
an optimization framework. Hereto, one of more target sites will be defined in the LA,
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after which, the optimization scheme will determine the optimal path given the patient-
specific constraints.
Task 3.5: Experimental validation of the simulation environment: The entire
biomechanical framework will be validated using the developed phantom models (Task
1.2). At this stage, we intend to verify if the system can estimate the optimal puncture
location and assess the differences when compared with the preliminary version
developed during WP1. Furthermore, we intend to prove that the estimated trajectory
(Task 3.4) guarantee maximum catheter dexterity at the target site, preventing second
puncture procedures.
3.4 Work package 4 – Real-time image fusion for interventional
guidance
During the second stage, an image fusion methodology will be developed in
order to combine the pre-interventional plan (WP3) with a peri-interventional
ultrasound imaging-based guidance system in real-time.
Task 4.1 Image fusion: The multi-modality registration algorithm will be based
on a non-rigid transformation using mutual information and a temporal alignment,
based on the ECG. In order to run this registration step in real-time – required for
guidance – the algorithm will be optimized for GPU processing. The catheter will be
guided using the image from the 3D TEE registered with the CT, creating an augmented
intervention system capable to indicate the optimal puncture position.
Task 4.2 Experimental validation: The final framework will be initially validated
using the dynamics phantom models developed throughout the task 1.2. Then, the entire
framework will be tested and validated in an experimental animal setting by planning
the intervention and verifying the accuracy of the puncture site and the target location.
Both experienced and inexperienced surgeons will be involved. All this experimental
work will be done in the Life and Health Sciences Research Institute (Braga) and at the
department of Cardiovascular Sciences (Leuven), both of which have the required
expertise, animal facilities and instrumentation.
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The integrated interventional planning and guidance framework proposed will
allow the physician to obtain objective information on the theoretical optimal site for
TSP and will show him this information during the intervention, as guidance. This will
not only avoid complications and the need for secondary punctures, but also decrease
the interventional time (and thus cost) and radiation dose.
As a final remark, an overview of the project timeline is presented in next
section.
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3.5 Timetable
Final Remarks
33
Final
Remarks
Final Remarks
34
4. Final Remarks
In this project, we intend to develop an integrated framework to guide the
physician throughout the TSP puncture based on a patient-specific biomechanical
model. As such, this novel framework will combines the intra-procedural planning
performed by the expert, with the real-time data obtained from three-dimensional
ultrasound imaging, in order to align/guide the transseptal needle position with the
optimal puncture site.
With the proposed framework, we expect to increase the success rate of TSPs
and avoid complications or the need for secondary punctures. Moreover, it will increase
the level of confidence of the physician during the procedure, thereby reducing the
interventional time and thus, its costs. The advantages are particularly relevant for
surgeons with less experience (e.g. trainees) and in patients with an abnormal septum, in
which TSP remains difficult even for the most experienced operator. Finally, the use of
ionizing radiation can be reduced, as the intra-procedural image guidance would be
based on volumetric ultrasound imaging.
Final Remarks
35
References
36
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[5] T. Akagi, Y. Kijima, Y. Takaya, K. Nakagawa, H. Oe, S. Sano, and H. Ito,
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[6] R. Weerasooriya, P. Khairy, J. Litalien, L. Macle, M. Hocini, F. Sacher, N.
Lellouche, S. Knecht, M. Wright, and I. Nault, "Catheter ablation for atrial
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Acknowledgement
45
Acknowledgement
This work was supported by Fundação para a Ciência e Tecnologia, Portugal, in
the scope of the PhD grant SFRH/BD/95438/2013.