KEYWORDS: Personal digital assistants, Data modeling, Image registration, X-ray imaging, X-rays, Spine, 3D image processing, Performance modeling, 3D modeling, Transducers
Accurate two-dimensional to three-dimensional (2-D/3-D) registration of preoperative 3-D data and intraoperative 2-D x-ray images is a key enabler for image-guided therapy. Recent advances in 2-D/3-D registration formulate the problem as a learning-based approach and exploit the modeling power of convolutional neural networks (CNN) to significantly improve the accuracy and efficiency of 2-D/3-D registration. However, for surgery-related applications, collecting a large clinical dataset with accurate annotations for training can be very challenging or impractical. Therefore, deep learning-based 2-D/3-D registration methods are often trained with synthetically generated data, and a performance gap is often observed when testing the trained model on clinical data. We propose a pairwise domain adaptation (PDA) module to adapt the model trained on source domain (i.e., synthetic data) to target domain (i.e., clinical data) by learning domain invariant features with only a few paired real and synthetic data. The PDA module is designed to be flexible for different deep learning-based 2-D/3-D registration frameworks, and it can be plugged into any pretrained CNN model such as a simple Batch-Norm layer. The proposed PDA module has been quantitatively evaluated on two clinical applications using different frameworks of deep networks, demonstrating its significant advantages of generalizability and flexibility for 2-D/3-D medical image registration when a small number of paired real-synthetic data can be obtained.
Atrial fibrillation (AFib) has been identified as a major cause of stroke. Radiofrequency
catheter ablation has become an increasingly important treatment option, especially
when drug therapy fails. Navigation under X-ray can be enhanced by using augmented fluoroscopy.
It renders overlay images from pre-operative 3-D data sets which are then fused with
X-ray images to provide more details about the underlying soft-tissue anatomy. Unfortunately,
these fluoroscopic overlay images are compromised by respiratory and cardiac motion. Various
methods to deal with motion have been proposed. To meet clinical demands, they have to be
fast. Methods providing a processing frame rate of 3 frames-per-second (fps) are considered
suitable for interventional electrophysiology catheter procedures if an acquisition frame rate of
2 fps is used. Unfortunately, when working at a processing rate of 3 fps, the delay until the actual
motion compensated image can be displayed is about 300 ms. More recent algorithms can
achieve frame rates of up to 20 fps, which reduces the lag to 50 ms. By using a novel approach
involving a 3-D catheter model, catheter segmentation and a distance transform, we can speed
up motion compensation to 25 fps which results in a display delay of only 40 ms on a standard
workstation for medical applications. Our method uses a constrained 2-D/3-D registration to
perform catheter tracking, and it obtained a 2-D tracking error of 0.61 mm.
Minimally invasive abdominal aortic aneurysm (AAA) stenting can be greatly facilitated by overlaying the preoperative
3-D model of the abdominal aorta onto the intra-operative 2-D X-ray images. Accurate 2-D/3-D registration in 3-D
space makes the 2-D/3-D overlay robust to the change of C-Arm angulations. By far, the 2-D/3-D registration methods
based on simulated X-ray projection images using multiple image planes have been shown to be able to provide
satisfactory 3-D registration accuracy. However, one drawback of the intensity-based 2-D/3-D registration methods is
that the similarity measure is usually highly non-convex and hence the optimizer can easily be trapped into local minima.
User interaction therefore is often needed in the initialization of the position of the 3-D model in order to get a successful
2-D/3-D registration. In this paper, a novel 3-D pose initialization technique is proposed, as an extension of our
previously proposed bi-plane 2-D/3-D registration method for AAA intervention [4]. The proposed method detects
vessel bifurcation points and spine centerline in both 2-D and 3-D images, and utilizes landmark information to bring the
3-D volume into a 15mm capture range. The proposed landmark detection method was validated on real dataset, and is
shown to be able to provide a good initialization for 2-D/3-D registration in [4], thus making the workflow fully
automatic.
Navigation and deployment of the prosthetic valve during trans-catheter aortic valve implantation (TAVI) can be greatly
facilitated with 3-D models showing detailed anatomical structures. Fast and robust automatic contrast detection at the
aortic root on X-ray images is indispensable for automatically triggering a 2-D/3-D registration to align the 3-D model.
Previously, we have proposed an automatic method for contrast detection at the aortic root on fluoroscopic and
angiographic sequences [4]. In this paper, we extend that algorithm in several ways, making it more robust to handle
more general and difficult cases. Specifically, the histogram likelihood ratio test is multiplied with the histogram portion
computation to handle faint contrast cases. Histogram mapping corrects sudden changes in the global brightness, thus
avoiding potential false positives. Respiration and heart beating check further reduces the false positive rate. In addition,
a probe mask is introduced to enhance the contrast feature curve when the dark ultrasound probe partially occludes the
aortic root. Lastly, a semi-global registration method for aligning the aorta shape model is implemented to improve the
robustness of the algorithm with respect to the selection of region of interest (ROI) containing the aorta. The extended
algorithm was evaluated on 100 sequences, and improved the detection accuracy from 94% to 100%, compared to the
original method. Also, the robustness of the extended algorithm was tested with 20 different shifts of the ROI, and the
error rate was as low as 0.2%, in comparison to 6.6% for the original method.
Presentation of detailed anatomical structures via 3-D models helps navigation and deployment of the prosthetic valve in
TAVI procedures. Fast and automatic contrast detection in the aortic root on X-ray images facilitates a seamless
workflow to utilize the 3-D models by triggering 2-D/3-D registration automatically when motion compensation is
needed. In this paper, we propose a novel method for automatic detection of contrast injection in the aortic root on
fluoroscopic and angiographic sequences. The proposed method is based on histogram analysis and likelihood ratio test,
and is robust to variations in the background, the density and volume of the injected contrast, and the size of the aorta.
The performance of the proposed algorithm was evaluated on 26 sequences from 5 patients and 3 clinical sites, with 16
out of 17 contrast injections correctly detected and zero false detections. The proposed method is of general form and
can be extended for detection of contrast injection in other organs and/or applications.
The treatment of atrial fibrillation has gained increasing importance in the field of
computer-aided interventions. State-of-the-art treatment involves the electrical isolation of the
pulmonary veins attached to the left atrium under fluoroscopic X-ray image guidance. Due to
the rather low soft-tissue contrast of X-ray fluoroscopy, the heart is difficult to see. To overcome
this problem, overlay images from pre-operative 3-D volumetric data can be used to add
anatomical detail. Unfortunately, these overlay images are static at the moment, i.e., they do not
move with respiratory and cardiac motion. The lack of motion compensation may impair X-ray
based catheter navigation, because the physician could potentially position catheters incorrectly.
To improve overlay-based catheter navigation, we present a novel two stage approach for respiratory
and cardiac motion compensation. First, a cascade of boosted classifiers is employed to
segment a commonly used circumferential mapping catheter which is firmly fixed at the ostium
of the pulmonary vein during ablation. Then, a 2-D/2-D model-based registration is applied to
track the segmented mapping catheter. Our novel hybrid approach was evaluated on 10 clinical
data sets consisting of 498 fluoroscopic monoplane frames. We obtained an average 2-D tracking
error of 0.61 mm, with a minimum error of 0.26 mm and a maximum error of 1.62 mm.
These results demonstrate that motion compensation using registration-based catheter tracking
is both feasible and accurate. Using this approach, we can only estimate in-plane motion. Fortunately,
compensating for this is often sufficient for EP procedures where the motion is governed
by breathing.
Radio-frequency catheter ablation (RFCA) of the pulmonary veins (PVs) attached to the left atrium (LA) is usually
carried out under fluoroscopy guidance. Overlay of detailed anatomical structures via 3-D CT and/or MR volumes onto
the fluoroscopy helps visualization and navigation in electrophysiology procedures (EP). Unfortunately, respiratory
motion may impair the utility of static overlay of the volume with fluoroscopy for catheter navigation. In this paper, we
propose a B-spline based method for tracking the circumferential catheter (lasso catheter) in monoplane fluoroscopy.
The tracked motion can be used for the estimation of the 3-D trajectory of breathing motion and for subsequent motion
compensation. A lasso catheter is typically used during EP procedures and is pushed against the ostia of the PVs to be
ablated. Hence this method does not require additional instruments, and achieves motion estimation right at the site of
ablation. The performance of the proposed tracking algorithm was evaluated on 340 monoplane frames with an average
error of 0.68 ± 0.36 mms. Our contributions in this work are twofold. First and foremost, we show how to design an
effective, practical, and workflow-friendly 3-D motion compensation scheme for EP procedures in a monoplane setup.
In addition, we develop an efficient and accurate method for model-based tracking of the circumferential lasso catheter
in the low-dose EP fluoroscopy.
Atrial fibrillation is the most common sustained heart arrhythmia and a leading cause
of stroke. Its treatment by radio-frequency catheter ablation, performed using fluoroscopic image
guidance, is gaining increasingly more importance. Two-dimensional fluoroscopic navigation
can take advantage of overlay images derived from pre-operative 3-D data to add anatomical
details otherwise not visible under X-ray. Unfortunately, respiratory motion may impair
the utility of these static overlay images for catheter navigation. We developed an approach for
image-based 3-D motion compensation as a solution to this problem. A bi-plane C-arm system
is used to take X-ray images of a special circumferential mapping catheter from two directions.
In the first step of the method, a 3-D model of the device is reconstructed. Three-dimensional
respiratory motion at the site of ablation is then estimated by tracking the reconstructed catheter
model in 3-D. This step involves bi-plane fluoroscopy and 2-D/3-D registration. Phantom data
and clinical data were used to assess our model-based catheter tracking method. Experiments
involving a moving heart phantom yielded an average 2-D tracking error of 1.4 mm and an average
3-D tracking error of 1.1 mm. Our evaluation of clinical data sets comprised 469 bi-plane
fluoroscopy frames (938 monoplane fluoroscopy frames). We observed an average 2-D tracking
error of 1.0 mm ± 0.4 mm and an average 3-D tracking error of 0.8 mm ± 0.5 mm. These results
demonstrate that model-based motion-compensation based on 2-D/3-D registration is both
feasible and accurate.
3D roadmap provided by pre-operative volumetric data that is aligned with fluoroscopy helps visualization and
navigation in Interventional Cardiology (IC), especially when contrast agent-injection used to highlight coronary vessels
cannot be systematically used during the whole procedure, or when there is low visibility in fluoroscopy for partially or
totally occluded vessels. The main contribution of this work is to register pre-operative volumetric data with intraoperative
fluoroscopy for specific vessel(s) occurring during the procedure, even without contrast agent injection, to
provide a useful 3D roadmap. In addition, this study incorporates automatic ECG gating for cardiac motion. Respiratory
motion is identified by rigid body registration of the vessels. The coronary vessels are first segmented from a multislice
computed tomography (MSCT) volume and correspondent vessel segments are identified on a single gated 2D
fluoroscopic frame. Registration can be explicitly constrained using one or multiple branches of a contrast-enhanced
vessel tree or the outline of guide wire used to navigate during the procedure. Finally, the alignment problem is solved
by Iterative Closest Point (ICP) algorithm. To be computationally efficient, a distance transform is computed from the
2D identification of each vessel such that distance is zero on the centerline of the vessel and increases away from the
centerline. Quantitative results were obtained by comparing the registration of random poses and a ground truth
alignment for 5 datasets. We conclude that the proposed method is promising for accurate 2D-3D registration, even for
difficult cases of occluded vessel without injection of contrast agent.
Presentation of detailed anatomical structures via 3D Computed Tomographic (CT) volumes helps visualization and
navigation in electrophysiology procedures (EP). Registration of the CT volume with the online fluoroscopy however is
a challenging task for EP applications due to the lack of discernable features in fluoroscopic images. In this paper, we
propose to use the coronary sinus (CS) catheter in bi-plane fluoroscopic images and the coronary sinus in the CT volume
as a location constraint to accomplish 2D-3D registration. Two automatic registration algorithms are proposed in this
study, and their performances are investigated on both simulated and real data. It is shown that compared to registration
using mono-plane fluoroscopy, registration using bi-plane images results in substantially higher accuracy in 3D and
enhanced robustness. In addition, compared to registering the projection of CS to the 2D CS catheter, it is more desirable
to reconstruct a 3D CS catheter from the bi-plane fluoroscopy and then perform a 3D-3D registration between the CS
and the reconstructed CS catheter. Quantitative validation based on simulation and visual inspection on real data
demonstrates the feasibility of the proposed workflow in EP procedures.
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