A dynamic model‐based approach to motion and deformation tracking of prosthetic valves from biplane x‐ray images

Wagner, Martin; Hatt, Charles R.; Dunkerley, David A. P.; Bodart, Lindsay E.; Raval, Amish N.; Speidel, Michael A.

doi:10.1002/mp.12913

Cited by 9 publications

(9 citation statements)

References 33 publications

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“…6(e)‐6(f)) and 3D TTE were acquired simultaneously. A 3D valve model was reconstructed from the biplane images using the method of Wagner et al 14 3D TTE was registered to the x‐ray system by applying the proposed pose estimation method to the PA x‐ray view.…”

Section: Methodsmentioning

confidence: 99%

“…A minimally invasive TTE/XRF image guidance solution that portrays the 3D relationship between catheter device and target anatomy would be desirable. To this end, a 3D echo/x‐ray concept was recently reported in which the 3D shape of an expandable TAVR valve stent is reconstructed from biplane fluoroscopy image sequences, 14 and, as shown in Fig. 1, the 3D device shape is visualized in real‐time relative to cut‐planes of simultaneously acquired 3D volumes from a transthoracic echo probe 15 …”

Section: Introductionmentioning

confidence: 99%

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Technical and clinical study of x‐ray‐based surface echo probe tracking using an attached fiducial apparatus

Bodart

Ciske

et al. 2021

Medical Physics

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Purpose: Several types of structural heart intervention (SHI) use information from multiple imaging modalities to complete an interventional task. For example, in transcatheter aortic valve replacement (TAVR), placement and deployment of a bioprosthetic aortic valve in the aorta is primarily guided by x-ray fluoroscopy (XRF), and echocardiography provides visualization of cardiac anatomy and blood flow. However, simultaneous interpretation of independent x-ray and echo displays remains a challenge for the interventionalist. The purpose of this work was to develop a novel echo/x-ray co-registration solution in which volumetric transthoracic echo (TTE) is transformed to the x-ray coordinate system by tracking the three-dimensional (3D) pose of a probe fiducial attachment from its appearance in two-dimensional (2D) x-ray images. Methods: A fiducial attachment for a commercial TTE probe consisting of rings of high-contrast ball bearings was designed and fabricated. The 3D pose (position and orientation) of the fiducial attachment is estimated from a 2D x-ray image using an algorithm in which a virtual point cloud model of the attachment is iteratively rotated, translated, and forward-projected onto the image until the average sum-of-squares of grayscale values at the projected points is minimized. Fiducial registration error (FRE) and target registration error (TRE) of this approach were evaluated in phantom studies using TAVR-relevant gantry orientations and four standard acoustic windows for the TTE probe. A patient study was conducted to assess the clinical suitability of the fiducial attachment prototype during TTE imaging of patients undergoing SHI. TTE image quality for the task of guiding a transcatheter procedure was evaluated in a reviewer study. Results: The 3D FRE ranged from 0.32 AE 0.03 mm (mean AE SD) to 1.31 AE 0.05 mm, depending on C-arm orientation and probe acoustic window. The 3D TRE ranged from 1.06 AE 0.03 mm to 2.42 AE 0.06 mm. Fiducial pose estimation was stable when >75% of the fiducial markers were visible in the x-ray image. A panel of reviewers graded the presentation of heart valves in TTE images from 48 SHI patients. While valve presentation did not differ significantly between acoustic windows (P > 0.05), the mitral valve did achieve a significantly higher image quality compared to the aortic and tricuspid valves (P < 0.001). Overall, reviewers perceived sufficient image quality in 76.5% of images of the mitral valve, 54.9% of images of the aortic valve, and 48.6% of images of the tricuspid valve. Conclusions: Fiducial-based tracking of a commercial TTE probe is compatible with clinical SHI workflows and yields 3D target registration error of less than 2.5 mm for a variety of x-ray gantry geometries and echo probe acoustic windows. Although TTE image quality with respect to target valve anatomy was sufficient for the majority of cases examined, prescreening of patients for sufficient TTE quality would be helpful.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Technical and clinical study of x‐ray‐based surface echo probe tracking using an attached fiducial apparatus

Bodart

Ciske

et al. 2021

Medical Physics

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“…Electromagnetic (EM) tracking has been suggested [ 1 – 4 ], which requires dedicated, often expensive equipment. Alternative, 3D localization of the 3D position from biplane X-ray (XR) projections has been suggested and proven accurate [ 5 – 9 ]. However, this approach demands quasi-simultaneous projection from two directions, which may cause a substantial increase of radiation dose and limits its application to biplane XR systems.…”

Section: Introductionmentioning

confidence: 99%

“…Refinement of the registration in course of an intervention may be necessary due to patient motion. Advanced motion compensation approaches [9,35,36] could make the monoplane 3D localization based on centerlines more robust and suitable for other vascular procedures, in which the target anatomy experiences cardiac and/or respiratory motion such as chronic total occlusions. Further small anatomic deformations between pre-interventional and interventional data have to be considered as a general limitation of using anatomic models for intervention guidance.…”

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confidence: 99%

3D localization from 2D X-ray projection

et al. 2022

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Purpose Most cardiology procedures are guided using X-ray (XR) fluoroscopy. However, the projective nature of the XR fluoroscopy does not allow for true depth perception as required for safe and efficient intervention guidance in structural heart diseases. For improving guidance, different methods have been proposed often being radiation-intensive, time-consuming, or expensive. We propose a simple 3D localization method based on a single monoplane XR projection using a co-registered centerline model. Methods The method is based on 3D anatomic surface models and corresponding centerlines generated from preprocedural imaging. After initial co-registration, 2D working points identified in monoplane XR projections are localized in 3D by minimizing the angle between the projection lines of the centerline points and the working points. The accuracy and reliability of the located 3D positions were assessed in 3D using phantom data and in patient data projected to 2D obtained during placement of embolic protection system in interventional procedures. Results With the proposed methods, 2D working points identified in monoplane XR could be successfully located in the 3D phantom and in the patient-specific 3D anatomy. Accuracy in the phantom (3D) resulted in 1.6 mm (± 0.8 mm) on average, and 2.7 mm (± 1.3 mm) on average in the patient data (2D). Conclusion The use of co-registered centerline models allows reliable and accurate 3D localization of devices from a single monoplane XR projection during placement of the embolic protection system in TAVR. The extension to different vascular interventions and combination with automatic methods for device detection and registration might be promising.

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“…All those applications rely on a prior requirement, which is the precise and automatic identification of the THV. Thus, detecting and tracking the bioprosthesis in angiographic X-ray imaging sequences has several new practical applications [ 8 ]. This automatic detection points directly to the hot topic of Artificial Intelligence (AI) in medical imaging [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning

et al. 2022

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Transcatheter aortic valve implantation (TAVI) has become the treatment of choice for patients with severe aortic stenosis and high surgical risk. Angiography has been established as an essential tool in TAVI, as this modality provides real-time images required to support the intervention. The automatic interpretation and parameter extraction on such images can lead to significative improvements and new applications in the procedure that, in most cases, rely on a prior identification of the transcatheter heart valve (THV). In this paper, U-Net architecture is proposed for the automatic segmentation of THV on angiographies, studying the role of its hyperparameters in the quality of the segmentations. Several experiments have been conducted, testing the methodology using multiple configurations and evaluating the results on different types of frames captured during the procedure. The evaluation has been performed in terms of conventional classification metrics, complemented with two new metrics, specifically defined for this problem. Those new metrics provide a more appropriate assessment of the quality of the results, given the class imbalance in the dataset. From an analysis of the evaluation results, it can be concluded that the method provides appropriate segmentation results for this dataset.

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A dynamic model‐based approach to motion and deformation tracking of prosthetic valves from biplane x‐ray images

Abstract: The proposed algorithm could be used to generate 3D visualization of the prosthetic valve from two projections. In combination with soft-tissue sensitive-imaging techniques like transesophageal echocardiography, this technique could enable 3D image guidance during TAVR procedures.

Cited by 9 publications

References 33 publications

Technical and clinical study of x‐ray‐based surface echo probe tracking using an attached fiducial apparatus

Technical and clinical study of x‐ray‐based surface echo probe tracking using an attached fiducial apparatus

3D localization from 2D X-ray projection

Automatic Identification of Bioprostheses on X-ray Angiographic Sequences of Transcatheter Aortic Valve Implantation Procedures Using Deep Learning

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