Real-time pose estimation of devices from x-ray images: Application to x-ray/echo registration for cardiac interventions

Abstract

In recent years, registration between x-ray fluoroscopy (XRF) and transesophageal echocardiography (TEE) has been rapidly developed, validated, and translated to the clinic as a tool for advanced image guidance of structural heart interventions. This technology relies on accurate pose-estimation of the TEE probe via standard 2D/3D registration methods. It has been shown that latencies caused by slow registrations can result in errors during untracked frames, and a real-time (> 15 hz) tracking algorithm is needed to minimize these errors. This paper presents two novel similarity metrics designed for accurate, robust, and extremely fast pose-estimation of devices from XRF images: Direct Splat Correlation (DSC) and Patch Gradient Correlation (PGC). Both metrics were implemented in CUDA C, and validated on simulated and clinical datasets against prior methods presented in the literature. It was shown that by combining DSC and PGC in a hybrid method (HYB), target registration errors comparable to previously reported methods were achieved, but at much higher speeds and lower failure rates. In simulated datasets, the proposed HYB method achieved a median projected target registration error (pTRE) of 0.33 mm and a mean registration frame-rate of 12.1 hz, while previously published methods produced median pTREs greater than 1.5 mm and mean registration frame-rates less than 4 hz. In clinical datasets, the HYB method achieved a median pTRE of 1.1 mm and a mean registration frame-rate of 20.5 hz, while previously published methods produced median pTREs greater than 1.3 mm and mean registration frame-rates less than 12 hz. The proposed hybrid method also had much lower failure rates than previously published methods.

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“…This is widely used in image-guided surgery and image-guided radiotherapy. One or more DRRs are iteratively compared to radiographs which are usually taken prior to or during an intervention; while early efforts suffered from the high computational load connected to the iterative DRRgeneration, the advent of general purpose computing on graphics processor units (GPGPU) [47,[83][84][85] boosted this application as a valuable tool, especially for patient positioning in radiotherapy [84,[86][87][88][89]. Figure 6 shows a screenshot from such a program for 2D/3D registration of projective images-the software and its underlying mechanism is described in more detail in Spoerk et al [83], Gendrin et al [84], Furtado et al [87], Li et al [88].…”

Radiation Awareness among Secondary School Students in Perak, Malaysia

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

“…Specifically, the 3D probe pose (position and orientation) in the x-ray coordinate system is determined by matching the forward projection of an a priori 3D probe model to the intrinsic x-ray-visible features of the probe. [10][11][12] These approaches were preceded by a proposed TEE probe tracking system using a sleeve with embedded, trackable ball bearing markers. 13 Although these commercial systems achieve a real-time, multi-modality display, their 2D format remains a limitation, and the transthoracic (TTE) probes used in minimalist interventions are not supported.…”