Towards the clinical integration of an image-guided navigation system for percutaneous liver tumor ablation using freehand 2D ultrasound images

Abstract

Primary and metastatic liver tumors constitute a significant challenge for contemporary medicine. Several improvements are currently being developed and implemented to advance image navigation systems for percutaneous liver focal lesion ablation in clinical applications at the diagnosis, planning and intervention stages. First, the automatic generation of an anatomically accurate parametric model of the preoperative patient liver was proposed in addition to a method to visually evaluate and make manual corrections. Second, a marker was designed to facilitate rigid registration between the model of the preoperative patient liver and the patient during treatment. A specific approach was implemented and tested for rigid mapping by continuously tracking a set of uniquely identified markers and by accounting for breathing motion, facilitating the determination of the optimal breathing phase for needle insertion into the liver tissue. Third, to overcome the challenge of tracking the absolute position of the planned target point, an intra-operative ultrasound (US) system was integrated based on the Public Software Library for UltraSound and OpenIGTLink protocol, which tracks breathing motion in a 2D time sequence of US images. Additionally, to improve the visibility of liver focal lesions, an approach to determine spatio-temporal correspondence between the US sequence and the 4D computed tomography (CT) examination was developed, implemented and tested. This proposed method of processing anatomical model, rigid registration approach and the implemented US tracking and fusion method were tested in 20 anonymized CT and in 10 clinical cases, respectively. The presented methodology can be applied and used with any older 2D US systems, which are currently commonly used in clinical practice.

Assessing the value of volume navigation during ultrasound-guided radiofrequency- and microwave-ablations of liver lesions

“…Navigation approaches help overcome all kinds of shortcomings of interventional imaging modalities such as fluorescence imaging, gamma-ray guidance, and US [ [13] , [14] , [15] , [16] , [17] , [18] ]. For the latter, volume based navigation (VNav) can be realized by manual and/or automatic co-registration of US-images with (pre-)interventional acquired CT- or MR-imaging data sets [ [19] , [20] , [21] , [22] ]. Although the technology is being implemented, few studies have reported on the technical feasibility of this approach [ 8 , 15 , 23 ].…”

Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration

“…Once the patient has recovered their breath, the registration becomes invalid due to the liver shift secondary to the respiration. In recent works, Spinczyk et al 3 and Pohlman et al 4 developed similar needle navigation systems, but for general purposes. As discussed in these works, the most important task in their navigation system is multi-modal image registration.…”

Two‐dimensional ultrasound‐computed tomography image registration for monitoring percutaneous hepatic intervention

“…16 Although image fusion tools for this purpose have been under development for 20 yr, the adoption of these tools has been delayed in the clinic due to the extra time required to achieve satisfactory alignment between the image modalities. 29,30 Intrinsic registration and fusion of preoperative CT/ MR images to US [31][32][33][34][35] has been utilized in some centers, [31][32][33] using common landmarks such as the liver surface and/or vasculature, [31][32][33][34][36][37][38][39][40][41][42][43][44] or electromagnetic antenna tracking. 33 These approaches require appropriate and careful clinical setup.…”

“…32 Data acquisition during breath holds or respiratory gating is essential to improve the accuracy of image fusion. 31,34 The registration/fusion of the images varies from real-time, 31 15 $ 20 s, 45 29-74 s, 36 to around 5-10 min. 46 Recent advancements in computing and automatic image analysis have been able to significantly reduce the extra time required for the image registration so that image fusion becomes a feasible option in ablation procedures.…”

“…These approaches require appropriate and careful clinical setup . Data acquisition during breath holds or respiratory gating is essential to improve the accuracy of image fusion . The registration/fusion of the images varies from real‐time, 15∼20 s, 29–74 s, to around 5–10 min .…”