Wennan Zhao, Trevor Kuhlengel, Qi Chang, Vahid Daneshpajooh, Yuxuan He, Austin Kao, Rebecca Bascom, Danish Ahmad, Yu Maw Htwe, Jennifer Toth, Thomas Schaer, Leslie Brewer, Rachel Hilliard, William E Higgins
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引用次数: 0
Abstract
Purpose: Lung cancer remains the leading cause of cancer death. This has brought about a critical need for managing peripheral regions of interest (ROIs) in the lungs, be it for cancer diagnosis, staging, or treatment. The state-of-the-art approach for assessing peripheral ROIs involves bronchoscopy. To perform the procedure, the physician first navigates the bronchoscope to a preplanned airway, aided by an assisted bronchoscopy system. They then confirm an ROI's specific location and perform the requisite clinical task. Many ROIs, however, are extraluminal and invisible to the bronchoscope's field of view. For such ROIs, current practice dictates using a supplemental imaging method, such as fluoroscopy, cone-beam computed tomography (CT), or radial endobronchial ultrasound (R-EBUS), to gather additional ROI location information. Unfortunately, fluoroscopy and cone-beam CT require substantial radiation and lengthen procedure time. As an alternative, R-EBUS is a safer real-time option involving no radiation. Regrettably, existing assisted bronchoscopy systems offer no guidance for R-EBUS confirmation, forcing the physician to resort to an unguided guess-and-check approach for R-EBUS probe placement-an approach that can produce R-EBUS placement errors exceeding 30 deg, an error that can result in missing many ROIs. Thus, because of physician skill variations, biopsy success rates using R-EBUS for ROI confirmation have varied greatly from 31% to 80%. This situation obliges the physician to turn to a radiation-based modality to gather sufficient information for ROI confirmation. We propose a two-phase registration method that provides guidance for R-EBUS probe placement.
Approach: After the physician navigates the bronchoscope to the airway near a target ROI, the two-phase registration method begins by registering a virtual bronchoscope to the real bronchoscope. A virtual 3D R-EBUS probe model is then registered to the real R-EBUS probe shape depicted in the bronchoscopic video using an iterative region-based alignment method drawing on a level-set-based optimization. This synchronizes the guidance system to the target ROI site. The physician can now perform the R-EBUS scan to confirm the ROI.
Results: We validated the method's efficacy for localizing extraluminal ROIs with a series of three studies. First, for a controlled phantom study, we observed that the mean accumulated position and direction errors (accounting for both registration phases) were 1.94 mm and 3.74 deg (equivalent to 1.30 mm position error for a 20 mm biopsy needle), respectively. Next, for a live animal study, these errors were 2.81 mm and 4.79 deg (2.41 mm biopsy needle error), respectively. For 100% of the ROIs considered in these two studies, the method enabled visualization of an ROI via R-EBUS in under 3 min per ROI. Finally, initial operating-room tests on lung cancer patients indicated the method's efficacy, functionality, efficiency, and safety under standard clinical conditions.
Conclusions: The method offers a quick, low-cost, radiation-free approach for examining peripheral extraluminal ROIs using R-EBUS. Although our studies focused on R-EBUS as the supplemental working channel instrument, the proposed method has general applicability to any clinical bronchoscopic task requiring a working channel instrument. Thus, the method has the potential to improve the efficiency and efficacy of bronchoscopic procedures for lung cancer patients.
期刊介绍:
JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.