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Known‐component 3D image reconstruction for improved intraoperative imaging in spine surgery: A clinical pilot study
Author(s) -
Zhang Xiaoxuan,
Uneri Ali,
Webster Stayman J.,
Zygourakis Corinna C.,
Lo Shengfu L.,
Theodore Nicholas,
Siewerdsen Jeffrey H.
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13652
Subject(s) - image quality , medicine , quality assurance , iterative reconstruction , nuclear medicine , image resolution , instrumentation (computer programming) , artificial intelligence , computer vision , computer science , radiology , image (mathematics) , external quality assessment , pathology , operating system
Purpose Intraoperative imaging plays an increased role in support of surgical guidance and quality assurance for interventional approaches. However, image quality sufficient to detect complications and provide quantitative assessment of the surgical product is often confounded by image noise and artifacts. In this work, we translated a three‐dimensional model‐based image reconstruction (referred to as “Known‐Component Reconstruction,” KC‐Recon) for the first time to clinical studies with the aim of resolving both limitations. Methods KC‐Recon builds upon a penalized weighted least‐squares (PWLS) method by incorporating models of surgical instrumentation (“known components”) within a joint image registration–reconstruction process to improve image quality. Under IRB approval, a clinical pilot study was conducted with 17 spine surgery patients imaged under informed consent using the O‐arm cone‐beam CT system (Medtronic, Littleton MA) before and after spinal instrumentation. Volumetric images were generated for each patient using KC‐Recon in comparison to conventional filtered backprojection (FBP). Imaging performance prior to instrumentation (“preinstrumentation”) was evaluated in terms of soft‐tissue contrast‐to‐noise ratio (CNR) and spatial resolution. The quality of images obtained after the instrumentation (“postinstrumentation”) was assessed by quantifying the magnitude of metal artifacts (blooming and streaks) arising from pedicle screws. The potential low‐dose advantages of the algorithm were tested by simulating low‐dose data (down to one‐tenth of the dose of standard protocols) from images acquired at normal dose. Results Preinstrumentation images (at normal clinical dose and matched resolution) exhibited an average 24.0% increase in soft‐tissue CNR with KC‐Recon compared to FBP (N = 16, P = 0.02), improving visualization of paraspinal muscles, major vessels, and other soft‐tissues about the spine and abdomen. For a total of 72 screws in postinstrumentation images, KC‐Recon yielded a significant reduction in metal artifacts: 66.3% reduction in overestimation of screw shaft width due to blooming ( P < 0.0001) and reduction in streaks at the screw tip (65.8% increase in attenuation accuracy, P < 0.0001), enabling clearer depiction of the screw within the pedicle and vertebral body for an assessment of breach. Depending on the imaging task, dose reduction up to an order of magnitude appeared feasible while maintaining soft‐tissue visibility and metal artifact reduction. Conclusions KC‐Recon offers a promising means to improve visualization in the presence of surgical instrumentation and reduce patient dose in image‐guided procedures. The improved soft‐tissue visibility could facilitate the use of cone‐beam CT to soft‐tissue surgeries, and the ability to precisely quantify and visualize instrument placement could provide a valuable check against complications in the operating room (cf., postoperative CT).