Known‐component 3D image reconstruction for improved intraoperative imaging in spine surgery: A clinical pilot study

Abstract

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).

A mobile isocentric C‐arm for intraoperative cone‐beam CT: Technical assessment of dose and 3D imaging performance

“…In vascular surgery, CBCT can better depict soft‐tissue anatomy, abnormalities, and hemorrhage . In each of these areas, CBCT provides a basis for surgical planning, quality assurance (QA), and improved patient safety . Integration with three‐dimensional (3D)‐3D image registration (CBCT to preoperative CT or MRI), 3D‐2D image registration (CBCT to radiographs), and surgical robotics are active areas of translational development …”

“…6 In each of these areas, CBCT provides a basis for surgical planning, quality assurance (QA), and improved patient safety. [7][8][9] Integration with three-dimensional (3D)-3D image registration (CBCT to preoperative CT or MRI), 10 3D-2D image registration (CBCT to radiographs), 11 and surgical robotics are active areas of translational development. 12,13 Mobile C-arms with CBCT capability using x-ray image intensifiers (XRIIs) have been available for many years (e.g., Arcadis Orbic 3D, Siemens Healthcare) but exhibit major limitations in image quality owing to limited field of view, image distortion, spatial resolution, and noise.…”

Optimization of virtual and real registration technology based on augmented reality in a surgical navigation system

“…However, continuous intraoperative X-ray images of the patient's focal points can expose both the physician and the patient to large amounts of radiation, which does not satisfy the doctor's need for spine surgery. Zhang et al [53] applied image reconstruction based on three-dimensional models to clinical studies. The image quality was improved by incorporating surgical instrument models ("known components") in the joint image register-reconstruction process.…”

Accuracy Assessment of Percutaneous Pedicle Screw Placement Using Cone Beam Computed Tomography with Metal Artifact Reduction

“…Metal artifacts originate from data inconsistencies caused by strong, energy-dependent attenuation [10,11]. Different metal artifact reduction (MAR) algorithms were developed to improve the intraoperative CBCT imaging quality based on the replacement of the projection data within the metal shadow by interpolation from surrounding detector pixel values [12][13][14]. This method can also be extended by known-component image reconstruction [13,14].…”