Metal artifacts present a challenge to cone-beam CT (CBCT) image-guided surgery, obscuring visualization of metal instruments and adjacent anatomy—often in the very region of interest pertinent to the imaging/surgical tasks. We present a method to reduce the influence of metal artifacts by prospectively defining an image acquisition protocol—viz., the C-arm source-detector orbit—that mitigates metal-induced biases in the projection data. The metal artifact avoidance (MAA) method is compatible with simple mobile C-arms, does not require exact prior information on the patient or metal implants, and is consistent with 3D filtered backprojection (FBP), more advanced (e.g. polyenergetic) model-based image reconstruction (MBIR), and metal artifact reduction (MAR) post-processing methods. The MAA method consists of: (i) coarse localization of metal objects in the field-of-view (FOV) via two or more low-dose scout projection views and segmentation (e.g. a simple U-Net) in coarse backprojection; (ii) model-based prediction of metal-induced x-ray spectral shift for all source-detector vertices accessible by the imaging system (e.g. gantry rotation and tilt angles); and (iii) identification of a circular or non-circular orbit that reduces the variation in spectral shift. The method was developed, tested, and evaluated in a series of studies presenting increasing levels of complexity and realism, including digital simulations, phantom experiment, and cadaver experiment in the context of image-guided spine surgery (pedicle screw implants). The MAA method accurately predicted tilted circular and non-circular orbits that reduced the magnitude of metal artifacts in CBCT reconstructions. Realistic distributions of metal instrumentation were successfully localized (0.71 median Dice coefficient) from 2–6 low-dose scout views even in complex anatomical scenes. The MAA-predicted tilted circular orbits reduced root-mean-square error (RMSE) in 3D image reconstructions by 46%–70% and ‘blooming’ artifacts (apparent width of the screw shaft) by 20–45%. Non-circular orbits defined by MAA achieved a further ∼46% reduction in RMSE compared to the best (tilted) circular orbit. The MAA method presents a practical means to predict C-arm orbits that minimize spectral bias from metal instrumentation. Resulting orbits—either simple tilted circular orbits or more complex non-circular orbits that can be executed with a motorized multi-axis C-arm—exhibited substantial reduction of metal artifacts in raw CBCT reconstructions by virtue of higher fidelity projection data, which are in turn compatible with subsequent MAR post-processing and/or polyenergetic MBIR to further reduce artifacts.

Purpose: Cone-beam computed tomography (CBCT) is commonly used in the operating room to evaluate the placement of surgical implants in relation to critical anatomical structures. A particularly problematic setting, however, is the imaging of metallic implants, where strong artifacts can obscure visualization of both the implant and surrounding anatomy. Such artifacts are compounded when combined with low-dose imaging techniques such as sparse-view acquisition. Approach: This work presents a dual convolutional neural network approach, one operating in the sinogram domain and one in the reconstructed image domain, that is specifically designed for the physics and setting of intraoperative CBCT to address the sources of beam hardening and sparse view sampling that contribute to metal artifacts. The networks were trained with images from cadaver scans with simulated metal hardware. Results: The trained networks were tested on images of cadavers with surgically implanted metal hardware, and performance was compared with a method operating in the image domain alone. While both methods removed most image artifacts, superior performance was observed for the dual-convolutional neural network (CNN) approach in which beam-hardening and view sampling effects were addressed in both the sinogram and image domain. Conclusion: The work demonstrates an innovative approach for eliminating metal and sparsity artifacts in CBCT using a dual-CNN framework which does not require a metal segmentation.

Purpose. A system for long-length intraoperative imaging is reported based on longitudinal motion of an O-arm gantry featuring a multi-slot collimator. We assess the utility of long-length tomosynthesis and the geometric accuracy of 3D image registration for surgical guidance and evaluation of long spinal constructs. Methods. A multi-slot collimator with tilted apertures was integrated into an O-arm system for long-length imaging. The multi-slot projective geometry leads to slight view disparity in both long-length projection images (referred to as ‘line scans’) and tomosynthesis ‘slot reconstructions’ produced using a weighted-backprojection method. The radiation dose for long-length imaging was measured, and the utility of long-length, intraoperative tomosynthesis was evaluated in phantom and cadaver studies. Leveraging the depth resolution provided by parallax views, an algorithm for 3D-2D registration of the patient and surgical devices was adapted for registration with line scans and slot reconstructions. Registration performance using single-plane or dual-plane long-length images was evaluated and compared to registration accuracy achieved using standard dual-plane radiographs. Results. Longitudinal coverage of ∼50–64 cm was achieved with a single long-length slot scan, providing a field-of-view (FOV) up to (40 × 64) cm², depending on patient positioning. The dose-area product (reference point air kerma × x-ray field area) for a slot scan ranged from ∼702–1757 mGy·cm², equivalent to ∼2.5 s of fluoroscopy and comparable to other long-length imaging systems. Long-length scanning produced high-resolution tomosynthesis reconstructions, covering ∼12–16 vertebral levels. 3D image registration using dual-plane slot reconstructions achieved median target registration error (TRE) of 1.2 mm and 0.6° in cadaver studies, outperforming registration to dual-plane line scans (TRE = 2.8 mm and 2.2°) and radiographs (TRE = 2.5 mm and 1.1°). 3D registration using single-plane slot reconstructions leveraged the ∼7–14° angular separation between slots to achieve median TRE ∼2 mm and <2° from a single scan. Conclusion. The multi-slot configuration provided intraoperative visualization of long spine segments, facilitating target localization, assessment of global spinal alignment, and evaluation of long surgical constructs. 3D-2D registration to long-length tomosynthesis reconstructions yielded a promising means of guidance and verification with accuracy exceeding that of 3D-2D registration to conventional radiographs.

Cone-beam CT (CBCT) systems commonly incorporate a flat-panel detector (FPD) with multiple-gain readout capability to reduce electronic noise and extend dynamic range. In this work, we report a penalized weighted least-squares (PWLS) method for CBCT image reconstruction with a system model that includes the electronic noise characteristics of FPDs, including systems with dynamic-gain or dual-gain (DG) readout in which the electronic noise is spatially varying. Statistical weights in PWLS were modified to account for the contribution of the electronic noise (algorithm denoted ), and the method was combined with a certainty-based approach that improves the homogeneity of spatial resolution (algorithm denoted ). The methods were tested in phantom studies designed to stress DG readout characteristics and translated to a clinical study for CBCT of patients with head traumas. The method demonstrated superior noise-resolution tradeoffs compared to filtered back-projection (FBP) and conventional PWLS. For example, with spatial resolution (edge-spread function width) matched at 0.65 mm, reduced variance by 28%–39% and 15%–25% compared to FBP and PWLS, respectively. The method achieved more homogeneous spatial resolution than while maintaining similar variance reduction. These findings were confirmed in clinical studies, which showed ~20% variance reduction in peripheral regions of the brain, potentially improving visual image quality in detection of epidural and/or subdural intracranial hemorrhage. The results are consistent with the general notion that incorporating a more accurate system model improves performance in optimization-based statistical CBCT reconstruction—in this case, a more accurate model for (spatially varying) electronic noise to improve detectability of low-contrast lesions.

PurposeMotivated by emerging cone-beam computed tomography (CBCT) systems and scan orbits, we aim to quantitatively assess the completeness of data for 3D image reconstruction—in turn, related to “cone-beam artifacts.” Fundamental principles of cone-beam sampling incompleteness are considered with respect to an analytical figure-of-merit [FOM, denoted tan ( ψmin ) ] and related to an empirical FOM (denoted zmod) for measurement of cone-beam artifact magnitude in a test phantom.ApproachA previously proposed analytical FOM [tan ( ψmin ) , defined as the minimum angle between a point in the 3D image reconstruction and the x-ray source over the scan orbit] was analyzed for a variety of CBCT geometries. A physical test phantom was configured with parallel disk pairs (perpendicular to the z-axis) at various locations throughout the field of view, quantifying cone-beam artifact magnitude in terms of zmod (the relative signal modulation between the disks). Two CBCT systems were considered: an interventional C-arm (Cios Spin 3D; Siemens Healthineers, Forcheim Germany) and a musculoskeletal extremity scanner; Onsight3D, Carestream Health, Rochester, United States)]. Simulations and physical experiments were conducted for various source–detector orbits: (a) a conventional 360 deg circular orbit, (b) tilted and untilted semi-circular (196 deg) orbits, (c) multi-source (three x-ray sources distributed along the z axis) semi-circular orbits, and (d) a non-circular (sine-on-sphere, SoS) orbit. The incompleteness of sampling [tan ( ψmin ) ] and magnitude of cone-beam artifacts (zmod) were evaluated for each system and orbit.ResultsThe results show visually and quantitatively the effect of system geometry and scan orbit on cone-beam sampling effects, demonstrating the relationship between analytical tan ( ψmin ) and empirical zmod. Advanced source–detector orbits (e.g., three-source and SoS orbits) exhibited superior sampling completeness as quantified by both the analytical and the empirical FOMs. The test phantom and zmod metric were sensitive to variations in CBCT system geometry and scan orbit and provided a surrogate measure of underlying sampling completeness.ConclusionFor a given system geometry and source–detector orbit, cone-beam sampling completeness can be quantified analytically (in terms arising from Tuy’s condition) and/or empirically (using a test phantom for quantification of cone-beam artifacts). Such analysis provides theoretical and practical insight on sampling effects and the completeness of data for emerging CBCT systems and scan trajectories.

Cardiac CT plays an important role in diagnosing heart diseases but is conventionally limited by its complex workflow that requires dedicated phase and bolus tracking devices [e.g., electrocardiogram (ECG) gating]. This work reports first progress towards robust and autonomous cardiac CT exams through joint deep learning (DL) and analytical analysis of pulsed-mode projections (PMPs). To this end, cardiac phase and its uncertainty were simultaneously estimated using a novel projection domain cardiac phase estimation network (PhaseNet), which utilizes sliding-window multi-channel feature extraction strategy and a long short-term memory (LSTM) block to extract temporal correlation between time-distributed PMPs. An uncertainty-driven Viterbi (UDV) regularizer was developed to refine the DL estimations at each time point through dynamic programming. Stronger regularization was performed at time points where DL estimations have higher uncertainty. The performance of the proposed phase estimation pipeline was evaluated using accurate physics-based emulated data. PhaseNet achieved improved phase estimation accuracy compared to the competing methods in terms of RMSE (~50% improvement vs. standard CNN-LSTM; ~24% improvement vs. multi-channel residual network). The added UDV regularizer resulted in an additional ~14% improvement in RMSE, achieving accurate phase estimation with <6% RMSE in cardiac phase (phase ranges from 0-100%). To our knowledge, this is the first publication of prospective cardiac phase estimation in the projection domain. Combined with our previous work on PMP-based bolus curve estimation, the proposed method could potentially be used to achieve autonomous cardiac scanning without ECG device and expert-in-the-loop bolus timing.