In image-guided spine surgery, robust three-dimensional to two-dimensional (3D–2D) registration of preoperative computed tomography (CT) and intraoperative radiographs can be challenged by the image content mismatch associated with the presence of surgical instrumentation and implants as well as soft-tissue resection or deformation. This work investigates image similarity metrics in 3D–2D registration offering improved robustness against mismatch, thereby improving performance and reducing or eliminating the need for manual masking. The performance of four gradient-based image similarity metrics (gradient information (GI), gradient correlation (GC), gradient information with linear scaling (GS), and gradient orientation (GO)) with a multi-start optimization strategy was evaluated in an institutional review board-approved retrospective clinical study using 51 preoperative CT images and 115 intraoperative mobile radiographs. Registrations were tested with and without polygonal masks as a function of the number of multistarts employed during optimization. Registration accuracy was evaluated in terms of the projection distance error (PDE) and assessment of failure modes (PDE > 30 mm) that could impede reliable vertebral level localization. With manual polygonal masking and 200 multistarts, the GC and GO metrics exhibited robust performance with 0% gross failures and median PDE < 6.4 mm (±4.4 mm interquartile range (IQR)) and a median runtime of 84 s (plus upwards of 1–2 min for manual masking). Excluding manual polygonal masks and decreasing the number of multistarts to 50 caused the GC-based registration to fail at a rate of >14%; however, GO maintained robustness with a 0% gross failure rate. Overall, the GI, GC, and GS metrics were susceptible to registration errors associated with content mismatch, but GO provided robust registration (median PDE = 5.5 mm, 2.6 mm IQR) without manual masking and with an improved runtime (29.3 s). The GO metric improved the registration accuracy and robustness in the presence of strong image content mismatch. This capability could offer valuable assistance and decision support in spine level localization in a manner consistent with clinical workflow.

Spinal screw placement is a challenging task due to small bone corridors and high risk of neurological or vascular complications, benefiting from precision guidance/navigation and quality assurance (QA). Implicit to both guidance and QA is the definition of a surgical plan-i.e. the desired trajectories and device selection for target vertebrae-conventionally requiring time-consuming manual annotations by a skilled surgeon. We propose automation of such planning by deriving the pedicle trajectory and device selection from a patient's preoperative CT or MRI. An atlas of vertebrae surfaces was created to provide the underlying basis for automatic planning-in this work, comprising 40 exemplary vertebrae at three levels of the spine (T7, T8, and L3). The atlas was enriched with ideal trajectory annotations for 60 pedicles in total. To define trajectories for a given patient, sparse deformation fields from the atlas surfaces to the input (CT or MR image) are applied on the annotated trajectories. Mean value coordinates are used to interpolate dense deformation fields. The pose of a straight trajectory is optimized by image-based registration to an accumulated volume of the deformed annotations. For evaluation, input deformation fields were created using coherent point drift (CPD) to perform a leave-one-out analysis over the atlas surfaces. CPD registration demonstrated surface error of 0.89  ±  0.10 mm (median  ±  interquartile range) for T7/T8 and 1.29  ±  0.15 mm for L3. At the pedicle center, registered trajectories deviated from the expert reference by 0.56  ±  0.63 mm (T7/T8) and 1.12  ±  0.67 mm (L3). The predicted maximum screw diameter differed by 0.45  ±  0.62 mm (T7/T8), and 1.26  ±  1.19 mm (L3). The automated planning method avoided screw collisions in all cases and demonstrated close agreement overall with expert reference plans, offering a potentially valuable tool in support of surgical guidance and QA.

This paper examines MRI analysis of neurodegeneration in Alzheimer’s Disease (AD) in a network of structures within the medial temporal lobe using diffeomorphometry methods coupled with high-field atlasing in which the entorhinal cortex is partitioned into eight subareas. The morphometry markers for three groups of subjects (controls, preclinical AD, and symptomatic AD) are indexed to template coordinates measured with respect to these eight subareas. The location and timing of changes are examined within the subareas as it pertains to the classic Braak and Braak staging by comparing the three groups. We demonstrate that the earliest preclinical changes in the population occur in the lateral most sulcal extent in the entorhinal cortex (alluded to as transentorhinal cortex by Braak and Braak), and then proceeds medially which is consistent with the Braak and Braak staging. We use high-field 11T atlasing to demonstrate that the network changes are occurring at the junctures of the substructures in this medial temporal lobe network. Temporal progression of the disease through the network is also examined via changepoint analysis, demonstrating earliest changes in entorhinal cortex. The differential expression of rate of atrophy with progression signaling the changepoint time across the network is demonstrated to be signaling in the intermediate caudal subarea of the entorhinal cortex, which has been noted to be proximal to the hippocampus. This coupled to the findings of the nearby basolateral involvement in amygdala demonstrates the selectivity of neurodegeneration in early AD.

Purpose Intraoperative x-ray radiography/fluoroscopy is commonly used to assess the placement of surgical devices in the operating room (e.g., spine pedicle screws), but qualitative interpretation can fail to reliably detect suboptimal delivery and/or breach of adjacent critical structures. We present a 3D-2D image registration method wherein intraoperative radiographs are leveraged in combination with prior knowledge of the patient and surgical components for quantitative assessment of device placement and more rigorous quality assurance (QA) of the surgical product. Methods The algorithm is based on known-component registration (KC-Reg) in which patient-specific preoperative CT and parametric component models are used. The registration performs optimization of gradient similarity, removes the need for offline geometric calibration of the C-arm, and simultaneously solves for multiple component bodies, thereby allowing QA in a single step (e.g., spinal construct with 4–20 screws). Performance was tested in a spine phantom, and first clinical results are reported for QA of transpedicle screws delivered in a patient undergoing thoracolumbar spine surgery. Results Simultaneous registration of 10 pedicle screws (5 contralateral pairs) demonstrated mean target registration error (TRE) of 1.1 ± 0.1 mm at the screw tip and 0.7 ± 0.4° in angulation when a prior geometric calibration was used. The calibration-free formulation, with the aid of component collision constraints, achieved TRE of 1.4 ± 0.6 mm. In all cases, a statistically significant improvement (p < 0.05) was observed for the simultaneous solutions in comparison to previously reported sequential solution of individual components. Initial application in clinical data in spine surgery demonstrated TRE of 2.7 ± 2.6 mm and 1.5 ± 0.8°. Conclusions The KC-Reg algorithm offers an independent check and quantitative QA of the surgical product using radiographic / fluoroscopic views acquired within standard OR workflow. Such intraoperative assessment could improve quality and safety, provide the opportunity to revise suboptimal constructs in the OR, and reduce the frequency of revision surgery.

In brain imaging and connectomics, the study of brain networks, estimating the mean of a population of graphs based on a sample is a core problem. Often, this problem is especially difficult because the sample or cohort size is relatively small, sometimes even a single subject, while the number of nodes can be very large with noisy estimates of connectivity. While the element-wise sample mean of the adjacency matrices is a common approach, this method does not exploit underlying structural properties of the graphs. We propose using a low-rank method which incorporates dimension selection and diagonal augmentation to smooth the estimates and improve performance over the naïve methodology for small sample sizes. Theoretical results for the stochastic blockmodel show that this method offers major improvements when there are many vertices. Similarly, we demonstrate that the low-rank methods outperform the standard sample mean for a variety of independent edge distributions as well as human connectome data derived from magnetic resonance imaging, especially when sample sizes are small. Moreover, the low-rank methods yield "eigen-connectomes", which correlate with the lobe-structure of the human brain and superstructures of the mouse brain, and enable estimation of latent connectome structure. These results indicate that low-rank methods are an important part of the toolbox for researchers studying populations of graphs in general, and statistical connectomics in particular.

Purpose To characterize the radiation dose and three‐dimensional (3D) imaging performance of a recently developed mobile, isocentric C‐arm equipped with a flat‐panel detector (FPD) for intraoperative cone‐beam computed tomography (CBCT) (Cios Spin 3D, Siemens Healthineers) and to identify potential improvements in 3D imaging protocols for pertinent imaging tasks. Methods The C‐arm features a 30 × 30 cm2 FPD and isocentric gantry with computer‐controlled motorization of rotation (0–195°), angulation (±220°), and height (0–45 cm). Geometric calibration was assessed in terms of 9 degrees of freedom of the x‐ray source and detector in CBCT scans, and the reproducibility of geometric calibration was evaluated. Standard and custom scan protocols were evaluated, with variation in the number of projections (100–400) and mAs per view (0.05–1.65 mAs). Image reconstruction was based on 3D filtered backprojection using “smooth,” “normal,” and “sharp” reconstruction filters as well as a custom, two‐dimensional 2D isotropic filter. Imaging performance was evaluated in terms of uniformity, gray value correspondence with Hounsfield units (HU), contrast, noise (noise‐power spectrum, NPS), spatial resolution (modulation transfer function, MTF), and noise‐equivalent quanta (NEQ). Performance tradeoffs among protocols were visualized in anthropomorphic phantoms for various anatomical sites and imaging tasks. Results Geometric calibration showed a high degree of reproducibility despite ~19 mm gantry flex over a nominal semicircular orbit. The dose for a CBCT scan varied from ~0.8–4.7 mGy for head protocols to ~6–38 mGy for body protocols. The MTF was consistent with sub‐mm spatial resolution, with f10 (frequency at which MTF = 10%) equal to 0.64 mm−1, 1.0 mm−1, and 1.5 mm−1 for smooth, standard, and sharp filters respectively. Implementation of a custom 2D isotropic filter improved CNR ~ 50–60% for both head and body protocols and provided more isotropic resolution and noise characteristics. The NPS and NEQ quantified the 3D noise performance and provided a guide to protocol selection, confirmed in images of anthropomorphic phantoms. Alternative scan protocols were identified according to body site and task — for example, lower‐dose body protocols (<3 mGy) sufficient for visualization of bone structures. Conclusion The studies provided objective assessment of the dose and 3D imaging performance of a new C‐arm, offering an important basis for clinical deployment and a benchmark for quality assurance. Modifications to standard 3D imaging protocols were identified that may improve performance or reduce radiation dose for pertinent imaging tasks.

Percutaneous screw fixation in pelvic trauma surgery is a challenging procedure that often requires long fluoroscopic exposure times and trial-and-error insertion attempts along narrow bone corridors of the pelvis. We report a method to automatically plan surgical trajectories using preoperative CT and assist device placement by augmenting the fluoroscopic scene with planned trajectories. A pelvic shape atlas was formed from 40 CT images and used to construct a statistical shape model (SSM). Each member of the atlas included expert definition of volumetric regions representing safe trajectory within bone corridors for fixating 10 common fracture patterns. Patient-specific planning is obtained by mapping the SSM to the (un-segmented) patient CT via active shape model (ASM) registration and free-form deformation (FFD), and the resulting transformation is used to transfer the atlas trajectory volumes to the patient CT. Fluoroscopic images acquired during K-wire placement are in turn augmented with projection of the planned trajectories via 3D–2D registration. Registration performance was evaluated via leave-one-out cross-validation over the 40-member atlas, computing the root mean square error (RMSE) in pelvic surface alignment (volumetric registration error), the positive predicted value (PPV) of volumetric trajectories within bone corridors (safety of the automatically planned trajectories), and the distance between trajectories within the planned volume and the bone cortex (absence of breach). A cadaver study was conducted in which K-wires were placed under fluoroscopic guidance to validate 3D–2D registration accuracy and evaluate the potential utility of augmented fluoroscopy with planned trajectories. The leave-one-out cross-validation achieved surface RMSE of 2.2  ±  0.3 mm after ASM registration and 1.8  ±  0.2 mm after FFD refinement. Automatically determined surgical plans conformed within bone corridors with PPV  >  90% and centerline trajectory within 3–5 mm of the bone cortex. 3D–2D registration in the cadaver study achieved 0.3  ±  0.8 mm accuracy (in-plane translation) and  <4° accuracy (in-plane rotation). Fluoroscopic images augmented with planning data exhibited  >90% conformance of volumetric planning data overlay within bone, and all centerline trajectories were within safe corridors. The approach yields a method for both automatic planning of pelvic fracture fixation and augmentation of fluoroscopy for improved surgical precision and safety. The method does not require segmentation of the patient CT, operates without additional hardware (e.g. tracking systems), and is consistent with common workflow in fluoroscopically guided procedures. The approach has the potential to reduce operating time and radiation dose by minimizing trial-and-error attempts in percutaneous screw placement.

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.