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.
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