M. D. Ketcha

Network Neurodegeneration in Alzheimer's Disease via MRI Based Shape Diffeomorphometry and High-Field Atlasing.

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.

3D-2D image registration for target localization in spine surgery: Investigation of similarity metrics providing robustness to content mismatch

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.

Utility of the LevelCheck Algorithm for Decision Support in Vertebral Localization.

Study Design. An automatic radiographic labeling algorithm called “LevelCheck” was analyzed as a means of decision support for target localization in spine surgery. The potential clinical utility and scenarios in which LevelCheck is likely to be the most beneficial were assessed in a retrospective clinical data set (398 cases) in terms of expert consensus from a multi-reader study (three spine surgeons). Objective. The aim of this study was to evaluate the potential utility of the LevelCheck algorithm for vertebrae localization. Summary of Background Data. Three hundred ninety-eight intraoperative radiographs and 178 preoperative computed tomographic (CT) images for patients undergoing spine surgery in cervical, thoracic, lumbar regions. Methods. Vertebral labels annotated in preoperative CT image were overlaid on intraoperative radiographs via 3D-2D registration. Three spine surgeons assessed the radiographs and LevelCheck labeling according to a questionnaire evaluating performance, utility, and suitability to surgical workflow. Geometric accuracy and registration run time were measured for each case. Results. LevelCheck was judged to be helpful in 42.2% of the cases (168/398), to improve confidence in 30.6% of the cases (122/398), and in no case diminished performance (0/398), supporting its potential as an independent check and assistant to decision support in spine surgery. The clinical contexts for which the method was judged most likely to be beneficial included the following scenarios: images with a lack of conspicuous anatomical landmarks; level counting across long spine segments; vertebrae obscured by other anatomy (e.g., shoulders); poor radiographic image quality; and anatomical variations/abnormalities. The method demonstrated 100% geometric accuracy (i.e., overlaid labels within the correct vertebral level in all cases) and did not introduce ambiguity in image interpretation. Conclusion. LevelCheck is a potentially useful means of decision support in vertebral level localization in spine surgery. Level of Evidence: N/A

Intraoperative evaluation of device placement in spine surgery using known-component 3D–2D image registration

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. 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. Simultaneous registration of ten pedicle screws (five 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°. 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.

Registration of MRI to intraoperative radiographs for target localization in spinal interventions

Decision support to assist in target vertebra localization could provide a useful aid to safe and effective spine surgery. Previous solutions have shown 3D-2D registration of preoperative CT to intraoperative radiographs to reliably annotate vertebral labels for assistance during level localization. We present an algorithm (referred to as MR-LevelCheck) to perform 3D-2D registration based on a preoperative MRI to accommodate the increasingly common clinical scenario in which MRI is used instead of CT for preoperative planning. Straightforward adaptation of gradient/intensity-based methods appropriate to CT-to-radiograph registration is confounded by large mismatch and noncorrespondence in image intensity between MRI and radiographs. The proposed method overcomes such challenges with a simple vertebrae segmentation step using vertebra centroids as seed points (automatically defined within existing workflow). Forwards projections are computed using segmented MRI and registered to radiographs via gradient orientation (GO) similarity and the CMA-ES (covariance-matrix-adaptation evolutionary-strategy) optimizer. The method was tested in an IRB-approved study involving 10 patients undergoing cervical, thoracic, or lumbar spine surgery following preoperative MRI. The method successfully registered each preoperative MRI to intraoperative radiographs and maintained desirable properties of robustness against image content mismatch and large capture range. Robust registration performance was achieved with projection distance error (PDE) (median  ±  IQR)  =  4.3  ±  2.6 mm (median  ±  IQR) and 0% failure rate. Segmentation accuracy for the continuous max-flow method yielded dice coefficient  =  88.1  ±  5.2, accuracy  =  90.6  ±  5.7, RMSE  =  1.8  ±  0.6 mm, and contour affinity ratio (CAR)  =  0.82  ±  0.08. Registration performance was found to be robust for segmentation methods exhibiting RMSE  <3 mm and CAR  >0.50. The MR-LevelCheck method provides a potentially valuable extension to a previously developed decision support tool for spine surgery target localization by extending its utility to preoperative MRI while maintaining characteristics of accuracy and robustness.

Fundamental limits of image registration performance: effects of image noise and resolution in CT-guided interventions

Purpose: In image-guided procedures, image acquisition is often performed primarily for the task of geometrically registering information from another image dataset, rather than detection / visualization of a particular feature. While the ability to detect a particular feature in an image has been studied extensively with respect to image quality characteristics (noise, resolution) and is an ongoing, active area of research, comparatively little has been accomplished to relate such image quality characteristics to registration performance. Methods: To establish such a framework, we derived Cramer-Rao lower bounds (CRLB) for registration accuracy, revealing the underlying dependencies on image variance and gradient strength. The CRLB was analyzed as a function of image quality factors (in particular, dose) for various similarity metrics and compared to registration accuracy using CT images of an anthropomorphic head phantom at various simulated dose levels. Performance was evaluated in terms of root mean square error (RMSE) of the registration parameters. Results: Analysis of the CRLB shows two primary dependencies: 1) noise variance (related to dose); and 2) sum of squared image gradients (related to spatial resolution and image content). Comparison of the measured RMSE to the CRLB showed that the best registration method, RMSE achieved the CRLB to within an efficiency factor of 0.21, and optimal estimators followed the predicted inverse proportionality between registration performance and radiation dose. Conclusions: Analysis of the CRLB for image registration is an important step toward understanding and evaluating an intraoperative imaging system with respect to a registration task. While the CRLB is optimistic in absolute performance, it reveals a basis for relating the performance of registration estimators as a function of noise content and may be used to guide acquisition parameter selection (e.g., dose) for purposes of intraoperative registration.

Spinal pedicle screw planning using deformable atlas registration

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 patients 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.

Deformable 3D-2D Registration of Known Components for Image Guidance in Spine Surgery

A 3D-2D image registration method is reported for guiding the placement of surgical devices (e.g., K-wires). The solution registers preoperative CT (and planning data therein) to intraoperative radiographs and computes the pose, shape, and deformation parameters of devices (termed “components”) known to be in the radiographic scene. The deformable known-component registration (dKC-Reg) method was applied in experiments emulating spine surgery to register devices (K-wires and spinal fixation rods) undergoing realistic deformation. A two-stage registration process (i) resolves patient pose from individual radiographs and (ii) registers components represented as polygonal meshes based on a B-spline model. The registration result can be visualized as overlay of the component in CT analogous to surgical navigation but without conventional trackers or fiducials. Target registration error in the tip and orientation of deformable K-wires was (1.5 ± 0.9) mm and (0.6° ± 0.2°), respectively. For spinal fixation rods, the registered components achieved Hausdorff distance of 3.4 mm. Future work includes testing in cadaver and clinical data and extension to more generalized deformation and component models.

Multi-stage 3D-2D registration for correction of anatomical deformation in image-guided spine surgery

A multi-stage image-based 3D-2D registration method is presented that maps annotations in a 3D image (e.g. point labels annotating individual vertebrae in preoperative CT) to an intraoperative radiograph in which the patient has undergone non-rigid anatomical deformation due to changes in patient positioning or due to the intervention itself. The proposed method (termed msLevelCheck) extends a previous rigid registration solution (LevelCheck) to provide an accurate mapping of vertebral labels in the presence of spinal deformation. The method employs a multi-stage series of rigid 3D-2D registrations performed on sets of automatically determined and increasingly localized sub-images, with the final stage achieving a rigid mapping for each label to yield a locally rigid yet globally deformable solution. The method was evaluated first in a phantom study in which a CT image of the spine was acquired followed by a series of 7 mobile radiographs with increasing degree of deformation applied. Second, the method was validated using a clinical data set of patients exhibiting strong spinal deformation during thoracolumbar spine surgery. Registration accuracy was assessed using projection distance error (PDE) and failure rate (PDE  >  20 mm-i.e. label registered outside vertebra). The msLevelCheck method was able to register all vertebrae accurately for all cases of deformation in the phantom study, improving the maximum PDE of the rigid method from 22.4 mm to 3.9 mm. The clinical study demonstrated the feasibility of the approach in real patient data by accurately registering all vertebral labels in each case, eliminating all instances of failure encountered in the conventional rigid method. The multi-stage approach demonstrated accurate mapping of vertebral labels in the presence of strong spinal deformation. The msLevelCheck method maintains other advantageous aspects of the original LevelCheck method (e.g. compatibility with standard clinical workflow, large capture range, and robustness against mismatch in image content) and extends capability to cases exhibiting strong changes in spinal curvature.

Deformable 3D-2D registration for guiding K-wire placement in pelvic trauma surgery

Pelvic Kirschner wire (K-wire) insertion is a challenging surgical task requiring interpretation of complex 3D anatomical shape from 2D projections (fluoroscopy) and delivery of device trajectories within fairly narrow bone corridors in proximity to adjacent nerves and vessels. Over long trajectories (~10-25 cm), K-wires tend to curve (deform), making conventional rigid navigation inaccurate at the tip location. A system is presented that provides accurate 3D localization and guidance of rigid or deformable surgical devices (“components” – e.g., K-wires) based on 3D-2D registration. The patient is registered to a preoperative CT image by virtually projecting digitally reconstructed radiographs (DRRs) and matching to two or more intraoperative x-ray projections. The K-wire is localized using an analogous procedure matching DRRs of a deformably parametrized model for the device component (deformable known-component registration, or dKC-Reg). A cadaver study was performed in which a K-wire trajectory was delivered in the pelvis. The system demonstrated target registration error (TRE) of 2.1 ± 0.3 mm in location of the K-wire tip (median ± interquartile range, IQR) and 0.8 ± 1.4º in orientation at the tip (median ± IQR), providing functionality analogous to surgical tracking / navigation using imaging systems already in the surgical arsenal without reliance on a surgical tracker. The method offers quantitative 3D guidance using images (e.g., inlet / outlet views) already acquired in the standard of care, potentially extending the advantages of navigation to broader utilization in trauma surgery to improve surgical precision and safety.