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We assessed interventional radiologists’ task-based image quality preferences for two- and three-dimensional images obtained with a complementary metal–oxide semiconductor (CMOS) flat-panel detector versus a hydrogenated amorphous silicon (a-Si:H) flat-panel detector. CMOS and a-Si:H detectors were implemented on identical mobile C-arms to acquire radiographic, fluoroscopic, and cone-beam computed tomography (CBCT) images of cadavers undergoing simulated interventional procedures using low- and high-dose settings. Images from both systems were displayed side by side on calibrated, diagnostic-quality displays, and three interventional radiologists evaluated task performance relevant to each image and ranked their preferences based on visibility of pertinent anatomy and interventional devices. Overall, CMOS images were preferred in fluoroscopy (
Convolutional neural networks (CNNs) offer a promising means to achieve fast deformable image registration with accuracy comparable to conventional, physics-based methods. A persistent question with CNN methods, however, is whether they will be able to generalize to data outside of the training set. We investigated this question of mismatch between train and test data with respect to first- and second-order image statistics (e.g., spatial resolution, image noise, and power spectrum). A UNet-based architecture was built and trained on simulated CT images for various conditions of image noise (dose), spatial resolution, and deformation magnitude. Target registration error was measured as a function of the difference in statistical properties between the test and training data. Generally, registration error is minimized when the training data exactly match the statistics of the test data; however, networks trained with data exhibiting a diversity in statistical characteristics generalized well across the range of statistical conditions considered. Furthermore, networks trained on simulated image content with first- and second-order statistics selected to match that of real anatomical data were shown to provide reasonable registration performance on real anatomical content, offering potential new means for data augmentation. Characterizing the behavior of a CNN in the presence of statistical mismatch is an important step in understanding how these networks behave when deployed on new, unobserved data. Such characterization can inform decisions on whether retraining is necessary and can guide the data collection and/or augmentation process for training.
Methods: An atlas was constructed with segmented pelvis shapes containing standard reference trajectories for screw placement. A statistical shape model computed from the atlas is used for deformable registration to the patient’s preoperative CT (without segmentation). By transferring the reference trajectories and surrounding acceptance windows (i.e., volumetric corridors of acceptable device placement) from the atlas, the system automatically computes reliable Kwire and screw trajectories for guidance (overlay in fluoroscopy) and QA.
Results: A leave-one-out analysis was performed to evaluate the accuracy or registration and overlay. The registration achieved average surface registration accuracy of 1.82 ± 0.39 mm. Automatically determined trajectories conformed within acceptable cortical bone margins, maintaining 3.75 ± 0.68 mm distance from cortex in narrow bone corridors and demonstrating accurate registration and surgical trajectory definition without breaching cortex.
Conclusions: The framework proposed in this work allows for multi-atlas based automatic planning of surgical trajectory without tracker or manual segmentation. The planning information can be further used to facilitate intraoperative guidance and post-operatively quality assurance in a manner consistent with surgical workflow.
Method: Registration of 2D US (slice) images is performed via the initialization obtained from a fast dictionary search that determines probe pose within a predefined set of pose configurations. 2D slices are extracted from a static 3D US (volume) image to construct a feature dictionary representing different probe poses. Haar features are computed in a fourlevel pyramid that transforms 2D image intensities to a 1D feature vector, which are in turn matched to the 2D target image. 3D-2D registration was performed with the Haar-based initialization with normalized cross-correlation as the metric and Powell’s method as the optimizer. Reduction to 1D feature vectors presents the potential for major gains in speed compared to registration of the 3D and 2D images directly. The method was validated in experiments conducted in a lumbar spine phantom and a cadaver specimen with known translations imparted by a computerized motion stage.
Results: The Haar feature matching method demonstrated initialization accuracy (mean ± std) = (1.9 ± 1.4) mm and (2.1 ± 1.2) mm in phantom and cadaver studies, respectively. The overall registration accuracy was (2.0 ± 1.3) mm and (1.7 ± 0.9) mm, and the initialization was a necessary and important step in the registration process.
Conclusions: The proposed image-based registration method demonstrated promising results for compensating motion of the US probe. This image-based solution could be an important step toward an entirely image-based, real-time registration method of 2D US to 3D US and pre-procedure MRI, eliminating hardware-based tracking systems in a manner more suitable to clinical workflow.
Methods: CT images from 41 subjects (21 males, 20 females) were derived from the Cancer Imaging Archive (TCIA) and segmented using manual/semi-automatic methods. A statistical shape model was constructed and incorporated in an active shape model (ASM) registration framework for atlas-to-patient registration. Further, we introduce a registration method that exploits clusters in the underlying distribution to iteratively perform registrations after selecting a patient relevant cluster (sub-atlas) that represents similar shape characteristics to the image being registered. Experiments were performed to evaluate surface-to-surface and atlas-to patient registration algorithms using this clustered iterative model. Initial investigation of improved registration based on using similar shapes, was first explored through the use of gender as a categorical way of selecting a possible sub-atlas for registration.
Results: The RMSE surface-to-surface registration error (mean ± std) was reduced from (2.1 ± 0.2) mm when registering according to the entire atlas (N=40 members) to (1.8 ± 0.1) mm when registering within clusters based on similarity of principal components (N=20 members), showing improved accuracy (p<0.001) with fewer atlas members – an efficiency gained by virtue of the proposed approach. The atlas showed clear clusters in the first two principal components corresponding to gender, and the proposed method demonstrated improved accuracy when using ASM registration as well as when applied to a coherent-point drift (CPD) non-rigid deformable registration.
Conclusions: The proposed framework improved atlas-to-patient registration accuracy and increased the efficiency of statistical shape models (i.e., equivalent registration using fewer atlas members) by guiding member selection according to similarity in principal components.
Methods: Two mobile C-arms were equipped with CMOS (Xineos 3030HS) and a-Si:H (PaxScan 3030X) FPDs. Technical assessment includes measurement of spatial resolution (MTF), image noise (NPS), and detective quantum efficiency (DQE). Evaluation of CBCT performance considers soft tissue visibility including axial image MTF, NPS, and noise-equivalent quanta (NEQ).
Results: The CMOS detector exhibited lower readout noise and slightly higher spatial resolution as expected. The a- Si:H detector showed about 10-15% higher DQE at low spatial frequencies while the CMOS detector showed greater resilience in DQE at higher spatial frequencies. In matched resolution CBCT, both detectors showed roughly equivalent performance.
Conclusion: CMOS detectors benefit performance with respect to high-frequency tasks, but the current work did not demonstrate strong advantage with respect to low-contrast soft-tissue visualization, in part due to light losses in scintillator-semiconductor coupling. Additional advantages include improved frame rate (reduced CBCT scan time). Ongoing work includes further investigation of modified bandwidth filters to take better advantage of underlying noiseresolution properties.
Methods: We apply a statistical framework that incorporates objective image quality factors such as spatial resolution and image noise combined with a statistical representation of anatomical clutter to predict the root-mean-squared error (RMSE) of transformation parameters in a rigid registration. Model predictions are compared to simulation studies in CT-to-CT slice registration using the cross-correlation (CC) similarity metric.
Results: RMSE predictions are shown to accurately model the impact of dose and soft-tissue clutter on measured RMSE performance. Further, these predictions reveal dose levels at which the registration becomes soft-tissue clutter limited, where further increase provides no improvement in registration performance.
Conclusions: Incorporating tissue deformation into a statistical registration model is an important step in understanding the limits of image registration performance and selecting pertinent registration methods for a particular registration task. The generalized noise model and RMSE analysis provide insight on how to optimize registration tasks with respect to image acquisition protocol (e.g., dose, reconstruction parameters) and registration method (e.g., level of blur).
Methods: The proposed method uses a calibration phantom consisting of multiple line-shaped wire segments. Geometric models relating the 3D line equations of the wires to the 2D line equations of their projections are used as the basis for system geometry estimation. This method was tested using a mobile C-arm CT system and comparisons were made to standard BB-based calibrations. Simulation studies were also conducted using a sinusoid-on-sphere orbit. Calibration performance was quantified in terms of Point Spread Function (PSF) width and back projection error. Visual image quality was assessed with respect to spatial resolution in trabecular bone in an anthropomorphic head phantom.
Results: The wire-based calibration method performed equal to or better than BB-based calibrations in all evaluated metrics. For the sinusoidal scans, the method provided reliable calibration, validating its application to non-circular trajectories. Furthermore, the ability to improve image quality using non-circular orbits in conjunction with this calibration method was demonstrated.
Conclusion: The proposed method has been shown feasible for conventional circular CBCT scans and offers a promising tool for non-circular scan orbits that can improve image quality, reduce dose, and extend field of view.
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.
Method: Geometric calibration of the C-arm was performed offline in two rotational directions (orbit α, orbit β). Patient registration was performed using image-based 3D-2D registration with an initially acquired radiograph of the patient. This approach for patient registration eliminated the requirement for external tracking devices inside the operating room, allowing virtual fluoroscopy using commonly available systems in fluoroscopically guided procedures within standard surgical workflow. Geometric accuracy was evaluated in terms of projection distance error (PDE) in anatomical fiducials. A pilot study was conducted to evaluate the utility of virtual fluoroscopy to aid C-arm positioning in image guided surgery, assessing potential improvements in time, dose, and agreement between the virtual and desired view.
Results: The overall geometric accuracy of DRRs in comparison to the actual radiographs at various C-arm positions was PDE (mean ± std) = 1.6 ± 1.1 mm. The conventional approach required on average 8.0 ± 4.5 radiographs spent “fluoro hunting” to obtain the desired view. Positioning accuracy improved from 2.6o ± 2.3o (in α) and 4.1o ± 5.1o (in β) in the conventional approach to 1.5o ± 1.3o and 1.8o ± 1.7o, respectively, with the virtual fluoroscopy approach.
Conclusion: Virtual fluoroscopy could improve accuracy of C-arm positioning and save time and radiation dose in the operating room. Such a system could be valuable to training of fluoroscopy technicians as well as intraoperative use in fluoroscopically guided procedures.
Method: The method, called MIND Demons, solves for the deformation field between two images by optimizing an energy functional that incorporates both the forward and inverse deformations, smoothness on the velocity fields and the diffeomorphisms, a modality-insensitive similarity function suitable to multi-modality images, and constraints on geodesics in Lagrangian coordinates. Direct optimization (without relying on an exponential map of stationary velocity fields used in conventional diffeomorphic Demons) is carried out using a Gauss-Newton method for fast convergence. Registration performance and sensitivity to registration parameters were analyzed in simulation, in phantom experiments, and clinical studies emulating application in image-guided spine surgery, and results were compared to conventional mutual information (MI) free-form deformation (FFD), local MI (LMI) FFD, and normalized MI (NMI) Demons.
Result: The method yielded sub-voxel invertibility (0.006 mm) and nonsingular spatial Jacobians with capability to preserve local orientation and topology. It demonstrated improved registration accuracy in comparison to the reference methods, with mean target registration error (TRE) of 1.5 mm compared to 10.9, 2.3, and 4.6 mm for MI FFD, LMI FFD, and NMI Demons methods, respectively. Validation in clinical studies demonstrated realistic deformation with sub-voxel TRE in cases of cervical, thoracic, and lumbar spine.
Conclusions: A modality-independent deformable registration method has been developed to estimate a viscoelastic diffeomorphic map between preoperative MR and intraoperative CT. The method yields registration accuracy suitable to application in image-guided spine surgery across a broad range of anatomical sites and modes of deformation.
Methods. Using fast, robust 3D-2D registration in combination with 3D models of known components (surgical devices), the 3D pose determination was solved to relate known components to 2D projection images and 3D preoperative CT in near-real-time. Exact and parametric models of the components were used as input to the algorithm to evaluate the effects of model fidelity. The proposed algorithm employs the covariance matrix adaptation evolution strategy (CMA-ES) to maximize gradient correlation (GC) between measured projections and simulated forward projections of components. Geometric accuracy was evaluated in a spine phantom in terms of target registration error at the tool tip (TREx), and angular deviation (TREΦ) from planned trajectory.
Results. Transpedicle surgical devices (probe tool and spine screws) were successfully guided with TREx<2 mm and TREΦ <0.5° given projection views separated by at least >30° (easily accommodated on a mobile C-arm). QA of the surgical product based on 3D-2D registration demonstrated the detection of pedicle screw breach with TREx<1 mm, demonstrating a trend of improved accuracy correlated to the fidelity of the component model employed.
Conclusions. 3D-2D registration combined with 3D models of known surgical components provides a novel method for near-real-time guidance and quality assurance using a mobile C-arm without external trackers or fiducial markers. Ongoing work includes determination of optimal views based on component shape and trajectory, improved robustness to anatomical deformation, and expanded preclinical testing in spine and intracranial surgeries.
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