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Purpose: We investigated the feasibility of dual-energy (DE) detection of bone marrow edema (BME) using a dedicated extremity cone-beam CT (CBCT) with a unique three-source x-ray unit. The sources can be operated at different energies to enable single-scan DE acquisitions. However, they are arranged parallel to the axis of rotation, resulting in incomplete sampling and precluding the application of DE projection-domain decompositions (PDD) for beam-hardening reduction. Therefore, we propose a novel combination of a model-based “one-step” DE two-material decomposition followed by a constrained image-domain change-of-basis to obtain virtual non-calcium (VNCa) images for BME detection.
Methods: DE projections were obtained using an “alternating-kV” protocol by operating the peripheral two sources of the CBCT system at low-energy (60 kV, 0.105 mAs/frame) and the central source at high-energy (100 kV, 0.028 mAs/frame), for a total of 600 frames over 216° of gantry rotation. Projections were processed with detector lag, glare and fast Monte Carlo (MC)-based iterative scatter corrections. Model-based material decomposition (MBMD) was then implemented to obtain aluminum (Al) and polyethylene (PE) volume fraction images with minimal beam-hardening. Statistical ray weights in MBMD were modified to account for regions with highly oblique sampling by the peripheral sources. To generate the VNCa maps, image-domain decomposition (IDD) constrained by the volume conservation principle (VCP) was performed to convert the Al and PE MBMD images into volume fractions of water, fat and cortical bone. Accuracy of BME detection was evaluated using physical phantom data acquired on the multi-source extremity CBCT scanner.
Results: The proposed framework estimated the volume of BME with ~10% error. The MC-based scatter corrections and the modified MBMD ray weights were essential to achieve such performance – the error without MC scatter corrections was <30%, whereas the uniformity of estimated VNCa images was 3x improved using the modified weights compared to the conventional weights.
Conclusions: The proposed DE decomposition framework was able to overcome challenges of high scatter and incomplete sampling to achieve BME detection on a CBCT system with axially-distributed x-ray sources.
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.
Methods. Two novel methodologies predicate the work: (1) Known-Component Registration (KC-Reg) for 3D localization of the patient and interventional devices from 2D radiographs; and (2) Penalized-Likelihood reconstruction (PLH) for improved 3D image quality and dose reduction. A thorough assessment of geometric stability, dosimetry, and image quality was performed to define algorithm parameters for imaging and guidance protocols. Laboratory studies included: evaluation of KC-Reg in localization of spine screws delivered in cadaver; and PLH performance in contrast, noise, and resolution in phantoms/cadaver compared to filtered backprojection (FBP).
Results. KC-Reg was shown to successfully register screw implants within ~1 mm based on as few as 3 radiographs. PLH was shown to improve soft-tissue visibility (61% improvement in CNR) compared to FBP at matched resolution. Cadaver studies verified the selection of algorithm parameters and the methods were successfully translated to clinical studies under an IRB protocol.
Conclusions. Model-based registration and reconstruction approaches were shown to reduce dose and provide improved visualization of anatomy and surgical instrumentation. Immediate future work will focus on further integration of KC-Reg and PLH for Known-Component Reconstruction (KC-Recon) to provide high-quality intraoperative imaging in the presence of dense instrumentation.
In this paper, we present an on-the-fly surgical support system that provides guidance using augmented reality and can be used in quasi-unprepared operating rooms. The proposed system builds upon a multi-modality marker and simultaneous localization and mapping technique to co-calibrate an optical see-through head mounted display to a C-Arm fluoroscopy system. Then, annotations on the 2-D X-Ray images can be rendered as virtual objects in 3-D providing surgical guidance. In a feasibility study on a semi-anthropomorphic phantom we found the accuracy of our system to be comparable to the traditional image-guided technique while substantially reducing the number of acquired X-Ray images as well as procedure time. Our promising results encourage further research on the interaction between virtual and real objects, that we believe will directly benefit the proposed method. Further, we would like to explore the capabilities of our on-the-fly augmented reality support system in a larger study directed towards common orthopaedic interventions.
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: The study involved 35 transilliac bone biopsy samples imaged on extremity CBCT (voxel size 75 μm, imaging dose ~13 mGy) and gold standard μCT (voxel size 7.67 μm). CBCT image segmentation was performed using (i) global Otsu’s thresholding, (ii) Bernsen’s local thresholding, (iii) Bernsen’s local thresholding with additional histogram-based global pre-thresholding, and (iv) the same as (iii) but combined with contrast enhancement using a Laplacian Pyramid. Correlations between extremity CBCT with the different segmentation algorithms and gold standard μCT were investigated for measurements of Bone Volume over Total Volume (BV/TV), Trabecular Thickness (Tb.Th), Trabecular Spacing (Tb.Sp), and Trabecular Number (Tb.N).
Results: The combination of local thresholding with global pre-thresholding and Laplacian contrast enhancement outperformed other CBCT segmentation methods. Using this optimal segmentation scheme, strong correlation between extremity CBCT and μCT was achieved, with Pearson coefficients of 0.93 for BV/TV, 0.89 for Tb.Th, 0.91 for Tb.Sp, and 0.88 for Tb.N (all results statistically significant). Compared to a simple global CBCT segmentation using Otsu’s algorithm, the advanced segmentation method achieved ~20% improvement in the correlation coefficient for Tb.Th and ~50% improvement for Tb.Sp.
Conclusions: Extremity CBCT combined with advanced image pre-processing and segmentation achieves high correlation with gold standard μCT in measurements of trabecular microstructure. This motivates ongoing development of clinical applications of extremity CBCT in in-vivo evaluation of bone health e.g. in early osteoarthritis and osteoporosis.
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.
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.
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