Accuracy of a new intraoperative cone beam CT imaging technique (Artis zeego II) compared to postoperative CT scan for assessment of pedicle screws placement and breaches detection

Cordemans, Virginie; Kaminski, Ludovic; Banse, Xavier; Francq, Bernard; Cartiaux, Olivier

doi:10.1007/s00586-017-5139-y

Cited by 37 publications

(27 citation statements)

References 34 publications

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“…The size of the CT system matrix is different according to different image sizes. The system matrix of the fan beam scanning mode [25] is used in the experiment. The point spread function of the CT image is approximated as a standard Gauss function process with different sizes, 20 × 20, 27 × 27, 18 × 18, 15 × 15, respectively and the same variance, σ 2 = 1.…”

Section: Resultsmentioning

confidence: 99%

A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Sun

Zhang

et al. 2019

Algorithms

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Computed tomography (CT) image reconstruction and restoration are very important in medical image processing, and are associated together to be an inverse problem. Image iterative reconstruction is a key tool to increase the applicability of CT imaging and reduce radiation dose. Nevertheless, traditional image iterative reconstruction methods are limited by the sampling theorem and also the blurring of projection data will propagate unhampered artifact in the reconstructed image. To overcome these problems, image restoration techniques should be developed to accurately correct a wide variety of image degrading effects in order to effectively improve image reconstruction. In this paper, a blind image restoration technique is embedded in the compressive sensing CT image reconstruction, which can result in a high-quality reconstruction image using fewer projection data. Because a small amount of data can be obtained by radiation in a shorter time, high-quality image reconstruction with less data is equivalent to reducing radiation dose. Technically, both the blurring process and the sparse representation of the sharp CT image are first modeled as a serial of parameters. The sharp CT image will be obtained from the estimated sparse representation. Then, the model parameters are estimated by a hierarchical Bayesian maximum posteriori formulation. Finally, the estimated model parameters are optimized to obtain the final image reconstruction. We demonstrate the effectiveness of the proposed method with the simulation experiments in terms of the peak signal to noise ratio (PSNR), and structural similarity index (SSIM).

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Section: Resultsmentioning

confidence: 99%

A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

Sun

Zhang

et al. 2019

Algorithms

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“…CBCT has been deployed for this purpose [13,23]. However, even with one of the most recent CBCT devices and compared to conventional postoperative CT, 23% of the cases of cortical breach were missed on intraoperative CBCT images primarily due to much stronger metal artifacts around the screw [5]. Improving the quality of intraoperative CBCT reconstructions for the task of pedicle screw placement in spinal fusion surgery, consequently, has a great potential to identify cortical breach during the operation, allowing for immediate revision, and therefore, reduction of both neurological complications and need for revision surgery.…”

Section: Introductionmentioning

confidence: 99%

A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance

et al. 2020

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Purpose During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality. Methods We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i.e., verification of screw placement. Adjustments are performed on-the-fly using a convolutional neural network that regresses a quality index over all possible next views given the current X-ray image. Adjusting the CBCT trajectory to acquire the recommended views results in non-circular source orbits that avoid poor images, and thus, data inconsistencies. Results We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory. Using both realistically simulated data as well as real CBCT acquisitions of a semianthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts. Conclusion The proposed method is a step toward online patient-specific C-arm CBCT source trajectories that enable high-quality tomographic imaging in the operating room. Since the optimization objective is implicitly encoded in a neural network trained on large amounts of well-annotated projection images, the proposed approach overcomes the need for 3D information at run-time.

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“…Despite substantial improvements in operative technique, spinal fusion surgery remains high-risk: In addition to usual complications, pedicle screws that breach cortex can result in nerve damage [5]. Surprisingly, the number of misplaced pedicle screws remains high [5,2]: Cortical breach occurs in up to 31% and 72% of the cases for freehand and fluoroscopy-guided techniques, respectively. Even when surgical navigation is employed, up to 19% of the screws are not fully contained in cortex [5].…”

Section: Introductionmentioning

confidence: 99%

“…C-arm X-ray systems is becoming widely available, it is not currently being used for spinal fusion 3D imaging, because compared to CT, C-arm CBCT images suffer from substantially stronger metal artifacts around the highly-attenuating titanium implants, which compromise the value of intra-operative CBCT for assessing cortical breach [2].…”

Section: Introductionmentioning

confidence: 99%

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

Zaech

Gao

Bier

et al. 2019

Lecture Notes in Computer Science

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Metal artifacts in computed tomography (CT) arise from a mismatch between physics of image formation and idealized assumptions during tomographic reconstruction. These artifacts are particularly strong around metal implants, inhibiting widespread adoption of 3D cone-beam CT (CBCT) despite clear opportunity for intra-operative verification of implant positioning, e. g. in spinal fusion surgery. On synthetic and real data, we demonstrate that much of the artifact can be avoided by acquiring better data for reconstruction in a task-aware and patientspecific manner, and describe the first step towards the envisioned taskaware CBCT protocol. The traditional short-scan CBCT trajectory is planar, with little room for scene-specific adjustment. We extend this trajectory by autonomously adjusting out-of-plane angulation. This enables C-arm source trajectories that are scene-specific in that they avoid acquiring "poor images", characterized by beam hardening, photon starvation, and noise. The recommendation of ideal out-of-plane angulation is performed on-the-fly using a deep convolutional neural network that regresses a detectability-rank derived from imaging physics.

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Accuracy of a new intraoperative cone beam CT imaging technique (Artis zeego II) compared to postoperative CT scan for assessment of pedicle screws placement and breaches detection

Abstract: Intraoperative CBCT provides accurate assessment of pedicle screw placement and enables intraoperative repositioning of misplaced screws. This technique may make postoperative CT imaging unnecessary.

Cited by 37 publications

References 34 publications

A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

A Novel Blind Restoration and Reconstruction Approach for CT Images Based on Sparse Representation and Hierarchical Bayesian-MAP

A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance

Learning to Avoid Poor Images: Towards Task-aware C-arm Cone-beam CT Trajectories

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