Enabling tomography with low-cost C-arm systems

Abella, Mónica; Molina, Claudia de; Ballesteros, Nerea; García-Santos, Alba; Martinez, Alvaro; Puerto, I. del; Desco, Manuel

doi:10.1371/journal.pone.0203817

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Closely related to our work, Abella et al [22] proposed a low-cost solution for tomographic reconstruction using a 3D scanner mounted on the C-arm. They identified three major issues that occur during tomographic reconstruction using a conventional C-arm device.…”

Section: Introductionmentioning

confidence: 82%

3D Reconstruction of Wrist Bones from C-Arm Fluoroscopy Using Planar Markers

et al. 2023

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In orthopedic surgeries, such as osteotomy and osteosynthesis, an intraoperative 3D reconstruction of the bone would enable surgeons to quickly assess the fracture reduction procedure with preoperative planning. Scanners equipped with such functionality are often more expensive than a conventional C-arm fluoroscopy device. Moreover, a C-arm fluoroscopy device is commonly available in many orthopedic facilities. Based on the widespread use of such equipment, this paper proposes a method to reconstruct the 3D structure of bone with a conventional C-arm fluoroscopy device. We focus on wrist bones as the target of reconstruction in this research as this will facilitate a flexible imaging scheme. Planar markers are attached to the target object and are tracked in the fluoroscopic image for C-arm pose estimation. The initial calibration of the device is conducted using a checkerboard pattern. In general, reconstruction algorithms are sensitive to geometric calibration errors. To assess the practicality of the method for reconstruction, a simulation study demonstrating the effect of checkerboard thickness and spherical marker size on reconstruction quality was conducted.

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Section: Introductionmentioning

confidence: 82%

3D Reconstruction of Wrist Bones from C-Arm Fluoroscopy Using Planar Markers

et al. 2023

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“…The impact of errors in the geometrical parameters on a specific system will depend on its mechanical tolerance. For example, in a non-motorized C-arm designed for 2D images, as the one used in Abella et al 21 , geometrical errors exceed the tolerances in all the cases except for detector roll. In the motorized Carm systems, with more accurate mechanics and lower magnification, the only parameter that exceeds the tolerance is the horizontal shift.…”

Section: Accepted Articlementioning

confidence: 98%

Tolerance to geometrical inaccuracies in CBCT systems: A comprehensive study

et al. 2021

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Purpose: The last decades have seen the consolidation of the Cone-Beam CT (CBCT) technology, which is nowadays widely used for different applications such as micro-CT for small animals, mammography, dentistry, or surgical procedures. Some CBCT systems may suffer mechanical strains due to the heavy load of the X-ray tube. This fact, together with tolerances in the manufacturing process, lead to different types of undesirable effects in the reconstructed image unless they are properly accounted for during the reconstruction. To obtain good quality images, it is necessary to have a complete characterization of the system geometry including the angular position of the gantry, the source-object and detector-object distances, and the position and pose of the detector. These parameters can be obtained through a calibration process done periodically, depending on the stability of the system geometry. To the best of our knowledge, there are no comprehensive works studying the effect of inaccuracies in the geometrical calibration of CBCT systems in a systematic and quantitative way. In this work, we describe the effects of detector misalignments (linear shifts, rotation and inclinations) on the image and define their tolerance as the maximum error that keeps the image free from artifacts.Methods: We used simulations of four phantoms including systematic and random misalignments.Reconstructions of these data with and without errors were compared to identify the artifacts introduced in the reconstructed image and the tolerance on miscalibration deemed to provide acceptable image quality.Results: Visual assessment provided an easy guideline to identify the sources of error by visual inspection of the artifactual images. Systematic errors result in blurring, shape distortion and/or reduction of the axial field of view while random errors produce streaks and blurring in all cases, with a tolerance which is more than twice that of systematic errors. The tolerance corresponding to errors in position of the detector along the tangential direction, i.e., skew (<0.2 degrees) and horizontal shift (<0.4 mm), is tighter than the tolerance to those errors affecting the position along the longitudinal direction or the magnification, i.e., vertical shift (<2 mm), roll (<1.5 degrees), tilt (<2 degrees) and SDD (<3 mm). Conclusion:We present a comprehensive study, based on realistic simulations, of the effects on the reconstructed image quality of errors in the geometrical characterization of a CBCT system and define their tolerance. These results could be used to guide the design of new systems, establishing the mechanical precision that must be achieved, and to help in the definition of an optimal geometrical calibration process. Also, the thorough visual assessment may be valuable to identify the most predominant sources of error based on the effects shown in the reconstructed image.

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“…Smekal et al 17 obtain an efficient analytical solution is but the system trajectory must be circular. Many methods consider a known calibration pattern 4,[18][19][20][21][22][23] (i.e., the relative 3D positions of the markers with respect to each other are known). The marker pattern can be adapted to exploit projection data redundancies 24 for highly reducing the reconstruction artefacts due to markers, but this approach needs a moving marker pattern.…”

Section: Introductionmentioning

confidence: 99%

Marker‐based C‐arm self‐calibration with unknown calibration pattern

Pivot,

Voros,

Chappard

et al. 2024

Medical Physics

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BackgroundAccurate tomographic reconstructions require the knowledge of the actual acquisition geometry. Many mobile C‐arm CT scanners have poorly reproducible acquisition geometries and thus need acquisition‐specific calibration procedures. Most of geometric self‐calibration methods based on projection data either need prior information or are limited to the estimation of a low number of geometric calibration parameters. Other self‐calibration methods generally use a calibration pattern with known geometry and are hardly implementable in practice for clinical applications.PurposeWe present a three‐step marker based self‐calibration method which does not require the prior knowledge of the calibration pattern and thus enables the use of calibration patterns with arbitrary markers positions.MethodsThe first step of the method aims at detecting the set of markers of the calibration pattern in each projection of the CT scan and is performed using the YOLO (You Only Look Once) Convolutional Neural Network. The projected marker trajectories are then estimated by a sequential projection‐wise marker association scheme based on the Linear Assignment Problem which uses Kalman filters to predict the markers 2D positions in the projections. The acquisition geometry is finally estimated from the marker trajectories using the Bundle‐adjustment algorithm.ResultsThe calibration method has been tested on realistic simulated images of the ICRP (International Commission on Radiological Protection) phantom, using calibration patterns with 10 and 20 markers. The backprojection error was used to evaluate the self‐calibration method and exhibited sub‐millimeter errors. Real images of two human knees with 10 and 30 markers calibration patterns were then used to perform a qualitative evaluation of the method, which showed a remarkable artifacts reduction and bone structures visibility improvement.ConclusionsThe proposed calibration method gave promising results that pave the way to patient‐specific geometric self‐calibrations in clinics.

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Enabling tomography with low-cost C-arm systems

Cited by 6 publications

References 23 publications

3D Reconstruction of Wrist Bones from C-Arm Fluoroscopy Using Planar Markers

3D Reconstruction of Wrist Bones from C-Arm Fluoroscopy Using Planar Markers

Tolerance to geometrical inaccuracies in CBCT systems: A comprehensive study

Marker‐based C‐arm self‐calibration with unknown calibration pattern

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