Enabling tomography with low-cost C-arm systems

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

BACKGROUND

Accurate 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.

PURPOSE

We 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.

METHODS

The 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.

RESULTS

The 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.

CONCLUSIONS

The proposed calibration method gave promising results that pave the way to patient-specific geometric self-calibrations in clinics.

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

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.

Tolerance to geometrical inaccuracies in CBCT systems: A comprehensive study

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 to 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, that is, skew (<0.2°) and horizontal shift (<0.4 mm), is tighter than the tolerance to those errors affecting the position along the longitudinal direction or the magnification, that is, vertical shift (<2 mm), roll (<1.5°), tilt (<2°), 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.