Simultaneous misalignment correction for approximate circular cone-beam computed tomography

Investigations into the Geometric Calibration and Systematic Effects of a Micro-CT System

Micro-Computed Tomography (µCT) systems are used for examining the internal structures of various objects, such as material samples, manufactured parts, and natural objects. Resolving fine details or performing accurate geometric measurements in the voxel data critically depends on the precise calibration of the µCT systems geometry. This paper presents a calibration method for µCT systems using projections of a calibration phantom, where the coordinates of the phantom are initially unknown. The approach involves detecting and tracking steel ball bearings and adjusting the unknown system geometry parameters using non-linear least squares optimization. Multiple geometric models are tested to verify their suitability for a self-calibration approach. The implementation is tested using a calibration phantom captured at different magnifications. The results demonstrate the system's capability to determine the geometry model parameters with a remaining error on the detector between 0.27 px and 0.18 px. Systematic errors that remain after calibration, as well as changing parameters due to system instabilities, are investigated. The source code of this work is published to enable further research.

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.

Reference-free calibration method for asynchronous rotation in robotic CT

BACKGROUND

Geometry calibration for robotic CT system is necessary for obtaining acceptable images under the asynchrony of two manipulators.

OBJECTIVE

We aim to evaluate the impact of different types of asynchrony on images and propose a reference-free calibration method based on a simplified geometry model.

METHODS

We evaluate the impact of different types of asynchrony on images and propose a novel calibration method focused on asynchronous rotation of robotic CT. The proposed method is initialized with reconstructions under default uncalibrated geometry and uses grid sampling of estimated geometry to determine the direction of optimization. Difference between the re-projections of sampling points and the original projection is used to guide the optimization direction. Images and estimated geometry are optimized alternatively in an iteration, and it stops when the difference of residual projections is close enough, or when the maximum iteration number is reached.

RESULTS

In our simulation experiments, proposed method shows better performance, with the PSNR increasing by 2%, and the SSIM increasing by 13.6% after calibration. The experiments reveal fewer artifacts and higher image quality.

CONCLUSION

We find that asynchronous rotation has a more significant impact on reconstruction, and the proposed method offers a feasible solution for correcting asynchronous rotation.

A nonconvex model-based combined geometric calibration scheme for micro cone-beam CT with irregular trajectories

BACKGROUND

Many dedicated cone-beam CT (CBCT) systems have irregular scanning trajectories. Compared with the standard CBCT calibration, accurate calibration for CBCT systems with irregular trajectories is a more complex task, since the geometric parameters for each scanning view are variable. Most of the existing calibration methods assume that the intrinsic geometric relationship of the fiducials in the phantom is precisely known, and rarely delve deeper into the issue of whether the phantom accuracy is adapted to the calibration model.

PURPOSE

A high-precision phantom and a highly robust calibration model are interdependent and mutually supportive, and they are both important for calibration accuracy, especially for the high-resolution CBCT. Therefore, we propose a calibration scheme that considers both accurate phantom measurement and robust geometric calibration.

METHODS

Our proposed scheme consists of two parts: (1) introducing a measurement model to acquire the accurate intrinsic geometric relationship of the fiducials in the phantom; (2) developing a highly noise-robust nonconvex model-based calibration method. The measurement model in the first part is achieved by extending our previous high-precision geometric calibration model suitable for CBCT with circular trajectories. In the second part, a novel iterative method with optimization constraints based on a back-projection model is developed to solve the geometric parameters of each view.

RESULTS

The simulations and real experiments show that the measurement errors of the fiducial ball bearings (BBs) are within the subpixel level. With the help of the geometric relationship of the BBs obtained by our measurement method, the classic calibration method can achieve good calibration based on far fewer BBs. All metrics obtained in simulated experiments as well as in real experiments on Micro CT systems with resolutions of 9 and 4.5 μm show that the proposed calibration method has higher calibration accuracy than the competing classic method. It is particularly worth noting that although our measurement model proves to be very accurate, the classic calibration method based on this measurement model can only achieve good calibration results when the resolution of the measurement system is close to that of the system to be calibrated, but our calibration scheme enables high-accuracy calibration even when the resolution of the system to be calibrated is twice that of the measurement system.

CONCLUSIONS

The proposed combined geometrical calibration scheme does not rely on a phantom with an intricate pattern of fiducials, so it is applicable in Micro CT with high resolution. The two parts of the scheme, the "measurement model" and the "calibration model," prove to be of high accuracy. The combination of these two models can effectively improve the calibration accuracy, especially in some extreme cases.

Online geometry calibration for retrofit computed tomography from a mouse rotation system and a small-animal imager

BACKGROUND

Computed tomography (CT) generates a three-dimensional rendering that can be used to interrogate a given region or desired structure from any orientation. However, in preclinical research, its deployment remains limited due to relatively high upfront costs. Existing integrated imaging systems that provide merged planar X-ray also dwarfs CT popularity in small laboratories due to their added versatility.

PURPOSE

In this paper, we sought to generate CT-like data using an existing small-animal X-ray imager with a specialized specimen rotation system, or MiSpinner. This setup conforms to the cone-beam CT (CBCT) geometry, which demands high spatial calibration accuracy. Therefore, a simple but robust geometry calibration algorithm is necessary to ensure that the entire imaging system works properly and accurately.

METHODS

Because the rotation system is not permanently affixed, we propose a structure tensor-based two-step online (ST-TSO) geometry calibration algorithm. Specifically, two datasets are needed, namely, calibration and actual measurements. A calibration measurement detects the background of the system forward X-ray projections. A study on the background image reveals the characteristics of the X-ray photon distribution, and thus, provides a reliable estimate of the imaging geometry origin. Actual measurements consisted of an X-ray of the intended object, including possible geometry errors. A comprehensive image processing technique helps to detect spatial misalignment information. Accordingly, the first processing step employs a modified projection matrix-based calibration algorithm to estimate the relevant geometric parameters. Predicted parameters are then fine-tuned in a second processing step by an iterative strategy based on the symmetry property of the sum of projections. Virtual projections calculated from the parameters after two-step processing compensate for the scanning errors and are used for CT reconstruction. Experiments on phantom and mouse imaging data were performed to validate the calibration algorithm.

RESULTS

Once system correction was conducted, CBCT of a CT bar phantom and a cohort of euthanized mice were analyzed. No obvious structure error or spatial artifacts were observed, validating the accuracy of the proposed geometry calibration method. Digital phantom simulation indicated that compared with the preset spatial values, errors in the final estimated parameters could be reduced to 0.05° difference in dominant angle and 0.5-pixel difference in dominant axis bias. The in-plane resolution view of the CT-bar phantom revealed that the resolution approaches 150 μ $\umu$ m.

CONCLUSIONS

A constrained two-step online geometry calibration algorithm has been developed to calibrate an integrated X-ray imaging system, defined by a first-step analytical estimation and a second-step iterative fine-tuning. Test results have validated its accuracy in system correction, thus demonstrating the potential of the described system to be modified and adapted for preclinical research.

Structure-guided computed tomography reconstruction from limited-angle projections

Limited-angle computed tomography (CT) imaging is one of the common imaging problems. The reconstructed images often encounter obvious artifacts and structure degradation. In recent years, the recoverability prior of image structure has been widely explored in limited-angle CT reconstruction, and the image quality has been greatly improved. However, the artifacts and structure degradation still exist. In this study, we establish a new reconstruction model based on weighted relative structure (wRS) determined by image gradients, which serves as weights to guide image reconstruction in order to reduce artifacts and preserve structures. Then, we develop an efficient algorithm using a surrogate function to solve this model. Moreover, this method is compared with some of other popular reconstruction methods, such as anisotropic total variation method and image gradient L0 norm minimization method and so on. Experiments on digital phantoms, real carved cheese and walnut projection are reported to demonstrate its superiority. Several quantitative indices including RMSE, PSNR, and SSIM of the reconstruction images from 90°-data of FORBILD head phantom are 0.0120, 43.52, and 0.9961. The experimental results indicate that the image obtained by our method is the closest to reference image. By comparing reconstruction images or their residual images, images reconstructed from real CT data, the experimental results of the residual images and the respective quantitative data analysis also demonstrate that the images reconstructed using our new method suffer from less artifacts and structure degradation.

Online Calibration of a Linear Micro Tomosynthesis Scanner

In a linear tomosynthesis scanner designed for imaging histologic samples of several centimeters size at 10 µm resolution, the mechanical instability of the scanning stage (±10 µm) exceeded the resolution of the image system, making it necessary to determine the trajectory of the stage for each scan to avoid blurring and artifacts in the images that would arise from the errors in the geometric information used in 3D reconstruction. We present a method for online calibration by attaching a layer of randomly dispersed micro glass beads or calcium particles to the bottom of the sample stage. The method was based on a parametric representation of the rigid body motion of the sample stage-marker layer assembly. The marker layer was easy to produce and proven effective in the calibration procedure.

Online Geometric Calibration of a Hybrid CT System for Ultrahigh-Resolution Imaging

A hybrid imaging system consisting of a standard computed tomography (CT) scanner and a low-profile photon-counting detector insert in contact with the patient's body has been used to produce ultrahigh-resolution images in a limited volume in chest scans of patients. The detector insert is placed on the patient bed as needed and not attached. Thus, its position and orientation in the scanner is dependent on the patient's position and scan settings. To allow accurate image reconstruction, we devised a method of determining the relative geometry of the detector insert and the CT scanner for each scan using fiducial markers. This method uses an iterative registration algorithm to align the markers in the reconstructed volume from the detector insert to that of the concurrent CT scan. After obtaining precise geometric information of the detector insert relative to the CT scanner, the two complementary sets of images are summed together to create a detailed image with reduced artifacts.

A motion correction approach for oral and maxillofacial cone-beam CT imaging

Patient movement affects image quality in oral and maxillofacial cone-beam computed tomography imaging. While many efforts are made to minimize the possibility of motion during a scan, relatively little attention has been given to motion correction after acquisition. We propose a novel method which can improve the image quality after an oral and maxillofacial scan. The proposed method is based on our previous work and is a retrospective motion estimation and motion compensation (ME/MC) approach that iteratively estimates and compensates for rigid pose change over time. During motion estimation, image update and motion update are performed alternately in a multi-resolution scheme to obtain the motion. We propose use of a feature-based motion update and patch-based image update in the iterative estimation process, to alleviate the effect of limited scan field of view on estimation. During motion compensation, a fine-resolution image reconstruction was performed with compensation for the estimated motion. The proposed ME/MC method was evaluated with simulations, phantom and patient studies. Two experts in dentomaxillofacial radiology assessed the diagnostic importance of the resulting motion artifact suppression. The quality of the reconstructed images was improved after motion compensation, and most of the image artifacts were eliminated. Quantitative analysis by comparison to a reference image and by calculation of a sharpness metric agreed with the qualitative observation. The results are promising, and further evaluation is required to assess the clinical value of the proposed method.

Motion estimation and correction in SPECT, PET and CT

Patient motion impacts single photon emission computed tomography (SPECT), positron emission tomography (PET) and X-ray computed tomography (CT) by giving rise to projection data inconsistencies that can manifest as reconstruction artifacts, thereby degrading image quality and compromising accurate image interpretation and quantification. Methods to estimate and correct for patient motion in SPECT, PET and CT have attracted considerable research effort over several decades. The aims of this effort have been two-fold: to estimate relevant motion fields characterizing the various forms of voluntary and involuntary motion; and to apply these motion fields within a modified reconstruction framework to obtain motion-corrected images. The aims of this review are to outline the motion problem in medical imaging and to critically review published methods for estimating and correcting for the relevant motion fields in clinical and preclinical SPECT, PET and CT. Despite many similarities in how motion is handled between these modalities, utility and applications vary based on differences in temporal and spatial resolution. Technical feasibility has been demonstrated in each modality for both rigid and non-rigid motion, but clinical feasibility remains an important target. There is considerable scope for further developments in motion estimation and correction, and particularly in data-driven methods that will aid clinical utility. State-of-the-art machine learning methods may have a unique role to play in this context.

Correction of geometric artifact in cone-beam computed tomography through a deep neural network

Cone-beam computed tomography is a noninvasive detection system that can obtain the three-dimensional structure of objects in a way that does not damage the object. It is widely applied in precision instruments, medical detection, and other fields. However, in the actual process, if a geometric artifact appears in the results, it will affect the quality of reconstructed images, including detail loss and decreased spatial resolution, which leads to inaccurate distinction of defects in detection. We propose a method for correcting a geometric artifact by means of data-driven projection and neural networks. The network designed is a deep neural network with six convolutional layers and six deconvolutional layers that can correct a geometric artifact by training a large number of labeled data and unlabeled data. Compared with other networks that require prior information for reconstructed images, the proposed method uses a projection data-driven approach that can avoid the requirement for prior information. The simulation data have been tested under varying degrees of noise, and satisfactory geometric artifact correction results have been obtained. Meanwhile, we use the actual data of line pairs and ball grid array solder joints to conduct experiments. The results obtained by our method are compared with two other phantom-based method and the U-net method, respectively. The results of similarity and spatial resolution show that the proposed method can achieve the comparable results as the two types of methods. At the same time, we apply a projection data-driven approach to avoid the requirement for prior information, which is more conducive to the correction of the geometric artifact in practical situations where prior information is lacking.

Deformable motion compensation for interventional cone-beam CT

Image-guided therapies in the abdomen and pelvis are often hindered by motion artifacts in cone-beam CT (CBCT) arising from complex, non-periodic, deformable organ motion during long scan times (5-30 s). We propose a deformable image-based motion compensation method to address these challenges and improve CBCT guidance. Motion compensation is achieved by selecting a set of small regions of interest in the uncompensated image to minimize a cost function consisting of an autofocus objective and spatiotemporal regularization penalties. Motion trajectories are estimated using an iterative optimization algorithm (CMA-ES) and used to interpolate a 4D spatiotemporal motion vector field. The motion-compensated image is reconstructed using a modified filtered backprojection approach. Being image-based, the method does not require additional input besides the raw CBCT projection data and system geometry that are used for image reconstruction. Experimental studies investigated: (1) various autofocus objective functions, analyzed using a digital phantom with a range of sinusoidal motion magnitude (4, 8, 12, 16, 20 mm); (2) spatiotemporal regularization, studied using a CT dataset from The Cancer Imaging Archive with deformable sinusoidal motion of variable magnitude (10, 15, 20, 25 mm); and (3) performance in complex anatomy, evaluated in cadavers undergoing simple and complex motion imaged on a CBCT-capable mobile C-arm system (Cios Spin 3D, Siemens Healthineers, Forchheim, Germany). Gradient entropy was found to be the best autofocus objective for soft-tissue CBCT, increasing structural similarity (SSIM) by 42%-92% over the range of motion magnitudes investigated. The optimal temporal regularization strength was found to vary widely (0.5-5 mm^-2) over the range of motion magnitudes investigated, whereas optimal spatial regularization strength was relatively constant (0.1). In cadaver studies, deformable motion compensation was shown to improve local SSIM by ∼17% for simple motion and ∼21% for complex motion and provided strong visual improvement of motion artifacts (reduction of blurring and streaks and improved visibility of soft-tissue edges). The studies demonstrate the robustness of deformable motion compensation to a range of motion magnitudes, frequencies, and other factors (e.g. truncation and scatter).

Data sustained misalignment correction in microscopic cone beam CT via optimization under the Grangeat Epipolar consistency condition

PURPOSE

The misalignment correction in cone beam computed tomography (CBCT), which is usually carried out in an offline manner, is a difficult and tedious process. It becomes even more challenging in microscopic CBCT due to the much higher requirements on spatial resolution. In practice, however, an offline approach for misalignment correction may not be readily implementable, especially in the situations where either time is of the essence or the process needs to be carried out repetitively. Thus, an online self-calibration (i.e., data sustained misalignment correction without the involvement of specific alignment phantom) would be more practical. In this work, we investigate the data sustained misalignment correction in microscopic CBCT via optimization under the Grangeat Epipolar Consistence Condition and evaluate its performance via phantom and specimen studies.

METHODS

With the cost function defined according to the Grangeat Epipolar Consistency Condition (G-ECC) and by minimizing the cost function using the simplex-simulated annealing algorithm (SIMPSA), we evaluate and verify the G-ECC optimization-based online self-calibration method's performance. Performance is measured in sensitivity, robustness, and accuracy using the projection data of phantoms generated by computer simulation and botanical specimens acquired by a prototype microscopic CBCT.

RESULTS

The online data sustained misalignment correction in microscopic CBCT via G-ECC optimization works very well in sensitivity and robustness, in addition to its accuracy of 0.27%, 0.48%, and 0.34% relative errors, respectively, in obtaining the three geometric parameters that are the most critical to image reconstruction in CBCT. Quantitatively, the performance in meeting the requirements on spatial resolution is comparable to, if not better than, that of the offline misalignment correction method, in which a specific alignment phantom has to be used.

CONCLUSIONS

The G-ECC optimization-based online self-calibration approach provides a practical solution (as long as no latitudinal (lateral) data truncation occurs) for misalignment correction in microscopic CBCT, an application that demands high accuracy in geometric alignment for biological (cellular) imaging at super high spatial resolutions in the order of micrometers (2.1 µm).

A method of determining geometry of cone beam CT scanner

The task of determining the geometry of a cone-beam CT scanner with flat panel detector and circular/spiral source trajectory is considered. Accomplishing this task implies analyzing projections of a set of points referred to as calibrating set or calibrating phantom. We take advantage of the fact that observed coordinates of a point’s projection are rational functions of the point’s location. Unknown coefficients of these functions can be recovered exactly from six projections of the point. Location of the source as well as position and orientation of the detector are determined in the scanner reference frame, which is constituted by rotation axis and central plane of the scanner. Two different projections of a calibrating set are enough to solve the task if the source trajectory is a circle. In applications where a shift of an object transversally to the central plane is required, two additional projections have to be collected in order to identify the direction of the shift. The developed formalism becomes especially simple when the detector is aligned with the rotation axis. In this case four projections of a single calibrating point rotated successfully about the rotation axis are sufficient. The error analysis carried out in the paper shows that the magnitude of deviation from the true values is of the order of the magnitude of measurement errors.

X-ray cone-beam computed tomography geometric artefact reduction based on a data-driven strategy

Cone-beam computed tomography (CBCT) enables three-dimensional imaging of the internal structure of objects in a non-invasive way with high accuracy. Practical misalignment of the CBCT system causes geometric artefacts in reconstructed images, which seriously degrades image quality in ways such as detail loss and decreased spatial resolution. This leads to inaccurate distinction of defects in detection, especially in precise industrial fields like aerospace and instrument manufacturing. This paper presents a method to reduce the geometric artefacts based on a data-driven strategy, which is an end-to-end modified fully convolutional neural network (M-FCNN). The designed M-FCCN contains five convolution layers and five deconvolution layers for feature extraction and output image rebuilding, respectively. In addition, the pooling layer is not used in the designed M-FCNN, considering the preservation of details in the reconstructed image. In this M-FCCN, artefact images with different features have been trained separately. After training, the M-FCNN can be applied to directly reduce geometric artefacts in the reconstructed image. The designed M-FCNN has been demonstrated with different types of synthetic data and has achieved accurate results. It is also validated with practical data, including carbon composite and medical oral phantoms with comparable quality to phantom-based methods, proving that it is an effective way to reduce geometric artefacts in the image domain by means of a data-driven strategy.

Technical Note: Intrinsic raw data-based CT misalignment correction without redundant data

PURPOSE

CT image reconstruction requires accurate knowledge of the used geometry or image quality might be degraded by misalignment artifacts. To overcome this issue, an intrinsic method, that is, a method not requiring a dedicated calibration phantom, to perform a raw data-based misalignment correction for CT is proposed herein that does not require redundant data and hence is applicable to measurements with less than 180 ∘ plus fan-angle of data.

METHODS

The forward projection of a volume reconstructed from a misaligned geometry resembles the acquired raw data if no redundant data are used, that is, if less than 180 ∘ plus fan-angle are used for image reconstruction. Hence, geometric parameters cannot be deduced from such data by an optimization of the geometry-dependent raw data fidelity. We propose to use a nonlinear transform applied to the reconstructed volume to introduce inconsistencies in the raw data that can be employed to estimate geometric parameters using less than 180 ∘ plus fan-angle of data. The proposed method is evaluated using simulations of the FORBILD head phantom and using actual measurements of a contrast-enhanced scan of a mouse acquired using a micro-CT.

RESULTS

Noisy simulations and actual measurements demonstrate that the proposed method is capable of correcting for artifacts arising from a misaligned geometry without redundant data while ensuring raw data fidelity.

CONCLUSIONS

The proposed method extends intrinsic raw data-based misalignment correction methods to an angular range of 180 ∘ or less and is thus applicable to systems with a limited scan range.

Design and Simulation Study of a CNT-Based Multisource Cubical CT System for Dynamic Objects

The purpose of this paper is to design and simulate a new computed tomography (CT) system with a high temporal resolution for dynamic objects. We propose a multisource cubical CT (MCCT) system with X-ray tubes and detectors installed on a cube. Carbon nanotube- (CNT-) based X-ray focal spots are distributed on the twelve edges of the cube. The distribution of X-ray focal spots and detectors completely avoids mechanical movements to scan an object under inspection. CNTs are excellent electron field emitters because the use of a "cold" cathode makes it possible to fabricate a cathode with multiple electron emission points, and the CNT-based X-ray focal spots possess little response time and programmable emission. The proposed rotation-free MCCT system can acquire a high scanning speed when using a high frame rate detector. A three-dimensional (3D) reconstruction algorithm with tensor framelet-based L0-norm (TF-L0) minimization is developed for the simulation study of the MCCT. Simulation experiment results demonstrate the feasibility of the MCCT system.

A line fiducial method for geometric calibration of cone-beam CT systems with diverse scan trajectories

Modern cone-beam CT systems, especially C-arms, are capable of diverse source-detector orbits. However, geometric calibration of these systems using conventional configurations of spherical fiducials (BBs) may be challenged for novel source-detector orbits and system geometries. In part, this is because the BB configurations are designed with careful forethought regarding the intended orbit so that BB marker projections do not overlap in projection views. Examples include helical arrangements of BBs (Rougee et al 1993 Proc. SPIE 1897 161-9) such that markers do not overlap in projections acquired from a circular orbit and circular arrangements of BBs (Cho et al 2005 Med. Phys. 32 968-83). As a more general alternative, this work proposes a calibration method based on an array of line-shaped, radio-opaque wire segments. With this method, geometric parameter estimation is accomplished by relating the 3D line equations representing the wires to the 2D line equations of their projections. The use of line fiducials simplifies many challenges with fiducial recognition and extraction in an orbit-independent manner. For example, their projections can overlap only mildly, for any gantry pose, as long as the wires are mutually non-coplanar in 3D. The method was tested in application to circular and non-circular trajectories in simulation and in real orbits executed using a mobile C-arm prototype for cone-beam CT. Results indicated high calibration accuracy, as measured by forward and backprojection/triangulation error metrics. Triangulation errors on the order of microns and backprojected ray deviations uniformly less than 0.2 mm were observed in both real and simulated orbits. Mean forward projection errors less than 0.1 mm were observed in a comprehensive sweep of different C-arm gantry angulations. Finally, successful integration of the method into a CT imaging chain was demonstrated in head phantom scans.

Evaluation of motion artifact metrics for coronary CT angiography

PURPOSE

This study quantified the performance of coronary artery motion artifact metrics relative to human observer ratings. Motion artifact metrics have been used as part of motion correction and best-phase selection algorithms for Coronary Computed Tomography Angiography (CCTA). However, the lack of ground truth makes it difficult to validate how well the metrics quantify the level of motion artifact. This study investigated five motion artifact metrics, including two novel metrics, using a dynamic phantom, clinical CCTA images, and an observer study that provided ground-truth motion artifact scores from a series of pairwise comparisons.

METHOD

Five motion artifact metrics were calculated for the coronary artery regions on both phantom and clinical CCTA images: positivity, entropy, normalized circularity, Fold Overlap Ratio (FOR), and Low-Intensity Region Score (LIRS). CT images were acquired of a dynamic cardiac phantom that simulated cardiac motion and contained six iodine-filled vessels of varying diameter and with regions of soft plaque and calcifications. Scans were repeated with different gantry start angles. Images were reconstructed at five phases of the motion cycle. Clinical images were acquired from 14 CCTA exams with patient heart rates ranging from 52 to 82 bpm. The vessel and shading artifacts were manually segmented by three readers and combined to create ground-truth artifact regions. Motion artifact levels were also assessed by readers using a pairwise comparison method to establish a ground-truth reader score. The Kendall's Tau coefficients were calculated to evaluate the statistical agreement in ranking between the motion artifacts metrics and reader scores. Linear regression between the reader scores and the metrics was also performed.

RESULTS

On phantom images, the Kendall's Tau coefficients of the five motion artifact metrics were 0.50 (normalized circularity), 0.35 (entropy), 0.82 (positivity), 0.77 (FOR), 0.77(LIRS), where higher Kendall's Tau signifies higher agreement. The FOR, LIRS, and transformed positivity (the fourth root of the positivity) were further evaluated in the study of clinical images. The Kendall's Tau coefficients of the selected metrics were 0.59 (FOR), 0.53 (LIRS), and 0.21 (Transformed positivity). In the study of clinical data, a Motion Artifact Score, defined as the product of FOR and LIRS metrics, further improved agreement with reader scores, with a Kendall's Tau coefficient of 0.65.

CONCLUSION

The metrics of FOR, LIRS, and the product of the two metrics provided the highest agreement in motion artifact ranking when compared to the readers, and the highest linear correlation to the reader scores. The validated motion artifact metrics may be useful for developing and evaluating methods to reduce motion in Coronary Computed Tomography Angiography (CCTA) images.

Geometric artifacts reduction for cone-beam CT via L0-norm minimization without dedicated phantoms

For cone-beam computed tomography (CBCT), transversal shifts of the rotation center exist inevitably, which will result in geometric artifacts in CT images. In this work, we propose a novel geometric calibration method for CBCT, which can also be used in micro-CT. The symmetry property of the sinogram is used for the first calibration, and then L0-norm of the gradient image from the reconstructed image is used as the cost function to be minimized for the second calibration. An iterative search method is adopted to pursue the local minimum of the L0-norm minimization problem. The transversal shift value is updated with affirmatory step size within a search range determined by the first calibration. In addition, graphic processing unit (GPU)-based FDK algorithm and acceleration techniques are designed to accelerate the calibration process of the presented new method. In simulation experiments, the mean absolute difference (MAD) and the standard deviation (SD) of the transversal shift value were less than 0.2 pixels between the noise-free and noisy projection images, which indicated highly accurate calibration applying the new calibration method. In real data experiments, the smaller entropies of the corrected images also indicated that higher resolution image was acquired using the corrected projection data and the textures were well protected. Study results also support the feasibility of applying the proposed method to other imaging modalities.

Simultaneous calibration phantom commission and geometry calibration in cone beam CT

Geometry calibration is a vital step for describing the geometry of a cone beam computed tomography (CBCT) system and is a prerequisite for CBCT reconstruction. In current methods, calibration phantom commission and geometry calibration are divided into two independent tasks. Small errors in ball-bearing (BB) positioning in the phantom-making step will severely degrade the quality of phantom calibration. To solve this problem, we propose an integrated method to simultaneously realize geometry phantom commission and geometry calibration. Instead of assuming the accuracy of the geometry phantom, the integrated method considers BB centers in the phantom as an optimized parameter in the workflow. Specifically, an evaluation phantom and the corresponding evaluation contrast index are used to evaluate geometry artifacts for optimizing the BB coordinates in the geometry phantom. After utilizing particle swarm optimization, the CBCT geometry and BB coordinates in the geometry phantom are calibrated accurately and are then directly used for the next geometry calibration task in other CBCT systems. To evaluate the proposed method, both qualitative and quantitative studies were performed on simulated and realistic CBCT data. The spatial resolution of reconstructed images using dental CBCT can reach up to 15 line pair cm^-1. The proposed method is also superior to the Wiesent method in experiments. This paper shows that the proposed method is attractive for simultaneous and accurate geometry phantom commission and geometry calibration.

Motion compensation in extremity cone-beam CT using a penalized image sharpness criterion

Cone-beam CT (CBCT) for musculoskeletal imaging would benefit from a method to reduce the effects of involuntary patient motion. In particular, the continuing improvement in spatial resolution of CBCT may enable tasks such as quantitative assessment of bone microarchitecture (0.1 mm-0.2 mm detail size), where even subtle, sub-mm motion blur might be detrimental. We propose a purely image based motion compensation method that requires no fiducials, tracking hardware or prior images. A statistical optimization algorithm (CMA-ES) is used to estimate a motion trajectory that optimizes an objective function consisting of an image sharpness criterion augmented by a regularization term that encourages smooth motion trajectories. The objective function is evaluated using a volume of interest (VOI, e.g. a single bone and surrounding area) where the motion can be assumed to be rigid. More complex motions can be addressed by using multiple VOIs. Gradient variance was found to be a suitable sharpness metric for this application. The performance of the compensation algorithm was evaluated in simulated and experimental CBCT data, and in a clinical dataset. Motion-induced artifacts and blurring were significantly reduced across a broad range of motion amplitudes, from 0.5 mm to 10 mm. Structure similarity index (SSIM) against a static volume was used in the simulation studies to quantify the performance of the motion compensation. In studies with translational motion, the SSIM improved from 0.86 before compensation to 0.97 after compensation for 0.5 mm motion, from 0.8 to 0.94 for 2 mm motion and from 0.52 to 0.87 for 10 mm motion (~70% increase). Similar reduction of artifacts was observed in a benchtop experiment with controlled translational motion of an anthropomorphic hand phantom, where SSIM (against a reconstruction of a static phantom) improved from 0.3 to 0.8 for 10 mm motion. Application to a clinical dataset of a lower extremity showed dramatic reduction of streaks and improvement in delineation of tissue boundaries and trabecular structures throughout the whole volume. The proposed method will support new applications of extremity CBCT in areas where patient motion may not be sufficiently managed by immobilization, such as imaging under load and quantitative assessment of subchondral bone architecture.

Geometry calibration method for a cone-beam CT system

PURPOSE

The positioning accuracy of each component is important to ensure the image quality of cone-beam CT. However, accurate positioning is not easy and requires experience and time. The option is to calibrate the geometric parameters and then plug them into a reconstruction algorithm which is the preferred solution in practice. In this case, the image quality is determined by the accuracy and precision of the calibration method. This work describes a method to independently calibrate an imaging system in each pose (projection angle) for a cone-beam CT with a nonideal circular trajectory.

METHODS

The calibration method uses a phantom with 12 beads on 2 planes that are observed on the radiographic images. This pose-independent calibration method (PIC) can decorrelate the relationships among the geometric parameters so that the parameters can be estimated one-by-one. This simplifies the calibration process. Besides the pose-independent calibration method, this paper also describes an extended calibration method with additional constraints on the system geometry. Both methods are validated with numerical simulations and then experimentally on a practical system with a scanning object loosely supported by rotating wheels. The object rotates during the CT data acquisition. The angular and pose information of the CT system are not accurately known a priori in this case.

RESULTS

The numerical simulations and the experiments both provide satisfactory results. The relative error of the calibrated source-to-detector distance in the simulation is less than 0.1%. The errors in the calibrated roll, pitch, and yaw angles are less than 0.04°. A sensitivity study using various bead position uncertainties in random directions shows that the pose-independent calibration method is robust to measurement errors. Tests were also done with a nonideal circular trajectory for further validation. Images reconstructed using the geometric parameters from both the pose-independent and the extended calibration methods were free of artifacts and blur from misalignment. All these results demonstrate the effectiveness of these methods.

CONCLUSIONS

The PIC method can be used to independently calibrate the geometric parameters of a cone-beam CT view-by-view. Thus, the PIC method can be implemented on commonly used systems such as circular, nonideal circular or C-arm cone-beam CTs. The PIC method can also be useful for some irregularly configured CT systems to fulfill special imaging requirements, for example, a CT system when the x ray source or the rotating platform cannot be easily located. The PIC method will reduce the costs of ensuring very precise mechanics and the labor in fine tuning CT systems.

Multimodality calibration for simultaneous fluoroscopic and nuclear imaging

Background

Simultaneous real-time fluoroscopic and nuclear imaging could benefit image-guided (oncological) procedures. To this end, a hybrid modality is currently being developed by our group, by combining a c-arm with a gamma camera and a four-pinhole collimator. Accurate determination of the system parameters that describe the position of the x-ray tube, x-ray detector, gamma camera, and collimators is crucial to optimize image quality. The purpose of this study was to develop a calibration method that estimates the system parameters used for reconstruction.

A multimodality phantom consisting of five point sources was created. First, nuclear and fluoroscopic images of the phantom were acquired at several distances from the image intensifier. The system parameters were acquired using physical measurement, and multimodality images of the phantom were reconstructed. The resolution and co-registration error of the point sources were determined as a measure of image quality. Next, the system parameters were estimated using a calibration method, which adjusted the parameters in the reconstruction algorithm, until the resolution and co-registration were optimized. For evaluation, multimodality images of a second set of phantom acquisitions were reconstructed using calibrated parameter sets. Subsequently, the resolution and co-registration error of the point sources were determined as a measure of image quality. This procedure was performed five times for different noise simulations. In addition, simultaneously acquired fluoroscopic and nuclear images of two moving syringes were obtained with parameter sets from before and after calibration.

Results

The mean FWHM was significantly lower after calibration than before calibration for 21 out of 25 point sources. The mean co-registration error was significantly lower after calibration than before calibration for all point sources. The simultaneously acquired fluoroscopic and nuclear images showed improved co-registration after calibration as compared with before calibration.

Conclusions

A calibration method was presented that improves the resolution and co-registration of simultaneously acquired hybrid fluoroscopic and nuclear images by estimating the geometric parameter set as compared with a parameter set acquired by direct physical measurement.

Electronic supplementary material

The online version of this article (doi:10.1186/s40658-016-0156-1) contains supplementary material, which is available to authorized users.

Self-calibration of cone-beam CT geometry using 3D-2D image registration

Robotic C-arms are capable of complex orbits that can increase field of view, reduce artifacts, improve image quality, and/or reduce dose; however, it can be challenging to obtain accurate, reproducible geometric calibration required for image reconstruction for such complex orbits. This work presents a method for geometric calibration for an arbitrary source-detector orbit by registering 2D projection data to a previously acquired 3D image. It also yields a method by which calibration of simple circular orbits can be improved. The registration uses a normalized gradient information similarity metric and the covariance matrix adaptation-evolution strategy optimizer for robustness against local minima and changes in image content. The resulting transformation provides a 'self-calibration' of system geometry. The algorithm was tested in phantom studies using both a cone-beam CT (CBCT) test-bench and a robotic C-arm (Artis Zeego, Siemens Healthcare) for circular and non-circular orbits. Self-calibration performance was evaluated in terms of the full-width at half-maximum (FWHM) of the point spread function in CBCT reconstructions, the reprojection error (RPE) of steel ball bearings placed on each phantom, and the overall quality and presence of artifacts in CBCT images. In all cases, self-calibration improved the FWHM-e.g. on the CBCT bench, FWHM  =  0.86 mm for conventional calibration compared to 0.65 mm for self-calibration (p  <  0.001). Similar improvements were measured in RPE-e.g. on the robotic C-arm, RPE  =  0.73 mm for conventional calibration compared to 0.55 mm for self-calibration (p  <  0.001). Visible improvement was evident in CBCT reconstructions using self-calibration, particularly about high-contrast, high-frequency objects (e.g. temporal bone air cells and a surgical needle). The results indicate that self-calibration can improve even upon systems with presumably accurate geometric calibration and is applicable to situations where conventional calibration is not feasible, such as complex non-circular CBCT orbits and systems with irreproducible source-detector trajectory.

Epipolar Consistency in Transmission Imaging

This paper presents the derivation of the Epipolar Consistency Conditions (ECC) between two X-ray images from the Beer-Lambert law of X-ray attenuation and the Epipolar Geometry of two pinhole cameras, using Grangeat's theorem. We motivate the use of Oriented Projective Geometry to express redundant line integrals in projection images and define a consistency metric, which can be used, for instance, to estimate patient motion directly from a set of X-ray images. We describe in detail the mathematical tools to implement an algorithm to compute the Epipolar Consistency Metric and investigate its properties with detailed random studies on both artificial and real FD-CT data. A set of six reference projections of the CT scan of a fish were used to evaluate accuracy and precision of compensating for random disturbances of the ground truth projection matrix using an optimization of the consistency metric. In addition, we use three X-ray images of a pumpkin to prove applicability to real data. We conclude, that the metric might have potential in applications related to the estimation of projection geometry. By expression of redundancy between two arbitrary projection views, we in fact support any device or acquisition trajectory which uses a cone-beam geometry. We discuss certain geometric situations, where the ECC provide the ability to correct 3D motion, without the need for 3D reconstruction.

A novel concept for CT with fixed anodes (FACT): Medical imaging based on the feasibility of thermal load capacity

Focussing primarily on thermal load capacity, we describe the performance of a novel fixed anode CT (FACT) compared with a 100 kW reference CT. Being a fixed system, FACT has no focal spot blurring of the X-ray source during projection. Monte Carlo and finite element methods were used to determine the fluence proportional to thermal capacity. Studies of repeated short-time exposures showed that FACT could operate in pulsed mode for an unlimited period. A virtual model for FACT was constructed to analyse various temporal sequences for the X-ray source ring, representing a circular array of 1160 fixed anodes in the gantry. Assuming similar detector properties at a very small integration time, image quality was investigated using an image reconstruction library. Our model showed that approximately 60 gantry rounds per second, i.e. 60 sequential targetings of the 1160 anodes per second, were required to achieve a performance level equivalent to that of the reference CT (relative performance, RP = 1) at equivalent image quality. The optimal projection duration in each direction was about 10 μs. With a beam pause of 1 μs between projections, 78.4 gantry rounds per second with consecutive source activity were thermally possible at a given thermal focal spot. The settings allowed for a 1.3-fold (RP = 1.3) shorter scan time than conventional CT while maintaining radiation exposure and image quality. Based on the high number of rounds, FACT supports a high image frame rate at low doses, which would be beneficial in a wide range of diagnostic and technical applications.

Fiducial marker-based correction for involuntary motion in weight-bearing C-arm CT scanning of knees. II. Experiment

PURPOSE

A C-arm CT system has been shown to be capable of scanning a single cadaver leg under loaded conditions by virtue of its highly flexible acquisition trajectories. In Part I of this study, using the 4D XCAT-based numerical simulation, the authors predicted that the involuntary motion in the lower body of subjects in weight-bearing positions would seriously degrade image quality and the authors suggested three motion compensation methods by which the reconstructions could be corrected to provide diagnostic image quality. Here, the authors demonstrate that a flat-panel angiography system is appropriate for scanning both legs of subjects in vivo under weight-bearing conditions and further evaluate the three motion-correction algorithms using in vivo data.

METHODS

The geometry of a C-arm CT system for a horizontal scan trajectory was calibrated using the PDS-2 phantom. The authors acquired images of two healthy volunteers while lying supine on a table, standing, and squatting at several knee flexion angles. In order to identify the involuntary motion of the lower body, nine 1-mm-diameter tantalum fiducial markers were attached around the knee. The static mean marker position in 3D, a reference for motion compensation, was estimated by back-projecting detected markers in multiple projections using calibrated projection matrices and identifying the intersection points in 3D of the back-projected rays. Motion was corrected using three different methods (described in detail previously): (1) 2D projection shifting, (2) 2D deformable projection warping, and (3) 3D rigid body warping. For quantitative image quality analysis, SSIM indices for the three methods were compared using the supine data as a ground truth.

RESULTS

A 2D Euclidean distance-based metric of subjects' motion ranged from 0.85 mm (±0.49 mm) to 3.82 mm (±2.91 mm) (corresponding to 2.76 to 12.41 pixels) resulting in severe motion artifacts in 3D reconstructions. Shifting in 2D, 2D warping, and 3D warping improved the SSIM in the central slice by 20.22%, 16.83%, and 25.77% in the data with the largest motion among the five datasets (SCAN5); improvement in off-center slices was 18.94%, 29.14%, and 36.08%, respectively.

CONCLUSIONS

The authors showed that C-arm CT control can be implemented for nonstandard horizontal trajectories which enabled us to scan and successfully reconstruct both legs of volunteers in weight-bearing positions. As predicted using theoretical models, the proposed motion correction methods improved image quality by reducing motion artifacts in reconstructions; 3D warping performed better than the 2D methods, especially in off-center slices.

Improving best-phase image quality in cardiac CT by motion correction with MAM optimization

PURPOSE

Research in image reconstruction for cardiac CT aims at using motion correction algorithms to improve the image quality of the coronary arteries. The key to those algorithms is motion estimation, which is currently based on 3-D/3-D registration to align the structures of interest in images acquired in multiple heart phases. The need for an extended scan data range covering several heart phases is critical in terms of radiation dose to the patient and limits the clinical potential of the method. Furthermore, literature reports only slight quality improvements of the motion corrected images when compared to the most quiet phase (best-phase) that was actually used for motion estimation. In this paper a motion estimation algorithm is proposed which does not require an extended scan range but works with a short scan data interval, and which markedly improves the best-phase image quality.

METHODS

Motion estimation is based on the definition of motion artifact metrics (MAM) to quantify motion artifacts in a 3-D reconstructed image volume. The authors use two different MAMs, entropy, and positivity. By adjusting the motion field parameters, the MAM of the resulting motion-compensated reconstruction is optimized using a gradient descent procedure. In this way motion artifacts are minimized. For a fast and practical implementation, only analytical methods are used for motion estimation and compensation. Both the MAM-optimization and a 3-D/3-D registration-based motion estimation algorithm were investigated by means of a computer-simulated vessel with a cardiac motion profile. Image quality was evaluated using normalized cross-correlation (NCC) with the ground truth template and root-mean-square deviation (RMSD). Four coronary CT angiography patient cases were reconstructed to evaluate the clinical performance of the proposed method.

RESULTS

For the MAM-approach, the best-phase image quality could be improved for all investigated heart phases, with a maximum improvement of the NCC value by 100% and of the RMSD value by 81%. The corresponding maximum improvements for the registration-based approach were 20% and 40%. In phases with very rapid motion the registration-based algorithm obtained better image quality, while the image quality of the MAM algorithm was superior in phases with less motion. The image quality improvement of the MAM optimization was visually confirmed for the different clinical cases.

CONCLUSIONS

The proposed method allows a software-based best-phase image quality improvement in coronary CT angiography. A short scan data interval at the target heart phase is sufficient, no additional scan data in other cardiac phases are required. The algorithm is therefore directly applicable to any standard cardiac CT acquisition protocol.

Online geometric calibration of cone-beam computed tomography for arbitrary imaging objects

A novel online method based on the symmetry property of the sum of projections (SOP) is proposed to obtain the geometric parameters in cone-beam computed tomography (CBCT). This method requires no calibration phantom and can be used in circular trajectory CBCT with arbitrary cone angles. An objective function is deduced to illustrate the dependence of the symmetry of SOP on geometric parameters, which will converge to its minimum when the geometric parameters achieve their true values. Thus, by minimizing the objective function, we can obtain the geometric parameters for image reconstruction. To validate this method, numerical phantom studies with different noise levels are simulated. The results show that our method is insensitive to the noise and can determine the skew (in-plane rotation angle of the detector), the roll (rotation angle around the projection of the rotation axis on the detector), and the rotation axis with high accuracy, while the mid-plane and source-to-detector distance will be obtained with slightly lower accuracy. However, our simulation studies validate that the errors of the latter two parameters brought by our method will hardly degrade the quality of reconstructed images. The small animal studies show that our method is able to deal with arbitrary imaging objects. In addition, the results of the reconstructed images in different slices demonstrate that we have achieved comparable image quality in the reconstructions as some offline methods.

A robust geometry estimation method for spiral, sequential and circular cone-beam micro-CT

PURPOSE

The authors propose a novel method for misalignment estimation of micro-CT scanners using an adaptive genetic algorithm.

METHODS

The proposed algorithm is able to estimate the rotational geometry, the direction vector of table movement and the displacement between different imaging threads of a dual source or even multisource scanner. The calibration procedure does not rely on dedicated calibration phantoms and a sequence scan of a single metal bead is sufficient to geometrically calibrate the whole imaging system for spiral, sequential, and circular scan protocols. Dual source spiral and sequential scan protocols in micro-computed tomography result in projection data that-besides the source and detector positions and orientations-also require a precise knowledge of the table direction vector to be reconstructed properly. If those geometric parameters are not known accurately severe artifacts and a loss in spatial resolution appear in the reconstructed images as long as no geometry calibration is performed. The table direction vector is further required to ensure that consecutive volumes of a sequence scan can be stitched together and to allow the reconstruction of spiral data at all.

RESULTS

The algorithm's performance is evaluated using simulations of a micro-CT system with known geometry and misalignment. To assess the quality of the algorithm in a real world scenario the calibration of a micro-CT scanner is performed and several reconstructions with and without geometry estimation are presented.

CONCLUSIONS

The results indicate that the algorithm successfully estimates all geometry parameters, misalignment artifacts in the reconstructed volumes vanish, and the spatial resolution is increased as can be shown by the evaluation of modulation transfer function measurements.

Image features for misalignment correction in medical flat-detector CT

PURPOSE

Misalignment artifacts are a serious problem in medical flat-detector computed tomography. Generally, the geometrical parameters, which are essential for reconstruction, are provided by preceding calibration routines. These procedures are time consuming and the later use of stored parameters is sensitive toward external impacts or patient movement. The method of choice in a clinical environment would be a markerless online-calibration procedure that allows flexible scan trajectories and simultaneously corrects misalignment and motion artifacts during the reconstruction process. Therefore, different image features were evaluated according to their capability of quantifying misalignment.

METHODS

Projections of the FORBILD head and thorax phantoms were simulated. Additionally, acquisitions of a head phantom and patient data were used for evaluation. For the reconstruction different sources and magnitudes of misalignment were introduced in the geometry description. The resulting volumes were analyzed by entropy (based on the gray-level histogram), total variation, Gabor filter texture features, Haralick co-occurrence features, and Tamura texture features. The feature results were compared to the back-projection mismatch of the disturbed geometry.

RESULTS

The evaluations demonstrate the ability of several well-established image features to classify misalignment. The authors elaborated the particular suitability of the gray-level histogram-based entropy on identifying misalignment artifacts, after applying an appropriate window level (bone window).

CONCLUSIONS

Some of the proposed feature extraction algorithms show a strong correlation with the misalignment level. Especially, entropy-based methods showed very good correspondence, with the best of these being the type that uses the gray-level histogram for calculation. This makes it a suitable image feature for online-calibration.

Automated recovery of the center of rotation in optical projection tomography in the presence of scattering

Finding the center of rotation is an essential step for accurate three-dimensional reconstruction in optical projection tomography (OPT). Unfortunately current methods are not convenient since they require either prior scanning of a reference phantom, small structures of high intensity existing in the specimen, or active participation during the centering procedure. To solve these problems this paper proposes a fast and automatic center of rotation search method making use of parallel programming in graphics processing units (GPUs). Our method is based on a two step search approach making use only of those sections of the image with high signal to noise ratio. We have tested this method both in non-scattering ex vivo samples and in in vivo specimens with a considerable contribution of scattering such as Drosophila melanogaster pupae, recovering in all cases the center of rotation with a precision 1/4 pixel or less.

Reliable automatic alignment of tomographic projection data by passive auto-focus

PURPOSE

The authors present a robust algorithm that removes the blurring and double-edge artifacts in high-resolution computed tomography (CT) images that are caused by misaligned scanner components. This alleviates the time-consuming process of physically aligning hardware, which is of particular benefit if components are moved or swapped frequently.

METHODS

The proposed method uses the experimental data itself for calibration. A parameterized model of the scanner geometry is constructed and the parameters are varied until the sharpest 3D reconstruction is found. The concept is similar to passive auto-focus algorithms of digital optical instruments. The parameters are used to remap the projection data from the physical detector to a virtual aligned detector. This is followed by a standard reconstruction algorithm, namely the Feldkamp algorithm. Feldkamp et al. [J. Opt. Soc. Am. A 1, 612-619 (1984)].

RESULTS

An example implementation is given for a rabbit liver specimen that was collected with a circular trajectory. The optimal parameters were determined in less computation time than that for a full reconstruction. The example serves to demonstrate that (a) sharpness is an appropriate measure for projection alignment, (b) our parameterization is sufficient to characterize misalignments for cone-beam CT, and (c) the procedure determines parameter values with sufficient precision to remove the associated artifacts.

CONCLUSIONS

The algorithm is fully tested and implemented for regular use at The Australian National University micro-CT facility for both circular and helical trajectories. It can in principle be applied to more general imaging geometries and modalities. It is as robust as manual alignment but more precise since we have quantified the effect of misalignment.

Estimation of CT cone-beam geometry using a novel method insensitive to phantom fabrication inaccuracy: implications for isocenter localization accuracy

PURPOSE

Mechanical instabilities that occur during gantry rotation of on-board cone-beam computed tomography (CBCT) imaging systems limit the efficacy of image-guided radiotherapy. Various methods for calibrating the CBCT geometry and correcting errors have been proposed, including some that utilize dedicated fiducial phantoms. The purpose of this work was to investigate the role of phantom fabrication imprecision on the accuracy of a particular CT cone-beam geometry estimate and to test a new method to mitigate errors in beam geometry arising from imperfectly fabricated phantoms.

METHODS

The authors implemented a fiducial phantom-based beam geometry estimation following the one described by Cho et al. [Med Phys 32(4), 968-983 (2005)]. The algorithm utilizes as input projection images of the phantom at various gantry angles and provides a full nine parameter beam geometry characterization of the source and detector position and detector orientation versus gantry angle. A method was developed for recalculating the beam geometry in a coordinate system with origin at the source trajectory center and aligned with the axis of gantry rotation, thus making the beam geometry estimation independent of the placement of the phantom. A second CBCT scan with the phantom rotated 180 degrees about its long axis was averaged with the first scan to mitigate errors from phantom imprecision. Computer simulations were performed to assess the effect of 2D fiducial marker positional error on the projections due to image discretization, as well as 3D fiducial marker position error due to phantom fabrication imprecision. Experimental CBCT images of a fiducial phantom were obtained and the algorithm used to measure beam geometry for a Varian Trilogy with an on-board CBCT.

RESULTS

Both simulations and experimental results reveal large sinusoidal oscillations in the calculated beam geometry parameters with gantry angle due to displacement of the phantom from CBCT isocenter and misalignment with the gantry axis, which are eliminated by recalculating the beam geometry in the source coordinate system. Simulations and experiments also reveal an additional source of oscillations arising from fiducial marker position error due to phantom fabrication imprecision that are mitigated by averaging the results with those of a second CBCT scan with phantom rotated. With a typical fiducial marker position error of 0.020 mm (0.001 in.), source and detector position are found in simulations to be within 250 microm of the true values, and detector and gantry angles less than 0.2 degrees. Detector offsets are within 100 microm of the known value. Experimental results verify the efficacy of the second scan in mitigating beam geometry errors, as well as large apparent source/detector isocenter offsets arising from phantom fabrication imprecision.

CONCLUSIONS

The authors have developed and validated a novel fiducial phantom-based CBCT beam geometry estimation algorithm that does not require precise positioning of the phantom at machine isocenter and is insensitive to positional imprecision of fiducial markers within the phantom due to fabrication errors. The method can accurately locate source and detector isocenters even when using an imprecise phantom, which is very important for measurement of isocenter coincidence of the therapy and on-board imaging systems.

Analysis of image sharpness reproducibility on a novel engineered micro-CT scanner with variable geometry and embedded recalibration software

This study investigates the reproducibility of the reconstructed image sharpness, after modifications of the geometry setup, for a variable magnification micro-CT (μCT) scanner. All the measurements were performed on a novel engineered μCT scanner for in vivo imaging of small animals (Xalt), which has been recently built at the Institute of Clinical Physiology of the National Research Council (IFC-CNR, Pisa, Italy), in partnership with the University of Pisa. The Xalt scanner is equipped with an integrated software for on-line geometric recalibration, which will be used throughout the experiments. In order to evaluate the losses of image quality due to modifications of the geometry setup, we have made 22 consecutive acquisitions by changing alternatively the system geometry between two different setups (Large FoV - LF, and High Resolution - HR). For each acquisition, the tomographic images have been reconstructed before and after the on-line geometric recalibration. For each reconstruction, the image sharpness was evaluated using two different figures of merit: (i) the percentage contrast on a small bar pattern of fixed frequency (f = 5.5 lp/mm for the LF setup and f = 10 lp/mm for the HR setup) and (ii) the image entropy. We have found that, due to the small-scale mechanical uncertainty (in the order of the voxel size), a recalibration is necessary for each geometric setup after repositioning of the system's components; the resolution losses due to the lack of recalibration are worse for the HR setup (voxel size = 18.4 μm). The integrated on-line recalibration algorithm of the Xalt scanner allowed to perform the recalibration quickly, by restoring the spatial resolution of the system to the reference resolution obtained after the initial (off-line) calibration.

Maximum-Likelihood Calibration of an X-ray Computed Tomography System

We present a maximum-likelihood (ML) method for calibrating the geometrical parameters of an x-ray computed tomography (CT) system. This method makes use of the full image data and not a reduced set of data. This algorithm is particularly useful for CT systems that change their geometry during the CT acquisition, such as an adaptive CT scan. Our ML search method uses a contracting-grid algorithm that does not require initial starting values to perform its estimate, thus avoiding problems associated with choosing initialization values.