A Grangeat-type half-scan algorithm for cone-beam CT

Modern CT and micro-CT scanners are rapidly moving from fan-beam toward cone-beam geometry. Half-scan CT algorithms are advantageous in terms of temporal resolution, and widely used in fan-beam and cone-beam geometry. While existing half-scan algorithms for cone-beam CT are in the Feldkamp framework, in this paper we compensate missing data explicitly in the Grangeat framework, and formulate a half-scan algorithm in the circular scanning case. The half-scan spans 180 degrees plus two cone angles that guarantee sufficient data for reconstruction of the midplane defined by the source trajectory. The smooth half-scan weighting functions are designed for the suppression of data inconsistency. Numerical simulation results are reported for verification of our formulas and programs. This Grangeat-type half-scan algorithm produces excellent image quality, without off-mid-plane artifacts associated with Feldkamp-type half-scan algorithms. The Grangeat-type half-scan algorithm seems promising for quantitative and dynamic biomedical applications of CT and micro-CT.     

A combination-weighted Feldkamp-based reconstruction algorithm for cone-beam CT

The combination-weighted Feldkamp algorithm (CW-FDK) was developed and tested in a phantom in order to reduce cone-beam artefacts and enhance cranio-caudal reconstruction coverage in an attempt to improve image quality when utilizing cone-beam computed tomography (CBCT). Using a 256-slice cone-beam CT (256CBCT), image quality (CT-number uniformity and geometrical accuracy) was quantitatively evaluated in phantom and clinical studies, and the results were compared to those obtained with the original Feldkamp algorithm. A clinical study was done in lung cancer patients under breath holding and free breathing. Image quality for the original Feldkamp algorithm is degraded at the edge of the scan region due to the missing volume, commensurate with the cranio-caudal distance between the reconstruction and central planes. The CW-FDK extended the reconstruction coverage to equal the scan coverage and improved reconstruction accuracy, unaffected by the cranio-caudal distance. The extended reconstruction coverage with good image quality provided by the CW-FDK will be clinically investigated for improving diagnostic and radiotherapy applications. In addition, this algorithm can also be adapted for use in relatively wide cone-angle CBCT such as with a flat-panel detector CBCT.     

Analysis of an exact inversion algorithm for spiral cone-beam CT

In this paper we continue studying a theoretically exact filtered backprojection inversion formula for cone beam spiral CT proposed earlier by the author. Our results show that if the phantom f is constant along the axial direction, the formula is equivalent to the 2D Radon transform inversion. Also, the inversion formula remains exact as spiral pitch goes to zero and in the limit becomes again the 2D Radon transform inversion formula. Finally, we show that according to the formula the processed cone beam projections should be backprojected using both the inverse distance squared law and the inverse distance law.     

Shading correction algorithm for improvement of cone-beam CT images in radiotherapy

Cone-beam CT (CBCT) images have recently become an established modality for treatment verification in radiotherapy. However, identification of soft-tissue structures and the calculation of dose distributions based on CBCT images is often obstructed by image artefacts and poor consistency of density calibration. A robust method for voxel-by-voxel enhancement of CBCT images using a priori knowledge from the planning CT scan has been developed and implemented. CBCT scans were enhanced using a low spatial frequency grey scale shading function generated with the aid of a planning CT scan from the same patient. This circumvents the need for exact correspondence between CBCT and CT and the process is robust to the appearance of unshared features such as gas pockets. Enhancement was validated using patient CBCT images. CT numbers in regions of fat and muscle tissue in the processed CBCT were both within 1% of the values in the planning CT, as opposed to 10-20% different for the original CBCT. Visual assessment of processed CBCT images showed improvement in soft-tissue visibility, although some cases of artefact introduction were observed.     

A knowledge-based cone-beam x-ray CT algorithm for dynamic volumetric cardiac imaging

With the introduction of spiral/helical multislice CT, medical x-ray CT began a transition into cone-beam geometry. The higher speed, thinner slice, and wider coverage with multislice/cone-beam CT indicate a great potential for dynamic volumetric imaging, with cardiac CT studies being the primary example. Existing ECG-gated cardiac CT algorithms have achieved encouraging results, but they do not utilize any time-varying anatomical information of the heart, and need major improvements to meet critical clinical needs. In this paper, we develop a knowledge-based spiral/helical multislice/cone-beam CT approach for dynamic volumetric cardiac imaging. This approach assumes the relationship between the cardiac status and the ECG signal, such as the volume of the left ventricle as a function of the cardiac phase. Our knowledge-based cardiac CT algorithm is evaluated in numerical simulation and patient studies. In the patient studies, the cardiac status is estimated initially from ECG data and subsequently refined with reconstructed images. Our results demonstrate significant image quality improvements in cardiac CT studies, giving clearly better clarity of the chamber boundaries and vascular structures. In conclusion, this approach seems promising for practical cardiac CT screening and diagnosis.     

A rebinned backprojection-filtration algorithm for image reconstruction in helical cone-beam CT

In the last few years, mathematically exact algorithms, including the backprojection-filtration (BPF) algorithm, have been developed for accurate image reconstruction in helical cone-beam CT. The BPF algorithm requires minimum data, and can reconstruct region-of-interest (ROI) images from data containing truncations. However, similar to other existing reconstruction algorithms for helical cone-beam CT, the BPF algorithm involves a backprojection with a spatially varying weighting factor, which is computationally demanding and, more importantly, can lead to undesirable numerical properties in reconstructed images. In this work, we develop a rebinned BPF algorithm in which the backprojection invokes no spatially varying weighting factor for accurate image reconstruction from helical cone-beam projections. This rebinned BPF algorithm is computationally more efficient and numerically more stable than the original BPF algorithm, while it also retains the nice properties of the original BPF algorithm such as minimum data requirement and ROI-image reconstruction from truncated data. We have also performed simulation studies to validate and evaluate the rebinned BPF algorithm.     

A backprojection-filtration algorithm for nonstandard spiral cone-beam CT with an n-PI-window

For applications in bolus-chasing computed tomography (CT) angiography and electron-beam micro-CT, the backprojection-filtration (BPF) formula developed by Zou and Pan was recently generalized by Ye et al to reconstruct images from cone-beam data collected along a rather flexible scanning locus, including a nonstandard spiral. A major implication of the generalized BPF formula is that it can be applied for n-PI-window-based reconstruction in the nonstandard spiral scanning case. In this paper, we design an n-PI-window-based BPF algorithm, and report the numerical simulation results with the 3D Shepp-Logan phantom and Defrise disk phantom. The proposed BPF algorithm consists of three steps: cone-beam data differentiation, weighted backprojection and inverse Hilbert filtration. Our simulated results demonstrate the feasibility and merits of the proposed algorithm.     

Evaluation of sparse-view reconstruction from flat-panel-detector cone-beam CT

Flat-panel-detector x-ray cone-beam computed tomography (CBCT) is used in a rapidly increasing host of imaging applications, including image-guided surgery and radiotherapy. The purpose of the work is to investigate and evaluate image reconstruction from data collected at projection views significantly fewer than what is used in current CBCT imaging. Specifically, we carried out imaging experiments using a bench-top CBCT system that was designed to mimic imaging conditions in image-guided surgery and radiotherapy; we applied an image reconstruction algorithm based on constrained total-variation (TV)-minimization to data acquired with sparsely sampled view-angles and conducted extensive evaluation of algorithm performance. Results of the evaluation studies demonstrate that, depending upon scanning conditions and imaging tasks, algorithms based on constrained TV-minimization can reconstruct images of potential utility from a small fraction of the data used in typical, current CBCT applications. A practical implication of the study is that the optimization of algorithm design and implementation can be exploited for considerably reducing imaging effort and radiation dose in CBCT.     

Evaluation of the spatial resolution characteristics of a cone-beam breast CT scanner

The purpose of this study was to examine the spatial resolution of a prototype pendant-geometry cone-beam breast computed tomography (CT) system. Modulation transfer functions (MTFs) of the reconstructed image in the coronal (x and y) plane were computed as a function of the cone angle, the radial distance from the axis of rotation, the size of the reconstruction matrix, the back-projection filter used, and the number of projections acquired for the reconstruction. The results show that the cone angle and size of the reconstruction matrix have minimal impact on the MTF, while the MTF degraded radially from the axis of rotation (from 0.76 at 2.6 mm from axis of rotation down to 0.37 at 76.9 mm from axis of rotation at f=0.5 mm(-1)). The Ramp reconstruction filter increases the MTF near the axis of rotation relative to the Shepp-Logan filter, while an increase in the number of projections from 500 to 1000 increased the MTF near the periphery of the reconstructed image. The MTF in the z direction (anterior-posterior direction) was also evaluated. The z-direction MTF values tend to be higher when compared to the coronal MTF (0.85 at f =0.5 mm(-1)), and tend to be very constant throughout the coronal plane direction. The results suggest that an increase in the MTF for the prototype breast CT system is possible by optimizing various scanning and reconstruction parameters.     

Volume-of-change cone-beam CT for image-guided surgery

C-arm cone-beam CT (CBCT) can provide intraoperative 3D imaging capability for surgical guidance, but workflow and radiation dose are the significant barriers to broad utilization. One main reason is that each 3D image acquisition requires a complete scan with a full radiation dose to present a completely new 3D image every time. In this paper, we propose to utilize patient-specific CT or CBCT as prior knowledge to accurately reconstruct the aspects of the region that have changed by the surgical procedure from only a sparse set of x-rays. The proposed methods consist of a 3D-2D registration between the prior volume and a sparse set of intraoperative x-rays, creating digitally reconstructed radiographs (DRRs) from the registered prior volume, computing difference images by subtracting DRRs from the intraoperative x-rays, a penalized likelihood reconstruction of the volume of change (VOC) from the difference images, and finally a fusion of VOC reconstruction with the prior volume to visualize the entire surgical field. When the surgical changes are local and relatively small, the VOC reconstruction involves only a small volume size and a small number of projections, allowing less computation and lower radiation dose than is needed to reconstruct the entire surgical field. We applied this approach to sacroplasty phantom data obtained from a CBCT test bench and vertebroplasty data with a fresh cadaver acquired from a C-arm CBCT system with a flat-panel detector. The VOCs were reconstructed from a varying number of images (10-66 images) and compared to the CBCT ground truth using four different metrics (mean squared error, correlation coefficient, structural similarity index and perceptual difference model). The results show promising reconstruction quality with structural similarity to the ground truth close to 1 even when only 15-20 images were used, allowing dose reduction by the factor of 10-20.     

Aperture weighted cardiac reconstruction for cone-beam CT

Multi-row detectors together with fast rotating gantries made cardiac imaging possible for CT. Due to the cardiac motion, ECG gating has to be integrated into the reconstruction of the data measured on a low pitch helical trajectory. Since the first multi-row scanners were introduced, it has been shown that approximative true cone-beam reconstruction methods are most suitable for the task of retrospectively gated cardiac volume CT. In this paper, we present the aperture weighted cardiac reconstruction (AWCR), which is a three-dimensional reconstruction algorithm of the filtered back-projection type. It is capable of handling all illumination intervals of an object point, which occur as a consequence of a low pitch helical cone-beam acquisition. Therefore, this method is able to use as much redundant data as possible, resulting in an improvement of the image homogeneity, the signal to noise ratio and the temporal resolution. Different optimization techniques like the heart rate adaptive cardiac weighting or the automatic phase determination can be adopted to AWCR. The excellent image quality achieved by AWCR is presented for medical datasets acquired with both a 40-slice and a 64-slice cone-beam CT scanner.     

Rebinning-based algorithms for helical cone-beam CT.

Several image reconstruction algorithms based on rebinning have been proposed recently for helical cone-beam CT. These algorithms separate the 3D reconstruction into a set of independent 2D reconstructions for a set of surfaces: planar or non-planar surfaces are defined and then reconstructed using 2D filtered backprojection from a 2D fan-beam or parallel-beam set of data estimated from the cone-beam (CB) measurements. The first part of this paper presents a unified derivation of rebinning algorithms for planar and non-planar surfaces. An integral equation is derived for the surface allowing the best rebinning and an iterative algorithm converging to the solution of that equation is given. The second part presents an efficient method to correct the residual reconstruction artefacts observed with rebinning algorithms when the cone-angle is too large for the required accuracy. This correction algorithm involves a CB backprojection and the reconstruction time is slightly longer than for the zero-boundary (ZB) method.     

Single-slice rebinning method for helical cone-beam CT

In this paper, we present reconstruction results from helical cone-beam CT data, obtained using a simple and fast algorithm, which we call the CB-SSRB algorithm. This algorithm combines the single-slice rebinning method of PET imaging with the weighting schemes of spiral CT algorithms. The reconstruction is approximate but can be performed using 2D multislice fan-beam filtered backprojection. The quality of the results is surprisingly good, and far exceeds what one might expect, even when the pitch of the helix is large. In particular, with this algorithm comparable quality is obtained using helical cone-beam data with a normalized pitch of 10 to that obtained using standard spiral CT reconstruction with a normalized pitch of 2.     

Ordered subset reconstruction for x-ray CT

Statistical methods for image reconstruction such as the maximum likelihood expectation maximization are more robust and flexible than analytical inversion methods and allow for accurate modelling of the counting statistics and photon transport during acquisition of projection data. Statistical reconstruction is prohibitively slow when applied to clinical x-ray CT due to the large data sets and the high number of iterations required for reconstructing high-resolution images. Recently, however, powerful methods for accelerating statistical reconstruction have been proposed which, instead of accessing all projections simultaneously for updating an image estimate, are based on accessing a subset of projections at the time during iterative reconstruction. In this paper we study images generated by the convex algorithm accelerated by the use of ordered subsets (the OS convex algorithm (OSC)) for data sets with sizes, noise levels and spatial resolution representative of x-ray CT imaging. It is only in the case of extremely high acceleration factors (higher than 50, corresponding to fewer than 20 projections per subset), that areas with incorrect grey values appear in the reconstructed images, and that image noise increases compared with the standard convex algorithm. These image degradations can be adequately corrected for by running the final iteration of OSC with a reduced number of subsets. Even by applying such a relatively slow final iteration, OSC produces almost an equal resolution and lesion contrast as the standard convex algorithm, but more than two orders of magnitude faster.     

An error-reduction-based algorithm for cone-beam computed tomography

Image reconstruction from cone-beam projections collected along a single circular source trajectory is commonly done using the Feldkamp algorithm, which performs well only with a small cone angle. In this report, we propose an error-reduction-based algorithm to increase the cone angle by several folds to achieve satisfactory image quality at the same radiation dose. In our scheme, we first reconstruct the object using the Feldkamp algorithm. Then, we synthesize cone-beam projection data from the reconstructed volume in the same geometry, and reconstruct the volume again from the synthesized projections. Finally, these two reconstruction results are combined to reduce the reconstruction error and produce a superior image volume. The merit of this algorithm is demonstrated in numerical simulation.     

Evaluation of x-ray scatter properties in a dedicated cone-beam breast CT scanner

The magnitude of scatter contamination on a first-generation prototype breast computed tomography (CT) scanner was evaluated using the scatter-to-primary ratio (SPR) metric. The SPR was measured and characterized over a wide range of parameters relevant to breast CT imaging, including x-ray beam energy, breast diameter, breast composition, isocenter-to-detector distance, collimated slot thickness, and grid ratio. The results demonstrated that in the absence of scatter reduction techniques, the SPR levels for the average breast (e.g., 14 cm diameter 50/50 composition cylindrical phantom) are quite high (approximately 0.5 at the center of the phantom for 80 kVp in true cone-beam CT geometry), and increases as the diameter of the phantom is increased (to approximately 1.0 at the center of a 18 cm diameter 50/50 phantom). The x-ray beam energy and the phantom compositions had only minimal impact on the measured SPR. When an ideal bowtie filter was used, the SPRs at the central axis of the 14 and 18 cm cylindrical phantoms were reduced while the SPRs at the edge of the phantoms were increased. Lastly, collimation in the vertical direction had a significant impact on the SPRs at the central axis of the phantoms. These high SPR levels might lead to cupping artifacts and increased noise in the reconstructed CT images, and this suggests that efficient scatter rejection and/or correction techniques may be required to improve the quality and accuracy of cone beam CT images.     

3D cone-beam CT reconstruction for circular trajectories

3D reconstruction from 2D projections obtained along a single circular source trajectory is most commonly done using an algorithm due to Feldkamp, Davis and Kress. In this paper we propose an alternative approach based on a cone-beam to parallel-beam rebinning step, a corresponding rebinning step into a rectangular virtual detector plane and a filtered backprojection. This approach yields an improved image quality reflected by a decreased low-intensity drop which is well known for 3D reconstruction from projection data obtained along circular trajectories. At the same time the computational complexity is lower than in Feldkamp's original approach. Based on this idea, a hybrid 3D cone-beam reconstruction method is formulated that enlarges the reconstruction volume in its dimension along the rotation axis of the cone-beam CT system. This enlargement is achieved by applying different reconstruction conditions for each voxel. An optimal ratio between the reconstructible and irradiated volume of the scanned object is achieved.     

Exact and approximate algorithms for helical cone-beam CT

This paper concerns image reconstruction for helical x-ray transmission tomography (CT) with multi-row detectors. We introduce two approximate cone-beam (CB) filtered-backprojection (FBP) algorithms of the Feldkamp type, obtained by extending to three dimensions (3D) two recently proposed exact FBP algorithms for 2D fan-beam reconstruction. The new algorithms are similar to the standard Feldkamp-type FBP for helical CT. In particular, they can reconstruct each transaxial slice from data acquired along an arbitrary segment of helix, thereby efficiently exploiting the available data. In contrast to the standard Feldkamp-type algorithm, however, the redundancy weight is applied after filtering, allowing a more efficient numerical implementation. To partially alleviate the CB artefacts, which increase with increasing values of the helical pitch, a frequency-mixing method is proposed. This method reconstructs the high frequency components of the image using the longest possible segment of helix, whereas the low frequencies are reconstructed using a minimal, short-scan, segment of helix to minimize CB artefacts. The performance of the algorithms is illustrated using simulated data.     

Feldkamp-based cone-beam reconstruction for gantry-tilted helical multislice CT

Depending on the clinical application, it is frequently necessary to tilt the gantry of an x-ray CT system with respect to the patient and couch. For single-slice fan-beam systems, tilting the gantry introduces no errors or artifacts. Most current systems, however, are helical multislice systems with up to 16 slices. The multislice helical reconstruction algorithms used to create CT images must be modified to account for tilting of the gantry. If they are not, the quality of reconstructed images will be poor with the presence of significant artifacts, such as smearing and double-imaging of anatomical structures. Current CT systems employ three primary types of reconstruction algorithms: helical fan-beam approximation, advanced single-slice rebinning, and Feldkamp-based algorithms. This paper presents a generalized helical cone-beam Feldkamp-based algorithm that is valid for both tilted and nontilted orientations of the gantry. Unlike some of the other algorithms, generalization of the Feldkamp algorithm to include gantry tilt is simple and straightforward with no significant increase in computational complexity. The effect of gantry tilt for helical Feldkamp reconstruction is to introduce a lateral shift in the isocenter of the reconstructed slice of interest, which is a function of the tilt, couch speed, and view angle. The lateral shift is easily calculated and incorporated into the helical Feldkamp backprojection algorithm. A tilt-generalized helical Feldkamp algorithm has been developed and incorporated into Aquilion 16-slice CT (Toshiba, Japan) scanners. This paper describes modifications necessary for the tilt generalization and its verification.