Streak artifacts caused by the presence of metal have been a significant problem in CT imaging since its inception in
1972. With the fast evolving medical device industry, the number of metal objects implanted in patients is increasing
annually. This correlates directly with an increased likelihood of encountering metal in a patient CT scan, thus
necessitating the need for an effective and reproducible metal artifact reduction (MAR) algorithm. Previous comparisons
between MAR algorithms have typically only evaluated a small number of patients and a limited range of metal
implants. Although the results of many methods are promising, the reproducibility of these results is key to
providing more tangible evidence of their effectiveness. This study presents a direct comparison between the
performances, assessed by board certified radiologists, of four MAR algorithms: 3 non-iterative and one iterative
method, all applied and compared to the original clinical images. The results of the evaluation indicated a negative mean
score in almost all uses for two of the non-iterative methods, signifying an overall decrease in the diagnostic quality of
the images, generally due to perceived loss of detail. One non-iterative algorithm showed a slight improvement. The
iterative algorithm was superior in all studies by producing a considerable improvement in all uses.
It is known that x-ray projections collected from a circular orbit of an x-ray source are insufficient for accurate
reconstruction of a 3D object. For each local region of the object (except in the plane containing the source
trajectory) there is a conical volume in the object's spatial frequency space that is unmeasured due to the circular
geometry. The Feldkamp, Davis and Kress (FDK) algorithm based on filtered backprojection (FBP) involves
a 3D backprojection step so that these unmeasured spatial frequencies are set to zero, resulting in cone beam
artifacts for certain objects. We present a new type of cone beam CT reconstruction algorithm based on the
Fourier rebinning (FORE) framework of Defrise et al. The cone beam x-ray projection data are rebinned into
a set of in-plane sinograms using the FORE rebinning approximation, followed by 2D FBP to reconstruct each
axial slice. The algorithm is able to extrapolate data into the missing region of the object's frequency space
in a computationally efficient way, allowing for a reduction of cone beam artifacts for certain objects. Unlike
FDK, the algorithm is exact for an impulse object located anywhere along the axis of rotation. Reconstruction
errors are dependent on the radial distance, cone angle, and the second-derivative of the projection data in the
longitudinal direction. Finally, an extension to the algorithm is presented that permits reconstruction in regions
of the object that are not seen by the detector in every view.
Inverse-geometry CT (IGCT) employs a large area x-ray source array opposite a small area detector array.
The system is expected to provide sub-second volumetric imaging with isotropic resolution and no cone-beam
effects. Due to the large amount of data, it is desirable to have an exact 3D reconstruction algorithm that is
fast. Currently known IGCT algorithms are either slow, due to 3D backprojection, and/or require a reprojection
step, or are inexact. Defrise et al. developed an exact Fourier rebinning algorithm (FORE-J) for 3D PET. This
algorithm first rebins the 3D PET data into in-plane sinograms and then reconstructs the series of axial slices
using any 2D method. FORE-J is fast, exact, and efficiently uses all of the acquired PET data. We modified this
algorithm to adapt it to the IGCT geometry. Experiments were performed using a numerical "Defrise" phantom
consisting of high-intensity discs spaced in z to assess the accuracy of the modified algorithm as well as highlight
any cone-beam effects. A noise simulation was performed to analyze the noise properties of FORE-J and the
modified algorithm. The modified algorithm is very fast and slightly more accurate than the original algorithm
with a very small noise penalty in the central axial slices.
Inverse-geometry CT (IGCT) is a promising new scanning geometry. Employing a scanned-anode x-ray source
array the system is expected to provide sub-second volumetric imaging with isotropic resolution and no conebeam effects. Three detector arrays spaced apart laterally can achieve a 50 cm in-plane FOV with a 31 cm source. However, when three separate detector arrays are used, motion artifacts are expected to be different than in conventional CT and need to be assessed. Simulations were performed for two objects representing slow and fast motion as well as periodic and non-periodic motion. The simulations were repeated at different points in the FOV to study motion effects in three regions: 1) the inner 15 cm region which is sampled only by the central detector array, 2) the transition between the inner and outer regions, and 3) the outer region which is sampled by all three detector arrays. 2D simulations assumed 125 "superviews" acquired in step-and-shoot mode over 360 degrees. A gridding algorithm was used to resample the data into parallel rays which were then filtered and backprojected. Artifacts from the inner region are exactly like those that arise in a traditional CT system. The most significant artifacts caused by the multi-detector nature of the system are in the outer region, at the angles where the object sampling transitions between detector arrays. These streaking artifacts are comparable to motion artifacts in conventional CT and can be reduced by increasing the overlap region at the expense of FOV size and SNR uniformity.
This work investigates the modulation transfer function (MTF) of a prototype table-top inverse-geometry volumetric CT (IGCT) system. The IGCT system has been proposed to acquire sufficient volumetric data in one circular rotation using a large-area scanned source and a narrower array of fast detectors. The source and detector arrays have the same axial, or slice, extent, thus providing sufficient volumetric coverage. A prototype system has been built using a NexRay Scanning-Beam Digital X-ray system (NexRay, Inc., Los Gatos, CA) with the C-arm gantry in the horizontal position and a stage placed between the source and detector to rotate the scanned object. The resulting system has a 16-cm in-plane field of view (FOV) and 5-cm axial FOV. Two phantoms were constructed for measuring the MTF. A 76 micron tungsten wire placed axially in a plastic frame was used to measure the in-plane MTF, and the same wire slanted at 45 degrees was used to test the isotropy of the MTF. The data were calibrated for flat-field intensity and geometric misalignment and reconstructed using a modified 3D PET algorithm. For both phantoms, slices perpendicular to the wires were reconstructed. Simulations which model the IGCT system were used to verify the MTF measurement, along with analytical predictions. The measured MTF curve was similar in shape to the predicted curve with a 10% point at 20 lp/cm compared to a predicted 18 lp/cm. Future work will also study the uniformity of the MTF across the FOV and further characterize the IGCT system.
The performance of an inverse geometry volumetric CT (IGCT) system with multiple detector arrays is being
investigated. The system is capable of a complete acquisition of a volume free from cone-beam artifacts with
only a single rotation of the gantry. The IGCT system is composed of a large source array opposite three small
detector arrays with a field-of-view (FOV) large enough for clinical imaging (45cm). Simulations were conducted to estimate the MTF at different points in the FOV. The simulations involved generating 2D projection data of a 100um circular object followed by a reconstruction algorithm that uses gridding and filtered backprojection. The simulations also modeled finite source spot and detector element sizes. The estimated MTF’s were compared with theoretical MTF’s at 0 cm, 10 cm, and 20 cm away from the isocenter. The simulated MTF’s closely matched the theoretical MTF’s. The MTF in the radial direction was over 10% at 16 lp/cm across the entire FOV while the azimuthal MTF 10% point degraded to 10.4 lp/cm at the edge of the FOV. This degradation in azimuth, which can be corrected for, is due to gridding in the angular direction which is magnified at large distances away from the isocenter. The simulations show promising results for the in-plane resolution of the multiple detector array IGCT system. Noise properties and other factors impacting performance are currently being investigated.
An inverse-geometry volumetric CT (IGCT) system for imaging in a single fast rotation without cone-beam artifacts is being developed. It employs a large scanned source array and a smaller detector array. For a single-source/single-detector implementation, the FOV is limited to a fraction of the source size. Here we explore options to increase the FOV without increasing the source size by using multiple detectors spaced apart laterally to increase the range of radial distances sampled. We also look at multiple source array systems for faster scans. To properly reconstruct the FOV, Radon space must be sufficiently covered and sampled in a uniform manner. Optimal placement of the detectors relative to the source was determined analytically given system constraints (5cm detector width, 25cm source width, 45cm source-to-isocenter distance). For a 1x3 system (three detectors per source) detector spacing (DS) was 18deg and source-to-detector distances (SDD) were 113, 100 and 113cm to provide optimum Radon sampling and a FOV of 44cm. For multiple-source systems, maximum angular spacing between sources cannot exceed 125deg since detectors corresponding to one source cannot be occluded by a second source. Therefore, for 2x3 and 3x3 systems using the above DS and SDD, optimum spacing between sources is 115deg and 61deg respectively, requiring minimum scan rotations of 115deg and 107deg. Also, a 3x3 system can be much faster for full 360deg dataset scans than a 2x3 system (120deg vs. 245deg). We found that a significantly increased FOV can be achieved while maintaining uniform radial sampling as well as a substantial reduction in scan time using several different geometries. Further multi-parameter optimization is underway.