Flat-panel detectors or, synonymously, flat detectors (FDs) have been developed for use in radiography and fluoroscopy with the defined goal to replace standard X-ray film, filmscreen combinations and image intensifiers by an advanced sensor system. FD technology in comparison to X-ray film and image intensifiers offers higher dynamic range, dose reduction, fast digital readout and the possibility for dynamic acquisitions of image series, yet keeping to a compact design. It appeared logical to employ FD designs also for computed tomography (CT) imaging. Respective efforts date back a few years only, but FD-CT has meanwhile become widely accepted for interventional and intraoperative imaging using C-arm systems. FD-CT provides a very efficient way of combining two-dimensional (2D) radiographic or fluoroscopic and 3D CT imaging. In addition, FD technology made its way into a number of dedicated CT scanner developments, such as scanners for the maxillo-facial region or for micro-CT applications. This review focuses on technical and performance issues of FD technology and its full range of applications for CT imaging. A comparison with standard clinical CT is of primary interest. It reveals that FD-CT provides higher spatial resolution, but encompasses a number of disadvantages, such as lower dose efficiency, smaller field of view and lower temporal resolution. FD-CT is not aimed at challenging standard clinical CT as regards to the typical diagnostic examinations; but it has already proven unique for a number of dedicated CT applications, offering distinct practical advantages, above all the availability of immediate CT imaging in the interventional suite or the operating room.
The empirical beam hardening correction is an interesting alternative to conventional iterative higher order beam hardening correction algorithms. It does not tend to over- or undercorrect the data. Apart from the segmentation step, EBHC does not require assumptions on the spectra or on the type of material involved. Potentially, it can therefore be applied to any CT image.
Metallic implants generate streak-like artifacts in flat-detector computed tomography (FD-CT) reconstructed volumetric images. This study presents a novel method for reducing these disturbing artifacts by inserting discarded information into the original rawdata using a three-step correction procedure and working directly with each detector element. Computation times are minimized by completely implementing the correction process on graphics processing units (GPUs). First, the original volume is corrected using a three-dimensional interpolation scheme in the rawdata domain, followed by a second reconstruction. This metal artifact-reduced volume is then segmented into three materials, i.e. air, soft-tissue and bone, using a threshold-based algorithm. Subsequently, a forward projection of the obtained tissue-class model substitutes the missing or corrupted attenuation values directly for each flat detector element that contains attenuation values corresponding to metal parts, followed by a final reconstruction. Experiments using tissue-equivalent phantoms showed a significant reduction of metal artifacts (deviations of CT values after correction compared to measurements without metallic inserts reduced typically to below 20 HU, differences in image noise to below 5 HU) caused by the implants and no significant resolution losses even in areas close to the inserts. To cover a variety of different cases, cadaver measurements and clinical images in the knee, head and spine region were used to investigate the effectiveness and applicability of our method. A comparison to a three-dimensional interpolation correction showed that the new approach outperformed interpolation schemes. Correction times are minimized, and initial and corrected images are made available at almost the same time (12.7 s for the initial reconstruction, 46.2 s for the final corrected image compared to 114.1 s and 355.1 s on central processing units (CPUs)).
A side effect of increased volume coverage by using multi-row and flat-panel detectors in computed tomography (CT) is the concurrently growing contribution of scattered radiation to the measured signal. In order to investigate the effect of scatter on x-ray projections used for CT imaging, our study aimed at the development of a simulation tool for fast calculation of primary and scatter intensities. We developed a deterministic method to assess the contribution of single-scatter events to the measured signal. The investigation of multiple scatter by Monte Carlo simulations showed that it results in a smooth signal as compared to single scatter. A hybrid method is proposed in order to optimize the performance of the scatter simulation: a fast and exact analytical calculation of the single-scatter intensity combined with a coarse Monte Carlo (MC) estimate of multiple scatter to reduce overall computational expenses, while assuring an acceptable signal quality. The results of the hybrid simulation of total scatter were in excellent agreement with the corresponding MC only simulations, thereby allowing us to reduce computational time by orders of magnitude. Estimates of two-dimensional scatter distributions for flat-panel CT imaging took about 30-40 s (per projection). The hybrid method provides a realistic simulation of x-ray scatter and offers a basis for scatter correction approaches.
In flat-detector CT, imperfect or defect detector elements may cause concentric ring artifacts due to their continuous over- or underestimation of attenuation values, which often disturb image quality. Especially due to the demand for high-spatial resolution images and the necessary pixel read-out without arbitrary pixel-binning, ring artifacts become more pronounced and the reduction of these artifacts becomes a necessity. We here present a comparison of two dedicated ring artifact correction methods for flat-detector CT, on the basis of different median and mean filterings of the reconstructed image but each working in different geometric planes. While the first method works in Cartesian coordinates, the second method performs a transformation to polar coordinates. Both post-processing methods efficiently reduce ring artifacts in the reconstructed images and improve image quality. The transformation to polar coordinates turned out to be a necessary step for efficient ring artifact correction, since correction in Cartesian coordinates suffers from newly introduced artifacts as well as insufficient correction of artifacts close to the center of rotation.
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