Carbon nanotube-based multiple source C-arm CT system: feasibility study with prototype system.

To extend the field of view while reducing dimensions of the C-arm, we propose a carbon nanotube (CNT)-based C-arm computed tomography (CT) system with multiple X-ray sources. A prototype system was developed using three CNT X-ray sources, enabling a feasibility study. Geometry calibration and image reconstruction were performed to improve the quality of image acquisition. However, the geometry of the prototype system led to projection truncation for each source and an overlap region of object area covered by each source in the two-dimensional Radon space, necessitating specific corrective measures. We addressed these problems by implementing truncation correction and applying weighting techniques to the overlap region during the image reconstruction phase. Furthermore, to enable image reconstruction with a scan angle less than 360°, we designed a weighting function to solve data redundancy caused by the short scan angle. The accuracy of the geometry calibration method was evaluated via computer simulations. We also quantified the improvements in reconstructed image quality using mean-squared error and structural similarity. Moreover, detector lag correction was applied to address the afterglow observed in the experimental data obtained from the prototype system. Our evaluation of image quality involved comparing reconstructed images obtained with and without incorporating the geometry calibration results and images with and without lag correction. The outcomes of our simulation study and experimental investigation demonstrated the efficacy of our proposed geometry calibration, image reconstruction method, and lag correction in reducing image artifacts.

Geometry calibration and image reconstruction for carbon-nanotube-based multisource and multidetector CT.

Conventional intraoperative computed tomography (CT) has a long scan time, degrading the image quality. Its large size limits the position of a surgeon during surgery. Therefore, this study proposes a CT system comprising of eight carbon-nanotube (CNT)-based x-ray sources and 16 detector modules to solve these limitations. Gantry only requires 45° of rotation to acquire the whole projection, reducing the scan time to 1/8 compared to the full rotation. Moreover, the volume and scan time of the system can be significantly reduced using CNT sources with a small volume and short pulse width and placing a heavy and large high-voltage generator outside the gantry. We divided the proposed system into eight subsystems and sequentially devised a geometry calibration method for each subsystem. Accordingly, a calibration phantom consisting of four polytetrafluoroethylene beads, each with 15 mm diameter, was designed. The geometry calibration parameters were estimated by minimizing the difference between the measured bead projection and the forward projection of the formulated subsystem. By reflecting the estimated geometry calibration parameters, the projection data were obtained via rebinning to be used in the filtered-backprojection reconstruction. The proposed calibration and reconstruction methods were validated by computer simulations and real experiments. Additionally, the accuracy of the geometry calibration method was examined by computer simulation. Furthermore, we validated the improved quality of the reconstructed image through the mean-squared error (MSE), structure similarity (SSIM), and visual inspections for both the simulated and experimental data. The results show that the calibrated images, reconstructed by reflecting the calibration results, have smaller MSE and higher SSIM values than the uncalibrated images. The calibrated images were observed to have fewer artifacts than the uncalibrated images in visual inspection, demonstrating that the proposed calibration and reconstruction methods effectively reduce artifacts caused by geometry misalignments.

A metal artifact reduction method for small field of view CT imaging.

Several sinogram inpainting based metal artifact reduction (MAR) methods have been proposed to reduce metal artifact in CT imaging. The sinogram inpainting method treats metal trace regions as missing data and estimates the missing information. However, a general assumption with these methods is that data truncation does not occur and that all metal objects still reside within the field-of-view (FOV). These assumptions are usually violated when the FOV is smaller than the object. Thus, existing inpainting based MAR methods are not effective. In this paper, we propose a new MAR method to effectively reduce metal artifact in the presence of data truncation. The main principle of the proposed method involves using a newly synthesized sinogram instead of the originally measured sinogram. The initial reconstruction step involves obtaining a small FOV image with the truncation artifact removed. The final step is to conduct sinogram inpainting based MAR methods, i.e., linear and normalized MAR methods, on the synthesized sinogram from the previous step. The proposed method was verified for extended cardiac-torso simulations, clinical data, and experimental data, and its performance was quantitatively compared with those of previous methods (i.e., linear and normalized MAR methods directly applied to the originally measured sinogram data). The effectiveness of the proposed method was further demonstrated by reducing the residual metal artifact that were present in the reconstructed images obtained using the previous method.

Rigid and non-rigid motion artifact reduction in X-ray CT using attention module.

Motion artifacts are a major factor that can degrade the diagnostic performance of computed tomography (CT) images. In particular, the motion artifacts become considerably more severe when an imaging system requires a long scan time such as in dental CT or cone-beam CT (CBCT) applications, where patients generate rigid and non-rigid motions. To address this problem, we proposed a new real-time technique for motion artifacts reduction that utilizes a deep residual network with an attention module. Our attention module was designed to increase the model capacity by amplifying or attenuating the residual features according to their importance. We trained and evaluated the network by creating four benchmark datasets with rigid motions or with both rigid and non-rigid motions under a step-and-shoot fan-beam CT (FBCT) or a CBCT. Each dataset provided a set of motion-corrupted CT images and their ground-truth CT image pairs. The strong modeling power of the proposed network model allowed us to successfully handle motion artifacts from the two CT systems under various motion scenarios in real-time. As a result, the proposed model demonstrated clear performance benefits. In addition, we compared our model with Wasserstein generative adversarial network (WGAN)-based models and a deep residual network (DRN)-based model, which are one of the most powerful techniques for CT denoising and natural RGB image deblurring, respectively. Based on the extensive analysis and comparisons using four benchmark datasets, we confirmed that our model outperformed the aforementioned competitors. Our benchmark datasets and implementation code are available at https://github.com/youngjun-ko/ct_mar_attention.

Correction of severe beam-hardening artifacts via a high-order linearization function using a prior-image-based parameter selection method.

PURPOSE: Polychromatic x-rays are used in most computed tomography scanners. In this case, a beam-hardening effect occurs, which degrades the image quality and distorts the shapes of objects in the reconstructed images. When the beam-hardening artifact is not severe, conventional correction methods can reduce the artifact reasonably well. However, highly dense materials, such as iron and titanium, can produce more severe beam-hardening artifacts, which often cannot be corrected by conventional methods. Moreover, when the size of the metal is large, severe darks bands due to photon starvation as well as beam-hardening are generated. The purpose of our study was to develop a new method for correcting severe beam-hardening artifacts and severe dark bands using a high-order polynomial correction function and a prior-image-based linearization method. METHODS: The initial estimate of an image free of beam-hardening (a prior image) was constructed from the initial reconstruction of the original projection data. Its corresponding beam-hardening-free projection data (a prior projection) were calculated by a projection operator onto the prior image. A new beam-hardening correction function G(praw ) with many high-order terms was effectively determined via a simple minimization process applied to the difference between the original projection data and the prior projection data. Using the determined correction function G(praw ), a corrected linearized sinogram pcorr can be obtained, which became effectively linear for the line integrals of the object. Final beam-hardening corrected images can be reconstructed from the linearized sinogram. The proposed method was evaluated in both simulation and real experimental studies. RESULTS: All investigated cases in both simulations and real experiments showed that the proposed method effectively removed not only streaks for moderate beam-hardening artifacts but also dark bands for severe beam-hardening artifacts without causing structural and contrast distortion. CONCLUSIONS: The prior-image-based linearization method exhibited better correction performance than conventional methods. Because the proposed method did not require time-consuming iterative reconstruction processes to obtain the optimal correction function, it can expedite the correction procedure and incorporate more high-order terms in the linearization correction function in comparison to the conventional methods.