Purpose
In recent years, health risks concerning high‐dose x‐ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low‐dose computed ...tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low‐dose x‐ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning.
Method
We used clinical dental CT data with low‐dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high‐quality labeled normal‐dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m‐WGAN, to remove artifacts and obtain high‐quality CT dental images in a clinical dental CT examination environment.
Results
The experimental results confirmed that the proposed algorithm effectively removes low‐dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low‐dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m‐WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m‐WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning.
Conclusions
The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone‐beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low‐dose dental CT artifact correction.
Perovskite materials in different dimensions show great potential in direct X‐ray detection, but each with limitations stemming from its own intrinsic properties. Particularly, the sensitivity of ...two‐dimensional (2D) perovskites is limited by poor carrier transport while ion migration in three‐dimensional (3D) perovskites causes the baseline drifting problem. To circumvent these limitations, herein a double‐layer perovskite film is developed with properly aligned energy level, where 2D (PEA)2MA3Pb4I13 (PEA=2‐phenylethylammonium, MA=methylammonium) is cascaded with vertically crystallized 3D MAPbI3. In this new design paradigm, the 3D layer ensures fast carrier transport while the 2D layer mitigates ion migration, thus offering a high sensitivity and a greatly stabilized baseline. Besides, the 2D layer increases the film resistivity and enlarges the energy barrier for hole injection without compromising carrier extraction. Consequently, the double‐layer perovskite detector delivers a high sensitivity (1.95 × 104 μC Gyair−1 cm−2) and a low detection limit (480 nGyair s−1). Also demonstrated is the X‐ray imaging capacity using a circuit board as the object. This work opens up a new avenue for enhancing X‐ray detection performance via cascade assembly of various perovskites with complementary properties.
By integrating a layered 2D perovskite with a vertically crystallized MAPbI3, a double‐layer perovskite is constructed for direct X‐ray detection, showing stable baseline, a high sensitivity of 19 503 μC Gyair−1 cm−2, and a low detection limit of 480 nGyair s−1. This work provides a strategy to unlock the performance limitations stemming from the intrinsic properties of the perovskite.
Grating-based x-ray differential phase contrast imaging (DPCI) often uses a phase stepping procedure to acquire data that enables the extraction of phase information. This method prolongs the time ...needed for data acquisition by several times compared with conventional x-ray absorption image acquisitions. A novel analyzer grating design was developed in this work to eliminate the additional data acquisition time needed to perform phase stepping in DPCI. The new analyzer grating was fabricated such that the linear grating structures are shifted from one detector row to the next; the amount of the lateral shift was equal to a fraction of the x-ray diffraction fringe pattern. The x-ray data from several neighboring detector rows were then combined to extract differential phase information. Initial experimental results have demonstrated that the new analyzer grating enables accurate DPCI signal acquisition from a single x-ray exposure like conventional x-ray absorption imaging.
High performance X-ray detector with ultra-high spatial and temporal resolution are crucial for biomedical imaging. This study reports a dynamic direct-conversion CMOS X-ray detector assembled with ...screen-printed CsPbBr
, whose mobility-lifetime product is 5.2 × 10
cm
V
and X-ray sensitivity is 1.6 × 10
µC Gy
cm
. Samples larger than 5 cmFormula: see text10 cm can be rapidly imaged by scanning this detector at a speed of 300 frames per second along the vertical and horizontal directions. In comparison to traditional indirect-conversion CMOS X-ray detector, this perovskite CMOS detector offers high spatial resolution (5.0 lp mm
) X-ray radiographic imaging capability at low radiation dose (260 nGy). Moreover, 3D tomographic images of a biological specimen are also successfully reconstructed. These results highlight the perovskite CMOS detector's potential in high-resolution, large-area, low-dose dynamic biomedical X-ray and CT imaging, as well as in non-destructive X-ray testing and security scanning.
A general theoretical framework is presented to explain the formation of the phase signal in an x-ray microscope integrated with a grating interferometer, which simultaneously enables the high ...spatial resolution imaging and the improved image contrast. By using this theory, several key parameters of phase contrast imaging can be predicted, for instance, the fringe visibility and period, and the conversion condition from the differential phase imaging (DPI) to the phase difference imaging (PDI). Additionally, numerical simulations are performed with certain x-ray optical components and imaging geometry. Comparison with the available experimental measurement
Appl. Phys. Lett.
113
063105 (2018) demonstrates the accuracy of this developed quantitative analysis method of x-ray phase-sensitive microscope imaging.
Aiming at reducing computed tomography (CT) scan radiation while ensuring CT image quality, a new low-dose CT super-resolution reconstruction method based on combining a random forest with coupled ...dictionary learning is proposed. The random forest classifier finds the optimal solution of the mapping relationship between low-dose CT (LDCT) images and high-dose CT (HDCT) images and then completes CT image reconstruction by coupled dictionary learning. An iterative method is developed to improve robustness, the important coefficients for the tree structure are discussed and the optimal solutions are reported. The proposed method is further compared with a traditional interpolation method. The results show that the proposed algorithm can obtain a higher peak signal-to-noise ratio (PSNR) and structural similarity index measurement (SSIM) and has better ability to reduce noise and artifacts. This method can be applied to many different medical imaging fields in the future and the addition of computer multithreaded computing can reduce time consumption.
Although perovskite wafers with a scalable size and thickness are suitable for direct X‐ray detection, polycrystalline perovskite wafers have drawbacks such as the high defect density, defective ...grain boundaries, and low crystallinity. Herein, PbI2‐DMSO powders are introduced into the MAPbI3 wafer to facilitate crystal growth. The PbI2 powders absorb a certain amount of DMSO to form the PbI2‐DMSO powders and PbI2‐DMSO is converted back into PbI2 under heating while releasing DMSO vapor. During isostatic pressing of the MAPbI3 wafer with the PbI2‐DMSO solid additive, the released DMSO vapor facilitates in situ growth in the MAPbI3 wafer with enhanced crystallinity and reduced defect density. A dense and compact MAPbI3 wafer with a high mobility‐lifetime (µτ) product of 8.70 × 10−4 cm2 V−1 is produced. The MAPbI3‐based direct X‐ray detector fabricated for demonstration shows a high sensitivity of 1.58 × 104 µC Gyair−1 cm−2 and a low detection limit of 410 nGyair s−1.
PbI2‐DMSO powders are adopted as a solid additive for isostatic pressing of MAPbI3 wafers to promote in situ crystal growth. A dense and compact MAPbI3 wafer exhibits high mobility‐lifetime product of 8.70 × 10−4 cm2 V–1. The X‐ray detector shows high sensitivity of 1.58 × 104 µC Gyair –1 cm–2 and low detection limit of 410 nGyair s–1.
Purpose:
This paper concerns the feasibility of x-ray differential phase contrast (DPC) tomosynthesis imaging using a grating-based DPC benchtop experimental system, which is equipped with a ...commercial digital flat-panel detector and a medical-grade rotating-anode x-ray tube. An extensive system characterization was performed to quantify its imaging performance.
Methods:
The major components of the benchtop system include a diagnostic x-ray tube with a 1.0 mm nominal focal spot size, a flat-panel detector with 96 μm pixel pitch, a sample stage that rotates within a limited angular span of ±30°, and a Talbot-Lau interferometer with three x-ray gratings. A total of 21 projection views acquired with 3° increments were used to reconstruct three sets of tomosynthetic image volumes, including the conventional absorption contrast tomosynthesis image volume (AC-tomo) reconstructed using the filtered-backprojection (FBP) algorithm with the ramp kernel, the phase contrast tomosynthesis image volume (PC-tomo) reconstructed using FBP with a Hilbert kernel, and the differential phase contrast tomosynthesis image volume (DPC-tomo) reconstructed using the shift-and-add algorithm. Three inhouse physical phantoms containing tissue-surrogate materials were used to characterize the signal linearity, the signal difference-to-noise ratio (SDNR), the three-dimensional noise power spectrum (3D NPS), and the through-plane artifact spread function (ASF).
Results:
While DPC-tomo highlights edges and interfaces in the image object, PC-tomo removes the differential nature of the DPC projection data and its pixel values are linearly related to the decrement of the real part of the x-ray refractive index. The SDNR values of polyoxymethylene in water and polystyrene in oil are 1.5 and 1.0, respectively, in AC-tomo, and the values were improved to 3.0 and 2.0, respectively, in PC-tomo. PC-tomo and AC-tomo demonstrate equivalent ASF, but their noise characteristics quantified by the 3D NPS were found to be different due to the difference in the tomosynthesis image reconstruction algorithms.
Conclusions:
It is feasible to simultaneously generate x-ray differential phase contrast, phase contrast, and absorption contrast tomosynthesis images using a grating-based data acquisition setup. The method shows promise in improving the visibility of several low-density materials and therefore merits further investigation.
The suppression of streak artifacts in computed tomography with a limited-angle configuration is challenging. Conventional analytical algorithms, such as filtered backprojection (FBP), are not ...successful due to incomplete projection data. Moreover, model-based iterative total variation algorithms effectively reduce small streaks but do not work well at eliminating large streaks. In contrast, FBP mapping networks and deep-learning-based postprocessing networks are outstanding at removing large streak artifacts; however, these methods perform processing in separate domains, and the advantages of multiple deep learning algorithms operating in different domains have not been simultaneously explored. In this paper, we present a hybrid-domain convolutional neural network (hdNet) for the reduction of streak artifacts in limited-angle computed tomography. The network consists of three components: the first component is a convolutional neural network operating in the sinogram domain, the second is a domain transformation operation, and the last is a convolutional neural network operating in the CT image domain. After training the network, we can obtain artifact-suppressed CT images directly from the sinogram domain. Verification results based on numerical, experimental and clinical data confirm that the proposed method can significantly reduce serious artifacts.
PDF
