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In radiation treatment, a cone-beam computed tomography (CBCT) scan is conducted for precise positioning of tumors, and the image quality is usually degraded by motion artifacts due to patient’s respiration and movement during scanning. Four-dimensional (4D) CBCT reconstruction with phase binning is typically used to overcome these difficulties. Albeit motion artifacts might be reduced with 4D CBCT, the overall image quality is typically worsened by severe streak artifacts due to the sparse-angle projections available in the 3D reconstruction for each motion phase. This study presents a method for reducing streak artifacts effectively in conventional 4D CBCT reconstruction by using a state-of-the-art convolutional neural network (a residual U-Net was used). We performed a computational simulation and an experiment to investigate the image quality and evaluate the effectiveness of the proposed method. The proposed 4D CBCT reconstruction method reduced streak artifacts noticeably, and its effectiveness was validated by comparing its results to those of other reconstruction methods such as the filtered-backprojection, a compressed-sensing, and a simple CNN-based algorithm for the 4D CBCT datasets.

In radiography, scattered X-rays cause contrast loss in X-ray images, limiting their clinical usefulness, and are often reduced using several scatter correction methods. One difficulty associated with the existing scatter correction methods is noise amplification due to scatter correction. In this study, we investigated a model-based noise reduction method using the U-Net, which is a deep convolutional neural network proposed for image segmentation, to provide a practical solution to the noise amplification problem in conventional scatter correction methods. In this method, the noise properties of an X-ray image after scatter correction are first analyzed using a Poisson-Gaussian mixture model, a trained U-Net model in which the corresponding noise parameters is used predicts image noise, and the scatter-corrected image is finally recovered by subtracting the predicted image noise. We performed a systematic simulation and an experiment to demonstrate its viability and investigated the image characteristics in terms of several image metrics. Our image results showed that the degradation of the image characteristics by scattered X-rays and noise was effectively removed by using the proposed method.

We investigated an efficient method for material decomposition in radiography, which can separate soft tissues and bones from a single radiograph with the aid of a surface image obtained using a three-dimensional laser scanner through which the attenuation length within an object is estimated. This approach does not require double radiation exposures; thus, it can eliminate the technical difficulties associated with the conventional dual-energy material decomposition (DEMD) method, such as increased patient doses, increased execution time, and misregistration errors between two scans. We implemented the proposed algorithm and performed a computational simulation and an experiment to demonstrate its viability for single-energy material decomposition in radiography (80 kVp was used). The image characteristics of the proposed method were investigated and compared with those obtained using the DEMD method (50 kVp and 80 kVp were used). Our results indicate that the estimate of the attenuation length by using the surface image of the examined object may substitute for one of the two dual-energy measurements in conventional DEMD. Accordingly, the proposed method yielded material decomposition results similar to the results elicited by the DEMD method in radiography.

We investigated a wide image stitching method in digital radiography where an X-ray tube is stationary while a detector moves from one exposure position to the next during acquisition of multiple images of a small field of view. This method completely emulates the common radiographic geometry and is free of the parallax artifacts inherent in the source-translation method, showing improved image quality with reduced geometric distortions. One difficulty associated with this method is that the exposure levels of the individual images vary according to the inverse square law of the X-ray intensity, which may result in heterogeneous stitched images. In this paper, we propose a new software exposure compensation (SEC) scheme able to overcome this difficulty, and in this study, we performed an experiment to demonstrate the feasibility of using the proposed SEC in wide stitching. Four X-ray images of a test phantom were obtained in different exposure positions and were intensity-compensated by using the SEC algorithm prior to phase-correlation-based registration and alpha blending. Our results indicate that the proposed SEC scheme effectively compensates for the exposure discrepancies of the individual images, and thereby homogeneous and seamless stitched images can be obtained with high accuracy in the wide stitching.

This study investigated a dictionary-learning (DL)-based image deblurring method for improving image performance in x-ray nondestructive testing. DL is a representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basic elements as well as those basic elements themselves. In this study, a DL-based algorithm was implemented, and a computational simulation and experiment were then performed to evaluate the algorithm’s effectiveness for image deblurring. The hardware system used in the experiment consisted of an x-ray tube with a focal spot size of 0.6 mm and a flat-panel detector with a pixel size of 100 μ m2. X-ray images of several electronic components were acquired at x-ray tube conditions of 80 kV p and 1.25 mAs. The image characteristics of the deblurred images generated by the DL-based algorithm were quantitatively evaluated in terms of intensity profile, universal-quality index, and noise power spectrum. Our results indicate that our DL-based image deblurring method effectively improves image performance in x-ray nondestructive testing.

Dual-energy radiography (DER) based on basis material decomposition (BMD) is a wellestablished X-ray technique that uses low- and high-kV radiographs to separate soft and dense tissues. Conventional DER methods often lead to reduced image contrast in resulting dual-energy images when extensive X-ray scatter is present in the images. In this study, we applied the weighted l1-norm scatter correction algorithm in conventional DER to create a scatter correction scheme able to overcome the reduced image contrast associated with dual-energy images. The proposed DER process consists of two main steps: (1) the generation of a scatter-corrected pairwise lookup table for equivalent acryl and aluminum thicknesses and (2) the separation of soft and dense tissues based on BMD. We performed a computational simulation and an experiment to investigate the image quality and evaluate the effectiveness of the proposed DER method. Polychromatic X-ray images were emulated at two different tube voltage settings (80 kVp and 140 kVp); then, the images were corrected for scatter prior to BMD. Our results indicate that the proposed scatter correction algorithm implemented in conventional DER effectively reduces X-ray scatter in radiography and results in improved DER image quality.

In dental computed tomography (DCT), metal artifact reduction (MAR) is a critical issue to improve the clinical usefulness of DCT and remains a challenging problem. Although various MAR methods have been developed in medical CT, those methods may not work robustly in DCT because teeth themselves, as well as metallic objects, have high X-ray attenuation. In this study, we investigated an MAR method that was based on sinogram normalization interpolation with an artifact-reduced prior for DCT. The method consisted of three main steps: segmentation of a metal trace, generation of an artifact-reduced prior image, and sinogram completion followed by DCT reconstruction. We performed a computational simulation and performed an experiment on a teeth phantom with several metal inserts to validate the proposed method. With respect to the root-mean-square error and the structural similarity, we compared our results with the ones obtained by using the combined prior-based MAR (CP-MAR) method. Our results indicate that the proposed MAR method reduced metal artifacts considerably in DCT images and showed an image performance that was better than that obtained by using the state of the art method (CP-MAR) in reducing streak artifacts without introducing any contrast anomaly.

Dual-energy material decomposition (DEMD) is a well-established theoretical x-ray technique that uses low- and high-kilovoltage radiographs to separate soft tissue and bone in radiography and compu...

In this study, we investigated sparse-view reconstruction in dental computed tomography (DCT) by using a dictionary-learning (DL)-based method to reduce excessive radiation dose to patients. In sparse-view DCT, only a small number (< 100) of projections, far less than what is required by the Nyquist sampling theory, are acquired from the imaging system and used for image reconstruction. DL is a representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basic elements (or atoms). We implemented a DL-based reconstruction algorithm and performed a systematic simulation and an experiment to evaluate the algorithm’s effectiveness for sparse-view reconstruction in DCT. DCT images were reconstructed using the three sparse-view projections of P30, P40, and P60, and their image qualities were quantitatively evaluated in terms of the intensity profile, the universal quality index, and the peak signal-to-noise ratio. The hardware system used in the experiment consisted of an X-ray tube, which was run at 90 kVp and 40 mA, and a flat-panel detector with a 388-μm pixel size. Our simulation and experimental results indicate that the DL-based method significantly reduced streak artifacts in the sparse-view DCT reconstruction when using P40, thus maintaining image quality.

Dark-field (DF) x-ray imaging (DFXI) is a technology that can obtain information relating to the small-angle x-ray scattering of a sample. In this paper, we report on the quantification of DF effects by measuring the real space correlation function of scattering samples in a single-shot grid-based x-ray imaging setup that enables a simple approach to DFXI. The experimental measurements of the DF effects in our imaging setup were in good agreement with the theoretical quantification over the entire range of test conditions, thus verifying its effectiveness for single-shot grid-based DFXI. Consequently, we were able to clearly understand the associated particle-scale selectivity, which can help us determine suitable applications for single-shot grid-based x-ray DFXI.

Background and objective Digital breast tomosynthesis (DBT) is a well-established multiplanar imaging modality in breast examinations designed to overcome the limitations of conventional mammography. However, reconstructed DBT images from the acquired projection data are often limited in image performance due mainly to blur artifacts resulting from inherent aspects of imaging systems, including detector resolution and the finite focal spot of the x-ray tube. Methods We investigated an effective blind-deblurring method based on a compressed-sensing scheme in an attempt to solve the blurring problem in DBT. We implemented the proposed algorithm and performed a systematic simulation and an experiment to demonstrate its viability. In both simulation and experiment, all of the projection data were taken with a tomographic angle of θ = 32° and an angle step of Δθ = 2°. The proposed deblurring algorithm was then applied to the projection data before performing the common filtered-backprojection-based DBT reconstruction process. Results The deblurred projection images showed much better image performance compared with the blurred projection images, demonstrating the viability of the proposed blind-deblurring scheme in conventional radiography. The PSNR and RMSE characteristics of the deblurred DBT image improved by factors of approximately 1.63 and 0.37, respectively, compared with those of the blurred DBT image. Conclusions Our results indicate that the proposed blind-deblurring method was effective in reducing the blurring problem in both DBT and in conventional radiography, excluding additional measurement of the system response function.

Digital tomosynthesis (DTS) images reconstructed from the limited-angle scanned projections are often limited in image performance due mainly to superimposed shadow artifacts caused by high attenua...

In common radiography, image contrast is often limited due mainly to scattered x-rays and noise, decreasing the quantitative usefulness of x-ray images. Several scatter reduction methods based on software correction schemes have been extensively investigated in an attempt to overcome these difficulties, most of which are based on measurement, mathematical-physical modeling, or a combination of both. However, those methods require special equipment, system geometry, and extra manual work to measure scatter characteristics. In this study, we investigated a new software scheme for scatter correction based on a simple radiographic scattering model where the intensity of the scattered x-rays was directly estimated from a single x-ray image using a weighted l1-norm contextual regularization framework. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. We also conducted some clinical image studies using patient's image data of breast and L-spine to verify the clinical effectiveness of the proposed scheme. Our results indicate that the degradation of image characteristics by scattered x-rays and noise was effectively recovered by using the proposed software scheme, thus improving radiographic visibility considerably. Graphical abstract The schematic illustrations of scatter suppression methods by using a an antiscatter grid and b a scatter estimation algorithm.

In conventional radiography, image visibility is often limited mainly due to the superimposition of the object’s structure under investigation and scattered x-rays. Several methods, including the antiscatter grid technique, the air-gap technique, and scatter correction methods using measurements, mathematical-physical modeling, or a combination of both, have been extensively investigated in an attempt to overcome these difficulties. However, these methods require special equipment, geometry, and extra work to measure the scatter characteristics. In this study, we propose a new image restoration method based on a simple radiographic scattering model in which the intensity of the scattered x-rays and the direct transmission function of a given object are estimated from a single x-ray image by using the dark-channel prior. We implemented the proposed algorithm and performed a systematic experiment by using a 450-kV industrial x-ray inspection system to demonstrate its viability for nondestructive testing. Our results indicated that the structure of the examined object was much more clearly visible in the restored image, considerably improving the radiographic visibility.

This study investigated analytic computed tomography (CT) reconstruction in sparse-angular sampling using a new sinogram interpolation method to reduce patient radiation dosage. In sparse-angle CT, only a small number of projections, far less than what is required by the Nyquist sampling theory, are taken from the CT system and used for image reconstruction. However, CT images reconstructed by using the standard filtered-backprojection algorithm usually suffer from severe streak artifacts due to theoretically insufficient angular sampling. In this study, a new sinogram interpolation method, the so-called sinogram-normalization interpolation, was introduced to the analytic sparse-angle CT reconstruction to alleviate such artifacts. To validate the proposed method, we performed a systematic simulation and experiment and investigated the image characteristics. CT images were reconstructed using the three sparse-angular samplings of 100, 120, and 150, and their image qualities were quantitatively evaluated in terms of the intensity profile, the peak signal-tonoise ratio, and the universal quality index. The results indicated that the proposed interpolation method effectively reduced streak artifacts in the analytic sparse-angle CT reconstruction, thus maintaining the image quality.

In this study, we investigated an effective low-dose digital tomosynthesis (DTS) where a multislit collimator placed between the X-ray tube and the patient oscillates during projection data acquisition, partially blocking the X-ray beam to the patient thereby reducing the radiation dosage. We performed a simulation using the proposed DTS with two sets of multislit collimators both having a 50% duty cycle and investigated the image characteristics to demonstrate the feasibility of this proposed approach. In the simulation, all projections were taken at a tomographic angle of θ = ± 5 0 ° and an angle step of Δ θ = 2 ° . We utilized an iterative algorithm based on a compressed-sensing (CS) scheme for more accurate DTS reconstruction. Using the proposed DTS, we successfully obtained CS-reconstructed DTS images with no bright-band artifacts around the multislit edges of the collimator, thus maintaining the image quality. Therefore, the use of multislit collimation in current real-world DTS systems can reduce the radiation dosage to patients.

In conventional planar radiography, image visibility is often limited mainly due to the superimposition of the object structure under investigation and the artifacts caused by scattered x-rays and noise. Several methods, including computed tomography (CT) as a multiplanar imaging modality, air-gap and grid techniques for the reduction of scatters, phase-contrast imaging as another image-contrast modality, etc., have extensively been investigated in attempt to overcome these difficulties. However, those methods typically require higher x-ray doses or special equipment. In this work, as another approach, we propose a new model-based radiography restoration method based on simple scatter-degradation scheme where the intensity of scattered x-rays and the transmission function of a given object are estimated from a single x-ray image to restore the original degraded image. We implemented the proposed algorithm and performed an experiment to demonstrate its viability. Our results indicate that the degradation of image characteristics by scattered x-rays and noise was effectively recovered by using the proposed method, which improves the image visibility in radiography considerably.

In recent digital tomosynthesis (DTS), iterative reconstruction methods are often used owing to the potential to provide multiplanar images of superior image quality to conventional filtered-backprojection (FBP)-based methods. However, they require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, we propose a new DTS reconstruction method incorporated with a dual-resolution voxelization scheme in attempt to overcome these difficulties, in which the voxels outside a small region-of-interest (ROI) containing target diagnosis are binned by 2 × 2 × 2 while the voxels inside the ROI remain unbinned. We considered a compressed-sensing (CS)-based iterative algorithm with a dual-constraint strategy for more accurate DTS reconstruction. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative DTS reconstruction, keeping the image quality inside the ROI not much degraded. A binning size of 2 × 2 × 2 required only about 31.9% computational memory and about 2.6% reconstruction time, compared to those for no binning case. The reconstruction quality was evaluated in terms of the root-mean-square error (RMSE), the contrast-to-noise ratio (CNR), and the universal-quality index (UQI).

We have recently developed precise x-ray grids having strip densities in the range of 100 – 250 lines/inch by adopting the precision sawing process and carbon interspace material for the demands of specific x-ray imaging techniques. However, quality assurance in the grid manufacturing has not yet satisfactorily conducted because grid strips of a high strip density are often invisible through an x-ray nondestructive testing with a flat-panel detector of an ordinary pixel resolution (>100 μ m). In this work, we propose a useful method to evaluate actual grid strip densities over the Nyquist sampling rate based on the moire artifact analysis. We performed a systematic simulation and experiment with several sample grids and a detector having a 143- μ m pixel resolution to verify the proposed quality assurance method. According to our results, the relative differences between the nominal and the evaluated grid strip densities were within 0.2% and 1.8% in the simulation and experiment, respectively, which demonstrates that the proposed method is viable with an ordinary detector having a moderate pixel resolution for quality assurance in grid manufacturing.

Dual-energy computed tomography (DECT) is a well-known imaging technique that can be used to differentiate and classify material composition by using projection data acquired at two different x-ray tube voltages. Dual-energy projection data can be also used to create virtual monochromatic images as the potential to reduce beam-hardening artifacts that are usually observed in conventional polychromatic images. Despite DECT’s merits, main concerns in the use of DECT in clinics may be high radiation dose imposed to patients during the examinations. In this study, we investigated sparse-view virtual monochromatic CT reconstruction using a dictionary-learning (DL)-based algorithm to provide quantitative measurements at reduced radiation dose. DL is an advanced representation learning theory that aims to find a sparse representation of the input signal in the form of a linear combination of basis elements. To validate the proposed method, we performed a systematic simulation and also we made an experiment on a skull phantom using a commercially-available dental cone-beam CT system. Two data sets of 60 projections were acquired at 80 kV p and 120 kV p separately from the system and used to create virtual monochromatic images at 90 keV and 130 keV. The image qualities were evaluated in terms of the image intensity and the peak signal-to-noise ratio.

Computed tomography (CT) images obtained at different monochromatic X-ray beam energies can be synthesized from conventional dual-energy CT scans. This approach to synthesizing monochromatic CT images is based on basis material decomposition and the knowledge of attenuation of basis materials. The main benefits of virtual monochromatic CT (VMCT) images include reduction of beam-hardening artifacts and provision of accurate atteuation measurements. Despite the VMCT's benefits, main concerns in the use of VMCT in clinics may be high radiation dose the patient is exposed to. In this study, we investigated a projection-based sparse-view VMCT method in an attempt to overcome these difficulties. We performed a computational simulation and evaluated the feasibility of using the VMCT method in sparse-view CT. Two polychromatic data sets of 90 projections, far less than what is required by the Nyquist sampling theory, were simulated at 80 kVp and 140 kVp and used to synthesize VMCT images at a monochromatic energy range of 40–140 keV . VMCT image characteristics were quantitatively evaluated in terms of intensity profile, the contrast-to-noise ratio, and the signal-to-noise ratio. Our results indicate that the CS-based algorithm produced high-quality sparse-view CT images, and thereby the proposed VMCT method yielded CT image results of improved beam-hardening artifacts and quantitative measurements.

This study investigated analytic digital tomosynthesis (DTS) reconstruction in partial sampling with a multislit collimator that partially blocks the x-ray beam to the patient during projection data acquisition, thereby reducing excessive radiation dose to patients. Partially-sampled DTS images reconstructed using the analytic filtered-backprojection (FBP) algorithm usually suffer from severe bright-band artifacts around multislit edges of the collimator due to incomplete spatial sampling. In this study, a new prior sinogram interpolation method was introduced to the analytic DTS reconstruction in partial sampling to alleviate such artifacts. To verify the proposed DTS method, we conducted a systematic simulation and investigated image characteristics. Three multislit collimator layouts of C(2/2), C(3/3), and C(4/4) with a 50% duty cycle were designed and used in the simulation. Here C(n/n) denotes a collimator layout that blocks the x-ray beam over n detector pixels vertically with a n-pixel interval. All projections were obtained at a tomographic angle of θ = ±40o and an angle step of Δθ = 2o and used to reconstruct DTS images using the FBP algorithm. Our results indicate that the proposed sinogram interpolation method effectively minimized bright-band artifacts in analytic DTS reconstruction in partial sampling, thus maintaining the image quality.

This study investigated the effectiveness of a new method for measuring the actual focal spot position of a diagnostic x-ray tube using a high-precision antiscatter grid and a digital x-ray detector in which grid magnification, which is directly related to the focal spot position, was determined from the Fourier spectrum of the acquired x-ray grid's image. A systematic experiment was performed to demonstrate the viability of the proposed measurement method. The hardware system used in the experiment consisted of an x-ray tube run at 50 kVp and 1 mA, a flat-panel detector with a pixel size of 49.5 µm, and a high-precision carbon-interspaced grid with a strip density of 200 lines/inch. The results indicated that the focal spot of the x-ray tube (Jupiter 5000, Oxford Instruments) used in the experiment was located approximately 31.10 mm inside from the exit flange, well agreed with the nominal value of 31.05 mm, which demonstrates the viability of the proposed measurement method. Thus, the proposed method can be utilized for system's performance optimization in many x-ray imaging applications.