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Objective: To improve the detection of lung abnormalities in chest X-rays by accurately suppressing overlapping bone structures in the lung area. According to literature on missed lung cancer in chest X-rays, such structures are a significant cause of chest-related diagnostic errors. Methods This study presents a deep-learning-based bone suppression method where a residual U-Net model is trained for chest X-rays using data set generated from the single-energy material decomposition (SEMD) technique on CT. Synthetic projection images and soft-tissue selective images were obtained from the CT data set via the SEMD, which were then used as the input and label data of the U-Net network. The trained network was tested on synthetic chest X-rays and two real chest radiographs. Results: Bone-suppressed images of the real chest radiographs obtained by the proposed method were similar to the results from the American Association of Physicists in Medicine lung CT data; pulmonary nodules in the soft-tissue selective images appeared more clearly than in the synthetic projection images. The peak signal-to-noise ratio and structural similarity values measured between the output and the corresponding label images were approximately 17.85 and 0.90, respectively. Conclusion: The proposed method effectively yielded bone-suppressed chest X-ray images, indicating its clinical usefulness, and it can improve the detection of lung abnormalities in chest X-rays. Advances in knowledge: The idea of using SEMD to obtain large amounts of paired images for deep-learning-based bone suppression algorithms is novel.

Conventional digital tomosynthesis (DTS) reconstruction by using the filtered-backprojection algorithm requires a full field-of-view scan and relatively dense projections to obtain high-quality images, which results in a high radiation dose to patients. Interior DTS (iDTS) with a proper collimator offers a possible imaging modality for reducing the dose of radiation delivered because the X-ray beam is able to target a small region-of-interest (ROI) containing the target area. Collimators for iDTS often have a fixed rectangular shape, but focusing the X-ray beam on an arbitrarily shaped ROI would be preferable because it further reduces the excessive radiation dose. In this study, we propose a new iDTS scan method to create an ROI with an arbitrary shape to minimize the radiation dose at each angle of view. We used a compressed-sensing-based algorithm for accurate iDTS reconstruction. To validate the proposed method, we performed a systematic simulation and an experiment, and we investigated the image characteristics. Our results indicate that the proposed method may effectively reduce radiation dose in iDTS in real imaging systems.

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.

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.

Purpose Conventional x-ray spectrum estimation methods from transmission measurement often lead to inaccurate results when extensive x-ray scatter is present in the measured projection. This study aims to apply the weighted L1-norm scatter correction algorithm in spectrum estimation for reducing residual differences between the estimated and true spectrum. Method The scatter correction algorithm is based on a simple radiographic scattering model where the intensity of scattered x-ray is directly estimated from a transmission measurement. Then, the scatter-corrected measurement is used for the spectrum estimation method that consists of deciding the weights of predefined spectra and representing the spectrum as a linear combination of the predefined spectra with the weights. The performances of the estimation method combined with scatter correction are evaluated on both simulated and experimental data. Results The results show that the estimated spectra using the scatter-corrected projection nearly match the true spectra. The normalized-root-mean-square-error and the mean energy difference between the estimated spectra and corresponding true spectra are reduced from 5.8% and 1.33 keV without the scatter correction to 3.2% and 0.73 keV with the scatter correction for both simulation and experimental data, respectively. Conclusions The proposed method is more accurate for the acquisition of x-ray spectrum than the estimation method without scatter correction and the spectrum can be successfully estimated even the materials of the filters and their thicknesses are unknown. The proposed method has the potential to be used in several diagnostic x-ray imaging applications.

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 cone-beam computed tomography (CBCT), reconstructed images are inherently degraded, restricting its image performance, due mainly to imperfections in the imaging process resulting from detector resolution, noise, X-ray tube’s focal spot, and reconstruction procedure as well. Thus, the recovery of CBCT images from their degraded version is essential for improving image quality. In this study, we investigated a compressed-sensing (CS)-based blind deconvolution method to solve the blurring problem in CBCT where both the image to be recovered and the blur kernel (or point-spread function) of the imaging system are simultaneously recursively identified. We implemented the proposed algorithm and performed a systematic simulation and experiment to demonstrate the feasibility of using the algorithm for image deblurring in dental CBCT. In the experiment, we used a commercially available dental CBCT system that consisted of an X-ray tube, which was operated at 90 kV(p) and 5 mA, and a CMOS flat-panel detector with a 200-μm pixel size. The image characteristics were quantitatively investigated in terms of the image intensity, the root-mean-square error, the contrast-to-noise ratio, and the noise power spectrum. The results indicate that our proposed method effectively reduced the image blur in dental CBCT, excluding repetitious measurement of the system’s blur kernel.

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 the effectiveness of reconstructing interior digital tomosynthesis (IDTS) images by using a dual-resolution voxellation method for achieving high-quality IDTS images at reduced computational cost. In the proposed IDTS, the X-ray beam span covered only a small region-of-interest (ROI) containing the diagnosis target to reduce the radiation dose received by the patient, and the voxels inside the target ROI had high resolution while the voxels outside the ROI were binned by 2×2×2 to reduce computational cost. The IDTS reconstruction algorithm was based on compressed-sensing (CS) theory. A systematic simulation and experiment were performed to evaluate the effectiveness of the proposed method. All projection data were taken at a tomographic angle of 40° and an angle step of 4°. The hardware system used in the experiment consisted of an X-ray tube run at 70 kVp and 5 mAs and a flat-panel detector with a pixel resolution of 198 μm. The results indicated that the proposed CS-based IDTS reconstruction method considerably reduced computational cost while still maintaining high fidelity for the reconstructed image of a region inside the target ROI.

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 mammography examination, compression of the breast is essential for reducing scattered X-rays and the radiation dose and for preventing motion artifacts, thereby producing optimal diagnostic breast images. However, mechanical compression of the breast often causes discomfort and pain during and after the examination and deters some patients from routine mammography screening. In this study, we propose a novel soft-compression mammography based on a weighted l1-norm scatter correction scheme in attempt to overcome these difficulties. We implemented the proposed algorithm and performed a simulation and experiment to demonstrate the feasibility of using the proposed method. According to our results, the structure of the breast phantom was much more clearly visible in the scatter-corrected image than in the original scatter-corrupted image. The contrast-to-noise ratio (CNR) for the scatter-corrected image was about 6.3, about 4.1 times larger than that for the scatter-corrupted image, indicating much improved image visibility. The proposed approach seems very promising for scatter correction in conventional mammography, thus allowing soft-compression breast examination in clinics.

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).

Digital tomosynthesis (DTS) is a geometric tomography technique by a limited-angle scan which has been popularly used in both medical and industrial X-ray imaging applications. However, conventional DTS remains limited by low contrast especially in imaging samples of low atomic number Z such as breast and cartilage tissues. In this work, we applied the recently proposed phase-contrast imaging (PCI) technique, the so-called single grid-based PCI, to DTS in attempt to overcome this limitation. PCI has superior soft-tissue imaging capability while DTS has improved image contrast. Combining the two techniques can therefore considerably improve the X-ray imaging performance. We developed a useful simulation platform for single grid-based phase-contrast DTS reconstruction and performed a systematic simulation using a three-dimensional (3D) numerical breast phantom. In the simulation, an X-ray grid having a lead strip density of 200 lines/inch was used and the DTS scan comprised 41 projections within an angle range of θ = ±40° at an X-ray energy of 25 keV. We successfully reconstructed DTS images of much improved contrast, compared to conventional DTS images, which demonstrates the viability of the proposed approach.

Conventional digital tomosynthesis (DTS) based on the filtered-backprojection (FBP) reconstruction requires full field-of-view scan and also relatively dense projections, which results in still high dose for medical imaging purposes. In this work, to overcome these difficulties, we propose a new type of DTS examinations, the so-called scout-view assisted interior DTS (iDTS), in which the x-ray beam span covers only a small region-of-interest (ROI) containing target diagnosis with the help of some scout views and they are used in the reconstruction to add additional information to interior ROI otherwise absent with conventional iDTS reconstruction methods. We considered an effective iterative algorithm based on compressed-sensing theory, rather than the FBP-based algorithm, for more accurate iDTS reconstruction. We implemented the proposed algorithm, performed a systematic simulation and experiment, and investigated the image characteristics. We successfully reconstructed iDTS images of substantially high accuracy and no truncation artifacts by using the proposed method, preserving superior image homogeneity, edge sharpening, and in-plane spatial resolution.

Background and objective Digital tomosynthesis (DTS) based on filtered-backprojection (FBP) reconstruction requires a full field-of-view (FOV) scan and relatively dense projections, which results in high doses for medical imaging purposes. To overcome these difficulties, we investigated region-of-interest (ROI) or interior DTS reconstruction where the x-ray beam span covers only a small ROI containing a target area. Methods An iterative method based on compressed-sensing (CS) scheme was compared with the FBP-based algorithm for ROI-DTS reconstruction. We implemented both algorithms and performed a systematic simulation and experiments on body and skull phantoms. The image characteristics were evaluated and compared. Results The CS-based algorithm yielded much better reconstruction quality in ROI-DTS compared to the FBP-based algorithm, preserving superior image homogeneity, edge sharpening, and in-plane resolution. The image characteristics of the CS-reconstructed images in ROI-DTS were not significantly different from those in full-FOV DTS. The measured CNR value of the CS-reconstructed ROI-DTS image was about 12.3, about 1.9 times larger than that of the FBP-reconstructed ROI-DTS image. Conclusions ROI-DTS images of substantially high accuracy were obtained using the CS-based algorithm and at reduced imaging doses and less computational cost, compared to typical full-FOV DTS images. We expect that the proposed method will be useful for the development of new DTS systems.

We theoretically investigate resonance fluorescence of a ladder- and a triangular-type three-level atomic system in the full quantization regime. By solving Heisenberg’s quantum kinetic equations of motion of an electron interacting with photons and two coherent laser fields, we obtain the firstorder electric field correlation function, which corresponds to photoluminescence spectrum. When the single electrons in both systems are resonantly excited by two strong cw-laser fields, in contrast to the ladder-type atomic systems emitting mainly incoherent photons, the triangular-type system shows that continuous coherent photons can be selectively generated from three optical transitions by trapping electronic population inversions and manipulated by changing the transition rates.

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.

Phase-contrast x-ray imaging (PCXI) is an emerging technology which allows for imaging of smaller features in the examined sample than conventional attenuation-based x-ray imaging with lower x-ray dose. In this work, we investigated a relatively simple approach to PCXI using a conventional x-ray grid, which has potential to open the way to further widespread use of the technique into many application fields. We performed a simulation using a useful PCXI software developed in our previous study to investigate the image characteristics. We also performed a preliminary experiment for PCXI using a table-top setup which consists of a focused-linear xray grid having a 200-lines/in strip density, a microfocus x-ray tube having a 55-μm focal spot, and a CMOS-type flat-panel detector having a 49-μm pixel resolution. We successfully extracted absorption, dark-field, and phase-contrast x-ray images of much enhanced visibility at a time from the raw image of the sample by Fourier analysis. Further, we made a composite image by combining the absorption image with the colored dark-field image and compared its image characteristic to that of the absorption image alone.

In recent computed tomography (CT) reconstruction, iterative methods have been often used owing to the potential to provide three-dimensional (3D) images of superior reconstruction quality to common filtered-backprojection (FBP)-based methods. However, these methods require enormous computational cost in the iterative process, which has still been an obstacle to put them to practical use. In this work, to overcome these difficulties, we propose a new cone-beam CT reconstruction with a dual-resolution voxelization strategy for a small region-of-interest (ROI) reconstruction in which the voxels outside the target ROI are binned by, for example, 2 × 2 × 2, 4 × 4 × 4, 8 × 8 × 8, etc., while the other voxels remain unbinned. We considered a compressed-sensing (CS)-based algorithm with a dual regularization strategy, rather than common FBP-based methods, for more accurate CT reconstruction. We implemented the proposed CS-based algorithm and performed a systematic simulation and experiment. Our results indicate that the proposed method seems to be effective for reducing computational cost considerably in iterative CT reconstruction, keeping the reconstruction quality inside the target ROI not much degraded.

Single grid-based phase-contrast x-ray imaging (g-PCXI) technique, which was recently proposed by Wen et al. to retrieve absorption, scattering, and phase-gradient images from the raw image of the examined object, seems a practical method for phase-contrast imaging with great simplicity and minimal requirements on the setup alignment. In this work, we developed a useful simulation platform for g-PCXI and performed a simulation to demonstrate its viability. We also established a table-top setup for g-PCXI which consists of a focused-linear grid (200-lines/in strip density), an x-ray tube (100-μm focal spot size), and a flat-panel detector (48-μm pixel size) and performed a preliminary experiment with some samples to show the performance of the simulation platform. We successfully obtained phase-contrast x-ray images of much enhanced contrast from both the simulation and experiment and the simulated contract seemed similar to the experimental contrast, which shows the performance of the developed simulation platform. We expect that the simulation platform will be useful for designing an optimal g-PCXI system.

In conventional mammography, contrast sensitivity remains limited due to the superimposition of breast tissue and scattered X-rays, which induces low visibility of lesions in the breast and, thus, an excessive number of false-positive findings. Several methods, including digital breast tomosynthesis as a multiplanar imaging modality, air-gap and slot techniques for the reduction of scatters, phase-contrast imaging as another image-contrast modality, etc., have been investigated in attempt to overcome these difficulties. However, those techniques typically require a higher imaging dose or special equipment. In this work, as an alternative, we propose a new image restoration method based on a radiographic scattering model in which the intensity of scattered X-rays and the direct transmission function of a given medium are estimated from a single projection by using the dark-channel prior. We implemented the proposed algorithm and performed an experiment to demonstrate its viability. Our results indicate that most of the structures in the examined breast were very discernable even with no adjustment in the display-window level, thus preserving superior image features and edge sharpening.

Digital tomosynthesis (DTS) has been widely used in both industrial nondestructive testing and medical x-ray imaging as a popular multiplanar imaging modality. However, although it provides some of the tomographic benefits of computed tomography (CT) at reduced dose and imaging time, the image characteristics are relatively poor due to blur artifacts originated from incomplete data sampling for a limited angular range and also aspects inherent to imaging system, including finite focal spot size of the x-ray source, detector resolution, etc. In this work, in order to overcome these difficulties, we propose an intuitive method in which a compressed-sensing (CS)-based deblurring scheme is applied to the projection images before common DTS reconstruction. We implemented the proposed deblurring algorithm and performed a systematic experiment to demonstrate its viability for improving the image characteristics in DTS. According to our results, the proposed method appears to be effective for the blurring...

We have established a high-voltage computed tomography (CT) system with a 1–3 MV LINAC x-ray generator for industrial nondestructive testing. However, reconstructed CT images are typically blurred, limiting its image performance, mainly due to the finite focal spot size of the x-ray source, aspects inherent to detector such as the detector pixel size and the detector resolution, and the reconstruction procedure as well. Thus the recovery of the reconstructed CT images from their degraded version is essential for improving the image characteristics. In this work, we investigated the compressed-sensing (CS)-based deconvolution scheme for more accurate image deblurring in CT. We implemented the proposed image deblurring algorithm for CT and performed a systematic simulation and experiment to demonstrate its viability. Our results indicate that the proposed deblurring scheme appears to be effective for the blurring problems in CT and is applicable to improve its present image characteristics.

In this work, we investigated a compressed-sensing (CS)-based deblurring scheme for image deblurring of high accuracy in digital tomosynthesis (DTS). We implemented the proposed deblurring scheme and performed a systematic simulation to demonstrate its viability for improved x-ray nondestructive testing. We also performed an experiment by using a table-top setup which consists of an x-ray tube (90 kVp, 6 mAs) and a CMOS-type flat-panel detector having a 198-μm pixel resolution. In both the simulation and the experiment, 51 projection images were taken with a tomographic angle range of θ=60° and an angle step of Δθ=1.2° and then deblurred by using the proposed algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. Our results indicate that the proposed deblurring scheme appears to be effective for the blurring problems in DTS and is applicable to improve the image characteristics in the present x-ray nondestructive testing.

An industrial x-ray inspection system has recently established by our group to examine large and dense objects available in industry. It consists of an industrial x-ray generator having a tube voltage of 450 kV and a focal spot size of 1 mm, a flat-panel detector having a pixel size of 200 µm and a pixel dimension of 2048 × 2048, and a mechanical support for object’s installation. For improving the image characteristics of the system, an effective blind deblurring method based on compressed-sensing scheme is reported. Blind deblurring is the image restoration by estimating the original image and the degradation mechanism using partial information on both. Compressed-sensing is a relatively new mathematical theory for solving the inverse problems. Systematic measurements were performed and the image characteristics of the restored images were quantitatively evaluated using several image-quality indicators. The results demonstrate that the deblurring method is effective for industrial x-ray inspecti...

We have recently developed very precise carbon-interspaced antiscatter grids having a wide range of grid strip densities ( i.e. , 85–234 lines/in.) by adopting the sawing process, depending on the demands of specific imaging modalities. However, because grid strips of the recently developed grids are mostly invisible through X-ray nondestructive testing due to their high strip densities, quality assurance in the manufacturing has not yet satisfactorily made. In this work, we proposed a pragmatic method to measure actual grid strip densities over the Nyquist sampling rate based on the moire pattern analysis. We prepared several samples of the carbon-interspaced grids having nominal grid strip densities of 85, 103, 130, 150, 178, 203, 215, and 234 lines/in. and measured their strip densities by using the proposed method to demonstrate its viability assessment. The differences between the nominal and the measured grid strip densities were less than about 1.8%, indicating the proposed method is useful for the quality assurance in grid manufacturing.

In this paper, we propose a new/next-generation type of CT examinations, the so-called Interior Computed Tomography (ICT), which may presumably lead to dose reduction to the patient outside the target region-of-interest (ROI), in dental x-ray imaging. Here an x-ray beam from each projection position covers only a relatively small ROI containing a target of diagnosis from the examined structure, leading to imaging benefits such as decreasing scatters and system cost as well as reducing imaging dose. We considered the compressed-sensing (CS) framework, rather than common filtered-backprojection (FBP)-based algorithms, for more accurate ICT reconstruction. We implemented a CS-based ICT algorithm and performed a systematic simulation to investigate the imaging characteristics. Simulation conditions of two ROI ratios of 0.28 and 0.14 between the target and the whole phantom sizes and four projection numbers of 360, 180, 90, and 45 were tested. We successfully reconstructed ICT images of substantially high image quality by using the CS framework even with few-view projection data, still preserving sharp edges in the images.

In this work, we propose a practical method that can combine the two functionalities of dental panoramic and cone-beam CT (CBCT) features in one by using a single panoramic detector. We implemented a CS-based reconstruction algorithm for the proposed method and performed a systematic simulation to demonstrate its viability for 3D dental X-ray imaging. We successfully reconstructed volumetric images of considerably high accuracy by using a panoramic detector having an active area of 198.4 mm × 6.4 mm and evaluated the reconstruction quality as a function of the pitch (p) and the angle step (Δθ). Our simulation results indicate that the CS-based reconstruction almost completely recovered the phantom structures, as in CBCT, for p≤2.0 and θ≤6°, indicating that it seems very promising for accurate image reconstruction even for large-pitch and few-view data. We expect the proposed method to be applicable to developing a cost-effective, volumetric dental X-ray imaging system.

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.

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.

In this work, we considered a compressed-sensing (CS)-based image deblurring scheme with a total-variation (TV) regularization penalty for improving image characteristics in digital tomosynthesis (DTS). We implemented the proposed image deblurring algorithm and performed a systematic simulation to demonstrate its viability. We also performed an experiment by using a table-top setup which consists of an x-ray tube operated at 90 kV p, 6 mAs and a CMOS-type flat-panel detector having a 198-μm pixel resolution. In the both simulation and experiment, 51 projection images were taken with a tomographic angle range of θ=60 o and an angle step of Δθ =1.2 o and then deblurred by using the proposed deblurring algorithm before performing the common filtered-backprojection (FBP)-based DTS reconstruction. According to our results, the image sharpness of the recovered x-ray images and the reconstructed DTS images were significantly improved and the cross-plane spatial resolution in DTS was also improved by a factor of about 1.4. Thus the proposed deblurring scheme appears to be effective for the blurring problems in both conventional radiography and DTS and is applicable to improve the present image characteristics.

In this work, we performed a proof-of-concept experiment for phase-contrast x-ray imaging (PCXI) based on a single antiscatter grid and a polychromatic x-ray source. We established a table-top setup which consists of a focused-linear grid having a strip density of 200 lines/inch, a microfocus x-ray tube having a focal-spot size of about 5 μm, and a CMOS-type flat-panel detector having a pixel size of 48 μm. By using our prototype PCXI system and the Fourier demodulation technique, we successfully obtained attenuation, scattering, and differential phase-contrast images of improved visibility from the raw images of several selected samples at x-ray tube conditions of 90 kV p and 0.1 mAs. Further, fusion image (e.g., the attenuation＋the scattering) may have an advantage in displaying details of the sample’s structures that are not clearly visible in the conventional attenuation image. Our experimental results indicate that single-grid-based approach seems a useful method for PCXI with great simplicity and minimal requirements on the setup alignment.

for cardiovascular disease and brachial-ankle pulse wave velocity (baPWV) is widely used as a marker of arterial stiffness, but there are a few data regarding the usefulness of measuring baPWV in patients with cerebral infarction. The purpose of this study was to assess the feasibility of baPWV in patients with cerebral infarction. Methods: Patients with cerebral infarction (Group I, n=105, 52 men, 66± 9 years) and without cerebral infarction (Group II, n=1147, 608 men, 62±10 years) who undergone testing a baPWV were included. The measurement of the baPWV was performed in a quiet controlled room (22±1 °C) in the supine positionwith using an automated device (VP2000, Colin, Co. Ltd, Komaki, Japan). Results: baPWV is significantly higher in Group I than Group II (1769±442 cm/s vs 1645±332 cm/s, Pb0.001). Multivariate analysis adjusting with age, sex, body mass index, heart rate andmean arterial pressure showed significantly higher baPWV of group I compared to group II (P=0.001). baPWV was significantly positively correlated with age, heart-femoral pulse wave velocity (hfPWV), heart rate (HR), systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure, blood urea nitrogen, creatinine and uric acid in both groups. In contrast, there was no significant correlation of baPWV with high-density lipoproteinand low-density lipoprotein-cholesterol. Multivariate linear regression analysis showed significant association of baPWV with age (beta= 13.966, P=0.001), HR (beta=6.018, P=0.001), SBP (beta=6.756, P=0.001), Diabetes mellitus (beta=72.696, Pb0.05) and smoking (beta=85.230, Pb0.05) in all study subjects. Conclusion: baPWV is higher in cerebral infarction patients and thus this study suggests that increased arterial stiffness is associated with cerebrovascular accident. Age is a major risk factor for cerebral infarction but baPWV might not be a predictor for cerebral infarction.