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The resulting image obtained from an X-ray imaging system depends significantly on the characteristics of the detector. In particular, when an X-ray image is acquired by thinning the detector, a relatively large amount of noise inevitably occurs. In addition, when a thick detector is used to reduce noise in X-ray images, blurring increases and the ability to distinguish target areas deteriorates. In this study, we aimed to derive the optimal X-ray image quality by deriving the optimal noise reduction parameters based on the non-local means (NLM) algorithm. The detectors used were of two thicknesses (96 and 140 μm), and images were acquired based on the IEC 62220-1-1:2015 RQA-5 protocol. The optimal parameters were derived by calculating the edge preservation index and signal-to-noise ratio according to the sigma value of the NLM algorithm. As a result, a sigma value of the optimized NLM algorithm (0.01) was derived, and this algorithm was applied to a relatively thin X-ray detector system to obtain appropriate noise level and spatial resolution data. The no-reference-based blind/referenceless image spatial quality evaluator value, which analyzes the overall image quality, was best when using the proposed method. In conclusion, we propose an optimized NLM algorithm based on a new method that can overcome the noise amplification problem in thin X-ray detector systems and is expected to be applied in various photon imaging fields in the future. [ABSTRACT FROM AUTHOR]

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise–power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images. [ABSTRACT FROM AUTHOR]

An indirect conversion X-ray detector uses a scintillator that utilizes the proportionality of the intensity of incident radiation to the amount of visible light emitted. A thicker scintillator reduces the patient's dose while decreasing the sharpness. A thin scintillator has an advantage in terms of sharpness; however, its noise component increases. Thus, the proposed method converts the spatial resolution of radiographic images acquired from a normal-thickness scintillation detector into a thin-thickness scintillation detector. Note that noise amplification and artifacts were minimized as much as possible after non-blind deconvolution. To accomplish this, the proposed algorithm estimates the optimal point-spread function (PSF) when the structural similarity index (SSIM) and feature similarity index (FSIM) are the most similar between thick and thin scintillator images. Simulation and experimental results demonstrate the viability of the proposed method. Moreover, the deconvolution images obtained using the proposed scheme show an effective image restoration method in terms of the human visible system compared to that of the traditional PSF measurement technique. Consequently, the proposed method is useful for restoring degraded images using the adaptive PSF while preventing noise amplification and artifacts and is effective in improving the image quality in the present X-ray imaging system. [ABSTRACT FROM AUTHOR]

Low-dose exposure and work convenience are required for mobile X-ray systems during the COVID-19 pandemic. We investigated a novel X-ray detector (FXRD-4343FAW, VIEWORKS, Anyang, Korea) composed of a thin-film transistor based on amorphous silicon with a flexible plastic substrate. This detector is composed of a thallium-doped cesium iodide scintillator with a pixel size of 99 μm, pixel matrix of 4316 × 4316, and weight of 2.95 kg. The proposed detector has the advantages of high-noise characteristics and low weight, which provide patients and workers with an advantage in terms of the dose and work efficiency, respectively. We performed a quantitative evaluation and an experiment to demonstrate its viability. The modulation transfer function, noise power spectrum, and detective quantum efficiency were identified using the proposed and comparative detectors, according to the International Electrotechnical Commission protocol. Additionally, the contrast-to-noise ratio and coefficient of variation were investigated using a human-like phantom. Our results indicate that the proposed detector efficiently increases the image performance in terms of noise characteristics. The detailed performance evaluation demonstrated that the outcomes of the use of the proposed detector confirmed the viability of mobile X-ray devices that require low doses. Consequently, the novel FXRD-4343FAW X-ray detector is expected to improve the image quality and work convenience in extended radiography. [ABSTRACT FROM AUTHOR]

Abstract Photon counting spectral X-ray imaging system has many advantages compared with the conventional energy integrated X-ray imaging system. The photon counting room temperature semiconductor detector (PCRTSD) splits the energy spectrum into various energy ranges and can use the K-edge imaging technique for elemental composition of objects. However, despite its advantages, this system has limitations with respect to image noise. The aim of this study was to design a patch group-based nonlocal self-similarity prior learning denoising (PGPD) algorithm and to evaluate its image performance with K-edge imaging technique in the CdTe photon counting spectral X-ray imaging system. We simulated CdTe PCRTSD and X-ray source using Monte Carlo simulation using Geant4 Application for Tomographic Emission version 6, and applied the median filter and proposed PGPD denoising algorithms in the acquired phantom image. The denoising algorithm provided significant improvement in image performance (contrast to noise ratio and coefficient of variation) with K-edge imaging technique in PCRTSD. [ABSTRACT FROM AUTHOR]

The noise reduction algorithms have been widely used in the field of medical X-ray imaging system using semiconductor photon counting detector (PCD). Among these algorithms, fast non-local means (FNLM) method is based on use of the encoded information and considering the local region in the image. The aim of this study was to design FNLM noise reduction algorithm using acceleration function with Euclidean distance, which is feasible to cope with practical problems, and to confirm its application feasibility in CdTe PCD. We simulated X-ray imaging system with PCD using Monte Carlo simulation tool (Geant4 Application for Tomographic Emission (GATE)) version 6 and applied conventional and proposed noise reduction algorithms in acquired water phantom image. We conclude that the results of the image performances associated with noise characteristics demonstrated the superiority of the proposed FNLM noise reduction algorithm in CdTe PCD imaging system. [ABSTRACT FROM AUTHOR]

The development of novel noise-reduction algorithms is one of the main research areas in the field of medical diagnostic X-ray imaging. The purpose of this study is to develop an improved total-variation (TV) noise-reduction technique using a gradient method that employs an iteration counter, and confirm its feasibility for applications in chest and abdominal X-ray imaging systems. We evaluated image qualities using contrast–to–noise ratio (CNR) and relative standard deviation (RSD) calculations with the proposed and conventional noise-reduction algorithms for both chest and abdominal X-ray images. The results of the quantitative CNR and RSD evaluations demonstrated the superiority of the proposed TV noise-reduction technique over other algorithms; in particular, the comparison between the conventional and proposed TV techniques revealed that the proposed method has improved noise-reduction characteristics in all cases. In conclusion, the results demonstrated that the improved TV noise-reduction technique with the gradient method using the iteration counter can be used to acquire images with a better quality than those of conventional algorithms. This can lead to lowering of the radiation dose in chest and abdominal X-ray imaging systems. [ABSTRACT FROM AUTHOR]

Recently, significant effort has been spent on the development of photons counting detector (PCD) based on a CdTe for applications in X-ray imaging system. The motivation of developing PCDs is higher image quality. Especially, the K-edge subtraction (KES) imaging technique using a PCD is able to improve image quality and useful for increasing the contrast resolution of a target material by utilizing contrast agent. Based on above-mentioned technique, we presented an idea for an improved K-edge log-subtraction (KELS) imaging technique. The KELS imaging technique based on the PCDs can be realized by using different subtraction energy width of the energy window. In this study, the effects of the KELS imaging technique and subtraction energy width of the energy window was investigated with respect to the contrast, standard deviation, and CNR with a Monte Carlo simulation. We simulated the PCD X-ray imaging system based on a CdTe and polymethylmethacrylate (PMMA) phantom which consists of the various iodine contrast agents. To acquired KELS images, images of the phantom using above and below the iodine contrast agent K-edge absorption energy (33.2 keV) have been acquired at different energy range. According to the results, the contrast and standard deviation were decreased, when subtraction energy width of the energy window is increased. Also, the CNR using a KELS imaging technique is higher than that of the images acquired by using whole energy range. Especially, the maximum differences of CNR between whole energy range and KELS images using a 1, 2, and 3 mm diameter iodine contrast agent were acquired 11.33, 8.73, and 8.29 times, respectively. Additionally, the optimum subtraction energy width of the energy window can be acquired at 5, 4, and 3 keV for the 1, 2, and 3 mm diameter iodine contrast agent, respectively. In conclusion, we successfully established an improved KELS imaging technique and optimized subtraction energy width of the energy window, and based on our results, we recommend using this technique for high image quality. [ABSTRACT FROM AUTHOR]

The median modified Wiener filter (MMWF) algorithm, which is a combination of median and Wiener filters, was applied to reduce the noise signal contained in digital image systems. In this study, we investigated the effect of MMWF algorithm with mask sizes of 3 × 3, 5 × 5, 7 × 7, and 9 × 9 and compared it to the median and Wiener filters with a breast American College of Radiology (ACR) phantom designed using 3D printing technology. To compare the similarity between the conventional and 3D printed breast ACR phantoms at various tube voltages and tube currents, the peak signal-to-noise (PSNR) ratio was recorded. Then, the noise level of the mammographic X-ray image applied with the MMWF algorithm using various mask sizes and the conventional filtering methods were compared and evaluated. According to the results, when the MMWF algorithm using a mask size of 9 × 9 was applied to the X-ray phantom image, superior signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and coefficient of variation (COV) values were derived. In addition, by comparing the average values obtained using the conventional noise reduction methods, we confirmed that the SNR, CNR, and COV values obtained from the MMWF algorithm, using a 9 × 9 mask size, improved by 1.91, 2.29, and 4.77 times, respectively. In conclusion, applicability of the mammographic X-ray field after ACR phantom production using 3D printing technology was proven in this study. An optimal mask size of 9 × 9 for the MMWF algorithm was proposed for improving the image quality of the acquired image. [ABSTRACT FROM AUTHOR]

The filters used in X-ray systems affect the image quality and radiation dose. Although using filters has advantages in terms of image quality and dose, image noise increases because the amount of X-rays in the energy range of the entire area is reduced. Thus, a newly improved non-local means (INLM) denoising algorithm was modeled and applied to X-ray images based on the thickness of the aluminum (Al) filter, which is most commonly used in X-ray imaging systems, to prove its potential use. For X-ray image acquisition, a high-performance cadmium telluride material-based detector and a tube containing Al filters (1-, 3-, and 5-mm thickness) were designed. The proposed INLM denoising algorithm was modeled by including the improved weights for the gradient information for each pixel in the conventional NLM-based equation. When the proposed INLM denoising algorithm was applied to the acquired photon-counting X-ray images, results showed superior performance for both noise characteristics and no-reference-based image evaluation. In particular, we confirmed that when the INLM was applied to X-ray images using 5 mm Al thickness, noise characteristics and no-reference-based evaluation results were improved by approximately 1.36 times and 1.06 times, respectively, compared to the conventional NLM. The study proved that choosing the INLM in photon-counting X-ray images using 5 mm-thick Al filtration is vital for the success of image processing applications. [ABSTRACT FROM AUTHOR]

In this study, we designed a photon-counting cadmium zinc telluride (CZT) gamma- and X-ray fusion imaging system and propose a new image processing method to improve image quality. The Geant4 Application for Tomographic Emission (GATE) platform was used to acquire gamma- and X-ray fusion images, and a stack-up parallel-hole collimator, which can greatly improve sensitivity, with a CZT detector was modeled. The newly proposed image processing algorithm to improve the image quality combines the median modified Wiener filter (MMWF), which can efficiently remove noise, and the Canny and Prewitt methods, which are representative edge detection algorithms. As a result, we confirmed that the two new algorithms improve noise and edge information compared to the conventional MMWF. In addition, when comparing the performance of the two proposed algorithms, we demonstrated that when the Canny is fused to MMWF, noise can be removed relatively efficiently, and the Prewitt can obtain improved edge information. The no-reference-based evaluation method was used to test these new algorithms with phantom images, with the same results. Thus, we demonstrate that the method combining MMWF and the edge detection algorithm provides superior image performance to the conventional filtering method in gamma- and X-ray fusion images with photon-counting CZT detectors. [ABSTRACT FROM AUTHOR]

Noise reduction is a fundamental early stage in X-ray image performance evaluation. In this study, the median-modified Wiener filter (MMWF) algorithm based on a nonlinear adaptive spatial filter is compared to two general noise-reduction techniques using median and Wiener filters to confirm an excellent denoising approach in X-ray images. To acquire images, a high-resolution complementary metal–oxide–semiconductor (CMOS) radio-magnetic X-ray imaging system (exposure conditions: 100, 400, and 700 μ A) and rat phantom were used. The performances of the denoising methods were evaluated in terms of contrast-to-noise ratio (CNR), coefficient of variation (COV), and no-reference image quality assessment using the Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). The average results for CNR, COV, and BRISQUE in the acquired X-ray image using the MMWF algorithm were 1.08, 1.10, and 1.03 times higher than those of the noisy image, respectively. On average, the MMWF algorithm provided better image restoration than general noise-reduction techniques and was found to be most effective in relatively lower exposure conditions. [ABSTRACT FROM AUTHOR]

Industrial high-energy X-ray imaging systems are widely used for non-destructive testing (NDT) to detect defects in the internal structure of objects. Research on X-ray image noise reduction techniques using image processing has been widely conducted with the aim of improving the detection of defects in objects. In this paper, we propose a non-local means (NLM) denoising algorithm to improve the quality of images obtained using an industrial 3 MeV high-energy X-ray imaging system. We acquired X-ray images using various castings and assessed the performance visually and by obtaining the intensity profile, contrast-to-noise ratio, coefficient of variation, and normalized noise power spectrum. Overall, the quality of images processed by the proposed NLM algorithm is superior to those processed by existing algorithms for the acquired casting images. In conclusion, the NLM denoising algorithm offers an efficient and competitive approach to overcome the noise problem in high-energy X-ray imaging systems, and we expect the accompanying image processing software to facilitate and improve image restoration. [ABSTRACT FROM AUTHOR]

X-ray systems with photon-counting cadmium telluride (CdTe) detectors can achieve greatly improved images by using the K-edge log-subtraction (KELS) imaging method. This paper discusses methods for acquiring KELS images with photon-counting CdTe detectors and applying the modeled total variation (TV) regularization noise reduction algorithm to the obtained images. Monte Carlo simulation using the Geant4 Application for Tomographic Emission platform was used to model the system. To demonstrate the usefulness of the proposed TV algorithm, we investigated the normalized noise power spectrum, contrast to noise ratio, and no reference-based assessment parameter using a natural image quality evaluator. The results demonstrate that the proposed TV noise reduction regularization algorithm can better preserve image details than the conventional denoising methods in all the evaluation parameters. [ABSTRACT FROM AUTHOR]

Abstract In chest radiography, a solitary pulmonary nodule, which may be a precursor of lung cancer, is a frequently detected finding. However, as the image quality is deteriorated owing to the increase in the noise, lung cancer screening studies revealed that the likelihood of finding a nodule is lower than those of other modalities. This study quantitatively evaluates three widely used filters (median, Wiener, and total variation) and a newly proposed filter (fast non-local means (FNLM)), which reduce image noise. Images of a phantom with lung nodules, obtained from a patient using the 3D printing technology, were acquired at the chest anterior–posterior, lateral, and posterior–anterior positions. To evaluate their denoising performance, normalized noise power spectrum, contrast to noise ratio and coefficient of variation were used. In the quantitative evaluation of the overall image, the proposed FNLM filter exhibited the best image performance. In the quantitative evaluation of the nodule image, the FNLM filter, which exhibits outstanding denoising performance and time efficiency, can be employed. Therefore, with the use of the FNLM filter in chest radiography, the detection probability of a nodule, which can be a precursor of lung cancer, is increased, and the cancer can be prevented even with a lower dose. [ABSTRACT FROM AUTHOR]

The purpose of this study is to compare and evaluate the radiation shielding efficiency of micro- and nanosized tungsten materials using powder type with 1-mm thick with respect to the X-ray exposure condition. We used an X-ray imaging system, and micro- and nanosized tungsten materials were manufactured and used. The exposure conditions were tube voltage of 41–140 kVp at fixed tube current of 10 mAs to evaluate radiation dose; we measured each transmissivity using an acrylic phantom. Also, a scanning electron microscope and X-ray diffraction spectroscopy were used for evaluation of quantitative microstructural analysis and the amount of crystalline material, respectively. The results of X-ray transmissivity measurements show that the nanosized tungsten shielding has much lower transmissivity than the microsized tungsten shielding from 41 to 100 kVp. At tube voltage of 100 kVp or more, there was almost no difference in the transmissivity of the two types of shielding. In conclusion, our results demonstrate that shielding effectiveness using nanosized tungsten material is better than that of microsized tungsten material at relatively low tube voltage of 41–100 kVp. [ABSTRACT FROM AUTHOR]

Although radiation dose based on X-ray source is becoming a concern, use of micro-computed tomography (m-CT) system continues to grow in diagnostic imaging. To address radiation dose concern in m-CT system, Monte Carlo simulation using Geant4 Application for Tomography Emission (GATE) tool and noise reduction technique have recently developed. Among these techniques, total variation (TV) noise reduction technique is well known to many researchers for X-ray imaging. Also, an important disadvantage of median filter and Wiener filter is low edge preservation compared with recently developed noise reduction methods Thus, the aim of this study was to design TV noise reduction technique based on L1-norm estimation with iterative method and compare to other techniques in m-CT system using GATE simulation. We used 4-dimension (4D) digital mouse whole body (MOBY) phantom and to clarify the feasibility of TV noise reduction technique in m-CT images, we compared the image performance using contrast-to-noise ratio (CNR) and coefficient of variation (COV). According to the results, the average for three image planes (coronal, sagittal and transverse) of CNR and COV of the TV noise reduction technique were 68.07 and 1.61%, respectively. In particular, the CNR and the COV difference between TV noise reduction technique and noisy image were maximum 5.28 and 2.89 times, respectively. In conclusion, the results of this study suggested that TV noise reduction technique can be achieved improved performance and the effect and feasibility of TV noise reduction technique for m-CT imaging can be investigated. [ABSTRACT FROM AUTHOR]

As a method for diagnosing chest lesions, a chest digital tomosynthesis (CDT) X-ray imaging system based on projection data using a limited angle has been widely used in the medical field since its development. In these CDT X-ray images, noise reduction methods using software that reduce the accuracy of lesion detection are essential. The aim of this study is to model the non-local means (NLM)-based algorithm, which is known to be very effective in removing noise from X-ray images, and to confirm its applicability in the CDT system. CDT X-ray images using a human phantom were generated employing the filtered back-projection reconstruction method with a projection angle of ± 36. 15 °. For quantitative evaluation, a reference image was obtained using the full radiation dose, and a noisy image was obtained using the minimum radiation dose to increase the amount of noise. The NLM noise reduction algorithm was modeled as a method for obtaining weights based on the similarity of the neighboring set of pixels. Conventional filtering methods were used in the comparison group to analyze the efficiency of the NLM algorithm. As a result, we confirmed that the coefficient of variation of the noise level was improved about 9.74 times compared with the noisy image when the NLM noise reduction algorithm was applied to the CDT X-ray image. When the similarity evaluation parameters were measured, we proved that 11%–53% better values were derived compared with the noisy image in the NLM algorithm. In particular, the performance of the NLM algorithm showed superior results to those obtained with the conventional filtering methods. In conclusion, the applicability of the NLM noise reduction algorithm to CDT X-ray images using limited projection data was demonstrated. [ABSTRACT FROM AUTHOR]

Anatomical and functional fusion imaging systems using cadmium zinc telluride (CZT) are widely used in the field of medical imaging and nuclear-medicine. However, noise is inevitable in the CZT images, and reducing noise is thus crucial for accurate diagnosis of diseases. Among various available techniques for noise reduction in images, deep learning-based noise reduction algorithm using patch group is considered the most efficient method. Therefore, this study is focused on designing deep learning-based noise reduction algorithm using patch group and evaluating it using simulated CZT fusion images with X-ray and gamma ray. We used the Geant4 Application for Tomographic Emission (version 6), which is a Monte Carlo simulation tool, and the normalized noise power spectrum, coefficient of variation (COV), and contrast to noise ratio (CNR) were evaluated. Furthermore, the proposed deep learning-based noise reduction algorithm exhibited better values compared with the conventional noise reduction algorithms. In particular, the COV and CNR values of our algorithm were approximately 8.46 and 1.85 times better than that of the original CZT image. Thus, we successfully demonstrated the feasibility of the proposed deep learning-based noise reduction algorithm using the patch group technique in a CZT fusion imaging system. [ABSTRACT FROM AUTHOR]