Journal of X-Ray Science and Technology - Volume 30, issue 2

Authors: Ghani, Muhammad U. | Omoumi, Farid H. | Wu, Xizeng | Fajardo, Laurie L. | Zheng, Bin | Liu, Hong

Article Type: Research Article

Abstract: PURPOSE: To compare imaging performance of a cadmium telluride (CdTe) based photon counting detector (PCD) with a CMOS based energy integrating detector (EID) for potential phase sensitive imaging of breast cancer. METHODS: A high energy inline phase sensitive imaging prototype consisting of a microfocus X-ray source with geometric magnification of 2 was employed. The pixel pitch of the PCD was 55μm, while 50μm for EID. The spatial resolution was quantitatively and qualitatively assessed through modulation transfer function (MTF) and bar pattern images. The edge enhancement visibility was assessed by measuring edge enhancement index (EEI) using the acrylic edge …acquired images. A contrast detail (CD) phantom was utilized to compare detectability of simulated tumors, while an American College of Radiology (ACR) accredited phantom for mammography was used to compare detection of simulated calcification clusters. A custom-built phantom was employed to compare detection of fibrous structures. The PCD images were acquired at equal, and 30% less mean glandular dose (MGD) levels as of EID images. Observer studies along with contrast to noise ratio (CNR) and signal to noise ratio (SNR) analyses were performed for comparison of two detection systems. RESULTS: MTF curves and bar pattern images revealed an improvement of about 40% in the cutoff resolution with the PCD. The excellent spatial resolution offered by PCD system complemented superior detection of the diffraction fringes at boundaries of the acrylic edge and resulted in an EEI value of 3.64 as compared to 1.44 produced with EID image. At equal MGD levels (standard dose), observer studies along with CNR and SNR analyses revealed a substantial improvement of PCD acquired images in detection of simulated tumors, calcification clusters, and fibrous structures. At 30% less MGD, PCD images preserved image quality to yield equivalent (slightly better) detection as compared to the standard dose EID images. CONCLUSION: CdTe-based PCDs are technically feasible to image breast abnormalities (low/high contrast structures) at low radiation dose levels using the high energy inline phase sensitive imaging technique. Show more

Keywords: Photon counting detector, phase contrast imaging, breast cancer detection, phase image retrieval, CdTe, ACR phantom

DOI: 10.3233/XST-211028

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 207-219, 2022

Authors: Liao, Qinghua | Feng, Huiying | Li, Yuan | Lai, Xiaoyu | Pan, Junping | Zhou, Fangjing | Zhou, Lin | Chen, Liang

Article Type: Research Article

Abstract: BACKGROUND: Although computer-aided detection (CAD) software employed with Artificial Intelligence (AI) system has been developed aiming to assist tuberculosis (TB) triage, screening, and diagnosis, its clinical performance for tuberculosis screening remains unknown. OBJECTIVE: To evaluate performance of an CAD software for detecting TB on chest X-ray images at a pilot active TB screening project. METHODS: A CAD software scheme employed with AI was used to screen chest X-ray images of participants and produce probability scores of cases being positive for TB. CAD-generated TB detection scores were compared with on-site and senior radiologists via several performance evaluation …indices including area under the ROC curves (AUC), specificity, sensitive, and positive predict value. Pycharm CE and SPSS statistics software packages were used for data analysis. RESULTS: Among 2,543 participants, eight TB patients were identified from this screening pilot program. The AI-based CAD system outperformed the onsite (AUC = 0.740) and senior radiologists (AUC = 0.805) either using thresholds of 30% (AUC = 0.978) and 50% (AUC = 0.859) when taking the final diagnosis as the ground truth. CONCLUSIONS: The AI-based CAD software successfully detects all TB patients as identified from this study at a threshold of 30%. It demonstrates feasibility and easy accessibility to carry out large scale TB screening using this CAD software equipped in medical vans with chest X-ray imaging machine. Show more

Keywords: Tuberculosis (TB), active TB case screening, artificial intelligence, computer-aided detection, chest X-ray

DOI: 10.3233/XST-211019

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 221-230, 2022

Authors: Rajesh Kannan, S. | Sivakumar, J. | Ezhilarasi, P.

Article Type: Research Article

Abstract: Since the infectious disease occurrence rate in the human community is gradually rising due to varied reasons, appropriate diagnosis and treatments are essential to control its spread. The recently discovered COVID-19 is one of the contagious diseases, which infected numerous people globally. This contagious disease is arrested by several diagnoses and handling actions. Medical image-supported diagnosis of COVID-19 infection is an approved clinical practice. This research aims to develop a new Deep Learning Method (DLM) to detect the COVID-19 infection using the chest X-ray. The proposed work implemented two methods namely, detection of COVID-19 infection using (i) a Firefly Algorithm …(FA) optimized deep-features and (ii) the combined deep and machine features optimized with FA. In this work, a 5-fold cross-validation method is engaged to train and test detection methods. The performance of this system is analyzed individually resulting in the confirmation that the deep feature-based technique helps to achieve a detection accuracy of > 92% with SVM-RBF classifier and combining deep and machine features achieves > 96% accuracy with Fine KNN classifier. In the future, this technique may have potential to play a vital role in testing and validating the X-ray images collected from patients suffering from the infection diseases. Show more

Keywords: Detection of COVID-19, chest X-ray, firefly algorithm, feature selection, combining deep and handcrafted features

DOI: 10.3233/XST-211050

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 231-244, 2022

Authors: Huang, Caiyun | Yin, Changhua

Article Type: Research Article

Abstract: Presence of plaque and coronary artery stenosis are the main causes of coronary heart disease. Detection of plaque and coronary artery segmentation have become the first choice in detecting coronary artery disease. The purpose of this study is to investigate a new method for plaque detection and automatic segmentation and diagnosis of coronary arteries and to test its feasibility of applying to clinical medical image diagnosis. A multi-model fusion coronary CT angiography (CTA) vessel segmentation method is proposed based on deep learning. The method includes three network layer models namely, an original 3-dimensional full convolutional network (3D FCN) and two …networks that embed the attention gating (AG) model in the original 3D FCN. Then, the prediction results of the three networks are merged by using the majority voting algorithm and thus the final prediction result of the networks is obtained. In the post-processing stage, the level set function is used to further iteratively optimize the results of network fusion prediction. The JI (Jaccard index) and DSC (Dice similarity coefficient) scores are calculated to evaluate accuracy of blood vessel segmentations. Applying to a CTA dataset of 20 patients, accuracy of coronary blood vessel segmentation using FCN, FCN-AG1, FCN-AG2 network and the fusion method are tested. The average values of JI and DSC of using the first three networks are (0.7962, 0.8843), (0.8154, 0.8966) and (0.8119, 0.8936), respectively. When using new fusion method, average JI and DSC of segmentation results increase to (0.8214, 0.9005), which are better than the best result of using FCN, FCN-AG1 and FCN-AG2 model independently. Show more

Keywords: Coronary computed tomography angiography (CTA), multi-model fusion, three-dimensional fully convolutional network (3D FCN), attention gate model, level set function

DOI: 10.3233/XST-211063

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 245-259, 2022

Authors: Zhou, Peng | Cui, Jingduo | Du, Zelin | Zhang, Tao | Liu, Zhiguo

Article Type: Research Article

Abstract: Parabolic monocapillary X-ray lens (PMXRL) is an ideal optical device for constraining the point divergent X-ray beams to quasi-parallel beams, but the overlap of direct X-rays and reflected X-rays through PMXRL deteriorates the outgoing beam divergence. Aiming to solve this problem, this study designs and tests a square-shaped lead occluder (SSLO) embedded in PMXRL to block the direct X-rays passing through the PMXRL. Python simulations are employed to determine the geometric parameters of the SSLO as well as the optimal position of the SSLO in the PMXRL according to our proposed model. The PMXRL with a conic parameter p …of 0.000939 mm and a length L of 60.8 mm is manufactured and the SSLO with a size of 0.472 mm×0.472 mm×3.4 mm is embedded into it. An optical path system based on this PMXRL is built to measure the divergence of the outgoing X-ray beam. The experimental results show that the quasi-parallel X-ray beam reaches a divergence of 0.36 mrad in the range from 15–45 mm at the PMXRL outlet. This divergence is 10 times lower than the theoretical divergence without SSLO. Our work provides an alternative method for obtaining highly parallel X-ray beam and is beneficial to generate or facilitate new applications of monocapillary optics in X-ray technology. Show more

Keywords: Monocapillary optics, quasi-parallel beam, beam stop, beam divergence

DOI: 10.3233/XST-211029

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 261-273, 2022

Authors: Albahli, Saleh | Ahmad Hassan Yar, Ghulam Nabi

Article Type: Research Article

Abstract: Diabetic retinopathy is an eye deficiency that affects retina as a result of the patient having diabetes mellitus caused by high sugar levels, which may eventually lead to macular edema. The objective of this study is to design and compare several deep learning models that detect severity of diabetic retinopathy, determine risk of leading to macular edema, and segment different types of disease patterns using retina images. Indian Diabetic Retinopathy Image Dataset (IDRiD) dataset was used for disease grading and segmentation. Since images of the dataset have different brightness and contrast, we employed three techniques for generating processed images from …the original images, which include brightness, color and, contrast (BCC) enhancing, color jitters (CJ), and contrast limited adaptive histogram equalization (CLAHE). After image preporcessing, we used pre-trained ResNet50, VGG16, and VGG19 models on these different preprocessed images both for determining the severity of the retinopathy and also the chances of macular edema. UNet was also applied to segment different types of diseases. To train and test these models, image dataset was divided into training, testing, and validation data at 70%, 20%, and 10% ratios, respectively. During model training, data augmentation method was also applied to increase the number of training images. Study results show that for detecting the severity of retinopathy and macular edema, ResNet50 showed the best accuracy using BCC and original images with an accuracy of 60.2% and 82.5%, respectively, on validation dataset. In segmenting different types of diseases, UNet yielded the highest testing accuracy of 65.22% and 91.09% for microaneurysms and hard exudates using BCC images, 84.83% for optic disc using CJ images, 59.35% and 89.69% for hemorrhages and soft exudates using CLAHE images, respectively. Thus, image preprocessing can play an important role to improve efficacy and performance of deep learning models. Show more

Keywords: CNN, deep learning, diabetic retinopathy, diabetes mellitus, ResNet50, VGG16, VGG19

DOI: 10.3233/XST-211073

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 275-291, 2022

Authors: Alqahtani, Mohammed S. | Hussein, Khalid I. | Afifi, Hesham | Reben, Manuela | Grelowska, Iwona | Zahran, Heba Y. | Yahia, I.S. | Yousef, El Sayed

Article Type: Research Article

Abstract: Shielding glass materials doped with heavy metal oxides show an improvement in the effectiveness of the materials used in radiation shielding. In this work, the photon shielding parameters of six tellurite glass systems doped with several metal oxides namely, 70TeO2 -10P2 O5 - 10ZnO- 5.0PbF2 - 0.0024Er2 O3 - 5.0X (where X represents different doped metail oxides namely, Nb2 O5 , TiO2 , WO3 , PbO, Bi2 O3 , and CdO) in a broad energy spectrum, ranging from 0.015 MeV to 15 MeV, were evaluated. The shielding parameters were calculated using the online software Phy-X/PSD. The highest linear and mass …attenuation coefficients recorded were obtaibed from the samples containing bismuth oxide (Bi2 O3 ), and the lowest half-value layer and mean free path were recorded among the other samples. Furthermore, the shielding effectiveness of tellurite glass systems was compared with commercial shielding materials (RS-369, RS-253 G18, chromite, ferrite, magnetite, and barite). The optical parameters viz, dispersion energy, single-oscillator energy, molar refraction, electronic polarizability, non-linear refractive indices, n2 , and third-order susceptibility were measured and reported at a different wavelength. Bi2 O3 has a strong effect on enhancing the optical and shielding properties. The outcome of this study suggests the potential of using the proposed glass samples as radiation-shielding materials for a broad range of imaging and therapeutic applications. Show more

Keywords: Radiation shielding, oxide glass, optical parameters, half value layer, mean free path, mass attenuation, exposure buildup factor (EBF)

DOI: 10.3233/XST-211017

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 293-305, 2022

Authors: Wang, Tao | Han, Yuxin | Lin, Liying | Yu, Changlu | Lv, Rong | Han, Li

Article Type: Research Article

Abstract: BACKGROUND: Previous studies have shown that using some post-processing methods, such as nonlinear-blending and linear blending techniques, has potential to improve dual-energy computed (DECT) image quality. OBJECTIVE: To improve DECT image quality of hepatic portal venography (CTPV) using a new non-linear blending method with computer-determined parameters, and to compare the results to additional linear and non-linear blending techniques. METHODS: DECT images of 60 patients who were clinically diagnosed with liver cirrhosis were selected and studied. Dual-energy scanning (80 kVp and Sn140 kVp) of CTPV was utilized in the portal venous phase through a dual-source CT scanner. For image …processing, four protocols were utilized including linear blending with a weighing factor of 0.3 (protocol A) and 1.0 (protocol B), non-linear blending with fixed blending width of 200 HU and set blending center of 150HU (protocol C), and computer-based blending (protocol D). Several image quality indicators, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and contrast of hepatic portal vein and hepatic parenchyma, were evaluated using the paired-sample t -test. A 5-grade scale scoring system was also utilized for subjective analysis. RESULTS: SNR of protocols A-D were 9.1±2.1, 12.1±3.0, 11.6±2.8 and 14.4±3.2, respectively. CNR of protocols A-D were 4.6±1.3, 8.0±2.3, 7.0±2.0 and 9.8±2.4, respectively. The contrast of protocols A-D were 37.7±11.6, 91.9±21.0, 66.2±19.0 and 107.7±21.3, respectively. The differences between protocol D and other three protocols were significant (P  < 0.01). In subjective evaluation, the modes of protocols A, B, C, and D were rated poor, good, generally acceptable, and excellent, respectively. CONCLUSION: The non-linear blending technique of protocol D with computer-determined blending parameters can help improve imaging quality of CTPV and contribute to a diagnosis of liver disease. Show more

Keywords: Non-linear blending, linear blending, dual-energy computed tomography (DECT), CT portal venography (CTPV), hepatic portal vein, signal-to-noise ratio, contrast-to-noise ratio

DOI: 10.3233/XST-210967

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 307-317, 2022

Authors: Shi, Lei | Qu, Gangrong

Article Type: Research Article

Abstract: BACKGROUND: Ultra-limited-angle image reconstruction problem with a limited-angle scanning range less than or equal to π 2 is severely ill-posed. Due to the considerably large condition number of a linear system for image reconstruction, it is extremely challenging to generate a valid reconstructed image by traditional iterative reconstruction algorithms. OBJECTIVE: To develop and test a valid ultra-limited-angle CT image reconstruction algorithm. METHODS: We propose a new optimized reconstruction model and Reweighted Alternating Edge-preserving Diffusion and Smoothing algorithm in which a reweighted method of improving the condition number is incorporated into …the idea of AEDS image reconstruction algorithm. The AEDS algorithm utilizes the property of image sparsity to improve partially the results. In experiments, the different algorithms (the Pre-Landweber, AEDS algorithms and our algorithm) are used to reconstruct the Shepp-Logan phantom from the simulated projection data with noises and the flat object with a large ratio between length and width from the real projection data. PSNR and SSIM are used as the quantitative indices to evaluate quality of reconstructed images. RESULTS: Experiment results showed that for simulated projection data, our algorithm improves PSNR and SSIM from 22.46db to 39.38db and from 0.71 to 0.96, respectively. For real projection data, our algorithm yields the highest PSNR and SSIM of 30.89db and 0.88, which obtains a valid reconstructed result. CONCLUSIONS: Our algorithm successfully combines the merits of several image processing and reconstruction algorithms. Thus, our new algorithm outperforms significantly other two algorithms and is valid for ultra-limited-angle CT image reconstruction. Show more

Keywords: Computed Tomography (CT), ultra-limited-angle reconstruction, condition number

DOI: 10.3233/XST-211069

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 319-331, 2022

Authors: Kim, Eunhye | Park, Hyemin | Kim, Kwanghyun | Yoon, Yongsu | Lim, Cheonghwan | Kim, Jungmin

Article Type: Research Article

Abstract: BACKGROUND: Infants admitted to neonate intensive care units (NICUs) are placed in incubators to maintain body temperature and condition, which undergo normal radiographs and are exposed to radiation. Furthermore, different incubator structures in different hospitals exhibit varying object to image receptor distance (OID), source to image receptor distance (SID), presence of canopy, which results in variations in X-ray radiation conditions and doses absorbed by the neonatal patients. OBJECTIVE: To measure organ dose exposed to neonatal patient in different incubator settings. METHODS: A portable X-ray was performed on a neonatal patient placed in an incubator to identify …disease progress, the injection path of the drug, and various factors. To minimize direct contact between neonatal patients and image receptor, radiologic technologists place the image receptor on a tray underneath the incubator and place the portable X-ray tube on top of the acrylic canopy of the incubators. SID and OID settings and value of organ dose exposed to the patient varied based on the incubator structure, and the organ absorbed dose was determined using Monte Carlo N-Particle (MCNP) simulation, PC-based Monte Carlo program (PCXMC) 2.0 simulation, and neonate phantoms. RESULTS: Evaluations of organ dose of neonatal patients in three hospitals with different incubator settings reveal that the average organ dose differs by 36% depending on change in OID and SID settings and reduces by 10% with an acrylic canopy. Therefore, owing to the presence of an acrylic canopy on the top of the incubator and the longer SID with the corresponding shorter OID, a lower dose was absorbed by organs of neonatal patient. CONCLUSION: Our results provide proof that proper incubator standard decreases organ dose to neonatal patient during continuously diagnostic X-ray procedure. Show more

Keywords: Neonatal patient, organ dose estimation, Monte Carlo simulation, X-ray source to image receptor distance, object to image receptor distance

DOI: 10.3233/XST-211091

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 333-342, 2022

Authors: Shen, Zhaoqiang | Zeng, Li | Gong, Changcheng | Guo, Yumeng | He, Yuanwei | Yang, Zhaojun

Article Type: Research Article

Abstract: In computed tomography (CT) image reconstruction problems, exterior CT is an important application in industrial non-destructive testing (NDT). Different from the limited-angle problem that misses part of the rotation angle, the rotation angle of the exterior problem is complete, but for each rotation angle, the projection data through the central region of the object cannot be collected, so that the exterior CT problem is ill-posed inverse problem. The results of traditional reconstruction methods like filtered back-projection (FBP) and simultaneous algebra reconstruction technique (SART) have artifacts along the radial direction edges for exterior CT reconstruction. In this study, we propose and …test an anisotropic relative total variation in polar coordinates (P-ARTV) model for addressing the exterior CT problem. Since relative total variation (RTV) can effectively distinguish edges from noises, and P-ARTV with different weights in radial and tangential directions can effectively enhance radial edges, a two-step iteration algorithm was developed to solve the P-ARTV model in this study. The fidelity term and the regularization term are solved in Cartesian and polar coordinate systems, respectively. Numerical experiments show that our new model yields better performance than the existing state-of-the-art algorithms. Show more

Keywords: Image reconstruction, computed tomography, exterior problem, polar coordinates, anisotropic relative total variation (ARTV)

DOI: 10.3233/XST-211042

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 343-364, 2022

Authors: Albahli, Saleh | Ahmad Hassan Yar, Ghulam Nabi

Article Type: Research Article

Abstract: BACKGROUND: Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Thus, there is a huge demand for developing computer-aided automated schemes of chest X-ray images to help doctors more accurately and efficiently detect lung diseases depicting on chest X-ray images. OBJECTIVE: To develop convolution neural network (CNN) based deep learning models and compare their feasibility and performance to classify 14 chest diseases or pathology patterns based on chest X-rays. METHOD: Several …CNN models pre-trained using ImageNet dataset are modified as transfer learning models and applied to classify between 14 different chest pathology and normal chest patterns depicting on chest X-ray images. In this process, a deep convolution generative adversarial network (DC-GAN) is also trained to mitigate the effects of small or imbalanced dataset and generate synthetic images to balance the dataset of different diseases. The classification models are trained and tested using a large dataset involving 91,324 frontal-view chest X-ray images. RESULTS: In this study, eight models are trained and compared. Among them, ResNet-152 model achieves an accuracy of 67% and 62% with and without data augmentation, respectively. Inception-V3, NasNetLarge, Xcaption, ResNet-50 and InceptionResNetV2 achieve accuracy of 68%, 62%, 66%, 66% and 54% respectively. Additionally, Resnet-152 with data augmentation achieves an accuracy of 83% but only for six classes. CONCLUSION: This study solves the problem of having fewer data by using GAN-based techniques to add synthetic images and demonstrates the feasibility of applying transfer learning CNN method to help classify 14 types of chest diseases depicting on chest X-ray images. Show more

Keywords: Convolution neural network (CNN), deep learning, chest diseases, chest X-ray images, radiographic findings, ResNet-152, inception-V3

DOI: 10.3233/XST-211082

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 365-376, 2022

Authors: Gai, Tiancheng | Thai, Theresa | Jones, Meredith | Jo, Javier | Zheng, Bin

Article Type: Research Article

Abstract: BACKGROUND: Pancreatic cancer is one of the most aggressive cancers with approximate 10% five-year survival rate. To reduce mortality rate, accurate detection and diagnose of suspicious pancreatic tumors at an early stage plays an important role. OBJECTIVE: To develop and test a new radiomics-based computer-aided diagnosis (CAD) scheme of computed tomography (CT) images to detect and classify suspicious pancreatic tumors. METHODS: A retrospective dataset consisting of 77 patients who had suspicious pancreatic tumors detected on CT images was assembled in which 33 tumors are malignant. A CAD scheme was developed using the following 5 steps namely, …(1) apply an image pre-processing algorithm to filter and reduce image noise, (2) use a deep learning model to detect and segment pancreas region, (3) apply a modified region growing algorithm to segment tumor region, (4) compute and select optimal radiomics features, and (5) train and test a support vector machine (SVM) model to classify the detected pancreatic tumor using a leave-one-case-out cross-validation method. RESULTS: By using the area under receiver operating characteristic (ROC) curve (AUC) as an evaluation index, SVM model yields AUC = 0.750 with 95% confidence interval [0.624, 0.885] to classify pancreatic tumors. CONCLUSIONS: Study results indicate that radiomics features computed from CT images contain useful information associated with risk of tumor malignancy. This study also built a foundation to support further effort to develop and optimize CAD schemes with more advanced image processing and machine learning methods to more accurately and robustly detect and classify pancreatic tumors in future. Show more

Keywords: Pancreatic cancer detection, classification of pancreatic tumor, computer-aided diagnosis (CAD), support vector machine, image segmentation using deep learning model

DOI: 10.3233/XST-211116

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 377-388, 2022

Authors: Yan, Cheng | Liu, Jing | Yang, Xue | Cai, Songqi | Lu, Xiuliang | Yang, Chun | Zeng, Mengsu | Zhou, Guofeng | Ji, Min

Article Type: Research Article

Abstract: BACKGROUND: Due to the limited temporal resolution and cardiac motion, coronary computed tomography angiography (CCTA) exam is one of the most challenging CT protocols which may require operating radiologist to apply additional phase adjustment or motion correction for image reconstruction. OBJECTIVE: To evaluate image quality between automatic and manual CCTA reconstruction in a 0.25 second rotation time, 16 cm coverage, single-beat, CT scanner with automated phase selection and AI-assisted motion correction. METHODS: CCTA exams of 535 consecutive patients were included. All exams were first reconstructed with an automatically selected phase. If there was an unacceptable motion artifact, …a manual reconstruction process was performed by radiologists. Additionally, automatic image series which consist of auto-phase selection and a follow-up motion correction were reconstructed. For these two manual and automatic image series, a four-point Likert scale rating system was used to evaluate image quality of the coronary artery segment by two experienced radiologists, according to the 18-segment model. RESULTS: Fifty-one patients (9.5%) did not have satisfactory image quality after auto-phase selection. In these patients, the heart rate during scanning was higher (78.3±18.4 bpm) than in the remaining 484 patients (68.9±13.1 bpm). Overall, 734 out of the 918 vessel segments were identified for quality evaluation among 51 patients. Automatic and manual image series were rated as having average Likert scores of 3.48±0.62 and 3.32±0.67 (P  < 0.001), respectively. CONCLUSIONS: Using a 0.25 second rotation speed, 16 cm z-coverage, CT scanner installed with an AI-assisted motion correction algorithm, the automatic image reconstruction with scanner equipped auto-phase-selection and motion correction algorithm outperforms manually controlled image reconstruction by radiologists. This suggests that the traditional CCTA exam reconstruction workflow could be altered allowing less radiologist involvement and becoming more efficient. Show more

Keywords: Computed tomography angiography, CTA protocol, artificial intelligence, image artifact correction, X-Ray computed tomography

DOI: 10.3233/XST-211048

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 389-398, 2022

Authors: Jeon, Pil-Hyun | Lee, Chang-Lae

Article Type: Research Article

Abstract: BACKGROUND: Expanding computed tomography (CT) detector coverage broadens the beam width, but inaccurate tube current application can reduce image quality at the boundaries between body regions with different attenuation values along the z-axis. OBJECTIVE: This study aims to develop and validate a new CT scanning technique with a fixed pitch to achieve higher imaging quality. METHODS: A cylindrical water phantom and an anthropomorphic chest phantom with different diameters represent a human body with different attenuation values. By optimizing the beam width and helical pitch, the pitch is fixed during scanning. The mean noise of the images …and the standard deviation were calculated, and the coefficient of variation (COV) was compared to evaluate the uniformity of image noise according to the beam width. RESULTS: At the boundaries between regions with different attenuation values, the 10 mm beam width (COV: 0.065) in the water phantom showed a 47.7% COV reduction of image noise compared with the 20 mm beam width (COV: 0.125). In addition, the 20 mm beam width (COV: 0.146) in the chest phantom showed a 29.3% COV reduction of image noise compared with the 40 mm beam width (COV: 0.206). Thus, as the beam was narrowed, the mean noise was similar, but the standard deviation was reduced. CONCLUSIONS: The proposed CT scanning technique with a fixed pitch, optimized beam width, and helical pitch demonstrates that image quality can be improved without increasing radiation dose at the boundary between regions with different attenuation values. Show more

Keywords: X-ray beam width, computed tomography (CT) scanning, helical pitch, CT image noise, CT image quality.

DOI: 10.3233/XST-211103

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 2, pp. 399-408, 2022