Journal of X-Ray Science and Technology - Volume 30, issue 6

Authors: Ertan, Ferihan | Sahin, Serdar | Azak, Can | Karakaya, Ebru | Altundag, Muzaffer Bedri | Goksel, Fatih

Article Type: Research Article

Abstract: PURPOSE: Voluntary deep inspiration breath hold (v-DIBH) reduces cardiac dose during left-sided breast irradiation. The purpose of this study is to evaluate the reproducibility and variability of breath-hold level (BHL) using breath-hold curves and lateral kV setup images together. MATERIAL/METHOD: A retrospective analysis of 30 left breast cancer patients treated using the v-DIBH technique in our department is performed. The BHL difference is measured from breath hold curves and lateral (LAT) kilo-Voltage (kV) setup images. The planning CT image and the selected treatment fraction data are collected. If the changes in BHL relate to the displacement of various …bones in the kV setup, images are assessed. Furthermore, the maximum heart distance inside the treatment field is compared from LAT MV portal images. RESULTS: The median and mean values of the BHL are nearly identical in different fractions (good reproducibility). However, the mean BHL values between planning and all measured fractions are statistically different; 16.3 vs. 20.8 mm for the planning and measured fractions (p  < 0.001), which indicates that the variability of BHL is significantly different. CONCLUSION: While reproducibility testing shows good agreement for inter-fractional breath-hold level, the variability between planning and fractions is relatively poor. Show more

Keywords: Reproducibility, breath-hold level, real-time position management (RPM), deep inspiration, interfraction.

DOI: 10.3233/XST-221228

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1057-1066, 2022

Authors: Byun, Sohyun | Jung, Julip | Hong, Helen | Kim, Bong-Seog

Article Type: Research Article

Abstract: BACKGROUND: Volumetric lung tumor segmentation is difficult due to the diversity of the sizes, locations and shapes of lung tumors, as well as the similarity in the intensity with surrounding tissue structures. OBJECTIVE: We propose a dual-coupling net for accurate lung tumor segmentation in chest CT images regardless of sizes, locations and shapes of lung tumors. METHODS To extract shape information from lung tumors and use it as shape prior, three-planar images including axial, coronal, and sagittal planes are trained on 2D-Nets. Two types of window images, lung and mediastinal window images, are trained on 2D-Nets …to distinguish lung tumors from the thoracic region and to better separate the boundaries of lung tumors from adjacent tissue structures. To prevent false-positive outliers to adjacent structures and to consider the spatial information of lung tumors, pairs of tumor volume-of-interest (VOI) and tumor shape prior are trained on 3D-Net. RESULTS In the first experiment, the dual-coupling net had the highest Dice Similarity Coefficient (DSC) of 75.7%, considering the shape prior as well as mediastinal window images to prevent the leakage of adjacent structures while maintaining the shape of the lung tumor, with 18.23% p, 3.7% p, 1.1% p, and 1.77% p higher DSCs than in the 2D-Net, 2.5D-Net, 3D-Net, and single-coupling net results, respectively. In the second experiment with annotations for two clinicians, the dual-coupling net showed outcomes of 67.73% and 65.07% regarding the DSC for each annotation. In the third experiment, the dual-coupling net showed 70.97% for the DSC. CONCLUSIONS The dual-coupling net enables accurate segmentation by distinguishing lung tumors from surrounding tissue structures and thus yields the highest DSC value. Show more

Keywords: Chest computed tomography (CT), lung tumor segmentation, deep learning, shape prior, mediastinal window image

DOI: 10.3233/XST-221191

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1067-1083, 2022

Authors: Gu, Yanan | Liu, Yi | Liu, Wenting | Yan, Rongbiao | Liu, Yuhang | Gui, Zhiguo

Article Type: Research Article

Abstract: OBJECTIVE: In order to solve the problem of image quality degradation of CT reconstruction under sparse angle projection, we propose to develop and test a new sparse angle CT reconstruction method based on group sparse. METHODS: In this method, the group-based sparse representation is introduced into the statistical iterative reconstruction framework as a regularization term to construct the objective function. The group-based sparse representation no longer takes a single patch as the minimum unit of sparse representation, while it uses Euclidean distance as a similarity measure, thus it divides similar patch into groups as basic units for sparse …representation. This method fully considers the local sparsity and non-local self-similarity of image. The proposed method is compared with several commonly used CT image reconstruction methods including FBP, SART, SART-TV and GSR-SART with experiments carried out on Sheep_Logan phantom and abdominal and pelvic images. RESULTS: In three experiments, the visual effect of the proposed method is the best. Under 64 projection angles, the lowest RMSE is 0.004776 and the highest VIF is 0.948724. FSIM and SSIM are all higher than 0.98. Under 50 projection angles, the index of the proposed method remains achieving the best image quality. CONCLUSION: Qualitative and quantitative results of this study demonstrate that this new proposed method can not only remove strip artifacts, but also effectively protect image details. Show more

Keywords: Computed tomography imaging, sparse angle, group sparse representation, dictionary learning

DOI: 10.3233/XST-221199

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1085-1097, 2022

Authors: Hwang, Jun-Ho | Kim, Sung-Bum | Choi, Man-Kyu | Lee, Kyung-Bae | Park, Chang-Kyu

Article Type: Research Article

Abstract: OBJECTIVE: To present an optimized examination model by analyzing the risk of disease and image quality according to the combination of the ion chamber of automatic exposure control (AEC) with digital radiography (DR). METHODS: The X-ray quality was analyzed by first calculating the percentage average error (PAE) of DR. After that, when using AEC, the combination of the ion chambers was the same as the left and centre and right, right and centre, left and centre, centre, right, and left, for a total of six. Accordingly, the entrance surface dose (ESD), risk of disease, and image quality were …evaluated. ESD was obtained by attaching a semiconductor dosimeter to the L4 level of the lumbar spine, and then irradiating X-rays to dosimeter centre through average and standard deviation of radiation dose. The calculated ESD was input into the PCXMC 2.0 programme to evaluate disease risk caused by radiation. Meanwhile, image quality according to chamber combination was quantified as the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) through Image J. RESULTS: X-ray quality of DR used in the experiment was within the normal range of±10. ESD of six ion chamber combinations was 1.363mGy, 0.964mGy, 0.946mGy, 0.866mGy, 0.748mGy, 0.726mGy for lumbar anteroposterior (AP), and the lumbar lateral values were 1.126mGy, 0.209mGy, 0.830mGy, 0.662mGy, 0.111mGy, and 0.250mGy, respectively. Meanwhile, disease risk analyzed through PCXMC 2.0 was bone marrow, colon, liver, lung, stomach, urinary and other tissue cancer, and disease risk showed a tendency to increase in proportion to ESD. SNR and CNR recorded the lowest values when three chambers were combined and did not show proportionality with dose, while showed the highest values when two chambers were combined. CONCLUSION: In this study, combination of three ion chambers showed the highest disease risk and lowest image quality. Using one ion chamber showed the lowest disease risk, but lower image quality than two ion chambers. Therefore, if considering all above factors, combination of two ion chambers can optimally maintain the disease risk and image quality. Thus, it is considered an optimal X-ray examination parameter. Show more

Keywords: Digital radiography (DR), Automatic exposure control (AEC), Ion chamber, Disease risk, Image quality

DOI: 10.3233/XST-221254

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1099-1114, 2022

Authors: Wei, Shu-Hua | Zhang, Jin-Mei | Shi, Bin | Gao, Fei | Zhang, Zhao-Xuan | Qian, Li-Ting

Article Type: Research Article

Abstract: OBJECTIVE: To investigate predictive value of CT-based radiomics features on visceral pleural invasion (VPI) in ≤3.0 cm peripheral type early non-small cell lung cancer (NSCLC). METHODS: A total of 221 NSCLC cases were collected. Among them, 115 are VPI-positive and 106 are VPI-negative. Using a stratified random sampling method, 70% cases were assigned to training dataset (n  = 155) and 30% cases (n  = 66) were assigned to validation dataset. First, CT findings, imaging features, clinical data and pathological findings were retrospectively analyzed, the size, location and density characteristics of nodules and lymph node status, the relationship between lesions and pleura …(RAP) were assessed, and their mean CT value and the shortest distance between lesions and pleura (DLP) were measured. Next, the minimum redundancy-maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) features were extracted from the imaging features. Then, CT imaging prediction model, texture feature prediction model and joint prediction model were built using multifactorial logistic regression analysis method, and the area under the ROC curve (AUC) was applied to evaluate model performance in predicting VPI. RESULTS: Mean diameter, density, fractal relationship with pleura, and presence of lymph node metastasis were all independent predictors of VPI. When applying to the validation dataset, the CT imaging model, texture feature model, and joint prediction model yielded AUC = 0.882, 0.824 and 0.894, respectively, indicating that AUC of the joint prediction model was the highest (p  < 0.05). CONCLUSION: The study demonstrates that the joint prediction model containing CT morphological features and texture features enables to predict the presence of VPI in early NSCLC preoperatively at the highest level. Show more

Keywords: Non-small cell lung cancer (NSCLC), Visceral pleural invasion (VPI), Computed tomography (CT), radiomics, predictive models

DOI: 10.3233/XST-221220

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1115-1126, 2022

Authors: Gong, Changcheng | Shen, Zhaoqiang | He, Yuanwei

Article Type: Research Article

Abstract: CT reconstruction from incomplete projection data is one of the key researches of X-ray CT imaging. The projection data acquired by few-view and limited-angle sampling are incomplete. In addition, few-view sampling often requires turning on and off the tube voltage, but rapid switching of tube voltage demands for high technical requirements. Limited-angle sampling is easy to realize. However, reconstructed images may encounter obvious artifacts. In this study we investigate a new segmental limited-angle (SLA) sampling strategy, which avoids rapid switching of tube voltage. Thus, the projection data has lower data correlation than limited-angle CT, which is conducive to reconstructing high-quality …images. To suppress potential artifacts, we incorporate image structural prior into reconstruction model to present a reconstruction method. The limited-angle CT reconstruction experiments on digital phantoms, real carved cheese and walnut projections are used to test and verify the effectiveness of the proposed method. Several image quality evaluation indices including RMSE, PSNR, and SSIM of the reconstructions in simulation experiments are calculated and listed to show the superiority of our method. The experimental results indicate that the CT image reconstructed using the proposed new method is closer to the reference image. Images from real CT data and their residual images also show that applying the proposed new method can more effectively reduce artifacts and image structures are well preserved. Show more

Keywords: Inverse problems, computed tomography, relative total variation, image sparsity, iterative reconstruction

DOI: 10.3233/XST-221222

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1127-1154, 2022

Authors: Li, Tingting | Liu, Yu | Guo, Jiuhong | Wang, Yuanjun

Article Type: Research Article

Abstract: PURPOSE: To investigate the value of a CT-based radiomics model in identification of Crohn’s disease (CD) active phase and remission phase. METHODS: CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t -test and the least absolute shrinkage and selection operator regression algorithm. …Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models. RESULTS: Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively. CONCLUSION: This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance. Show more

Keywords: Crohn’s disease, activity, radiomics, machine learning, ensemble algorithm

DOI: 10.3233/XST-221224

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1155-1168, 2022

Authors: Malik, Yushaa Shafqat | Tamoor, Maria | Naseer, Asma | Wali, Aamir | Khan, Ayesha

Article Type: Research Article

Abstract: BACKGROUND: Medical image processing has gained much attention in developing computer-aided diagnosis (CAD) of diseases. CAD systems require deep understanding of X-rays, MRIs, CT scans and other medical images. The segmentation of the region of interest (ROI) from those images is one of the most crucial tasks. OBJECTIVE: Although active contour model (ACM) is a popular method to segment ROIs in medical images, the final segmentation results highly depend on the initial placement of the contour. In order to overcome this challenge, the objective of this study is to investigate feasibility of developing a fully automated initialization process …that can be optimally used in ACM to more effectively segment ROIs. METHODS: In this study, a fully automated initialization algorithm namely, an adaptive Otsu-based initialization (AOI) method is proposed. Using this proposed method, an initial contour is produced and further refined by the ACM to produce accurate segmentation. For evaluation of the proposed algorithm, the ISIC-2017 Skin Lesion dataset is used due to its challenging complexities. RESULTS: Four different supervised performance evaluation metrics are employed to measure the accuracy and robustness of the proposed algorithm. Using this AOI algorithm, the ACM significantly (p ≤0.05) outperforms Otsu thresholding method with 0.88 Dice Score Coefficients (DSC) and 0.79 Jaccard Index (JI) and computational complexity of 0(mn). CONCLUSIONS: After comparing proposed method with other state-of-the-art methods, our study demonstrates that the proposed methods is superior to other skin lesion segmentation methods, and it requires no training time, which also makes the new method more efficient than other deep learning and machine learning methods. Show more

Keywords: Active contour, skin lesion, image segmentation, region of interest (ROI)

DOI: 10.3233/XST-221245

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1169-1184, 2022

Authors: Zhang, Aining | Hu, Qiming | Ma, Zhanlong | Song, Jiacheng | Chen, Ting

Article Type: Research Article

Abstract: OBJECTIVE: To investigate the value of nomogram analysis based on conventional features and radiomics features of computed tomography (CT) venous phase to differentiate metastatic ovarian tumors (MOTs) from epithelial ovarian tumors (EOTs). METHODS: A dataset involving 286 patients pathologically confirmed with EOTs (training cohort: 133 cases, validation cohort: 68 cases) and MOTs (training cohort: 54 cases, validation cohort: 31 cases) is assembled in this study. Radiomics features are extracted from the venous phase of CT images. Logistic regression is employed to build models based on conventional features (model 1), radiomics features (model 2), and the combination of model …1 and model 2 (model 3). Diagnostic performance is assessed and compared. Additionally, a nomogram is plotted for model 3, and decision curve analysis is applied for clinical use. RESULTS: Age, abdominal metastasis, para-aortic lymph node metastasis, location, and septation are chosen to build Model 1. Ten optimal radiomics features are ultimately selected and radiomics score (rad-score) is calculated to build Model 2. Nomogram score is calculated to build model 3 that shows optimal diagnostic performance in both the training (AUC = 0.952) and validation cohorts (AUC = 0.720), followed by model 1 (AUC = 0.872 for training cohort and AUC = 0.709 for validation cohort) and model 2 (AUC = 0.833 for training cohort and AUC = 0.620 for validation cohort). Additionally, Model 3 achieves accuracy, sensitivity, and specificity of 0.893, 0.880, and 0.926 in the training cohort and 0.737, 0.853, and 0.613 in the validation cohort. CONCLUSION: Model 3 demonstrates the best diagnostic performance for preoperative differentiation of MOTs from EOTs. Thus, nomogram analysis based on Model 3 may be used as a biomarker to differentiate MOTs from EOTs. Show more

Keywords: Radiomics, ovarian tumors, computed tomography, metastasis

DOI: 10.3233/XST-221244

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1185-1199, 2022

Authors: Saglam, Yucel | Samanci, Yavuz | Bolukbasi, Yasemin | Peker, Selcuk

Article Type: Research Article

Abstract: BACKGROUND: Multi Fractionated stereotactic radiosurgery (MF-SRS) of Linac has an essential role in the treatment of skull base meningiomas (sbMNG). However, Gamma Knife Icon (GK) allows MF-SRS using mask immobilization with onboard image guidance. OBJECTIVE: This dosimetric study aims to investigate whether equivalent plan quality can be achieved with Volumetric Modulated Arc Therapy (VMAT) in patients with large sbMNG (>10 cm3 ) previously treated with GK. METHODS: Twenty patients with the median target volume of 19.7cm3 are re-planned by using VMAT with 20 Gy in 5 fractions. Plan qualities are compared to tumor coverage, paddick conformity …index (PCI), gradient index (GI), V4  Gy , V10  Gy , V12  Gy , optic chiasm V20  Gy , brainstem V23  Gy , optic nerve V25  Gy volumes, and maximum doses for all. Additionally, beam-on time and approximate planning time are also analyzed and compared. RESULTS: All plans provide adequate clinical requirements. First, the CI is comparable for the GK and VMAT (0.99±0.01 vs. 1.13±0.20; p  = 0.18). Second, VMAT has a significantly higher GI than GK (3.81±0.35 vs. 2.63±0.09; p  < 0.001). Third, the PCI is significantly higher in GK than VMAT (0.76±0.05 vs. 0.70±0.07; p  < 0.001). The lower GI of the GK also results in significantly lower V4  Gy (156.1±43.8 vs. 207.5±40.1 cm3 , p  < 0.001) and V10  Gy (26.1±9.0 vs. 28.9±7.7 cm3 , p  <  0.001) compared to VMAT. Last, the VMAT reduces beam-on time (4.8±0.5 vs. 19±1.1 min.; p  < 0.001). CONCLUSION: Although both systems have succeeded in creating effective plans in clinical practice, the GK reveals more effective lower normal brain tissue doses. However, the shorter treatment time with LINAC, excluding the total procedure time, can be considered advantageous over GK. Show more

Keywords: Dosimetric study, gamma knife, LINAC, meningioma

DOI: 10.3233/XST-221264

Citation: Journal of X-Ray Science and Technology, vol. 30, no. 6, pp. 1201-1211, 2022