Journal of X-Ray Science and Technology - Volume Pre-press, issue Pre-press
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Journal of X-Ray Science and Technology is an international journal designed for the diverse community (biomedical, industrial and academic) of users and developers of novel x-ray imaging techniques. The purpose of the journal is to provide clear and full coverage of new developments and applications in the field.
Areas such as x-ray microlithography, x-ray astronomy and medical x-ray imaging as well as new technologies arising from fields traditionally considered unrelated to x rays (semiconductor processing, accelerator technology, ionizing and non-ionizing medical diagnostic and therapeutic modalities, etc.) present opportunities for research that can meet new challenges as they arise.
Abstract: OBJECTIVE: To develop and test an optimal machine learning model based on the enhanced computed tomography (CT) to preoperatively predict pathological grade of clear cell renal cell carcinoma (ccRCC). METHODS: A retrospective analysis of 53 pathologically confirmed cases of ccRCC was performed and 25 consecutive ccRCC cases were selected as a prospective testing set. All patients underwent routine preoperative abdominal CT plain and enhanced scans. Renal tumor lesions were segmented on arterial phase images and 396 radiomics features were extracted. In the training set, seven discrimination classifiers for high- and low-grade ccRCCs were constructed based on seven different…machine learning models, respectively, and their performance and stability for predicting ccRCC grades were evaluated through receiver operating characteristic (ROC) analysis and cross-validation. Prediction accuracy and area under ROC curve were used as evaluation indices. Finally, the diagnostic efficacy of the optimal model was verified in the testing set. RESULTS: The accuracies and AUC values achieved by support vector machine with radial basis function kernel (svmRadial), random forest and naïve Bayesian models were 0.860±0.158 and 0.919±0.118, 0.840±0.160 and 0.915±0.138, 0.839±0.147 and 0.921±0.133, respectively, which showed high predictive performance, whereas K-nearest neighborhood model yielded lower accuracy of 0.720±0.188 and lower AUC value of 0.810±0.150. Additionally, svmRadial had smallest relative standard deviation (RSD, 0.13 for AUC, 0.17 for accuracy), which indicates higher stability. CONCLUSION: svmRadial performs best in predicting pathological grades of ccRCC using radiomics features computed from the preoperative CT images, and thus may have high clinical potential in guiding preoperative decision.
Abstract: BACKGROUND: Convolutional neural network has achieved a profound effect on cardiac image segmentation. The diversity of medical imaging equipment brings the challenge of domain shift for cardiac image segmentation. OBJECTIVE: In order to solve the domain shift existed in multi-modality cardiac image segmentation, this study aims to investigate and test an unsupervised domain adaptation network RA-SIFA, which combines a parallel attention module (PAM) and residual attention unit (RAU). METHODS: First, the PAM is introduced in the generator of RA-SIFA to fuse global information, which can reduce the domain shift from the respect of image alignment. Second,…the shared encoder adopts the RAU, which has residual block based on the spatial attention module to alleviate the problem that the convolution layer is insensitive to spatial position. Therefore, RAU enables to further reduce the domain shift from the respect of feature alignment. RA-SIFA model can realize the unsupervised domain adaption (UDA) through combining the image and feature alignment, and then solve the domain shift of cardiac image segmentation in a complementary manner. RESULTS: The model is evaluated using MM-WHS2017 datasets. Compared with SIFA, the Dice of our new RA-SIFA network is improved by 8.4%and 3.2%in CT and MR images, respectively, while, the average symmetric surface distance (ASD) is reduced by 3.4 and 0.8mm in CT and MR images, respectively. CONCLUSION: The study results demonstrate that our new RA-SIFA network can effectively improve the accuracy of whole-heart segmentation from CT and MR images.
Keywords: Cardiac image segmentation, deep learning model in image segmentation, domain shift, multi-modality fusion, domain adaptation
Abstract: BACKGROUND: Interest exists in dual-energy computed tomography (DECT) imaging with scanning arcs of limited-angular ranges (LARs) for reducing scan time and radiation dose, and for enabling scan configurations of C-arm CT that can avoid possible collision between the rotating X-ray tube/detector and the imaged subject. OBJECTIVE: In this work, we investigate image reconstruction for a type of configurations of practical DECT interest, referred to as the two-orthogonal-arc configuration, in which low- and high-kVp data are collected over two non-overlapping arcs of equal LAR α, ranging from 30° to 90°, separated by 90°. The configuration can readily be implemented,…e.g., on CT with dual sources separated by 90° or with the slow-kVp-switching technique. METHODS: The directional-total-variation (DTV) algorithm developed previously for image reconstruction in conventional, single-energy CT is tailored to enable image reconstruction in DECT with two-orthogonal-arc configurations. RESULTS: Performing visual inspection and quantitative analysis of monochromatic images obtained and effective atomic numbers estimated, we observe that the monochromatic images of the DTV algorithm from LAR data are with substantially reduced LAR artifacts, which are observed otherwise in those of existing algorithms, and thus visually correlate reasonably well, in terms of metrics PCC and nMI, with their reference images obtained form full-angular-range data. In addition, effective atomic numbers estimated from LAR data of DECT with two-orthogonal-arc configurations are in reasonable agreement, with relative errors up to ∼ 10%, with those estimated from full-angular-range data in DECT. CONCLUSIONS: The results acquired in the work may yield insights into the design of LAR configurations of practical dual-energy application relevance in diagnostic CT or C-arm CT imaging.
Keywords: DECT, limited-angular range, directional total variation, two-orthogonal-arc
Abstract: OBJECTIVE: To assess clinical application of applying deep learning image reconstruction (DLIR) algorithm to contrast-enhanced portal venous phase liver computed tomography (CT) for improving image quality and lesions detection rate compared with using adaptive statistical iterative reconstruction (ASIR-V) algorithm under routine dose. METHODS: The raw data from 42 consecutive patients who underwent contrast-enhanced portal venous phase liver CT were reconstructed using three strength levels of DLIRs (low [DL-L]; medium [DL-M]; high [DL-H]) and two levels of ASIR-V (30%[AV-30]; 70%[AV-70]). Objective image parameters, including noise, signal-to-noise (SNR), and the contrast-to-noise ratio (CNR) relative to muscle, as well as subjective…parameters, including noise, artifact, hepatic vein-clarity, index lesion-clarity, and overall scores were compared pairwise. For the lesions detection rate, the five reconstructions in patients who underwent subsequent contrast-enhanced magnetic resonance imaging (MRI) examinations were compared. RESULTS: For objective parameters, DL-H exhibited superior image quality of lower noise and higher SNR than AV-30 and AV-70 (all P < 0.05). CNR was not statistically different between AV-70, DL-M, and DL-H (all P > 0.05). In both objective and subjective parameters, only image noise was statistically reduced as the strength of DLIR increased compared with ASIR-V (all P < 0.05). Regarding the lesions detection rate, a total of 45 lesions were detected by MRI examination and all five reconstructions exhibited similar lesion-detection rate (25/45, 55.6%). CONCLUSION: Compared with AV-30 and AV 70, DLIR leads to better image quality with equal lesion detection rate for liver CT imaging under routine dose.
Abstract: OBJECTIVE: To evaluate dose differences predicted between using Anisotropic Analytical Algorithm (AAA) and Acuros XB (AXB) in patients diagnosed with locally advanced non-small cell lung cancer (NSCLC) treated with intensity modulated radiation therapy (IMRT). METHODS: A phantom study was done to evaluate the dose prediction accuracy of AXB and AAA beyond low-density medium by comparing the calculated measurement results. Thirty-two advanced NSCLC patients were subjected to IMRT. The dose regimen was 60 Gy over 30 fractions. Effects on planning target volume (PTV) and organ-at-risk (OAR) were evaluated. Clinically acceptable treatment plans with AAA were re-calculated using AXB algorithms with…two modes Dw and Dm at the same beam arrangements and multileaf collimator leaf settings as with AAA. RESULTS: Using AXB yielded better agreement with the measurements and the average dose difference for all points was about 0.5%. Conversely, using AAA showed a larger disagreement with measured values and the average difference was up to 5.9%. The maximum relative difference was between AXB_Dm and AAA for PTV dose (D98 % ). The percentage dose differences of plans calculated by AAA, AXB_Dw and AAA, AXB_Dm revealed that AAA overestimated the dose than AXB. Regarding OAR, results showed significant difference for lungs-PTV. CONCLUSIONS: AXB algorithm yields more accurate dose prediction than AAA in heterogeneous medium. Differences in dose distribution are observed when plans re-calculated with AXB indicating that AAA apparently overestimates dose, particularly the PTV dose. Thus, AXB algorithm should be used in preference to AAA for cases in which PTVs are involved with tissues of highly different densities, such as lung.
Abstract: BACKGROUND: The limited-angle reconstruction problem is of both theoretical and practical importance. Due to the severe ill-posedness of the problem, it is very challenging to get a valid reconstructed result from the known small limited-angle projection data. The theoretical ill-posedness leads the normal equation A T Ax = A T b of the linear system derived by discretizing the Radon transform to be severely ill-posed, which is quantified as the large condition number of A T A . OBJECTIVE: To develop and test a new valid algorithm for improving…the limited-angle image reconstruction with the known appropriately small angle range from [ 0 , π 3 ] ∼ [ 0 , π 2 ] . METHODS: We propose a reweighted method of improving the condition number of A T Ax = A T b and the corresponding preconditioned Landweber iteration scheme. The weight means multiplying A T Ax = A T b by a matrix related to A T A , and the weighting process is repeated multiple times. In the experiment, the condition number of the coefficient matrix in the reweighted linear system decreases monotonically to 1 as the weighting times approaches infinity. RESULTS: The numerical experiments showed that the proposed algorithm is significantly superior to other iterative algorithms (Landweber, Cimmino, NWL-a and AEDS) and can reconstruct a valid image from the known appropriately small angle range. CONCLUSIONS: The proposed algorithm is effective for the limited-angle reconstruction problem with the known appropriately small angle range.
Keywords: Landweber iteration, limited-angle image reconstruction, condition number
Abstract: OBJECTIVE: To compare and evaluate diagnostic capabilities of preoperative ultrasonography (US) and magnetic resonance imaging (MRI) in the cervical lymph nodes of patients with papillary thyroid cancer. METHODS: A retrospective dataset involving 156 patients who had undergone thyroidectomy and preoperative US and MRI was assembled. Among these, 69 had cervical lymph node metastasis and 87 did not. At least four radiologists unilaterally and spontaneously investigated the US and MRI attributes of the cervical lymph nodes. The efficiency of diagnostic imaging for cervical lymph nodes, including their true-positive rate or sensitivity, true-negative rate or specificity, positive predictive value, negative…predictive value, and predictive accuracy were analysed and assessed. RESULTS: In the assessment of cervical lymph node metastases of papillary thyroid cancer, the diagnostic sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of diagnostic US vs. MRI were 58.0% vs. 79.7%, 69.0% vs. 83.9%, 59.7% vs. 79.7%, 67.4% vs. 83.9%, and 64.1% vs. 82.1%, respectively. The accuracy consistency of the two imaging modalities was 83.5%. CONCLUSIONS: MRI is more effective than US in diagnosing and assessing cervical lymph node metastases of papillary thyroid cancer.
Keywords: Magnetic resonance imaging (MRI), ultrasound imaging (US), cervical lymph node, thyroid cancer, comparison between MRI and US
Abstract: Limited-angle computed tomography (CT) may appear in restricted CT scans. Since the available projection data is incomplete, the images reconstructed by filtered back-projection (FBP) or algebraic reconstruction technique (ART) often encounter shading artifacts. However, using the anisotropy property of the shading artifacts that coincide with the characteristic of limited-angle CT images can reduce the shading artifacts. Considering this concept, we combine the anisotropy property of the shading artifacts with the anisotropic structure property of an image to develop a new algorithm for image reconstruction. Specifically, we propose an image reconstruction method based on adaptive weighted anisotropic total variation (AwATV). This…method, termed as AwATV method for short, is designed to preserve image structures and then remove the shading artifacts. It characterizes both of above properties. The anisotropy property of the shading artifacts accounts for reducing artifacts, and the anisotropic structure property of an image accounts for preserving structures. In order to evaluate the performance of AwATV, we use the simulation projection data of FORBILD head phantom and real CT data for image reconstruction. Experimental results show that AwATV can always reconstruct images with higher SSIM and PSNR, and smaller RMSE, which means that AwATV enables to reconstruct images with higher quality in term of artifact reduction and structure preservation.
Abstract: Precise segmentation of lung parenchyma is essential for effective analysis of the lung. Due to the obvious contrast and large regional area compared to other tissues in the chest, lung tissue is less difficult to segment. Special attention to details of lung segmentation is also needed. To improve the quality and speed of segmentation of lung parenchyma based on computed tomography (CT) or computed tomography angiography (CTA) images, the 4th International Symposium on Image Computing and Digital Medicine (ISICDM 2020) provides interesting and valuable research ideas and approaches. For the work of lung parenchyma segmentation, 9 of the 12 participating…teams used the U-Net network or its modified forms, and others used the methods to improve the segmentation accuracy include attention mechanism, multi-scale feature information fusion. Among them, U-Net achieves the best results including that the final dice coefficient of CT segmentation is 0.991 and the final dice coefficient of CTA segmentation is 0.984. In addition, attention U-Net and nnU-Net network also performs well. In this review paper, the methods chosen by 12 teams from different research groups are evaluated and their segmentation results are analyzed for the study and references to those involved.
Keywords: Deep learning, computed tomography (CT), computed tomography angiography (CTA), segmentation of lung parenchyma, U-Net, nnU-Net
Abstract: OBJECTIVE: To evaluate diagnostic value of Thyroid Imaging Reporting and Data System published by American College of Radiology (ACR TI-RADS) in 2017, ultrasound-guided fine-needle aspiration (US-FNA), and the combination of both methods in differentiation between benign and malignant thyroid nodules. METHODS: The data of US-FNA and ACR TI-RADS are collected from 159 patients underwent thyroid surgery in our hospital, which include a total of 178 thyroid nodules. A Bethesda System for Reporting Thyroid Cytopathology category of ≥IV and an ACR TI-RADS category ≥4 are regarded as diagnosis standards for malignancy in US-FNA and ACR TI-RADS, respectively. The pathological…results after surgery are considered as the gold standard. The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the ACR TI-RADS, US-FNA and the combination of both methods for the differential diagnosis of thyroid nodules are calculated, respectively. RESULTS: The sensitivity, specificity and accuracy of ACR TI-RADS are 85.4%, 37.5%and 72.5%, respectively. The sensitivity, specificity and accuracy of US-FNA are 70.0%, 100%and 78.1%, respectively. After combining these two methods, the sensitivity, specificity and accuracy increase to 99.23%, 37.50%and 82.58%, respectively. The sensitivity of ACR TI-RADS is higher than that of US-FAN, and the sensitivity of combining these two methods is also higher than that of using ACR TI-RADS and US-FNA alone. CONCLUSION: The established ACR TI-RADS can help in selecting the target during nodule puncture, while the combination of ACR TI-RADS and US-FAN can further improve diagnostic ability for detecting malignant thyroid nodules.