Journal of X-Ray Science and Technology最新文献

Purpose: Low-dose computed tomography (LDCT) has promising potential for dose reduction in medical applications, while suffering from low image quality caused by noise. Therefore, it is in urgent need for developing new algorithms to obtain high-quality images for LDCT.

Methods: This study tries to exploit the sparse and low-rank properties of images and proposes a new algorithm based on subspace identification. The collection of transmission data is sparsely represented by singular value decomposition and the eigen-images are then denoised by block-matching frames. Then, the projection is regularized by the correlation information under the frame of prior image compressed sensing (PICCS). With the application of a typical analytical algorithm on the processed projection, the target images are obtained. Both numerical simulations and real data verifications are carried out to test the proposed algorithm. The numerical simulations data is obtained based on real clinical scanning three-dimensional data and the real data is obtained by scanning experimental head phantom.

Results: In simulation experiment, using new algorithm boots the means of PSNR and SSIM by 1 dB and 0.05, respectively, compared with BM3D under the Gaussian noise with variance 0.04. Meanwhile, on the real data, the proposed algorithm exhibits superiority over compared algorithms in terms of noise suppression, detail preservation and computational overhead. The means of PSNR and SSIM are improved by 1.84 dB and 0.1, respectively, compared with BM3D under the Gaussian noise with variance 0.04.

Conclusion: This study demonstrates the feasibility and advantages of a new algorithm based on subspace identification for LDCT. It exploits the similarity among three-dimensional data to improve the image quality in a concise way and shows a promising potential on future clinical diagnosis.

Objective: To investigate the use of non-contrast-enhanced (NCE) and contrast-enhanced (CE) CT radiomics signatures (Rad-scores) as prognostic factors to help improve the prediction of the overall survival (OS) of postoperative colorectal cancer (CRC) patients.

Methods: A retrospective analysis was performed on 65 CRC patients who underwent surgical resection in our hospital as the training set, and 19 patient images retrieved from The Cancer Imaging Archive (TCIA) as the external validation set. In training, radiomics features were extracted from the preoperative NCE/CE-CT, then selected through 5-fold cross validation LASSO Cox method and used to construct Rad-scores. Models derived from Rad-scores and clinical factors were constructed and compared. Kaplan-Meier analyses were also used to compare the survival probability between the high- and low-risk Rad-score groups. Finally, a nomogram was developed to predict the OS.

Results: In training, a clinical model achieved a C-index of 0.796 (95% CI: 0.722-0.870), while clinical and two Rad-scores combined model performed the best, achieving a C-index of 0.821 (95% CI: 0.743-0.899). Furthermore, the models with the CE-CT Rad-score yielded slightly better performance than that of NCE-CT in training. For the combined model with CE-CT Rad-scores, a C-index of 0.818 (95% CI: 0.742-0.894) and 0.774 (95% CI: 0.556-0.992) were achieved in both the training and validation sets. Kaplan-Meier analysis demonstrated a significant difference in survival probability between the high- and low-risk groups. Finally, the areas under the receiver operating characteristics (ROC) curves for the model were 0.904, 0.777, and 0.843 for 1, 3, and 5-year survival, respectively.

Conclusion: NCE-CT or CE-CT radiomics and clinical combined models can predict the OS for CRC patients, and both Rad-scores are recommended to be included when available.

Objective: To explore the value of applying computed tomography (CT) radiomics based on different CT-enhanced phases to determine the immunotherapeutic efficacy of non-small cell lung cancer (NSCLC).

Methods: 106 patients with NSCLC who underwent immunotherapy are randomly divided into training (74) and validation (32) groups. CT-enhanced arterial and venous phase images of patients before treatment are collected. Region-of-interest (ROI) is segmented on the CT-enhanced images, and the radiomic features are extracted. One-way analysis of variance and least absolute shrinkage and selection operator (LASSO) are used to screen the optimal radiomics features and analyze the association between radiomics features and immunotherapy efficacy. The area under receiver-operated characteristic curves (AUC) along with the sensitivity and specificity are computed to evaluate diagnostic effectiveness.

Results: LASSO regression analysis screens and selects 6 and 8 optimal features in the arterial and venous phases images, respectively. Applying to the training group, AUCs based on CT-enhanced arterial and venous phase images are 0.867 (95% CI:0.82-0.94) and 0.880 (95% CI:0.86-0.91) with the sensitivities of 73.91% and 76.19%, and specificities of 66.67% and 72.19%, respectively, while in validation group, AUCs of the arterial and venous phase images are 0.732 (95% CI:0.71-0.78) and 0.832 (95% CI:0.78-0.91) with sensitivities of 75.00% and 76.00%, and specificities of 73.07% and 75.00%, respectively. There are no significant differences between AUC values computed from arterial phases and venous phases images in both training and validation groups (P < 0.05).

Conclusion: The optimally selected radiomics features computed from CT-enhanced different-phase images can provide new imaging marks to evaluate efficacy of the targeted therapy in NSCLC with a high diagnostic value.

Background: The Mueller, Siddon and Joseph weighting algorithms are frequently used for projection and back-projection, which are relatively complicated when they are implemented in computer code.

Objective: This study aims to reduce the actual complexity of the projection and back-projection.

Methods: First, we neglect the exact shape of the pixel, so that its shadow is a rectangle projecting precisely to a detector bin, which implies that all the pixel weights are exactly 1 for each ray through them, otherwise are exactly 0. Next, a one-to-one reversible image rotation algorithm (RIRA) is proposed to compute the projection and back-projection, where two one-to-one mapping lists namely, U and V, are used to store the coordinates of a rotated pixel and its corresponding new coordinates, respectively. For each 2D projection, the projection is simply the column sum in each orientation according to the lists U and V. For each 2D back-projection, it is simply to arrange the projection to the corresponding column element according to the lists U and V. Thus, there is no need for an interpolation in the projection and back-projection. Last, a rotating image computed tomography (RICT) based on RIRA is proposed to reconstruct the image.

Results: Experiments show the RICT reconstructs a good image that is close to the result of filtered back-projection (FBP) method according to the RMSE, PSNR and MSSIM values. What's more, our weight, projection and back-projection are much easier to be implemented in computer code than the FBP method.

Conclusion: This study demonstrates that the RIRA method has potential to be used to simplify many computed tomography image reconstruction algorithms.

Computed laminography (CL) is one of the best methods for nondestructive testing of plate-like objects. If the object and the detector move continually while the scanning is being done, the data acquisition efficiency of CL will be significantly increased. However, the projection images will contain motion artifact as a result. A multi-angle fusion network (MAFusNet) is presented in order to correct the motion artifact of CL projection images considering the properties of CL projection images. The multi-angle fusion module significantly increases the ability of MAFusNet to deblur by using data from nearby projection images, and the feature fusion module lessens information loss brought on by data flow between the encoders. In contrast to conventional deblurring networks, the MAFusNet network employs synthetic datasets for training and performed well on realistic data, proving the network's outstanding generalization. The multi-angle fusion-based network has a significant improvement in the correction effect of CL motion artifact through ablation study and comparison with existing classical deblurring networks, and the synthetic training dataset can also significantly lower the training cost, which can effectively improve the quality and efficiency of CL imaging in industrial nondestructive testing.

Background: Several physical factors such as photon beam energy, electron beam energy, and dose rate may affect the dosimetric properties of polymer gel dosimeters. The photon beam energy and dose rate dependence of PASSAG gel dosimeter were previously evaluated.

Objective: This study aims to assess the dosimetric properties of the optimized PASSAG gel samples in various electron beam energies.

Methods: The optimized PASSAG gel samples are first fabricated and irradiated to various electron energies (5, 7, 10 and 12 MeV). Then, the response (R2) and sensitivity of gel samples are analyzed by magnetic resonance imaging technique at a dose range of 0 to 10 Gy, scanning room temperature range of 15 to 22 °C, and post-irradiation time range of 1 to 30 days.

Results: The R2-dose response and sensitivity of gel samples do not change under the evaluated electron beam energies (the differences are less than 5%). Furthermore, a dose resolution range of 11 to 38 cGy is obtained for the gel samples irradiated to different electron beam energies. Moreover, the findings show that the R2-dose response and sensitivity dependence of gel samples on electron beam energy varies over different scanning room temperatures and post-irradiation times.

Conclusion: The dosimetric assessment of the optimized PASSAG gel samples provides the promising data for this dosimeter during electron beam radiotherapy.

Objective: To explore the value of color-coded virtual touch tissue imaging (CCV) using acoustic radiation force pulse technology (ARFI) in diagnosing malignant thyroid nodules.

Methods: Images including 189 thyroid nodules were collected as training samples and a binary logistic regression analysis was used to calculate regression coefficients for Thyroid Imaging Reporting and Data System (TI-RADS) and CCV. An integrated prediction model (TI-RADS+CCV) was then developed based on the regression coefficients. Another testing dataset involving 40 thyroid nodules was used to validate and compare the diagnostic performance of TI-RADS, CCV, and the integrated predictive models using the receiver operating characteristic (ROC) curves.

Results: Both TI-RADS and CCV are independent predictors. The diagnostic performance advantage of CCV is insignificant compared to TI-RADS (P = 0.61). However, the diagnostic performance of the integrated prediction model is significantly higher than that of TI-RADS or CCV (all P < 0.05). Applying to the validation image dateset, the integrated predictive model yields an area under the curve (AUC) of 0.880.

Conclusions: Developing a new predictive model that integrates the regression coefficients calculated from TI-RADS and CCV enables to achieve the superior performance of thyroid nodule diagnosis to that of using TI-RADS or CCV alone.

Background: Lung cancer has the second highest cancer mortality rate in the world today. Although lung cancer screening using CT images is a common way for early lung cancer detection, accurately detecting lung nodules remains a challenged issue in clinical practice.

Objective: This study aims to develop a new weighted bidirectional recursive pyramid algorithm to address the problems of small size of lung nodules, large proportion of background region, and complex lung structures in lung nodule detection of CT images.

Methods: First, the weighted bidirectional recursive feature pyramid network (BiPRN) is proposed, which can increase the ability of network model to extract feature information and achieve multi-scale fusion information. Second, a CBAM_CSPDarknet53 structure is developed to incorporate an attention mechanism as a feature extraction module, which can aggregate both spatial information and channel information of the feature map. Third, the weighted BiRPN and CBAM_CSPDarknet53 are applied to the YOLOvX model for lung nodule detection experiments, named BiRPN-YOLOvX, where YOLOvX represents different versions of YOLO. To verify the effectiveness of our weighted BiRPN and CBAM_ CSPDarknet53 algorithm, they are fused with different models of YOLOv3, YOLOv4 and YOLOv5, and extensive experiments are carried out using the publicly available lung nodule datasets LUNA16 and LIDC-IDRI. The training set of LUNA16 contains 949 images, and the validation and testing sets each contain 118 images. There are 1987, 248 and 248 images in LIDC-IDRI's training, validation and testing sets, respectively.

Results: The sensitivity of lung nodule detection using BiRPN-YOLOv5 reaches 98.7% on LUNA16 and 96.2% on LIDC-IDRI, respectively.

Conclusion: This study demonstrates that the proposed new method has potential to help improve the sensitivity of lung nodule detection in future clinical practice.

Background: Molecular breast imaging uses Tc-99 m sestamibi to obtain functional images of the breast. Determining the Tc-99 m sestamibi uptake in volumes of interest in the breast may be useful in assessing the response to neoadjuvant chemotherapy or for the purposes of breast cancer risk assessment.

Purpose: To determine, using Monte Carlo simulation, if emission tomography can be used to quantify the uptake of Tc-99 m sestamibi in molecular breast imaging and if so, to determine the accuracy as a function of the number of projections used in the reconstruction process.

Methods: In this study, two voxelized breast models are implemented with different ratios of fibroglandular to fatty tissue and tumoral masses of varying dimensions. Monte Carlo simulation is used to calculate sets of projections, which assumes that each tumoral mass contains a given Tc-99 m activity. Projections are also calculated for a calibration phantom in order to correlate the known activity with the image pixel value. For each case, the total number of calculated projections is 36 and the reconstruction is carried out for 36, 18, 9, 7 and 5 projections, respectively, using an open source image reconstruction toolbox.

Results: Study data show that determination of Tc-99 m sestamibi uptake with and average error of 7% can be carried out with as little as 7 projections.

Conclusions: Molecular breast emission tomography enables to accurately determine the Tc-99 m sestamibi tumoral mass uptake with the number of projections very close to the number of images currently acquired in clinical practice.

Objective: This study aims to develop and test a new technique by using the convergent arcTAN (cATAN) method capable of dealing with the virtual source position delivered by different carbon ion energies from the pattern of scanning-passive scatter beam.

Materials and methods: A homemade large-format CMOS sensor and Gaf Chromic EBT3 films are used for the virtual source position measurement. The Gaf films are embedded in a self-designed rectangular plastic frame to tighten films and set up on a treatment couch for irradiation in air with the film perpendicular to the carbon ion beam at the nominal source-axis-distance (SAD) as well as upstream and downstream from the SAD. The horizontal carbon ion beam with 5 energies at a machine opening field size is carried out in this study. The virtual source position is determined by using the convergent arcTAN (cATAN) method and compared with a linear regression by back projecting FWHM to zero at a distance upstream from the various source-film-distance.

Results: The film FWHM measurement error of 0.5 mm leads to 0.001% deviation of α (cATAN) at every assumed textend. The overall uncertainty for the reproducibility of calculated virtual source position by the assumed textend in the vertical and horizontal directions amounts to 0.1%. The errors of calculated virtual source position by assumed textend with back projecting FWHM to zero methods are within 1.1±0.001, p = 0.033.

Conclusion: We develop a new technique capable of dealing with the virtual source position with a convergent arcTAN method to avoid any manual measurement mistakes in scanning-passive scatter carbon ion beam. The readers are encouraged to conduct the proposed cATAN method in this study to investigate the virtual source position in the Linac-based external electron beams and the proton beams.