Journal of X-Ray Science and Technology最新文献

Background: Dynamic cerebral perfusion CT (DCPCT) can provide valuable insight into cerebral hemodynamics by visualizing changes in blood within the brain. However, the associated high radiation dose of the standard DCPCT scanning protocol has been a great concern for the patient and radiation physics. Minimizing the x-ray exposure to patients has been a major effort in the DCPCT examination. A simple and cost-effective approach to achieve low-dose DCPCT imaging is to lower the x-ray tube current in data acquisition. However, the image quality of low-dose DCPCT will be degraded because of the excessive quantum noise.

Objective: To obtain high-quality DCPCT images, we present a statistical iterative reconstruction (SIR) algorithm based on penalized weighted least squares (PWLS) using adaptive prior image constrained total generalized variation (APICTGV) regularization (PWLS-APICTGV).

Methods: APICTGV regularization uses the precontrast scanned high-quality CT image as an adaptive structural prior for low-dose PWLS reconstruction. Thus, the image quality of low-dose DCPCT is improved while essential features of targe image are well preserved. An alternating optimization algorithm is developed to solve the cost function of the PWLS-APICTGV reconstruction.

Results: PWLS-APICTGV algorithm was evaluated using a digital brain perfusion phantom and patient data. Compared to other competing algorithms, the PWLS-APICTGV algorithm shows better noise reduction and structural details preservation. Furthermore, the PWLS-APICTGV algorithm can generate more accurate cerebral blood flow (CBF) map than that of other reconstruction methods.

Conclusions: PWLS-APICTGV algorithm can significantly suppress noise while preserving the important features of the reconstructed DCPCT image, thus achieving a great improvement in low-dose DCPCT imaging.

Background: Deformable image registration (DIR) plays an important part in many clinical tasks, and deep learning has made significant progress in DIR over the past few years.

Objective: To propose a fast multiscale unsupervised deformable image registration (referred to as FMIRNet) method for monomodal image registration.

Methods: We designed a multiscale fusion module to estimate the large displacement field by combining and refining the deformation fields of three scales. The spatial attention mechanism was employed in our fusion module to weight the displacement field pixel by pixel. Except mean square error (MSE), we additionally added structural similarity (ssim) measure during the training phase to enhance the structural consistency between the deformed images and the fixed images.

Results: Our registration method was evaluated on EchoNet, CHAOS and SLIVER, and had indeed performance improvement in terms of SSIM, NCC and NMI scores. Furthermore, we integrated the FMIRNet into the segmentation network (FCN, UNet) to boost the segmentation task on a dataset with few manual annotations in our joint leaning frameworks. The experimental results indicated that the joint segmentation methods had performance improvement in terms of Dice, HD and ASSD scores.

Conclusions: Our proposed FMIRNet is effective for large deformation estimation, and its registration capability is generalizable and robust in joint registration and segmentation frameworks to generate reliable labels for training segmentation tasks.

Background: Besides the direct impact on the cardiovascular system, hypertension is closely associated with organ damage in the kidneys, liver, and pancreas. Chronic liver and pancreatic damage in hypertensive patients may be detectable via imaging.

Objective: To explore the correlation between hypertension-related indicators and extracellular volume fraction (ECV) of liver and pancreas measured by iodine maps, and to evaluate corresponding clinical value in chronic damage of liver and pancreas in hypertensive patients.

Methods: A prospective study from June to September 2023 included abdominal patients who underwent contrast-enhanced spectral CT. Normal and various grades of hypertensive blood pressure groups were compared. Upper abdominal iodine maps were constructed, and liver and pancreatic ECVs calculated. Kruskal-Wallis and Spearman analyses evaluated ECV differences and correlations with hypertension indicators.

Results: In 300 patients, hypertensive groups showed significantly higher liver and pancreatic ECV than the normotensive group, with ECV rising alongside hypertension severity. ECVliver displayed a stronger correlation with hypertension stages compared to ECVpancreas. Regression analysis identified hypertension severity as an independent predictor for increased ECV.

Conclusions: ECVliver and ECVpancreas positively correlates with hypertension indicators and serves as a potential clinical marker for chronic organ damage due to hypertension, with ECVliver being more strongly associated than ECVpancreas.

Introduction: Intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) are the main radiotherapy techniques for treating and managing rectal cancer. Collimator rotation is one of the crucial parameters in radiotherapy planning, and its alteration can cause dosimetric variations. This study assessed the effect of collimator rotation on the dosimetric results of various IMRT and VMAT plans for rectal cancer.

Materials and methods: Computed tomography (CT) images of 20 male patients with rectal cancer were utilized for IMRT and VMAT treatment planning with various collimator angles. Nine different IMRT techniques (5, 7, and 9 coplanar fields with collimator angles of 0°, 45°, and 90°) and six different VMAT techniques (1 and 2 full coplanar arcs with collimator angles of 0°, 45°, and 90°) were planned for each patient. The dosimetric results of various treatment techniques for target tissue (conformity index [CI] and homogeneity index [HI]) and organs at risk (OARs) sparing (parameters obtained from OARs dose-volume histograms [DVH]) as well as radiobiological findings were analyzed and compared.

Results: The 7-fields IMRT technique demonstrated lower bladder doses (V40Gy, V45Gy), unaffected by collimator rotation. The 9-fields IMRT and 2-arcs VMAT (excluding the 90-degree collimator) had the lowest V35Gy and V45Gy. A 90-degree collimator rotation in 2-arcs VMAT significantly increased small bowel and bladder V45Gy, femoral head doses, and HI values. Radiobiologically, the 90-degree rotation had adverse effects on small bowel NTCP (normal tissue complication probability). No superiority was found for a 45-degree collimator rotation over 0 or 30 degrees in VMAT techniques.

Conclusion: Collimator rotation had minimal impact on dosimetric parameters in IMRT planning but is significant in VMAT techniques. A 90-degree rotation in VMAT, particularly in a 2-full arc technique, adversely affects PTV homogeneity index, bladder dose, and small bowel NTCP. Other evaluated collimator angles did not significantly affect VMAT dosimetrical or radiobiological outcomes.

Objective: Content-based medical image retrieval (CBMIR) has become an important part of computer-aided diagnostics (CAD) systems. The complex medical semantic information inherent in medical images is the most difficult part to improve the accuracy of image retrieval. Highly expressive feature vectors play a crucial role in the search process. In this paper, we propose an effective deep convolutional neural network (CNN) model to extract concise feature vectors for multiple semantic X-ray medical image retrieval.

Methods: We build a feature pyramid based CNN model with ResNet50V2 backbone to extract multi-level semantic information. And we use the well-known public multiple semantic annotated X-ray medical image data set IRMA to train and test the proposed model.

Results: Our method achieves an IRMA error of 32.2, which is the best score compared to the existing literature on this dataset.

Conclusions: The proposed CNN model can effectively extract multi-level semantic information from X-ray medical images. The concise feature vectors can improve the retrieval accuracy of multi-semantic and unevenly distributed X-ray medical images.

X-ray communication is a kind of space communication technology which uses X-ray as information carrier. In order to improve the information transmission capacity, communication rate and anti-interference ability of X-ray communication, we proposes to design a novel multi-target X-ray source. The source is composed of a fast switching module of light channels based on FPGA technology and four photoelectric X-ray tubes with different target materials: Cr, Fe, Ni, and Cu. Using Geant4 software, we determined the optimal target thickness for each material, which enabled us to fully leverage the characteristic X-rays for multi-channel signal modulation transmission. Moreover, using CST software for particle trajectory simulation and optimization of the electron beam revealed that at a tube voltage of 20 kV, the focus area measures approximately 1.2 mm×1.2 mm. The simulations show that four kinds of spectra with high distinctiveness can be generated from the Cr, Fe, Ni, and Cu targets. Within a single modulation period, these spectra can be combined in various ways to create 16 different X-ray spectra signals, thereby increasing the number of communication elements and enhancing the information transmission rate.

Background: Geometry calibration for robotic CT system is necessary for obtaining acceptable images under the asynchrony of two manipulators.

Objective: We aim to evaluate the impact of different types of asynchrony on images and propose a reference-free calibration method based on a simplified geometry model.

Methods: We evaluate the impact of different types of asynchrony on images and propose a novel calibration method focused on asynchronous rotation of robotic CT. The proposed method is initialized with reconstructions under default uncalibrated geometry and uses grid sampling of estimated geometry to determine the direction of optimization. Difference between the re-projections of sampling points and the original projection is used to guide the optimization direction. Images and estimated geometry are optimized alternatively in an iteration, and it stops when the difference of residual projections is close enough, or when the maximum iteration number is reached.

Results: In our simulation experiments, proposed method shows better performance, with the PSNR increasing by 2%, and the SSIM increasing by 13.6% after calibration. The experiments reveal fewer artifacts and higher image quality.

Conclusion: We find that asynchronous rotation has a more significant impact on reconstruction, and the proposed method offers a feasible solution for correcting asynchronous rotation.

Background: Chest X-rays (CXR) are widely used to facilitate the diagnosis and treatment of critically ill and emergency patients in clinical practice. Accurate hemi-diaphragm detection based on postero-anterior (P-A) CXR images is crucial for the diaphragm function assessment of critically ill and emergency patients to provide precision healthcare for these vulnerable populations.

Objective: Therefore, an effective and accurate hemi-diaphragm detection method for P-A CXR images is urgently developed to assess these vulnerable populations' diaphragm function.

Methods: Based on the above, this paper proposes an effective hemi-diaphragm detection method for P-A CXR images based on the convolutional neural network (CNN) and graphics. First, we develop a robust and standard CNN model of pathological lungs trained by human P-A CXR images of normal and abnormal cases with multiple lung diseases to extract lung fields from P-A CXR images. Second, we propose a novel localization method of the cardiophrenic angle based on the two-dimensional projection morphology of the left and right lungs by graphics for detecting the hemi-diaphragm.

Results: The mean errors of the four key hemi-diaphragm points in the lung field mask images abstracted from static P-A CXR images based on five different segmentation models are 9.05, 7.19, 7.92, 7.27, and 6.73 pixels, respectively. Besides, the results also show that the mean errors of these four key hemi-diaphragm points in the lung field mask images abstracted from dynamic P-A CXR images based on these segmentation models are 5.50, 7.07, 4.43, 4.74, and 6.24 pixels,respectively.

Conclusion: Our proposed hemi-diaphragm detection method can effectively perform hemi-diaphragm detection and may become an effective tool to assess these vulnerable populations' diaphragm function for precision healthcare.