Journal of X-Ray Science and Technology

Cylindrical phantoms are often imaged by X-ray computed tomography (CT) to evaluate the extent of beam hardening (or cupping artifact) resulting from a polychromatic X-ray source.

Our goal was to derive analytical expressions for the reconstructed image of a homogeneous cylindrical phantom exhibiting a cupping artifact, to permit a quantitative comparison with experimental cupping data.

A filtered backprojection method was employed to obtain the analytical cupping profile for the phantom, assuming that the projection data could be approximated as a power series with respect to the sample penetration thickness.

The cupping profile was obtained analytically as a series of functions by employing Ramachandran filtering with an infinite Nyquist wavenumber. The quantitative relationship between the power series of the projection and the nth moment of the linear attenuation coefficient spectrum of the phantom was also determined. Application of the obtained cupping profile to the evaluation of the practical reconstruction filters with a finite Nyquist wavenumber and to the best choice of the contrast agent was demonstrated.

The set of exact solutions derived in this work should be applicable to the analysis of cylindrical phantom experiments intended to evaluate CT systems.

X-ray CT/micro-CT methods with photon-counting detectors (PCDs) and high Z materials are a hot research topic. One method using PCDs allows for spectral imaging in 5 energy windows while conventional X-ray detectors only collect energy-integrating data.

To demonstrate the enhanced separation of contrast materials by using PCDs, multivariate analysis, and linear discriminant methods.

Phantoms containing iodine and aqueous nanomaterials were scanned on a MARS spectral micro-CT. Image volumes were segmented into separate material-specific populations. Contrast comparisons were made by calculating T² test statistics in the univariate, pseudo-conventional and multivariate, spectral CT data sets. Separability after Fisher discriminant analysis (FDA) was also assessed.

The T² values calculated for material comparisons increased as a result of the spectral expansion. The majority of the tested contrast agents showed increased T² values by a factor of ∼2 -3. The total significant T² statistics in the pure and mixed lanthanide image sets increased in the spectral data set.

This work consolidates the groundwork for photon-counting-based material decomposition with micro-CT, facilitating future development of novel nanomaterials and their preclinical applications.

Accurate measurement of bone mineral density (BMD) in dual-energy X-ray absorptiometry (DXA) is essential for proper diagnosis of osteoporosis. Calculation of BMD requires precise bone segmentation and subtraction of soft tissue absorption. Femur segmentation remains a challenge as many existing methods fail to correctly distinguish femur from soft tissue. Reasons for this failure include low contrast and noise in DXA images, bone shape variability, and inconsistent X-ray beam penetration and attenuation, which cause shadowing effects and person-to-person variation.

To present a new method namely, a Pixel Label Decision Tree (PLDT), and test whether it can achieve higher accurate performance in femur segmentation in DXA imaging.

PLDT involves mainly feature extraction and selection. Unlike photographic images, X-ray images include features on the surface and inside an object. In order to reveal hidden patterns in DXA images, PLDT generates seven new feature maps from existing high energy (HE) and low energy (LE) X-ray features and determines the best feature set for the model. The performance of PLDT in femur segmentation is compared with that of three widely used medical image segmentation algorithms, the Global Threshold (GT), Region Growing Threshold (RGT), and artificial neural networks (ANN).

PLDT achieved a higher accuracy of femur segmentation in DXA imaging (91.4%) than either GT (68.4%), RGT (76%) or ANN (84.4%).

The study demonstrated that PLDT outperformed other conventional segmentation techniques in segmenting DXA images. Improved segmentation should help accurate computation of BMD which later improves clinical diagnosis of osteoporosis.

The aim of this study was to apply texture analysis to investigate whether there was a change in the lens following radiotherapy.

Patients who received radiotherapy (RT) for head and neck cancer or brain tumor were enrolled. Computed tomography (CT) images taken in the last month before RT and the most recent images after RT were compared. Entropy values were calculated using lens attenuation values. The lens doses were obtained from the dose-volume histogram data.

A total of 55 lenses were evaluated. The mean Hounsfield Unit value of the lenses was 66.14±12.16 before RT and 72.02±9.12 after RT (p = 0.007). The mean entropy value was 1.87±0.31 before RT and this reduced to 1.31±0.34 after RT (p < 0.001), respectively. As time increased, the difference in entropy also increased (p = 0.007). A correlation close to statistical significance was determined between the entropy difference and minimum, maximum and mean lens radiation dose (p = 0.052, p = 0.052, p = 0.063, respectively). The entropy difference was significantly reduced in the >4 Gy group (p = 0.046).

Study results indicated that the entropy values in the lens were signifcantly changed after radiotherapy and the degree of the change associated with dose and time.

In clinical computed tomography (CT) applications, when a patient is obese or improperly positioned, the final tomographic scan is often partially truncated. Images directly reconstructed by the conventional reconstruction algorithms suffer from severe cupping and direct current bias artifacts. Moreover, the current methods for projection extension have limitations that preclude incorporation from clinical workflows, such as prohibitive computational time for iterative reconstruction, extra radiation dose, hardware modification, etc.

In this study, we first established a geometrical constraint and estimated the patient habitus using a modified scout configuration. Then, we established an energy constraint using the integral invariance of fan-beam projections. Two constraints were extracted from the existing CT scan process with minimal modification to the clinical workflows. Finally, we developed a novel dual-constraint based optimization model that can be rapidly solved for projection extrapolation and accurate local reconstruction.

Both numerical phantom and realistic patient image simulations were performed, and the results confirmed the effectiveness of our proposed approach.

We establish a dual-constraint-based optimization model and correspondingly develop an accurate extrapolation method for partially truncated projections. The proposed method can be readily integrated into the clinical workflow and efficiently solved by using a one-dimensional optimization algorithm. Moreover, it is robust for noisy cases with various truncations and can be further accelerated by GPU based parallel computing.

Various imaging modalities have been used to diagnose suspicious breast lesions. Purpose of this study is to compare the diagnostic accuracy for breast cancer using mammography, ultrasonography and magnetic resonance imaging (MRI).

Total 107 patients aged from 19 to 62 years are included in this retrospective study. Mammography, ultrasonography and MRI scans were performed for each patient detected with suspected breast tumor within a month. In addition, the tumor diversity (10 types of benign and 5 types of malignant) was confirmed by pathological findings of tumor biopsy. To compare the diagnosis performance of the three imaging modalities, the overall fraction correct (accuracy), positive predict value (PPV), negative predict value (NPV), sensitivity and specificity were calculated. Meanwhile, the receiver operating characteristic (ROC) analysis was also performed.

The diagnostic accuracy ranged from 78.5% to 86.9% among three imaging modalities. All modalities yielded a PPV lower than 77.8% and a NPV higher than 90.0% in identifying the presence of malignant tumors. MRI presented a diagnostic accuracy of 86.9%, as well as a sensitivity of 95.5% and an area under curve (AUC) of 0.948, which are higher than mammography and ultrasonography.

By using a diverse dataset and comparing the diagnostic accuracy of three imaging modalities commonly used in breast cancer detection and diagnosis, this study also demonstrated that mammography, ultrasonography and MRI had different diagnostic performance in breast tumor identification. Among them, MRI yielded the highest performance even though the unexpected specificity may lead to over-diagnosis, and ultrosonography is slightly better than mammography.

Images of industrial cone-beam computed tomography (CBCT) contain noise and beam hardening artifacts, which induce difficulty and low precision in segmenting regions of interest.

The primary objective of this study is to improve the segmentation precision of CBCT series slice images.

This paper presents a method based on the Phansalkar to segment CBCT series slice images precisely. First, the basics of the proposed method and the necessity of changing the local window size are analysed. The adaptive accumulated Phansalkar, which collects each pixel’s classification results in different local windows, is proposed. Second, the bimodal distribution of the histogram is used to calculate the appropriate local window size for each pixel adaptively. Third, the characteristics of the accumulated probability (the accumulated classification results divided by the accumulated times) are analysed, from which an adaptive method is applied to segment the accumulated probability. Last, experiments are conducted on CBCT series slice images of three workpieces and one computer-aided design (CAD) model with internal defects.

The proposed new method can segment CBCT images with noise and beam-hardening well. Moreover, for the segmentation of all four CBCT series slice images, the new method acquired the highest BF and AOM scores (1 and 0.9981) with the smallest standard deviation (0.0013) as compared with other existing methods including CMF (continuous max-flow/min cut), MS (mean-shift), DRLSE (distance regularized level set evolution), and ARKFCM (adaptively regularized kernel-based fuzzy c-means clustering).

The experimental results support that our new method can more precisely segment CBCT series slice images with noise and artifacts than many existing methods. Thus, the new method has prospective application value and can provide valuable technical support for the industrial CBCT image post-processing system.

This study aims to evaluate the effect of dentoalveolar distraction extraction (DDE) on site preservation, and to evaluate how the technique keeps the height and width of alveolar bones to a greater extent.

12 beagle dogs, randomly divided into three groups (DDE group, NH group, BOG group), were used. In the dogs of three groups, the root of the left or right third mandibular premolars were respectively extracted by three methods namely, DDE, traditional extraction with natural healing, and traditional extraction with Bio-Oss bone dust implanted and guided bone regeneration (GBR). Cone-beam computed tomography (CBCT) scans and X-rays were taken immediately and three months after the tooth extraction. The height and width of the alveolar ridges were compared among different groups.

Three months after tooth extraction, at the 1 mm level below the alveolar ridge crest, the amount and degree of buccal alveolar ridge width resorption in DDE group were significantly lower than that of NH and BOG group (P < 0.05). At the 2 mm and 3 mm level below the alveolar ridge crest, the amount and degree of buccal alveolar ridge width resorption in DDE group and BOG had no significant difference, and both were significant lower than that of NH group (P < 0.05). The height resorption of alveolar ridge in DDE group was significantly lower than NH and BOG groups (P < 0.05), while NH and BOG group had no statistically significant.

To a greater extent, the alveolar ridge preservation through DDE could preserve the height and width of alveolar ridge crest.

To evaluate the clinical efficacy of percutaneous trans-hepatic integrated ¹²⁵I seed stents implantation for malignant lower biliary tract obstruction.

Thirty-two patients with malignant lower biliary obstruction were randomly divided into two groups. One group underwent the therapy with integrated ¹²⁵I seed stents (Test group, n = 13), and another group received conventional metal stents implantation for treatment (Control group, n = 19). The pre- and post-operative changes in biochemical indices, white blood cell count, IgG level, stent patency, survival time, tumor size and complications were compared between the two groups. RECIST 1.1 (Response Evaluation Criteria In Solid Tumors) was used to evaluate therapeutic effects. The average follow-up time was 12.3 months.

The differences between pre- and post-operative (30 days) intragroup biochemical indices had statistically significant difference (P < 0.05), but there were no significant differences (P > 0.05) in leukocyte counts and IgG levels. As to the median time of stent patency and patients’ survival, there were significant differences (P < 0.05) between Control and Test groups (3.9 months vs. 8.1 months, 139 days vs. 298 days, respectively). Three months after the operation, the average tumor size was reduced in the Test group, but was increased in the Control group (P < 0.05). There was no significant difference in the incidence of complications between the two groups. The evaluation results using RECIST 1.1 showed that there were statistically significant differences between the two groups in terms of the rates of remission, control, and progression (χ² = 17.5, P < 0.05).

The study indicates that integrated ¹²⁵I seed stents are effective in reducing jaundice symptoms, inhibiting tumor growth, improving stent patency and prolonging patient survival, which may serve as a safer and more feasible method in treating malignant lower biliary obstruction with minimal invasiveness.