ABSTRACT
@#Introduction: Metal artifacts can degrade the image quality of computed tomography (CT) images which lead to errors in diagnosis. This study aims to evaluate the performance of Laplace interpolation (LI) method for metal artifacts reduction (MAR) in CT images in comparison with cubic spline (CS) interpolation. Methods: In this study, the proposed MAR algorithm was developed using MATLAB platform. Firstly, the virtual sinogram was acquired from CT image using Radon transform function. Then, dual-adaptive thresholding detected and segmented the metal part within the CT sinogram. Performance of the two interpolation methods to replace the missing part of segmented sinogram were evaluated. The interpolated sinogram was reconstructed, prior to image fusion to obtain the final corrected image. The qualitative and quantitative evaluations were performed on the corrected CT images (both phantom and clinical images) to evaluate the effectiveness of the proposed MAR technique. Results: From the findings, LI method had successfully replaced the missing data on both simple and complex thresholded sinogram as compared to CS method (p-value = 0.17). The artifact index was significantly reduced by LI method (p-value = 0.02). For qualitative analysis, the mean scores by radiologists for LI-corrected images were higher than original image and CS-corrected images. Conclusion: In conclusion, LI method for MAR produced better results as compared to CS interpolation method, as it worked more effective by successfully interpolated all the missing data within sinogram in most of the CT images.
ABSTRACT
Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.
Subject(s)
Humans , Algorithms , China , Disease Progression , Multiple Pulmonary Nodules , Neural Networks, Computer , Tomography, X-Ray Computed/methodsABSTRACT
Aim of Study: The goal of this research was to investigate if application of optimized imaging parameters, recommended in literature, would be effective in producing the image quality required for treatment planning of spinal radiation fields with metallic implants. Materials and Methods: CT images from an anthropomorphic torso phantom with and without spinal implants were acquired using different imaging protocols: raising kVp and mAs, reducing the pitch and applying an extended CT scale (ECTS) technique. Profiles of CT number (CT#) were produced using DICOM data of each image. The effect of artifact on dose calculation accuracy was investigated using the image data in the absence of implant as a reference and the recommended electron density tolerance levels (Δρe). Results: Raising the kVp was the only method that produced improvement to some degree in CT# in artifact regions. Application of ECTS improved CT# values only for metal. Conclusions: Although raising the kVp was effective in reducing metallic artifact, the significance of this effect on Δρe values in corrected images depends on the required tolerance for treatment planning dose calculation accuracy. ECTS method was only successful in correcting the CT number range in the metal. Although, application of ECTS method did not have any effect on artifact regions, its use is necessary in order to improve delineation of metal and accuracy of attenuation calculations in metal, provided that the treatment planning system can use an extended CT# calibration curve. Also, for Monte Carlo calculations using patient's images, ECTS-post-processed-CT images improve dose calculation accuracy for impure metals
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This paper explores the relationship between the cardiac volume and time, which is applied to control dynamic heart phantom. We selected 50 patients to collect their cardiac computed tomography angiography (CTA) images, which have 20 points in time series CTA images using retrospective electrocardiograph gating, and measure the volume of four chamber in 20-time points with cardiac function analysis software. Then we grouped patients by gender, age, weight, height, heartbeat, and utilize repeated measurement design to conduct statistical analyses. We proposed structured sparse learning to estimate the mathematic expression of cardiac volume variation. The research indicates that all patients' groups are statistically significant in time factor ( = 0.000); there are interactive effects between time and gender groups in left ventricle ( = 8.597, = 0.006) while no interactive effects in other chambers with the remaining groups; and the different weight groups' volume is statistically significant in right ventricle ( = 9.004, = 0.005) while no statistical significance in other chambers with remaining groups. The accuracy of cardiac volume and time relationship utilizing structured sparse learning is close to the least square method, but the former's expression is more concise and more robust. The number of nonzero basic function of the structured sparse model is just 2.2 percent of that of least square model. Hence, the work provides more the accurate and concise expression of the cardiac for cardiac motion simulation.
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Pectus carinatum (PC) is one of the most common chest wall anomalies, which is characterized by the protrusion of the anterior chest wall including the sternum and adjacent costal cartilages. Mildly patients suffer from mental problems such as self-abasement, while severely suffering patients are disturbed by significant cardiopulmonary symptoms. The traditional Haller index, which is widely used clinically to evaluate the severity of PC, is deficient in diagnosis efficiency and classification. This paper presents an improved Haller index algorithm for PC: first, the contour of the patient chest in the axial computed tomography (CT) slice where the most convex thorax presents is extracted; and then a cubic B-spline curve is employed to fit the extracted contour followed by an eclipse fitting procedure; finally, the improved Haller index and the classification index are automatically calculated based on the analytic curves. The results of CT data analysis using 22 preoperative and postoperative patient CT datasets show that the proposed diagnostic index for PC can diagnose and classify PC patients correctly, which confirms the feasibility of the evaluation index. Furthermore, digital measurement techniques can be employed to improve the diagnostic efficiency of PC, achieving one small step towards the computer-aided intelligent diagnosis and treatment for pediatric chest wall malformations.
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PURPOSE: To perform virtual colonoscopy using electron beam tomography(EBT) in potients in whom a colonicmass was present, and to compare the results with those obtained using barium enema, colonoscopy and grosspathologic specimens. MATERIALS AND METHODS: Ten patients in whom colonic masses were diagnosed by either bariumenema or colonoscopy were involved in this study. There were nine cases of adenocarcinoma and one of tubulovillousadenoma. Using EBT preoperative abdominopelvic CT scans were performed. Axial scans were then three-dimensionallyreconstructed to produce virtual colonoscopic images and were compared with barium enema, colonoscopy and grosspathologic specimens. Virtual colonoscopic images of the masses were classified as either 1)polypoid, 2)sessile,3)fungating, or 4)annular constrictive. We also determined whether ulcers were present within the lesions andwhether there was obstruction. RESULT: After virtval colonoscopy, two lesions were classified as polypoid, oneas sessile, five as fungating and two as annular constrictive. Virtual colonoscopic images showed good correlationwith the findings of barium enema, colonoscopy and gross pathologic specimens. Three of six ulcerative lesionswere observed on colonoscopy; in seven adenocarcinomas with partial or total luminal obstruction, virtualcolonoscopy visualized the colon beyond the obstructed sites. In one case, barium contrast failed to pass throughthe obstructed portion and in six cases, the colonoscope similarly failed. CONCLUSION: Virtual colonoscopiescorrelated well with barium enema, colonoscopy and gross patholoic specimens. They provide three dimensionalimages of colonic masses and are helpful for the evaluation of obstructive lesions.