Determining Trapped Gas in Foam From Computed-Tomography Images

SPE Journal ◽  
2010 ◽  
Vol 16 (01) ◽  
pp. 24-34 ◽  
Author(s):  
R.A.. A. Kil ◽  
Q.P.. P. Nguyen ◽  
W.R.. R. Rossen
Keyword(s):  
Computed Tomography ◽  
Oil Recovery ◽  
Ct Images ◽  
Axial Position ◽  
Tracer Concentration ◽  
Computed Tomography Images ◽  
Gas Trapping ◽  
Capacitance Model ◽  
The Difference ◽  
Trapped Gas

Summary Gas trapping by foam is a key mechanism of foam mobility and foam effectiveness in applications such as acid diversion in well stimulation, enhanced oil recovery (EOR), and aquifer remediation. Previous studies have attempted to quantify the extent of gas trapping by injecting a tracer gas within the foam and then fitting the effluent profile to a 1D capacitance model. In this model, at any given axial position along the core, all flowing gas and all trapped gas are each characterized by a single tracer concentration. Computed-tomography (CT) images of experiments using xenon (Xe) tracer show that this characterization is not accurate: Trapped gas near flowing gas comes rapidly to equilibrium with flowing gas long before tracer diffuses into trapped gas farther away. We introduce a method that uses the CT images directly to estimate flowing-gas fraction. In the CT images, tracer advances in many small channels and diffuses outward into surrounding regions of trapped gas a few millimeters in diameter. The difference between the higher tracer concentration at the center of these channels and the lower concentration at the edge can be related to the diffusion coefficient of the tracer and the flowing-gas fraction within the channel. For the CT images of Xe tracer in one experiment, this method gives flowing-gas fractions one or two orders of magnitude smaller than what is estimated using the 1D capacitance model. The model can be used to estimate flowing-gas fraction in different regions of a core in spite of different average gas velocities in the different regions.

scholarly journals Multi-Cycle-Consistent Adversarial Networks for Edge Denoising of Computed Tomography Images

2021 ◽  
Vol 17 (4) ◽  
pp. 1-16
Author(s):  
Xiaowe Xu ◽  
Jiawei Zhang ◽  
Jinglan Liu ◽  
Yukun Ding ◽  
Tianchen Wang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Denoising ◽  
Ct Images ◽  
High Dose ◽  
Ct Image ◽  
Paired Data ◽  
High Quality ◽  
Computed Tomography Images ◽  
Significant Difference ◽  
Global Cycle

As one of the most commonly ordered imaging tests, the computed tomography (CT) scan comes with inevitable radiation exposure that increases cancer risk to patients. However, CT image quality is directly related to radiation dose, and thus it is desirable to obtain high-quality CT images with as little dose as possible. CT image denoising tries to obtain high-dose-like high-quality CT images (domain Y ) from low dose low-quality CT images (domain X ), which can be treated as an image-to-image translation task where the goal is to learn the transform between a source domain X (noisy images) and a target domain Y (clean images). Recently, the cycle-consistent adversarial denoising network (CCADN) has achieved state-of-the-art results by enforcing cycle-consistent loss without the need of paired training data, since the paired data is hard to collect due to patients’ interests and cardiac motion. However, out of concerns on patients’ privacy and data security, protocols typically require clinics to perform medical image processing tasks including CT image denoising locally (i.e., edge denoising). Therefore, the network models need to achieve high performance under various computation resource constraints including memory and performance. Our detailed analysis of CCADN raises a number of interesting questions that point to potential ways to further improve its performance using the same or even fewer computation resources. For example, if the noise is large leading to a significant difference between domain X and domain Y , can we bridge X and Y with a intermediate domain Z such that both the denoising process between X and Z and that between Z and Y are easier to learn? As such intermediate domains lead to multiple cycles, how do we best enforce cycle- consistency? Driven by these questions, we propose a multi-cycle-consistent adversarial network (MCCAN) that builds intermediate domains and enforces both local and global cycle-consistency for edge denoising of CT images. The global cycle-consistency couples all generators together to model the whole denoising process, whereas the local cycle-consistency imposes effective supervision on the process between adjacent domains. Experiments show that both local and global cycle-consistency are important for the success of MCCAN, which outperforms CCADN in terms of denoising quality with slightly less computation resource consumption.

scholarly journals Correlation between clinical risk factors and tracheal intubation difficulty in infants with Pierre-Robin syndrome: A systematic review

2020 ◽  
Author(s):  
Yanli Liu ◽  
Jiashuo Wang ◽  
Shan Zhong
Keyword(s):  
Computed Tomography ◽  
Tracheal Intubation ◽  
Surface Area ◽  
Body Surface ◽  
Pierre Robin Syndrome ◽  
Intubation Difficulty ◽  
Computed Tomography Images ◽  
The Difference ◽  
And Gender ◽  
Pierre Robin

Abstract Background: Difficult tracheal intubation is a problem commonly encountered by anesthesiologists in the clinic. Methods: In this retrospective study, case-level clinical data and computed tomography images of 96 infants with Pierre-Robin syndrome were included in the analysis. First, computed tomography images were labeled by a clinically experienced physician. Then color space conversion, binarization, contour acquisition, and area calculation processing were performed on the annotated files. Finally, we calculated the correlation coefficient between the seven clinical factors and tracheal intubation difficulty, and the difference in each risk factor under tracheal intubation difficulty. Results: The absolute value of the correlation coefficient between throat area and tracheal intubation difficulty is 0.54, and the difference of throat area under tracheal intubation difficulty is significant. Body surface area, weight and gender also show significant difference under tracheal intubation difficulty. Conclusions: There is a significant correlation between throat area and tracheal intubation difficulty in infants with Pierre-Robin syndrome. Body surface area, weight and gender may have an impact on tracheal intubation difficulty in infants with Pierre-Robin syndrome.

Role of post-mapping computed tomography in virtual-assisted lung mapping

2017 ◽  
Vol 25 (2) ◽  
pp. 123-130 ◽  
Author(s):  
Masaaki Sato ◽  
Kazuhiro Nagayama ◽  
Hideki Kuwano ◽  
Jun-ichi Nitadori ◽  
Masaki Anraku ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Guidance ◽  
Virtual Bronchoscopy ◽  
3 Dimensional ◽  
Computed Tomography Images ◽  
Significant Difference ◽  
Marking Technique ◽  
The Difference ◽  
Dye Marking

Background Virtual-assisted lung mapping is a novel bronchoscopic preoperative lung marking technique in which virtual bronchoscopy is used to predict the locations of multiple dye markings. Post-mapping computed tomography is performed to confirm the locations of the actual markings. This study aimed to examine the accuracy of marking locations predicted by virtual bronchoscopy and elucidate the role of post-mapping computed tomography. Methods Automated and manual virtual bronchoscopy was used to predict marking locations. After bronchoscopic dye marking under local anesthesia, computed tomography was performed to confirm the actual marking locations before surgery. Discrepancies between marking locations predicted by the different methods and the actual markings were examined on computed tomography images. Forty-three markings in 11 patients were analyzed. Results The average difference between the predicted and actual marking locations was 30 mm. There was no significant difference between the latest version of the automated virtual bronchoscopy system (30.7 ± 17.2 mm) and manual virtual bronchoscopy (29.8 ± 19.1 mm). The difference was significantly greater in the upper vs. lower lobes (37.1 ± 20.1 vs. 23.0 ± 6.8 mm, for automated virtual bronchoscopy; p < 0.01). Despite this discrepancy, all targeted lesions were successfully resected using 3-dimensional image guidance based on post-mapping computed tomography reflecting the actual marking locations. Conclusions Markings predicted by virtual bronchoscopy were dislocated from the actual markings by an average of 3 cm. However, surgery was accurately performed using post-mapping computed tomography guidance, demonstrating the indispensable role of post-mapping computed tomography in virtual-assisted lung mapping.

scholarly journals CT Imaging and Differential Diagnosis of COVID-19

2020 ◽  
Vol 71 (2) ◽  
pp. 195-200 ◽  
Author(s):  
Wei-cai Dai ◽  
Han-wen Zhang ◽  
Juan Yu ◽  
Hua-jian Xu ◽  
Huan Chen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Differential Diagnosis ◽  
Lung Diseases ◽  
Ct Imaging ◽  
Medical Institution ◽  
Computed Tomography Images ◽  
The Difference

Since the beginning of 2020, coronavirus disease 2019 (COVID-19) has spread throughout China. This study explains the findings from lung computed tomography images of some patients with COVID-19 treated in this medical institution and discusses the difference between COVID-19 and other lung diseases.

scholarly journals Radiomic prediction of radiation pneumonitis on pretreatment planning computed tomography images prior to lung cancer stereotactic body radiation therapy

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Taka-aki Hirose ◽  
Hidetaka Arimura ◽  
Kenta Ninomiya ◽  
Tadamasa Yoshitake ◽  
Jun-ichi Fukunaga ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Radiation Therapy ◽  
Stereotactic Body Radiation Therapy ◽  
Prediction Models ◽  
Ct Images ◽  
Imaging Biomarkers ◽  
Stereotactic Body Radiation ◽  
Computed Tomography Images ◽  
Radiation Induced

AbstractThis study developed a radiomics-based predictive model for radiation-induced pneumonitis (RP) after lung cancer stereotactic body radiation therapy (SBRT) on pretreatment planning computed tomography (CT) images. For the RP prediction models, 275 non-small-cell lung cancer patients consisted of 245 training (22 with grade ≥ 2 RP) and 30 test cases (8 with grade ≥ 2 RP) were selected. A total of 486 radiomic features were calculated to quantify the RP texture patterns reflecting radiation-induced tissue reaction within lung volumes irradiated with more than x Gy, which were defined as LVx. Ten subsets consisting of all 22 RP cases and 22 or 23 randomly selected non-RP cases were created from the imbalanced dataset of 245 training patients. For each subset, signatures were constructed, and predictive models were built using the least absolute shrinkage and selection operator logistic regression. An ensemble averaging model was built by averaging the RP probabilities of the 10 models. The best model areas under the receiver operating characteristic curves (AUCs) calculated on the training and test cohort for LV5 were 0.871 and 0.756, respectively. The radiomic features calculated on pretreatment planning CT images could be predictive imaging biomarkers for RP after lung cancer SBRT.

scholarly journals Rapid screening for COVID-19 by applying artificial intelligence to chest computed tomography images: A feasibility study

2021 ◽  
Vol 5 ◽  
pp. 239920262110136
Author(s):  
Pedro Galván ◽  
José Fusillo ◽  
Felipe González ◽  
Oraldo Vukujevic ◽  
Luciano Recalde ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Pleural Effusion ◽  
Feasibility Study ◽  
Ct Images ◽  
Public Hospitals ◽  
Rt Pcr ◽  
Chest Computed Tomography ◽  
Computed Tomography Images ◽  
Respiratory Conditions

Aim: The aim of the study was to present the results and impact of the application of artificial intelligence (AI) in the rapid diagnosis of COVID-19 by telemedicine in public health in Paraguay. Methods: This is a descriptive, multi-centered, observational design feasibility study based on an AI tool for the rapid detection of COVID-19 in chest computed tomography (CT) images of patients with respiratory difficulties attending the country’s public hospitals. The patients’ digital CT images were transmitted to the AI diagnostic platform, and after a few minutes, radiologists and pneumologists specialized in COVID-19 downloaded the images for evaluation, confirmation of diagnosis, and comparison with the genetic diagnosis (reverse transcription polymerase chain reaction (RT-PCR)). It was also determined the percentage of agreement between two similar AI systems applied in parallel to study the viability of using it as an alternative method of screening patients with COVID-19 through telemedicine. Results: Between March and August 2020, 911 rapid diagnostic tests were carried out on patients with respiratory disorders to rule out COVID-19 in 14 hospitals nationwide. The average age of patients was 50.7 years, 62.6% were male and 37.4% female. Most of the diagnosed respiratory conditions corresponded to the age group of 27–59 years (252 studies), the second most frequent corresponded to the group over 60 years, and the third to the group of 19–26 years. The most frequent findings of the radiologists/pneumologists were severe pneumonia, bilateral pneumonia with pleural effusion, bilateral pulmonary emphysema, diffuse ground glass opacity, hemidiaphragmatic paresis, calcified granuloma in the lower right lobe, bilateral pleural effusion, sequelae of tuberculosis, bilateral emphysema, and fibrotic changes, among others. Overall, an average of 86% agreement and 14% diagnostic discordance was determined between the two AI systems. The sensitivity of the AI system was 93% and the specificity 80% compared with RT-PCR. Conclusion: Paraguay has an AI-based telemedicine screening system for the rapid stratified detection of COVID-19 from chest CT images of patients with respiratory conditions. This application strengthens the integrated network of health services, rationalizing the use of specialized human resources, equipment, and inputs for laboratory diagnosis.

Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images

2021 ◽  
Author(s):  
S. Vishwa Kiran ◽  
Inderjeet Kaur ◽  
K. Thangaraj ◽  
V. Saveetha ◽  
R. Kingsy Grace ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Data Science ◽  
Processing Technique ◽  
Ct Images ◽  
Lung Cancer Diagnosis ◽  
Computed Tomography Images ◽  
Diagnosis And Classification

In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-the-art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%.

Identification of selected log characteristics from computed tomography images of sugar maple logs using maximum likelihood classifier and textural analysis

Holzforschung ◽  
2008 ◽  
Vol 62 (4) ◽  
Author(s):  
Qiang Wei ◽  
Brigitte Leblon ◽  
Ying Hei Chui ◽  
Shu Yin Zhang
Keyword(s):  
Computed Tomography ◽  
Maximum Likelihood ◽  
Sugar Maple ◽  
Ct Images ◽  
Textural Analysis ◽  
Textural Features ◽  
Training Images ◽  
Computed Tomography Images ◽  
Class Separability ◽  
Maximum Likelihood Classifier

Abstract In recent years, computed tomography (CT) was investigated to acquire internal log information non-destructively. This paper studied the feasibility of identifying internal log characteristics in CT images by means of maximum likelihood classifier. The log characteristics to be identified include heartwood, sapwood, inner bark, and knots in sugar maple. A total of 100 CT images were sampled from one log to develop the classifier and 20 images were selected from another log for validation. Besides spectral and distance features, textural features were also assessed. In total, nine of them were selected as the input features for the classifier based on the class separability analysis. The classifier developed in this study produced overall accuracies of 79.8% and 72.2% for the training images and the validation images, respectively. This study indicates that the developed maximum likelihood classifier relying on a combination of spectral, textural, and distance features may be applicable to identify the internal log characteristics in the CT images of sugar maple.

Abilities of a Densitometric Analysis of Computed Tomography Images and Hemorrhagic Parameters to Predict Outcome Favorability in Patients With Intracerebral Hemorrhage

Neurosurgery ◽  
2017 ◽  
Vol 83 (2) ◽  
pp. 226-236 ◽  
Author(s):  
Hakseung Kim ◽  
Xiaoke Yang ◽  
Young Hun Choi ◽  
Byung C Yoon ◽  
Keewon Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Intracerebral Hemorrhage ◽  
Rapid Assessment ◽  
Ct Images ◽  
Interquartile Range ◽  
Densitometric Analysis ◽  
Computed Tomography Images ◽  
Outcome Favorability ◽  
Actual Volume ◽  
Hemorrhage Volume

Abstract BACKGROUND Intracerebral hemorrhage (ICH) is one of the most devastating subtypes of stroke. A rapid assessment of ICH severity involves the use of computed tomography (CT) and derivation of the hemorrhage volume, which is often estimated using the ABC/2 method. However, these estimates are highly inaccurate and may not be feasible for anticipating outcome favorability. OBJECTIVE To predict patient outcomes via a quantitative, densitometric analysis of CT images, and to compare the predictive power of these densitometric parameters with the conventional ABC/2 volumetric parameter and segmented hemorrhage volumes. METHODS Noncontrast CT images of 87 adult patients with ICH (favorable outcomes = 69, unfavorable outcomes = 12, and deceased = 6) were analyzed. In-house software was used to calculate the segmented hemorrhage volumes, ABC/2 and densitometric parameters, including the skewness and kurtosis of the density distribution, interquartile ranges, and proportions of specific pixels in sets of CT images. Nonparametric statistical analyses were conducted. RESULTS The densitometric parameter interquartile range exhibited greatest accuracy (82.7%) in predicting favorable outcomes. The combination of skewness and the interquartile range effectively predicted mortality (accuracy = 83.3%). The actual volume of the ICH exhibited good coherence with ABC/2 (R = 0.79). Both parameters predicted mortality with moderate accuracy (&lt;78%) but were less effective in predicting unfavorable outcomes. CONCLUSION Hemorrhage volume was rapidly estimated and effectively predicted mortality in patients with ICH; however, this value may not be useful for predicting favorable outcomes. The densitometric analysis exhibited significantly higher power in predicting mortality and favorable outcomes in patients with ICH.

scholarly journals Evaluation of Deformable Image Registration for Three-Dimensional Temporal Subtraction of Chest Computed Tomography Images

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Yan ◽  
Yoshie Kodera ◽  
Kazuhiro Shimamoto
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Three Dimensional ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Temporal Changes ◽  
Transformation Model ◽  
Chest Computed Tomography ◽  
Temporal Subtraction ◽  
Computed Tomography Images

Purpose. To perform lung image registration for reducing misregistration artifacts on three-dimensional (3D) temporal subtraction of chest computed tomography (CT) images, in order to enhance temporal changes in lung lesions and evaluate these changes after deformable image registration (DIR). Methods. In 10 cases, mutual information (MI) lung mask affine mapping combined with cross-correlation (CC) lung diffeomorphic mapping was used to implement lung volume registration. With advanced normalization tools (ANTs), we used greedy symmetric normalization (greedy SyN) as a transformation model, which involved MI-CC-SyN implementation. The resulting displacement fields were applied to warp the previous (moving) image, which was subsequently subtracted from the current (fixed) image to obtain the lung subtraction image. Results. The average minimum and maximum log-Jacobians were 0.31 and 3.74, respectively. When considering 3D landmark distance, the root-mean-square error changed from an average of 20.82 mm for Pfixed to Pmoving to 0.5 mm for Pwarped to Pfixed. Clear shadows were observed as enhanced lung nodules and lesions in subtraction images. The lesion shadows showed lesion shrinkage changes over time. Lesion tissue morphology was maintained after DIR. Conclusions. DIR (greedy SyN) effectively and accurately enhanced temporal changes in chest CT images and decreased misregistration artifacts in temporal subtraction images.

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