scholarly journals Evaluation of Hand Motion Capture Protocol Using Static Computed Tomography Images: Application to an Instrumented Glove

2014 ◽  
Vol 136 (12) ◽  
Author(s):  
James H. Buffi ◽  
Joaquín Luis Sancho Bru ◽  
Joseph J. Crisco ◽  
Wendy M. Murray
Keyword(s):  
Computed Tomography ◽  
Motion Capture ◽  
Ct Images ◽  
Technical Note ◽  
Ct Scanner ◽  
Hand Motion ◽  
Computed Tomography Images ◽  
Accuracy And Precision ◽  
Principal Axes ◽  
Instrumented Glove

There has been a marked increase in the use of hand motion capture protocols in the past 20 yr. However, their absolute accuracies and precisions remain unclear. The purpose of this technical brief was to present a method for evaluating the accuracy and precision of the joint angles determined by a hand motion capture protocol using simultaneously collected static computed tomography (CT) images. The method consists of: (i) recording seven functional postures using both the motion capture protocol and a CT scanner; (ii) obtaining principal axes of the bones in each method; (iii) calculating the flexion angle at each joint for each method as the roll angle of the composite, sequential, roll-pitch-yaw rotations relating the orientation of the distal bone to the proximal bone; and (iv) comparing corresponding joint angle measurements. For demonstration, we applied the method to a Cyberglove protocol. Accuracy and precision of the instrumented-glove protocol were calculated as the mean and standard deviation, respectively, of the differences between the angles determined from the Cyberglove output and the CT images across the seven postures. Implementation in one subject highlighted substantial errors, especially for the distal joints of the fingers. This technical note both clearly demonstrates the need for future work and introduces a solid, technical approach with the potential to improve the current state of such assessments in our field.

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.

Technical Note: Synthesizing of lung tumors in computed tomography images

2020 ◽  
Vol 47 (10) ◽  
pp. 5070-5076
Author(s):  
Teaghan B. O'Briain ◽  
Kwang Moo Yi ◽  
Magdalena Bazalova‐Carter
Keyword(s):  
Computed Tomography ◽  
Lung Tumors ◽  
Technical Note ◽  
Computed Tomography Images

scholarly journals Measurement of snow density and microstructure using computed tomography

2002 ◽  
Vol 48 (161) ◽  
pp. 312-316 ◽  
Author(s):  
Chris C. Lundy ◽  
Michael Q. Edens ◽  
Robert L. Brown
Keyword(s):  
Computed Tomography ◽  
Plane Surface ◽  
Ct Images ◽  
Two Dimensional ◽  
Ct Scanner ◽  
Cross Sectional ◽  
Thin Sections ◽  
Kinetic Growth ◽  
Similar Images ◽  
Non Destructive

AbstractIn the past, two-dimensional images of internal snow structure have been obtained through plane surface sections or thin sections. These techniques are time-consuming and necessarily destroy the snow specimen. Computed tomography (CT) allows similar images to be obtained, but in a more efficient and non-destructive manner. To demonstrate the methodology, a CT scanner was used to obtain cross-sectional images over time of a snow sample undergoing kinetic-growth metamorphism. Densities calculated from the CT images correlated well to density measured using a traditional method. A procedure was developed that allows the CT image to be converted to binary in an objective manner. Employing innovative stereological software, the microstructural properties (grain diameter, bond diameter, neck length and intercept length) of the snow were then measured from the two-dimensional CT images. The presented methodology provides significant improvements over previous techniques, requiring less time and labor to obtain high-quality microstructural data.

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 (<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.

On the use of X-ray computed tomography for determining wood properties: a review1This article is a contribution to the series The Role of Sensors in the New Forest Products Industry and Bioeconomy.

2011 ◽  
Vol 41 (11) ◽  
pp. 2120-2140 ◽  
Author(s):  
Qiang Wei ◽  
Brigitte Leblon ◽  
Armand La Rocque
Keyword(s):  
Computed Tomography ◽  
Growth Ring ◽  
Back Propagation ◽  
Forest Products ◽  
Ct Images ◽  
Ct Scanner ◽  
Forest Products Industry ◽  
X Ray ◽  
Feed Forward Back Propagation ◽  
X Ray Computed

In several processes of the forest products industry, an in-depth knowledge of log and board internal features is required and their determination needs fast scanning systems. One of the possible technologies is X-ray computed tomography (CT) technology. Our paper reviews applications of this technology in wood density measurements, in wood moisture content monitoring, and in locating internal log features that include pith, sapwood, heartwood, knots, and other defects. Annual growth ring measurements are more problematic to be detected on CT images because of the low spatial resolution of the images used. For log feature identification, our review shows that the feed-forward back-propagation artificial neural network is the most efficient CT image processing method. There are also some studies attempting to reconstruct three-dimensional log or board images from two-dimensional CT images. Several industrial prototypes have been developed because medical CT scanners were shown to be inappropriate for the wood industry. Because of the high cost of X-ray CT scanner equipment, other types of inexpensive sensors should also be investigated, such as electric resistivity tomography and microwaves. It also appears that the best approach uses various different sensors, each of them having its own strengths and weaknesses.

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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