Attenuation-based estimation of patient size for the purpose of size specific dose estimation in CT. Part II. Implementation on abdomen and thorax phantoms using cross sectional CT images and scanned projection radiograph images

Abstract Background Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwide concern. This study aims to investigate the correlation between the extent of lung infection and relevant clinical laboratory testing indicators in COVID-19 and to analyse its underlying mechanism. Methods Chest high-resolution computer tomography (CT) images and laboratory examination data of 31 patients with COVID-19 were extracted, and the lesion areas in CT images were quantitatively segmented and calculated using a deep learning (DL) system. A cross-sectional study method was carried out to explore the differences among the proportions of lung lobe infection and to correlate the percentage of infection (POI) of the whole lung in all patients with clinical laboratory examination values. Results No significant difference in the proportion of infection was noted among various lung lobes (P > 0.05). The POI of total lung was negatively correlated with the peripheral blood lymphocyte percentage (L%) (r = − 0.633, P < 0.001) and lymphocyte (LY) count (r = − 0.555, P = 0.001) but positively correlated with the neutrophil percentage (N%) (r = 0.565, P = 0.001). Otherwise, the POI was not significantly correlated with the peripheral blood white blood cell (WBC) count, monocyte percentage (M%) or haemoglobin (HGB) content. In some patients, as the infection progressed, the L% and LY count decreased progressively accompanied by a continuous increase in the N%. Conclusions Lung lesions in COVID-19 patients are significantly correlated with the peripheral blood lymphocyte and neutrophil levels, both of which could serve as prognostic indicators that provide warning implications, and contribute to clinical interventions in patients.

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Method of Determining Patient Size Surrogates Using CT Images

Diagnostic Radiology Physics with MATLAB® ◽

10.1201/9781351188197-15 ◽

2020 ◽

pp. 163-170

Author(s):

Christiane Sarah Burton

Keyword(s):

Ct Images ◽

Patient Size

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Cascaded U-net for Kidney and Tumor Segmentation from CT volumes

Artificial Intelligence in Oncology ◽

10.52454/aio.v2i1.8 ◽

2020 ◽

Vol 2 (1) ◽

pp. 004-008

Author(s):

Asha K Kumaraswamy ◽

Chandrashekar Patil

Keyword(s):

Semantic Segmentation ◽

Ct Images ◽

Difficult Problem ◽

Training Data ◽

Tumor Segmentation ◽

Computer Assisted ◽

Kidney Tumors ◽

Cross Sectional ◽

Morphological Heterogeneity ◽

Enhanced Computed Tomography

Contrast-enhanced Computed Tomography (CT) imaging is most useful tool in diagnosing and locating the kidney lesions. An automated kidney and tumor segmentation are very helpful because it can provide the precise information about the location and size of lesions which can be used in quantitative analysis of the tumor. Semantic segmentation of kidney is very challenging as it requires large dataset for training and its morphological heterogeneity makes it a difficult problem. The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was a competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) has publicly released a 210 cross sectional CT images with kidney tumors along with corresponding semantic segmentation masks. In this work we proposed a novel two stage 2D segmentation method to automatically segment kidney and tumor using the combination of Unet++ and squeeze and excite approach. The proposed network is trained in keras framework. Our method achieves a dice score of 0.98 and 0.965 with kidney and tumor respectively on training data and the results demonstrates the accuracy of our proposed method. Proposed method was able to segment kidney and tumor from abdominal CT images which can provide the exact location and size of the tumor. This information can also be used to analyze treatment response.

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Cross-sectional area and fat content in dachshund epaxial muscles: an MRI and CT reliability study

Veterinary Record Open ◽

10.1136/vetreco-2017-000256 ◽

2018 ◽

Vol 5 (1) ◽

pp. e000256 ◽

Cited By ~ 3

Author(s):

Anna Fredrika Boström ◽

Anu K Lappalainen ◽

Lieven Danneels ◽

Tarja S Jokinen ◽

Outi Laitinen-Vapaavuori ◽

...

Keyword(s):

Ct Images ◽

Fat Content ◽

Cross Sectional Area ◽

Spinal Diseases ◽

Intervertebral Disc Herniation ◽

Sectional Area ◽

Cross Sectional ◽

Rater Reliability ◽

Epaxial Muscles ◽

Epaxial Muscle

MRI and CT are frequently used to diagnose spinal diseases in dogs. These modalities have detected epaxial muscle degeneration in dachshunds with intervertebral disc herniation. However, research on the reliability of epaxial muscular measurements is limited in veterinary medicine. The aims of the study were to assess the intrarater and inter-rater reliability of epaxial muscle cross-sectional area (CSA) and fat content measurements on MRI and CT images in dachshunds, and to compare the CSA measurement between the two modalities. MRI and CT images of 10 healthy dachshunds were evaluated. Two blinded observers assessed MRI CSA, MRI fat content, CT CSA and CT muscle attenuation of three thoracolumbar epaxial muscles using OsiriX. The results showed ‘substantial’ to ‘almost perfect’ intrarater reliability (intraclass correlation coefficient (ICC) 0.828–0.998) and inter-rater reliability (ICC 0.685–0.854) for all variables. When individual spinal segments were analysed, the intrarater and inter-rater reliability decreased and the confidence intervals increased. There was positive correlation (r= 0.719–0.841, P=0.001) and high agreement (0.824–0.894) for the measured CSA between MRI and CT. Epaxial muscle CSA and fat content can be reliably measured on MRI and CT, bearing in mind that measurement of certain segments requires adequate training.

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MO-D-134-05: Size Specific Dose Estimation in Abdominal CT: Impact of Longitudinal Variations in Patient Size

Medical Physics ◽

10.1118/1.4815263 ◽

2013 ◽

Vol 40 (6Part24) ◽

pp. 402-403 ◽

Cited By ~ 2

Author(s):

S Leng ◽

M Shiung ◽

X Duan ◽

L Yu ◽

Y Zhang ◽

...

Keyword(s):

Dose Estimation ◽

Abdominal Ct ◽

Longitudinal Variations ◽

Patient Size

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Intra- and inter-rater reliability in the cross-sectional area of feline lumbar epaxial musculature evaluated via abdominal CT scan

Journal of Feline Medicine and Surgery ◽

10.1177/1098612x19880997 ◽

2019 ◽

Vol 22 (8) ◽

pp. 721-728

Author(s):

Laura H Rayhel ◽

Jessica M Quimby ◽

Eric M Green ◽

Valerie J Parker ◽

Shasha Bai

Keyword(s):

Ct Scan ◽

Muscle Mass ◽

Ct Images ◽

Cross Sectional Area ◽

Area Measurement ◽

Thoracic Vertebrae ◽

Sectional Area ◽

Cross Sectional ◽

Rater Reliability ◽

Epaxial Muscle

Objectives The aim of this study was to evaluate the intra- and inter-rater reliability of epaxial muscle cross-sectional area measurement on feline CT images and to determine the relationship between normalized epaxial muscle area (EMA) and subjective muscle condition score (MCS). Methods Feline transverse CT images including the junction of the 13th thoracic vertebrae/13th rib head were retrospectively reviewed. Right and left epaxial muscle circumference and vertebral body height were measured and an average normalized EMA (ratio of epaxial area:vertebral height) was calculated for each image. Measurements were performed by three individuals blinded to the clinical data and were repeated 1 month later. Intra- and inter-rater reliability of EMA was assessed with concordance correlation coefficient (CCC), and Bland–Altman analysis was performed to assess bias and limits of agreement (LoA) between and within observers at different time points. In cats for which MCS data were available, EMA was compared between differing MCSs via the Kruskal–Wallis test, with Bonferroni-corrected Wilcoxon rank-sum post-hoc analysis. Results In total, 101 CT scans met the inclusion criteria for reliability analysis, 29 of which had muscle condition information available for analysis. Intra-rater EMA CCC ranged from 0.84 to 0.99 with minimal bias (range –0.16 to 0.08) and narrow LoA. Inter-rater EMA CCC ranged from 0.87 to 0.94, bias was larger (range –0.46 to 0.66) and LoA were wider when assessed between observers. Median EMA was significantly lower in cats with severe muscle atrophy (2.76, range 1.28–3.96) than in all other MCS groups ( P <0.0001 for all comparisons). Conclusions and relevance Measurement of EMA on CT showed strong intra-rater reliability, and median EMA measurements were significantly lower in cats with severe muscle wasting, as assessed on physical examination. Further studies correlating EMA to lean muscle mass in cats are needed to determine whether this method may be useful to quantify muscle mass in patients undergoing a CT scan.

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Usefulness of Coronal Reconstruction CT Images for Quantitative Evaluation of the Cross-Sectional Area of Small Pulmonary Vessels

Academic Radiology ◽

10.1016/j.acra.2014.05.024 ◽

2014 ◽

Vol 21 (11) ◽

pp. 1411-1415 ◽

Cited By ~ 4

Author(s):

Shin Matsuoka ◽

Tsuneo Yamashiro ◽

Shoichiro Matsushita ◽

Akiyuki Kotoku ◽

Atsuko Fujikawa ◽

...

Keyword(s):

Quantitative Evaluation ◽

Ct Images ◽

Cross Sectional Area ◽

Sectional Area ◽

Cross Sectional ◽

Pulmonary Vessels ◽

The Cross

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Effectiveness of a Dynamic Cross-Sectional Anatomy Learning Tool (3D-X) at Improving Students’ Ability to Interpret CT Images

Medical Science Educator ◽

10.1007/bf03341648 ◽

2013 ◽

Vol 23 (3) ◽

pp. 364-376 ◽

Cited By ~ 2

Author(s):

Kyle Dorosh ◽

Siddhartha Bhattacharyya ◽

Peter Haase ◽

Marjorie Johnson

Keyword(s):

Ct Images ◽

Learning Tool ◽

Cross Sectional ◽

Sectional Anatomy

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Measurement of snow density and microstructure using computed tomography

Journal of Glaciology ◽

10.3189/172756502781831485 ◽

2002 ◽

Vol 48 (161) ◽

pp. 312-316 ◽

Cited By ~ 17

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.

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