Slice89: A Single Slice Tomography Experiment

1991 ◽  
pp. 81-86 ◽  
Author(s):  
Bruce M. Howe ◽  
James A. Mercer ◽  
Robert C. Spindel ◽  
Peter F. Worcester ◽  
John A. Hildebrand ◽  
...  
Keyword(s):  
Single Slice

An Industrial Single‐Slice Si‐MBE Apparatus

1989 ◽  
Vol 136 (4) ◽  
pp. 1154-1158 ◽  
Author(s):  
E. Kasper ◽  
H. Kibbel ◽  
F. Schäffler
Keyword(s):  
Single Slice

scholarly journals Dynamic single-slice CT estimates whole-lung dual-energy CT variables in pigs with and without experimental lung injury

2019 ◽  
Vol 7 (1) ◽  
Author(s):  
John N. Cronin ◽  
João Batista Borges ◽  
Douglas C. Crockett ◽  
Andrew D. Farmery ◽  
Göran Hedenstierna ◽  
...  
Keyword(s):  
Lung Injury ◽  
Dual Energy ◽  
Dual Energy Ct ◽  
Lung Density ◽  
Density Distributions ◽  
Single Slice ◽  
Injury Model ◽  
Lung Injury Model ◽  
Ct Density ◽  
Surfactant Depletion

Abstract Background Dynamic single-slice CT (dCT) is increasingly used to examine the intra-tidal, physiological variation in aeration and lung density in experimental lung injury. The ability of dCT to predict whole-lung values is unclear, especially for dual-energy CT (DECT) variables. Additionally, the effect of inspiration-related lung movement on CT variables has not yet been quantified. Methods Eight domestic pigs were studied under general anaesthesia, including four following saline-lavage surfactant depletion (lung injury model). DECT, dCT and whole-lung images were collected at 12 ventilatory settings. Whole-lung single energy scans images were collected during expiratory and inspiratory apnoeas at positive end-expiratory pressures from 0 to 20 cmH2O. Means and distributions of CT variables were calculated for both dCT and whole-lung images. The cranio-caudal displacement of the anatomical slice was measured from whole-lung images. Results Mean CT density and volume fractions of soft tissue, gas, iodinated blood, atelectasis, poor aeration, normal aeration and overdistension correlated between dCT and the whole lung (r2 0.75–0.94) with agreement between CT density distributions (r 0.89–0.97). Inspiration increased the matching between dCT and whole-lung values and was associated with a movement of 32% (SD 15%) of the imaged slice out of the scanner field-of-view. This effect introduced an artefactual increase in dCT mean CT density during inspiration, opposite to that caused by the underlying physiology. Conclusions Overall, dCT closely approximates whole-lung aeration and density. This approximation is improved by inspiration where a decrease in CT density and atelectasis can be interpreted as physiological rather than artefactual.

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

2021 ◽  
pp. 197140092199897
Author(s):  
Sarv Priya ◽  
Caitlin Ward ◽  
Thomas Locke ◽  
Neetu Soni ◽  
Ravishankar Pillenahalli Maheshwarappa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Central Nervous System ◽  
Nervous System ◽  
Diagnostic Performance ◽  
Model Performance ◽  
Area Under The Curve ◽  
Central Nervous System Lymphoma ◽  
First Order ◽  
Single Slice ◽  
Contrast Enhanced

Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

Computed tomography-based radiomics analysis to predict lymphovascular invasion in esophageal squamous cell carcinoma

2021 ◽  
Author(s):  
Hui Peng ◽  
Qiuxing Yang ◽  
Ting Xue ◽  
Qiaoling Chen ◽  
Manman Li ◽  
...  
Keyword(s):  
Squamous Cell Carcinoma ◽  
Esophageal Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Lymphovascular Invasion ◽  
Validation Cohort ◽  
Regression Method ◽  
Training Cohort ◽  
Volume Model ◽  
Single Slice ◽  
Full Volume

Objective The present study explored the value of preoperative CT radiomics in predicting lymphovascular invasion (LVI) in esophageal squamous cell carcinoma (ESCC). Methods A retrospective analysis of 294 pathologically confirmed ESCC patients undergoing surgical resection and their preoperative chest-enhanced CT arterial images were used to delineate the target area of the lesion. All patients were randomly divided into a training cohort and a validation cohort at a ratio of 7:3. Radiomics features were extracted from single-slice, three-slice, and full-volume regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) regression method was applied to select valuable radiomics features. Radiomics models were constructed using logistic regression method and were validated using leave group out cross-validation (LGOCV) method. The performance of the three models was evaluated using the receiver characteristic curve (ROC) and decision curve analysis (DCA). Results A total of 1218 radiomics features were separately extracted from single-slice ROIs, three-slice ROIs, and full-volume ROIs, and 16, 13 and 18 features, respectively, were retained after optimization and screening to construct a radiomics prediction model. The results showed that the AUC of the full-volume model was higher than that of the single-slice and three-slice models. According to LGOCV, the full-volume model showed the highest mean AUC for the training cohort and the validation cohort. Conclusion The full-volume radiomics model has the best predictive performance and thus can be used as an auxiliary method for clinical treatment decision making. Advances in knowledge: LVI is considered to be an important initial step for tumor dissemination. CT radiomics features correlate with LVI in ESCC and can be used as potential biomarkers for predicting LVI in ESCC.

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