Three-Dimensions Segmentation of Pulmonary Vascular Trees for Low Dose CT Scans

2016 ◽  
Vol 17 (1) ◽  
Author(s):  
Jun Lai ◽  
Ying Huang ◽  
Ying Wang ◽  
Jun Wang
Keyword(s):  
Low Dose ◽  
Three Dimensions ◽  
Ct Scans ◽  
Low Dose Ct ◽  
Vascular Trees

scholarly journals Screening for lung cancer with low-dose CT scans

2013 ◽  
Vol 51 (4) ◽  
pp. 205-206 ◽  
Author(s):  
James R. Jett
Keyword(s):  
Lung Cancer ◽  
Low Dose ◽  
Ct Scans ◽  
Low Dose Ct

Radiomic Phenotyping of the Lung Parenchyma: Feasibility Study in a Screening Cohort

2021 ◽  
Author(s):  
Babak Haghighi ◽  
Hannah Horng ◽  
Peter B Noël ◽  
Eric Cohen ◽  
Lauren Pantalone ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Screening ◽  
Low Dose ◽  
Lung Cancer Screening ◽  
Lung Parenchyma ◽  
Ct Scans ◽  
Lung Field ◽  
Reconstruction Algorithms ◽  
Low Dose Ct ◽  
Significant Difference

Abstract Rationale: High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. Methods: We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015-2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f), sharp (I50f)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The features extracted included gray-level histogram, co-occurrence, and run-length descriptors. Each feature was averaged for each scan within a range of lattice window sizes (W) ranging from 4-20mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchal clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between? phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Results: Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant difference for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns where similar across the two reconstructed kernels, specifically when smaller window sizes (W=4 and 8mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years.ConclusionsRadiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

scholarly journals Body region localization in whole-body low-dose CT images of PET/CT scans using virtual landmarks

2019 ◽  
Vol 46 (3) ◽  
pp. 1286-1299 ◽  
Author(s):  
Peirui Bai ◽  
Jayaram K. Udupa ◽  
Yubing Tong ◽  
ShiPeng Xie ◽  
Drew A. Torigian
Keyword(s):  
Low Dose ◽  
Ct Images ◽  
Whole Body ◽  
Ct Scans ◽  
Body Region ◽  
Low Dose Ct ◽  
Pet Ct

Estimation of the total effective dose from low-dose CT scans and radiopharmaceutical administrations delivered to patients undergoing SPECT/CT explorations

2013 ◽  
Vol 27 (7) ◽  
pp. 610-617 ◽  
Author(s):  
Carlos Montes ◽  
Pilar Tamayo ◽  
Jorge Hernandez ◽  
Felipe Gomez-Caminero ◽  
Sofia García ◽  
...  
Keyword(s):  
Effective Dose ◽  
Low Dose ◽  
Ct Scans ◽  
Low Dose Ct ◽  
Total Effective Dose

Automated detection of nodules attached to the pleural and mediastinal surface in low-dose CT scans

2008 ◽  
Author(s):  
Bram van Ginneken ◽  
Andre Tan ◽  
Keelin Murphy ◽  
Bart-Jan de Hoop ◽  
Mathias Prokop
Keyword(s):  
Low Dose ◽  
Automated Detection ◽  
Ct Scans ◽  
Low Dose Ct

The use of ultra-low-dose CT scans for the evaluation of limb fractures

2016 ◽  
Vol 98-B (12) ◽  
pp. 1668-1673 ◽  
Author(s):  
S. R. Konda ◽  
A. M. Goch ◽  
P. Leucht ◽  
A. Christiano ◽  
S. Gyftopoulos ◽  
...  
Keyword(s):  
Low Dose ◽  
Ct Scans ◽  
Low Dose Ct ◽  
Limb Fractures

scholarly journals Intrathoracic airway trees: segmentation and airway morphology analysis from low-dose CT scans

2005 ◽  
Vol 24 (12) ◽  
pp. 1529-1539 ◽  
Author(s):  
J. Tschirren ◽  
E.A. Hoffman ◽  
G. McLennan ◽  
M. Sonka
Keyword(s):  
Low Dose ◽  
Ct Scans ◽  
Low Dose Ct ◽  
Morphology Analysis ◽  
Airway Trees ◽  
Airway Morphology
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