scholarly journals Quantitative assessment of emphysema from whole lung CT scans: comparison with visual grading

2009 ◽  
Author(s):  
Brad M. Keller ◽  
Anthony P. Reeves ◽  
Tatiyana V. Apanosovich ◽  
Jianwei Wang ◽  
David F. Yankelevitz ◽  
...  
Keyword(s):  
Quantitative Assessment ◽  
Ct Scans ◽  
Visual Grading ◽  
Lung Ct

scholarly journals Motion correction for routine X-ray lung CT imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Doil Kim ◽  
Jiyoung Choi ◽  
Duhgoon Lee ◽  
Hyesun Kim ◽  
Jiyoung Jung ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Motion Correction ◽  
Ct Imaging ◽  
Motion Artifacts ◽  
Ct Scans ◽  
Breath Hold ◽  
X Ray ◽  
Wide Range ◽  
Thorax Ct ◽  
Lung Ct

AbstractA novel motion correction algorithm for X-ray lung CT imaging has been developed recently. It was designed to perform for routine chest or thorax CT scans without gating, namely axial or helical scans with pitch around 1.0. The algorithm makes use of two conjugate partial angle reconstruction images for motion estimation via non-rigid registration which is followed by a motion compensated reconstruction. Differently from other conventional approaches, no segmentation is adopted in motion estimation. This makes motion estimation of various fine lung structures possible. The aim of this study is to explore the performance of the proposed method in correcting the lung motion artifacts which arise even under routine CT scans with breath-hold. The artifacts are known to mimic various lung diseases, so it is of great interest to address the problem. For that purpose, a moving phantom experiment and clinical study (seven cases) were conducted. We selected the entropy and positivity as figure of merits to compare the reconstructed images before and after the motion correction. Results of both phantom and clinical studies showed a statistically significant improvement by the proposed method, namely up to 53.6% (p < 0.05) and up to 35.5% (p < 0.05) improvement by means of the positivity measure, respectively. Images of the proposed method show significantly reduced motion artifacts of various lung structures such as lung parenchyma, pulmonary vessels, and airways which are prominent in FBP images. Results of two exemplary cases also showed great potential of the proposed method in correcting motion artifacts of the aorta which is known to mimic aortic dissection. Compared to other approaches, the proposed method provides an excellent performance and a fully automatic workflow. In addition, it has a great potential to handle motions in wide range of organs such as lung structures and the aorta. We expect that this would pave a way toward innovations in chest and thorax CT imaging.

scholarly journals The metopic index: an anthropometric index for the quantitative assessment of trigonocephaly from metopic synostosis

2016 ◽  
Vol 18 (3) ◽  
pp. 275-280 ◽  
Author(s):  
Joanna Y. Wang ◽  
Amir H. Dorafshar ◽  
Ann Liu ◽  
Mari L. Groves ◽  
Edward S. Ahn
Keyword(s):  
Quantitative Assessment ◽  
Surgical Repair ◽  
Preoperative Evaluation ◽  
Ct Scans ◽  
Control Group ◽  
3D Reconstructions ◽  
Metopic Synostosis ◽  
The Mean ◽  
Age Range ◽  
Age And Sex

OBJECTIVE Because the metopic suture normally fuses during infancy, there are varying degrees of severity in head shape abnormalities associated with premature fusion. A method for the objective and reproducible assessment of metopic synostosis is needed to guide management, as current methods are limited by their reliance on aesthetic markers. The object of this study was to describe the metopic index (MI), a simple anthropometric cranial measurement. The measurements can be obtained from CT scans and, more importantly, from palpable cranial landmarks, and the index provides a rapid tool for evaluating patients in both pre- and postoperative settings. METHODS High-resolution head CT scans obtained in 69 patients (age range 0–24 months) diagnosed with metopic craniosynostosis were retrospectively reviewed. Preoperative 3D reconstructions were available in 15 cases, and these were compared with 3D reconstructions of 324 CT scans obtained in a control group of 316 infants (age range 0–24 months) who did not have any condition that might affect head size or shape and also in a subset of this group, comprising 112 patients precisely matched to the craniosynostosis patients with respect to age and sex. Postoperative scans were available and reviewed in 9 of the craniosynostosis patients at a mean time of 7.1 months after surgical repair. 3D reconstructions of these scans were matched with controls based upon age and sex. RESULTS The mean preoperative MI for patients with trigonocephaly was 0.48 (SD 0.05), significantly lower than the mean values of 0.57 (SD 0.04) calculated on the basis of all 324 scans obtained in controls (p < 0.001) and 0.58 (SD 0.04) for the subset of 112 age- and sex-matched controls (p < 0.001). For 7 patients with both pre- and postoperative CT scans available for evaluation, the mean postoperative MI was 0.55 (SD 0.03), significantly greater than their preoperative MIs (mean 0.48 [SD 0.04], p = 0.001) and comparable to the mean MI of the controls (p = 0.30). In 4 patients, clinically obtained postoperative MIs by caliper measurement were comparable to measurements derived from CT (p = 0.141). CONCLUSIONS The MI is a useful measurement of the severity of trigonocephaly in patients with metopic synostosis. This simple quantitative assessment can potentially be used in the clinical setting to guide preoperative evaluation, surgical repair, and postoperative degree of correction.

Automated Detection of Small Pulmonary Nodules in Whole Lung CT Scans

2007 ◽  
Vol 14 (5) ◽  
pp. 579-593 ◽  
Author(s):  
Andinet A. Enquobahrie ◽  
Anthony P. Reeves ◽  
David F. Yankelevitz ◽  
Claudia I. Henschke
Keyword(s):  
Pulmonary Nodules ◽  
Automated Detection ◽  
Ct Scans ◽  
Small Pulmonary Nodules ◽  
Lung Ct

scholarly journals The quantitative assessment of interstitial lung disease with positron emission tomography scanning in systemic sclerosis patients

Rheumatology ◽  
2019 ◽  
Vol 59 (6) ◽  
pp. 1407-1415 ◽  
Author(s):  
Daphne M Peelen ◽  
Ben G J C Zwezerijnen ◽  
Esther J Nossent ◽  
Lilian J Meijboom ◽  
Otto S Hoekstra ◽  
...  
Keyword(s):  
High Resolution ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Fdg Pet ◽  
Quantitative Assessment ◽  
Ct Scanning ◽  
Ct Scans ◽  
Pet Ct ◽  
18F Fdg ◽  
Fdg Pet Ct

Abstract Objectives The reversibility of interstitial lung disease (ILD) in SSc is difficult to assess by current diagnostic modalities and there is clinical need for imaging techniques that allow for treatment stratification and monitoring. 18F-Fluorodeoxyglucose (FDG) PET/CT scanning may be of interest for this purpose by detection of metabolic activity in lung tissue. This study aimed to investigate the potential role of 18F-FDG PET/CT scanning for the quantitative assessment of SSc-related active ILD. Methods 18F-FDG PET/CT scans and high resolution CT scans of eight SSc patients, including five with ILD, were analysed. For comparison, reference groups were included: eight SLE patients and four primary Sjögren’s syndrome (pSS) patients, all without ILD. A total of 22 regions of interest were drawn in each patient at apical, medial and dorsobasal lung levels. 18F-FDG uptake was measured as mean standardized uptake value (SUVmean) in each region of interest. Subsequently, basal/apical (B/A) and medial/apical (M/A) ratios were calculated at patient level (B/A-p and M/A-p) and at tissue level (B/A-t and M/A-t). Results SUVmean values in dorsobasal ROIs and B/A-p ratios were increased in SSc with ILD compared with SSc without ILD (P = 0.04 and P = 0.07, respectively), SLE (P = 0.003 and P = 0.002, respectively) and pSS (P = 0.03 and P = 0.02, respectively). Increased uptake in the dorsobasal lungs and increased B/A-t ratios corresponded to both ground glass and reticulation on high resolution CT. Conclusion Semi-quantitative assessment of 18F-FDG PET/CT is able to distinguish ILD from non-affected lung tissue in SSc, suggesting that it may be used as a new biomarker for SSc-ILD disease activity.

scholarly journals Quantitative Computerized Two-Point Correlation Analysis of Lung CT Scans Correlates With Pulmonary Function in Pulmonary Sarcoidosis

CHEST Journal ◽  
2012 ◽  
Vol 142 (6) ◽  
pp. 1589-1597 ◽  
Author(s):  
Barbaros Selnur Erdal ◽  
Elliott D. Crouser ◽  
Vedat Yildiz ◽  
Mark A. King ◽  
Andrew T. Patterson ◽  
...  
Keyword(s):  
Correlation Analysis ◽  
Pulmonary Function ◽  
Pulmonary Sarcoidosis ◽  
Ct Scans ◽  
Point Correlation ◽  
Lung Ct

scholarly journals Deep CNN-Based CAD System for COVID-19 Detection Using Multiple Lung CT Scans (Preprint)

10.2196/27468 ◽  
2021 ◽  
Author(s):  
Mustafa Ghaderzadeh ◽  
Farkhondeh Asadi ◽  
Ramezan Jafari ◽  
Davood Bashash ◽  
Hassan Abolghasemi ◽  
...  
Keyword(s):  
Ct Scans ◽  
Cad System ◽  
Deep Cnn ◽  
Lung Ct

scholarly journals DIAG a Diagnostic Web Application Based on Lung CT Scan Images and Deep Learning

2021 ◽  
Author(s):  
Amel Imene Hadj Bouzid ◽  
Said Yahiaoui ◽  
Anis Lounis ◽  
Sid-Ahmed Berrani ◽  
Hacène Belbachir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Web Application ◽  
Patient Management ◽  
Image Interpretation ◽  
Ct Scans ◽  
Diagnostic Tools ◽  
The World ◽  
Aided Diagnosis ◽  
Lung Ct ◽  
Lung Ct Scan

Coronavirus disease is a pandemic that has infected millions of people around the world. Lung CT-scans are effective diagnostic tools, but radiologists can quickly become overwhelmed by the flow of infected patients. Therefore, automated image interpretation needs to be achieved. Deep learning (DL) can support critical medical tasks including diagnostics, and DL algorithms have successfully been applied to the classification and detection of many diseases. This work aims to use deep learning methods that can classify patients between Covid-19 positive and healthy patient. We collected 4 available datasets, and tested our convolutional neural networks (CNNs) on different distributions to investigate the generalizability of our models. In order to clearly explain the predictions, Grad-CAM and Fast-CAM visualization methods were used. Our approach reaches more than 92% accuracy on 2 different distributions. In addition, we propose a computer aided diagnosis web application for Covid-19 diagnosis. The results suggest that our proposed deep learning tool can be integrated to the Covid-19 detection process and be useful for a rapid patient management.

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