Improved precision of noise estimation in CT with a volume-based approach

AbstractAssessment of image noise is a relevant issue in computed tomography (CT). Noise is routinely measured by the standard deviation of density values (Hounsfield units, HU) within a circular region of interest (ROI). We explored the effect of a spherical volume of interest (VOI) on noise measurements. Forty-nine chronic obstructive pulmonary disease patients underwent CT with clinical protocol (regular dose [RD], volumetric CT dose index [CTDIvol] 3.04 mGy, 64-slice unit), and ultra-low dose (ULD) protocol (median CTDIvol 0.38 mGy, dual-source unit). Noise was measured in 27 1-cm2 ROIs and 27 0.75-cm3 VOIs inside the trachea. Median true noise was 21 HU (range 17-29) for RD-CT and 33 HU (26-39) for ULD-CT. The VOI approach resulted in a lower mean distance between limits of agreement compared to ROI: 5.9 versus 10.0 HU for RD-CT (−40%); 4.7 versus 9.9 HU for ULD-CT (−53%). Mean systematic bias barely changed: −1.6 versus −0.9HU for RD-CT; 0.0 to 0.4HU for ULD-CT. The average measurement time was 6.8 s (ROI) versus 9.7 (VOI), independent of dose level. For chest CT, measuring noise with a VOI-based instead of a ROI-based approach reduces variability by 40-53%, without a relevant effect on systematic bias and measurement time.

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COPD Imaging on a 3rd Generation Dual-Source CT: Acquisition of Paired Inspiratory-Expiratory Chest Scans at an Overall Reduced Radiation Risk

Diagnostics ◽

10.3390/diagnostics10121106 ◽

2020 ◽

Vol 10 (12) ◽

pp. 1106

Author(s):

Joshua Gawlitza ◽

Thomas Henzler ◽

Frederik Trinkmann ◽

Elke Nekolla ◽

Holger Haubenreisser ◽

...

Keyword(s):

Radiation Risk ◽

Chronic Obstructive ◽

Dual Source Ct ◽

Organ Doses ◽

Obstructive Pulmonary Disease ◽

Risk Levels ◽

Copd Patients ◽

Dual Source ◽

Effective Doses ◽

Conventional Ct

As stated by the Fleischner Society, an additional computed tomography (CT) scan in expiration is beneficial in patients with chronic obstructive pulmonary disease (COPD). It was thus the aim of this study to evaluate the radiation risk of a state-of-the-art paired inspiratory-expiratory chest scan compared to inspiration-only examinations. Radiation doses to 28 organs were determined for 824 COPD patients undergoing routine chest examinations at three different CT systems–a conventional multi-slice CT (MSCT), a 2nd generation (2nd-DSCT), and 3rd generation dual-source CT (3rd-DSCT). Patients examined at the 3rd-DSCT received a paired inspiratory-expiratory scan. Organ doses, effective doses, and lifetime attributable cancer risks (LAR) were calculated. All organ and effective doses were significantly lower for the paired inspiratory-expiratory protocol (effective doses: 4.3 ± 1.5 mSv (MSCT), 3.0 ± 1.2 mSv (2nd-DSCT), and 2.0 ± 0.8 mSv (3rd-DSCT)). Accordingly, LAR was lowest for the paired protocol with an estimate of 0.025 % and 0.013% for female and male patients (50 years) respectively. Image quality was not compromised. Paired inspiratory-expiratory scans can be acquired on 3rd-DSCT systems at substantially lower dose and risk levels when compared to inspiration-only scans at conventional CT systems, offering promising prospects for improved COPD diagnosis.

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U-Net Based Lung Image Segmentation for Lung Disease Detection

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1336.0982s1119 ◽

2019 ◽

Vol 8 (2S11) ◽

pp. 2743-2748

Keyword(s):

Image Segmentation ◽

Rural Areas ◽

Lung Diseases ◽

Region Of Interest ◽

Semantic Segmentation ◽

World Health ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Treatment And Prevention ◽

Health Organization

Lung diseases are becoming a worldwide health problem. World Health Organization estimates that by 2030, lung diseases such as Chronic Obstructive Pulmonary Disease will be one of the leading cause of mortality. Accurate and timely detection of lung diseases may prevent further death. It is therefore vital that its early detection will lead to treatment and prevention of mortality among patients. However, the scarcity of expert or well-trained radiologists reading CXR images might delay the timely diagnosis of lung diseases especially in rural areas where the scarcity is felt. In order to aid radiologist in reading CXR images, a computer aided tool is proposed for faster and more accurate reading of CXR images. To prepare the image for processing, it need to be segmented to make it easier for the computer to understand. The goal of image segmentation in medical field is to extract the region of interest in the organ. This study is focused on developing a model that will segment the lung from CXR images. Using U-Net architecture based semantic segmentation, the researchers were able to develop and train a model using a set of 562 CXR images and lung mask images, 70 percent of the images were used for training and 30 percent for testing. The developed model achieved a final training accuracy of 97.55 percent and validation accuracy of 97.37 percent. Validation loss and training loss are also low which indicates that the model can segment lung from CXR images with minimal error. The developed model can then be used in classifying lung diseases by focusing on the segmented image rather than focusing on the entire CXR image.

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Oral and Oropharyngeal Sensory Function in Adults With Chronic Obstructive Pulmonary Disease

American Journal of Speech-Language Pathology ◽

10.1044/2019_ajslp-19-00095 ◽

2020 ◽

Vol 29 (2) ◽

pp. 864-872

Author(s):

Fernanda Borowsky da Rosa ◽

Adriane Schmidt Pasqualoto ◽

Catriona M. Steele ◽

Renata Mancopes

Keyword(s):

Chronic Obstructive Pulmonary Disease ◽

Oral Cavity ◽

Pulmonary Disease ◽

Thermal Sensation ◽

Sensory Function ◽

Control Group ◽

Chronic Obstructive ◽

Obstructive Pulmonary Disease ◽

Age Related ◽

Copd Patients

Introduction The oral cavity and pharynx have a rich sensory system composed of specialized receptors. The integrity of oropharyngeal sensation is thought to be fundamental for safe and efficient swallowing. Chronic obstructive pulmonary disease (COPD) patients are at risk for oropharyngeal sensory impairment due to frequent use of inhaled medications and comorbidities including gastroesophageal reflux disease. Objective This study aimed to describe and compare oral and oropharyngeal sensory function measured using noninstrumental clinical methods in adults with COPD and healthy controls. Method Participants included 27 adults (18 men, nine women) with a diagnosis of COPD and a mean age of 66.56 years ( SD = 8.68). The control group comprised 11 healthy adults (five men, six women) with a mean age of 60.09 years ( SD = 11.57). Spirometry measures confirmed reduced functional expiratory volumes (% predicted) in the COPD patients compared to the control participants. All participants completed a case history interview and underwent clinical evaluation of oral and oropharyngeal sensation by a speech-language pathologist. The sensory evaluation explored the detection of tactile and temperature stimuli delivered by cotton swab to six locations in the oral cavity and two in the oropharynx as well as identification of the taste of stimuli administered in 5-ml boluses to the mouth. Analyses explored the frequencies of accurate responses regarding stimulus location, temperature and taste between groups, and between age groups (“≤ 65 years” and “> 65 years”) within the COPD cohort. Results We found significantly higher frequencies of reported use of inhaled medications ( p < .001) and xerostomia ( p = .003) in the COPD cohort. Oral cavity thermal sensation ( p = .009) was reduced in the COPD participants, and a significant age-related decline in gustatory sensation was found in the COPD group ( p = .018). Conclusion This study found that most of the measures of oral and oropharyngeal sensation remained intact in the COPD group. Oral thermal sensation was impaired in individuals with COPD, and reduced gustatory sensation was observed in the older COPD participants. Possible links between these results and the use of inhaled medication by individuals with COPD are discussed.

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