4 results match your criteria: "2406 Seamans Center for the Engineering Art and Science[Affiliation]"

A computed tomography imaging-based subject-specific whole-lung deposition model.

Eur J Pharm Sci

October 2022

Department of Mechanical Engineering, 2406 Seamans Center for the Engineering Art and Science, University of Iowa, Iowa City, Iowa 52242, USA; IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA; Department of Radiology, University of Iowa, Iowa City, Iowa, USA. Electronic address:

The respiratory tract is an important route for beneficial drug aerosol or harmful particulate matter to enter the body. To assess the therapeutic response or disease risk, whole-lung deposition models have been developed, but were limited by compartment, symmetry or stochastic approaches. In this work, we proposed an imaging-based subject-specific whole-lung deposition model.

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Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease and the traditional variables extracted from computed tomography (CT) images may not be sufficient to describe all the topological features of lung tissues in COPD patients. We employed an unsupervised three-dimensional (3D) convolutional autoencoder (CAE)-feature constructor (FC) deep learning network to learn from CT data and derive tissue pattern-clusters jointly. We then applied exploratory factor analysis (EFA) to discover the unobserved latent traits (factors) among pattern-clusters.

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Background: Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping.

Methods: An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics.

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Background: Classification of COPD is usually based on the severity of airflow, which may not sensitively differentiate subpopulations. Using a multiscale imaging-based cluster analysis (MICA), we aim to identify subpopulations for current smokers with COPD.

Methods: Among the SPIROMICS subjects, we analyzed computed tomography images at total lung capacity (TLC) and residual volume (RV) of 284 current smokers.

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