98%
921
2 minutes
20
In patients with asthma, magnetic resonance imaging (MRI) provides direct measurements of regional ventilation heterogeneity, the etiology of which is not well-understood, nor is the relationship of ventilation abnormalities with lung mechanics. In addition, respiratory resistance and reactance are often abnormal in asthmatics and the frequency dependence of respiratory resistance is thought to reflect ventilation heterogeneity. We acquiredMRIventilation defect maps, forced expiratory volume in one-second (FEV1), and airways resistance (Raw) measurements, and used a computational airway model to explore the relationship of ventilation defect percent (VDP) with simulated measurements of respiratory system resistance (Rrs) and reactance (Xrs).MRIventilation defect maps were experimentally acquired in 25 asthmatics before, during, and after methacholine challenge and these were nonrigidly coregistered to the airway tree model. Using the model coregistered to ventilation defect maps, we narrowed proximal (9th) and distal (14th) generation airways that were spatially related to theMRIventilation defects. The relationships forVDPwith Raw measured using plethysmography (r = 0.79), and model predictions of Rrs>14(r = 0.91,P < 0.0001) and Rrs>9(r = 0.88,P < 0.0001) were significantly stronger (P = 0.005;P = 0.03, respectively) than withFEV1(r = -0.68,P = 0.0001). The slopes for the relationship ofVDPwith simulated lung mechanics measurements were different (P < 0.0001); among these, the slope for theVDP-Xrs0.2relationship was largest, suggesting thatVDPwas dominated by peripheral airway heterogeneity in these patients. In conclusion, as a first step toward understanding potential links between lung mechanics and ventilation defects, impedance predictions were made using a computational airway tree model with simulated constriction of airways related to ventilation defects measured in mild-moderate asthmatics.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4831329 | PMC |
http://dx.doi.org/10.14814/phy2.12761 | DOI Listing |
IEEE Trans Neural Netw Learn Syst
September 2025
In industrial scenarios, semantic segmentation of surface defects is vital for identifying, localizing, and delineating defects. However, new defect types constantly emerge with product iterations or process updates. Existing defect segmentation models lack incremental learning capabilities, and direct fine-tuning (FT) often leads to catastrophic forgetting.
View Article and Find Full Text PDFCell
August 2025
Department of Cardiac Surgery, Jiangsu Provincial Key Laboratory of Critical Care Medicine, Zhongda Hospital, Key Laboratory of Developmental Genes and Human Disease, State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, School of Life Science and
Early organogenesis is a crucial stage in embryonic development, characterized by extensive cell fate specification to initiate organ formation but also by a high susceptibility to developmental defects. Here, we profiled 285 serial sections from six E7.5-E8.
View Article and Find Full Text PDFJ Magn Reson Imaging
September 2025
Department of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Background: Radiation-free four-dimensional (4D) dynamic ultrashort echo time MRI (UTE MRI) enables quantification of ventilation defects in chronic obstructive pulmonary disease (COPD) and preserved ratio impaired spirometry (PRISm) populations.
Purpose: To quantify pulmonary ventilation using 4D UTE MRI in PRISm and COPD populations, and determine its ability to distinguish PRISm from non-COPD subjects.
Study Type: Prospective, cross-sectional.
Brain Commun
August 2025
CNNP Lab (www.cnnp-lab.com), School of Computing, Newcastle University, Newcastle upon Tyne NE4 5BX, United Kingdom.
Non-invasive neuroimaging is important in epilepsy to help identify cerebral abnormalities. Abnormally reduced fractional anisotropy (FA) in deep white matter (WM) from diffusion-weighted imaging (DWI) is widely reported in large multi-cohort studies across all types of epilepsies. However, abnormalities in FA for superficial WM are rarely investigated in epilepsy.
View Article and Find Full Text PDFMach Learn Med Imaging
October 2024
Martinos Center for Biomedical Imaging, MGH & Harvard Medical School.
Parcellation of mesh models for cortical analysis is a central problem in neuroimaging. Most classical and deep learning methods have requisites in terms of mesh topology, requiring inputs that are homeomorphic to a sphere (i.e.
View Article and Find Full Text PDF