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Purpose: To implement and validate an algorithm to determine the statistical errors in self-gated non-contrast-enhanced functional lung imaging.
Methods: A bootstrapping residuals approach to determine the error in quantitative functional lung imaging is proposed. Precision and accuracy of the median error over the lungs, as well as reproducibility of the approach were investigated in 7 volunteers. The algorithm was additionally applied to data acquired in a patient with cystic fibrosis.
Results: The obtained bootstrapping error maps appear comparable to the error maps determined from repeated measurements, and median absolute error values for both methods show comparable median errors when reducing the number of averages. In a volunteer in whom 10 consecutive measurements were carried out, the median functional parameters were ventilation = 0.22 mL gas/mL lung tissue, perfusion amplitude = 0.028, perfusion timing = -82 ms, whereas precision and accuracy of the median error were below 3.2 × 10 mL gas/mL lung for ventilation tissue, 4.4 × 10 for perfusion amplitude, and 11 ms for perfusion timing. In the measurement of the patient, low errors in areas with reduced ventilation support the assessment as real defects.
Conclusion: Using a bootstrapping residuals method, the error of functional lung MRI could be determined without the need for repeated measurements. The error values can be determined reproducibly and can be used as a future means of quality control for functional lung MRI.
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http://dx.doi.org/10.1002/mrm.30367 | DOI Listing |
CNS Neurosci Ther
December 2024
Department of Anesthesiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, China.
Aims: This study aimed to explore the role and underlying mechanisms of brain-derived exosomes in traumatic brain injury-induced acute lung injury (TBI-induced ALI), with a particular focus on the potential regulation of ferroptosis through miRNAs and Scd1.
Methods: To elucidate TBI-induced ALI, we used a TBI mouse model. Exosomes were isolated from the brains of these mice and characterized using TEM and NTA.
Biometrics
October 2024
Evidence Generation and Advanced Analytics Biogen Digital Health, Biogen, Cambridge, MA 02142, United States.
In many clinical contexts, the event of interest could occur multiple times for the same patient. Considerable advancement has been made on developing recurrent event models based on or that use biomarker information. However, less attention has been given to evaluating the prognostic accuracy of a biomarker or a composite score obtained from a fitted recurrent event-rate model.
View Article and Find Full Text PDFEur J Clin Invest
December 2024
First Department of Cardiology, AHEPA University Hospital, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Background: Adults with congenital heart disease (ACHD) can face a lifelong risk of premature cardiovascular events. Endothelial dysfunction and arterial stiffness may be some of the key mechanisms involved. Early identification of endothelial damage in ACHD could be crucial to mitigate the adverse events.
View Article and Find Full Text PDFAnn Ital Chir
December 2024
Department of Anesthesiology, Institute of Anesthesia, Emergency and Critical Care, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, 225002 Yangzhou, Jiangsu, China.
Aim: Intraoperative lung-protective ventilation strategies (LPVS) have been shown to improve lung oxygenation and prevent postoperative pulmonary problems in surgical patients. However, the application of positive end-expiratory pressure (PEEP)-based LPVS in emergency traumatic brain injury (TBI) has not been thoroughly explored. The purpose of this study is to evaluate the effects of drive pressure-guided individualized PEEP on perioperative pulmonary oxygenation, postoperative pulmonary complications, and recovery from neurological injury in patients with TBI.
View Article and Find Full Text PDFPhenomics
October 2024
School of Kinesiology, Shanghai University of Sport, Qingyuanhuan Road, #650, Yangpu District, Shanghai, 200438 China.
Unlabelled: The field of competitive swimming lacks broadly applicable predictive models for talent identification across various age groups of adolescent swimmers. This study aimed to construct a predictive model for athletic talent using machine learning methods based on anthropometric and physiological data. Baseline data were collected from 5444 participants aged 10-18 in Shanghai, China, between 2015 and 2018, with 4969 completing a 3-year follow-up.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!