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1.
BMJ Open Respir Res ; 9(1)2022 09.
Article in English | MEDLINE | ID: covidwho-2137828

ABSTRACT

INTRODUCTION: Breathing pattern disorder (BPD) is an abnormal breathing pattern associated with biochemical, biomechanical and psychophysiological changes. While physiotherapy is often offered, limited evidence-based therapies for BPD are available. Music therapy-based singing exercises have been shown to improve quality of life for individuals with respiratory conditions and may also be beneficial for individuals living with BPD. No study has previously compared these participatory interventions in the treatment of people living with BPD. METHODS AND ANALYSIS: This is a study protocol for an assessor blinded 1:1 randomised controlled trial and qualitative interview study. Forty participants aged 18-40 years who score at least 19 on the Nijmegen Questionnaire (NQ) and do not have any underlying respiratory conditions will be recruited. Participants will be randomised to receive either physiotherapy-led or music therapy-led breathing exercises for 6 weeks. The primary outcome will be between-group difference in NQ post-intervention. Semistructured interviews with a purposive sample of participants will be performed. Qualitative data will be analysed using thematic analysis to better understand participants' intervention and trial experiences. ETHICS AND DISSEMINATION: This study has received ethical approval by Brunel University London College of Health, Medicine and Life Science's Research Ethics Committee (32483-MHR-Mar/2022-38624-3). The anonymised completed dataset will be made available as an open-access file via Brunel University London Figshare and the manuscript containing anonymised patient data will be published in an open-access journal. TRIAL REGISTRATION NUMBER: This trial is registered on the Open Science Framework Registry (https://osf.io/u3ncw).


Subject(s)
Music , Physical Therapists , Adolescent , Adult , Breathing Exercises/methods , Humans , Pilot Projects , Quality of Life , Randomized Controlled Trials as Topic , Respiration , Young Adult
4.
J Appl Physiol (1985) ; 133(5): 1175-1191, 2022 11 01.
Article in English | MEDLINE | ID: covidwho-2108366

ABSTRACT

The longer-term effects of COVID-19 on lung physiology remain poorly understood. Here, a new technique, computed cardiopulmonography (CCP), was used to study two COVID-19 cohorts (MCOVID and C-MORE-LP) at both ∼6 and ∼12 mo after infection. CCP is comprised of two components. The first is collection of highly precise, highly time-resolved measurements of gas exchange with a purpose-built molecular flow sensor based around laser absorption spectroscopy. The second component is estimation of physiological parameters by fitting a cardiopulmonary model to the data set. The measurement protocol involved 7 min of breathing air followed by 5 min of breathing pure O2. One hundred seventy-eight participants were studied, with 97 returning for a repeat assessment. One hundred twenty-six arterial blood gas samples were drawn from MCOVID participants. For participants who had required intensive care and/or invasive mechanical ventilation, there was a significant increase in anatomical dead space of ∼30 mL and a significant increase in alveolar-to-arterial Po2 gradient of ∼0.9 kPa relative to control participants. Those who had been hospitalized had reductions in functional residual capacity of ∼15%. Irrespectively of COVID-19 severity, participants who had had COVID-19 demonstrated a modest increase in ventilation inhomogeneity, broadly equivalent to that associated with 15 yr of aging. This study illustrates the capability of CCP to study aspects of lung function not so easily addressed through standard clinical lung function tests. However, without measurements before infection, it is not possible to conclude whether the findings relate to the effects of COVID-19 or whether they constitute risk factors for more serious disease.NEW & NOTEWORTHY This study used a novel technique, computed cardiopulmonography, to study the lungs of patients who have had COVID-19. Depending on severity of infection, there were increases in anatomical dead space, reductions in absolute lung volumes, and increases in ventilation inhomogeneity broadly equivalent to those associated with 15 yr of aging. However, without measurements taken before infection, it is unclear whether the changes result from COVID-19 infection or are risk factors for more severe disease.


Subject(s)
COVID-19 , Humans , Respiratory Function Tests , Respiration, Artificial , Lung , Respiration
6.
Sensors (Basel) ; 22(19)2022 Sep 26.
Article in English | MEDLINE | ID: covidwho-2043923

ABSTRACT

The worldwide outbreak of the novel Coronavirus (COVID-19) has highlighted the need for a screening and monitoring system for infectious respiratory diseases in the acute and chronic phase. The purpose of this study was to examine the feasibility of using a wearable near-infrared spectroscopy (NIRS) sensor to collect respiratory signals and distinguish between normal and simulated pathological breathing. Twenty-one healthy adults participated in an experiment that examined five separate breathing conditions. Respiratory signals were collected with a continuous-wave NIRS sensor (PortaLite, Artinis Medical Systems) affixed over the sternal manubrium. Following a three-minute baseline, participants began five minutes of imposed difficult breathing using a respiratory trainer. After a five minute recovery period, participants began five minutes of imposed rapid and shallow breathing. The study concluded with five additional minutes of regular breathing. NIRS signals were analyzed using a machine learning model to distinguish between normal and simulated pathological breathing. Three features: breathing interval, breathing depth, and O2Hb signal amplitude were extracted from the NIRS data and, when used together, resulted in a weighted average accuracy of 0.87. This study demonstrated that a wearable NIRS sensor can monitor respiratory patterns continuously and non-invasively and we identified three respiratory features that can distinguish between normal and simulated pathological breathing.


Subject(s)
COVID-19 , Adult , COVID-19/diagnosis , Humans , Monitoring, Physiologic , Respiration , Spectroscopy, Near-Infrared
7.
Physiol Rep ; 10(17): e15452, 2022 09.
Article in English | MEDLINE | ID: covidwho-2030378

ABSTRACT

Split ventilation (using a single ventilator to ventilate multiple patients) is technically feasible. However, connecting two patients with acute respiratory distress syndrome (ARDS) and differing lung mechanics to a single ventilator is concerning. This study aimed to: (1) determine functionality of a split ventilation system in benchtop tests, (2) determine whether standard ventilation would be superior to split ventilation in a porcine model of ARDS and (3) assess usability of a split ventilation system with minimal specific training. The functionality of a split ventilation system was assessed using test lungs. The usability of the system was assessed in simulated clinical scenarios. The feasibility of the system to provide modified lung protective ventilation was assessed in a porcine model of ARDS (n = 30). In bench testing a split ventilation system independently ventilated two test lungs under conditions of varying compliance and resistance. In usability tests, a high proportion of naïve operators could assemble and use the system. In the porcine model, modified lung protective ventilation was feasible with split ventilation and produced similar respiratory mechanics, gas exchange and biomarkers of lung injury when compared to standard ventilation. Split ventilation can provide some elements of lung protective ventilation and is feasible in bench testing and an in vivo model of ARDS.


Subject(s)
Respiratory Distress Syndrome , Animals , Lung , Respiration , Respiration, Artificial , Respiratory Distress Syndrome/therapy , Respiratory Mechanics , Swine
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4599-4603, 2022 07.
Article in English | MEDLINE | ID: covidwho-2018760

ABSTRACT

The COVID-19 pandemic has fueled exponential growth in the adoption of remote delivery of primary, specialty, and urgent health care services. One major challenge is the lack of access to physical exam including accurate and inexpensive measurement of remote vital signs. Here we present a novel method for machine learning-based estimation of patient respiratory rate from audio. There exist non-learning methods but their accuracy is limited and work using machine learning known to us is either not directly useful or uses non-public datasets. We are aware of only one publicly available dataset which is small and which we use to evaluate our algorithm. However, to avoid the overfitting problem, we expand its effective size by proposing a new data augmentation method. Our algorithm uses the spectrogram representation and requires labels for breathing cycles, which are used to train a recurrent neural network for recognizing the cycles. Our augmentation method exploits the independence property of the most periodic frequency components of the spectrogram and permutes their order to create multiple signal representations. Our experiments show that our method almost halves the errors obtained by the existing (non-learning) methods. Clinical Relevance- We achieve a Mean Absolute Error (MAE) of 1.0 for the respiratory rate while relying only on an audio signal of a patient breathing. This signal can be collected from a smartphone such that physicians can automatically and reliably determine respiratory rate in a remote setting.


Subject(s)
COVID-19 , Respiratory Rate , COVID-19/diagnosis , Humans , Machine Learning , Pandemics , Respiration
9.
Sci Rep ; 12(1): 14412, 2022 08 24.
Article in English | MEDLINE | ID: covidwho-2016837

ABSTRACT

This paper describes a novel way to measure, process, analyze, and compare respiratory signals acquired by two types of devices: a wearable sensorized belt and a microwave radar-based sensor. Both devices provide breathing rate readouts. First, the background research is presented. Then, the underlying principles and working parameters of the microwave radar-based sensor, a contactless device for monitoring breathing, are described. The breathing rate measurement protocol is then presented, and the proposed algorithm for octave error elimination is introduced. Details are provided about the data processing phase; specifically, the management of signals acquired from two devices with different working principles and how they are resampled with a common processing sample rate. This is followed by an analysis of respiratory signals experimentally acquired by the belt and microwave radar-based sensors. The analysis outcomes were checked using Levene's test, the Kruskal-Wallis test, and Dunn's post hoc test. The findings show that the proposed assessment method is statistically stable. The source of variability lies in the person-triggered breathing patterns rather than the working principles of the devices used. Finally, conclusions are derived, and future work is outlined.


Subject(s)
Microwaves , Radar , Algorithms , Humans , Monitoring, Physiologic/methods , Respiration , Respiratory Rate , Signal Processing, Computer-Assisted
10.
Indoor Air ; 32(8): e13099, 2022 08.
Article in English | MEDLINE | ID: covidwho-2005271

ABSTRACT

Particle size removal efficiencies for 0.1-1.0 µm ( PSE 0.1 - 1.0 $$ {PSE}_{0.1-1.0} $$ ) and 0.3-1.0 µm ( PSE 0.3 - 1.0 $$ {PSE}_{0.3-1.0} $$ ) diameter of Minimum Efficiency Reporting Value (MERV) filters, an electrostatic enhanced air filter (EEAF), and their two-stage filtration systems were evaluated. Considering the most penetrating particle size was 0.1-0.4 µm particulate matter (PM), the PSE 0.1 - 1.0 $$ {PSE}_{0.1-1.0} $$ as an evaluation parameter deserves more attention during the COVID-19 pandemic, compared to the PSE 0.3 - 1.0 $$ {PSE}_{0.3-1.0} $$ . The MERV 13 filters were recommended for a single-stage filtration system because of their superior quality factor (QF) compared to MERV 6, MERV 8, MERV 11 filters, and the EEAF. Combined MERV 8 + MERV 11 filters have the highest QF compared to MERV 6 + MERV 11 filters and EEAF + MERV 11 filters; regarding 50% of PSE 0.1 - 1.0 $$ {PSE}_{0.1-1.0} $$ as the filtration requirements of two-stage filtration systems, the MERV 8 + MERV 11 filtration system can achieve this value at 1.0 m/s air velocity, while PSE 0.1 - 1.0 $$ {PSE}_{0.1-1.0} $$ values were lower than 50% at 1.5 m/s and 2.0 m/s. EEAF obtained a better PSE 0.3 - 1.0 $$ {PSE}_{0.3-1.0} $$ in the full-recirculated test rig than in the single-pass mode owing to active ionization effects when EEAF was charged by alternating current.


Subject(s)
Air Filters , Air Pollution, Indoor , COVID-19 , Air Conditioning , Air Pollution, Indoor/analysis , Filtration , Heating , Humans , Pandemics , Respiration , Ventilation
12.
J Healthc Eng ; 2022: 6965083, 2022.
Article in English | MEDLINE | ID: covidwho-1950424

ABSTRACT

The upgrading of an emergency use ventilator from a single mandatory volume control mode of ventilation (VEMERS 1.0) to 8 modes of ventilation (VEMERS 2.0) is described. The original VEMERS 1.0 was developed in the midst of the COVID-19 crisis in Chile (April to August 2020) following special but nonetheless strict guidelines specified by local medical associations and national health and scientific ministries. The upgrade to 8 modes of ventilation in VEMERS 2.0 was made possible with minor but transcendental changes to the original architecture. The main contribution of this research is that starting from a functional block diagram of an ICU mechanical ventilator and carrying a systematic analysis, the main function blocks are implemented in such a way that combinations of standard off-the-shelf pneumatic and electronic components can be used. This approach has both economical and technical advantages. No special parts need to be fabricated at all, and because of a wider variety of options, the use of extensively field-proven off-the-shelf commercial components assures better availability and lower costs when compared to that of conventional ICU mechanical ventilators, without sacrificing reliability. Given the promising results obtained with VEMERS 2.0 in the subsequent national certification process, the production of 40 VEMERS 2.0 units was sponsored by the Ministry of Science and the Ministry of Economy. Twenty units have been distributed among hospitals along the country. The purpose of VEMERS 2.0, as a low-cost but very reliable option, is to increase the number of mechanical ventilators available (3,000 for a population of 18,000,000) in the country to eventually reach a ratio similar to that of more developed countries. VEMERS is an open-source project for others to use the knowledge gained.


Subject(s)
COVID-19 , COVID-19/therapy , Humans , Reproducibility of Results , Respiration , Respiration, Artificial/methods , Ventilators, Mechanical
13.
IEEE Trans Biomed Circuits Syst ; 16(4): 664-678, 2022 08.
Article in English | MEDLINE | ID: covidwho-1948843

ABSTRACT

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.


Subject(s)
COVID-19 , Radar , Algorithms , Humans , Machine Learning , Respiration , Signal Processing, Computer-Assisted
14.
Methods Mol Biol ; 2511: 321-332, 2022.
Article in English | MEDLINE | ID: covidwho-1941386

ABSTRACT

Inflammatory diseases caused by infectious agents such as the SARS-CoV-2 virus can lead to impaired reductive-oxidative (REDOX) balance and disrupted mitochondrial function. Peripheral blood mononuclear cells (PBMCs) provide a useful model for studying the effects of inflammatory diseases on mitochondrial function but can be limited by the need to store these cells by cryopreservation prior to assay. Here, we describe a method for improving and determining PBMC viability with normalization of values to number of living cells. The approach can be applied not only to PBMC samples derived from patients with diseases marked by an altered inflammatory response such as viral infections.


Subject(s)
COVID-19 , Leukocytes, Mononuclear , Cryopreservation/methods , Humans , Leukocytes, Mononuclear/metabolism , Mitochondria , Respiration , SARS-CoV-2
15.
Int J Environ Res Public Health ; 19(11)2022 05 26.
Article in English | MEDLINE | ID: covidwho-1892854

ABSTRACT

This study investigates the effects of face masks on physiological and voice parameters, focusing on cyclists that perform incremental sports activity. Three healthy male subjects were monitored in a climatic chamber wearing three types of masks with different acoustic properties, breathing resistance, and air filtration performance. Masks A and B were surgical masks made of hydrophobic fabric and three layers of non-woven fabric of 100% polypropylene, respectively. Mask S was a multilayer cloth mask designed for sports activity. Mask B and Mask S behave similarly and show lower sound attenuation and sound transmission loss and lower breathing resistance than Mask A, although Mask A exhibits slightly higher filtration efficiency. Similar cheek temperatures were observed for Masks A and B, while a significantly higher temperature was measured with Mask S at incremental physical activity. No differences were found between the masks and the no-mask condition for voice monitoring. Overall, Mask B and Mask S are suitable for sports activities without adverse effects on voice production while ensuring good breathing resistance and filtration efficiency. These outcomes support choosing appropriate masks for sports activities, showing the best trade-off between breathing resistance and filtration efficiency, sound attenuation, and sound transmission loss.


Subject(s)
Masks , Textiles , Bicycling , Filtration , Humans , Male , Respiration
16.
Molecules ; 27(11)2022 Jun 04.
Article in English | MEDLINE | ID: covidwho-1884286

ABSTRACT

Wearing surgical face masks is among the measures taken to mitigate coronavirus disease (COVID-19) transmission and deaths. Lately, concern was expressed about the possibility that gases from respiration could build up in the mask over time, causing medical issues related to the respiratory system. In this research study, the carbon dioxide concentration and ethylene in the breathing zone were measured before and immediately after wearing surgical face masks using the photoacoustic spectroscopy method. From the determinations of this study, the C2H4 was established to be increased by 1.5% after one hour of wearing the surgical face mask, while CO2 was established to be at a higher concentration of 1.2% after one hour of wearing the surgical face mask, when the values were correlated with the baseline (control).


Subject(s)
COVID-19 , Masks , COVID-19/prevention & control , Gases , Humans , Respiration , SARS-CoV-2 , Spectrum Analysis
17.
IEEE J Biomed Health Inform ; 26(6): 2481-2492, 2022 06.
Article in English | MEDLINE | ID: covidwho-1878964

ABSTRACT

OBJECTIVE: At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. METHODS: In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. MAJOR RESULTS: We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. CONCLUSION: ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. SIGNIFICANCE: We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.


Subject(s)
COVID-19 , Respiratory Rate , Artifacts , Electrocardiography , Humans , Respiration , Signal Processing, Computer-Assisted
18.
Biosensors (Basel) ; 12(5)2022 May 15.
Article in English | MEDLINE | ID: covidwho-1875483

ABSTRACT

Respiration monitoring is a very important indicator of health status. It can be used as a marker in the recognition of a variety of diseases, such as sleep apnea, asthma or cardiac arrest. The purpose of the present study is to overcome limitations of the current state of the art in the field of respiration monitoring systems. Our goal was the development of a lightweight handheld device with portable operation and low power consumption. The proposed approach includes a textile capacitive sensor with interdigitated electrodes embroidered into the facemask, integrated with readout electronics. Readout electronics is based on the direct interface of the capacitive sensor and a microcontroller through just one analog and one digital pin. The microcontroller board and sensor are powered by a smartphone or PC through a USB cable. The developed mobile application for the Android™ operating system offers reliable data acquisition and acts as a bridge for data transfer to the remote server. The embroidered sensor was initially tested in a humidity-controlled chamber connected to a commercial impedance analyzer. Finally, in situ testing with 10 volunteering subjects confirmed stable operation with reliable respiration monitoring.


Subject(s)
Masks , Respiration , Humans , Monitoring, Physiologic , Smartphone , Textiles
19.
Crit Care ; 26(1): 157, 2022 06 01.
Article in English | MEDLINE | ID: covidwho-1875020

ABSTRACT

BACKGROUND: Increasing evidence indicates the potential benefits of restricted fluid management in critically ill patients. Evidence lacks on the optimal fluid management strategy for invasively ventilated COVID-19 patients. We hypothesized that the cumulative fluid balance would affect the successful liberation of invasive ventilation in COVID-19 patients with acute respiratory distress syndrome (ARDS). METHODS: We analyzed data from the multicenter observational 'PRactice of VENTilation in COVID-19 patients' study. Patients with confirmed COVID-19 and ARDS who required invasive ventilation during the first 3 months of the international outbreak (March 1, 2020, to June 2020) across 22 hospitals in the Netherlands were included. The primary outcome was successful liberation of invasive ventilation, modeled as a function of day 3 cumulative fluid balance using Cox proportional hazards models, using the crude and the adjusted association. Sensitivity analyses without missing data and modeling ARDS severity were performed. RESULTS: Among 650 patients, three groups were identified. Patients in the higher, intermediate, and lower groups had a median cumulative fluid balance of 1.98 L (1.27-7.72 L), 0.78 L (0.26-1.27 L), and - 0.35 L (- 6.52-0.26 L), respectively. Higher day 3 cumulative fluid balance was significantly associated with a lower probability of successful ventilation liberation (adjusted hazard ratio 0.86, 95% CI 0.77-0.95, P = 0.0047). Sensitivity analyses showed similar results. CONCLUSIONS: In a cohort of invasively ventilated patients with COVID-19 and ARDS, a higher cumulative fluid balance was associated with a longer ventilation duration, indicating that restricted fluid management in these patients may be beneficial. Trial registration Clinicaltrials.gov ( NCT04346342 ); Date of registration: April 15, 2020.


Subject(s)
COVID-19 , Noninvasive Ventilation , Respiratory Distress Syndrome , COVID-19/therapy , Cohort Studies , Humans , Respiration , Respiratory Distress Syndrome/therapy , Water-Electrolyte Balance
20.
Invest Radiol ; 57(11): 742-751, 2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-1874065

ABSTRACT

OBJECTIVES: With the COVID-19 pandemic, repetitive lung examinations have become necessary to follow-up symptoms and associated alterations. Low-field MRI, benefiting from reduced susceptibility effects, is a promising alternative for lung imaging to limit radiations absorbed by patients during CT examinations, which also have limited capability to assess functional alterations. The aim of this investigative study was to explore the functional abnormalities that free-breathing 0.55 T MRI in combination with the phase-resolved functional lung (PREFUL) analysis could identify in patients with persistent symptoms after COVID-19 infection. MATERIALS AND METHODS: Seventy-four COVID-19 patients and 8 healthy volunteers were prospectively scanned in free-breathing with a balanced steady-state free-precession sequence optimized at 0.55 T, 5 months postinfection on average. Normalized perfusion (Q), fractional ventilation (FV), and flow-volume loop correlation (FVLc) maps were extracted with the PREFUL technique. Q, FV, and FVLc defects as well as defect overlaps between these metrics were quantified. Morphological turbo-spin-echo images were also acquired, and the extent of abnormalities was scored by a board-certified radiologist. To investigate the functional correlates of persistent symptoms, a recursive feature elimination algorithm was applied to find the most informative variables to detect the presence of persistent symptoms with a logistic regression model and a cross-validation strategy. All MRI metrics, sex, age, body mass index, and the presence of preexisting lung conditions were included. RESULTS: The most informative variables to detect persistent symptoms were the percentage of concurrent Q and FVLc defects and of areas free of those defects. A detection accuracy of 71.4% was obtained with these 2 variables when fitting the model on the entire dataset. Although none of the single variables differed between patients with and without persistent symptoms ( P > 0.05), the combined score of these 2 variables did ( P < 0.02). This score also showed a consistent increase from healthy volunteers (7.7) to patients without persistent symptoms (8.2) and with persistent symptoms (8.6). The morphological abnormality score showed poor correlation with the functional parameters. CONCLUSIONS: Functional pulmonary examinations using free-breathing 0.55 T MRI with PREFUL analysis revealed potential quantitative markers of impaired lung function in patients with persistent symptoms after COVID-19 infection, potentially complementing morphologic imaging. Future work is needed to explore the translational relevance and clinical implication of these findings.


Subject(s)
COVID-19 , Humans , Lung/diagnostic imaging , Magnetic Resonance Imaging/methods , Pandemics , Respiration
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