Real-time Breath Research Articles

Protocol for Project Fizzyo, an analytic longitudinal observational cohort study of physiotherapy for children and young people with cystic fibrosis, with interrupted time-series design

IntroductionDaily physiotherapy is believed to mitigate the progression of cystic fibrosis (CF) lung disease. However, physiotherapy airway clearance techniques (ACTs) are burdensome and the evidence guiding practice remains weak. This...

Novel barium hexaferrite based highly selective and stable trace ammonia sensor for detection of renal disease by exhaled breath analysis

Exhaled human breath bears the fingerprints of multifarious pathophysiological conditions, and diseases. In this paper we report for the first time a highly sensitive, selective, and stable barium hexaferrite based sensor for the detection of trace ammonia vapor in exhaled human breath, the biomarker for renal diseases. Barium hexaferrite nanoparticles were synthesized by a facile solid-state reaction route. The as prepared nanopowder and the sensor film were well characterized by using XRD, FESEM, TEM, EDX, XPS, BET, and I-V measurements. The fabricated sensor delineates p-type behavior and the capability to detect ammonia down to 0.2 ppm with reasonably high response of ∼1.46 folds. Further, the sensor showed remarkable response of ∼2.34 folds towards 1 ppm ammonia. The sensor is practically insensitive towards similar concentrations of other major interfering breath volatiles, viz. acetone, ethanol, and saturated moisture. Also, the sensor exhibited fast response (∼2.88 s), and recovery (∼39.4 s) times ensuring real time breath analysis. Finally, long-term stability of the sensor for more than three months renders it suitable for commercial applications.

Real-time breath analysis of exhaled compounds upon peppermint oil ingestion by secondary electrospray ionization-high resolution mass spectrometry: technical aspects

Breath analysis by secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) has potential for clinical diagnosis and drug monitoring. However, there is still a lack of benchmarking data that shows the capability of this technique and allows comparability with other breath analysis techniques. In this regard, the goal of this study was the identification of volatile compounds upon ingestion of a specific peppermint oil capsule to get benchmark data for real-time breath analysis with SESI-HRMS. This was done in the framework of a consortium set up by the International Association of Breath Research (IABR), aimed at comparing several analytical instruments for breath analysis.Breath temporal profiles of two subjects were analyzed with SESI-HRMS before and after ingestion of a peppermint oil capsule. The measurements were performed at two different locations using identical SESI-HRMS platforms to allow for comparability and benchmarking. Remarkably, along with the four major compounds (monoterpenes/cineole, menthone, menthofuran and menthol) reported by other members of the consortium, we detected 57 additional features significantly associated (ρ > 0.8) with the peppermint oil capsule, suggesting that this relatively simple intervention might trigger a more complex metabolic cascade than initially expected. This observation was made on both sites. Additional replicate experiments for one of the subjects suggested that a core of 35–40 unique molecules are consistently detected in exhaled breath upon ingestion of the capsule. In addition, we illustrate the analytical capabilities of real-time SESI-HRMS/MS to assist in the identification of unknown compounds. The results outlined herein showcase the performance of SESI-HRMS and enable comparison with other breath analysis techniques. Along with that, they strengthen the potential of this analytical technique for non-invasive drug monitoring and clinical diagnostic purposes.

Textile-Based Capacitive Sensor for Physical Rehabilitation via Surface Topological Modification.

Wearable sensor technologies, especially continuous monitoring of various human health conditions, are attracting increased attention. However, current rigid sensors present obvious drawbacks, like lower durability and poor comfort. Here, a strategy is proposed to efficiently yield wearable sensors using cotton fabric as an essential component, and conductive materials conformally coat onto the cotton fibers, leading to a highly electrically conductive interconnecting network. To improve the conductivity and durability of conductive coatings, a topographical modification approach is developed with genus-3 and genus-5 structures, and topological genus structures enable cage metallic seeds on the surface of substrates. A textile-based capacitive sensor with flexible, comfortable, and durable properties has been demonstrated. High sensitivity and convenience of signal collection have been achieved by the excellent electrical conductivity of this sensor. Based on results of deep investigation on capacitance, effects of distance and angles between two conductive fabrics contribute to the capacitive sensitivity. In addition, the textile-based capacitive sensor has successfully been used for real-time monitoring human breathing, speaking, blinking, and joint motions during physical rehabilitation exercises.

Microstructured optical fiber based Fabry\u2013P\xe9rot interferometer as a humidity sensor utilizing chitosan polymeric matrix for breath monitoring

This study reports a method for humidity sensing based on a specialty microstructured optical fiber (MOF). A suspended tri-core MOF was fabricated using the stack and draw technique. A low finesse sensing head was prepared by depositing a chitosan polymer matrix within the holes of the MOF, forming a Fabry-Pérot interferometer as a sensing platform while the chitosan film acts as the sensing material. The use of the probe for real-time breath monitoring was also successfully demonstrated. The probe possessed a maximum sensitivity of 81.05 pm/(%RH) for 90–95%RH range while the linear region of the sensor ranged from 70–95%RH. The temperature cross correlation was also experimented, and a lower influence of external temperature was observed. The probe shows an ultrafast response during human breath monitoring with a rising time and recovery time of 80 ms and 70 ms, respectively.

Development and Validation of a MATLAB Software Program for Decoding the Treatment Errors in Real-time Position Management™ Gating-generated Breathing Trace.

Introduction:The Real-time Position Management™ (RPM) is used as a motion management tool to reduce normal tissue complication. However, no commercial software is available to quantify the “beam-on” errors in RPM-generated breathing traces. This study aimed to develop and validate an in-house-coded MATLAB program to quantify the “beam-on” errors in the breathing trace.Materials and Methods:A graphical user interface (GUI) was developed using MATLAB (Matrix Laboratory Ra2016) software. The GUI was validated using two phantoms (Varian-gated phantom and Brainlab ET gating phantom) with three regular motion profiles. Treatment time delay was calculated using regular sinusoidal motion profile. Ten patient's irregular breathing profiles were also analyzed using this GUI.Results:The beam-on comparison between the recorded reference trace and irradiated trace profile was done in two ways: (1) beam-on time error and (2) beam-on displacement error. These errors were ≤1.5% with no statistical difference for phase- and amplitude-based treatments. The predicated amplitude levels of reference phase-based profiles, and the actual amplitude levels of amplitude-based irradiated profiles were almost equal. The average treatment time delay was 47 ± 0.003 ms. The irregular breathing profile analysis showed that the amplitude-based gating treatment was more accurate than phase based.Conclusion:The developed GUI gave the same and acceptable results for all regular profiles. These errors were due to the lag time of the linear accelerator with gating treatment. This program can be used as to quantifying the intrafraction “beam-on” errors in breathing trace with both mode of gating techniques for irregular breathing trace, and in addition, it is capable to convert phase-based gating parameters to amplitude-based gating parameters for treatment.

A Multimodal Wearable System for Continuous and Real-Time Breathing Pattern Monitoring During Daily Activity.

This study aims to understand breathing patterns during daily activities by developing a wearable respiratory and activity monitoring (WRAM) system. A novel multimodal fusion architecture is proposed to calculate the respiratory and exercise parameters and simultaneously identify human actions. A hybrid hierarchical classification (HHC) algorithm combining deep learning and threshold-based methods is presented to distinguish 15 complex activities for accuracy enhancement and fast computation. A series of signal processing algorithms are utilized and integrated to calculate breathing and motion indices. The designed wireless communication structure achieves the interactions among chest bands, mobile devices, and the data processing center. The advantage of the proposed HHC method is evaluated by comparing the average accuracy (97.22%) and predictive time (0.0094s) with machine learning and deep learning approaches. The nine breathing patterns during 15 activities were analyzed by investigating the data from 12 subjects. With 12 hours of naturalistic data collected from one participant, the WRAM system reports the breathing and exercise performance within the identified motions. The demonstration shows the ability of the WRAM system to monitor multiple users breathing and exercise status in real-time. The present system demonstrates the usefulness of the framework of breathing pattern monitoring during daily activities, which may be potentially used in healthcare. The proposed multimodal based WRAM system offers new insights into the breathing function of exercise in action and presents a novel approach for precision medicine and health state monitoring.

Design of seed respiration measurement system using virtual instrument

In the seed breathing CO2 detection system, the traditional method cannot measure the concentration of CO2 in the seed breathing in real time. According to the characteristics of seed breathing CO2, a seed breathing detection system based on virtual instrument LabVIEW is designed based on tunable diode laser absorption spectroscopy (TDLAS). The system mainly includes laser light source and its controller, seed breathing container based on multiple reflecting pool structure. The upper computer software is mainly set with data acquisition, signal processing, concentration inversion and other functional modules, in which the concentration inversion uses the orthogonal vector phase-locked amplification algorithm to avoid the error caused by the phase difference between the reference signal and the signal to be measured. The experimental results show that the CO2 detection system for seed respiration implemented by virtual instrument software can effectively detect the change of seed respiration, and has good anti-interference and stability, which lays a foundation for the subsequent experimental research and development.

Non-Invasive Assessment of Metabolic Adaptation in Paediatric Patients Suffering from Type 1 Diabetes Mellitus.

An analysis of exhaled volatile organic compounds (VOC) may deliver systemic information quicker than available invasive techniques. Metabolic aberrations in pediatric type 1 diabetes (T1DM) are of high clinical importance and could be addressed via breathomics. Real-time breath analysis was combined with continuous glucose monitoring (CGM) and blood tests in children suffering from T1DM and age-matched healthy controls in a highly standardized setting. CGM and breath-resolved VOC analysis were performed every 5 minutes for 9 hours and blood was sampled at pre-defined time points. Per participant (n = 44) food intake and physical activity were identical and a total of 22 blood samples and 93 minutes of breath samples were investigated. The inter-individual variability of glucose, insulin, glucagon, leptin, and soluble leptin receptor relative to food intake differed distinctly between patients and controls. In T1DM patients, the exhaled amounts of acetone, 2-propanol, and pentanal correlated to glucose concentrations. Of note, the strength of these correlations strongly depended on the interval between food intake and breath sampling. Our data suggests that metabolic adaptation through postprandial hyperglycemia and related oxidative stress is immediately reflected in exhaled breath VOC concentrations. Clinical translations of our findings may enable point-of-care applicability of online breath analysis towards personalized medicine.

Wearable Cardiopulmonary Function Evaluation System for Six-Minute Walking Test.

As a submaximal exercise test, a 6-min walking test (6MWT) can be considered a suitable index for the exercise capacity of patients with a respiratory problem. Traditionally, medical staff manually collect cardiopulmonary information using different devices. However, no integrated monitoring system is currently available to simultaneously record the real-time breathing sound, heart rhythm, and precise walking information (i.e., walking distance, speed, and acceleration) during the 6MWT. In this study, a wearable and wireless multiparameter monitoring system is proposed to simultaneously monitor the breathing sound, oxygen saturation (SpO2), electrocardiograph (ECG) signals, and precise walking information during the 6MWT. Here, a wearable mechanical design was successfully used to reduce the effect of motion artifacts on the breathing sound and ECG signal. A multiparameter detection algorithm was designed to effectively estimate heart and breathing rates. Finally, the cardiopulmonary function of smokers was evaluated using the proposed system. The evaluation indicated that this system could reveal dynamic changes and differences in the breathing rate, heart rate, SpO2, walking speed, and acceleration during the 6MWT. The proposed system can serve as a more integrated approach to monitor cardiopulmonary parameters and obtain precise walking information simultaneously during the 6MWT.

Low-dose ramosetron accelerates gastric emptying in the early phase: A crossover study in healthy volunteers using a continuous real-time 13C breath test (BreathID System).

The aim of this study was to determine the correlation between low-dose ramosetron pre-treatment and gastric emptying using a novel, non-invasive technique for measuring gastric emptying, namely, the continuous real-time 13C breath test (BreathID system: Exalenz Bioscience Ltd., Israel). Twelve healthy male volunteers participated in this randomized two-way crossover study. The subjects fasted overnight and were randomly assigned to receive the test meal (200 kcal per 200 mL) after an hour pre-treatment with 5 µg ramosetron or the test meal alone. Gastric emptying was monitored for 4 hours after administration of the test meal with the 13C-acetic acid breath test performed continuously using the BreathID system. Using Oridion Research Software (β version), T 1/2, T lag, GEC and the regression-estimated constants (β and κ) were calculated. The differences in the parameters measured at two time-points were analyzed using Wilcoxon's signed-rank test. There was a significant difference in the calculated parameter β. No significant differences in the calculated parameters T 1/2, T lag, GEC or κ were observed between the test meal with ramosetron group and the test meal alone group. This study showed that ramosetron pre-treatment enhances the early gastric emptying of liquid nutrients.

Breath-based human–machine interaction system using triboelectric nanogenerator

Human-machine interface (HMI) is an emerging technology to exchange information between humans and electric devices. In current HMI devices, their crucial sensing components are mainly based on the interaction of physical movements and languages. However, these conventional HMI interacting methods are not friendly to some disabled persons owing to the difficulty to clearly express their intentions with gestures, limb motions, or languages. Additionally, these HMI sensing parts rely on traditional batteries, which require frequent charging or replacing. Here, we report a breath-driven single-electrode triboelectric nanogenerator (TENG) serving as a self-powered sensor to deliver control command through breathing for the HMI interaction. The TENG is on the basis of a flexible nanowire-structured polyethylene terephthalate (PET) polymer thin film, which can harvest mechanical energy of the airflow from human breathing and produce responsive electrical signals. By using this TENG as a self-powered sensor together with signal processing and transmitting circuits, a smart wireless breath-driven HMI system is further developed. Compared with the conventional HMI interacting methods, this smart system can convert real-time human breathing into command signals to control electrical appliances without relying on physical movements or languages. This work introduces a self-powered breath-based interacting method to the field of HMI technology. It could greatly reduce the threshold of using modern electrical equipment and personal electronics for disabled people, and make the HMI interaction become more convenient and captivating.

Proton transfer reaction time-of-flight mass spectrometric measurements of volatile compounds contained in peppermint oil capsules of relevance to real-time pharmacokinetic breath studies

With the growing interest in the use of breath volatiles in the health sciences, the lack of standardization for the sampling and analysis of exhaled breath is becoming a major issue leading to an absence of conformity, reproducibility and reliability in spectrometric measurements. Through the creation of a worldwide ‘peppermint consortium’, the International Association of Breath Research has set up a task force to deal with this problem. Pharmacokinetic studies are proposed, and a real-time analytical technique that is being used is proton transfer reaction-time-of-flight-mass spectrometry (PTR-ToF-MS). This paper presents details on how the volatile compounds contained in a peppermint oil capsule, and hence on breath, appear in a PTR-ToF-MS. To aid that study, the key volatiles in the headspace of peppermint oil were first identified using gas chromatography-mass spectrometry, notably: menthol, menthone, 1,8-cineole, menthofuran, limonene, α-pinene and β-pinene. A PTR-ToF-MS analysis of these compounds has been undertaken, divorced from the complexity of the peppermint oil matrix using ‘normal’ and ‘saturated’ humidity drift-tube conditions, with the latter used to mimic breath samples, and over a range of reduced electric fields. There are no characteristic product ions that can distinguish monoterpenes and 1,8-cineole, and hence, without pre-separation, a combined washout for these volatiles can only be provided. By operating the drift tube above about 130 Td, there are characteristic product ions for menthone, menthofuran and menthol, namely m/z 155.14 (protonated menthone), m/z 151.11 (protonated menthofuran), m/z 139.15 (loss of H2O from protonated menthol) and m/z 83.09 (a fragment ion, C6H11+, from menthol). These have been used to monitor, with a high specificity, the temporal profile of these three compounds in breath following the ingestion of a peppermint oil capsule. To aid in the analyses, the proton affinities and gas-phase basicities for the key volatiles investigated have been determined using density functional theory.

Breathing detection from tracheal sounds in both temporal and frequency domains in the context of phrenic nerve stimulation.

Electrical stimulation of the phrenic nerves via implanted devices allows to counteract some disadvantages of mechanical ventilation in patients with high tetraplegia or Ondine's syndrome. Existing devices do not allow to monitor breathing or to adapt the electroventilation to patients' actual needs. A reliable breathing monitor with an inbuilt alarm function would improve patient safety. In our study, a real-time acoustic breathing detection method is proposed as a possible solution to improve implanted phrenic stimulation. A new algorithm to process tracheal sounds has been developed. It combines breathing detection in both temporal and frequency domains. The algorithm has been applied on recordings from 18 healthy participants. The obtained average sensitivity, specificity and accuracy of the detection are: 99.31%, 96.84% and 98.02%, respectively. These preliminary results show a first positive proof of the interest of such an approach. Additional experiments are needed, including longer recordings from individuals with tetraplegia or Ondine Syndrome in various environments to go further in the validation.

An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network applied for simultaneously detection of exhaled CO and N2O.

A compact high-resolution spectroscopic sensor using a thermoelectrically (TE) cooled continuous-wave (CW) room temperature (RT) quantum cascade laser (QCL) was demonstrated for simultaneous measurements of exhaled carbon monoxide (CO) and nitrous oxide (N2O). The sampling pressure was optimized to improve the sensitivity, the optimal pressure was determined to be 150 mbar based on an optical density analysis of simulated and measured absorption spectra. An adaptive Kalman filtering algorithm based on back-propagation (BP) neural network was developed and proposed for real-time exhaled breath analysis in order to perform fast and high precision on-line measurements. The detection limits (1σ) of 1.14 ppb and 1.12 ppb were experimentally achieved for CO and N2O detection, respectively. Typical concentrations of exhaled CO and N2O from smokers and non-smokers were analyzed. The experimental results indicated that the state-of-the-art CW-QCL based sensor has a great potential for non-invasive, on-line identification and quantification of biomarkers in human breath.

Detection of Intestinal Tissue Perfusion by Real-Time Breath Methane Analysis in Rat and Pig Models of Mesenteric Circulatory Distress.

Methane (CH4) breath test is an established diagnostic method for gastrointestinal functional disorders. Our aim was to explore the possible link between splanchnic circulatory changes and exhaled CH4 in an attempt to recognize intestinal perfusion failure. Randomized, controlled in vivo animal study. University research laboratory. Anesthetized, ventilated Sprague-Dawley rats (280 ± 30 g) and Vietnamese minipigs (31 ± 7 kg). In the first series, CH4 was administered intraluminally into the ileum before 45 minutes mesenteric ischemia or before reperfusion in non-CH4 producer rats to test the appearance of the gas in the exhaled air. In the porcine experiments, the superior mesenteric artery was gradually obstructed during consecutive, 30-minute flow reductions and 30-minute reperfusions achieving complete occlusion after four cycles (n = 6), or nonocclusive mesenteric ischemia was induced by pericardial tamponade (n = 12), which decreased superior mesenteric artery flow from 351 ± 55 to 182 ± 67 mL/min and mean arterial pressure from 96.7 ± 18.2 to 41.5 ± 4.6 mm Hg for 60 minutes. Macrohemodynamics were monitored continuously; RBC velocity of the ileal serosa or mucosa was recorded by intravital videomicroscopy. The concentration of exhaled CH4 was measured online simultaneously with high-sensitivity photoacoustic spectroscopy. The intestinal flow changes during the occlusion-reperfusion phases were accompanied by parallel changes in breath CH4 output. Also in cardiac tamponade-induced nonocclusive intestinal ischemia, the superior mesenteric artery flow and RBC velocity correlated significantly with parallel changes in CH4 concentration in the exhaled air (Pearson's r = 0.669 or r = 0.632, respectively). we report a combination of in vivo experimental data on a close association of an exhaled endogenous gas with acute mesenteric macro- and microvascular flow changes. Breath CH4 analysis may offer a noninvasive approach to follow the status of the splanchnic circulation.

Wearable and Skin‐Mountable Fiber‐Optic Strain Sensors Interrogated by a Free‐Running, Dual‐Comb Fiber Laser

AbstractWearable and skin‐mountable strain sensors are highly demanding for monitoring skin deformations induced by human activities. Realization of such sensor devices based on fiber‐optic approaches offers attractive advantages such as electromagnetic immunity and inherent electric safety in comparison to their electronic counterparts. However, fiber‐optic sensors, conventionally made of stiff silica fibers, are not mechanically compliant with the soft human skins and have limited strain range (<1%) for measuring large deformations. Here, a stretchable fiber Bragg grating (FBG)‐based optical (SFO) strain sensor with skin‐like compliance for human activity monitoring is presented. The SFO sensor is fabricated by a sinuous‐shaped FBG incorporated with a stretchable substrate that responds to strain deformations by shifting of the Bragg wavelength. This structural design enables measurement of various dynamic strains associated with tension, bending and torsion in a large sensing range up to 50%. To facilitate wearable integrations, a novel free‐running fiber laser with coherent dual‐comb output is developed to interrogate the SFO sensors by dual‐comb spectroscopy, which enables fast spectral acquisition with a single photodiode. It is shown that the SFO strain sensors can be used for wearable and skin‐mountable detection of diverse human activities including breathing, phonation, facial expression, and joint movements in real time.

Standardization procedures for real-time breath analysis by secondary electrospray ionization high-resolution mass spectrometry

Despite the attractiveness of breath analysis as a non-invasive means to retrieve relevant metabolic information, its introduction into routine clinical practice remains a challenge. Among all the different analytical techniques available to interrogate exhaled breath, secondary electrospray ionization high-resolution mass spectrometry (SESI-HRMS) offers a number of advantages (e.g., real-time, yet wide, metabolome coverage) that makes it ideal for untargeted and targeted studies. However, so far, SESI-HRMS has relied mostly on lab-built prototypes, making it difficult to standardize breath sampling and subsequent analysis, hence preventing further developments such as multi-center clinical studies. To address this issue, we present here a number of new developments. In particular, we have characterized a new SESI interface featuring real-time readout of critical exhalation parameters such as CO2, exhalation flow rate, and exhaled volume. Four healthy subjects provided breath specimens over a period of 1 month to characterize the stability of the SESI-HRMS system. A first assessment of the repeatability of the system using a gas standard revealed a coefficient of variation (CV) of 2.9%. Three classes of aldehydes, namely 4-hydroxy-2-alkenals, 2-alkenals and 4-hydroxy-2,6-alkedienals―hypothesized to be markers of oxidative stress―were chosen as representative metabolites of interest to evaluate the repeatability and reproducibility of this breath analysis analytical platform. Median and interquartile ranges (IQRs) of CVs for CO2, exhalation flow rate, and exhaled volume were 3.2% (1.5%), 3.1% (1.9%), and 5.0% (4.6%), respectively. Despite the high repeatability observed for these parameters, we observed a systematic decay in the signal during repeated measurements for the shorter fatty aldehydes, which eventually reached a steady state after three/four repeated exhalations. In contrast, longer fatty aldehydes showed a steady behavior, independent of the number of repeated exhalation maneuvers. We hypothesize that this highly molecule-specific and individual-independent behavior may be explained by the fact that shorter aldehydes (with higher estimated blood-to-air partition coefficients; approaching 100) mainly get exchanged in the airways of the respiratory system, whereas the longer aldehydes (with smaller estimated blood-to-air partition coefficients; approaching 10) are thought to exchange mostly in the alveoli. Exclusion of the first three exhalations from the analysis led to a median CV (IQR) of 6.7 % (5.5 %) for the said classes of aldehydes. We found that such intra-subject variability is in general much lower than inter-subject variability (median relative differences between subjects 48.2%), suggesting that the system is suitable to capture such differences. No batch effect due to sampling date was observed, overall suggesting that the intra-subject variability measured for these series of aldehydes was biological rather than technical. High correlations found among the series of aldehydes support this notion. Finally, recommendations for breath sampling and analysis for SESI-HRMS users are provided with the aim of harmonizing procedures and improving future inter-laboratory comparisons.Graphical abstract

Fitting of single-exhalation profiles using a pulmonary gas exchange model—application to carbon monoxide

Real-time breath gas analysis coupled to gas exchange modeling is emerging as promising strategy to enhance the information gained from breath tests. It is shown for exhaled breath carbon monoxide (eCO), a potential biomarker for oxidative stress and respiratory diseases, that a weighted, nonlinear least-squares fit of simulated to measured expirograms can be used to extract physiological parameters, such as airway and alveolar concentrations and diffusing capacities. Experimental CO exhalation profiles are acquired with high time-resolution and precision using mid-infrared tunable diode laser absorption spectroscopy and online breath sampling. A trumpet model with axial diffusion is employed to generate eCO profiles based on measured exhalation flow rates and volumes. The concept is demonstrated on two healthy non-smokers exhaling at a flow rate of 250 ml s−1 during normal breathing and at 120 ml s−1 after 10 s of breath-holding. The obtained gas exchange parameters of the two subjects are in a similar range, but clearly distinguishable. Over a series of twenty consecutive expirograms, the intra-individual variation in the alveolar parameters is less than 6%. After a 2 h exposure to 10 ± 2 ppm CO, end-tidal and alveolar CO concentrations are significantly increased (by factors of 2.7 and 4.9 for the two subjects) and the airway CO concentration is slightly higher, while the alveolar diffusing capacity is unchanged compared to before exposure. Using model simulations, it is found that a three-fold increase in maximum airway CO flux and a reduction in alveolar diffusing capacity by 60% lead to clearly distinguishable changes in the exhalation profile shape. This suggests that extended breath CO analysis has clinical relevance in assessing airway inflammation and chronic obstructive pulmonary disease. Moreover, the novel methodology contributes to the standardization of real-time breath gas analysis.

High-quality reconstruction of four-dimensional cone beam CT from motion registration prior image

Four-dimensional cone beam CT (4D-CBCT) imaging can provide accurate location information of real-time breathing for imaging-guided radiotherapy. How to improve the accuracy of 4D-CBCT reconstruction image is a hot topic in current studies. PICCS algorithm performs remarkably in all 4D-CBCT reconstruction algorithms based on CS theory. The improved PICCS algorithm proposed in this paper improves the prior image on the basis of the traditional PICCS algorithm. According to the location information of each phase, the corresponding prior image is constructed, which completely eliminates the motion blur of the reconstructed image caused by the mismatch of the projection data. Meanwhile, the data fidelity model of the proposed method is consistent with the traditional PICCS algorithm. The experimental results showed that the reconstructed image using the proposed method had a clearer organization boundary compared with that of images reconstructed using the traditional PICCS algorithm. This proposed method significantly reduced the motion artifact and improved the image resolution.