Breathing Phases Research Articles

Investigation of thoracic four-dimensional CT-based dimension reduction technique for extracting the robust radiomic features

Robust feature selection in radiomic analysis is often implemented using the RIDER test-retest datasets. However, the CT Protocol between the facility and test-retest datasets are different. Therefore, we investigated possibility to select robust features using thoracic four-dimensional CT (4D-CT) scans that are available from patients receiving radiation therapy.In 4D-CT datasets of 14 lung cancer patients who underwent stereotactic body radiotherapy (SBRT) and 14 test-retest datasets of non-small cell lung cancer (NSCLC), 1170 radiomic features (shape: n = 16, statistics: n = 32, texture: n = 1122) were extracted. A concordance correlation coefficient (CCC) > 0.85 was used to select robust features. We compared the robust features in various 4D-CT group with those in test-retest.The total number of robust features was a range between 846/1170 (72%) and 970/1170 (83%) in all 4D-CT groups with three breathing phases (40%–60%); however, that was a range between 44/1170 (4%) and 476/1170 (41%) in all 4D-CT groups with 10 breathing phases. In test-retest, the total number of robust features was 967/1170 (83%); thus, the number of robust features in 4D-CT was almost equal to that in test-retest by using 40–60% breathing phases.In 4D-CT, respiratory motion is a factor that greatly affects the robustness of features, thus by using only 40–60% breathing phases, excessive dimension reduction will be able to be prevented in any 4D-CT datasets, and select robust features suitable for CT protocol of your own facility.

An Inhaler Tracking System Based on Acoustic Analysis: Hardware and Software

In treating asthma and chronic obstructive pulmonary disorder (COPD), acquisition of authentic and effective feedback from patients on regimen adherence is difficult. Face-to-face and oral reporting methods do not satisfy current intelligent medication best practices. This paper presents a system to track and analyze daily inhaler usage. A portable electronic device that attaches to the inhaler uses an accelerometer and capacitive sensors to detect users’ motion and an embedded digital microphone to capture sounds while the inhaler is in use. In terms of analysis, sound features are extracted, and breath phases are identified by employing a hidden Markov model with a Gaussian mixture model. A feature template is also constructed and used to search for and identify “canister pressed” events. The system provides objective feedback, quantifying asthma, and COPD patients’ adherence to medication regimens. Although interest in asthma adherence to medication regimens is growing, there is still a relative paucity of research and, indeed, compliance devices in this area; the tracking system can help doctors better understand the patient’s condition and choose an appropriated treatment plan. At the same time, patients can also improve their self-management by system feedback.

Tirumūlar on evaluating the qualitative standard of the pace of exhaling breath in pranayama practitioners

Tirumūlar's Tirumantiram is the earliest known Tamil treatise on yoga, in which 14 poems are dedicated to the practice of pranayama. These poems give a detailed insight on the methods and benefits of pranayama. The purpose of the study is to advance ideas related to pranayama by proposing a test to identify the stages of progression of a pranayama practitioner. The proposed test is based on the method prescribed in the poems of pranayama in Tirumantiram on evaluating the qualitative standard of the pace of exhaling breath in pranayama practitioners. The verse 567 from the research edition of Tirumantiram critically edited by Annamalai was selected for decoding in which the highest quality standard for the three phases of breath in a pranayama cycle, namely Pūraka (inhalation), Kumbhaka (retention) and Rēsaka (exhalation) is described. On the highest quality standard of Rēsaka, Tirumūlar says 'the exhaling breath should not cause flutters in powder of bran kept nearby'. The description exhibits a method to visually evaluate the pace of exhaling breath by observing its impact on powder of bran. Tirumūlar's ideology on “the exhaling breath and its impact on rice bran powder” could be developed in the name of 'Bran test' to identify the stages of progression of a pranayama practitioner.

Geometrical form assessment of a CFD based breathing thermal manikins, designed by simplified polygonal shapes

The presented paper focuses on a CFD based analyses of the complexity in the geometrical shape of the breathing thermal manikins, associated with their main functionalities. Both impacts of the external manikin’s form were studied – over the velocity and over the temperature fields in the thermal plume zone above the head. Three different geometrical shapes are analysed – a physiologically identified (called Humanoid Manikin) and two other shapes, designed to match the overall 95th percentile of the anthropometric size of the standard person (called Polygonal Manikins). The first model represents a comprehensive multifaceted figure of a manikin with high degree of physiological identity with a female human being. The second and third one, are simplified, but still with anatomically realistic component forms, accurately representing the anthropometric size of a standard person. The difference between them is in the presence of additional flow optimization collars in the third model. The numerical results demonstrate the clear impact of the manikins’ geometrical characteristics over the simulated breathing and convective flows. The optimization with the proposed collars had a positive effect over the resulted flow acceleration at top head and chest zones. However, the improvement of the flow characteristics was observed for two of the simulated three breathing phases and further shape optimization is required.

Experimental and Preliminary Clinical Study of Real-Time Registration in Liver Tumors During Respiratory Motion Based on a Multimodality Image Navigation System.

Purpose:To develop a fusion imaging system that combines ultrasound and computed tomography for real-time tumor tracking and to validate the accuracy of performing registration via this approach during a specific breathing phase.Materials and Methods:The initial part of the experimental study was performed using iodized oil injection in pig livers and was focused on determining the accuracy of registration. Eight points (A1-4 and B1-4) at different positions and with different target sizes were selected as target points. During respiratory motion, we used our self-designed system to perform the procedure either with (experimental group, E) or without (control group, C) the respiratory monitoring module. The registration errors were then compared between the 2 groups and within group E. The second part of this study was designed as a preliminary clinical study and was performed in 18 patients. Screening was performed to determine the combination of points on the body surface that provided the highest sensitivity to respiratory motion. Registration was performed either with (group E) or without (group C) the respiratory monitoring module. Registration errors were compared between the 2 groups.Results:In part 1 of this study, there were fewer registration errors at each point in group E than at the corresponding points in group C (P < .01). In group E, there were more registration errors at points A1 and B1 than at the other points (P < .05). There was no significant difference in registration errors among the remaining points. During part 2 of the study, there was a significant difference in the registration errors between the 2 groups (P < .01).Conclusions:Real-time fusion registration is feasible and can be accurately performed during respiratory motions when using this system.

47 Automated respiratory cycle binning for liver 4D-MR imaging

Introduction Stereotactic body radiation therapy (SBRT) has been validated for liver lesions [1] . Because high dose per fraction is delivered on small volume, very accurate lesion segmentation is necessary. MRI is the most relevant imaging for liver lesion contouring but it is prone to motion artifacts if breathing is not managed. Evaluation of liver lesion motion induced by breathing is necessary in radiotherapy to guide patient treatment planning (respiratory gating). A 4D-MRI sequence was previously validated for liver [2] . In this study, an automated method was developed to sort images according to the breathing cycle. Methods The 4D-MRI acquisition is performed with an experimental sequence (bSSFP TrueFISP [3] ) on 1.5T Magnetom AeraTM (Siemens). This sequence allows very fast axial and sagittal 2D acquisition during free breathing. Axial slice positions (0.88 s/slice) are incremented to cover the liver volume. Sagittal slices (navigators) are acquired at a fixed position. The navigators are used to assign a phase for each axial slice which depend on an amplitude sequencing of the respiratory cycle (0% = inspiration, 16%, 33%, 50% = expiration, 66%, 83%). Lung volume is estimated on each navigator by automatic segmentation and allows to assign a phase for the coupled axial image in the breathing cycle. As the image number per respiratory cycle is low, the sequence is repeated 20 times in order to achieve satisfactory sampling. The sequence was evaluated on 4 volunteers with an audio coaching (5 and 6 s period) and 2 patients without audio coaching. Results Patient image sorting according to their position in the breathing cycle was realized by automated phase detection (Fig. 1) and images were imported into the treatment planning system Eclipse 13.7TM (Varian) in 6 phases. Audio coaching is not used here. It is required for patients with irregular breathing cycle. Download : Download high-res image (278KB) Download : Download full-size image From axial images, Eclipse reconstructs slices in sagittal and coronal plans, and allows to display a cine view with the 6 breathing phases in order to analyze the dynamic behavior of the liver. Conclusions 4D-MRI images from volunteers and patients were acquired with bSSFP TrueFISP sequence. After automated sorting with the method developed in this study, 6 breathing cycle phases were imported into Eclipse. Patient 4D-MRI can be registered with 4D-CT to increase the physician’s segmentation accuracy.

The impact of 2D cine MR imaging parameters on automated tumor and organ localization for MR-guided real-time adaptive radiotherapy

2D cine MR imaging may be utilized to monitor rapidly moving tumors and organs-at-risk for real-time adaptive radiotherapy. This study systematically investigates the impact of geometric imaging parameters on the ability of 2D cine MR imaging to guide template-matching-driven autocontouring of lung tumors and abdominal organs.Abdominal 4D MR images were acquired of six healthy volunteers and thoracic 4D MR images were obtained of eight lung cancer patients. At each breathing phase of the images, the left kidney and gallbladder or lung tumor, respectively, were outlined as volumes of interest. These images and contours were used to create artificial 2D cine MR images, while simultaneously serving as 3D ground truth. We explored the impact of five different imaging parameters (pixel size, slice thickness, imaging plane orientation, number and relative alignment of images as well as strategies to create training images). For each possible combination of imaging parameters, we generated artificial 2D cine MR images as training and test images. A template-matching algorithm used the training images to determine the tumor or organ position in the test images. Subsequently, a 3D base contour was shifted to the determined position and compared to the ground truth via centroid distance and Dice similarity coefficient.The median centroid distance between adapted and ground truth contours was 1.56 mm for the kidney, 3.81 mm for the gallbladder and 1.03 mm for the lung tumor (median Dice similarity coefficient: 0.95, 0.72 and 0.93). We observed that a decrease in image resolution led to a modest decrease in localization accuracy, especially for the small gallbladder. However, for all volumes of interest localization accuracy varied substantially more between subjects than due to the different imaging parameters.Automated tumor and organ localization using 2D cine MR imaging and template-matching-based autocontouring is robust against variation of geometric imaging parameters. Future work and optimization efforts of 2D cine MR imaging for real-time adaptive radiotherapy is needed to characterize the influence of sequence- and anatomical site-specific imaging contrast.

Selection of the respiratory phase in minimally invasive interventions for target registration error minimization

BackgroundIn minimally invasive surgery, the main challenge is precisely locating the target during the intervention. For abdominal intervention, one of most important factors causing target motion is breathing. The aim of the study is to efficiently predict target localization during the respiratory in breathing cycle. MethodAnalysis of target registration error (TRE) for the registration circuits method was used to find the breathing phase corresponding to the preoperative Computed Tomography spatial configuration. Then, Elastic Body Spline (EBS) for modeling deformation field and Particle Swarm Optimization method were used to find the desired values of EBS parameters: ∝ and stiffness were used. ResultsThe proposed methodology was verified during experiments conducted on 21 patients diagnosed with liver tumors. This ability of TRE reduction has been achieved for the respiratory phases founded in registration chain analysis. ConclusionsThe proposed method presents the usability of spatio-temporal analysis of collected real breathing data in order to estimate the position of a target during the respiratory cycle. This method has been developed to perform operations in real-time on a standard workstation.

UTE-SENCEFUL: first results for 3D high-resolution lung ventilation imaging.

This study aimed to develop a 3D MRI technique to assess lung ventilation in free-breathing and without the administration of contrast agent. A 3D-UTE sequence with a koosh ball trajectory was developed for a 3 Tesla scanner. An oversampled k-space was acquired, and the direct current signal from the k-space center was used as a navigator to sort the acquired data into 8 individual breathing phases. Gradient delays were corrected, and iterative SENSE was used to reconstruct the individual timeframes. Subsequently, the signal changes caused by motion were eliminated using a 3D image registration technique, and ventilation-weighted maps were created by analyzing the signal changes in the lung tissue. Six healthy volunteers and 1 patient with lung cancer were scanned with the new 3D-UTE and the standard 2D technique. Image quality and quantitative ventilation values were compared between both methods. UTE-based self-gated noncontrast-enhanced functional lung (SENCEFUL) MRI provided a time-resolved reconstruction of the breathing motion, with a 49% increase of the SNR. Ventilation quantification for healthy subjects was in statistical agreement with 2D-SENCEFUL and the literature, with a mean value of 0.11 ± 0.08 mL/mL for the whole lung. UTE-SENCEFUL was able to visualize and quantify ventilation deficits in a patient with lung tumor that were not properly depicted by 2D-SENCEFUL. UTE-SENCEFUL represents a robust MRI method to assess both morphological and functional information of the lungs in 3D. When compared to the 2D approach, 3D-UTE offered ventilation maps with higher resolution, improved SNR, and reduced ventilation artifacts.

Characterization and classification of asthmatic wheeze sounds according to severity level using spectral integrated features

ObjectiveThis study aimed to investigate and classify wheeze sounds of asthmatic patients according to their severity level (mild, moderate and severe) using spectral integrated (SI) features. MethodSegmented and validated wheeze sounds were obtained from auscultation recordings of the trachea and lower lung base of 55 asthmatic patients during tidal breathing manoeuvres. The segments were multi-labelled into 9 groups based on the auscultation location and/or breath phases. Bandwidths were selected based on the physiology, and a corresponding SI feature was computed for each segment. Univariate and multivariate statistical analyses were then performed to investigate the discriminatory behaviour of the features with respect to the severity levels in the various groups. The asthmatic severity levels in the groups were then classified using the ensemble (ENS), support vector machine (SVM) and k-nearest neighbour (KNN) methods. Results and conclusionAll statistical comparisons exhibited a significant difference (p < 0.05) among the severity levels with few exceptions. In the classification experiments, the ensemble classifier exhibited better performance in terms of sensitivity, specificity and positive predictive value (PPV). The trachea inspiratory group showed the highest classification performance compared with all the other groups. Overall, the best PPV for the mild, moderate and severe samples were 95% (ENS), 88% (ENS) and 90% (SVM), respectively. With respect to location, the tracheal related wheeze sounds were most sensitive and specific predictors of asthma severity levels. In addition, the classification performances of the inspiratory and expiratory related groups were comparable, suggesting that the samples from these locations are equally informative.

A Monte-Carlo-based and GPU-accelerated 4D-dose calculator for a pencil beam scanning proton therapy system.

The presence of respiratory motion during radiation treatment leads to degradation of the expected dose distribution, both for target coverage and healthy tissue sparing, particularly for techniques like pencil beam scanning proton therapy which have dynamic delivery systems. While tools exist to estimate this degraded four-dimensional (4D) dose, they typically have one or more deficiencies such as not including the particular effects from a dynamic delivery, using analytical dose calculations, and/or using nonphysical dose-accumulation methods. This work presents a clinically useful 4D-dose calculator that addresses each of these shortcomings. To quickly compute the 4D dose, the three main tasks of the calculator were run on graphics processing units (GPUs). These tasks were (a) simulating the delivery of the plan using measured delivery parameters to distribute the plan amongst 4DCT phases characterizing the patient breathing, (b) using an in-house Monte Carlo simulation (MC) dose calculator to determine the dose delivered to each breathing phase, and (c) accumulating the doses from the various breathing phases onto a single phase for evaluation. The accumulation was performed by individually transferring the energy and mass of dose-grid subvoxels, a technique that models the transfer of dose in a more physically realistic manner. The calculator was run on three test cases, with lung, esophagus, and liver targets, respectively, to assess the various uncertainties in the beam delivery simulation as well as to characterize the dose-accumulation technique. Four-dimensional doses were successfully computed for the three test cases with computation times ranging from 4-6min on a server with eight NVIDIA Titan X graphics cards; the most time-consuming component was the MC dose engine. The subvoxel-based dose-accumulation technique produced stable 4D-dose distributions at subvoxel scales of 0.5-1.0mm without impairing the total computation time. The uncertainties in the beam delivery simulation led to moderate variations of the dose-volume histograms for these cases; the variations were reduced by implementing repainting or phase-gating motion mitigation techniques in the calculator. A MC-based and GPU-accelerated 4D-dose calculator was developed to estimate theeffects of respiratory motion on pencil beam scanning proton therapy treatments. After future validation, the calculator could be used to assess treatment plans and its quick runtime would make it easily usable in a future 4D-robust optimization system.

Feasibility of deriving a novel imaging biomarker based on patient-specific lung elasticity for characterizing the degree of COPD in lung SBRT patients.

Lung tissue elasticity is an effective spatial representation for Chronic Obstructive Pulmonary Disease phenotypes and pathophysiology. We investigated a novel imaging biomarker based on the voxel-by-voxel distribution of lung tissue elasticity. Our approach combines imaging and biomechanical modeling to characterize tissue elasticity. We acquired 4DCT images for 13 lung cancer patients with known COPD diagnoses based on GOLD 2017 criteria. Deformation vector fields (DVFs) from the deformable registration of end-inhalation and end-exhalation breathing phases were taken to be the ground-truth. A linear elastic biomechanical model was assembled from end-exhalation datasets with a density-guided initial elasticity distribution. The elasticity estimation was formulated as an iterative process, where the elasticity was optimized based on its ability to reconstruct the ground-truth. An imaging biomarker (denoted YM1-3) derived from the optimized elasticity distribution, was compared with the current gold standard, RA950 using confusion matrix and area under the receiver operating characteristic (AUROC) curve analysis. The estimated elasticity had 90 % accuracy when representing the ground-truth DVFs. The YM1-3 biomarker had higher diagnostic accuracy (86% vs 71 %), higher sensitivity (0.875 vs 0.5), and a higher AUROC curve (0.917 vs 0.875) as compared to RA950. Along with acting as an effective spatial indicator of lung pathophysiology, the YM1-3 biomarker also proved to be a better indicator for diagnostic purposes than RA950. Overall, the results suggest that, as a biomarker, lung tissue elasticity will lead to new end points for clinical trials and new targeted treatment for COPD subgroups. The derivation of elasticity information directly from 4DCT imaging data is a novel method for performing lung elastography. The work demonstrates the need for a mechanics-based biomarker for representing lung pathophysiology.

Automated Vessel Segmentation in Pancreas 4D-MRI using a Novel Transferred Convolutional Neural Network

Pancreatic cancer has dismal prognosis with five-year survival of ∼5%. Although surgery is considered the most effective treatment, only 10-20% of the patients present with resectable disease. Stereotactic body radiation therapy (SBRT) with simultaneous integrated boost (SIB) to tumor sub-volumes abutting major vessels has the potential to sterilize the tumor sub-volumes and down-stage patients for subsequent successful resection. However, abdominal tumor and organs are mobile. SBRT-SIB to small tumor sub-volumes is greatly challenged by ventilation induced tumor and organ motion, making it difficult to define treatment margins. 4D imaging has been used to assess tumor motion in radiation therapy. In a 4D image set, 5 to 10 3D volumetric images are usually generated, making manual segmentation of involved vessels time-consuming and not practical in real clinical setting. The purpose of this study is to apply a novel deep-learning method, two-scale convolutional neural network (CNN) followed by distance and contour regularized level set evolution (DCRLSE), on pancreas 4D-MR data sets to automatically delineate vessels limiting surgery. The 4D-MR images of ten patients were used. Each patient has 10 3D respiration-resolved image sets. For each 3D image set, there are approximately 40 2D axial slices that include vessels limiting surgery. For each patient’s 4D-MRI data-set, 300 image frames were analyzed. The proposed transferred CNN routine is composed of two tasks. In the source task, a relatively large amount of data is used to train the paired CNN. In the target task, a relatively small amount of data is used to fine-tune the network. In the test phase, Paired CNN is used to locate and roughly segment the vessel. Small holes in the segmentation are filled using morphological operation. The DCRLSE model is then used to refine the contours. Transferred learning is used to improve the performance by exploiting the similarity among different patients. After the binary mask is trained on one patient, the network weights are transferred to initialize the CNN training of the second patient’s network. The weights are fine-tuned on selected breathing phases to find the minimal number of images needed for the new segmentation task. A widely used segmentation method, level-set, was used for comparison. Manual segmentation was used as ground truth. The segmentation results were evaluated visually, as well as quantitatively using four metrics: sensitivity, specificity, dice similarity coefficient (DSC) and modified Hausdorff distance (HD). CNN-DCRLSE method visually showed more consistent segmentation results comparing to level-set method. It was able to achieve average sensitivity of 0.876±0.070 (mean±σ), specificity of 0.999±0.001, DSC of 0.863±0.081, and HD (mm) of 0.099±0.059. The CNN-DCRLSE method was successfully implemented on 4D-MR images to automatically segment major blood vessels that limit surgery for pancreatic cancer patients.

A fiducial-less tracking method for radiation therapy of liver tumors by diaphragm disparity analysis part 2: validation study by using clinical data

Motion management must be considered in treating liver tumors. One effective approach is real-time tumor tracking, which can be performed by the CyberKnife® Robotic Radiosurgery System through the Synchrony® Respiratory Tracking System. It uses a combination of kV images, LED markers, an infrared camera, and surgically implanted fiducial markers to track tumors under the influence of respiration. However, the use of fiducial markers through an invasive procedure can lead to complications. In our previous simulation study, we were able to demonstrate the feasibility of our proposed fiducial-less tracking technique using a digital phantom under regular respiratory motion. The aim of this study is to further validate this innovative method by using a digital phantom data under the irregular respiratory cycles as well as clinical data from patients under the Cyberknife environment. As performed in our previous simulation study, abdominal 4DCT datasets of one breathing cycle, from the digital phantom and from four patients, were previously generated or acquired. Associated with the breathing cycles in the 4DCT datasets, one set of DRR images (+ 45° or − 45°) was produced for each breathing phase. On each DRR, an outline of the lung-diaphragm border was detected using an edge detection algorithm. The tracked target volume’s gravity center was identified for each phase of the breathing cycle by a MATLAB program, serving as the ground truth for the validation. Using artificial neural networks (ANN), four models for the phantom and six models for the patient data, correlating the diaphragm’s location with the corresponding 3D location of the tracked target volume, were compared. Assessment was performed by using the root-mean-squared error (RMSE) values through the leave-one-out (LOO) validation criterion. The averaged RMSE for the phantom data was 1.05 ± 1.14 mm. When using the patient data from the + 45° projection, the averaged RMSE was 2.13 ± 1.79 mm, while from the − 45° projection, the averaged RMSE was 2.26 ± 2.40 mm. Using the proposed method in both phantom validation and patient data validation, the RMSE is closely related to the 4DCT reconstruction error and to the distance from the lung-diaphragm border to the tracked tumor. We proposed and investigated the fiducial-less tracking method to follow tumor motion in the real-time under the influence of respiration. The study shows the feasibility of accurately predicting the tumor’s position with the use of lung-diaphragm border’s information through available kV images without gold fiducial markers. This developed diaphragm disparity-analysis-based approach, verified with clinically accepted errors, has the potential to replace fiducial markers in clinical applications.

Effects of Reslizumab on Asthma Outcomes in a Subgroup of Eosinophilic Asthma Patients with Self-Reported Chronic Rhinosinusitis with Nasal Polyps

An estimated 7% of patients with asthma have chronic rhinosinusitis with nasal polyps (CRSwNP), and more than 80% have at least some radiographic evidence of sinonasal inflammation. Aspirin sensitivity is strongly associated with elevated blood eosinophil levels and increased asthma severity. Intravenous (IV) reslizumab has been shown to improve asthma control in patients with nasal polyps. These post hoc analyses of pooled data from 2 BREATH phase 3 clinical trials, studies 1 and 2 (NCT01287039 and NCT01285323), examined asthma-related outcomes in patients with comorbid, self-reported CRSwNP with and without aspirin sensitivity. Patients aged 12-75 years with elevated blood eosinophils (≥400 cells/μL) and inadequately controlled asthma were randomized to receive placebo or reslizumab (3 mg/kg IV) every 4 weeks for 52 weeks. Patients continued their background asthma maintenance therapy during the study. Information regarding the presence of CRSwNP was obtained through patient-reported medical history. Add-on reslizumab treatment reduced the frequency of clinical asthma exacerbations by 83% versus placebo among patients with CRSwNP. Among patients with and without aspirin sensitivity, reductions of 79% and 84%, respectively, were observed. Patients with CRSwNP (with and without aspirin sensitivity) treated with reslizumab add-on therapy also had significant improvements in lung function, as measured by forced expiratory volume in 1 second, compared with placebo. Among patients with CRSwNP, reslizumab was also associated with improvements in patient-reported asthma control and asthma quality of life. Patients with eosinophilic asthma and self-reported CRSwNP, with and without aspirin sensitivity, are highly responsive to treatment with reslizumab for asthma-related outcomes. These findings suggest that prospective investigation of reslizumab in this patient population is warranted.

Optimized breath detection algorithm in electrical impedance tomography

Objective: This paper defines a method for optimizing the breath delineation algorithms used in electrical impedance tomography (EIT). In lung EIT the identification of the breath phases is central for generating tidal impedance variation images, subsequent data analysis and clinical evaluation. The optimisation of these algorithms is particularly important in neonatal care since the existing breath detectors developed for adults may give insufficient reliability in neonates due to their very irregular breathing pattern. Approach: Our approach is generic in the sense that it relies on the definition of a gold standard and the associated definition of detector sensitivity and specificity, an optimisation criterion and a set of detector parameters to be investigated. The gold standard has been defined by 11 clinicians with previous experience with EIT and the performance of our approach is described and validated using a neonatal EIT dataset acquired within the EU-funded CRADL project. Main results: Three different algorithms are proposed that improve the breath detector performance by adding conditions on (1) maximum tidal breath rate obtained from zero-crossings of the EIT breathing signal, (2) minimum tidal impedance amplitude and (3) minimum tidal breath rate obtained from time-frequency analysis. As a baseline a zero-crossing algorithm has been used with some default parameters based on the Swisstom EIT device. Significance: Based on the gold standard, the most crucial parameters of the proposed algorithms are optimised by using a simple exhaustive search and a weighted metric defined in connection with the receiver operating characterics. This provides a practical way to achieve any desirable trade-off between the sensitivity and the specificity of the detectors.

Maturation of Breathing-Related Inhibitory Neurotransmission in the Medulla Oblongata of the Embryonic and Perinatal Zebra Finch (Taeniopygia guttata).

The medullary portion of the embryonic zebra finch hindbrain was isolated and superfused with physiologically relevant artificial cerebral spinal fluid. This in vitro preparation produced uninterrupted rhythmic episodes of neural activity via cranial nerve IX (glossopharyngeal) from embryonic day 4 (E4) through hatching on E14. Cranial nerve IX carries motor activity to the glottis during the inspiratory phase of breathing, and we focused on the role of synaptic inhibition during the embryonic and perinatal maturation of this branchiomotor outflow. We show that spontaneous neural activity (SNA) is first observed on E4 and temporally transforms as the embryo ages. To start, SNA is dependent on the excitatory actions of GABAA and glycine. As the embryo continues to develop, GABAergic and glycinergic neurotransmission take on a modulatory role, albeit an excitatory one, through E10. After that, data show that GABAergic and glycinergic neurotransmission switches to a phenotype consistent with inhibition, coincident with the onset of functional breathing. We also report that the inhibitory action of GABAergic and glycinergic receptor gating is not necessary for the spontaneous generation of branchiomotor motor rhythms in these birds near hatching. This is the first report focusing on the development of central breathing-related inhibitory neurotransmission in birds during the entire period of embryogenesis.

Use of lung treatment plans to evaluate DIR algorithms.

The purpose of the study is to evaluate the accuracy of two deformable image registration algorithms by examining their influence on the dose summation results obtained using 4DCT (four dimensional computed tomography) dose distributions based on '4D' planned and '4D optimal' IMRT (intensity modulated radiation therapy) plans. For ten lung cancer patients, 4D step and shoot IMRT plans were produced. The breathing cycle was divided into ten parts and for each part a set of CT images was acquired. For each patient the treatment plan was copied to the CTs of each phase and subsequently recalculated. Each phase CT was then registered to the average intensity projection (AIP) CT using a deformable image registration (DIR) algorithm and the composite dose distribution was then calculated by summing up the deformed dose distributions from all the phases ('4D' treatment plan). The '4D optimal' treatment plan was created by producing an optimal plan on the CTs of each phase of the respiratory cycle and summing up the deformed dose distributions from all the phases. The results indicate that it is possible to map the dose distributions of different breathing phases in lung using DIR, and that different DIR methods and target characteristics (motion amplitude, size, location) affect the differences between original plan, '4D' and '4D optimal' dose distributions. Although the '4D optimal' plans were designed to achieve 95% target coverage, both of the used DIR methods failed to translate that coverage in some instances. The same variation between these methods was also observed in the '4D' plan comparison. This study shows that it is feasible to perform an acceptably accurate calculation of the composite deformed dose. However, it is important to account for tumor motion and body deformation especially when the tumor volume is small and/or located in the lower lobe of the lung.

Four-dimensional Plan Optimization for the Treatment of Lung Tumors Using Pencil-beam Scanning Proton Radiotherapy.

Purpose This study aimed to evaluate the effectiveness of four-dimensional (4D) robust optimization for proton pencil-beam scanning (PBS) treatment of lung tumors.Patients and methods In seven patients with lung cancer, proton beam therapy was planned using 4D robust optimization over 4D computed tomography (CT) data sets. The gross target volume (GTV) was contoured based on individual breathing phases, and a 5-mm expansion was used to generate the clinical target volume (CTV) for each phase. The 4D optimization was conducted directly on the 4D CT data set. The robust optimization settings included a CT Hounsfield unit (HU) uncertainty of 4% and a setup uncertainty of 5 mm to obtain the CTV. Additional target dose objectives such as those for the internal target volume (ITV) as well as the organ-at-risk (OAR) dose requirements were placed on the average CT. For comparison, three-dimensional (3D) robust optimization was also performed on the average CT. An additional verification 4D CT was performed to verify plan robustness against inter-fractional variations.Results Target coverages were generally higher for 4D optimized plans. The difference was most pronounced for ITV V70Gy when evaluating individual breathing phases. The 4D optimized plans were shown to be able to maintain the ITV coverage at full prescription, while 3D optimized plans could not. More importantly, this difference in ITV V70Gy between the 4D and 3D optimized plans was also consistently observed when evaluating the verification 4D CT, indicating that the 4D optimized plans were more robust against inter-fractional variations. Less difference was seen between the 4D and 3D optimized plans in the lungs criteria: V5Gy and V20Gy.Conclusion The proton PBS treatment plans optimized directly on the 4D CT were shown to be more robust when compared to those optimized on a regular 3D CT. Robust 4D optimization can improve the target coverage for the proton PBS lung treatments.

New insights into the timing and potential mechanisms of respiratory-induced cortical arousals in obstructive sleep apnea

A negative intrathoracic pressure threshold is one commonly proposed mechanism for triggering respiratory-induced arousals in obstructive sleep apnea (OSA). If so, they should occur during inspiration, shortly after maximal negative pressure swings. Alternatively, respiratory-induced arousals may occur throughout the respiratory cycle if other mechanisms also contribute. However, arousal timing has been minimally investigated. This study aimed to (1) determine the temporal relationship between respiratory-induced arousals and breathing phase and (2) characterize neuromuscular and load compensation responses prior to arousal. Fifty-one CPAP-treated OSA patients underwent a sleep physiology study with genioglossus and tensor palatini EMG, nasal mask/pneumotachograph, and epiglottic pressure. Transient CPAP reductions were delivered to induce respiratory-related arousals. Of 354 arousals, 65(60-70)%[mean(CI)] occurred during inspiration, 35(30-40)% during expiration. Nadir epiglottic pressure occurred 68(66-69)% into inspiration while inspiratory arousals had a uniform distribution throughout inspiration. Expiratory arousals occurred predominantly in early expiration. CPAP reductions initially reduced minute ventilation by ~2.5 liter/min, which was restored immediately prior to expiratory but not inspiratory arousals. Duty cycle just prior to arousal was greater for inspiratory versus expiratory arousals [0.20(0.18-0.21) vs. 0.13(0.11-0.15)Δbaseline, p = 0.001]. Peak tensor palatini EMG was higher for expiratory versus inspiratory arousals during prearousal breaths [7.6(5.8-9.6) vs. 3.7(3.0-4.5)%Δbaseline, p = 0.001], whereas genioglossus and tonic tensor palatini EMG were similar between arousal types. Over one third of respiratory-induced arousals occur during expiration. These findings highlight the importance of nonpressure threshold mechanisms of respiratory-induced arousals in OSA and suggest that expiratory arousals may be a novel marker of enhanced tensor palatini neuromuscular compensation.