Pulmonary inflammation decreases with ultra-protective ventilation in experimental ARDS under VV-ECMO: a positron emission tomography study.

Background: Experimentally, ultra-protective ventilation (UPV, tidal volumes [VT] < 4 mL.kg-1) strategies in conjunction with veno-venous extracorporeal membrane oxygenation (VV-ECMO) are associated with lesser ventilator-induced lung injuries (VILI) during acute respiratory distress syndrome (ARDS). However, whether these strategies reduce lung inflammation more effectively than protective ventilation (PV) remains unclear. We aimed to demonstrate that a UPV strategy decreases acute lung inflammation in comparison with PV in an experimental swine model of ARDS. Methods: ARDS was induced by tracheal instillation of chlorhydric acid in sedated and paralyzed animals under mechanical ventilation. Animals were randomized to receive either UPV (VT 1 mL.kg-1, positive end-expiration pressure [PEEP] set to obtain plateau pressure between 20 and 25 cmH2O and respiratory rate [RR] at 5 min-1 under VV-ECMO) or PV (VT 6 mL.kg-1, PEEP set to obtain plateau pressure between 28 and 30 cmH2O and RR at 25 min-1) during 4 h. After 4 h, a positron emission tomography with [11C](R)-PK11195 (ligand to TSPO-bearing macrophages) injection was realized, coupled with quantitative computerized tomography (CT). Pharmacokinetic multicompartment models were used to quantify regional [11C](R)-PK11195 lung uptake. [11C](R)-PK11195 lung uptake and CT-derived respiratory variables were studied regionally across eight lung regions distributed along the antero-posterior axis. Results: Five pigs were randomized to each study group. Arterial O2 partial pressure to inspired O2 fraction were not significantly different between study groups after experimental ARDS induction (75 [68-80] mmHg in a PV group vs. 87 [69-133] mmHg in a UPV group, p = 0.20). Compared to PV animals, UPV animals exhibited a significant decrease in the regional non-aerated compartment in the posterior lung levels, in mechanical power, and in regional dynamic strain and no statistical difference in tidal hyperinflation after 4 h. UPV animals had a significantly lower [11C](R)-PK11195 uptake, compared to PV animals (non-displaceable binding potential 0.35 [IQR, 0.20-0.59] in UPV animals and 1.01 [IQR, 0.75-1.59] in PV animals, p = 0.01). Regional [11C](R)-PK11195 uptake was independently associated with the interaction of regional tidal hyperinflation and regional lung compliance. Conclusion: In an experimental model of ARDS, 4 h of UPV strategy significantly decreased lung inflammation, in relation to the control of VT-derived determinants of VILI.

Precision of CT-derived alveolar recruitment assessed by human observers and a machine learning algorithm in moderate and severe ARDS.

BACKGROUND: Assessing measurement error in alveolar recruitment on computed tomography (CT) is of paramount importance to select a reliable threshold identifying patients with high potential for alveolar recruitment and to rationalize positive end-expiratory pressure (PEEP) setting in acute respiratory distress syndrome (ARDS). The aim of this study was to assess both intra- and inter-observer smallest real difference (SRD) exceeding measurement error of recruitment using both human and machine learning-made lung segmentation (i.e., delineation) on CT. This single-center observational study was performed on adult ARDS patients. CT were acquired at end-expiration and end-inspiration at the PEEP level selected by clinicians, and at end-expiration at PEEP 5 and 15 cmH2O. Two human observers and a machine learning algorithm performed lung segmentation. Recruitment was computed as the weight change of the non-aerated compartment on CT between PEEP 5 and 15 cmH2O. RESULTS: Thirteen patients were included, of whom 11 (85%) presented a severe ARDS. Intra- and inter-observer measurements of recruitment were virtually unbiased, with 95% confidence intervals (CI95%) encompassing zero. The intra-observer SRD of recruitment amounted to 3.5 [CI95% 2.4-5.2]% of lung weight. The human-human inter-observer SRD of recruitment was slightly higher amounting to 5.7 [CI95% 4.0-8.0]% of lung weight, as was the human-machine SRD (5.9 [CI95% 4.3-7.8]% of lung weight). Regarding other CT measurements, both intra-observer and inter-observer SRD were close to zero for the CT-measurements focusing on aerated lung (end-expiratory lung volume, hyperinflation), and higher for the CT-measurements relying on accurate segmentation of the non-aerated lung (lung weight, tidal recruitment…). The average symmetric surface distance between lung segmentation masks was significatively lower in intra-observer comparisons (0.8 mm [interquartile range (IQR) 0.6-0.9]) as compared to human-human (1.0 mm [IQR 0.8-1.3] and human-machine inter-observer comparisons (1.1 mm [IQR 0.9-1.3]). CONCLUSIONS: The SRD exceeding intra-observer experimental error in the measurement of alveolar recruitment may be conservatively set to 5% (i.e., the upper value of the CI95%). Human-machine and human-human inter-observer measurement errors with CT are of similar magnitude, suggesting that machine learning segmentation algorithms are credible alternative to humans for quantifying alveolar recruitment on CT.

Prone position decreases acute lung inflammation measured by [11C](R)-PK11195 positron emission tomography in experimental acute respiratory distress syndrome.

Whether prone positioning (PP) modulates acute lung inflammation by the modulation of biomechanical forces of ventilator-induced lung injuries (VILIs) remains unclear. We aimed to demonstrate that PP decreases acute lung inflammation in animals with experimental acute respiratory distress syndrome (ARDS). Animals were under general anesthesia and protective ventilation (tidal volume 6 mL·kg-1, PEEP 5 cmH2O). ARDS was induced by intratracheal instillation of chlorohydric acid. Animals were then randomized to PP, or to supine position (SP). After 4 h, a positron emission tomography (PET) acquisition with [11C](R)-PK11195 was performed coupled with computerized tomography (CT) acquisitions, allowing the CT quantification of VILI-associated parameters. [11C](R)-PK11195 lung uptake was quantified using pharmacokinetic multicompartment models. Analyses were performed on eight lung sections distributed along the antero-posterior dimension. Six animals were randomized to PP, five to SP (median [Formula: see text]/[Formula: see text] [interquartile range]: 164 [102-269] mmHg). The normally aerated compartment was significantly redistributed to the posterior lung regions of animals in PP, compared with SP. Dynamic strain was significantly increased in posterior regions of SP animals, compared with PP. After 4 h, animals in PP had a significantly lower uptake of [11C](R)-PK11195, compared with SP. [11C](R)-PK11195 regional uptake was independently associated with the study group, dynamic strain, tidal hyperinflation, and regional respiratory system compliance in multivariate analysis. In an experimental model of ARDS, 4 h of PP significantly decreased acute lung inflammation assessed with PET. The beneficial impact of PP on acute lung inflammation was consecutive to the combination of decreased biomechanical forces and changes in the respiratory system mechanics.NEW & NOTEWORTHY Prone position decreases acute lung macrophage inflammation quantified in vivo with [11C](R)-PK11195 positron emission tomography in an experimental acute respiratory distress syndrome. Regional macrophage inflammation is maximal in the most anterior and posterior lung section of supine animals, in relation with increased regional tidal strain and hyperinflation, and reduced regional lung compliance.

Response to PEEP in COVID-19 ARDS patients with and without extracorporeal membrane oxygenation. A multicenter case-control computed tomography study.

BACKGROUND: PEEP selection in severe COVID-19 patients under extracorporeal membrane oxygenation (ECMO) is challenging as no study has assessed the alveolar recruitability in this setting. The aim of the study was to compare lung recruitability and the impact of PEEP on lung aeration in moderate and severe ARDS patients with or without ECMO, using computed tomography (CT). METHODS: We conducted a two-center prospective observational case-control study in adult COVID-19-related patients who had an indication for CT within 72 h of ARDS onset in non-ECMO patients or within 72  h after ECMO onset. Ninety-nine patients were included, of whom 24 had severe ARDS under ECMO, 59 severe ARDS without ECMO and 16 moderate ARDS. RESULTS: Non-inflated lung at PEEP 5 cmH2O was significantly greater in ECMO than in non-ECMO patients. Recruitment induced by increasing PEEP from 5 to 15 cmH2O was not significantly different between ECMO and non-ECMO patients, while PEEP-induced hyperinflation was significantly lower in the ECMO group and virtually nonexistent. The median [IQR] fraction of recruitable lung mass between PEEP 5 and 15 cmH2O was 6 [4-10]%. Total superimposed pressure at PEEP 5 cmH2O was significantly higher in ECMO patients and amounted to 12 [11-13] cmH2O. The hyperinflation-to-recruitment ratio (i.e., a trade-off index of the adverse effects and benefits of PEEP) was significantly lower in ECMO patients and was lower than one in 23 (96%) ECMO patients, 41 (69%) severe non-ECMO patients and 8 (50%) moderate ARDS patients. Compliance of the aerated lung at PEEP 5 cmH2O corrected for PEEP-induced recruitment (CBABY LUNG) was significantly lower in ECMO patients than in non-ECMO patients and was linearly related to the logarithm of the hyperinflation-to-recruitment ratio. CONCLUSIONS: Lung recruitability of COVID-19 pneumonia is not significantly different between ECMO and non-ECMO patients, with substantial interindividual variations. The balance between hyperinflation and recruitment induced by PEEP increase from 5 to 15 cmH2O appears favorable in virtually all ECMO patients, while this PEEP level is required to counteract compressive forces leading to lung collapse. CBABY LUNG is significantly lower in ECMO patients, independently of lung recruitability.

Computed Tomography Registration-Derived Regional Ventilation Indices Compared to Global Lung Function Parameters in Patients With COPD.

CT registration-derived indices provide data on regional lung functional changes in COPD. However, because unlike spirometry which involves dynamic maximal breathing maneuvers, CT-based functional parameters are assessed between two static breath-holds, it is not clear how regional and global lung function parameters relate to each other. We assessed the relationship between CT-density change (dHU), specific volume change (dsV), and regional lung tissue deformation (J) with global spirometric and plethysmographic parameters, gas exchange, exercise capacity, dyspnoea, and disease stage in a prospective cohort study in 102 COPD patients. There were positive correlations of dHU, dsV, and J with spirometric variables, DLCO and gas exchange, 6-min walking distance, and negative correlations with plethysmographic lung volumes and indices of trapping and lung distension as well as GOLD stage. Stepwise regression identified FEV1/FVC (standardized ß = 0.429, p < 0.0001), RV/TLC (ß = -0.37, p < 0.0001), and BMI (ß = 0.27, p=<0.001) as the strongest predictors of CT intensity-based metrics dHU, with similar findings for dsV, while FEV1/FVC (ß = 0.32, p=<0.001) and RV/TLC (ß = -0.48, p=<0.0001) were identified as those for J. These data suggest that regional lung function is related to two major pathophysiological processes involved in global lung function deterioration in COPD: chronic airflow obstruction and gas trapping, with an additional contribution of nutritional status, which in turn determines respiratory muscle strength. Our data confirm previous findings in the literature, suggesting the potential of CT image-based regional lung function metrics as the biomarkers of disease severity and provide mechanistic insight into the interpretation of regional lung function indices in patients with COPD.

Carotid Ultrasound Boundary Study (CUBS): Technical considerations on an open multi-center analysis of computerized measurement systems for intima-media thickness measurement on common carotid artery longitudinal B-mode ultrasound scans.

After publishing an in-depth study that analyzed the ability of computerized methods to assist or replace human experts in obtaining carotid intima-media thickness (CIMT) measurements leading to correct therapeutic decisions, here the same consortium joined to present technical outlooks on computerized CIMT measurement systems and provide considerations for the community regarding the development and comparison of these methods, including considerations to encourage the standardization of computerized CIMT measurements and results presentation. A multi-center database of 500 images was collected, upon which three manual segmentations and seven computerized methods were employed to measure the CIMT, including traditional methods based on dynamic programming, deformable models, the first order absolute moment, anisotropic Gaussian derivative filters and deep learning-based image processing approaches based on U-Net convolutional neural networks. An inter- and intra-analyst variability analysis was conducted and segmentation results were analyzed by dividing the database based on carotid morphology, image signal-to-noise ratio, and research center. The computerized methods obtained CIMT absolute bias results that were comparable with studies in literature and they generally were similar and often better than the observed inter- and intra-analyst variability. Several computerized methods showed promising segmentation results, including one deep learning method (CIMT absolute bias = 106 ± 89 µm vs. 160 ± 140 µm intra-analyst variability) and three other traditional image processing methods (CIMT absolute bias = 139 ± 119 µm, 143 ± 118 µm and 139 ± 136 µm). The entire database used has been made publicly available for the community to facilitate future studies and to encourage an open comparison and technical analysis (https://doi.org/10.17632/m7ndn58sv6.1).

Non-invasive quantification of acute macrophagic lung inflammation with [11C](R)-PK11195 using a three-tissue compartment kinetic model in experimental acute respiratory distress syndrome.

PURPOSE: Imaging of acute lung inflammation is pivotal to evaluate innovative ventilation strategies. We aimed to develop and validate a three-tissue compartment kinetic model (3TCM) of [11C](R)-PK11195 lung uptake in experimental acute respiratory distress syndrome (ARDS) to help quantify macrophagic inflammation, while accounting for the impact of its non-specific and irreversible uptake in lung tissues. MATERIAL AND METHODS: We analyzed the data of 38 positron emission tomography (PET) studies performed in 21 swine with or without experimental ARDS, receiving general anesthesia and mechanical ventilation. Model input function was a plasma, metabolite-corrected, image-derived input function measured in the main pulmonary artery. Regional lung analysis consisted in applying both the 3TCM and the two-tissue compartment model (2TCM); in each region, the best model was selected using a selection algorithm with a goodness-of-fit criterion. Regional best model binding potentials (BPND) were compared to lung macrophage presence, semi-quantified in pathology. RESULTS: The 3TCM was preferred in 142 lung regions (62%, 95% confidence interval: 56 to 69%). BPND determined by the 2TCM was significantly higher than the value computed with the 3TCM (overall median with interquartile range: 0.81 [0.44-1.33] vs. 0.60 [0.34-0.94], p < 0.02). Regional macrophage score was significantly associated with the best model BPND (p = 0.03). Regional BPND was significantly increased in the hyperinflated lung compartment, compared to the normally aerated one (median with interquartile range: 0.8 [0.6-1.7] vs. 0.6 [0.3-0.8], p = 0.03). CONCLUSION: To assess the intensity and spatial distribution of acute macrophagic lung inflammation in the context of experimental ARDS with mechanical ventilation, PET quantification of [11C](R)-PK11195 lung uptake was significantly improved in most lung regions using the 3TCM. This new methodology offers the opportunity to non-invasively evaluate innovative ventilatory strategies aiming at controlling acute lung inflammation.

Improving motion-mask segmentation in thoracic CT with multiplanar U-nets.

PURPOSE: Motion-mask segmentation from thoracic computed tomography (CT) images is the process of extracting the region that encompasses lungs and viscera, where large displacements occur during breathing. It has been shown to help image registration between different respiratory phases. This registration step is, for example, useful for radiotherapy planning or calculating local lung ventilation. Knowing the location of motion discontinuity, that is, sliding motion near the pleura, allows a better control of the registration preventing unrealistic estimates. Nevertheless, existing methods for motion-mask segmentation are not robust enough to be used in clinical routine. This article shows that it is feasible to overcome this lack of robustness by using a lightweight deep-learning approach usable on a standard computer, and this even without data augmentation or advanced model design. METHODS: A convolutional neural-network architecture with three 2D U-nets for the three main orientations (sagittal, coronal, axial) was proposed. Predictions generated by the three U-nets were combined by majority voting to provide a single 3D segmentation of the motion mask. The networks were trained on a database of nonsmall cell lung cancer 4D CT images of 43 patients. Training and evaluation were done with a K-fold cross-validation strategy. Evaluation was based on a visual grading by two experts according to the appropriateness of the segmented motion mask for the registration task, and on a comparison with motion masks obtained by a baseline method using level sets. A second database (76 CT images of patients with early-stage COVID-19), unseen during training, was used to assess the generalizability of the trained neural network. RESULTS: The proposed approach outperformed the baseline method in terms of quality and robustness: the success rate increased from 53 % to 79 % without producing any failure. It also achieved a speed-up factor of 60 with GPU, or 17 with CPU. The memory footprint was low: less than 5 GB GPU RAM for training and less than 1 GB GPU RAM for inference. When evaluated on a dataset with images differing by several characteristics (CT device, pathology, and field of view), the proposed method improved the success rate from 53 % to 83 % . CONCLUSION: With 5-s processing time on a mid-range GPU and success rates around 80 % , the proposed approach seems fast and robust enough to be routinely used in clinical practice. The success rate can be further improved by incorporating more diversity in training data via data augmentation and additional annotated images from different scanners and diseases. The code and trained model are publicly available.

Validation of a novel system to assess end-expiratory lung volume and alveolar recruitment in an ARDS model.

BACKGROUND: Personalizing mechanical ventilation requires the development of reliable bedside monitoring techniques. The multiple-breaths nitrogen washin-washout (MBNW) technique is currently available to measure end-expiratory lung volume (EELVMBNW), but the precision of the technique may be poor, with percentage errors ranging from 28 to 57%. The primary aim of the study was to evaluate the reliability of a novel MBNW bedside system using fast mainstream sensors to assess EELV in an experimental acute respiratory distress syndrome (ARDS) model, using computed tomography (CT) as the gold standard. The secondary aims of the study were: (1) to evaluate trending ability of the novel system to assess EELV; (2) to evaluate the reliability of estimated alveolar recruitment induced by positive end-expiratory pressure (PEEP) changes computed from EELVMBNW, using CT as the gold standard. RESULTS: Seven pigs were studied in 6 experimental conditions: at baseline, after experimental ARDS and during a decremental PEEP trial at PEEP 16, 12, 6 and 2 cmH2O. EELV was computed at each PEEP step by both the MBNW technique (EELVMBNW) and CT (EELVCT). Repeatability was assessed by performing replicate measurements. Alveolar recruitment between two consecutive PEEP levels after lung injury was measured with CT (VrecCT), and computed from EELV measurements (VrecMBNW) as ΔEELV minus the product of ΔPEEP by static compliance. EELVMBNW and EELVCT were significantly correlated (R2 = 0.97). An acceptable non-constant bias between methods was identified, slightly decreasing toward more negative values as EELV increased. The conversion equation between EELVMBNW and EELVCT was: EELVMBNW = 0.92 × EELVCT + 36. The 95% prediction interval of the bias amounted to ± 86 mL and the percentage error between both methods amounted to 13.7%. The median least significant change between repeated measurements amounted to 8% [CI95%: 4-10%]. EELVMBNW adequately tracked EELVCT changes over time (concordance rate amounting to 100% [CI95%: 87%-100%] and angular bias amounting to - 2° ± 10°). VrecMBNW and VrecCT were significantly correlated (R2 = 0.92). A non-constant bias between methods was identified, slightly increasing toward more positive values as Vrec increased. CONCLUSIONS: We report a new bedside MBNW technique that reliably assesses EELV in an experimental ARDS model with high precision and excellent trending ability.

Estimating Arterial Wall Deformations from Automatic Key-Point Detection and Matching.

Assessing arterial-wall motion and deformations may reveal pathologic alterations in biomechanical properties of the parietal tissues and, thus, contribute to the detection of vascular disease onset. Ultrasound image sequences allow the observation of this motion and many methods have been developed to estimate temporal changes in artery diameter and wall thickness and to track 2-D displacements of selected points. Some methods enable the assessment of shearing or stretching within the wall, but none of them can estimate all these deformations simultaneously. The method herein proposed was devised to simultaneously estimate translation, compression, stretching and shearing of the arterial wall in ultrasound B-mode image sequences representing the carotid artery longitudinal section. Salient blob-like patterns, called key points, are automatically detected in each frame and matched between successive frames. A robust estimator based on an affine transformation model is then used to assess frame-to-frame motion explaining at best the key-point matches and to reject outliers. Realistic simulated image sequences were used to evaluate the accuracy and robustness of the method against ground truth. The method was also visually assessed on clinical image sequences, for which true deformations are unknown.

Quantitative-analysis of computed tomography in COVID-19 and non COVID-19 ARDS patients: A case-control study.

PURPOSE: The aim of this study was to assess whether the computed tomography (CT) features of COVID-19 (COVID+) ARDS differ from those of non-COVID-19 (COVID-) ARDS patients. MATERIALS AND METHODS: The study is a single-center prospective observational study performed on adults with ARDS onset ≤72 h and a PaO2/FiO2 ≤ 200 mmHg. CT scans were acquired at PEEP set using a PEEP-FiO2 table with VT adjusted to 6 ml/kg predicted body weight. RESULTS: 22 patients were included, of whom 13 presented with COVID-19 ARDS. Lung weight was significantly higher in COVID- patients, but all COVID+ patients presented supranormal lung weight values. Noninflated lung tissue was significantly higher in COVID- patients (36 ± 14% vs. 26 ± 15% of total lung weight at end-expiration, p < 0.01). Tidal recruitment was significantly higher in COVID- patients (20 ± 12 vs. 9 ± 11% of VT, p < 0.05). Lung density histograms of 5 COVID+ patients with high elastance (type H) were similar to those of COVID- patients, while those of the 8 COVID+ patients with normal elastance (type L) displayed higher aerated lung fraction.

Carotid Wall Longitudinal Motion in Ultrasound Imaging: An Expert Consensus Review.

Motion extracted from the carotid artery wall provides unique information for vascular health evaluation. Carotid artery longitudinal wall motion corresponds to the multiphasic arterial wall excursion in the direction parallel to blood flow during the cardiac cycle. While this motion phenomenon has been well characterized, there is a general lack of awareness regarding its implications for vascular health assessment or even basic vascular physiology. In the last decade, novel estimation strategies and clinical investigations have greatly advanced our understanding of the bi-axial behavior of the carotid artery, necessitating an up-to-date review to summarize and classify the published literature in collaboration with technical and clinical experts in the field. Within this review, the state-of-the-art methodologies for carotid wall motion estimation are described, and the observed relationships between longitudinal motion-derived indices and vascular health are reported. The vast number of studies describing the longitudinal motion pattern in plaque-free arteries, with its putative application to cardiovascular disease prediction, point to the need for characterizing the added value and applicability of longitudinal motion beyond established biomarkers. To this aim, the main purpose of this review was to provide a strong base of theoretical knowledge, together with a curated set of practical guidelines and recommendations for longitudinal motion estimation in patients, to foster future discoveries in the field, toward the integration of longitudinal motion in basic science as well as clinical practice.

Voxel-wise assessment of lung aeration changes on CT images using image registration: application to acute respiratory distress syndrome (ARDS).

PURPOSE: (1) To improve the accuracy of global and regional alveolar-recruitment quantification in CT scan pairs by accounting for lung-tissue displacements and deformation, (2) To propose a method for local-recruitment calculation. METHODS: Recruitment was calculated by subtracting the quantity of non-aerated lung tissues between expiration and inspiration. To assess global recruitment, lung boundaries were first interactively delineated at inspiration, and then they were warped based on automatic image registration to define the boundaries at expiration. To calculate regional recruitment, the lung mask defined at inspiration was cut into pieces, and these were also warped to encompass the same tissues at expiration. Local-recruitment map was calculated as follows: For each voxel at expiration, the matching location at inspiration was determined by image registration, non-aerated voxels were counted in the neighborhood of the respective locations, and the voxel count difference was normalized by the neighborhood size. The methods were evaluated on 120 image pairs of 12 pigs with experimental acute respiratory distress syndrome. RESULTS: The dispersion of global- and regional-recruitment values decreased when using image registration, compared to the conventional approach neglecting tissue motion. Local-recruitment maps overlaid onto the original images were visually consistent, and the sum of these values over the whole lungs was very close to the global-recruitment estimate, except four outliers. CONCLUSIONS: Image registration can compensate lung-tissue displacements and deformation, thus improving the quantification of alveolar recruitment. Local-recruitment calculation can also benefit from image registration, and its values can be overlaid onto the original image to display a local-recruitment map. They also can be integrated over arbitrarily shaped regions to assess regional or global recruitment.

Noninvasive quantification of macrophagic lung recruitment during experimental ventilation-induced lung injury.

Macrophagic lung infiltration is pivotal in the development of lung biotrauma because of ventilation-induced lung injury (VILI). We assessed the performance of [11C](R)-PK11195, a positron emission tomography (PET) radiotracer binding the translocator protein, to quantify macrophage lung recruitment during experimental VILI. Pigs (n = 6) were mechanically ventilated under general anesthesia, using protective ventilation settings (baseline). Experimental VILI was performed by titrating tidal volume to reach a transpulmonary end-inspiratory pressure (∆PL) of 35-40 cmH2O. We acquired PET/computed tomography (CT) lung images at baseline and after 4 h of VILI. Lung macrophages were quantified in vivo by the standardized uptake value (SUV) of [11C](R)-PK11195 measured in PET on the whole lung and in six lung regions and ex vivo on lung pathology at the end of experiment. Lung mechanics were extracted from CT images to assess their association with the PET signal. ∆PL increased from 9 ± 1 cmH2O under protective ventilation, to 36 ± 6 cmH2O during experimental VILI. Compared with baseline, whole-lung [11C](R)-PK11195 SUV significantly increased from 1.8 ± 0.5 to 2.9 ± 0.5 after experimental VILI. Regional [11C](R)-PK11195 SUV was positively associated with the magnitude of macrophage recruitment in pathology (P = 0.03). Compared with baseline, whole-lung CT-derived dynamic strain and tidal hyperinflation increased significantly after experimental VILI, from 0.6 ± 0 to 2.0 ± 0.4, and 1 ± 1 to 43 ± 19%, respectively. On multivariate analysis, both were significantly associated with regional [11C](R)-PK11195 SUV. [11C](R)-PK11195 lung uptake (a proxy of lung inflammation) was increased by experimental VILI and was associated with the magnitude of dynamic strain and tidal hyperinflation.NEW & NOTEWORTHY We assessed the performance of [11C](R)-PK11195, a translocator protein-specific positron emission tomography (PET) radiotracer, to quantify macrophage lung recruitment during experimental ventilation-induced lung injury (VILI). In this proof-of-concept study, we showed that the in vivo quantification of [11C](R)-PK11195 lung uptake in PET reflected the magnitude of macrophage lung recruitment after VILI. Furthermore, increased [11C](R)-PK11195 lung uptake was associated with harmful levels of dynamic strain and tidal hyperinflation applied to the lungs.

A tree-matching algorithm: Application to airways in CT images of subjects with the acute respiratory distress syndrome.

To match anatomical trees such as airways, we propose a graph-based strategy combined with an appropriate distance function. The strategy was devised to cope with topological and geometrical differences that may arise between trees corresponding to the same subject, but extracted from images acquired in different conditions. The proposed distance function, called father/family distance, combines topological and geometrical information in a single measure, by calculating a sum of path-to-path distances between sub-trees of limited extent. To use it successfully, the branches of these sub-trees need to be brought closer, which is obtained by successively translating the roots of these sub-trees prior to their actual matching. The work herein presented contributes to a study of the acute respiratory distress syndrome, where a series of pulmonary CT images from the same subject is acquired at varying settings (pressure and volume) of the mechanical ventilation. The method was evaluated on 45 combinations of synthetic trees, as well as on 15 pairs of real airway trees: nine corresponding to end-expiration and end-inspiration with the same pressure, and six corresponding to end-inspiration with significantly different pressures. It achieved a high rate of successful matches with respect to a hand-made reference containing a total of 2391 matches in real data: sensitivity of 94.3% and precision of 92.8%, when using the basic parameter settings of the algorithm.

A Fully-Automatic Method to Segment the Carotid Artery Layers in Ultrasound Imaging: Application to Quantify the Compression-Decompression Pattern of the Intima-Media Complex During the Cardiac Cycle.

The aim of this study was to introduce and evaluate a contour segmentation method to extract the interfaces of the intima-media complex in carotid B-mode ultrasound images. The method was applied to assess the temporal variation of intima-media thickness during the cardiac cycle. The main methodological contribution of the proposed approach is the introduction of an augmented dimension to process 2-D images in a 3-D space. The third dimension, which is added to the two spatial dimensions of the image, corresponds to the tentative local thickness of the intima-media complex. The method is based on a dynamic programming scheme that runs in a 3-D space generated with a shape-adapted filter bank. The optimal solution corresponds to a single medial axis representation that fully describes the two anatomical interfaces of the arterial wall. The method is fully automatic and does not require any input from the user. The method was trained on 60 subjects and validated on 184 other subjects from six different cohorts and four different medical centers. The arterial wall was successfully segmented in all analyzed images (average pixel size = 57 ± 20 mm), with average segmentation errors of 47 ± 70 mm for the lumen-intima interface, 55 ± 68 mm for the media-adventitia interface and 66 ± 90 mm for the intima-media thickness. The amplitude of the temporal variations in IMT during the cardiac cycle was significantly higher in the diseased population than in healthy volunteers (106 ± 48 vs. 86 ± 34 mm, p = 0.001). The introduced framework is a promising approach to investigate an emerging functional parameter of the arterial wall by assessing the cyclic compression-decompression pattern of the tissues.

Progressive attenuation of the longitudinal kinetics in the common carotid artery: preliminary in vivo assessment.

Longitudinal kinetics (LOKI) of the arterial wall consists of the shearing motion of the intima-media complex over the adventitia layer in the direction parallel to the blood flow during the cardiac cycle. The aim of this study was to investigate the local variability of LOKI amplitude along the length of the vessel. By use of a previously validated motion-estimation framework, 35 in vivo longitudinal B-mode ultrasound cine loops of healthy common carotid arteries were analyzed. Results indicated that LOKI amplitude is progressively attenuated along the length of the artery, as it is larger in regions located on the proximal side of the image (i.e., toward the heart) and smaller in regions located on the distal side of the image (i.e., toward the head), with an average attenuation coefficient of -2.5 ± 2.0%/mm. Reported for the first time in this study, this phenomenon is likely to be of great importance in improving understanding of atherosclerosis mechanisms, and has the potential to be a novel index of arterial stiffness.

Comparing algorithms for automated vessel segmentation in computed tomography scans of the lung: the VESSEL12 study.

The VESSEL12 (VESsel SEgmentation in the Lung) challenge objectively compares the performance of different algorithms to identify vessels in thoracic computed tomography (CT) scans. Vessel segmentation is fundamental in computer aided processing of data generated by 3D imaging modalities. As manual vessel segmentation is prohibitively time consuming, any real world application requires some form of automation. Several approaches exist for automated vessel segmentation, but judging their relative merits is difficult due to a lack of standardized evaluation. We present an annotated reference dataset containing 20 CT scans and propose nine categories to perform a comprehensive evaluation of vessel segmentation algorithms from both academia and industry. Twenty algorithms participated in the VESSEL12 challenge, held at International Symposium on Biomedical Imaging (ISBI) 2012. All results have been published at the VESSEL12 website http://vessel12.grand-challenge.org. The challenge remains ongoing and open to new participants. Our three contributions are: (1) an annotated reference dataset available online for evaluation of new algorithms; (2) a quantitative scoring system for objective comparison of algorithms; and (3) performance analysis of the strengths and weaknesses of the various vessel segmentation methods in the presence of various lung diseases.

Bone canalicular network segmentation in 3D nano-CT images through geodesic voting and image tessellation.

Recent studies emphasized the role of the bone lacuno-canalicular network (LCN) in the understanding of bone diseases such as osteoporosis. However, suitable methods to investigate this structure are lacking. The aim of this paper is to introduce a methodology to segment the LCN from three-dimensional (3D) synchrotron radiation nano-CT images. Segmentation of such structures is challenging due to several factors such as limited contrast and signal-to-noise ratio, partial volume effects and huge number of data that needs to be processed, which restrains user interaction. We use an approach based on minimum-cost paths and geodesic voting, for which we propose a fully automatic initialization scheme based on a tessellation of the image domain. The centroids of pre-segmented lacunæ are used as Voronoi-tessellation seeds and as start-points of a fast-marching front propagation, whereas the end-points are distributed in the vicinity of each Voronoi-region boundary. This initialization scheme was devised to cope with complex biological structures involving cells interconnected by multiple thread-like, branching processes, while the seminal geodesic-voting method only copes with tree-like structures. Our method has been assessed quantitatively on phantom data and qualitatively on real datasets, demonstrating its feasibility. To the best of our knowledge, presented 3D renderings of lacunæ interconnected by their canaliculi were achieved for the first time.

A semi-automatic method to extract canal pathways in 3D micro-CT images of Octocorals.

The long-term goal of our study is to understand the internal organization of the octocoral stem canals, as well as their physiological and functional role in the growth of the colonies, and finally to assess the influence of climatic changes on this species. Here we focus on imaging tools, namely acquisition and processing of three-dimensional high-resolution images, with emphasis on automated extraction of canal pathways. Our aim was to evaluate the feasibility of the whole process, to point out and solve - if possible - technical problems related to the specimen conditioning, to determine the best acquisition parameters and to develop necessary image-processing algorithms. The pathways extracted are expected to facilitate the structural analysis of the colonies, namely to help observing the distribution, formation and number of canals along the colony. Five volumetric images of Muricea muricata specimens were successfully acquired by X-ray computed tomography with spatial resolution ranging from 4.5 to 25 micrometers. The success mainly depended on specimen immobilization. More than [Formula: see text] of the canals were successfully detected and tracked by the image-processing method developed. Thus obtained three-dimensional representation of the canal network was generated for the first time without the need of histological or other destructive methods. Several canal patterns were observed. Although most of them were simple, i.e. only followed the main branch or "turned" into a secondary branch, many others bifurcated or fused. A majority of bifurcations were observed at branching points. However, some canals appeared and/or ended anywhere along a branch. At the tip of a branch, all canals fused into a unique chamber. Three-dimensional high-resolution tomographic imaging gives a non-destructive insight to the coral ultrastructure and helps understanding the organization of the canal network. Advanced image-processing techniques greatly reduce human observer's effort and provide methods to both visualize and quantify the structures of interest.