Impact of Humidification Modality on Incidence of Endotracheal Tube Occlusion in COVID-19 Patients.

Background: Endotracheal tube (ETT) occlusion is reported at a higher frequency among coronavirus disease-2019 (COVID-19) patients. Prior to the COVID-19 pandemic, literature examining patient and ventilator characteristics, including humidification, as etiologies of ETT occlusion yielded mixed results. Our study examines the relationship of humidification modality with ETT occlusion in COVID-19 patients undergoing invasive mechanical ventilation (IMV). Methods: We conducted a retrospective chart review of COVID-19 patients requiring IMV at a tertiary care center in New York from April 2020 to April 2021. Teleflex Neptune heated wire heated humidification (HH) and hygroscopic Intersurgical FiltaTherm and Sunmed Ballard 1500 heat and moisture exchangers (HME) were used. Episodes of ETT occlusion were recorded. Univariate and multivariable logistic regression models were used to investigate the relationship between humidification modality and the occurrence of ETT occlusion. Findings: A total of 201 eligible patients were identified. Teleflex HH was utilized in 50.2% of the population and the others Intersurgical and Sunmed HME devices. Median age was 62 years and 78.6% of patients had at least one medical comorbidity. Precisely, 24% of patients experienced an ETT occlusion after a median of 12 days. The HME group was younger (58.5 vs 64 years), predominantly male (75% vs 59.4%), and experienced more total ventilator days than the HH group (24 vs 12). Those using the studied HME devices had significantly higher odds of ETT occlusion (OR 4.4, 95% CI 1.8-10.6, P = .0011). Three patients (6.1%) experienced cardiac arrest as a consequence of their occlusion. There were no deaths directly attributed to ETT occlusion. Conclusions: The studied HME devices were significantly associated with higher odds of ETT occlusion in COVID-19 patients requiring invasive mechanical ventilation. These events are not without significant clinical consequences. Prolonged use of under-performing HME devices remains suspect in the occurrence of ETT occlusions.

A multimodal submillimeter MRI atlas of the human cerebellum.

The human cerebellum is engaged in a broad array of tasks related to motor coordination, cognition, language, attention, memory, and emotional regulation. A detailed cerebellar atlas can facilitate the investigation of the structural and functional organization of the cerebellum. However, existing cerebellar atlases are typically limited to a single imaging modality with insufficient characterization of tissue properties. Here, we introduce a multifaceted cerebellar atlas based on high-resolution multimodal MRI, facilitating the understanding of the neurodevelopment and neurodegeneration of the cerebellum based on cortical morphology, tissue microstructure, and intra-cerebellar and cerebello-cerebral connectivity.

Endotoxemia Correlates with Kidney Function and Length of Stay in Critically Ill Patients.

INTRODUCTION: Endotoxin is a key driver of sepsis, which frequently causes acute kidney injury (AKI). However, endotoxins may also be found in non-bacteremic critically ill patients, likely from intestinal translocation. Preclinical models show that endotoxins can directly injure the kidneys, and in COVID-19 patients, endotoxemia correlated with AKI. We sought to determine correlations between endotoxemia and kidney and hospital outcomes in a broad group of critically ill patients. METHODS: In this single-center, serial prospective study, 124 predominantly Caucasian adult patients were recruited within 48 h of admission to Stony Brook University Hospital Intensive Care Unit (ICU). Demographics, vital signs, laboratory data, and outcomes were collected. Circulating endotoxin was measured on days 1, 4, and 8 using the endotoxin activity assay (EAA). The association of EAA with outcomes was examined with EAA: (1) categorized as <0.6, ≥0.6, and nonresponders (NRs); and (2) used as a continuous variable. RESULTS: Patients with EAA ≥0.6 had a higher prevalence of proteinuria, and lower arterial oxygen saturation (SaO2) to fraction of inspired oxygen (FiO2) (SaO2/FiO2) ratio versus patients with EAA <0.6. EAA levels positively correlated with serum creatinine (sCr) levels on day 1. Patients whose EAA level stayed ≥0.6 had a slower decline in sCr compared to those whose EAA started at ≥0.6 and subsequently declined. Patients with AKI stage 1 and EAA ≥0.6 on day 1 showed slower decline in sCr compared to patients with stage 1 AKI and EAA <0.6. EAA ≥0.6 and NR patients had longer hospital stay and delayed ICU discharge versus EAA <0.6. CONCLUSIONS: High EAA levels correlated with worse kidney function and outcomes. Patients whose EAA levels fell, and those with AKI stage I and day 1 EAA <0.6 recovered more quickly compared to those with EAA ≥0.6, suggesting that removal of circulating endotoxins may be beneficial in critically ill patients.

MeshDeform: Surface Reconstruction of Subcortical Structures in Human Brain MRI.

Surface reconstruction of cortical and subcortical structures is crucial for brain morphological studies. Existing deep learning surface reconstruction methods, such as DeepCSR and Vox2Surf, learn an implicit field function for computing the isosurface, but do not consider mesh topology. In this paper, we propose a novel and efficient deep learning mesh deformation network, called MeshDeform, to reconstruct topologically correct surfaces of subcortical structures using brain MR images. MeshDeform combines features extracted from a U-Net encoder with mesh deformation blocks to predict surfaces of subcortical structures by deforming spherical mesh templates. MeshDeform is able to reconstruct in less than 10 seconds the surfaces of a left-right pair of subcortical structures with subvoxel accuracy. Reconstruction of all 17 subcortical structures takes less than one and a half minutes. By contrast, Vox2Surf takes about 20-30 minutes for all subcortical structures. Visual and quantitative evaluation on the Human Connectome Project (HCP) dataset demonstrate that MeshDeform generates accurate subcortical surfaces in limited time while preserving mesh topology.

Organ-aware CBCT enhancement via dual path learning for prostate cancer treatment.

BACKGROUND: Cone-beam computed tomography (CBCT) plays a crucial role in the intensity modulated radiotherapy (IMRT) of prostate cancer. However, poor image contrast and fuzzy organ boundaries pose challenges to precise targeting for dose delivery and plan reoptimization for adaptive therapy. PURPOSE: In this work, we aim to enhance pelvic CBCT images by translating them to high-quality CT images with a particular focus on the anatomical structures important for radiotherapy. METHODS: We develop a novel dual-path learning framework, covering both global and local information, for organ-aware enhancement of the prostate, bladder and rectum. The global path learns coarse inter-modality translation at the image level. The local path learns organ-aware translation at the regional level. This dual-path learning architecture can serve as a plug-and-play module adaptable to other medical image-to-image translation frameworks. RESULTS: We evaluated the performance of the proposed method both quantitatively and qualitatively. The training dataset consists of unpaired 40 CBCT and 40 CT scans, the validation dataset consists of 5 paired CBCT-CT scans, and the testing dataset consists of 10 paired CBCT-CT scans. The peak signal-to-noise ratio (PSNR) between enhanced CBCT and reference CT images is 27.22 ± 1.79, and the structural similarity (SSIM) between enhanced CBCT and the reference CT images is 0.71 ± 0.03. We also compared our method with state-of-the-art image-to-image translation methods, where our method achieves the best performance. Moreover, the statistical analysis confirms that the improvements achieved by our method are statistically significant. CONCLUSIONS: The proposed method demonstrates its superiority in enhancing pelvic CBCT images, especially at the organ level, compared to relevant methods.

Comparison of cognitive functional therapy and movement system impairment treatment in chronic low back pain patients: a randomized controlled trial.

BACKGROUND: This study aimed to compare the effects of cognitive functional therapy (CFT) and movement system impairment (MSI)-based treatment on pain intensity, disability, Kinesiophobia, and gait kinetics in patients with chronic non-specific low back pain (CNSLBP). METHODS: In a single-blind randomized clinical trial, we randomly assigned 91 patients with CNSLBP into CFT (n = 45) and MSI-based treatment (n = 46) groups. An 8-week training intervention was given to both groups. The researchers measured the primary outcome, which was pain intensity (Numeric rating scale), and the secondary outcomes, including disability (Oswestry disability index), Kinesiophobia (Tampa Kinesiophobia Scale), and vertical ground reaction force (VGRF) parameters at self-selected and faster speed (Force distributor treadmill). We evaluated patients at baseline, at the end of the 8-week intervention (post-treatment), and six months after the first treatment. We used mixed-model ANOVA to evaluate the effects of the interaction between time (baseline vs. post-treatment vs. six-month follow-up) and group (CFT vs. MSI-based treatment) on each measure. RESULTS: CFT showed superiority over MSI-based treatment in reducing pain intensity (P < 0.001, Effect size (ES) = 2.41), ODI (P < 0.001, ES = 2.15), and Kinesiophobia (P < 0.001, ES = 2.47) at eight weeks. The CFT also produced greater improvement in VGRF parameters, at both self-selected (FPF[P < 0.001, ES = 3], SPF[P < 0.001, ES = 0.5], MSF[P < 0.001, ES = 0.67], WAR[P < 0.001, ES = 1.53], POR[P < 0.001, ES = 0.8]), and faster speed, FPF(P < 0.001, ES = 1.33, MSF(P < 0.001, ES = 0.57), WAR(P < 0.001, ES = 0.67), POR(P < 0.001, ES = 2.91)] than the MSI, except SPF(P < 0.001, ES = 0.0) at eight weeks. CONCLUSION: This study suggests that the CFT is associated with better results in clinical and cognitive characteristics than the MSI-based treatment for CNSLBP, and the researchers maintained the treatment effects at six-month follow-up. Also, This study achieved better improvements in gait kinetics in CFT. CTF seems to be an appropriate and applicable treatment in clinical setting. TRIAL REGISTRATION: The researchers retrospectively registered the trial 10/11/2022, at https://www.umin.ac.jp/ with identifier number (UMIN000047455).

Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network.

Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of image contents. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both tissue contrasts and anatomical details.

A paired dataset of T1- and T2-weighted MRI at 3 Tesla and 7 Tesla.

Brain magnetic resonance imaging (MRI) provides detailed soft tissue contrasts that are critical for disease diagnosis and neuroscience research. Higher MRI resolution typically comes at the cost of signal-to-noise ratio (SNR) and tissue contrast, particularly for more common 3 Tesla (3T) MRI scanners. At ultra-high magnetic field strength, 7 Tesla (7T) MRI allows for higher resolution with greater tissue contrast and SNR. However, the prohibitively high costs of 7T MRI scanners deter their widespread adoption in clinical and research centers. To obtain higher-quality images without 7T MRI scanners, algorithms that can synthesize 7T MR images from 3T MR images are under active development. Here, we make available a dataset of paired T1-weighted and T2-weighted MR images at 3T and 7T of 10 healthy subjects to facilitate the development and evaluation of 3T-to-7T MR image synthesis models. The quality of the dataset is assessed using image quality metrics implemented in MRIQC.

Multifaceted atlases of the human brain in its infancy.

Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface-volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.

Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding.

In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.

Utilizing a novel point of care ultrasound (POCUS) protocol to guide diuresis - A case series.

Volume assessment is an important, but challenging but crucial aspect of patient care. Fluid balance is affected by volume expansion, sepsis/shock states, cardiac and kidney failure and is present in the majority of patients. Similarly, in critically ill patients on diuretics, both excessive and inadequate diuresis can worsen outcomes. Chest X-ray (CXR) and auscultation are poorly predictive of volume status, while bioimpedance and blood volume monitoring have limitations at the bedside [4]. Inferior vena cava (IVC) diameter as measured by POCUS is a marker of intravascular volume that can provide a real-time assessment to guide diuresis. The Reverse Falls Protocol combines lung and IVC US to enable the clinician to visualize, in real-time, the patient's intravascular and extravascular volume and to set diuresis goals. We present a series of cases where euvolemia was achieved using the principles described by O'Hara, Chabra & Ahmad's Reverse Falls Protocol.

Recurrent Tissue-Aware Network for Deformable Registration of Infant Brain MR Images.

Deformable registration is fundamental to longitudinal and population-based image analyses. However, it is challenging to precisely align longitudinal infant brain MR images of the same subject, as well as cross-sectional infant brain MR images of different subjects, due to fast brain development during infancy. In this paper, we propose a recurrently usable deep neural network for the registration of infant brain MR images. There are three main highlights of our proposed method. (i) We use brain tissue segmentation maps for registration, instead of intensity images, to tackle the issue of rapid contrast changes of brain tissues during the first year of life. (ii) A single registration network is trained in a one-shot manner, and then recurrently applied in inference for multiple times, such that the complex deformation field can be recovered incrementally. (iii) We also propose both the adaptive smoothing layer and the tissue-aware anti-folding constraint into the registration network to ensure the physiological plausibility of estimated deformations without degrading the registration accuracy. Experimental results, in comparison to the state-of-the-art registration methods, indicate that our proposed method achieves the highest registration accuracy while still preserving the smoothness of the deformation field. The implementation of our proposed registration network is available online https://github.com/Barnonewdm/ACTA-Reg-Net.

Harmonization of Multi-site Cortical Data Across the Human Lifespan.

Neuroimaging data harmonization has become a prerequisite in integrative data analytics for standardizing a wide variety of data collected from multiple studies and enabling interdisciplinary research. The lack of standardized image acquisition and computational procedures introduces non-biological variability and inconsistency in multi-site data, complicating downstream statistical analyses. Here, we propose a novel statistical technique to retrospectively harmonize multi-site cortical data collected longitudinally and cross-sectionally between birth and 100 years. We demonstrate that our method can effectively eliminate non-biological disparities from cortical thickness and myelination measurements, while preserving biological variation across the entire lifespan. Our harmonization method will foster large-scale population studies by providing comparable data required for investigating developmental and aging processes.

Inhaled nitric oxide in adults with in-hospital cardiac arrest: A feasibility study.

BACKGROUND: While inhaled nitric oxide (iNO) has revealed benefit in cardiac arrest in an animal model, no published data has yet demonstrated the impact of iNO in humans with cardiac arrest. METHODS: In this pilot study, we administered iNO, along with standard post-resuscitative care, in adults with in-hospital cardiac arrest (IHCA) following achievement of return of spontaneous circulation (ROSC) at an academic tertiary medical center. Patients receiving iNO were compared to age-matched controls with IHCA receiving standard care from an institutional registry. The primary outcome was survival to discharge; secondary outcome was favorable neurologic outcome, defined by a Glasgow Outcome Score of 4 or 5. Propensity-score (PS) matching analysis was performed between patients receiving iNO versus controls. RESULTS: Twenty adults with IHCA receiving iNO were compared to 199 controls with IHCA. Similar age, Charlson comorbidity index, and initial rhythm were noted in both groups. Patients receiving iNO had higher rates of survival to discharge compared to controls (35% vs 11%, p < 0.0001) but no difference in favorable neurologic outcome (15% vs 9%, p = 0.39) in the unmatched population. In the PS-matched analysis, patients receiving iNO had higher survival to discharge (35% vs 20%, p = 0.0344) than the control group but no difference in favorable neurologic outcome (15% vs 20%, p = 0.13) were noted between both groups. CONCLUSIONS: In this pilot study, iNO was associated with significantly higher rates of survival to discharge but not favorable neurologic outcome among patients with IHCA compared to controls. This benefit was also observed in the PS-matched analysis. A large scale randomized controlled trial comparing standard of care supplemented with iNO to standard of care alone is warranted in patients with cardiac arrest (Funded by Stony Brook University Renaissance School of Medicine, ClinicalTrials.gov number, NCT04134078).

Brainwide functional networks associated with anatomically- and functionally-defined hippocampal subfields using ultrahigh-resolution fMRI.

The hippocampus is critical for learning and memory and may be separated into anatomically-defined hippocampal subfields (aHPSFs). Hippocampal functional networks, particularly during resting state, are generally analyzed using aHPSFs as seed regions, with the underlying assumption that the function within a subfield is homogeneous, yet heterogeneous between subfields. However, several prior studies have observed similar resting-state functional connectivity (FC) profiles between aHPSFs. Alternatively, data-driven approaches investigate hippocampal functional organization without a priori assumptions. However, insufficient spatial resolution may result in a number of caveats concerning the reliability of the results. Hence, we developed a functional Magnetic Resonance Imaging (fMRI) sequence on a 7 T MR scanner achieving 0.94 mm isotropic resolution with a TR of 2 s and brain-wide coverage to (1) investigate the functional organization within hippocampus at rest, and (2) compare the brain-wide FC associated with fine-grained aHPSFs and functionally-defined hippocampal subfields (fHPSFs). This study showed that fHPSFs were arranged along the longitudinal axis that were not comparable to the lamellar structures of aHPSFs. For brain-wide FC, the fHPSFs rather than aHPSFs revealed that a number of fHPSFs connected specifically with some of the functional networks. Different functional networks also showed preferential connections with different portions of hippocampal subfields.

Pneumothorax in critically ill patients with COVID-19 infection: Incidence, clinical characteristics and outcomes in a case control multicenter study.

BACKGROUND: The clinical features and outcomes of mechanically ventilated patients with COVID-19 infection who develop a pneumothorax has not been rigorously described or compared to those who do not develop a pneumothorax. PURPOSE: To determine the incidence, clinical characteristics, and outcomes of critically ill patients with COVID-19 infection who developed pneumothorax. In addition, we compared the clinical characteristics and outcomes of mechanically ventilated patients who developed a pneumothorax with those who did not develop a pneumothorax. METHODS: This study was a multicenter retrospective analysis of all adult critically ill patients with COVID-19 infection who were admitted to intensive care units in 4 tertiary care centers in the United States. RESULTS: A total of 842 critically ill patients with COVID-19 infection were analyzed, out of which 594 (71%) were mechanically ventilated. The overall incidence of pneumothorax was 85/842 (10%), and 80/594 (13%) in those who were mechanically ventilated. As compared to mechanically ventilated patients in the non-pneumothorax group, mechanically ventilated patients in the pneumothorax group had worse respiratory parameters at the time of intubation (mean PaO2:FiO2 ratio 105 vs 150, P<0.001 and static respiratory system compliance: 30ml/cmH2O vs 39ml/cmH2O, P = 0.01) and significantly higher in-hospital mortality (63% vs 49%, P = 0.04). CONCLUSION: The overall incidence of pneumothorax in mechanically ventilated patients with COVID-19 infection was 13%. Mechanically ventilated patients with COVID-19 infection who developed pneumothorax had worse gas exchange and respiratory mechanics at the time of intubation and had a higher mortality compared to those who did not develop pneumothorax.

Ultrasound for Pleural Disease. Beyond a Pocket of Pleural Fluid.

The evolution of pleural disease imaging modalities through the years has helped the scientific community understand and treat various disease states. Ultrasound (US) has been an image modality that has reigned superior to those used in the past such as chest X-ray and computed tomographic scan in terms of cost effectiveness, portability, and reduction in unwarranted radiation exposure to patients. Here we provide a succinct review of US use in pleural disease including imaging techniques, identifying safe pleural space for access, and predicting pleural fluid volume and etiology along with specificities regarding trapped lung identification and pleural mass biopsy. We believe bedside chest US is an adjunct to the physical exam adding superior diagnostic abilities. Further research is warranted in more specific aspects of sonographic use such as in fibrinolytic therapy management, evaluation for trapped lung, and the utility of specific modes like the color flow Doppler.

Surface-Guided Image Fusion for Preserving Cortical Details in Human Brain Templates.

Human brain templates are a basis for comparison of brain features across individuals. They should ideally capture an atomical details at both coarse and fine scales to facilitate comparison at varying granularity. Brain template construction typically involves spatial normalization and image fusion. While significant efforts have been dedicated to improving brain templates with sophisticated spatial normalization algorithms, image fusion is typically carried out using intensity-based averaging, causing blurring of anatomical structures. Here, we present an image fusion method that exploits cortical surfaces as guidance to help preserve details in brain templates. Our method encodes cortical boundary information given by a cortical surface mesh in a signed distance function (SDF) map. We use the SDF map to help determine localized contributions of the individual images, especially at cortical boundaries, in image fusion. Experimental results demonstrate that our method significantly improves the preservation of fine gyral and sulcal details, resulting in detailed brain templates with good surface-volume agreement.