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).
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.
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,
= .0011). Three patients (6.1%) experienced cardiac arrest as a consequence of their occlusion. There were no deaths directly attributed to ETT occlusion.
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.
•Inter-subject non-rigid deformations are modeled as elastic waves.•The underlying transformation is governed by elastodynamics wave equation.•The proposed dynamic elasticity model recovers both ...global shape differences and large local deformations.•The proposed registration scheme pursues in a step-by-step approach thus avoiding local optimal transformations.
The purpose of this paper is to present a new approach for inter-subject non-rigid registration of 3D MR brain images which could assist in automatic labeling of brain structures. A physical dynamic elasticity model (DEM) is developed which tends to represent the complex non-linear deformations as elastic waves. The transformation is governed by elastodynamics wave equation. The registration process ensues in a hierarchical fashion, thus reducing the risk of obtaining a local optimal transformation. Along with the correction of local misalignments, it also removes global shape differences without any prior initialization. The proposed scheme was compared against high ranking registration methods including: DROP, SyN, ART and DRAMMS. The results were quantitatively analyzed by computing and testing the statistical significance of the volume overlap measures and Hausdorff distance for segmented structures with DROP, SyN, ART, DRAMMS and DEM registration methods. Experimental results demonstrate that the proposed DEM registration method leads to very promising results when applied to the problem of inter-subject registration and that favorably compares against classical registration approaches. Since DEM registration method is able to reduce registration errors significantly, hence it could be used to automatically label the anatomical structures for clinical studies.
The collapsibility index of the inferior vena cava is traditionally visualized from the subcostal region in the sagittal plane, referred to here as cIVCSS. Alternatively, the collapsibility index of ...the inferior vena cava can be visualized from the right midaxillary line in the coronal plane, referred to here as cIVCRC. It is unclear whether values of cIVCRC are comparable with values of cIVCSS because the inferior vena cava collapses asymmetrically into an elliptical form, quantified as the flat ratio of the inferior vena cava (F-IVC). This study aimed (1) to establish if cIVCRC is concordant or discordant to cIVCSS, and (2) to describe how this concordance or discordance is related to F-IVC.
This single-center cross-sectional study enrolled 110 spontaneously breathing patients. Values of cIVCRC were compared with cIVCSS. Performance of cIVCRC ≥ 42% in predicting fluid responsiveness, defined as cIVCSS ≥ 42%, was assessed. F-IVC was also correlated to the difference between cIVCSS and cIVCRC.
cIVCRC ≥ 42% was 61.5% sensitive (95% CI, 31.58%-86.14%) and 67.1% specific (95% CI, 55.81%-77.06%) for predicting cIVCSS ≥ 42%. cIVCRC underestimated cIVCSS. The degree of discordance between cIVCRC and cIVCSS was proportional to the value of F-IVC.
cIVCRC and cIVCSS measures are discordant, where cIVCRC underestimates cIVCSS. The degree of discordance is directly proportional to the value of F-IVC. Therefore, we recommend that cIVCRC ≥ 42% be used to rule in, but not to rule out, fluid responsivity. Wherever possible, F-IVC should be assessed to understand the clinical relevance of cIVCRC.
•We propose a trustworthy adversarial learning metamorphosis framework that accounts for both the appearance and structural changes in infant brain MRI.•A spatial-frequency transfer block captures ...structural changes from multiple frequency bands with wavelet decomposition.•A quality-guided learning strategy is incorporated to improve predictions in challenging regions.•A multi-scale hybrid loss function is employed to improve prediction of textural details and anatomical boundaries.
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.
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.
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.
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.
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.
The proposed method demonstrates its superiority in enhancing pelvic CBCT images, especially at the organ level, compared to relevant methods.
Abstract In this paper, we present a new non-rigid image registration method that imposes a topology preservation constraint on the deformation. We propose to incorporate the time varying elasticity ...model into the deformable image matching procedure and constrain the Jacobian determinant of the transformation over the entire image domain. The motion of elastic bodies is governed by a hyperbolic partial differential equation, generally termed as elastodynamics wave equation, which we propose to use as a deformation model. We carried out clinical image registration experiments on 3D magnetic resonance brain scans from IBSR database. The results of the proposed registration approach in terms of Kappa index and relative overlap computed over the subcortical structures were compared against the existing topology preserving non-rigid image registration methods and non topology preserving variant of our proposed registration scheme. The Jacobian determinant maps obtained with our proposed registration method were qualitatively and quantitatively analyzed. The results demonstrated that the proposed scheme provides good registration accuracy with smooth transformations, thereby guaranteeing the preservation of topology.
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