Pediatric sleep-related breathing disorders: advances in imaging and computational modeling

Graph-based rotational nonuniformity correction for localized compliance measurement in the human nasopharynx

Recent advancements in the high-speed long-range optical coherence tomography (OCT) endoscopy allow characterization of tissue compliance in the upper airway, an indicator of collapsibility. However, the resolution and accuracy of localized tissue compliance measurement are currently limited by the lack of a reliable nonuniform rotational distortion (NURD) correction method. In this study, we developed a robust 2-step NURD correction algorithm that can be applied to the dynamic OCT images obtained during the compliance measurement. We demonstrated the utility of the NURD correction algorithm by characterizing the local compliance of nasopharynx from an awake human subject for the first time.

Segmentation of 4D images via space-time neural networks

Medical imaging techniques currently produce 4D images that portray the dynamic behaviors and phenomena associated with internal structures. The segmentation of 4D images poses challenges different from those arising in segmenting 3D static images due to different patterns of variation of object shape and appearance in the space and time dimensions. In this paper, different network models are designed to learn the pattern of slice-to-slice change in the space and time dimensions independently. The two models then allow a gamut of strategies to actually segment the 4D image, such as segmentation following just the space or time dimension only, or following first the space dimension for one time instance and then following all time instances, or vice versa, etc. This paper investigates these strategies in the context of the obstructive sleep apnea (OSA) application and presents a unified deep learning framework to segment 4D images. Because of the sparse tubular nature of the upper airway and the surrounding low-contrast structures, inadequate contrast resolution obtainable in the magnetic resonance (MR) images leaves many challenges for effective segmentation of the dynamic airway in 4D MR images. Given that these upper airway structures are sparse, a Dice coefficient (DC) of ~0.88 for their segmentation based on our preferred strategy is similar to a DC of >0.95 for large non-sparse objects like liver, lungs, etc., constituting excellent accuracy.

MR Image Analytics to Characterize the Upper Airway Structure in Obese Children with Obstructive Sleep Apnea Syndrome

Purpose

Quantitative image analysis in previous research in obstructive sleep apnea syndrome (OSAS) has focused on the upper airway or several objects in its immediate vicinity and measures of object size. In this paper, we take a more general approach of considering all major objects in the upper airway region and measures pertaining to their individual morphological properties, their tissue characteristics revealed by image intensities, and the 3D architecture of the object assembly. We propose a novel methodology to select a small set of salient features from this large collection of measures and demonstrate the ability of these features to discriminate with very high prediction accuracy between obese OSAS and obese non-OSAS groups.

Materials and Methods

Thirty children were involved in this study with 15 in the obese OSAS group with an apnea-hypopnea index (AHI) = 14.4 ± 10.7) and 15 in the obese non-OSAS group with an AHI = 1.0 ± 1.0 (p<0.001). Subjects were between 8–17 years and underwent T1- and T2-weighted magnetic resonance imaging (MRI) of the upper airway during wakefulness. Fourteen objects in the vicinity of the upper airways were segmented in these images and a total of 159 measurements were derived from each subject image which included object size, surface area, volume, sphericity, standardized T2-weighted image intensity value, and inter-object distances. A small set of discriminating features was identified from this set in several steps. First, a subset of measures that have a low level of correlation among the measures was determined. A heat map visualization technique that allows grouping of parameters based on correlations among them was used for this purpose. Then, through T-tests, another subset of measures which are capable of separating the two groups was identified. The intersection of these subsets yielded the final feature set. The accuracy of these features to perform classification of unseen images into the two patient groups was tested by using logistic regression and multi-fold cross validation.

Results

A set of 16 features identified with low inter-feature correlation (< 0.36) yielded a high classification accuracy of 96% with sensitivity and specificity of 97.8% and 94.4%, respectively. In addition to the previously observed increase in linear size, surface area, and volume of adenoid, tonsils, and fat pad in OSAS, the following new markers have been found. Standardized T2-weighted image intensities differed between the two groups for the entire neck body region, pharynx, and nasopharynx, possibly indicating changes in object tissue characteristics. Fat pad and oropharynx become less round or more complex in shape in OSAS. Fat pad and tongue move closer in OSAS, and so also oropharynx and tonsils and fat pad and tonsils. In contrast, fat pad and oropharynx move farther apart from the skin object.

Conclusions

The study has found several new anatomic bio-markers of OSAS. Changes in standardized T2-weighted image intensities in objects may imply that intrinsic tissue composition undergoes changes in OSAS. The results on inter-object distances imply that treatment methods should respect the relationships that exist among objects and not just their size. The proposed method of analysis may lead to an improved understanding of the mechanisms underlying OSAS.

Minimally interactive segmentation of 4D dynamic upper airway MR images via fuzzy connectedness

PURPOSE

There are several disease conditions that lead to upper airway restrictive disorders. In the study of these conditions, it is important to take into account the dynamic nature of the upper airway. Currently, dynamic magnetic resonance imaging is the modality of choice for studying these diseases. Unfortunately, the contrast resolution obtainable in the images poses many challenges for an effective segmentation of the upper airway structures. No viable methods have been developed to date to solve this problem. In this paper, the authors demonstrate a practical solution by employing an iterative relative fuzzy connectedness delineation algorithm as a tool.

METHODS

3D dynamic images were collected at ten equally spaced instances over the respiratory cycle (i.e., 4D) in 20 female subjects with obstructive sleep apnea syndrome. The proposed segmentation approach consists of the following steps. First, image background nonuniformities are corrected which is then followed by a process to correct for the nonstandardness of MR image intensities. Next, standardized image intensity statistics are gathered for the nasopharynx and oropharynx portions of the upper airway as well as the surrounding soft tissue structures including air outside the body region, hard palate, soft palate, tongue, and other soft structures around the airway including tonsils (left and right) and adenoid. The affinity functions needed for fuzzy connectedness computation are derived based on these tissue intensity statistics. In the next step, seeds for fuzzy connectedness computation are specified for the airway and the background tissue components. Seed specification is needed in only the 3D image corresponding to the first time instance of the 4D volume; from this information, the 3D volume corresponding to the first time point is segmented. Seeds are automatically generated for the next time point from the segmentation of the 3D volume corresponding to the previous time point, and the process continues and runs without human interaction and completes in 10 s for segmenting the airway structure in the whole 4D volume.

RESULTS

Qualitative evaluations performed to examine smoothness and continuity of motions of the entire upper airway as well as its transverse sections at critical anatomic locations indicate that the segmentations are consistent. Quantitative evaluations of the separate 200 3D volumes and the 20 4D volumes yielded true positive and false positive volume fractions around 95% and 0.1%, respectively, and mean boundary placement errors under 0.5 mm. The method is robust to variations in the subjective action of seed specification. Compared with a segmentation approach based on a registration technique to propagate segmentations, the proposed method is more efficient, accurate, and less prone to error propagation from one respiratory time point to the next.

CONCLUSIONS

The proposed method is the first demonstration of a viable and practical approach for segmenting the upper airway structures in dynamic MR images. Compared to registration-based methods, it effectively reduces error propagation and consequently achieves not only more accurate segmentations but also more consistent motion representation in the segmentations. The method is practical, requiring minimal user interaction and computational time.

A Multidisciplinary Children's Airway Center: Impact on the Care of Patients With Tracheostomy

BACKGROUND

Children with complex airway problems see multiple specialists. To improve outcomes and coordinate care, we developed a multidisciplinary Children's Airway Center. For children with tracheostomies, aspects of care targeted for improvement included optimizing initial hospital discharge, promoting effective communication between providers and caregivers, and avoiding tracheostomy complications.

METHODS

The population includes children up to 21 years old with tracheostomies. The airway center team includes providers from pediatric pulmonology, pediatric otolaryngology/head and neck surgery, and pediatric gastroenterology. Improvement initiatives included enhanced educational strategies, weekly care conferences, institutional consensus guidelines and care plans, personalized clinic schedules, and standardized intervals between airway examinations. A patient database allowed for tracking outcomes over time.

RESULTS

We initially identified 173 airway center patients including 123 with tracheostomies. The median number of new patients evaluated by the center team each year was 172. Median hospitalization after tracheostomy decreased from 37 days to 26 days for new tracheostomy patients <1 year old discharged from the hospital. A median of 24 care plans was evaluated at weekly conferences. Consensus protocol adherence increased likelihood of successful decannulation from 68% to 86% of attempts. The median interval of 8 months between airway examinations aligned with published recommendations.

CONCLUSIONS

For children with tracheostomies, our Children's Airway Center met and sustained goals of optimizing hospitalization, promoting communication, and avoiding tracheostomy complications by initiating targeted improvements in a multidisciplinary team setting. A multidisciplinary approach to management of these patients can yield measurable improvements in important outcomes.

Dynamic 3-D MR Visualization and Detection of Upper Airway Obstruction During Sleep Using Region-Growing Segmentation

GOAL

We demonstrate a novel and robust approach for visualization of upper airway dynamics and detection of obstructive events from dynamic 3-D magnetic resonance imaging (MRI) scans of the pharyngeal airway.

METHODS

This approach uses 3-D region growing, where the operator selects a region of interest that includes the pharyngeal airway, places two seeds in the patent airway, and determines a threshold for the first frame.

RESULTS

This approach required 5 s/frame of CPU time compared to 10 min/frame of operator time for manual segmentation. It compared well with manual segmentation, resulting in Dice Coefficients of 0.84 to 0.94, whereas the Dice Coefficients for two manual segmentations by the same observer were 0.89 to 0.97. It was also able to automatically detect 83% of collapse events.

CONCLUSION

Use of this simple semiautomated segmentation approach improves the workflow of novel dynamic MRI studies of the pharyngeal airway and enables visualization and detection of obstructive events.

SIGNIFICANCE

Obstructive sleep apnea (OSA) is a significant public health issue affecting 4-9% of adults and 2% of children. Recently, 3-D dynamic MRI of the upper airway has been demonstrated during natural sleep, with sufficient spatiotemporal resolution to noninvasively study patterns of airway obstruction in young adults with OSA. This study makes it practical to analyze these long scans and visualize important factors in an MRI sleep study, such as the time, site, and extent of airway collapse.