Quantitative analysis of the regional motion of the left hemi-diaphragm (LHD) and right hemi-diaphragm (RHD) can provide information regarding the distribution and severity of abnormalities in individual patients with conditions that affect respiration such as thoracic insufficiency syndrome (TIS). Such motion can be captured effectively from dynamic magnetic resonance imaging (dMRI) which does not involve ionizing radiation and can be obtained under free-breathing conditions. The analysis of motion can be performed on the diaphragm using 4D images constructed from dMRI, which in turn requires diaphragm segmentation in the 4D images. In this paper, we present our methodology for segmentation of the left and right diaphragms, which has been implemented in three steps: recognition of diaphragm, delineation of diaphragm, and splitting of diaphragm along the mid-sagittal plane into LHD and RHD. The challenges involved in dMRI images are low resolution, motion blur, suboptimal contrast resolution, inconsistent meaning of gray-level intensities for the same object across multiple scans, and low signal-to-noise ratio. Utilizing 200 and 100 3D images for training and testing, respectively, an average location error of one and a half voxels is achieved for the recognition step. For the delineation step, an average mean-HD of one and a half pixels is achieved. The mid-sagittal plane is identified within a quarter of a voxel. These results are promising, showing that our system can cope with the aforesaid challenges.
In pediatric patients with respiratory abnormalities, it is important to understand the alterations in regional dynamics of the lungs and other thoracoabdominal components, which in turn requires a quantitative understanding of what is considered as normal in healthy children. Currently, such a normative database of regional respiratory structure and function in healthy children does not exist. The purpose of this study is to introduce a large open-source normative database from our ongoing Virtual Growing Child (VGC) project, which includes measurements of volumes, architecture, and regional dynamics in healthy children (six to 20 years) derived via dynamic Magnetic Resonance Imaging (dMRI) images. The database provides four categories of regional respiratory measurement parameters including morphological, architectural, dynamic, and developmental. The database has 3,820 3D segmentations (around 100,000 2D slices with segmentations), which to our knowledge is the largest dMRI dataset of healthy children. The database is unique and provides dMRI images, object segmentations, and quantitative regional respiratory measurement parameters for healthy children. The database can serve as a reference standard to quantify regional respiratory abnormalities on dMRI in young patients with various respiratory conditions and facilitate treatment planning and response assessment. The database can be useful to advance future AI-based research on MRI-based object segmentation and analysis.
Purpose: This study investigates Thoracic Insufficiency Syndrome (TIS) in pediatric patients, a condition impacting respiratory function due to spinal and thoracic deformities. The research focuses on the use of the Vertical Expandable Prosthetic Titanium Rib (VEPTR) surgery to address deformities hindering normal development and to promote lung growth. A unique non-invasive approach utilizing free-breathing dynamic magnetic resonance imaging (dMRI) is employed to analyze the 3D motion and shape of each hemi-diaphragm (HD) surface. Method: In this study of 49 TIS patients who underwent VEPTR surgery, free-breathing dMRI was used before and after surgery. After 4D image construction of diaphragm, we manually delineated the HDs on sagittal slice images at endexpiration and end-inspiration time points. We then constructed the 3D surface of the HDs, automatically selected 25 points on each HD surface, and estimated velocities and both sagittal curvature and coronal curvature at each point. Then, we identified 13 homologous regions for each HD surface and categorized subjects based on changes in Cobb angle to compare HD velocities and curvatures before and after surgery. Results: The study group consisted of 27 males and 22 females with mean age of 3.51 ± 3.49 years before surgery and 5.9 ± 3.63 years after surgery. Right HD regions exhibited statistically significantly higher velocities compared to homologous regions in the left HD, and posterior regions showed higher velocity than other regions in both HDs. The most statistically significant differences in diaphragm shape were observed in the lateral regions, with particular emphasis on the coronal curvatures. Conclusion: Through analysis of pre- and post-surgical data, we observed significant improvement in diaphragm motion following VEPTR surgery, despite minimal changes in diaphragm shape.
Quantitative analysis of the dynamic properties of thoraco-abdominal organs such as lungs during respiration could lead to more accurate surgical planning for disorders such as Thoracic Insufficiency Syndrome (TIS). This analysis can be done from semi-automatic delineations of the aforesaid organs in scans of the thoraco-abdominal body region. Dynamic magnetic resonance imaging (dMRI) is a practical and preferred imaging modality for this application, although automatic segmentation of the organs in these images is very challenging. In this paper, we describe an auto-segmentation system we built and evaluated based on dMRI acquisitions from 95 healthy subjects. For the three recognition approaches, the system achieves a best average location error (LE) of about one voxel for the lungs. The standard deviation (SD) of LE is about one to two voxels. For the delineation approach, the average Dice Coefficient (DC) is about 0.95 for the lungs. The standard deviation of DC is about 0.01 to 0.02 for the lungs. The system seems to be able to cope with the challenges posed by low resolution, motion blur, inadequate contrast, and image intensity non-standardness quite well. We are in the process of testing its effectiveness on TIS patient dMRI data and on other thoraco-abdominal organs including liver, kidneys, and spleen.
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