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27 February 2018 Lung parenchymal analysis on dynamic MRI in thoracic insufficiency syndrome to assess changes following surgical intervention
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General surgeons, orthopedists, and pulmonologists individually treat patients with thoracic insufficiency syndrome (TIS). The benefits of growth-sparing procedures such as Vertical Expandable Prosthetic Titanium Rib (VEPTR)insertionfor treating patients with TIS have been demonstrated. However, at present there is no objective assessment metricto examine different thoracic structural components individually as to their roles in the syndrome, in contributing to dynamics and function, and in influencing treatment outcome. Using thoracic dynamic MRI (dMRI), we have been developing a methodology to overcome this problem. In this paper, we extend this methodology from our previous structural analysis approaches to examining lung tissue properties. We process the T2-weighted dMRI images through a series of steps involving 4D image construction of the acquired dMRI images, intensity non-uniformity correction and standardization of the 4D image, lung segmentation, and estimation of the parameters describing lung tissue intensity distributions in the 4D image. Based on pre- and post-operative dMRI data sets from 25 TIS patients (predominantly neuromuscular and congenital conditions), we demonstrate how lung tissue can be characterized by the estimated distribution parameters. Our results show that standardized T2-weighted image intensity values decrease from the pre- to post-operative condition, likely reflecting improved lung aeration post-operatively. In both pre- and post-operative conditions, the intensity values decrease also from end-expiration to end-inspiration, supporting the basic premise of our results.
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Basavaraj N. Jagadale, Jayaram K. Udupa, Yubing Tong, Caiyun Wu, Joseph McDonough, Drew A. Torigian, and Robert M. Campbell Jr. "Lung parenchymal analysis on dynamic MRI in thoracic insufficiency syndrome to assess changes following surgical intervention", Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753M (27 February 2018);

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