The development of ultra low power wireless sensors for customized wearable and implantable medical devices requires patient specific models for radio frequency simulation to understand wave propagation in the body. In practice, the creation of a patient specific whole-body model is difficult and time consuming to create. It is therefore necessary to establish a method for studying a population in a statistical manner. In this paper, we present a statistical shape model for the whole body for RF simulation. It is built from 10 male and 10 female subjects of varying size and height. This model has the ability to instantiate a new surface mesh with the parameters allowed by the training set. This model would provide shapes of varying sizes for studies, without the requirement of obtaining subject specific whole body models. Results from finite-differences time-domain simulation are presented on the extreme shapes from the model and demonstrate the need for a full understanding of the range in body shapes.
Late Gadolinium-Enhanced Cardiac MRI (LGE CMRI) is an emerging non-invasive technique to image and quantify preablation native and post-ablation atrial scarring. Previous studies have reported that enhanced image intensities of the atrial scarring in the LGE CMRI inversely correlate with the left atrial endocardial voltage invasively obtained by electro-anatomical mapping. However, the reported reproducibility of using LGE CMRI to identify and quantify atrial scarring is variable. This may be due to two reasons: first, delineation of the left atrium (LA) and pulmonary veins (PVs) anatomy generally relies on manual operation that is highly subjective, and this could substantially affect the subsequent atrial scarring segmentation; second, simple intensity based image features may not be good enough to detect subtle changes in atrial scarring. In this study, we hypothesized that texture analysis can provide reliable image features for the LGE CMRI images subject to accurate and objective delineation of the heart anatomy based on a fully-automated whole heart segmentation (WHS) method. We tested the extracted texture features to differentiate between pre-ablation and post-ablation LGE CMRI studies in longstanding persistent atrial fibrillation patients. These patients often have extensive native scarring and differentiation from post-ablation scarring can be difficult. Quantification results showed that our method is capable of solving this classification task, and we can envisage further deployment of this texture analysis based method for other clinical problems using LGE CMRI.
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