Multiplanar reconstruction with incomplete data via enhanced fuzzy radial basis function neural networks

Abstract

Slice images based on a single slicing direction often contain incomplete data and cannot be used by clinicians for diagnosis or observation. It is thus necessary to reconstruct the slices using multiplanar reconstruction technology. In the case of complete data, it is not difficult to obtain a series of clear images from other slicing directions. In the case of incomplete data, interpolation methods are commonly employed on reconstructed images to compensate for the missing information. However, such results are often not ideal. In this study, we propose a new method based on an enhanced fuzzy radial basis function neural network. First, a series of incomplete transverse section images that have been accurately registered are adopted. Then, we superpose the sequence images to obtain the three-dimensional data volume. Thereafter, we can acquire the coronal or sagittal images by reformatting this data volume. For a reconstructed image, the proposed system was applied to compensate for the lost data. We used 15 sets of proposed neural networks to obtain 15 sets of output data, with the final output data acquired via the inverse distance-weighted algorithm. We trained the system via a gravitational search algorithm. Finally, we repaired all the interpolated data. In this experiment, we used two types of datasets, i.e., images obtained via brain magnetic resonance imaging and abdominal computed tomography. Subjective observations and objective evaluations confirm the superiority and effectiveness of the proposed method compared to other state-of-the-art methods.