Deep learning based MRI contrast synthesis using full volume prediction

In Magnetic Resonance Imaging (MRI), depending on the image acquisition settings, a large number of image types or contrasts can be generated showing complementary information of the same imaged subject. This multi-spectral information is highly beneficial since can improve MRI analysis tasks such as segmentation and registration, thanks to pattern ambiguity reduction. However, the acquisition of several contrasts is not always possible due to time limitations and patient comfort constraints. Contrast synthesis has emerged recently as an approximate solution to generate other image types different from those acquired originally. Most of the previously proposed methods for contrast synthesis are slice-based which result in intensity inconsistencies between neighbor slices when applied in 3D. We propose the use of a 3D convolutional neural network (CNN) capable of generating T2 and FLAIR images from a single anatomical T1 source volume. The proposed network is a 3D variant of the UNet that processes the whole volume at once breaking with the inconsistency in the resulting output volumes related to 2D slice or patch-based methods. Since working with a full volume at once has a huge memory demand we have introduced a spatial-to-depth and a reconstruction layer that allows working with the full volume but maintain the required network complexity to solve the problem. Our approach enhances the coherence in the synthesized volume while improving the accuracy thanks to the integrated three-dimensional context-awareness. Finally, the proposed method has been validated with a segmentation method, thus demonstrating its usefulness in a direct and relevant application.

CODAS HFLTS Method to Appraise Organizational Culture of Innovation and Complex Technological Changes Environments

Sustainable development implies establishing principles, objectives and strategies within organizations that impact the organizational culture in innovation. However, a method needs to be defined in order to know the critical factors that allow the strengthening of the organizational culture in innovation with emphasis on Industry 4.0 and sustainable development in a highly changing environment for a specific organization. In this sense, the paper identifies the set of factors that are documented through reviews and analysis of the literature, subsequently proposes a Multi-Criteria Decision Making (MCDM) methodology using hesitant fuzzy linguistic term sets (HFLTS) and combinative distance-based assessment (CODAS), where factors are evaluated to obtain a score and hierarchy value. Weight values were calculated using the ambiguity reduction method, which incorporates the knowledge acquired by researchers in organizational culture of innovation and expert judgment under the Saaty scale used in analytic hierarchy process (AHP). Finally, a model of organizational culture in innovation is proposed that can be used by organizations to focus strategies on the factors of greater hierarchy and thereby optimize their resources considering the sustainable development and the Industry 4.0 approach.