Dynamically Scalable NoC Architecture for Implementing Run-Time Reconfigurable Applications.

The paper proposes two architectures for a dynamically scalable network-on-chip (NoC) for dynamically reconfigurable intellectual properties (IPs) to save power. The first architecture is a run-time scalable column-based NoC, where the columns of the NoC are scaled up and down at run-time depending on the demands to connect reconfigurable IPs. The second architecture is an extension of the first, where both the rows and columns of the NoC are dynamically scaled up and down on demand. A robust control manager is developed to control the IP and sub-NoC reconfigurations by optimizing the reconfiguration costs. The proposed architectures have been implemented and tested in actual prototypes on a Virtex 6 FPGA mounted on the ML605 board. The results show that dynamically scalable architectures are capable of significant power reduction as compared to traditional static architectures for the same size of the NoC. It is anticipated that the scalable NoC can be very useful for sharing the FPGA resources among IPs at runtime.

Automatic deformable PET/MRI registration for preclinical studies based on B-splines and non-linear intensity transformation.

PET images deliver functional data, whereas MRI images provide anatomical information. Merging the complementary information from these two modalities is helpful in oncology. Alignment of PET/MRI images requires the use of multi-modal registration methods. Most of existing PET/MRI registration methods have been developed for humans and few works have been performed for small animal images. We proposed an automatic tool allowing PET/MRI registration for pre-clinical study based on a two-level hierarchical approach. First, we applied a non-linear intensity transformation to the PET volume to enhance. The global deformation is modeled by an affine transformation initialized by a principal component analysis. A free-form deformation based on B-splines is then used to describe local deformations. Normalized mutual information is used as voxel-based similarity measure. To validate our method, CT images acquired simultaneously with the PET on tumor-bearing mice were used. Results showed that the proposed algorithm outperformed affine and deformable registration techniques without PET intensity transformation with an average error of 0.72 ± 0.44 mm. The optimization time was reduced by 23% due to the introduction of robust initialization. In this paper, an automatic deformable PET-MRI registration algorithm for small animals is detailed and validated. Graphical abstract ᅟ.