Promising Theory Approach for Sense Control under Childhood Environment  
  Authors : P.Hari Prasad; N. Suresh; S. Koteswara Rao

 

Although tremendous progress has been made in the past to get High resolution 3D cardiac MRI but still it is considered as area of concern in the medical image processing filed. The existing anisotropic 2D stack volumes are typical, and improving the resolution of these is strongly motivated by both visualization and analysis. The lack of suitable reconstruction techniques that handle non-rigid motion means that cardiac image enhancement is still often attained by simple interpolation. A novel approach is proposed in this paper which accurately precise the High resolution 3D cardiac MRI in easy way. We explore the use of example-based super-resolution, to enable high fidelity patchbased reconstruction, using training data that does not need to be accurately aligned with the target data.

 

Published In : IJCAT Journal Volume 3, Issue 10

Date of Publication : October 2016

Pages : 454-458

Figures :03

Tables : 02

Publication Link :Optimized Super Resolution Reconstruction Framework for Cardiac MRI Images Perception

 

 

 

P.Hari Prasad : post graduate student of ASIST,Paritala,His area of interest image processing.

N.Suresh : worked as an assistant professor in ECE Dept of ASIST,Paritala,His area of I nterests are EDC,Signal processing.

S.Koteswararao : Graduated in Electronics Communication Engineering from Dr.S.G.I.E.T and post graduated in instrumentation and Control Systems from JNTUK, Kakinada, Submitted his thesis to JNTUK, Kakinada his research interests includes optimized routing for Wireless sensor networks and Digital communication.

 

 

 

 

 

 

 

MRI Analysis, Patch, Super resolution, Reconstruction, Dictionary building

A novel approach is proposed in this paper which accurately precise the High resolution 3D cardiac MRI in easy way. We explore the use of example-based superresolution, to enable high fidelity patch-based reconstruction, using training data that does not need to be accurately aligned with the target data. By moving to a patch scale, we are able to exploit the data redundancy present in cardiac image sequences, without the need for registration. To do this, dictionaries of high-resolution and low-resolution patches are co-trained on high-resolution sequences, in order to enforce a common relationship between high- and low-resolution patch representations. These dictionaries are then used to reconstruct from a lowresolution view of the same anatomy. We demonstrate marked improvements of the reconstruction algorithm over standard interpolation.

 

 

 

 

 

 

 

 

 

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