Radiologists are habituated to view scans one after another. So, compression algorithms for medical scan have been concentrated on 2D images. Today, PACS database is dominated by X-ray images (CR, DR, DX). Presently the image volume is increasing rapidly with the increase in speed of acquisition device and decrease in slice thickness. Soon, CT and MR will dominate the PACS database. It poses a big challenge to radiologists to sustain their productivity if they want to view the fine slices one after another. The other big challenge will be to transport such huge volume of data over network without undue delay. 3D image compression provides a promising approach to solve these problems at the same time. The extra dimension will provide more compressing allowing more efficient storage and transport over network. 3D data will allow radiologists to view voxel data rather than pixels. Using multi-resolution in axial dimension they can navigate quickly through low-resolution data (in axial direction) and on reaching the right place look at the high-resolution data and make the diagnosis. The asymmetric codec will help them to sustain their productivity and improve it further.
3D compression algorithms exploit the redundancy of information in Z dimension to provide better compression ratio
3D compression algorithms exploit the redundancy of information in Z dimension to provide better compression ratio. The multi-resolution in z- dimension provides averaged frames. This will help radiologist to sustain its productivity and improve it while reading big volume scans. (S.M., S.B., and V.N., Employees, GE India Technology Centre; A.R., Employee, Wipro GE Medical Systems; D.H. and D.X., Employees, GE Medical Systems.)
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