Abstract
The widespread use of multi-detector CT scanners has been associated with a remarkable increase in the number of CT slices as well as a substantial decrease in the average thickness of individual slices. This increased number of thinner slices has created a marked increase in archival and network bandwidth requirements associated with storage and transmission of these studies. We demonstrate that although compression can be used to decrease the size of these image files, thinner CT slices are less compressible than thicker slices when measured by either a visual discrimination model (VDM) or the more traditional peak signal to noise ratio. The former technique (VDM) suggests that the discrepancy in compressibility between thin and thick slices becomes greater at greater compression levels while the latter technique (PSNR), suggests that this is not the case. Previous studies that we and others have performed suggest that the VDM model probably corresponds more closely with human observers than does the PSNR model. Additionally we demonstrated that the poor relative compressibility of thin sections can be substantially negated by the use of JPEG 2000 3D compression which yields superior image quality at a given level of compression in comparison with 2D compression. Additionally, thin and thick sections are approximately equally compressible for 3D compression with little change with increasing levels of compression.
Original language | English (US) |
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Article number | 5748-21 |
Pages (from-to) | 162-170 |
Number of pages | 9 |
Journal | Progress in Biomedical Optics and Imaging - Proceedings of SPIE |
Volume | 5748 |
DOIs | |
State | Published - 2005 |
Event | Medical Imaging 2005 - PACS and Imaging Informatics - San Diego, CA, United States Duration: Feb 15 2005 → Feb 17 2005 |
Keywords
- 2D
- 3D
- CT
- Compression
- DICOM
- JND
- JPEG 2000
- MSCT
- PSNR
- VDM
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Biomaterials
- Radiology Nuclear Medicine and imaging