A NEW METHOD FOR COMPUTED-TOMOGRAPHY IMAGE COMPRESSION USING ADJACENTSLICE DATA

RATIONALE AND OBJECTIVES. The authors developed and subjectively evalu ated an interslice compression algorithm that explores the redundancy among adjacent slices of an x-ray computed tomography (CT) scan. This algorithm has been compared to an intraslice compression algorithm bas ed on the two-dimensional discrete cosine transform. METHODS. Nine x-r ay CT head images from three patients were compressed with this inters lice method at compression ratios of 5:1, 10:1, and 15:1. The same ima ges were also compressed with the intraslice method at the same ratios . Six radiologists judged quality of randomly selected compressed and decompressed images compared to that of the originals. The evaluation data were analyzed statistically with the analysis of variance and Tuk ey's multiple comparison. Kappa-like statistics (Williams index and O' Connell and Dobson indexes) were also calculated to measure the agreem ent among readers beyond the amount expected by chance. RESULTS. The i nterslice coding algorithm showed significantly better quality than th e intraslice method at significance level 0.05, even though there was no difference in the objective distortion measure (signal-to-noise rat io). Also, the quality of 10:1 compressed images with the interslice c oding algorithm was not significantly different from that of the origi nals at level 0.05. While large variations in agreement occurred among readers, the overall agreement was statistically significant. CONCLUS IONS. By using adjacent slice information in compressing x-ray CT imag es, significantly better quality in compressed and decompressed images was achieved. While 10:1 compressed images with the interslice algori thm were not significantly different from the originals in quality at level 0.05, effect on diagnostic accuracy remains to be investigated.