Near-lossless compression of PET/CT images using singular value decomposition. Issue 10 (22nd August 2022) - Chaudhary, Jagrati; Pandey, Anil Kumar; Sharma, Param D.; Jaleel, Jasim; Patel, Chetan; Bal, Chandra Sekhar; Kumar, Rakesh
Abstract : Aims and objectives: The aim of the study is to compare the single matrix approach and slice-by-slice approach for computing singular value decomposition (SVD) to achieve near-lossless compression of PET/CT images. Materials and methods: The parameters used for comparison were SVD computation time, percentage compression and percentage difference between ROI counts on compressed and original images. SVD of 49 F-18-FDG PET/CT studies (33 370 PET/CT images) was computed using both approaches. The smaller singular values contributing insignificant information to the image were truncated, and then, the compressed image was reconstructed. A mask (101 × 101pixels) was used to extract the ROI counts from compressed and original images. Two nuclear medicine physicians compared compressed images with their corresponding original images for loss of clinical details and the presence of generated artifacts. Structural Similarity Index Measure, blur, brightness, contrast per pixel and global contrast factor were used for objective assessment of image quality. Wilcoxon test was applied to find a statistically significant difference between the parameters used for comparison at alpha = 0.05. Results: Nuclear medicine physicians found compressed image identical to the corresponding original image. The values of comparation parameters were significantly larger for the single matrix approach in comparison with the slice-by-slice approach. The maximum percentage error between the compressed image and original image was less than 5%. Conclusions: Up to 64 % and 44% near-lossless compression of PET and CT images were achieved, respectively using the slice-by-slice approach, and up to 58 and 53% near-lossless compression of PET and CT images were achieved respectively using the single matrix approach.