RESUMEN
Advances in computed tomography (CT) have resulted in a substantial increase in the size of datasets. We built a new concept of medical image compression that provides the best compromise between compression rate and image quality. The method is based on multiple contexts and regions-of-interest (ROI) defined according to the degree of clinical interest. High priority areas (primary ROIs) are assigned a lossless compression. Other areas (secondary ROIs and background) are compressed with moderate or heavy losses. The method is applied to a whole dataset of CT angiography (CTA) of the lower extremity vasculature. It is compared to standard lossy compression techniques in terms of quantitative and qualitative image quality. It is also compared to standard lossless compression techniques in terms of image size reduction and compression ratio. The proposed compression method met quantitative criteria for high-quality encoding. It obtained the highest qualitative image quality rating score, with a statistically significant difference compared to other methods. The average compressed image size was up to 61% lower compared to standard compression techniques, with a 9:1 compression ratio compared with original non-compressed images. Our new adaptive 3D compression method for CT images can save data storage space while preserving clinically relevant information.
RESUMEN
BACKGROUND: In chest computed tomography (CT), iterative reconstruction (IR) algorithms maintain diagnostic image quality (IQ) while significantly reducing the dose. PURPOSE: To evaluate the impact of IR on IQ of chest CT at effective doses below 0.3 mSv. MATERIAL AND METHODS: Twenty chest CT scans performed at effective dose below 0.3 mSv (CT1) were reconstructed varying three parameters: filtered back-projection and IR iDose(4) algorithms; 512 × 512 and 768 × 768 matrices; and sharp and soft kernels, thus generating eight series per patient. The qualitative evaluation of the IQ was performed by ranking series from 1 to 8 (8 corresponding to the highest rank) which was subsequently compared to quantitative assessment of IQ by using an appropriated merit formula. Intra- and inter-reader IQ ranking reliability was also evaluated using Cohen's kappa. Analysis of lung findings was finally compared between the best CT1 series and the reference CT (CT0). RESULTS: The best series in terms of qualitative and quantitative IQ was obtained using IR, 512(2) matrix and soft kernel. The best CT1 series detected nodules greater than 4 mm with an almost perfect match with CT0. CONCLUSION: Chest CT performed at effective doses below 0.3 mSv may be used to confidently diagnose lesions greater than 4 mm using iDose(4), soft kernel and 512 × 512 matrix.
