Computed tomography (CT) images are stored at a 12-bit depth. However, many deep learning libraries and pre-trained models are designed for 8-bit images, requiring an intermediate compression step before restoring the original 12-bit physical range. This process causes information loss and can compromise image reliability. This study investigated the impact of two CT resampling methods (8-bit compression; 12-bit decompression) on dose calculation and image quality. Ten total marrow and lymphoid irradiation patients were selected. CT scans were resampled using linear and non-linear look-up tables (lLUT/nlLUT). Original and resampled CTs were evaluated considering: (i) Hounsfield unit (HU) root mean squared error (RMSE) ; (ii) dose-volume histogram (DVH) statistics for target volume and several organs; (iii) 3D gamma passing rate (GPR) with a 1%/1. 25 mm criterion; (iv) lymph nodes contouring and diagnostic quality (scale 1–5). The RMSE for lLUT vs. nlLUT was 7 ± 1 vs. 10 ± 1 HU. Maximum differences in DVH statistics were 0. 4%, with a 3D-GPR = 100% for all cases. CTs resampled with lLUT exhibited evident brain pixelation (score = 1), whereas nlLUT matched the original CT quality (score = 4). Both LUTs were acceptable for lymph nodes delineation. The nlLUT optimized the CT resampling process, providing a more efficient method for possible deep learning applications in synthetic CT generation.
Ml et al. (Fri,) studied this question.