Applicability Evaluation of Full-Reference Image Quality Assessment Methods for Computed Tomography Images.

Image quality assessments (IQA) are an important task for providing appropriate medical care. Full-reference IQA (FR-IQA) methods, such as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), are often used to evaluate imaging conditions, reconstruction conditions, and image processing algorithms, including noise reduction and super-resolution technology. However, these IQA methods may be inapplicable for medical images because they were designed for natural images. Therefore, this study aimed to investigate the correlation between objective assessment by some FR-IQA methods and human subjective assessment for computed tomography (CT) images. For evaluation, 210 distorted images were created from six original images using two types of degradation: noise and blur. We employed nine widely used FR-IQA methods for natural images: PSNR, SSIM, feature similarity (FSIM), information fidelity criterion (IFC), visual information fidelity (VIF), noise quality measure (NQM), visual signal-to-noise ratio (VSNR), multi-scale SSIM (MSSSIM), and information content-weighted SSIM (IWSSIM). Six observers performed subjective assessments using the double stimulus continuous quality scale (DSCQS) method. The performance of IQA methods was quantified using Pearson's linear correlation coefficient (PLCC), Spearman rank order correlation coefficient (SROCC), and root-mean-square error (RMSE). Nine FR-IQA methods developed for natural images were all strongly correlated with the subjective assessment (PLCC and SROCC > 0.8), indicating that these methods can apply to CT images. Particularly, VIF had the best values for all three items, PLCC, SROCC, and RMSE. These results suggest that VIF provides the most accurate alternative measure to subjective assessments for CT images.

Preoperative assessment of pleural adhesion by Four-Dimensional Ultra-Low-Dose Computed Tomography (4D-ULDCT) with Adaptive Iterative Dose Reduction using Three-Dimensional processing (AIDR-3D).

PURPOSE: To assess the feasibility of Four-Dimensional Ultra-Low-Dose Computed Tomography (4D-ULDCT) for distinguishing pleural aspects with localized pleural adhesion (LPA) from those without. METHODS: Twenty-seven patients underwent 4D-ULDCT during a single respiration with a 16cm-coverage of the body axis. The presence and severity of LPA was confirmed by their intraoperative thoracoscopic findings. A point on the pleura and a corresponding point on the outer edge of the costal bone were placed in identical axial planes at end-inspiration. The distance of the two points (PCD), traced by automatic tracking functions respectively, was calculated at each respiratory phase. The maximal and average change amounts in PCD (PCDMCA and PCDACA) were compared among 110 measurement points (MPs) without LPA, 16MPs with mild LPA and 10MPs with severe LPA in upper lung field cranial to the bronchial bifurcation (ULF), and 150MPs without LPA, 17MPs with mild LPA and 9MPs with severe LPA in lower lung field caudal to the bronchial bifurcation (LLF) using the Mann-Whitney U test. RESULTS: In the LLF, PCDACA as well as PCDMCA demonstrated a significant difference among non-LPA, mild LPA and severe LPA (18.1±9.2, 12.3±6.2 and 5.0±3.3mm) (p<0.05). Also in the ULF, PCDACA showed a significant difference among three conditions (9.2±5.5, 5.7±2.8 and 2.2±0.4mm, respectively) (p<0.05), whereas PCDMCA for mild LPA was similar to that for non-LPA (12.3±5.9 and 17.5±11.0mm). CONCLUSIONS: Four D-ULDCT could be a useful non-invasive preoperative assessment modality for the detection of the presence or severity of LPA.