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.

Usefulness of preset count acquisition in pediatric 99m Tc-DMSA planar imaging.

OBJECTIVE: This study was aimed at determining the minimum acquisition count to provide diagnosable image quality (DIQ) and investigating the usefulness of preset count acquisition (PCA) for planar images of pediatric 99mTc-dimercaptosuccinic acid (DMSA) scintigraphy. METHODS: First, we calculated a coefficient of variation (CV) for DIQ with the shortest acquisition time through visual evaluation in 12 pediatric patients who underwent 99mTc-DMSA scintigraphy. Second, a minimum acquisition count to achieve the CV for DIQ was determined with the single regression analysis using CV as an explanatory variable and the total acquisition count as an objective variable in 81 pediatric patients. Finally, we compared PCA images based on the minimum acquisition count and preset time acquisition (PTA) images for 5 min in terms of the acquisition time, CV, and renal uptake ratio in another 23 pediatric patients. RESULTS: The visual evaluation showed that the CV corresponding to DIQ with the shortest acquisition time was 27.1%. The total acquisition count corresponding to DIQ was revealed to be 299,764 in the single regression analysis and was determined to be 300,000 after rounding. The CV and its standard deviation in PCA at 300,000 counts and PTA for 5 min were 26.4 ± 0.6% and 24.8 ± 1.3%, respectively. The standard deviation of CV in PCA at 300,000 counts was smaller than that in PTA for 5 min, indicating little variation in image quality between cases. The acquisition time in PCA at 300,000 counts (3.1 ± 0.7 min) was shorter than that in PTA for 5 min (5.0 ± 0.0 min). The intraclass correlation coefficient between renal uptake ratios for PCA and PTA was 0.98, indicating an extremely high concordance. CONCLUSIONS: The minimum acquisition count required for the DIQ was 300,000. In addition, PCA at 300,000 counts was demonstrated to be useful by providing stable image quality at the shortest acquisition time.

Peripheral bronchial luminal conspicuity on dynamic-ventilation computed tomography: association with radiation doses and temporal resolution by using an ex vivo porcine lung phantom.

BACKGROUND: It is still unclear which image reconstruction algorithm is appropriate for peripheral bronchial luminal conspicuity (PBLC) on dynamic-ventilation computed tomography (DVCT). PURPOSE: To assess the influence of radiation doses and temporal resolution (TR) on the association between movement velocity (MV) and PBLC on DVCT. MATERIAL AND METHODS: An ex vivo porcine lung phantom with simulated respiratory movement was scanned by 320-row CT at 240 mA and 10 mA. Peak and dip CT density and luminal area adjusted by values at end-inspiration (CTDpeak and CTDdip, luminal area ratio [LAR]) for PBLC and MVs were measured and visual scores (VS) were obtained at 12 measurement points on 13 frame images obtained at half and full reconstructions (TR 340 and 190 ms) during expiration. Size-specific dose estimate (SSDE) was applied to presume radiation dose. VS, CTDpeak, CTDdip, LAR, and their cross-correlation coefficients with MV (CCC) were compared among four methods with combinations of two reconstruction algorithms and two doses. RESULTS: The dose at 10 mA was presumed as 26 mA by SSDE for standard proportion adults. VS, CTDdip, CTDpeak, and LAR with half reconstruction at 10 mA (2.52 ± 0.59, 1.016 ± 0.221, 0.948 ± 0.103, and 0.990 ± 0.527) were similar to those at 240 mA except for VS, and different from those with full reconstruction at both doses (2.24 ± 0.85, 0.830 ± 0.209, 0.986 ± 0.065, and 1.012 ± 0.438 at 240 mA) (P < 0.05). CCC for CTDdip with half reconstruction (-0.024 ± 0.552) at 10 mA was higher compared with full reconstruction (-0.503 ± 0.291) (P < 0.05). CONCLUSION: PBLC with half reconstruction at 10 mA was comparable to that at 240 mA and better than those with full reconstruction on DVCT.

Deblurring Algorithm for Blurred Images Caused by Patient Motion in Radiography.

In radiography, when a blurred image caused by patient motion was acquired, radiologists retake an image as needed. However, retaking an image leads to extra radiation exposure to patients and reducing work efficiency. This study proposes the deblurring algorithm for blurred images caused by patient motion in radiography. In the proposed algorithm, we first take a video using an optical device during radiography. Second, we calculate the optical flow between each frame, and estimate a point spread function (PSF) based on the optical flows. Finally, we restore the blurred image by deconvolution processing. In this study, blurred images with the blur width from 1.0 mm to 5.0 mm at 0.5 mm intervals were obtained by using own moving body phantom, and applied proposed algorithm for each blurred image. To evaluate the algorithm, we measured blur area and structural similarity (SSIM) of the blurred images and deblurred images, and compared the values. As a result, a significant decrease in blur area and a significant increase in SSIM were confirmed in each blur condition. These results suggest the usefulness of the proposed algorithm.