Noise in computed tomography (CT) is inevitably generated, which lowers the accuracy of disease diagnosis. The non-local means approach, a software technique for reducing noise, is widely used in medical imaging. In this study, we propose a noise reduction algorithm based on fast non-local means (FNLMs) and apply it to CT images of a phantom created using 3D printing technology. The self-produced phantom was manufactured using filaments with similar density to human brain tissues. To quantitatively evaluate image quality, the contrast-to-noise ratio (CNR), coefficient of variation (COV), and normalized noise power spectrum (NNPS) were calculated. The results demonstrate that the optimized smoothing factors of FNLMs are 0.08, 0.16, 0.22, 0.25, and 0.32 at 0.001, 0.005, 0.01, 0.05, and 0.1 of noise intensities, respectively. In addition, we compared the optimized FNLMs with noisy, local filters and total variation algorithms. As a result, FNLMs showed superior performance compared to various denoising techniques. Particularly, comparing the optimized FNLMs to the noisy images, the CNR improved by 6.53 to 16.34 times, COV improved by 6.55 to 18.28 times, and the NNPS improved by 10⁻² mm² on average. In conclusion, our approach shows significant potential in enhancing CT image quality with anthropomorphic phantoms, thus addressing the noise issue and improving diagnostic accuracy.

In low-dose computed tomography (LDCT), lung segmentation effectively improves the accuracy of lung cancer diagnosis. However, excessive noise is inevitable in LDCT, which can decrease lung segmentation accuracy. To address this problem, it is necessary to derive an optimized kernel size when using the median modified Wiener filter (MMWF) for noise reduction. Incorrect application of the kernel size can result in inadequate noise removal or blurring, degrading segmentation accuracy. Therefore, various kernel sizes of the MMWF were applied in this study, followed by region-growing-based segmentation and quantitative evaluation. In addition to evaluating the segmentation performance, we conducted a similarity assessment. Our results indicate that the greatest improvement in segmentation performance and similarity was at a kernel size 5 × 5. Compared with the noisy image, the accuracy, F1-score, intersection over union, root mean square error, and peak signal-to-noise ratio using the optimized MMWF were improved by factors of 1.38, 33.20, 64.86, 7.82, and 1.30 times, respectively. In conclusion, we have demonstrated that by applying the MMWF with an appropriate kernel size, the optimization of noise and blur reduction can enhance segmentation performance.

In low-dose cone beam computed tomography (CT), the insufficient number of photons inevitably results in noise, which reduces the accuracy of disease diagnosis. One approach to improving the image quality of CT images acquired using a low-dose protocol involves the utilization of a reconstruction algorithm that efficiently reduces noise. In this study, we modeled the Feldkamp–Davis–Kress (FDK) algorithm using various filters and projection angles and applied it to the reconstruction process using CT simulation. To quantitatively evaluate the quality of the reconstruction images, we measured the coefficient of variation (COV), and signal-to-noise ratio (SNR) in the air, brain, and bone regions to evaluate the noise level. Furthermore, we calculated root mean square error (RMSE), universal image quality index (UQI), and blind/referenceless image spatial quality evaluator (BRISQUE) as similarity and no-reference evaluation. The Hann filter of the FDK algorithm showed superior performance in terms of COV, SNR, RMSE, and UQI compared to the other filters. In addition, when analyzing the COV and SNR results, we observed that image quality increased significantly at projection angles smaller than approximately 2.8°. Moreover, based on BRISQUE results, we confirm that the Shepp–Logan filter exhibited the most superior performance. In conclusion, we believe that the application of the Hann filter in the FDK reconstruction process offers significant advantages in improving the image quality acquired under a low-dose protocol, and we expect that our study will be a preliminary study of no-reference evaluation of CT reconstruction images.

In this study, we applied and optimized a fast non-local means (FNLM) algorithm to reduce noise in pediatric abdominal virtual monoenergetic images (VMIs). To analyze various contrast agent concentrations, we produced contrast agent concentration samples (20, 40, 60, 80, and 100%) and inserted them into a phantom model of a one-year-old pediatric patient. Single-energy computed tomography (SECT) and dual-energy computed tomography (DECT) images were acquired from the phantom, and 40 kilo-electron-volt (keV) VMI was acquired based on the DECT images. For the 40 keV VMI, the smoothing factor of the FNLM algorithm was applied from 0.01 to 1.00 in increments of 0.01. We derived the optimized value of the FNLM algorithm based on quantitative evaluation and performed a comparative assessment with SECT, DECT, and a total variation (TV) algorithm. As a result of the analysis, we found that the average contrast to noise ratio (CNR) and coefficient of variation (COV) of each concentration were most improved at a smoothing factor of 0.02. Based on these results, we derived the optimized smoothing factor value of 0.02. Comparative evaluation shows that the optimized FNLM algorithm improves the CNR and COV results by approximately 3.14 and 2.45 times, respectively, compared with the DECT image, and the normalized noise power spectrum result shows a ${10}^{-1} {\mathrm{mm}}^2$ improvement. The main contribution of this study is to demonstrate the effectiveness of an optimized FNLM algorithm in reducing noise in pediatric abdominal VMI, allowing high-quality images to be acquired while reducing contrast dose. This advancement has significant implications for minimizing the risk of contrast-induced toxicity, especially in pediatric patients. Our approach addresses the problem of limited datasets in pediatric imaging by providing a computationally efficient noise reduction technique and highlights the clinical applicability of the FNLM algorithm. In addition, effective noise reduction enables high-contrast imaging with minimal radiation and contrast exposure, which is expected to be suitable for repeat CT examinations of pediatric liver cancer patients and other abdominal diseases.

Computed tomography (CT) imaging using low-dose radiation effectively reduces radiation exposure; however, it introduces noise amplification in the resulting image. This study models an adaptive nonlocal total variation (NL-TV) algorithm that efficiently reduces noise in X-ray-based images and applies it to low-dose CT images. In this study, an AAPM CT performance phantom is used, and the resulting image is obtained by applying an annotation filter and a high-pitch protocol. The adaptive NL-TV filter was designed by applying the optimal window value calculated by confirming the difference between Gaussian filtering and the basic NL-TV approach. For quantitative image quality evaluation parameters, contrast-to-noise ratio (CNR), coefficient of variation (COV), and sigma value were used to confirm the noise reduction effectiveness and spatial resolution value. The CNR and COV values in low-dose CT images using the adaptive NL-TV filter, which performed an optimization process, improved by approximately 1.29 and 1.45 times, respectively, compared with conventional NL-TV. In addition, the adaptive NL-TV filter was able to acquire spatial resolution data that were similar to a CT image without applying noise reduction. In conclusion, the proposed NL-TV filter is feasible and effective in improving the quality of low-dose CT images.

Accurate segmentation of abdominal aortic aneurysm (AAA) from computed tomography (CT) images is critical for early diagnosis and treatment planning of vascular diseases. However, noise in CT images obscures vessel boundaries, reducing segmentation accuracy. U-Net is widely used for medical image segmentation, where noise removal is critical. This study applied various denoising filters for U-Net segmentation and classified the severity of segmented AAA images to evaluate accuracy. Poisson–Gaussian noise was added to AAA CT images, and then average, median, Wiener, and median-modified Wiener filters (MMWF) were applied. U-Net-based segmentation was performed, and the segmentation accuracy of the output images obtained per filter was quantitatively assessed. Furthermore, the Hough circle algorithm was applied to the segmented images for diameter measurement, enabling severity classification and evaluation of classification accuracy. MMWF application improved the Matthews correlation coefficient, Dice score, Jaccard coefficient, and mean surface distance by 31.09%, 34.25%, 53.99%, and 3.70%, respectively, compared with images with added noise. Moreover, classification based on the output images obtained after MMWF application demonstrated the highest accuracy, with sensitivity, precision, and accuracy reaching 100%. Thus, U-Net-based segmentation yields more accurate results when images are processed with the MMWF and analyzed using the Hough circle algorithm.