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Ultrasound imaging is widely used for accurate diagnosis due to its noninvasive nature and the absence of radiation exposure, which is achieved by controlling the scan frequency. In addition, Gaussian and speckle noises degrade image quality. To address this issue, filtering techniques are typically used in the spatial domain. Recently, deep learning models have been increasingly applied in the field of medical imaging. In this study, we evaluated the effectiveness of a convolutional neural network-based residual network (ResNet) deep learning model for noise reduction when Gaussian and speckle noises were present. We compared the results with those obtained from conventional filtering techniques. A dataset of 500 images was prepared, and Gaussian and speckle noises were added to create noisy input images. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. The ResNet deep learning model, comprising 16 residual blocks, was trained using optimized hyperparameters, including the learning rate, optimization function, and loss function. For quantitative analysis, we calculated the normalized noise power spectrum, peak signal-to-noise ratio, and root mean square error. Our findings showed that the ResNet deep learning model exhibited superior noise reduction performance to median, Wiener, and median-modified Wiener filter algorithms. [ABSTRACT FROM AUTHOR]

In gamma-ray imaging, a collimator plays a crucial role in determining the sensitivity of the system and the spatial resolution of the acquired image. Herein, we presented a novel concept for a variable collimator in a pixelated photon-counting detector. The gamma-ray imaging system was modeled using the Monte Carlo simulation tool Geant4 Application for Tomographic Emission program. A detector using cadmium telluride and a 99m Tc point source was used. The collimators comprised three tungsten layers, and the septal height the beam finally penetrated from the incident gamma ray was adjusted differently. The first collimator layer constituted a hole with four times the pixel size, and the second and third layers comprised a hole with twice the pixel size, overlapping asymmetrically. Finally, the emitted gamma ray equaled the pixel. The spatial resolution showed different results depending on the source-to-collimator distance for each collimator configuration. Additionally, we determined that the sensitivity improved as the collimator height where gamma rays are incident and initially interact increased. We have proposed a novel collimator concept in a gamma-ray imaging system using a pixelated photon-counting detector, which may be applied in various fields in the future. [ABSTRACT FROM AUTHOR]

Thallium bromide (TlBr) semiconductor materials are widely used to detect gamma rays. They have excellent detection efficiency owing to their high stopping and effective atomic number. The purpose of this study was to compare image quality using TlBr and conventional detector materials according to parallel-hole collimator geometry in gamma camera scintigraphy. The Geant4 application for tomographic emission (GATE) simulation tool was used. Cadmium telluride (CdTe), cadmium zinc telluride (CZT), and TlBr semiconductor materials were used; the parallel-hole collimator lengths were designed as 0.4, 0.6, 0.8, and 1.0 cm. For qualitative analysis, images were acquired using a quadrant-bar phantom with different bar thicknesses. Quantitative analyses, such as sensitivity (cps/MBq) and scatter fraction using a point source with 99m Tc of 1 mCi, were conducted. The sensitivity with TlBr material with collimator lengths of 0.4, 0.6, 0.8, and 1.0 cm was 2.38, 2.39, 2.39, and 2.36 times greater than with CdTe and CZT materials, and the scatter fraction with TlBr material was 1.26, 1.25, 1.24, and 1.31 times less than the scatter fraction with CdTe and CZT materials with the same collimator length, respectively. There was no significant difference in sensitivity and scatter fraction between CdTe and CZT materials. Thus, we confirmed that TlBr is a potential detector material with improved sensitivity and scatter fraction in gamma camera scintigraphy. [ABSTRACT FROM AUTHOR]

Quantitative analysis in mammography is commonly carried out using a customized breast phantom. Noise reduction algorithms such as Wiener, median, and median modified Wiener filter (MMWF) with 3 × 3, 5 × 5, 7 × 7, and 9 × 9 mask sizes are applied to digital medical images because the digital images contain noise distributions such as Gaussian noise. The purpose of this study was to validate a customized breast phantom consisting of three positions for the inner structure (Condition A: 0.5 cm; condition B: 2.0 cm, condition C: 3.0 cm from the top surface) by applying the noise reduction algorithms listed above. The acquired images were evaluated using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and normalized noise power spectrum (NNPS) parameters. When comparing conditions A, B and C, the SNR results using condition A were 1.2 and 1.3 times better, the CNR results were 1.9 and 3.3 times better, and the NNPS results were 1.2 and 1.3 times better than for conditions B and C, respectively. The application of algorithms was useful for the reduction of noise distribution; in particular, the MMWF algorithm with a 9 × 9 mask size presented the best performance in terms of SNR, CNR, and NNPS results. In conclusion, we confirmed that the location of the breast lesion can affect the image quality under the same scan conditions and that noise reduction algorithms are essential processes to improve image quality. [ABSTRACT FROM AUTHOR]

The median modified Wiener filter (MMWF) algorithm, which is a combination of median and Wiener filters, was applied to reduce the noise signal contained in digital image systems. In this study, we investigated the effect of MMWF algorithm with mask sizes of 3 × 3, 5 × 5, 7 × 7, and 9 × 9 and compared it to the median and Wiener filters with a breast American College of Radiology (ACR) phantom designed using 3D printing technology. To compare the similarity between the conventional and 3D printed breast ACR phantoms at various tube voltages and tube currents, the peak signal-to-noise (PSNR) ratio was recorded. Then, the noise level of the mammographic X-ray image applied with the MMWF algorithm using various mask sizes and the conventional filtering methods were compared and evaluated. According to the results, when the MMWF algorithm using a mask size of 9 × 9 was applied to the X-ray phantom image, superior signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and coefficient of variation (COV) values were derived. In addition, by comparing the average values obtained using the conventional noise reduction methods, we confirmed that the SNR, CNR, and COV values obtained from the MMWF algorithm, using a 9 × 9 mask size, improved by 1.91, 2.29, and 4.77 times, respectively. In conclusion, applicability of the mammographic X-ray field after ACR phantom production using 3D printing technology was proven in this study. An optimal mask size of 9 × 9 for the MMWF algorithm was proposed for improving the image quality of the acquired image. [ABSTRACT FROM AUTHOR]

Quality control was performed using a dedicated phantom in nuclear medicine imaging. However, a dedicated phantom is expensive, and it is challenging to modify the phantom structure. In this study, we fabricated two customized nuclear medicine phantoms using a three-dimensional (3D) printing technique with lead and copper materials, which were acquired with efficient gamma rays owing to their higher physical density. The customized phantom was designed with six spheres, each with different diameters, using the fusion 360 software. In addition, a customized phantom was printed using a 3D printer created by Ultimaker after slicing using the CURA software. The gamma images according to lead-based and copper-based customized phantoms were obtained with the technetium-99 m radioisotope of 1 mCi at 50 kcounts. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were applied to evaluate the image quality. The SNR and CNR values for the lead-based customized phantom image were higher (1.11 and 1.21 times higher than those of the copper-based customized phantom image, respectively). We confirmed the possibility of an alternative nuclear medicine phantom based on the 3D printing technique, as well as the reasonable price and reorganization of conventional structures. In addition, the image quality of the lead-based customized phantom was superior to that of the copper-based customized phantom images. [ABSTRACT FROM AUTHOR]