Purpose: To improve the image resolution of magnetic resonance imaging (MRI), conventional interpolation methods are commonly used to magnify images via various image processing approaches;however, these methods tend ...Purpose: To improve the image resolution of magnetic resonance imaging (MRI), conventional interpolation methods are commonly used to magnify images via various image processing approaches;however, these methods tend to produce artifacts. While super-resolution (SR) schemes have been introduced as an alternative approach to apply medical imaging, previous studies applied SR only to medical images in 8-bit image format. This study aimed to evaluate the effectiveness of sparse-coding super-resolution (ScSR) for improving the image quality of reconstructed high-resolution MR images in 16-bit digital imaging and communications in medicine (DICOM) image format. Materials and Methods: Fifty-nine T1-weighted images (T1), 84 T2-weighted images (T2), 85 fluid attenuated inversion recovery (FLAIR) images, and 30 diffusion-weighted images (DWI) were sampled from The Repository of Molecular Brain Neoplasia Data as testing datasets, and 1307 non-medical images were sampled from the McGill Calibrated Color Image Database as a training dataset. We first trained the ScSR to prepare dictionaries, in which the relationship between low- and high-resolution images was learned. Using these dictionaries, a high-resolution image was reconstructed from a 16-bit DICOM low-resolution image downscaled from the original test image. We compared the image quality of ScSR and 4 interpolation methods (nearest neighbor, bilinear, bicubic, and Lanczos interpolations). For quantitative evaluation, we measured the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Results: The PSNRs and SSIMs for the ScSR were significantly higher than those of the interpolation methods for all 4 MRI sequences (PSNR: p p Conclusion: ScSR provides significantly higher image quality in terms of enhancing the resolution of MR images (T1, T2, FLAIR, and DWI) in 16-bit DICOM format compared to the interpolation methods.展开更多
A development of an acquisition of the characteristic of a solar panel by automatic load variation system is put into play and coupled to an instrumentation chain for taking account of temperature. A programmed digita...A development of an acquisition of the characteristic of a solar panel by automatic load variation system is put into play and coupled to an instrumentation chain for taking account of temperature. A programmed digital microprocessor control enables this automation. Design and implementation of a device for automation of variations of the resistive load are powered by solar panel. It is provided by a PIC 16F877A running a computer program that we have developed on the basis of an algorithm according to the operation that we have set. By varying automatically the resistive load, we were able to automatically acquire the characteristic I-V and temperature of the solar panel. With automatic combinations of the 10 resistors, we have obtained 1024 measures of the characteristic curve of the solar cell which has a good accuracy. The change in load and temperature measurement allows us to have the characteristic curves parameterized by temperature.展开更多
文摘Purpose: To improve the image resolution of magnetic resonance imaging (MRI), conventional interpolation methods are commonly used to magnify images via various image processing approaches;however, these methods tend to produce artifacts. While super-resolution (SR) schemes have been introduced as an alternative approach to apply medical imaging, previous studies applied SR only to medical images in 8-bit image format. This study aimed to evaluate the effectiveness of sparse-coding super-resolution (ScSR) for improving the image quality of reconstructed high-resolution MR images in 16-bit digital imaging and communications in medicine (DICOM) image format. Materials and Methods: Fifty-nine T1-weighted images (T1), 84 T2-weighted images (T2), 85 fluid attenuated inversion recovery (FLAIR) images, and 30 diffusion-weighted images (DWI) were sampled from The Repository of Molecular Brain Neoplasia Data as testing datasets, and 1307 non-medical images were sampled from the McGill Calibrated Color Image Database as a training dataset. We first trained the ScSR to prepare dictionaries, in which the relationship between low- and high-resolution images was learned. Using these dictionaries, a high-resolution image was reconstructed from a 16-bit DICOM low-resolution image downscaled from the original test image. We compared the image quality of ScSR and 4 interpolation methods (nearest neighbor, bilinear, bicubic, and Lanczos interpolations). For quantitative evaluation, we measured the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). Results: The PSNRs and SSIMs for the ScSR were significantly higher than those of the interpolation methods for all 4 MRI sequences (PSNR: p p Conclusion: ScSR provides significantly higher image quality in terms of enhancing the resolution of MR images (T1, T2, FLAIR, and DWI) in 16-bit DICOM format compared to the interpolation methods.
文摘A development of an acquisition of the characteristic of a solar panel by automatic load variation system is put into play and coupled to an instrumentation chain for taking account of temperature. A programmed digital microprocessor control enables this automation. Design and implementation of a device for automation of variations of the resistive load are powered by solar panel. It is provided by a PIC 16F877A running a computer program that we have developed on the basis of an algorithm according to the operation that we have set. By varying automatically the resistive load, we were able to automatically acquire the characteristic I-V and temperature of the solar panel. With automatic combinations of the 10 resistors, we have obtained 1024 measures of the characteristic curve of the solar cell which has a good accuracy. The change in load and temperature measurement allows us to have the characteristic curves parameterized by temperature.
