随着计算机软硬件技术及超大规模集成电路技术的发展,电子设计自动化(EDA)逐渐取代了传统的电子设计方法,而成为现代电子设计的基本手段。仿真是EDA技术的典型特征,从一个既定的设计任务(16bits比较器)开始,从设计构思、在EDA仿真软件(M...随着计算机软硬件技术及超大规模集成电路技术的发展,电子设计自动化(EDA)逐渐取代了传统的电子设计方法,而成为现代电子设计的基本手段。仿真是EDA技术的典型特征,从一个既定的设计任务(16bits比较器)开始,从设计构思、在EDA仿真软件(Modelsim SE 5．6)平台上编写VHDL仿真软件、进行仿真测试等方面全面地介绍了进行系统计算机仿真的全过程。展开更多
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
文摘随着计算机软硬件技术及超大规模集成电路技术的发展,电子设计自动化(EDA)逐渐取代了传统的电子设计方法,而成为现代电子设计的基本手段。仿真是EDA技术的典型特征,从一个既定的设计任务(16bits比较器)开始,从设计构思、在EDA仿真软件(Modelsim SE 5．6)平台上编写VHDL仿真软件、进行仿真测试等方面全面地介绍了进行系统计算机仿真的全过程。
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.
