Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography

Ota, Junko; Umehara, Kensuke; Ishimaru, Naoki; Ohno, Shunsuke; Okamoto, K.; Suzuki, Takanori; Shirai, Naoki; Ishida, Takayuki

doi:10.1117/12.2253582

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…Recent literature 8 suggests that DDPM has surpassed traditional generative adversarial networks in the synthesis field, literature 9 extends the use of conditional DDPM to image synthesis, image colorization, image restoration, image super-resolution research, and other new directions. For medical CT images, Styner et al 10 . proposed a sparse coding superresolution method, which significantly improved the visual quality and objective evaluation metrics of CT images, effectively recovering high-resolution images from low-dimensional images using sparse representation techniques.…”

Section: Related Workmentioning

confidence: 99%

A denoising diffusion probabilistic model method for colorectal tissue unit pathological images super-resolution

Peng,

Ge,

Chen

et al. 2024

Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024)

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Section: Related Workmentioning

confidence: 99%

A denoising diffusion probabilistic model method for colorectal tissue unit pathological images super-resolution

Peng,

Ge,

Chen

et al. 2024

Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024)

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“…In computer vision, various example-based super-resolution methods have been proposed [9][10][11]. It has been shown that the use of the sparsecoding super-resolution method [10], which is an example-based super-resolution method, yielded higher image quality over that of the conventional linear interpolation methods in chest CT images [12]. However, for real-time clinical applications in medical imaging, the computation time of the conventional examplebased super-resolution methods still remains a challenge.…”

Section: Introductionmentioning

confidence: 99%

Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT

2017

Self Cite

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In this study, the super-resolution convolutional neural network (SRCNN) scheme, which is the emerging deep-learning-based super-resolution method for enhancing image resolution in chest CT images, was applied and evaluated using the post-processing approach. For evaluation, 89 chest CT cases were sampled from The Cancer Imaging Archive. The 89 CT cases were divided randomly into 45 training cases and 44 external test cases. The SRCNN was trained using the training dataset. With the trained SRCNN, a high-resolution image was reconstructed from a low-resolution image, which was down-sampled from an original test image. For quantitative evaluation, two image quality metrics were measured and compared to those of the conventional linear interpolation methods. The image restoration quality of the SRCNN scheme was significantly higher than that of the linear interpolation methods (p < 0.001 or p < 0.05). The high-resolution image reconstructed by the SRCNN scheme was highly restored and comparable to the original reference image, in particular, for a ×2 magnification. These results indicate that the SRCNN scheme significantly outperforms the linear interpolation methods for enhancing image resolution in chest CT images. The results also suggest that SRCNN may become a potential solution for generating high-resolution CT images from standard CT images.

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“…Super-resolution (SR) is a class of techniques that increase the resolution of an imaging system [2] and has been widely applied on natural images and is increasingly being explored in medical imaging. Traditional SR methods use linear or non-linear functions (e.g., bilinear/bicubic interpolation and example-based methods [3,4]) to estimate and simulate image distributions. These methods, however, produce blurring and jagged edges in images, which introduce artifacts and may negatively impact the ability of computer-aided detection (CAD) systems to detect subtle nodules.…”

Section: Introductionmentioning

confidence: 99%

Computed Tomography Image Enhancement Using 3D Convolutional Neural Network

Shen

Gao

et al. 2018

Lecture Notes in Computer Science

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Computed tomography (CT) is increasingly being used for cancer screening, such as early detection of lung cancer. However, CT studies have varying pixel spacing due to differences in acquisition parameters. Thick slice CTs have lower resolution, hindering tasks such as nodule characterization during computer-aided detection due to partial volume effect. In this study, we propose a novel 3D enhancement convolutional neural network (3DECNN) to improve the spatial resolution of CT studies that were acquired using lower resolution/slice thicknesses to higher resolutions. Using a subset of the LIDC dataset consisting of 20,672 CT slices from 100 scans, we simulated lower resolution/thick section scans then attempted to reconstruct the original images using our 3DECNN network. A significant improvement in PSNR (29.3087dB vs. 28.8769dB, p-value < 2.2e − 16) and SSIM (0.8529dB vs. 0.8449dB, pvalue < 2.2e − 16) compared to other state-of-art deep learning methods is observed.

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Evaluation of the sparse coding super-resolution method for improving image quality of up-sampled images in computed tomography

Cited by 9 publications

References 8 publications

A denoising diffusion probabilistic model method for colorectal tissue unit pathological images super-resolution

A denoising diffusion probabilistic model method for colorectal tissue unit pathological images super-resolution

Application of Super-Resolution Convolutional Neural Network for Enhancing Image Resolution in Chest CT

Computed Tomography Image Enhancement Using 3D Convolutional Neural Network

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