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

2017 ◽  
Author(s):  
Junko Ota ◽  
Kensuke Umehara ◽  
Naoki Ishimaru ◽  
Shunsuke Ohno ◽  
Kentaro Okamoto ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Sparse Coding ◽  
Super Resolution ◽  
Resolution Method

scholarly journals Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

scholarly journals Reconstruction of High-Resolution Computed Tomography Image in Sinogram Space

2021 ◽  
Vol 15 ◽  
pp. 84-88
Author(s):  
Osama A. Omer
Keyword(s):  
Computed Tomography ◽  
High Resolution ◽  
Image Quality ◽  
Computational Cost ◽  
Super Resolution ◽  
Reconstruction Method ◽  
High Resolution Computed Tomography ◽  
Resolution Image ◽  
Reconstruction Methods

An important part of any computed tomography (CT) system is the reconstruction method, which transforms the measured data into images. Reconstruction methods for CT can be either analytical or iterative. The analytical methods can be exact, by exact projector inversion, or non-exact based on Back projection (BP). The BP methods are attractive because of thier simplicity and low computational cost. But they produce suboptimal images with respect to artifacts, resolution, and noise. This paper deals with improve of the image quality of BP by using super-resolution technique. Super-resolution can be beneficial in improving the image quality of many medical imaging systems without the need for significant hardware alternation. In this paper, we propose to reconstruct a high-resolution image from the measured signals in Sinogram space instead of reconstructing low-resolution images and then post-process these images to get higher resolution image.

scholarly journals Novel Assistive Device for Tomographic Ultrasound Neck Imaging vs. Freehand

2020 ◽  
Vol 6 (3) ◽  
pp. 28-31
Author(s):  
Marcel Köhler ◽  
Elmer Jeto Gomes Ataide ◽  
Jens Ziegle ◽  
Axel Boese ◽  
Michael Friebe
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Diagnostic Procedures ◽  
Assistive Device ◽  
Volume Data ◽  
Data Set ◽  
Scanning Method ◽  
Neck Area ◽  
2D Ultrasound

AbstractFor assessing clinically relevant structures in the neck area, especially the thyroid, it has been shown that 3D or tomographic ultrasound (3D US or tUS) is able to outperform standard 2D ultrasound [1] and computed tomography [2] for certain diagnostic procedures. However, when using a freehand and unassisted scanning method to acquire a 3D US volume data set in this area overlapping image slices, a variation of the probe angulation or differences in training might lead to unusable scanning results. Based on previous works [3] [4] we propose the design - with subsequent testing - of an assistive device that is able to aid physicians during the tUS scanning process on the neck. To validate the feasibility and efficacy we compared the image quality of both freehand and assisted scanning.

Influence of cone-beam computed tomography milliamperage settings on image quality of the mandibular third molar region

2013 ◽  
Vol 30 (1) ◽  
pp. 27-31 ◽  
Author(s):  
Frederico Sampaio Neves ◽  
Thaís de Camargo Souza ◽  
Sérgio Lins de-Azevedo-Vaz ◽  
Paulo Sérgio Flores Campos ◽  
Frab Norberto Bóscolo
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Cone Beam Computed Tomography ◽  
Third Molar ◽  
Cone Beam ◽  
Mandibular Third Molar ◽  
Molar Region

scholarly journals Effect of metal artifact reduction software on image quality of C-arm cone-beam computed tomography during intracranial aneurysm treatment

2018 ◽  
Vol 24 (3) ◽  
pp. 303-308 ◽  
Author(s):  
Yukiko Enomoto ◽  
Keita Yamauchi ◽  
Takahiko Asano ◽  
Katharina Otani ◽  
Toru Iwama
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Intracranial Aneurysms ◽  
Clinical Findings ◽  
Cone Beam ◽  
Artifact Reduction ◽  
Metal Artifact ◽  
Metal Artifact Reduction ◽  
Cbct Image

Background and purpose C-arm cone-beam computed tomography (CBCT) has the drawback that image quality is degraded by artifacts caused by implanted metal objects. We evaluated whether metal artifact reduction (MAR) prototype software can improve the subjective image quality of CBCT images of patients with intracranial aneurysms treated with coils or clips. Materials and methods Forty-four patients with intracranial aneurysms implanted with coils (40 patients) or clips (four patients) underwent one CBCT scan from which uncorrected and MAR-corrected CBCT image datasets were reconstructed. Three blinded readers evaluated the image quality of the image sets using a four-point scale (1: Excellent, 2: Good, 3: Poor, 4: Bad). The median scores of the three readers of uncorrected and MAR-corrected images were compared with the paired Wilcoxon signed-rank and inter-reader agreement of change scores was assessed by weighted kappa statistics. The readers also recorded new clinical findings, such as intracranial hemorrhage, air, or surrounding anatomical structures on MAR-corrected images. Results The image quality of MAR-corrected CBCT images was significantly improved compared with the uncorrected CBCT image ( p < 0.001). Additional clinical findings were seen on CBCT images of 70.4% of patients after MAR correction. Conclusion MAR software improved image quality of CBCT images degraded by metal artifacts.

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