Spectral detector CT applications in advanced liver imaging

2021 ◽  
pp. 20201290
Author(s):  
Noor Fatima Majeed ◽  
Marta Braschi Amirfarzan ◽  
Christoph Wald ◽  
Jeremy R Wortman
Keyword(s):  
Clinical Practice ◽  
Image Quality ◽  
Radiation Dose ◽  
Diagnostic Information ◽  
Liver Imaging ◽  
Post Processing ◽  
Improve Image Quality ◽  
Spectral Detector Ct ◽  
Processing Techniques

Objective: Spectral detector CT (SDCT) has many applications in advanced liver imaging. If appropriately utilized, this technology has the potential to improve image quality, provide new diagnostic information, and allow for decreased radiation dose. The purpose of this review is to familiarize radiologists with the uses of SDCT in liver imaging. Conclusion: SDCT has a variety of post-processing techniques, which can be used in advanced liver imaging and can significantly add value in clinical practice.

scholarly journals ANALYSIS EXPOSURE INDEX AS AN OPTIMIZATION EFFORT ON EXAMINATION CHEST POSTERIOR ANTERIOR

2020 ◽  
Vol 4 (2) ◽  
pp. 50
Author(s):  
Muhammad Irsal ◽  
Firdha Adlia Syuhada ◽  
Yolanda Pangestu Ananda ◽  
Andre Galih Pratama Putra ◽  
Muhammad Rizky Syahputera ◽  
...  
Keyword(s):  
Image Quality ◽  
Radiation Dose ◽  
Imaging System ◽  
Radiographic Examination ◽  
Exposure Index ◽  
Post Processing ◽  
Improve Image Quality ◽  
Medical Physicists ◽  
Quantitative Descriptive ◽  
Posterior Anterior

Background: Radiographers are responsible for producing image quality which can provide accurate diagnostic information by considering the lowest possible radiation dose according to the As Low As Reasonably Achievable (ALARA) principle. Participation between radiographers and medical physicists is needed in optimizing efforts to control the selection of exposure factors by the required clinical radiographic examination. Purpose: To analyze the exposure index on examination chest posterior-anterior. Methods: Quantitative descriptive by analyzing the percentage of exposure index results used in chest PA radiographs as an effort to optimize: image quality and radiation dose indicators on chest PA examinations. Results: Optimization of exposure percentage results of 68%, 25% underexposure, 4% underexposure, 2% overexposure, 1% overexposure (noise). Radiographers have tried to optimize: image quality and image radiation dose by selecting exposure factors that are tailored to the patient’s condition and maximizing post-processing for increased quality. Conclusion: In optimizing the CR imaging system, it is necessary to understand exposure index, this is related to the underexposed, optimal, and overexposed categories, besides radiographers can take advantage of post-processing to improve image quality.

A Review of Diagnostic Reference Levels in Computed Tomography

2021 ◽  
Vol 17 ◽  
Author(s):  
Jemal Edris Dawd ◽  
Dilber Uzun Ozsahin ◽  
Ilker Ozsahin
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Radiation Dose ◽  
Good Practice ◽  
Ct Scanning ◽  
Diagnostic Information ◽  
The Body ◽  
Reference Level ◽  
Technical Parameters ◽  
Wide Range

: Computed tomography (CT) scanning generate 3-D images of the inside structures of the body by delivering comparatively radiation dose to the patient. This requires high concern of optimization via establishing diagnostic reference level (DRL). DRL values can be estimated based on reference patient percentiles (such as 90th, 75th and 50th) dose distribution. DRL has significant uses in professional judgments by generating harmonized evidence about the radiation dose received by the patient. The primary goal of this review is to assess the practical application of DRL in CT procedures internationally. The main objective of establishing DRLs is to optimize the patient dose and without compromising the image quality in order to obtain adequate diagnostic information. That means inescapability of DRL for a country in medical diagnosis is to reduce the limitation of dose dispersion, to harmonize and expand good practice, to narrow large dispersion of doses, and to create systematic supervision for unwanted radiological doses. The review presents that international records have a wide-range of mean dose distributions due to the variation of exam protocols and technical parameters in use. Hence, this review recommends that each CT health facilities are required exercising careful dose reduction strategies by accounting adequate image quality with sufficient diagnostic information via through follow up of concerned bodies.

scholarly journals Radiation dose reduction in chest dual-energy computed tomography: effect on image quality and diagnostic information

2018 ◽  
Vol 51 (6) ◽  
pp. 377-384 ◽  
Author(s):  
Rodrigo Canellas ◽  
Subba Digumarthy ◽  
Azadeh Tabari ◽  
Alexi Otrakji ◽  
Shaunagh McDermott ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Radiation Dose ◽  
Dual Energy ◽  
Diagnostic Information ◽  
Lesion Detection ◽  
Kappa Statistics ◽  
Dual Energy Computed Tomography ◽  
Reduced Dose ◽  
Image Series

Abstract Objective: To determine whether dual-energy computed tomography (DECT) of the chest can be performed at a reduced radiation dose, with an emphasis on images generated with post-processing techniques. Materials and Methods: In 21 patients undergoing DECT of the chest in a dual-source scanner, an additional image series was acquired at a reduced radiation dose. Four thoracic radiologists assessed both image series for image quality, normal thoracic structures, as well as pulmonary and mediastinal abnormalities, on virtual monochromatic images at 40 keV and 60 keV. Data were analyzed with Student's t-test, kappa statistics, analysis of variance, and the Wilcoxon signed-rank test. Results: The overall image quality of 60 keV virtual monochromatic images at a reduced radiation dose was considered optimal in all patients, and no abnormalities were missed. Contrast enhancement and lesion detection performance were comparable between reduced-dose images at 40 keV and standard-of-care images at 60 keV. The intraobserver and interobserver agreement were both good. The mean volumetric CT dose index (CTDIvol), size-specific dose estimate (SSDE), dose-length product (DLP), and effective dose (ED) for reduced-dose DECT were 3.0 ± 0.6 mGy, 4.0 ± 0.6 mGy, 107 ± 30 mGy.cm, and 1.5 ± 0.4 mSv, respectively. Conclusion: DECT of the chest can be performed at a reduced radiation dose (CTDIvol < 3 mGy) without loss of diagnostic information.

Microwave Imaging Techniques for Non-Destructive Testing of Materials

1992 ◽  
Vol 269 ◽  
Author(s):  
J.Ch. Bolomey ◽  
Ch. Pichot
Keyword(s):  
Image Quality ◽  
Spatial Resolution ◽  
Imaging Techniques ◽  
Microwave Imaging ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Improve Image Quality ◽  
Technological Advances ◽  
Processing Techniques ◽  
Non Destructive

ABSTRACTRecent technological advances for microwave multiport sensors lead us to look for efficient wavefront processing techniques in order to improve image quality, in terms of spatial resolution and contrast. This paper reviews some of these techniques which can be used for Non-Destructive Testing purposes. Mutual advantages and limitations are analysed.

scholarly journals REMOTE SENSING IMAGE QUALITY ASSESSMENT EXPERIMENT WITH POST-PROCESSING

2018 ◽  
Vol XLII-3 ◽  
pp. 665-668
Author(s):  
W. Jiang ◽  
S. Chen ◽  
X. Wang ◽  
Q. Huang ◽  
H. Shi ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Quality Assessment ◽  
Physical Simulation ◽  
Image Quality Assessment ◽  
Imaging System ◽  
Post Processing ◽  
Improve Image Quality ◽  
System Parameters ◽  
Imaging Parameters

This paper briefly describes the post-processing influence assessment experiment, the experiment includes three steps: the physical simulation, image processing, and image quality assessment. The physical simulation models sampled imaging system in laboratory, the imaging system parameters are tested, the digital image serving as image processing input are produced by this imaging system with the same imaging system parameters. The gathered optical sampled images with the tested imaging parameters are processed by 3 digital image processes, including calibration pre-processing, lossy compression with different compression ratio and image post-processing with different core. Image quality assessment method used is just noticeable difference (JND) subject assessment based on ISO20462, through subject assessment of the gathered and processing images, the influence of different imaging parameters and post-processing to image quality can be found. The six JND subject assessment experimental data can be validated each other. Main conclusions include: image post-processing can improve image quality; image post-processing can improve image quality even with lossy compression, image quality with higher compression ratio improves less than lower ratio; with our image post-processing method, image quality is better, when camera MTF being within a small range.

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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