scholarly journals Quality assessment of anatomical MRI images from Generative Adversarial Networks: human assessment and image quality metrics

2022 ◽  
Author(s):  
Matthias S Treder ◽  
Ryan Codrai ◽  
Kamen A Tsvetanov
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Quality Metrics ◽  
Generative Adversarial Networks ◽  
Brain Images ◽  
Human Data ◽  
Adversarial Networks ◽  
Image Quality Metrics ◽  
Statistical Relationships ◽  
Human Assessment

Background: Generative Adversarial Networks (GANs) can synthesize brain images from image or noise input. So far, the gold standard for assessing the quality of the generated images has been human expert ratings. However, due to limitations of human assessment in terms of cost, scalability, and the limited sensitivity of the human eye to more subtle statistical relationships, a more automated approach towards evaluating GANs is required. New method: We investigated to what extent visual quality can be assessed using image quality metrics and we used group analysis and spatial independent components analysis to verify that the GAN reproduces multivariate statistical relationships found in real data. Reference human data was obtained by recruiting neuroimaging experts to assess real Magnetic Resonance (MR) images and images generated by a Wasserstein GAN. Image quality was manipulated by exporting images at different stages of GAN training. Results: Experts were sensitive to changes in image quality as evidenced by ratings and reaction times, and the generated images reproduced group effects (age, gender) and spatial correlations moderately well. We also surveyed a number of image quality metrics which consistently failed to fully reproduce human data. While the metrics Structural Similarity Index Measure (SSIM) and Naturalness Image Quality Evaluator (NIQE) showed good overall agreement with human assessment for lower-quality images (i.e. images from early stages of GAN training), only a Deep Quality Assessment (QA) model trained on human ratings was sensitive to the subtle differences between higher-quality images. Conclusions: We recommend a combination of group analyses, spatial correlation analyses, and both distortion metrics (SSIM, NIQE) and perceptual models (Deep QA) for a comprehensive evaluation and comparison of brain images produced by GANs.

Multi-gene Genetic Programming based Predictive Models for Full-reference Image Quality Assessment

2021 ◽  
Author(s):  
Naima Merzougui ◽  
Leila Djerou
Keyword(s):  
Image Quality ◽  
Genetic Programming ◽  
Quality Assessment ◽  
Predictive Models ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Symbolic Regression ◽  
Quality Metrics ◽  
Reference Image ◽  
Image Quality Metrics

Many objective quality metrics for assessing the visual quality of images have been developed during the last decade. A simple way to fine tune the efficiency of assessment is through permutation and combination of these metrics. The goal of this fusion approach is to take advantage of the metrics utilized and minimize the influence of their drawbacks. In this paper, a symbolic regression technique using an evolutionary algorithm known as multi-gene genetic programming (MGGP) is applied for predicting subject scores of images in datasets using a combination of objective scores of a set of image quality metrics (IQM). By learning from image datasets, the MGGP algorithm can determine appropriate image quality metrics, from 21 metrics utilized, whose objective scores employed as predictors in the symbolic regression model, by optimizing simultaneously two competing objectives of model ‘goodness of fit’ to data and model ‘complexity’. Six large image databases (namely LIVE, CSIQ, TID2008, TID2013, IVC and MDID) that are available in public domain are used for learning and testing the predictive models, according the k-fold-cross-validation and the cross dataset strategies. The proposed approach is compared against state-of-the-art objective image quality assessment approaches. Results of comparison reveal that the proposed approach outperforms other state-of-the-art recently developed fusion approaches.

scholarly journals Quality Assessment of 2.5D Prints Using 2D Image Quality Metrics

2021 ◽  
Vol 11 (16) ◽  
pp. 7470
Author(s):  
Altynay Kadyrova ◽  
Vlado Kitanovski ◽  
Marius Pedersen
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Quality Metrics ◽  
Reference Image ◽  
Single Shot ◽  
Current State ◽  
Image Quality Metrics ◽  
Objective Quality ◽  
Multiple Shot ◽  
Set Up

Quality assessment is an important aspect in a variety of application areas. In this work, the objective quality assessment of 2.5D prints was performed. The work is done on camera captures under both diffuse (single-shot) and directional (multiple-shot) illumination. Current state-of-the-art 2D full-reference image quality metrics were used to predict the quality of 2.5D prints. The results showed that the selected metrics can detect differences between the prints as well as between a print and its 2D reference image. Moreover, the metrics better detected differences in the multiple-shot set-up captures than in the single-shot set-up ones. Although the results are based on a limited number of images, they show existing metrics’ ability to work with 2.5D prints under limited conditions.

scholarly journals Evaluation of Organ Dose and Image Quality Metrics of Pediatric CT Chest-Abdomen-Pelvis (CAP) Examination: An Anthropomorphic Phantom Study

2021 ◽  
Vol 11 (5) ◽  
pp. 2047
Author(s):  
Nor Azura Muhammad ◽  
Zunaide Kayun ◽  
Hasyma Abu Hassan ◽  
Jeannie Hsiu Ding Wong ◽  
Kwan Hoong Ng ◽  
...  
Keyword(s):  
Image Quality ◽  
Multidetector Ct ◽  
Organ Dose ◽  
Quality Metrics ◽  
Ct Scanner ◽  
Image Quality Metrics ◽  
Tube Current ◽  
Significant Difference ◽  
Noise Ratio ◽  
The Impact

The aim of this study is to investigate the impact of CT acquisition parameter setting on organ dose and its influence on image quality metrics in pediatric phantom during CT examination. The study was performed on 64-slice multidetector CT scanner (MDCT) Siemens Definition AS (Siemens Sector Healthcare, Forchheim, Germany) using various CT CAP protocols (P1–P9). Tube potential for P1, P2, and P3 protocols were fixed at 100 kVp while P4, P5, and P6 were fixed at 80 kVp with used of various reference noise values. P7, P8, and P9 were the modification of P1 with changes on slice collimation, pitch factor, and tube current modulation (TCM), respectively. TLD-100 chips were inserted into the phantom slab number 7, 9, 10, 12, 13, and 14 to represent thyroid, lung, liver, stomach, gonads, and skin, respectively. The image quality metrics, signal to noise ratio (SNR) and contrast to noise ratio (CNR) values were obtained from the CT console. As a result, this study indicates a potential reduction in the absorbed dose up to 20% to 50% along with reducing tube voltage, tube current, and increasing the slice collimation. There is no significant difference (p > 0.05) observed between the protocols and image metrics.

Image-quality metrics for characterizing adaptive optics system performance

1997 ◽  
Vol 36 (26) ◽  
pp. 6583 ◽  
Author(s):  
Robert T. Brigantic ◽  
Michael C. Roggemann ◽  
Kenneth W. Bauer ◽  
Byron M. Welsh
Keyword(s):  
Image Quality ◽  
Adaptive Optics ◽  
System Performance ◽  
Quality Metrics ◽  
Image Quality Metrics ◽  
Adaptive Optics System

scholarly journals Contrast agent dose reduction in computed tomography with deep learning using a conditional generative adversarial network

2021 ◽  
Author(s):  
Johannes Haubold ◽  
René Hosch ◽  
Lale Umutlu ◽  
Axel Wetter ◽  
Patrizia Haubold ◽  
...  
Keyword(s):  
Image Quality ◽  
Contrast Media ◽  
Animal Experiments ◽  
Turing Test ◽  
Dual Energy ◽  
Generative Adversarial Networks ◽  
Arterial Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks

Abstract Objectives To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks. Methods Dual-energy CTs in the arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (−50% and −80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency. Results The −80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the −50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use. Conclusions The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results. Key Points • The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks. • Not only the image quality but especially the pathological consistency must be evaluated to assess safety. • A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80%.

scholarly journals Repeatability of a Dual-Scheimpflug Placido Disc Corneal Tomographer/Topographer in Eyes with Keratoconus

2020 ◽  
Author(s):  
Anne Poulsen ◽  
Diane Jang ◽  
Mahmood Khan ◽  
Zaina Nabil Al-Mohtaseb ◽  
Michael Chen ◽  
...  
Keyword(s):  
Image Quality ◽  
Spherical Aberration ◽  
Correlation Coefficients ◽  
Quality Metrics ◽  
Corneal Surgery ◽  
Image Quality Metrics ◽  
Interclass Correlation ◽  
Intrastromal Corneal Ring ◽  
History Of ◽  
Image Quality Metric

Purpose: To investigate the repeatability of a combined Dual-Scheimpflug placido disc corneal topographer (Zeimer Galilei G4) with respect to keratometric indices used to monitor progression of keratoconus (KCN). Methods: Patients with KCN were prospectively enrolled. For each eye lacking history of corneal surgery, 5 measurements were taken in succession. Eyes in which 3 or more measurements could be obtained (defined by the device's 4 image quality metrics) were included in the analysis. The repeatability limits (RL) and interclass correlation coefficients (ICC) were calculated for various parameters. Results: 32 eyes from 25 patients met all image quality metrics, and 54 eyes from 38 patients met at least 3/4 criteria (all except the placido image quality metric). RLs for key parameters when 4/4 or 3/4 image quality metrics were met included: 0.37 and 0.77 diopters (D) for steep simulated keratometry, 0.79 and 1.65 D for maximum keratometry, 13.80 and 13.88 degrees for astigmatism axis, 0.64 and 0.56 um for vertical coma magnitude, and 3.76 and 3.84 um for thinnest pachymetry, respectively. The ICCs for all parameters were excellent [above 0.87 except for spherical aberration (0.77)]. Conclusions: The dual-Scheimpflug placido disc corneal topographer is highly repeatable in quantifying parameters used in monitoring KCN. Excellent placido images are difficult to capture in eyes with KCN, but when available, increase the reliability of the measurements. The RLs may be especially helpful in detecting progression in mild KCN when interventions such as corneal cross-linking or intrastromal corneal ring segments are most beneficial.

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