No‐reference quality measure in brain MRI images using binary operations, texture and set analysis

Osadebey, Michael; Pedersen, Marius; Arnold, Douglas L.; Wendel-Mitoraj, Katrina

doi:10.1049/iet-ipr.2016.0560

Cited by 17 publications

(24 citation statements)

References 60 publications

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“…In this study, image quality scores were obtained by a subjective metric, that is, evaluation from two radiologists. The human visual system is the gold standard in image quality evaluation . However, subjective evaluation by human observers suffers from high intra‐reader and inter‐reader variability.…”

Section: Discussionmentioning

confidence: 99%

Impact of patient comfort on diagnostic image quality during PET/MR exam: A quantitative survey study for clinical workflow management

Chen

et al. 2019

J Applied Clin Med Phys

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Background PET/MR is transferring from a powerful scientific research tool to an imaging modality in clinical routine practice. Whole body PET/MR screening usually takes 30–50 minutes to finish, during which a few factors might induce patient discomfort and further cause degraded image quality. The aim of this report is to investigate the patients' perception of the imaging procedure and its correlation with image quality. Methods One hundred and twenty patients (63 males and 57 females, average age = 51.3 years, range 22–70 years) who had been diagnosed with cancer or had previous history of cancer were recruited and scanned with a simultaneous PET/MR system. A questionnaire was given to all patients retrospectively after the PET/MR scan, which has nine questions to assess patients' feeling of the scan on a Likert scale scoring system (1–5, 1 as most satisfied). All PET/MR images were also visually examined by two experts independently to evaluate the quality of the images. Six body locations were assessed and each location was evaluated also with a Likert scale scoring system (1–5, 5 as the best quality). Mann–Whitney Utest was used for statistical analysis to check if there is significant correlation between image quality and patient perceptions. Results With a total of 120 patients, 118 questionnaires were filled and returned for analysis. The patients’ characteristics were summarized in Table 4. The statistics of the patients’ perception in the questionnaire were illustrated in Tables 5–7. Statistical significant correlations were found between MR image quality and patients’ characteristics/perception. Conclusion Our results show that PET/MR scanning is generally safe and comfortable for most of the patients. Statistical analysis does not support the hypothesis that bad patient’s perception leads to degraded image quality.

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Section: Discussionmentioning

confidence: 99%

Impact of patient comfort on diagnostic image quality during PET/MR exam: A quantitative survey study for clinical workflow management

Chen

et al. 2019

J Applied Clin Med Phys

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“…Below the threshold, where blur dominates, the mean of the LCF image is lower than the mean of the observed image. Both degradation processes results in loss of sharpness but in opposite direction on either side of the threshold [41] .…”

Section: Methodsmentioning

confidence: 99%

“…Mortamet et al [39] combine the detection of artifacts and estimation of noise level to measure image quality. Recently the report in [41] propose a no-reference method which predict brain MRI quality based on five quality attributes. The attributes are lightness, contrast, sharpness, texture details and noise.…”

Section: Introductionmentioning

confidence: 99%

Image Quality Evaluation in Clinical Research: A Case Study on Brain and Cardiac MRI Images in Multi-Center Clinical Trials

Osadebey

Pedersen

Arnold

et al. 2018

IEEE J. Transl. Eng. Health Med.

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Magnetic resonance imaging (MRI) system images are important components in the development of drugs because it can reveal the underlying pathology in diseases. Unfortunately, the processes of image acquisition, storage, transmission, processing, and analysis can influence image quality with the risk of compromising the reliability of MRI-based data. Therefore, it is necessary to monitor image quality throughout the different stages of the imaging workflow. This report describes a new approach to evaluate the quality of an MRI slice in multi-center clinical trials. The design philosophy assumes that an MRI slice, such as all natural images, possess statistical properties that can describe different levels of contrast degradation. A unique set of pixel configuration is assigned to each possible level of contrast-distorted MRI slice. Invocation of the central limit theorem results in two separate Gaussian distributions. The central limit theorem says that the mean and standard deviation of pixel configuration assigned to each possible level of contrast degradation will follow a normal distribution. The mean of each normal distribution corresponds to the mean and standard deviation of the underlying ideal image. Quality prediction processes for a test image can be summarized into four steps. The first step extracts local contrast feature image from the test image. The second step computes the mean and standard deviation of the feature image. The third step separately standardizes each normal distribution using the mean and standard deviation computed from the feature image. This gives two separate z-scores. The fourth step predicts the lightness contrast quality score and the texture contrast quality score from cumulative distribution function of the appropriate normal distribution. The proposed method was evaluated objectively on brain and cardiac MRI volume data using four different types and levels of degradation. The four types of degradation are Rician noise, circular blur, motion blur, and intensity nonuniformity also known as bias fields. Objective evaluation was validated using a proposed variation of difference of mean opinion scores. Results from performance evaluation show that the proposed method will be suitable to monitor and standardize image quality throughout the different stages of imaging workflow in large clinical trials. MATLAB implementation of the proposed objective quality evaluation method can be downloaded from ().

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“…The reconstructed brain images were assessed using BRISQUE by Sandilya and Nirmala . Binary brain images were considered by Osadebey et al Esteban et al proposed a tool for visual inspection of MR images in which the binary classification of images based on features generated by quality metrics was performed. A neural network model for the detection of motion artifacts in the head and the abdomen images was proposed by Kustner et al, while Sujit et al used deep learning for binary classification of structural brain images.…”

Section: Introductionmentioning

confidence: 99%

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Oszust

Piórkowski

Obuchowicz

2020

Magnetic Resonance in Med

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Purpose: Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. Methods: A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve lowfrequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. Results: The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. Conclusions: It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development. K E Y W O R D Shigh-boost filtering, image quality assessment, local features, magnetic resonance images, no-reference, subjective tests

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No‐reference quality measure in brain MRI images using binary operations, texture and set analysis

Cited by 17 publications

References 60 publications

Impact of patient comfort on diagnostic image quality during PET/MR exam: A quantitative survey study for clinical workflow management

Impact of patient comfort on diagnostic image quality during PET/MR exam: A quantitative survey study for clinical workflow management

Image Quality Evaluation in Clinical Research: A Case Study on Brain and Cardiac MRI Images in Multi-Center Clinical Trials

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

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