BackgroundRician noise, bias fields and blur are the common distortions that degrade MRI images during acquisition. Blur is unique in comparison to Rician noise and bias fields because it can be introduced into an image beyond the acquisition stage such as postacquisition processing and the manifestation of pathological conditions. Most current blur assessment algorithms are designed and validated on consumer electronics such as television, video and mobile appliances. The few algorithms dedicated to medical images either requires a reference image or incorporate manual approach. For these reasons it is difficult to compare quality measures from different images and images with different contents. Furthermore, they will not be suitable in environments where large volumes of images are processed. In this report we propose a new blind blur assessment method for different types of MRI images and for different applications including automated environments.MethodsTwo copies of the test image are generated. Edge map is extracted by separately convolving each copy of the test image with two parallel difference of Gaussian filters. At the start of the multiscale representation, the initial output of the filters are equal. In subsequent scales of the multiscale representation, each filter is tuned to different operating parameters over the same fixed range of Gaussian scales. The filters are termed low and high energy filters based on their characteristics to successively attenuate and highlight edges over the range of multiscale representation. Quality score is predicted from the distance between the normalized mean of the edge maps at the final output of the filters.ResultsThe proposed method was evaluated on cardiac and brain MRI images. Performance evaluation shows that the quality index has very good correlation with human perception and will be suitable for application in routine clinical practice and clinical research.

We propose a new application-specific post-acquisition quality evaluation method for brain MRI images. The domain of a MRI slice is regarded as the universal set. Four feature images; grayscale, local entropy, local contrast and local standard deviation are extracted from the slice and transformed into the binary domain. Each feature image is regarded as a set enclosed by the universal set. Four qualities attribute; lightness, contrast, sharpness and texture details are described by four different combinations of the feature sets. In an ideal MRI slice the four feature sets are identically equal. The degree of distortion in real MRI slice is quantified by the fidelity between the sets that describe a quality attribute. Noise is the fifth quality attribute and it is described by the slice Euler number region property. The total quality score is the weighted sum of the five quality scores. Our proposed method addresses the current challenges in image quality evaluation. It is simple, easy-to-use and easy-to-understand. Incorporation of binary transformation in the proposed method reduces computational as well as operational complexity of the algorithm. We provide experimental results that demonstrate the efficacy of our proposed method on good quality images and on common distortions in MRI images of the brain.

Popular algorithms for quality evaluation of medical images are generic, global and distortion-specific. Performance is limited by complexity introduced by presence of disease signatures such as lesions. Application of classical statistics ignores spatial dependency and the unique image attributes in different imaging modalities. We propose a new no-reference method that overcomes some draw-backs of current algorithms and correlate with human visual system in terms of 'fidelity', 'usefulness' and 'naturalness'. We define the sample space as local entropy at pixel locations in a MRI volume data. Furthermore we segment each slice into eight equal angular segments and partition the sample space into low and high entropy regions. These regions, in a MRI volume data, are regarded as regionalized random variables which exhibit distinct and organized geographic patterns. Our proposal has 'usefulness' because it exploits similarity in geometry of human anatomy to build quality models from 250 brain MRI images of different subjects sourced from the Alzheimer's disease neuroimaging initiative (ADNI) database. 'Fidelity' is impacted by comparing quality models built from ADNI images with quality features extracted from a test image. 'Naturalness' comes from application of spatial statistics to describe distinct geographic patterns of different anatomic structures of brain. Quality scores computed using law of total probability takes into account presence of lesions in MRI data. Partition of feature space encourages focus on region of interest. Experimental results show that our proposal correlates with different levels of degradation and can improve discernment of a physician or a trained reader towards reliable diagnosis.

Effective performance of many image processing and image analysis algorithms is strongly dependent on accurate estimation of noise level. We exploit the simplicity and similarity of statistics of human anatomy among different subjects to develop new noise level estimation algorithm for magnetic resonance images of brain. Objects of the experiment are noise‐free 3D brain MRI of 422 subjects. There are 21 slices for each subject. For each slice, total clique potential (TCP) of Markov random field, computed from local clique potential, is indexed by 200 different levels of noise. The sample space is the set of TCP‐noise level data of each slice. The random variable is the set of indices of noise level of TCP in each element of sample space that is closest in numerical value to TCP measured from a test MRI slice. Noise level is estimated from the mean and variance of the random variable. We also report the formulation of a generalized mathematical model describing relationship between TCP and Rician noise level in brain MRI images. Our proposal can operate in the absence of signals in the background and significantly reduce modeling errors inherent in strong parametric assumptions adopted by some of the current algorithms. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 304–413, 2013

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 ().