Estimating the Rician noise level in brain MR image

Evaluation on the generalization of a learned convolutional neural network for MRI reconstruction

Reduction of breast dose in abdominal CT examinations: Effectiveness of automatic exposure control system

Estimating the gaze direction with a digital video-based pupil and corneal reflection (P-CR) eye tracker is challenging partly since a video camera is limited in terms of spatial and temporal resolution, and because the captured eye images contain noise. Through computer simulation, we evaluated the localization accuracy of pupil-, and CR centers in the eye image for small eye rotations (≪ 1 deg). Results highlight how inaccuracies in center localization are related to 1) how many pixels the pupil and CR span in the eye camera image, 2) the method to compute the center of the pupil and CRs, and 3) the level of image noise. Our results provide a possible explanation to why the amplitude of small saccades may not be accurately estimated by many currently used video-based eye trackers. We conclude that eye movements with arbitrarily small amplitudes can be accurately estimated using the P-CR eye-tracking principle given that the level of image noise is low and the pupil and CR span enough pixels in the eye camera, or if localization of the CR is based on the intensity values in the eye image instead of a binary representation.

A local Zernike moment-based unbiased nonlocal means fuzzy C-Means algorithm for segmentation of brain magnetic resonance images

Estimation of additive white Gaussian noise levels in images has a variety of image processing applications including image enhancement, segmentation and feature extraction. Designing an algorithm with a consistent performance across a range of noise levels and image contents is a challenging problem; without any prior information, it is difficult to differentiate the noise signal from the underlying image signal. In this paper, an adaptive block-based noise level estimation algorithm in the singular value decomposition domain is proposed. The algorithm has the ability to change the singular value tail length according to the observed noise levels. A number of different choices of block size are considered and, for each choice, a mathematical model is proposed to describe how to adjust the singular value tail length as a function of the initial noise level estimates. In comparison with a seminal fixed singular value tail length algorithm, the proposed algorithm significantly improves the noise level estimation accuracy at low noise levels at the expense of a small increase in computational time; for example, for the block size of 64 × 64 and AWGN level σ = 1 , the MSE is reduced by 65%, whilst the computational time is increased by less than 1.3%.

Acoustic emission (AE) detection, as a non-electrical detection method, is very suitable for effective fault detection of power equipment with a strong electromagnetic field. However, the AE signal collected at industrial sites often contains a lot of interference noise, affecting the analysis and prediction of faults. In this study, a wavelet denoising method based on a new threshold function is proposed, to achieve a noise reduction in the low signal-to-noise ratio (SNR) signals. Simulation experiment results show that the proposed threshold function not only overcomes the shortcomings of the discontinuous hard threshold function, but also solves the constant deviation of the soft threshold function. What’s more, the proposed function achieves a good adaptability. When SNR = 10 dB: The SNR of the new threshold is 20.6622, and the RMSE is 0.0026. The SNR of the hard threshold is 20.2246 and the RMSE is 0.0027, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 2.16% and the root mean square error (RMSE) is reduced by 3.7%; the SNR of the soft threshold is 15.5656, and the RMSE is 0.0047, compared with the traditional soft threshold method, the new threshold has a 32.74% increase in SNR and a 40.43% reduction in RMSE. When SNR = -10 dB: The SNR of the new threshold is 4.2602, and the RMSE is 0.0172. The SNR of the hard threshold is 3.8558 and the RMSE is 0.0182, compared with the traditional hard threshold method, the SNR of the new threshold is increased by 10.49% and the RMSE is reduced by 5.49%; the SNR of the soft threshold is 2.1625, and the RMSE is 0.0212, compared with the soft threshold method, SNR is improved by 97% and RMSE is reduced by 18.87%. Performance analyses have proved that the improved wavelet denoising method can obtain a good noise reduction effect. It is very helpful for AE signal analysis with the generally low SNR, which can improve the accuracy of failure identification in subsequent acts.

To compare Rician and non-Rician noise models for quantitative R2 * magnetic resonance imaging (MRI), in a simulation, phantom, and human study. Synthetic 12-echo spoiled GRE (SGRE) datasets were generated with various R2 * rates (0-2000 sec(-1) ) at a signal-to-noise ratio (SNR) of 50, 20, 10, and 5. Phantoms of different MnCl2 concentrations (0-25 mM) were constructed and imaged using a 12-echo 3D SGRE sequence at 1.5T. Increasing levels of synthetic noise was added to the original data to simulate sequentially lower SNR conditions. Sixteen patients with suspected or known iron overload were imaged using 12-echo 3D SGRE at 1.5T. Various R2 * quantification methods, based on Rician and non-Rician noise models, were compared in the simulation, phantom, and human datasets. Non-Rician R2 * estimates were variably inaccurate in the high R2 * range (>500 sec(-1) ), with SNR-dependent linear goodness-of-fit statistic (R(2) ) of 0.373-0.999. Rician R2 * estimates were accurate even in the high R2 * range, with high R(2) of 0.940-0.999 regardless of SNR. Non-Rician R2 * estimates were variably nonlinear at high MnCl2 concentrations, with SNR-dependent R(2) of 0.345-0.994. Rician R2 * estimates were linear even at high MnCl2 concentrations, with high R(2) of 0.923-0.994 regardless of SNR. Between-method agreement of the R2 * estimates was excellent in patients with low ferritin but poor in patients with high ferritin. Rician R2 * estimates had excellent correlation with ferritin (r = 0.966 P < 0.001). Rician R2 * estimates were most consistent in the high R2 * conditions and under varying SNR, and may be more reliable when high iron load is suspected.

Parallel magnetic resonance imaging (MRI) with non-Cartesian sampling pattern is a promising technique that increases the scan speed using multiple receiver coils with reduced samples. However, reconstruction is challenging due to the increased complexity. Three reconstruction methods were evaluated: gridding, blocked uniform resampling (BURS) and non-uniform FFT (NUFFT). Computer simulations of parallel reconstruction were performed. Root mean square error (RMSE) of the reconstructed images to the simulated phantom were used as image quality criterion. Gridding method showed best RMSE performance. Two type of a priori constraints to reduce noise and artifacts were evaluated: edge preserving penalty, which suppresses noise and aliasing artifact in image while preventing over-smoothness, and object support penalty, which reduces background noise amplification. A trust region based step-ratio method that iteratively calculates the penalty coefficient was proposed for the penalty functions. Two methods to alleviate computation burden were evaluated: smaller over sampling ratio, and interpolation coefficient matrix compression. The performance were individually tested using computer simulations. Edge preserving penalty and object support penalty were shown to have consistent improvement on RMSE. The performance of calculated penalty coefficients on the two penalties were close to the best RMSE. Oversampling ratio as low as 1.125 was shown to have impact of less than one percent on RMSE for 2 the radial sampling pattern reconstruction. The value reduced the three dimensional data requirement to less than 1/5 of what the conventional 2x grid needed. Interpolation matrix compression with compression ratio up to 50 percent showed small impact on RMSE. The proposed method was validated on 25 MR data set from a GEMR scanner. Six image quality metrics were used to evaluate the performance. RMSE, normalized mutual information (NMI) and joint entropy (JE) relative to a reference image from a separate body coil scan were used to verify the fidelity of reconstruction to the reference. Region of interest (ROI) signal to noise ratio (SNR), two-data SNR and background noise were used to validate the quality of the reconstruction. The proposed method showed higher ROI SNR, two-data SNR, and lower background noise over conventional method with comparable RMSE, NMI and JE to the reference image at reduced computer resource requirement. Abstract Approved: Thesis SupervisorApproved: Thesis Supervisor Title and Department

The additive Gaussian white noise (AGWN) level in real-life images is usually unknown, for which the empirical setting will make the denoising methods over-smooth fine structures or remove noise incompletely. The previous noise level estimation methods are easily lost in accurately estimating them from images with complicated structures. To cope with this issue, we propose a novel noise level estimation scheme based on Chi-square distribution, including the following key points: First, a degraded image is divided into many image patches through a sliding window. Then, flat patches are selected by using a patch selection strategy on the gradient maps of those image patches. Next, the initial noise level is calculated by employing Chi-square distribution on the selected flat patches. Finally, the stable noise level is optimized by an iterative strategy. Quantitative, with association, to qualitative results of experiments on synthetic real-life images validate that the proposed noise level estimation method is effective and even superior to the state-of-the-art methods. Extensive experiments on noise removal using BM3D further illustrate that the proposed noise level estimation method is more beneficial for achieving favorable denoising performance with detail preservation.

A wavelet-based method for improving signal-to-noise ratio and contrast in MR images

The dynamic range of the gray level of a digital image is limited by the noise it contains. Two statistical methods called "runs test" and "join-count statistic" are used to measure the noise level in a digital image. A residual image is formed by subtracting an original image from its smoothed version. Theoretically, the noise level in the residual image should be identical to that in the original image. The noise level is determined by examining each bit plane of the residual image individually starting from the least significant bit up to the bit plane whose statistic does not show a random pattern. Images from three digital modalities: computerized tomography, magnetic resonance, and computed radiography are used to evaluate the gray-level dynamic range. Both methods are easy to implement and fast to perform.

Brain magnetic resonance imaging (MRI) provides detailed soft tissue contrasts that are critical for disease diagnosis and neuroscience research. Higher MRI resolution typically comes at the cost of signal-to-noise ratio (SNR) and tissue contrast, particularly for more common 3 Tesla (3T) MRI scanners. At ultra-high magnetic field strength, 7 Tesla (7T) MRI allows for higher resolution with greater tissue contrast and SNR. However, the prohibitively high costs of 7T MRI scanners deter their widespread adoption in clinical and research centers. To obtain higher-quality images without 7T MRI scanners, algorithms that can synthesize 7T MR images from 3T MR images are under active development. Here, we make available a dataset of paired T1-weighted and T2-weighted MR images at 3T and 7T of 10 healthy subjects to facilitate the development and evaluation of 3T-to-7T MR image synthesis models. The quality of the dataset is assessed using image quality metrics implemented in MRIQC.

Filter-based vessel enhancement algorithms facilitate the extraction of vascular networks from medical images. Traditional filter-based algorithms struggle with high noise levels in images with false vessel extraction, and a low standard deviation (σ) value may introduce gaps at the centers of wide vessels. In this paper, a robust technique with less sensitivity to parameter tuning and better noise suppression than other filter-based methods for two-dimensional and three-dimensional images is implemented. In this study, we propose a filter that employs non-local means (NLM) for denoising, applying the vesselness function to suppress blob-like structures and filling the gaps in wide vessels without compromising edge quality or details. Acoustic resolution photoacoustic microscopy (AR-PAM) systems generate high-resolution volumetric photoacoustic images, but their vascular structure imaging suffers from out-of-focal signal-to-noise ratio (SNR) and lateral resolution loss. Implementing a synthetic aperture focusing technique (SAFT) based on a virtual detector (VD) improves out-of-focal region resolution and SNR. Combining the proposed filter with the SAFT algorithm enhances vascular structural imaging in AR-PAM systems. The proposed method is robust and applicable for animal tissues with less error of vasculature structure extraction in comparison to traditional fliter-based methods like Frangi and Sato filter. Also, the method is faster in terms of processing speed and less tuning parameters. We applied the method to a digital phantom to validate our approach and conducted in vivo experiments to demonstrate its superiority for real volumetric tissue imaging.

Short stature is a common concern that necessitates pediatric endocrinology evaluation. Growth hormone (GH) deficiency is often included as an etiology. Brain and pituitary Magnetic Resonance Imaging (MRI) with gadolinium-based contrast agents (GBCAs) is the imaging modality of choice in assessing patients with GH deficiency. Given the significant strides made in MRI technology that allow improved spatial and contrast resolution, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. We preformed a retrospective review of otherwise healthy patients with short stature and/or GH deficiency who underwent brain and pituitary MRI without and with contrast, to assess the benefit of contrast administration. Introduction: Short stature is a common concern that necessitates pediatric endocrinology evaluation. The etiologies of short stature are diverse. GH deficiency is often included as an etiology although it accounts for only 1-2% of short stature cases. The prevalence of GH deficiency is reported to be ~ 1:3500. The vast majority of GH deficiency cases are idiopathic in nature with only 20% due to organic causes. The organic causes of GH deficiency include congenital central nervous system (CNS) anomalies, tumors and other pathologic conditions that involve the pituitary-hypothalamic region. As a result, the radiological assessment of the hypothalamic-pituitary region is considered standard of care for evaluating patients with GH deficiency. Although brain and pituitary MRI is the imaging modality of choice in assessing patients with GH deficiency, its yield in cases of isolated GH deficiency is very low. In a study of 40 otherwise normal patients with isolated GH deficiency, 35 (87.5%) had normal brain MRIs. The abnormal findings of brain MRI in the minority of isolated GH deficiency cases included pituitary hypoplasia, pituitary stalk agenesis, lack of the normal T1-weighted pituitary hyperintensity in the posterior part of the sella turcica, and the presence of a high-intensity signal at the infundibular level representing ectopic neurohypophysis. Traditionally, these brain and pituitary MRI images are obtained with the use of contrast material (gadolinium). The main purpose of using contrast material is for the evaluation of pituitary microadenomas. Given the fact that significant strides made in MRI technology and pituitary microadenomas are not appeared to be associated with GH deficiency, the necessity of using contrast material when obtaining brain and pituitary MRI in cases of short stature and isolated GH deficiency should be reassessed. GBCAs have been shown to deposit in different tissues including the kidneys and the brain. The risk increases with repeated doses. The clinical significance of this deposition is unclear at this time but warrants caution especially in pediatric population who have a longer expected lifespan to manifest any delayed effects. Allergic reactions and gastrointestinal symptoms in pediatric patients can occur with GBCA administration, although the incidence is low. Using contrast material also increases the total cost of the MRI study and prolongs the time needed to complete it. Moreover, in order to use contrast material, intravenous venous (IV) access is required which causes discomfort and additional stress to children and their families. Therefore, we performed a retrospective review of otherwise healthy patients with short stature and/or growth hormone deficiency who underwent brain and pituitary MRI without and with contrast, to assess whether contrast administration led to diagnoses that would have otherwise been missed and/or impacted the patient’s clinical course.Objectives: - To compare the diagnostic yield of non-contrast MRI with pre and post-contrast MRI of the brain and pituitary in evaluation of pediatric patients with short stature and/or growth hormone deficiency.- A secondary objective is to measure the size of the pituitary gland and correlates it with peak growth hormone levels (using insulin/argenine). Methodology: We included patients who underwent brain/pituitary MRI with/without contrast performed at our institution between Jan 2013-Dec 2018 who have short stature/GH deficiency. We excluded patients with known diagnosis of other pituitary hormone deficiencies prior to obtaining MRI studies, genetic and neurological disorders, known tumors/malignancies of any type, or renal failure. Two pediatric neuroradiologists independently reviewed the brain and pituitary MRI of these patients (each read 50% of the cohort) blinded to the clinical data and diagnoses. Each radiologist initially reviewed only the non-contrast portions of the studies, and subsequently, the same radiologist reviewed the entire study, including pre- and post-contrast portions in a separate session. The two sessions were 6 weeks apart to avoid recall bias. Several imaging findings including size and morphology of pituitary gland, presence of congenital anomalies or focal lesions and any associated intracranial findings systematically recorded, and subsequently analyzed. Hypotheses: 1.The incidence of finding congenital pituitary cysts is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 2.The incidence of discovering abnormal infundibulum is the same when obtaining brain/pituitary MRI imaging using gadolinium contrast versus when not using contrast in patients with short stature and or isolate GH deficiency. 3.Small pituitary size correlate with GH deficiency. Results: -We identified 327 patients with short stature/GH deficiency from Jan 2013-Dec 2018-224 (68.5%) are males and 103 (31.5%) are females. -The mean age at the time of imaging is 10 years and the median is 11 years. -161 (49.24%) have height z-score &amp;lt; -2.25 and 166 (50.76%) have height z-score &amp;gt; -2.25.-82 (25.07%) have IGF1 z-score for age &amp;lt; -2, 102 (31.19%) have z-score ≥-2 to ≤ -1, 141 (43.12%) have z-score &amp;gt; -1 and 2 (0.62%) have no level done.-63 (19.27%) have GH peak &amp;lt;5, 87 (26.61%) have GH peak 5-7.99, 53 (16.21%) have GH peak 8-9.99, 30 (9.17%) have GH peak &amp;gt; 10 and 94 (28.75%) did not undergo GH provocative testing. -The kappa coefficient for pars intermedia cyst on pre vs. post contrast imaging is 0.74 and 0.55 for the infundibulum on pre vs. post contrast imaging. -The mean pituitary height for patients with IGF z-score &amp;lt; -2 is 3.9 mm, 4 mm for z-score ≥- 2 to ≤ -1 and 4.3 mm for z-score &amp;gt; -1-The mean pituitary height for patients with peak GH &amp;lt; 5 is 3.8 mm, 4.2 mm for peak 5-7.99, 4.3 mm for peak 8-9.99 and 4.4 mm for peak &amp;gt; 10. Conclusion: This question has not been answered or even raised in the literature. Our findings suggest that the there is no added benefit to use gadolinium when obtaining brain/pituitary MRI for the evaluation of GH deficiency/short stature. Furthermore, it seems that there is an association between the pituitary height and the GH status of the cohort which is in line with previous published studies.

Automated assessment of noise level in clinical computed tomography (CT) images is a crucial technique for evaluating and ensuring the quality of these images. There are various factors that can impact CT image noise, such as statistical noise, electronic noise, structure noise, texture noise, artifact noise, etc. In this study, a method was developed to measure the global noise index (GNI) in clinical CT scans due to the fluctuation of x-ray quanta. Initially, a noise map is generated by sliding a 10 × 10 pixel for calculating Hounsfield unit (HU) standard deviation and the noise map is further combined with the gradient magnitude map. By employing Boolean operation, pixels with high gradients are excluded from the noise histogram generated with the noise map. By comparing the shape of the noise histogram from this method with Christianson’s tissue-type global noise measurement algorithm, it was observed that the noise histogram computed in anthropomorphic phantoms had a similar shape with a close GNI value. In patient CT images, excluding the HU deviation due the structure change demonstrated to have consistent GNI values across the entire CT scan range with high heterogeneous tissue compared to the GNI values using Christianson’s tissue-type method. The proposed GNI was evaluated in phantom scans and was found to be capable of comparing scan protocols between different scanners. The variation of GNI when using different reconstruction kernels in clinical CT images demonstrated a similar relationship between noise level and kernel sharpness as observed in uniform phantom: sharper kernel resulted in noisier images. This indicated that GNI was a suitable index for estimating the noise level in clinical CT images with either a smooth or grainy appearance. The study’s results suggested that the algorithm can be effectively utilized to screen the noise level for a better CT image quality control.