Image Noise Level Research Articles

On micromechanical parameter identification with integrated DIC and the role of accuracy in kinematic boundary conditions

Integrated Digital Image Correlation (IDIC) is nowadays a well established full-field experimental procedure for reliable and accurate identification of material parameters. It is based on the correlation of a series of images captured during a mechanical experiment, that are matched by displacement fields derived from an underlying mechanical model. In recent studies, it has been shown that when the applied boundary conditions lie outside the employed field of view, IDIC suffers from inaccuracies. A typical example is a micromechanical parameter identification inside a Microstructural Volume Element (MVE), whereby images are usually obtained by electron microscopy or other microscopy techniques but the loads are applied at a much larger scale. For any IDIC model, MVE boundary conditions still need to be specified, and any deviation or fluctuation in these boundary conditions may significantly influence the quality of identification. Prescribing proper boundary conditions is generally a challenging task, because the MVE has no free boundary, and the boundary displacements are typically highly heterogeneous due to the underlying microstructure. The aim of this paper is therefore first to quantify the effects of errors in the prescribed boundary conditions on the accuracy of the identification in a systematic way. To this end, three kinds of mechanical tests, each for various levels of material contrast ratios and levels of image noise, are carried out by means of virtual experiments. For simplicity, an elastic compressible Neo-Hookean constitutive model under plane strain assumption is adopted. It is shown that a high level of detail is required in the applied boundary conditions. This motivates an improved boundary condition application approach, which considers constitutive material parameters as well as kinematic variables at the boundary of the entire MVE as degrees of freedom in the IDIC procedure, assuring that both are identified with equal precision and importance. This problem has been studied in the literature with a different method, i.e. Finite Element Method Updating framework.

Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

In this paper, an automatic estimation of additive white Gaussian noise technique is proposed. This technique is built according to the local statistics of Gaussian noise. In the field of digital signal processing, estimation of the noise is considered as pivotal process that many signal processing tasks relies on. The main aim of this paper is to design a patch-based estimation technique in order to estimate the noise level in natural images and use it in blind image removal technique. The estimation processes is utilized selected patches which is most contaminated sub-pixels in the tested images sing principal component analysis (PCA). The performance of the suggested noise level estimation technique is shown its superior to state of the art noise estimation and noise removal algorithms, the proposed algorithm produces the best performance in most cases compared with the investigated techniques in terms of PSNR, IQI and the visual perception.

Patch based fast noise level estimation using DCT and standard deviation

Image noise level estimation play a vital role in image processing applications such as medical imaging and celestial imaging. The noise level estimation of image is hard to estimate due to texture of image. Conventional methods segment image in blocks to identify noise in image. The method provides erroneous noise detection in high textured image such as medical images. In this paper, we propose a Patch based DCT (PDCT) model to decompose image in spatial domain in parallel pool loop for medical image slices. The PDCT model split noisy image into patches to exhibit noise in image. The PDCT model estimates noise level accurately in complex images compared to conventional noise level estimation methods such as principle component analysis and weak textured patch methods.

Clinically Acceptable Optimized Dose Reduction in Computed Tomographic Imaging of Necrotizing Pancreatitis Using a Noise Addition Software Tool.

This study aimed to determine potential radiation dose reduction of contrast-enhanced computed tomography (CECT) for imaging necrotizing pancreatitis (NP) using a noise addition tool. Eighty-four patients were identified with at least 1 abdominopelvic CECT for NP within a 2-year period. Sixty consecutive scans were selected as reference radiation dose data sets. A noise addition software was used to simulate 4 data sets of increased noise. Readers rated confidence for identifying (i) anatomic structures, (ii) complications of NP, and (iii) diagnostic acceptability. Noise and dose levels were identified at acceptability threshold where observer scores were statistically indistinguishable from full-dose computed tomographies. Observers' perception of image tasks decreased progressively with increasing noise (P < 0.05). Acceptability and statistical analysis indicated that noise can be increased from 10 to 25 HU corresponding to an 84% reduction in dose without change in observer perception (P < 0.05). Higher image noise levels may be tolerated in CECT in patients with NP.

A Single Radio Source in the High-redshift Dual AGN LBQS 0302-0019

By discovering a narrow-line emitter embedded in an extended Ly-alpha nebula, Husemann et al. (2018) found that the luminous radio-quiet quasar LBQS 0302-0019 is a dual active galactic nucleus (AGN) system. By combining available archival radio interferometric data taken at 8.4 GHz with the Very Large Array (VLA), we found that the weak 0.4-mJy radio source in this system positionally coincides with the unobscured optical quasar while its newly discovered obscured companion has no detectable radio emission (<0.045 mJy/beam, 3-sigma image noise level). However, the non-detection of this object with the VLA does not rule out the presence of a radio-quiet AGN there.

Influence of imaging configurations on the accuracy of digital image correlation measurement

Compared to other approaches to improving digital image correlation (DIC) measurement accuracy, improving image quality is becoming increasingly effective. Image noise level, which is the main indicator of image quality, is very sensitive to the configurations of an imaging system and affects DIC measurement accuracy. However, the mechanism by which the imaging configurations affect accuracy is unclear. In this study, the influence of imaging configuration on DIC measurement accuracy was investigated using the common factor of noise. Firstly, the type and levels of noise introduced into actual images by different imaging system configurations were investigated. Next, the effect of noise on DIC measurement accuracy was determined using simulated images. Finally, the effects of imaging configuration on DIC measurement accuracy were experimentally investigated using actual measurements. The results show that both camera-related and lens-related configurations affect DIC measurement accuracy. In particular, lengthening the exposure time and reducing the f-number of the lens achieve similar improvement in DIC measurement accuracy, and a prime lens with high light transmittance is recommended for full-field optical measurements. In contrast, the imaging gain does not affect the accuracy of DIC measurements.

Improved Opacification of a Suboptimally Enhanced Pulmonary Artery in Chest CT: Experience Using a Dual-Layer Detector Spectral CT.

The objective of our study was to evaluate the quality of virtual monoenergetic imaging (VMI) from dual-layer detector spectral CT and the effect of virtual monoenergetic images obtained at low energies on the detection of pulmonary embolism (PE) in patients with a suboptimally enhanced pulmonary artery on chest CT. Of 1552 consecutive chest CT examinations performed with dual-layer detector spectral CT using a routine protocol with a tube voltage of 120 kVp, 79 examinations with suboptimal enhancement of the pulmonary artery (i.e., mean attenuation of pulmonary artery ≤ 180 HU) were included. The mean attenuation of the pulmonary artery, noise, contrast-to-noise ratio (CNR), and signal-to-noise ratio (SNR) of virtual monoenergetic images obtained at 40-200 keV were compared with those of the conventional 120-kVp images. The virtual monoenergetic images with the best CNR were compared with the 120-kVp images with regard to subjective image quality and diagnostic accuracy for detecting PE. Sufficient attenuation of the pulmonary artery (> 180 HU) was obtained using VMI for 78 of the 79 examinations. The noise levels of the virtual monoenergetic images were gradually increased with decreasing energy level (i.e., kiloelectron volt setting). The CNR and SNR of virtual monoenergetic images at 40-65 keV were significantly higher (both, p < 0.001) than the CNR and SNR of the 120-kVp images. The CNR was the highest at 40 keV for all cases. Diagnostic accuracy for detecting PE was significantly higher for 40-keV images (reader 1: AUC = 0.992, p = 0.033; reader 2: AUC = 0.986, p = 0.043) than for 120-kVp images (reader 1, AUC = 0.911; reader 2, AUC = 0.933). The subjective quality was not different between these two images. In chest CT examinations in which the pulmonary artery is suboptimally enhanced, obtaining virtual monoenergetic images at a low energy setting using dual-layer detector spectral CT allows sufficient attenuation of the pulmonary artery to be achieved while preserving image quality and increasing diagnostic performance for detecting PE.

Deep Shrinkage Convolutional Neural Network for Adaptive Noise Reduction

The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. But denoising methods based on deep convolutional neural networks (deep-CNN) do not have such adjustable parameters. Therefore, a deep-CNN whose training data contain limited levels of noise does not effectively restore images whose noise level is different from the training data. If the range of noise levels of training data is extended to solve the problem, the maximum performance of a produced deep-CNN is limited. To solve the tradeoff, we propose a deep-CNN that is adjustable to the noise level of the input image immediately. We use soft shrinkage for activation functions of our deep-CNN. The soft shrinkage has thresholds proportional to the noise level given by the user. We also propose an optimization method for proportionality coefficients for the thresholds of soft shrinkage. Our method optimizes the coefficients for various noise levels simultaneously. In our experiment using a test set whose noise level is from 5 to 50, the proposed method showed higher PSNR than that in the case of the conventional method using only one deep-CNN, and PSNR comparable to that in the case of the conventional method using multiple noise-level-specific CNNs.

Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

We proposed a noniterative principal component analysis (PCA)-based noise level estimation (NLE) algorithm that addresses the problem of estimating the noise level with a two-step scheme. First, we randomly extracted a number of raw patches from a given noisy image and took the smallest eigenvalue of the covariance matrix of the raw patches as the preliminary estimation of the noise level. Next, the final estimation was directly obtained with a nonlinear mapping (rectification) function that was trained on some representative noisy images corrupted with different known noise levels. Compared with the state-of-art NLE algorithms, the experiment results show that the proposed NLE algorithm can reliably infer the noise level and has robust performance over a wide range of image contents and noise levels, showing a good compromise between speed and accuracy in general.

Optimized digital speckle patterns for digital image correlation by consideration of both accuracy and efficiency.

The technique of digital image correlation (DIC), which has been widely used for noncontact deformation measurements in both the scientific and engineering fields, is greatly affected by the quality of speckle patterns in terms of its performance. This study was concerned with the optimization of the digital speckle pattern (DSP) for DIC in consideration of both the accuracy and efficiency. The root-mean-square error of the inverse compositional Gauss-Newton algorithm and the average number of iterations were used as quality metrics. Moreover, the influence of subset sizes and the noise level of images, which are the basic parameters in the quality assessment formulations, were also considered. The simulated binary speckle patterns were first compared with the Gaussian speckle patterns and captured DSPs. Both the single-radius and multi-radius DSPs were optimized. Experimental tests and analyses were conducted to obtain the optimized and recommended DSP. The vector diagram of the optimized speckle pattern was also uploaded as reference.

Fast GPU-based Monte Carlo code for SPECT/CT reconstructions generates improved 177Lu images

BackgroundFull Monte Carlo (MC)-based SPECT reconstructions have a strong potential for correcting for image degrading factors, but the reconstruction times are long. The objective of this study was to develop a highly parallel Monte Carlo code for fast, ordered subset expectation maximum (OSEM) reconstructions of SPECT/CT images. The MC code was written in the Compute Unified Device Architecture language for a computer with four graphics processing units (GPUs) (GeForce GTX Titan X, Nvidia, USA). This enabled simulations of parallel photon emissions from the voxels matrix (1283 or 2563). Each computed tomography (CT) number was converted to attenuation coefficients for photo absorption, coherent scattering, and incoherent scattering. For photon scattering, the deflection angle was determined by the differential scattering cross sections. An angular response function was developed and used to model the accepted angles for photon interaction with the crystal, and a detector scattering kernel was used for modeling the photon scattering in the detector. Predefined energy and spatial resolution kernels for the crystal were used. The MC code was implemented in the OSEM reconstruction of clinical and phantom 177Lu SPECT/CT images. The Jaszczak image quality phantom was used to evaluate the performance of the MC reconstruction in comparison with attenuated corrected (AC) OSEM reconstructions and attenuated corrected OSEM reconstructions with resolution recovery corrections (RRC).ResultThe performance of the MC code was 3200 million photons/s. The required number of photons emitted per voxel to obtain a sufficiently low noise level in the simulated image was 200 for a 1283 voxel matrix. With this number of emitted photons/voxel, the MC-based OSEM reconstruction with ten subsets was performed within 20 s/iteration. The images converged after around six iterations. Therefore, the reconstruction time was around 3 min. The activity recovery for the spheres in the Jaszczak phantom was clearly improved with MC-based OSEM reconstruction, e.g., the activity recovery was 88% for the largest sphere, while it was 66% for AC-OSEM and 79% for RRC-OSEM.ConclusionThe GPU-based MC code generated an MC-based SPECT/CT reconstruction within a few minutes, and reconstructed patient images of 177Lu-DOTATATE treatments revealed clearly improved resolution and contrast.

The Most Compact Bright Radio-loud AGNs. II. VLBA Observations of 10 Sources at 43 and 86 GHz

Abstract Radio-loud active galactic nuclei (AGNs), hosting powerful relativistic jet outflows, provide an excellent laboratory for studying jet physics. Very long baseline interferometry (VLBI) enables high-resolution imaging on milli-arcsecond (mas) and sub-mas scales, making it a powerful tool to explore the inner jet structure, shedding light on the formation, acceleration, and collimation of AGN jets. In this paper, we present Very Long Baseline Array observations of 10 radio-loud AGNs at 43 and 86 GHz that were selected from the Planck catalog of compact sources and are among the brightest in published VLBI images at and below 15 GHz. The image noise levels in our observations are typically 0.3 and 1.5 mJy beam−1 at 43 and 86 GHz, respectively. Compared with the VLBI data observed at lower frequencies from the literature, our observations with higher resolutions (with the highest resolution being up to 0.07 mas at 86 GHz and 0.18 mas at 43 GHz) and at higher frequencies detected new jet components at sub-parsec scales, offering valuable data for studies of the physical properties of the innermost jets. These include the compactness factor of the radio structure (the ratio of core flux density to total flux density), and core brightness temperature ( ). In all these sources, the compact core accounts for a significant fraction ( ) of the total flux density. Their correlated flux density at the longest baselines is higher than 0.16 Jy. The compactness of these sources make them good phase calibrators of millimeter-wavelength ground-based and space VLBI.

Robust denoising technique for ultrasound images by splicing of low rank filter and principal component analysis

Robust image denoising techniques are still a significant challenge for medical ultrasound images. Though the difficulty of the recently proposed strategies, most techniques have not yet achieved desired level of applicability. The principle focus of this paper is a thresholding strategy together with a splicing of Low Rank Filtering (LRF) and Principal Component Analysis (PCA) techniques. By means of the proposed robust denoising technique, the denoised medical ultrasound image is assessed until the point that it reaches desired level. The input noisy medical ultrasound image is given to the hybridization technique of LRF mechanism and PCA transformation which is used for removing the noise in medical ultrasound image. For effective image denoising, the segmentation process is done with the guide of sparse decomposition framework. Consequently, denoised image threshold is compared with the threshold value of training images in the database and this is repeated until we get a better denoised image. For analysis purpose, the set of five medical ultrasound images of artery have been considered. These medical ultrasound images are used for quantitative analysis. Performance of these recent filters as two phase matrix decomposition denoised filter, new adaptive denoised filter, and fast fourier bessel steerable PCA (FFBsPCA) denoised filter have been compared with our proposed splicing denoised method in terms of peak signal to noise ratio (PSNR) value and mean square error (MSE) value performance index under various noise density selection. Analytical results for set of five medical ultrasound images have shown that this proposed splice filtering method is more robust for noise reduction. So, it has shown that proposed splice filtering method is robust for medical ultrasound image denoising which preserves the clinical details and minimizing the noise level in medical ultrasound images.

Noise Robust Face Image Super-Resolution Through Smooth Sparse Representation.

Face image super-resolution has attracted much attention in recent years. Many algorithms have been proposed. Among them, sparse representation (SR)-based face image super-resolution approaches are able to achieve competitive performance. However, these SR-based approaches only perform well under the condition that the input is noiseless or has small noise. When the input is corrupted by large noise, the reconstruction weights (or coefficients) of the input low-resolution (LR) patches using SR-based approaches will be seriously unstable, thus leading to poor reconstruction results. To this end, in this paper, we propose a novel SR-based face image super-resolution approach that incorporates smooth priors to enforce similar training patches having similar sparse coding coefficients. Specifically, we introduce the fused least absolute shrinkage and selection operator-based smooth constraint and locality-based smooth constraint to the least squares representation-based patch representation in order to obtain stable reconstruction weights, especially when the noise level of the input LR image is high. Experiments are carried out on the benchmark FEI face database and CMU+MIT face database. Visual and quantitative comparisons show that the proposed face image super-resolution method yields superior reconstruction results when the input LR face image is contaminated by strong noise.

Towards reliable object detection in noisy images

Image noise is a common problem frequently caused by insufficient lighting, low-quality cameras, image compression and other factors. While low image quality is expected to degrade results of visual recognition, most of the current methods and benchmarks for object recognition, such as Pascal Visual Object Classes Challenge and Microsoft Common Objects in Context Challenge, focus on relatively high-quality images. Meanwhile, object recognition in noisy images is a common problem in surveillance and other domains. In this work we address object detection in noisy images and propose a novel low-cost method for image denoising. When combined with the standard Deformable Parts Model and Regions with Convolutional Neural Network object detectors, our method shows improvements of object detection under varying levels of image noise. We present a comprehensive experimental evaluation and compare our method to other denoising techniques as well as to standard detectors re-trained on noisy images. Results are presented for the common Pascal Visual Object Classes benchmark for object detection and KAIST Multispectral Pedestrian Detection Benchmark with the real noise presence in night images.

Enhancing quality in Diffusion Tensor Imaging with anisotropic anomalous diffusion filter

Abstract Introduction: Diffusion tensor imaging (DTI) is an important medical imaging modality that has been useful to the study of microstructural changes in neurological diseases. However, the image noise level is a major practical limitation, in which one simple solution could be the average signal from a sequential acquisition. Nevertheless, this approach is time-consuming and is not often applied in the clinical routine. In this study, we aim to evaluate the anisotropic anomalous diffusion (AAD) filter in order to improve the general image quality of DTI. Methods A group of 20 healthy subjects with DTI data acquired (3T MR scanner) with different numbers of averages (N=1,2,4,6,8, and 16), where they were submitted to 2-D AAD and conventional anisotropic diffusion filters. The Relative Mean Error (RME), Structural Similarity Index (SSIM), Coefficient of Variation (CV) and tractography reconstruction were evaluated on Fractional Anisotropy (FA) and Apparent Diffusion Coefficient (ADC) maps. Results The results point to an improvement of up to 30% of CV, RME, and SSIM for the AAD filter, while up to 14% was found for the conventional AD filter (p<0.05). The tractography revealed a better estimative in fiber counting, where the AAD filter resulted in less FA variability. Furthermore, the AAD filter showed a quality improvement similar to a higher average approach, i.e. achieving an image quality equivalent to what was seen in two additional acquisitions. Conclusions In general, the AAD filter showed robustness in noise attenuation and global image quality improvement even in DTI images with high noise level.

Bias-variance tradeoff in anticorrelated noise reduction for spectral CT.

In spectral CT, basis material decomposition is commonly used to generate a set of basis images showing the material composition at each point in the field of view. The noise in these images typically contains anticorrelations between the different basis images, which leads to increased noise in each basis image. These anticorrelations can be removed by changing the basis functions used in the material decomposition, but the resulting basis images can then no longer be used for quantitative measurements. Recent studies have demonstrated that reconstruction methods which take the anticorrelations into account give reduced noise in the reconstructed image. The purpose of this work is to analyze an analytically solvable denoising model problem and investigate its effect on the noise level and bias in the image as a function of spatial frequency. A denoising problem with a quadratic regularization term is studied as a mathematically tractable model for such a reconstruction method. An analytic formula for the resulting image in the spatial frequency domain is presented, and this formula is applied to a simple mathematical phantom consisting of an iodinated contrast agent insert embedded in soft tissue. We study the effect of the denoising on the image in terms of its transfer function and the visual appearance, the noise power spectrum and the Fourier component correlation coefficient of the resulting image, and compare the result to a denoising problem which does not model the anticorrelations in the image. Including the anticorrelations in the noise model of the denoising method gives 3-40% lower noise standard deviation in the soft-tissue image while leaving the iodine standard deviation nearly unchanged (0-1% difference). It also gives a sharper edge-spread function. The studied denoising method preserves the noise level and the anticorrelated structure at low spatial frequencies but suppresses the noise and removes the anticorrelations at higher spatial frequencies. Cross-talk between images gives rise to artifacts at high spatial frequencies. Modeling anticorrelations in a denoising problem can decrease the noise level in the basis images by removing anticorrelations at high spatial frequencies while leaving low spatial frequencies unchanged. In this way, basis image cross-talk does not lead to low spatial frequency bias but it may cause artifacts at edges in the image. This theoretical insight will be useful for researchers analyzing and designing reconstruction algorithms for spectral CT.

Functional overestimation due to spatial smoothing of fMRI data

BackgroundPearson correlation (simply correlation) is a basic technique for neuroimage function analysis. It has been observed that the spatial smoothing may cause functional overestimation, which however remains a lack of complete understanding. Herein, we present a theoretical explanation from the perspective of correlation scale invariance. New methodsFor a task-evoked spatiotemporal functional dataset, we can extract the functional spatial map by calculating the temporal correlations (tcorr) of voxel timecourses against the task timecourse. From the relationship between image noise level (changed through spatial smoothing) and the tcorr map calculation, we show that the spatial smoothing causes a noise reduction, which in turn smooths the tcorr map and leads to a spatial expansion on neuroactivity blob estimation. ResultsThrough numerical simulations and subject experiments, we show that the spatial smoothing of fMRI data may overestimate activation spots in the correlation functional map. Our results suggest a small spatial smoothing (with a smoothing kernel with a full width at half maximum (FWHM) of no more than two voxels) on fMRI data processing for correlation-based functional mapping Comparison with existing methodsIn extreme noiselessness, the correlation of scale-invariance property defines a meaningless binary tcorr map. In reality, a functional activity blob in a tcorr map is shaped due to the spoilage of image noise on correlative responses. We may reduce data noise level by smoothing processing, which poses a smoothing effect on correlation. This logic allows us to understand the noise dependence and the smoothing effect of correlation-based fMRI data analysis.

Micro-CT imaging of live insects using carbon dioxide gas-induced hypoxia as anesthetic with minimal impact on certain subsequent life history traits

BackgroundLive imaging of whole invertebrates can be accomplished with X-ray micro-computed tomography (micro-CT) at 10-100 μm spatial resolution. However, image quality could be compromised by the movement of live subjects, producing image artefacts. We tested the feasibility of using CO2 gas to induce temporary full-immobilization of sufficient duration to image live insects based on their ability to tolerate hypoxic conditions. Additionally, we investigated the effects of these prolonged hypoxic conditions on several life history traits of a lepidopteran species.MethodsLive Colorado potato beetle (CPB) and true armyworm (TAW) adults were immobilized under a constant CO2 gas flow (0.5 L/min), and scanned using micro-CT (80 kVp; 450 μA). An L8 (24) orthogonal array (OA) was used to evaluate the effects of prolonged CO2-induced anesthesia on the recovery, longevity, and incidence of mating of TAW adults. The variable factors were age (immature and mature), sex (female and male), exposure time (3 and 7 h), and exposure regime (single and repeated).ResultsWith this method, successful 3D reconstruction and visualizations of CPB and TAW adults were produced at 20 micron voxel spacing at an acceptable radiation dose and image noise level. From the inverse-square relationship found between the radiation doses and image noise levels, the optimal scanning protocol produced an entrance dose of 6.2 ± 0.04 Gy with images of 129.6 ± 5.1 HU noise level during a 2.7 h scan.Independent OA experiments indicated that CO2 gas did not result in death of exposed TAW adults, except when older males were exposed for longer durations. Exposure time and sex were more influential factors affecting recovery, longevity, and mating success than age and exposure regime following CO2 exposure.ConclusionWe have demonstrated that using CO2 gas during micro-CT imaging effectively induces safe, repeatable, whole-body, and temporary immobilization of live insects for 3D visualizations without motion artefacts. Moreover, we have shown that exposed TAW individuals made a full recovery with very little impact on subsequent longevity, and mating success post hypoxia. This method is applicable to other imaging modalities and could be used for routine exploratory and time-course studies, for repeated scanning of live and intact individuals.

Optimization of common CT protocols evaluating statistical measures on real patient images

The present work aimed to assess a methodology, in order to propose the minimal exposure levels that yield images suited to diagnostic purposes, for routine computed tomography (CT) studies in three hospitals in Havana, through the evaluation of image quality and exposure levels. The main novelty of this work is that quantitative measures of image quality were calculated over regions of interest on images of real CT studies, with the purpose of determining the influence of dose on image quality. The volume CT air kerma index (CVOL) was measured using the IAEA protocols, and the assessment of clinical criteria proposed in the guidelines was performed by experienced radiologists, which evidenced high potential for optimization. Coherence in the behaviors of statistical metrics determined on real patient images was proven, as well as their ability to compare different scanners in terms of image contrast and noise levels. Finally, the existence of a positive correlation between the statistical image quality measures and volume CT air kerma index values made it possible to determine optimal values of the latter quantity. These results are part of a larger project intended to establish Cuban diagnostic reference levels for CT, and the methodology proved to be readily implementable, as well as applicable to individual institutions.