Objective Image Quality Measures Research Articles

Assessing the Ability of Generative Adversarial Networks to Learn Canonical Medical Image Statistics.

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for potential applications in medical imaging, such as medical image synthesis, restoration, reconstruction, translation, as well as objective image quality assessment. Despite the impressive progress in generating high-resolution, perceptually realistic images, it is not clear if modern GANs reliably learn the statistics that are meaningful to a downstream medical imaging application. In this work, the ability of a state-of-the-art GAN to learn the statistics of canonical stochastic image models (SIMs) that are relevant to objective assessment of image quality is investigated. It is shown that although the employed GAN successfully learned several basic first- and second-order statistics of the specific medical SIMs under consideration and generated images with high perceptual quality, it failed to correctly learn several per-image statistics pertinent to the these SIMs, highlighting the urgent need to assess medical image GANs in terms of objective measures of image quality.

Feasibility of accelerated non-contrast-enhanced whole-heart bSSFP coronary MR angiography by deep learning-constrained compressed sensing.

To examine a compressed sensing artificial intelligence (CSAI) framework to accelerate image acquisition in non-contrast-enhanced whole-heart bSSFP coronary magnetic resonance (MR) angiography. Thirty healthy volunteers and 20 patients with suspected coronary artery disease (CAD) scheduled for coronary computed tomography angiography (CCTA) were enrolled. Non-contrast-enhanced coronary MR angiography was performed with CSAI, compressed sensing (CS), and sensitivity encoding (SENSE) methods in healthy participants and with CSAI in patients. Acquisition time, subjective image quality score, and objective image quality measurement (blood pool homogeneity, signal-to-noise ratio [SNR], and contrast-to-noise ratio [CNR]) were compared among the three protocols. The diagnostic performance of CASI coronary MR angiography for predicting significant stenosis (≥ 50% diameter stenosis) on CCTA was evaluated. The Friedman test was performed to compare the three protocols. Acquisition time was significantly shorter in the CSAI and CS groups than in the SENSE group (10.2 ± 3.2min vs. 10.9 ± 2.9min vs. 13.0 ± 4.1min, p < 0.001). However, the CSAI approach had the highest image quality scores, blood pool homogeneity, mean SNR value, and mean CNR value (all p < 0.001) compared with the CS and SENSE approaches. The sensitivity, specificity, and accuracy of CSAI coronary MR angiography per patient were 87.5% (7/8), 91.7% (11/12), and 90.0% (18/20); those per vessel were 81.8% (9/11), 93.9% (46/49), and 91.7% (55/60); and those per segment were 84.6% (11/13), 98.0% (244/249), and 97.3% (255/262), respectively. CSAI yielded superior image quality within a clinically feasible acquisition time in healthy participants and patients with suspected CAD. The non-invasive and radiation-free CSAI framework could be a promising tool for rapid screening and comprehensive examination of the coronary vasculature in patients with suspected CAD. • This prospective study showed that CSAI enables a reduction in acquisition time by 22% with superior diagnostic image quality compared with the SENSE protocol. • CSAI replaces the wavelet transform with a CNN as a sparsifying transform in the CS algorithm, achieving high coronary MR image quality with reduced noise. • CSAI achieved per-patient sensitivity of 87.5% (7/8) and specificity of 91.7% (11/12) respectively for detecting significant coronary stenosis.

High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses

To explore the use of 70-kVp tube voltage combined with high-strength deep learning image reconstruction (DLIR-H) in reducing radiation and contrast doses in coronary CT angiography (CCTA) in patients with body mass index (BMI) < 26kg/m2, in comparison with the conventional scan protocol using 120 kVp and adaptive statistical iterative reconstruction (ASIR-V). A total of 100 patients referred to CCTA were prospectively enrolled and randomly divided into two groups: low-dose group (n = 50) with 70 kVp, Smart mA for noise index (NI) of 36HU, contrast dose rate of 16mgI/kg/s, and DLIR-H, and conventional group (n = 50) with 120kV, Smart mA for NI of 25HU, contrast dose rate of 32mgI/kg/s, and 60%ASIR-V. Radiation and contrast dose, subjective image quality score, and objective image quality measurement (image noise, contrast-noise-ratio (CNR), and signal-noise-ratio (SNR) for vessel) were compared between the two groups. Low-dose group used significantly reduced contrast dose (23.82 ± 3.69mL, 50.6% reduction) and radiation dose (0.75 ± 0.14mSv, 54.5% reduction) compared to the conventional group (48.23 ± 6.38mL and 1.65 ± 0.66mSv, respectively) (all p < 0.001). Both groups had similar enhancement in vessels. However, the low-dose group had lower background noise (23.57 ± 4.74 HU vs. 35.04 ± 8.41 HU), higher CNR in RCA (48.63 ± 10.76 vs. 29.32 ± 5.52), LAD (47.33 ± 10.20 vs. 29.27 ± 5.12), and LCX (46.74 ± 9.76 vs. 28.58 ± 5.12) (all p < 0.001) compared to the conventional group. The use of 70-kVp tube voltage combined with DLIR-H for CCTA in normal size patients significantly reduces radiation dose and contrast dose while further improving image quality compared with the conventional 120-kVp tube voltage with 60%ASIR-V. •The combination of 70-kVp tube voltage and high-strength deep learning image reconstruction (DLIR-H) algorithm protocol reduces approximately 50% of radiation and contrast doses in coronary computed tomography angiography (CCTA) compared with the conventional scan protocol. •CCTA of normal size (BMI < 26kg/m2) patients acquired at sub-mSv radiation dose and 24mL contrast dose through the combination of 70-kVp tube voltage and DLIR-H algorithm achieves excellent diagnostic image quality with a good inter-rater agreement. •DLIR-H algorithm shows a higher capacity of significantly reducing image noise than adaptive statistical iterative reconstruction algorithm in CCTA examination.

3-D Stochastic Numerical Breast Phantoms for Enabling Virtual Imaging Trials of Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is an emerging imaging modality for breast imaging that can produce quantitative images that depict the acoustic properties of tissues. Computer-simulation studies, also known as virtual imaging trials, provide researchers with an economical and convenient route to systematically explore imaging system designs and image reconstruction methods. When simulating an imaging technology intended for clinical use, it is essential to employ realistic numerical phantoms that can facilitate the objective, or task-based, assessment of image quality (IQ). Moreover, when computing objective IQ measures, an ensemble of such phantoms should be employed, which displays the variability in anatomy and object properties that are representative of the to-be-imaged patient cohort. Such stochastic phantoms for clinically relevant applications of USCT are currently lacking. In this work, a methodology for producing realistic 3-D numerical breast phantoms for enabling clinically relevant computer-simulationstudies of USCT breast imaging is presented. By extending and adapting an existing stochastic 3-D breast phantom for use with USCT, methods for creating ensembles of numerical acoustic breast phantoms are established. These breast phantoms will possess clinically relevant variations in breast size, composition, acoustic properties, tumor locations, and tissue textures. To demonstrate the use of the phantoms in virtual USCT studies, two brief case studies are presented, which addresses the development and assessment of image reconstruction procedures. Examples of breast phantoms produced by use of the proposed methods and a collection of 52 sets of simulated USCT measurement data have been made open source for use in image reconstruction development.

Impact of deep learning-based image super-resolution on binary signal detection.

Purpose: Deep learning-based image super-resolution (DL-SR) has shown great promise in medical imaging applications. To date, most of the proposed methods for DL-SR have only been assessed using traditional measures of image quality (IQ) that are commonly employed in the field of computer vision. However, the impact of these methods on objective measures of IQ that are relevant to medical imaging tasks remains largely unexplored. We investigate the impact of DL-SR methods on binary signal detection performance. Approach: Two popular DL-SR methods, the super-resolution convolutional neural network and the super-resolution generative adversarial network, were trained using simulated medical image data. Binary signal-known-exactly with background-known-statistically and signal-known-statistically with background-known-statistically detection tasks were formulated. Numerical observers (NOs), which included a neural network-approximated ideal observer and common linear NOs, were employed to assess the impact of DL-SR on task performance. The impact of the complexity of the DL-SR network architectures on task performance was quantified. In addition, the utility of DL-SR for improving the task performance of suboptimal observers was investigated. Results: Our numerical experiments confirmed that, as expected, DL-SR improved traditional measures of IQ. However, for many of the study designs considered, the DL-SR methods provided little or no improvement in task performance and even degraded it. It was observed that DL-SR improved the task performance of suboptimal observers under certain conditions. Conclusions: Our study highlights the urgent need for the objective assessment of DL-SR methods and suggests avenues for improving their efficacy in medical imaging applications.

A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

Partial differential equation based anisotropic diffusion techniques are used extensively in computer vision for image enhancement and edge detection. Anisotropic Diffusion which is found to be a low computational complexity approach which has overcome the undesirable effects of linear smoothing filters and now is popular in prominent research areas of enhancing the quality of low contrast images and speckle noise reduction from geological, industrial and medical images. This paper presents a comprehensive survey on various state-of-the-art anisotropic diffusion techniques for image enhancement. The capability of anisotropic diffusion for enhancing the quality of low contrast images and speckle noise reduction from medical images are further explored. Various objective image quality measures are studied which are used to validate the performance of enhancement approaches. The major research issuesand possible future scopes in this diffusion filtering approach are also discussed.

Determination of a cone-beam CT low-dose protocol for root fracture diagnosis in non-endodontically treated anterior maxillary teeth.

The aim of this study was to determine a "low-dose protocol" which provides acceptable diagnostic accuracy for detection of root fractures in unrestored anterior maxillary teeth, using an ex vivo model. 48 maxillary anterior teeth, half with horizontal or oblique root fractures, were imaged using CBCT in an anthropomorphic model. Nine X-ray exposure combinations were used, including the manufacturer's standard ("reference") exposure and high-resolution settings ("HiRes"), by varying kV, exposure time, and rotation angle. Measurements of Dose Area Product (DAP) were recorded. Five dental radiologists assessed the scans for root fractures and judged image quality. Parameters of diagnostic accuracy were calculated, including area under the Receiver Operating Characteristic curve (Az). Objective measures of image quality were made at the same exposure combinations using an image quality phantom. Although there was a significant linear relationship between DAP and mean Az, only the lowest DAP exposure combination had a mean Az significantly different to the reference exposure. There was no significant effect on other diagnostic accuracy parameters when using HiRes compared with the reference exposure. There was a significant positive relationship between DAP and contrast resolution. HiRes did not significantly improve contrast resolution and made a small improvement to spatial resolution. Scope existed for radiation dose reduction compared with the manufacturer's guidance. There was no improvement in diagnostic accuracy using HiRes settings. A cautious recommendation for this CBCT machine is that it is possible to achieve a dose reduction of about 20% compared with the reference exposure parameters.

Comparison of 2D and 3D quiescent-interval slice-selective non-contrast MR angiography in patients with peripheral artery disease.

To evaluate the potential clinical benefit of the superior spatial resolution of 3D prototype thin-slab stack-of-stars (tsSOS) quiescent-interval slice-selective (QISS) MRA over standard 2D-QISS MRA for the detection peripheral artery disease (PAD), using computed tomography angiography (CTA) as reference. Twenty-three patients (70 ± 8 years, 18 men) with PAD who had previously undergone run-off CTA were prospectively enrolled. Patients underwent non-contrast MRA using 2D-QISS and tsSOS-QISS at 1.5 T. Eighteen arterial segments were evaluated for subjective and objective image quality (normalized signal-to-noise, nSNR), vessel sharpness, and area under the curve (AUC) for > 50% stenosis detection. Overall subjective image quality ratings for the entire run-off were not different between tsSOS-QISS and 2D-QISS (3 [3; 4] vs 4 [3; 4], respectively; P = 0.813). Sharpness of primary branch vessels demonstrated improved image quality using tsSOS-QISS compared with 2D-QISS (4 [3; 4] vs 3 [2; 3], P = 0.008). Objective image quality measures were not different between 2D-QISS and tsSOS-QISS (nSNR 5.0 ± 1.9 vs 4.2 ± 1.8; P = 0.132). AUCs for significant stenosis detection by tsSOS-QISS and 2D-QISS were 0.877 and 0.856, respectively (P = 0.336). The prototype 3D tsSOS-QISS technique provides similar accuracy in patients with PAD to a standard commercially available 2D-QISS technique, indicating that the use of relatively thick slices does not limit the diagnostic performance of 2D-QISS. However, subjective image quality for branch vessel depiction is improved using the 3D approach.

Evaluation of Golden-Angle-Sampled Dynamic Contrast-Enhanced MRI Reconstruction Using Objective Image Quality Measures: A Simulated Phantom Study.

We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.

Correlation of objective image quality and working length measurements in different CBCT machines: An ex vivo study

To investigate potential correlations between objective CBCT image parameters and accuracy in endodontic working length determination ex vivo. Contrast-to-noise ratio (CNR) and spatial resolution (SR) as fundamental objective image parameters were examined using specific phantoms in seven different CBCT machines. Seven experienced observers were instructed and calibrated. The order of the CBCTs was randomized for each observer and observation. To assess intra-operator reproducibility, the procedure was repeated within six weeks with a randomized order of CBCT images. Multivariate analysis (MANOVA) did not reveal any influence of the combined image quality factors CNR and SR on measurement accuracy. Inter-operator reproducibility as assessed between the two observations was poor, with a mean intra-class correlation (ICC) of 0.48 (95%-CI 0.38, 0.59) for observation No. 1. and 0.40 (95%-CI 0.30, 0.51) for observation No. 2. Intra-operator reproducibility pooled over all observers between both observations was only moderate, with a mean ICC of 0.58 (95%-CI 0.52 to 0.64). Within the limitations of the study, objective image quality measures and exposure parameters seem not to have a significant influence on accuracy in determining endodontic root canal lengths in CBCT scans. The main factor of variance is the observer.

Image-quality assessment for full-disk solar observations with generative adversarial networks

Context. In recent decades, solar physics has entered the era of big data and the amount of data being constantly produced from ground- and space-based observatories can no longer be purely analyzed by human observers. Aims. In order to assure a stable series of recorded images of sufficient quality for further scientific analysis, an objective image-quality measure is required. Especially when dealing with ground-based observations, which are subject to varying seeing conditions and clouds, the quality assessment has to take multiple effects into account and provide information about the affected regions. The automatic and robust identification of quality-degrading effects is critical for maximizing the scientific return from the observations and to allow for event detections in real time. In this study, we develop a deep-learning method that is suited to identify anomalies and provide an image-quality assessment of solar full-disk Hα filtergrams. The approach is based on the structural appearance and the true image distribution of high-quality observations. Methods. We employ a neural network with an encoder–decoder architecture to perform an identity transformation of selected high-quality observations. The encoder network is used to achieve a compressed representation of the input data, which is reconstructed to the original by the decoder. We use adversarial training to recover truncated information based on the high-quality image distribution. When images of reduced quality are transformed, the reconstruction of unknown features (e.g., clouds, contrails, partial occultation) shows deviations from the original. This difference is used to quantify the quality of the observations and to identify the affected regions. In addition, we present an extension of this architecture that also uses low-quality samples in the training step. This approach takes characteristics of both quality domains into account, and improves the sensitivity for minor image-quality degradation. Results. We apply our method to full-disk Hα filtergrams from the Kanzelhöhe Observatory recorded during 2012−2019 and demonstrate its capability to perform a reliable image-quality assessment for various atmospheric conditions and instrumental effects. Our quality metric achieves an accuracy of 98.5% in distinguishing observations with quality-degrading effects from clear observations and provides a continuous quality measure which is in good agreement with the human perception. Conclusions. The developed method is capable of providing a reliable image-quality assessment in real time, without the requirement of reference observations. Our approach has the potential for further application to similar astrophysical observations and requires only coarse manual labeling of a small data set.

An Ultrasound Image Enhancement Method Using Neutrosophic Similarity Score.

Ultrasound images, having low contrast and noise, adversely impact in the detection of abnormalities. In view of this, an enhancement method is proposed in this work to reduce noise and improve contrast of ultrasound images. The proposed method is based on scaling with neutrosophic similarity score (NSS), where an image is represented in the neutrosophic domain through three membership subsets T, I, and F denoting the degree of truth, indeterminacy, and falseness, respectively. The NSS measures the belonging degree of pixel to the texture using multi-criteria that is based on intensity, local mean intensity and edge detection. Then, NSS is utilized to extract the enhanced coefficient and this enhanced coefficient is applied to scale the input image. This scaling reflects contrast improvement and denoising effect on ultrasound images. The performance of proposed enhancement method is evaluated on clinical ultrasound images, using both subjective and objective image quality measures. In subjective evaluation, with proposed method, overall best score of 4.3 was obtained and that was 44% higher than the score of original images. These results were also supported by objective measures. The results demonstrated that the proposed method outperformed the other methods in terms of mean brightness preservation, edge preservation, structural similarity, and human perception-based image quality assessment. Thus, the proposed method can be used in computer-aided diagnosis systems and to visually assist radiologists in their interactive-decision-making task.

Objective Image Quality Measures for Disparity Maps Evaluation

The evaluation of disparity (range) maps includes the selection of an objective image quality (or error) measure. Among existing measures, the percentage of bad matched pixels is commonly used. However, it requires a disparity error tolerance and ignores the relationship between range and disparity. In this research, twelve error measures are characterized in order to provide the bases to select accurate stereo algorithms during the evaluation process. Adaptations of objective quality measures for disparity maps’ accuracy evaluation are proposed. The adapted objective measures operate in a manner similar to the original objective measures, but allow special handling of missing data. Additionally, the adapted objective measures are sensitive to errors in range and surface structure, which cannot be measured using the bad matched pixels. Their utility was demonstrated by evaluating a set of 50 stereo disparity algorithms known in the literature. Consistency evaluation of the proposed measures was performed using the two conceptually different stereo algorithm evaluation methodologies—ordinary ranking and partition and grouping of the algorithms with comparable accuracy. The evaluation results showed that partition and grouping make a fair judgment about disparity algorithms’ accuracy.

Approximating the Ideal Observer for Joint Signal Detection and Localization Tasks by use of Supervised Learning Methods.

Medical imaging systems are commonly assessed and optimized by use of objective measures of image quality (IQ). The Ideal Observer (IO) performance has been advocated to provide a figure-of-merit for use in assessing and optimizing imaging systems because the IO sets an upper performance limit among all observers. When joint signal detection and localization tasks are considered, the IO that employs a modified generalized likelihood ratio test maximizes observer performance as characterized by the localization receiver operating characteristic (LROC) curve. Computations of likelihood ratios are analytically intractable in the majority of cases. Therefore, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) techniques have been developed to approximate the likelihood ratios. However, the applications of MCMC methods have been limited to relatively simple object models. Supervised learning-based methods that employ convolutional neural networks have been recently developed to approximate the IO for binary signal detection tasks. In this paper, the ability of supervised learning-based methods to approximate the IO for joint signal detection and localization tasks is explored. Both background-known-exactly and background-known-statistically signal detection and localization tasks are considered. The considered object models include a lumpy object model and a clustered lumpy model, and the considered measurement noise models include Laplacian noise, Gaussian noise, and mixed Poisson-Gaussian noise. The LROC curves produced by the supervised learning-based method are compared to those produced by the MCMC approach or analytical computation when feasible. The potential utility of the proposed method for computing objective measures of IQ for optimizing imaging system performance is explored.

Image Quality Assessment: Unifying Structure and Texture Similarity.

Objective measures of image quality generally operate by comparing pixels of a "degraded" image to those of the original. Relative to human observers, these measures are overly sensitive to resampling of texture regions (e.g., replacing one patch of grass with another). Here, we develop the first full-reference image quality model with explicit tolerance to texture resampling. Using a convolutional neural network, we construct an injective and differentiable function that transforms images to multi-scale overcomplete representations. We demonstrate empirically that the spatial averages of the feature maps in this representation capture texture appearance, in that they provide a set of sufficient statistical constraints to synthesize a wide variety of texture patterns. We then describe an image quality method that combines correlations of these spatial averages ("texture similarity") with correlations of the feature maps ("structure similarity"). The parameters of the proposed measure are jointly optimized to match human ratings of image quality, while minimizing the reported distances between subimages cropped from the same texture images. Experiments show that the optimized method explains human perceptual scores, both on conventional image quality databases, as well as on texture databases. The measure also offers competitive performance on related tasks such as texture classification and retrieval. Finally, we show that our method is relatively insensitive to geometric transformations (e.g., translation and dilation), without use of any specialized training or data augmentation. Code is available at https://github.com/dingkeyan93/DISTS.

Dual-energy CT for routine imaging of the abdomen and pelvis: radiation dose and image quality.

To assess the radiation dose and image quality of routine dual energy CT (DECT) of the abdomen and pelvis performed in the emergency department setting, compared with single energy CT (SECT). Seventy-five consecutive routine contrast-enhanced SECT scans of the abdomen and pelvis meeting inclusion criteria were compared with 75 routine contrast-enhanced DECT scans matched by size and patient weight (within 10 lbs), performed on the same dual-source DECT scanner. Cohorts were compared in terms of radiation dose metrics of CT dose index (CTDIvol) and dose length product (DLP), objective measurements of image quality (signal, noise, and signal-to-noise ratio of a variety of anatomical landmarks), and subjective measurements of image quality scored by two emergency radiologists. Demographics and patient size were not statistically different between DECT and SECT cohorts. Both average scans CTDIvol and DLP were significantly lower with DECT than with SECT. Average scan CTDIvol for SECT was 14.7 mGy (± 6.6) and for DECT was 10.9 mGy (± 3.8) (p < 0.0001). Average scan DLP for SECT was 681.5 mGy cm (± 339.3) and for DECT was 534.8 mGy cm (± 201.9) (p < 0.0001). For objective image quality metrics, for all structures measured, noise was significantly lower and SNR was significantly higher with DECT compared with SECT. For subjective image quality, for both readers, there was no significant difference between SECT and DECT in subjective image quality for soft tissues and vascular structures, or for subjective image noise. DECT was performed with decreased radiation dose when compared with SECT, demonstrated improved objective measurements of image quality, and equivalent subjective image quality.

Quality assessment of fusing Sentinel-2 and WorldView-4 imagery on Sentinel-2 spectral band values: a case study of Zagreb, Croatia

ABSTRACT Image fusion methods aim at fusing low resolution and high-resolution image to obtain a new image that provides new information for the specific application. The main goal of this article is multitemporal Sentinel-2 image fusion using single WorldView-4 satellite image for urban area monitoring. Fusing those images should provide Sentinel-2 image with similar radiometric band value as original Sentinel-2 image, but with a spatial resolution of WorldView-4. Ehlers, Brovey Transform, Modified Intensity-Hue-Saturation, High-Pass Filtering, Hyperspherical Colour Space and Wavelet resolution merge fusion techniques were used for spatial enhancement of Sentinel-2 images. Original and fused images were first compared using standard statistical parameters, mean, median and standard deviation. Image quality analysis was conducted with different objective image quality measures like root mean square error, peak signal to noise ratio, universal image quality index, structural similarity index, relative dimensionless global error, spatial correlation coefficient, relative average spectral error, spectral angle mapper, multi-scale structural similarity index. Using these quality measures helped in determining the spectral and spatial preservation of fused images. Hyperspherical colour space method was selected as the best method for image fusion of Sentinel-2 and WorldView-4 image-based on standard statistical parameters and quality measures.

The use of 80 kV versus 100 kV in pulmonary CT angiography: An evaluation of the impact on radiation dose and image quality on two CT scanners

IntroductionUse of CT in the investigation of pulmonary embolism in radiosensitive patients such as pregnant and young female patients entails the need for protocol optimization. The aim of this study was to analyze the dose reduction and image quality achieved by using 80 kV instead of 100 kV in CT pulmonary angiography protocols. Methods80 examinations of non-obese patients were analyzed (40 consecutive patients for each protocol, equally distributed on two CT scanners). Objective image quality was assessed by measurements of HU values (average and standard deviation) in five ROIs in pulmonary arteries and calculations of signal-to-noise (SNR) and contrast-to-noise ratios (CNR). Subjective image quality was independently evaluated by two radiologists in terms of perceived noise, sharp reproduction of pulmonary arteries and overall diagnostic quality. Radiation dose parameters (CTDIvol, DLP, SSDE and effective dose) and effective risk were compared. Differences in radiation dose and objective measures of image quality for the two protocols were assessed using the independent t test; comparison of subjective grading of image quality was performed with the Mann–Whitney U test. ResultsUse of 80 kV significantly increased both arterial contrast enhancement and image noise. Differences in SNR and CNR between protocols were not statistically significant. Achieved dose reduction by using 80 kV was significant on both scanners (SSDE reduction 35% and 46%, p < 0.001; effective dose reduction 40% and 53%, p < 0.001). ConclusionUse of 80 kV protocols for CT examinations of pulmonary arteries in non-obese patients with bodyweight below 80 kg results in significant reduction of radiation doses without compromising image quality.

Quality control for engineers

In 2011 ‘Quality Control Measurements for digital X-ray detectors’ was published by Marshall, Mackenzie and Honey. These outlined a series of objective measurements that could be done in a short period of time and with limited equipment that would pick up most foreseeable issues with image quality. Obtaining these images could be done by service engineers on routine service visits thereby tripling the frequency with which certain measurements are taken (potentially reducing the number of patients imaged on equipment that has failed a QC test). A program written in Matlab for engineers will be presented which analyses; uniformity, SNR, STP curve, dark noise, image lag (ghosting), MTF, NPS and DQE. Initial results compared to manufacturer specifications for both mammography and conventional DR imaging detectors will be presented. Objective image quality measurements benefit from highly reproducible and not operator dependent, which will hopefully increase confidence in the validity of these reports. The algorithms used in the program and potential collaboration will be discussed as well as pitfalls- particularly around NPS measurements. In 2011 ‘Quality Control Measurements for digital X-ray detectors’ was published by Marshall, Mackenzie and Honey. These outlined a series of objective measurements that could be done in a short period of time and with limited equipment that would pick up most foreseeable issues with image quality. Obtaining these images could be done by service engineers on routine service visits thereby tripling the frequency with which certain measurements are taken (potentially reducing the number of patients imaged on equipment that has failed a QC test). A program written in Matlab for engineers will be presented which analyses; uniformity, SNR, STP curve, dark noise, image lag (ghosting), MTF, NPS and DQE. Initial results compared to manufacturer specifications for both mammography and conventional DR imaging detectors will be presented. Objective image quality measurements benefit from highly reproducible and not operator dependent, which will hopefully increase confidence in the validity of these reports. The algorithms used in the program and potential collaboration will be discussed as well as pitfalls- particularly around NPS measurements.

Heavily Calcified Coronary Arteries: Advanced Calcium Subtraction Improves Luminal Visualization and Diagnostic Confidence in Dual-Energy Coronary Computed Tomography Angiography.

The aim of this study was to evaluate a prototype dual-energy computed tomography calcium subtraction algorithm and its impact on luminal visualization in patients with heavily calcified coronary arteries. Twenty-nine patients (62% male; mean age, 64 ± 7 years) who had undergone dual-energy coronary computed tomography angiography were retrospectively included in this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study. Linearly blended (M_0.6) and calcium-subtracted images were reconstructed. Two independent observers assessed luminal visualization of the coronary arteries in a segment-based analysis, subjective image quality, and diagnostic confidence using 5-point Likert scales. Contrast-to-noise ratios for both data sets were calculated. Wilcoxon testing and Cohen's κ were used for statistical comparisons. Calcium-subtracted image series showed improved lumen visualization of the coronary arteries (P = 0.008), with excellent interreader agreement (mean score, 3.3; κ = 0.82), compared with M_0.6 series (mean score, 2.9; κ = 0.77). The calcium subtraction algorithm improved diagnostic confidence compared with the M_0.6 reconstructions (mean scores, 4.0 and 3.1, respectively; all P ≤ 0.002). The image quality analysis showed no significant differences between calcium-subtracted and M_0.6 data sets (subjectively: mean scores, 4.1 and 4.2, respectively, P = 0.442; objectively: mean contrast-to-noise ratio, 37.0 and 38.2, respectively, P = 0.733). A prototype algorithm for calcium subtraction improves coronary lumen visualization and diagnostic confidence in patients with heavy coronary calcifications without differences in conventional subjective and objective measures of image quality.