Perceived Image Quality Research Articles

Luminance and Detail Enhancement for HDR Images Based on Surround-Aware Perceptual Quantization Under Ambient Illumination

The lighting conditions of mobile devices could severely affect the viewing experience of the displayed image, such as loss of details and contrast, especially for high dynamic range (HDR) contents. In this paper, a content independent luminance and detail enhancement method is proposed for HDR images under ambient illumination. The perceptual luminance at the given lighting conditions is first quantified based on the human visual adaption. Combining the quantization curve and the display model, the mapping operator for the luminance channel could then be efficiently constructed. Both psychophysical evaluation and objective measurement indicate that the proposed method significantly improves the perceptual quality of HDR images under ambient illumination.

Impact of artificial intelligence-based color constancy on dermoscopical assessment of skin lesions: A comparative study.

The quality of dermoscopic images is affected by lighting conditions, operator experience, and device calibration. Color constancy algorithms reduce this variability by making images appear as if they were acquired under the same conditions, allowing artificial intelligence (AI)-based methods to achieve better results. The impact of color constancy algorithms has not yet been evaluated from a clinical dermatologist's workflow point of view. Here we propose an in-depth investigation of the impact of an AI-based color constancy algorithm, called DermoCC-GAN, on the skin lesion diagnostic routine. Three dermatologists, with different experience levels, carried out two assignments. The clinical experts evaluated key parameters such as perceived image quality, lesion diagnosis, and diagnosis confidence. When the DermoCC-GAN color constancy algorithm was applied, the dermoscopic images were perceived to be of better quality overall. An increase in classification performance was observed, reaching a maximum accuracy of 74.67% for a six-class classification task. Finally, the use of normalized images results in an increase in the level of self-confidence in the qualitative diagnostic routine. From the conducted analysis, it is evident that the impact of AI-based color constancy algorithms, such as DermoCC-GAN, is positive and brings qualitative benefits to the clinical practitioner.

Reducing the risk of hallucinations with interpretable deep learning models for low-dose CT denoising: comparative performance analysis

Objective. Reducing CT radiation dose is an often proposed measure to enhance patient safety, which, however results in increased image noise, translating into degradation of clinical image quality. Several deep learning methods have been proposed for low-dose CT (LDCT) denoising. The high risks posed by possible hallucinations in clinical images necessitate methods which aid the interpretation of deep learning networks. In this study, we aim to use qualitative reader studies and quantitative radiomics studies to assess the perceived quality, signal preservation and statistical feature preservation of LDCT volumes denoised by deep learning. We aim to compare interpretable deep learning methods with classical deep neural networks in clinical denoising performance. Approach. We conducted an image quality analysis study to assess the image quality of the denoised volumes based on four criteria to assess the perceived image quality. We subsequently conduct a lesion detection/segmentation study to assess the impact of denoising on signal detectability. Finally, a radiomic analysis study was performed to observe the quantitative and statistical similarity of the denoised images to standard dose CT (SDCT) images. Main results. The use of specific deep learning based algorithms generate denoised volumes which are qualitatively inferior to SDCT volumes(p < 0.05). Contrary to previous literature, denoising the volumes did not reduce the accuracy of the segmentation (p > 0.05). The denoised volumes, in most cases, generated radiomics features which were statistically similar to those generated from SDCT volumes (p > 0.05). Significance. Our results show that the denoised volumes have a lower perceived quality than SDCT volumes. Noise and denoising do not significantly affect detectability of the abdominal lesions. Denoised volumes also contain statistically identical features to SDCT volumes.

Virtual reality simulation as a training tool for perfusionists in extracorporeal circulation: Establishing face and content validity.

We conducted a prospective study to assess the face and content validity of a new virtual reality (VR) extracorporeal circulation simulator (ECC) developed for perfusionists to facilitate training and practice. We evaluated the opinions of students and staff members about the feasibility of the simulation. The 2 groups consisted of experts (qualified perfusionists) and novices (trainee perfusionists). Perfusionists (n=12 experts and n=11 trainees) received instructions on how to use the VR simulator and then proceeded to perform the start of cardiopulmonary bypass in the VR environment. Participants then completed a Usefulness, Satisfaction, and Ease of Use Questionnaire. The questions were rated on a 5-point Likert scale, ranging from 1 (fully disagree) to 5 (fully agree), to assess the face validity and content validity of this simulator. Participants reported a predominantly positive experience with the VR-ECC simulator, with 96% (n=22) agreeing that the simulator was a useful way of training ECC scenarios. All participants found it easy to interact with the software (100%, n=23), and 82% of students (n=9) believed it helped them remember the steps involved with initiating ECC. Finally, (87% [n=20]) of participants believed the image quality and depth perception were good. Our next-generation simulator was valid for face and content constructs, and almost all participants found it to be a useful way of training for ECC scenarios. This simulator represents a first step toward truly blended digital learning and a new interactive, flexible, and innovative modality for perfusion training.

Improving Structural MRI Preprocessing with Hybrid Transformer GANs.

Magnetic resonance imaging (MRI) is a technique that is widely used in practice to evaluate any pathologies in the human body. One of the areas of interest is the human brain. Naturally, MR images are low-resolution and contain noise due to signal interference, the patient's body's radio-frequency emissions and smaller Tesla coil counts in the machinery. There is a need to solve this problem, as MR tomographs that have the capability of capturing high-resolution images are extremely expensive and the length of the procedure to capture such images increases by the order of magnitude. Vision transformers have lately shown state-of-the-art results in super-resolution tasks; therefore, we decided to evaluate whether we can employ them for structural MRI super-resolution tasks. A literature review showed that similar methods do not focus on perceptual image quality because upscaled images are often blurry and are subjectively of poor quality. Knowing this, we propose a methodology called HR-MRI-GAN, which is a hybrid transformer generative adversarial network capable of increasing resolution and removing noise from 2D T1w MRI slice images. Experiments show that our method quantitatively outperforms other SOTA methods in terms of perceptual image quality and is capable of subjectively generalizing to unseen data. During the experiments, we additionally identified that the visual saliency-induced index metric is not applicable to MRI perceptual quality assessment and that general-purpose denoising networks are effective when removing noise from MR images.

Poster Session: Perceived image quality of natural images through different bifocal corrections after adaptation to sharp or blur.

Simultaneous Vision, present in bifocal and multifocal contact or intraocular lenses, is a common clinical strategy for presbyopia. Neural adaptation is known to occur, but it is not predictable nor well understood. Previous works have studied the short-term neural adaptation to bifocal images using Adaptive Optics and digital convolutions of a face image (with pure defocus and simultaneous images) as adaptation images, and also as test images for perceptual judgments (Radhakrishnan et al., 2014). In this study, we use a programmable see-through head-mounted visual simulator (SimVis Gekko) to analyze the effect of neural adaptation on the perceived image quality of different bifocal corrections (BC) with different balances of energy between far and near, and for different transitions between adaptation and test. Eight subjects judged a video sequence of urban natural images, through two monofocal corrections (0 and 3D) and nine BCs (far/near balance from 90/10 to 10/90 and an addition of 3.00D), optically simulated monocularly. Two initial adaptation states of 10s were used: 0.00D-SHARP and 3.00D-BLUR, followed a transition from these states to the BC that can be ABRUPT (with 1, 5 or 30s of adaptation), or SMOOTH (linear transition lasting 1, 5 or 30s). Our results show a significant increase in perceptual quality after adaptation to BLUR, particularly for intermediate energy balances. However, subjects are quite insensitive to the type and duration of the transition.

Radiation dose reduction and image quality evaluation for lateral lumbar spine projection

PurposeOptimization studies in digital radiology help to reduce the radiological risk to patients and maximize the benefits associated with their clinical purpose. The aim of this study was to assess the optimization of lateral lumbar spine projection via a combination of exposure parameters adjustments and additional filtration using a sectional anthropomorphic phantom. Materials and methodsWe evaluated the effects of peak voltage, tube loading, and low-cost filters made of copper, titanium, brass, and nickel on both the perceived and physical quality of 125 radiographs obtained in a computer radiography system. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) with their Figure of Merit (FOM), based on the entrance surface air kerma with backscatter (ESAK), was used to assess physical image quality. ResultsThe standard image had a perceived image quality, SNR, FOMSNR, CNR, FOMCNR and ESAK of 3.4, 22.3, 386.4, 23.6, 433.7 and 1.28 mGy, respectively. Copper (90.3% purity) and titanium (95.0% purity) filters reduced ESAK by an average of 60% without compromising diagnostic quality, while brass and nickel filters increased dose under the conditions of the study. ConclusionsOur findings show that optimizing lumbar spine projection can reduce radiation dose without compromising image quality. Low-cost copper and titanium filters can be valuable in resource-limited settings. Further research can explore additional strategies for radiological optimization.

Konx: cross-resolution image quality assessment

Scale-invariance is an open problem in many computer vision subfields. For example, object labels should remain constant across scales, yet model predictions diverge in many cases. This problem gets harder for tasks where the ground-truth labels change with the presentation scale. In image quality assessment (IQA), down-sampling attenuates impairments, e.g., blurs or compression artifacts, which can positively affect the impression evoked in subjective studies. To accurately predict perceptual image quality, cross-resolution IQA methods must therefore account for resolution-dependent discrepancies induced by model inadequacies as well as for the perceptual label shifts in the ground truth. We present the first study of its kind that disentangles and examines the two issues separately via KonX, a novel, carefully crafted cross-resolution IQA database. This paper contributes the following: 1. Through KonX, we provide empirical evidence of label shifts caused by changes in the presentation resolution. 2. We show that objective IQA methods have a scale bias, which reduces their predictive performance. 3. We propose a multi-scale and multi-column deep neural network architecture that improves performance over previous state-of-the-art IQA models for this task. We thus both raise and address a novel research problem in image quality assessment.

Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.

To assess image quality and liver metastasis detection of reduced-dose dual-energy CT (DECT) with deep learning image reconstruction (DLIR) compared to standard-dose single-energy CT (SECT) with DLIR or iterative reconstruction (IR). In this prospective study, two groups of 40 participants each underwent abdominal contrast-enhanced scans with full-dose SECT (120-kVp images, DLIR and IR algorithms) or reduced-dose DECT (40- to 60-keV virtual monochromatic images [VMIs], DLIR algorithm), with 122 and 106 metastases, respectively. Groups were matched by age, sex ratio, body mass index, and cross-sectional area. Noise power spectrum of liver images and task-based transfer function of metastases were calculated to assess the noise texture and low-contrast resolution. The image noise, signal-to-noise ratios (SNR) of liver and portal vein, liver-to-lesion contrast-to-noise ratio (LLR), lesion conspicuity, lesion detection rate, and the subjective image quality metrics were compared between groups on 1.25-mm reconstructed images. Compared to 120-kVp images with IR, 40- and 50-keV VMIs with DLIR showed similar noise texture and LLR, similar or higher image noise and low-contrast resolution, improved SNR and lesion conspicuity, and similar or better perceptual image quality. When compared to 120-kVp images with DLIR, 50-keV VMIs with DLIR had similar low-contrast resolution, SNR, LLR, lesion conspicuity, and perceptual image quality but lower frequency noise texture and higher image noise. For the detection of hepatic metastases, reduced-dose DECT by 34% maintained observer lesion detection rates. DECT assisted with DLIR enables a 34% dose reduction for detecting hepatic metastases while maintaining comparable perceptual image quality to full-dose SECT. Reduced-dose dual-energy CT with deep learning image reconstruction is as accurate as standard-dose single-energy CT for the detection of liver metastases and saves more than 30% of the radiation dose. • The 40- and 50-keV virtual monochromatic images (VMIs) with deep learning image reconstruction (DLIR) improved lesion conspicuity compared with 120-kVp images with iterative reconstruction while providing similar or better perceptual image quality. • The 50-keV VMIs with DLIR provided comparable perceptual image quality and lesion conspicuity to 120-kVp images with DLIR. • The reduction of radiation by 34% by DLIR in low-keV VMIs is clinically sufficient for detecting low-contrast hepatic metastases.

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Objective. Human beings perceive stereoscopic image quality through the cerebral visual cortex, which is a complex brain activity. As a solution, the quality of stereoscopic images can be evaluated more accurately by attempting to replicate the human perception from electroencephalogram (EEG) signals on image quality in a machine, which is different from previous stereoscopic image quality assessment methods focused only on the extraction of image features. Approach. Our proposed method is based on a novel image-to-brain (I2B) cross-modality model including a spatial-temporal EEG encoder (STEE) and an I2B deep convolutional generative adversarial network (I2B-DCGAN). Specifically, the EEG representations are first learned by STEE as real samples of I2B-DCGAN, which is designed to extract both quality and semantic features from the stereoscopic images by a semantic-guided image encoder, and utilize a generator to conditionally create the corresponding EEG features for images. Finally, the generated EEG features are classified to predict the image perceptual quality level. Main results. Extensive experimental results on the collected brain-visual multimodal stereoscopic image quality ranking database, demonstrate that the proposed I2B cross-modality model can better emulate the visual perception mechanism of the human brain and outperform the other methods by achieving an average accuracy of 95.95. Significance. The proposed method can convert the learned stereoscopic image features into brain representations without EEG signals during testing. Further experiments verify that the proposed method has good generalization ability on new datasets and the potential for practical applications.

Unifying Dual-Attention and Siamese Transformer Network for Full-Reference Image Quality Assessment

Image Quality Assessment (IQA) is a critical task of computer vision. Most Full-Reference (FR) IQA methods have limitation in the accurate prediction of perceptual qualities of the traditional distorted images and the Generative Adversarial Networks (GANs) based distorted images. To address this issue, we propose a novel method by Unifying Dual-Attention and Siamese Transformer Network (UniDASTN) for FR-IQA. An important contribution is the spatial attention module composed of a Siamese Transformer Network and a feature fusion block. It can focus on significant regions and effectively maps the perceptual differences between the reference and distorted images to a latent distance for distortion evaluation. Another contribution is the dual-attention strategy that exploits channel attention and spatial attention to aggregate features for enhancing distortion sensitivity. In addition, a novel loss function is designed by jointly exploiting Mean Square Error (MSE), bidirectional Kullback–Leibler divergence, and rank order of quality scores. The designed loss function can offer stable training and thus enables the proposed UniDASTN to effectively learn visual perceptual image quality. Extensive experiments on standard IQA databases are conducted to validate the effectiveness of the proposed UniDASTN. The IQA results demonstrate that the proposed UniDASTN outperforms some state-of-the-art FR-IQA methods on the LIVE, CSIQ, TID2013, and PIPAL databases.

First experience of evaluation of the impact of high-matrix size reconstruction in image quality in arterial CT runoff studies of the lower extremities.

To determine whether image reconstruction with a higher matrix size improves image quality for lower extremity CTA studies. Raw data from 50 consecutive lower extremity CTA studies acquired on two MDCT scanners (SOMATOM Flash, Force) in patients evaluated for peripheral arterial disease (PAD) were retrospectively collected and reconstructed with standard (512 × 512) and higher resolution (768 × 768, 1024 × 1024) matrix sizes. Five blinded readers reviewed representative transverse images in randomized order (150 total). Readers graded image quality (0 (worst)-100 (best)) for vascular wall definition, image noise, and confidence in stenosis grading. Ten patients' stenosis scores on CTA images were compared to invasive angiography. Scores were compared using mixed effects linear regression. Reconstructions with 1024 × 1024 matrix were ranked significantly better for wall definition (mean score 72, 95% CI = 61-84), noise (74, CI = 59-88), and confidence (70, CI = 59-80) compared to 512 × 512 (wall = 65, CI = 53 × 77; noise = 67, CI = 52 × 81; confidence = 62, CI = 52 × 73; p = 0.003, p = 0.01, and p = 0.004, respectively). Compared to 512 × 512, the 768 × 768 and 1024 × 1024 matrix improved image quality in the tibial arteries (wall = 51 vs 57 and 59, p < 0.05; noise = 65 vs 69 and 68, p = 0.06; confidence = 48 vs 57 and 55, p < 0.05) to a greater degree than the femoral-popliteal arteries (wall = 78 vs 78 and 85; noise = 81 vs 81 and 84; confidence = 76 vs 77 and 81, all p > 0.05), though for the 10 patients with angiography accuracy of stenosis grading was not significantly different. Inter-reader agreement was moderate (rho = 0.5). Higher matrix reconstructions of 768 × 768 and 1024 × 1024 improved image quality and may enable more confident assessment of PAD. Higher matrix reconstructions of the vessels in the lower extremities can improve perceived image quality and reader confidence in making diagnostic decisions based on CTA imaging. • Higher than standard matrix sizes improve perceived image quality of the arteries in the lower extremities. • Image noise is not perceived as increased even at a matrix size of 1024 × 1024 pixels. • Gains from higher matrix reconstructions are higher in smaller, more distal tibial and peroneal vessels than in femoropopliteal vessels.

Underwater-image super-resolution via range-dependency learning of multiscale features

Optical imaging instruments have been widely deployed in underwater-engineering systems, playing an important role in underwater-object localization, detection, and recognition. However, underwater images suffer from the adverse effects of local distortion and global haze. Consequently, raw underwater images have limitations when used for display and vision tasks. Super resolution (SR) has been increasingly exploited for perceptual image quality improvement. However, underwater-image SR is a relatively underexplored area, most existing methods being unable to reduce the adverse effects of underwater images. Moreover, in contrast to land-based high-performance platforms, the power supply and computational resources of underwater platforms are limited, making them difficult to use in large-scale models. To solve these problems, this study developed a novel range-dependency learning network to present the short- and long-range dependency of multiscale features. Such a mechanism could provide more detailed and accurate texture information for underwater-image SR, improving underwater-image SR performance. Moreover, a channel-splitting module was designed to generate the channel bands which could extract texture details and global structural information at different scales while reducing the number of parameters, thus accelerating the training speed of the model and maintaining good performance. Our novel network could reach an optimal tradeoff between the underwater-image SR performance and efficiency, which was demonstrated by experimental comparisons and an ablation study.

Unsupervised Low-Light Image Enhancement Based on Generative Adversarial Network.

Low-light image enhancement aims to improve the perceptual quality of images captured under low-light conditions. This paper proposes a novel generative adversarial network to enhance low-light image quality. Firstly, it designs a generator consisting of residual modules with hybrid attention modules and parallel dilated convolution modules. The residual module is designed to prevent gradient explosion during training and to avoid feature information loss. The hybrid attention module is designed to make the network pay more attention to useful features. A parallel dilated convolution module is designed to increase the receptive field and capture multi-scale information. Additionally, a skip connection is utilized to fuse shallow features with deep features to extract more effective features. Secondly, a discriminator is designed to improve the discrimination ability. Finally, an improved loss function is proposed by incorporating pixel loss to effectively recover detailed information. The proposed method demonstrates superior performance in enhancing low-light images compared to seven other methods.

An Optimization-Based Family of Predictive, Fusion-Based Models for Full-Reference Image Quality Assessment.

Given the reference (distortion-free) image, full-reference image quality assessment (FR-IQA) algorithms seek to assess the perceptual quality of the test image. Over the years, many effective, hand-crafted FR-IQA metrics have been proposed in the literature. In this work, we present a novel framework for FR-IQA that combines multiple metrics and tries to leverage the strength of each by formulating FR-IQA as an optimization problem. Following the idea of other fusion-based metrics, the perceptual quality of a test image is defined as the weighted product of several already existing, hand-crafted FR-IQA metrics. Unlike other methods, the weights are determined in an optimization-based framework and the objective function is defined to maximize the correlation and minimize the root mean square error between the predicted and ground-truth quality scores. The obtained metrics are evaluated on four popular benchmark IQA databases and compared to the state of the art. This comparison has revealed that the compiled fusion-based metrics are able to outperform other competing algorithms, including deep learning-based ones.

MDNet: A Fusion Generative Adversarial Network for Underwater Image Enhancement

Underwater images are widely used in ocean resource exploration and ocean environment surveillance. However, due to the influence of light attenuation and noise, underwater images usually display degradation phenomena such as blurring and color deviation; an enhancement method is required to make the images more visible. Currently, there are two major approaches for image enhancement: the traditional methods based on physical or non-physical models, and the deep learning method. Inspired by the fusion-based idea, this paper attempts to combine traditional methods with deep learning and proposes a multi-input dense connection generator network (MDNet) for underwater image enhancement. Raw images and processed images are input into the network together, the shallow information is fully utilized by dense connection, and the network is trained in generative and adversarial manner. We also design a multiple loss function to improve the visual quality of the generated images. We conduct both qualitative and quantitative experiments, and then compare the results with state-of-the-art approaches comprehensively using three representative datasets. Results show that the proposed method can effectively improve the perceptual and statistical quality of underwater images.

87‐4: Invited Paper: Specular‐Free Surface for Excellent Image Quality under a Bright Environment

In this paper, we propose specular‐free (SF) surface to provide excellent image quality under a bright environment. By optimizing the anti‐glare (AG) layer and low‐reflection layer, specular reflection and diffuse reflection reduced up to 78% and 25%, respectively, compared to conventional AG. Using a SF technique, it is possible to remove the inconvenience of glare and improve the perceptual image quality.

Spatial and directional contrast dependence in Lissajous-scanning projection systems.

Scanning-based image formation fundamentally differs from its classical lens-based counterpart. Therefore, established classical performance evaluation methods fail to determine the theoretical limitations of scanning-based optical systems. We developed a simulation framework and a novel performance evaluation process to evaluate the achievable contrast in scanning systems. Applying these tools, we conducted a study determining the resolution limits of different Lissajous scanning approaches. For the first time, we identify and quantify spatial and directional dependencies of the optical contrast and demonstrate their significant impact on the perceived image quality. We prove that the observed effects are more pronounced for Lissajous systems with high ratios of the two scanning frequencies. The presented method and results can lay the foundation for a more sophisticated application-specific design of next-generation scanning systems.

Dual-energy CT iodine overlay improves efficiency of oral contrast leak assessment.

Evaluation for gastrointestinal leak is a frequent imaging indication, and dual-energy computed tomography (DECT) with oral or rectally administered contrast can be used to improve efficiency and diagnostic confidence. To assess the value of the DECT iodine overlay (IO) reconstruction as a stand-alone image set compared to routine CT in assessing oral or rectal contrast leak from the gastrointestinal system. A blinded, retrospective audit study was performed by three readers who each interpreted 50 studies performed for assessment of oral or rectal contrast leak that were acquired using DECT. Each reader independently assessed both the routine CT images and the images of the reconstructed IO for contrast leak in random order with a six-week "wash-out period" between readings. Clinical follow-up provided the reference standard. Readers recorded the presence/absence of a leak, diagnostic confidence, image quality score, and interpretation time for each image set. Pooled data for overall accuracy in identification of a leak increased from 0.81 (95% confidence interval [CI]=0.74-0.87) for routine CT to 0.91 (95% CI=0.85-0.95) with IO, and the area under the curve (AUC) was significantly higher for IO than routine CT (P = 0.015). Readers required significantly less time to interpret IO than routine CT (median improvement of 12.5 s per image using pooled data; P < 0.001) while maintaining diagnostic confidence and perceived image quality. Use of DECT IO reconstructions for identification of oral or rectal contrast leak requires less time to interpret than routine CT with improved accuracy and maintained diagnostic confidence and perceived image quality.

A comparison of perceived image quality between computer display monitors and augmented reality smart glasses

IntroductionAugmented-reality (AR) smart glasses provide an alternative to standard computer display monitors (CDM). AR smart glasses may provide an opportunity to improve visualisation during fluoroscopy and interventional radiology (IR) procedures when there can be difficulty in viewing intra-procedural images on a CDM. The aim of this study was to evaluate radiographer perception of image quality (IQ) when comparing CDM and AR smart glasses. Methods38 radiographers attending an international congress evaluated ten fluoroscopic-guided surgery and IR images on both a CDM (1920 × 1200 pixels) and a set of Epson Moverio BT-40 AR smart glasses (1920 × 1080 pixels). Participants provided oral responses to pre-defined IQ questions generated by study researchers. Summative IQ scores for each participant/image were compared between CDM and AR smart glasses. ResultsOf the 38 participants, the mean age was 39 ± 1 years. 23 (60.5%) participants required corrective glasses. In terms of generalisability, participants were from 12 different countries, the majority (n = 9, 23.7%) from the United Kingdom. For eight out of ten images, the AR smart glasses demonstrated a statistically significant increase in perceived IQ (median [IQR] 2.0 [-1.0 to 7.0] points) when compared to the CDM. ConclusionAR smart glasses appear to show improvements in perceived IQ when compared to a CDM. AR smart glasses could provide an option for improving the experiences of radiographers involved in image-guided procedures and should be subject to further clinical evaluations. Implications for practiceOpportunities exist to improve perceived IQ for radiographers when reviewing fluoroscopy and IR images. AR smart glasses should be further evaluated as a potential opportunity to improve practice when visual attention is split between positioning equipment and image review.