Showing papers on "Contrast (vision) published in 2022"
TL;DR: Li et al. as discussed by the authors proposed an efficient and robust underwater image enhancement method, called MLLE, which adjusts the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy.
Abstract: Underwater images typically suffer from color deviations and low visibility due to the wavelength-dependent light absorption and scattering. To deal with these degradation issues, we propose an efficient and robust underwater image enhancement method, called MLLE. Specifically, we first locally adjust the color and details of an input image according to a minimum color loss principle and a maximum attenuation map-guided fusion strategy. Afterward, we employ the integral and squared integral maps to compute the mean and variance of local image blocks, which are used to adaptively adjust the contrast of the input image. Meanwhile, a color balance strategy is introduced to balance the color differences between channel a and channel b in the CIELAB color space. Our enhanced results are characterized by vivid color, improved contrast, and enhanced details. Extensive experiments on three underwater image enhancement datasets demonstrate that our method outperforms the state-of-the-art methods. Our method is also appealing in its fast processing speed within 1s for processing an image of size 1024×1024×3 on a single CPU. Experiments further suggest that our method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection. The project page is available at https://li-chongyi.github.io/proj_MMLE.html.
61 citations
TL;DR: A case study on FEMTO-ST datasets shows that the fine-tuned model is competent for incipient fault detection, outperforming other state-of-the-art methods.
59 citations
TL;DR: In this article , the authors examined the major drivers of energy consumption and carbon dioxide emissions in an augmented model framework and found that the role of eco-innovation, economic complexity, institutions, and globalization are examined in the context of two sets of heterogeneous panel observations, namely the G7 and E7 countries from 1991 to 2017.
45 citations
TL;DR: In this paper , a novel underwater image enhancement method based on Retinex-inspired color correction and detail preserved fusion technology is proposed to cope with color cast, blurring, and low contrast of underwater images for display and further analysis.
44 citations
TL;DR: In this paper , a self-supervised pre-training via contrast learning (SSPCL) is introduced to learn discriminative representations from unlabeled bearing datasets, and a specific architecture for SSPCL deployment on bearing vibration signals by presenting several data augmentations for 1D sequences.
41 citations
TL;DR: This article proposes an underwater image color correction method that employs a dual-histogram-based iterative threshold method and a limited histogram method with Rayleigh distribution to improve the global and local contrast of the color-corrected image, thus achieving a global contrast-enhanced version and a local Contrast enhanced version.
Abstract: An underwater image often suffers from quality degradation issues, such as color deviations, low contrast, and blurred details, due to the absorption and scattering of light. In this article, we propose to address the aforementioned degradation issues via attenuated color channel correction and detail preserved contrast enhancement. Concretely, we first propose an underwater image color correction method. Considering the differences between superior and inferior color channels of an underwater image, the inferior color channels are compensated via especially designed attenuation matrices. We then employ a dual-histogram-based iterative threshold method and a limited histogram method with Rayleigh distribution to improve the global and local contrast of the color-corrected image, thus achieving a global contrast-enhanced version and a local contrast-enhanced version, respectively. To integrate the complementary merits between the global contrast-enhanced version and the local contrast-enhanced version, we adopt a multiscale fusion strategy to fuse them. Finally, we propose a multiscale unsharp masking strategy to further sharpen the fused image for better visual quality. Extensive experiments on four underwater image enhancement benchmark data sets demonstrate that our method effectively enhances underwater images qualitatively and quantitatively. Besides, our method also generalizes well to the enhancement of low-light images and hazy images.
39 citations
TL;DR: A step-by-step guide to select a cancer biomarker and subsequent approaches to design imaging agents for in vivo use is provided and the outlook and future perspective in this exciting field is provided.
Abstract: Photoacoustic (PA) imaging has emerged as a powerful technique for the high resolution visualization of biological processes within deep tissue. Through the development and application of exogenous targeted contrast agents and activatable probes that can respond to a given cancer biomarker, researchers can image molecular events in vivo during cancer progression. This information can provide valuable details that can facilitate cancer diagnosis and therapy monitoring. In this tutorial review, we provide a step-by-step guide to select a cancer biomarker and subsequent approaches to design imaging agents for in vivo use. We envision this information will be a useful summary to those in the field, new members to the community, and graduate students taking advanced imaging coursework. We also highlight notable examples from the recent literature, with emphasis on the molecular designs and their in vivo PA imaging performance. To conclude, we provide our outlook and future perspective in this exciting field.
36 citations
15 Jul 2022
TL;DR: This paper presents a novel multi-grained contrastive model, namely X-CLIP, and proposes the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and Words on retrieval results.
Abstract: Video-text retrieval has been a crucial and fundamental task in multi-modal research. The development of video-text retrieval has been considerably promoted by large-scale multi-modal contrastive pre-training, which primarily focuses on coarse-grained or fine-grained contrast. However, cross-grained contrast, which is the contrast between coarse-grained representations and fine-grained representations, has rarely been explored in prior research. Compared with fine-grained or coarse-grained contrasts, cross-grained contrast calculate the correlation between coarse-grained features and each fine-grained feature, and is able to filter out the unnecessary fine-grained features guided by the coarse-grained feature during similarity calculation, thus improving the accuracy of retrieval. To this end, this paper presents a novel multi-grained contrastive model, namely X-CLIP, for video-text retrieval. However, another challenge lies in the similarity aggregation problem, which aims to aggregate fine-grained and cross-grained similarity matrices to instance-level similarity. To address this challenge, we propose the Attention Over Similarity Matrix (AOSM) module to make the model focus on the contrast between essential frames and words, thus lowering the impact of unnecessary frames and words on retrieval results. With multi-grained contrast and the proposed AOSM module, X-CLIP achieves outstanding performance on five widely-used video-text retrieval datasets, including MSR-VTT (49.3 R@1), MSVD (50.4 R@1), LSMDC (26.1 R@1), DiDeMo (47.8 R@1) and ActivityNet (46.2 R@1).
35 citations
TL;DR: The authors assesses different econometric approaches to working with count-based outcome variables and other outcomes with similar distributions, which are increasingly common in corporate finance applications and demonstrate that the common practice of estimating linear regressions of the log of 1 plus the outcome produces estimates with no natural interpretation that can have the wrong sign in expectation.
34 citations
TL;DR: The Multilingual Eye-Movement Corpus (MECO) as mentioned in this paper ) is a corpus of eye-tracking data from 13 languages recorded during text reading, including English, French, German, Dutch, Italian, and Spanish.
Abstract: Scientific studies of language behavior need to grapple with a large diversity of languages in the world and, for reading, a further variability in writing systems. Yet, the ability to form meaningful theories of reading is contingent on the availability of cross-linguistic behavioral data. This paper offers new insights into aspects of reading behavior that are shared and those that vary systematically across languages through an investigation of eye-tracking data from 13 languages recorded during text reading. We begin with reporting a bibliometric analysis of eye-tracking studies showing that the current empirical base is insufficient for cross-linguistic comparisons. We respond to this empirical lacuna by presenting the Multilingual Eye-Movement Corpus (MECO), the product of an international multi-lab collaboration. We examine which behavioral indices differentiate between reading in written languages, and which measures are stable across languages. One of the findings is that readers of different languages vary considerably in their skipping rate (i.e., the likelihood of not fixating on a word even once) and that this variability is explained by cross-linguistic differences in word length distributions. In contrast, if readers do not skip a word, they tend to spend a similar average time viewing it. We outline the implications of these findings for theories of reading. We also describe prospective uses of the publicly available MECO data, and its further development plans.
31 citations
TL;DR: Zhang et al. as mentioned in this paper found that passive social media use was positively associated with both upward contrast and downward identification, which in turn predicted a higher level of stress. But cognitive reappraisal was negatively associated with unhealthy social comparison, but was positively related to healthy social comparison such as upward identification.
TL;DR: Zhang et al. as discussed by the authors found that passive social media use was positively associated with both upward contrast and downward identification, which in turn predicted a higher level of stress. But cognitive reappraisal was negatively associated with unhealthy social comparison, but was positively related to healthy social comparison such as upward identification.
TL;DR: The ACR Committee on Drugs and Contrast Media, within the ACR Commission on Quality and Safety, is aware of the current global shortage of iodinated contrast media and has made some recommendations on how providers may address this emergency locally as discussed by the authors .
Abstract: The ACR Committee on Drugs and Contrast Media, within the ACR Commission on Quality and Safety, is aware of the current global shortage of iodinated contrast media. The following statement offers some recommendations on how providers may address this emergency locally. The recommendations are not exhaustive or prescriptive. They are intended as a resource for imaging providers and their institutions to continue to provide high-quality patient care during times of shortage of contrast media. Providers and administrative leaders are encouraged to incorporate sound clinical judgment in all decisions affecting patient care.
TL;DR: In this article , the authors evaluated visual performance and patient-reported outcomes after bilateral implantation of a new nondiffractive wavefront-shaping extended depth-offocus (EDoF) intraocular lens (IOL).
Abstract: To evaluate visual performance and patient-reported outcomes after bilateral implantation of a new nondiffractive wavefront-shaping extended depth-of-focus (EDoF) intraocular lens (IOL).Department of Ophthalmology, Goethe University, Frankfurt, Germany.Prospective, single-arm, single-center study.Patient population: 16 patients (32 eyes) who received bilateral implantation of a nondiffractive wavefront-shaping EDoF IOL (AcrySof IQ Vivity) were included. Target refraction in both eyes was emmetropia. Observation procedure: Monocular and binocular uncorrected (UCVA) and distance-corrected (DCVA) visual acuity (VA), refractive outcome, defocus curve, and contrast sensitivity (CS) were evaluated 3 months after surgery with a questionnaire on optical phenomena and spectacle independence. Main outcome measure: 3-month postoperative monocular and binocular UCVA and CDVA (logMAR), defocus curve, CS, and quality of vision (QoV) questionnaire results.16 patients with 32 eyes were included. Mean spherical equivalent was -0.16 ± 0.37 diopters (D) 3 months postoperatively. Binocular uncorrected distance VA at distance, intermediate, and near was 0.01 ± 0.05 logMAR at 4 m, 0.05 ± 0.05 logMAR at 80 cm, 0.07 ± 0.06 logMAR at 66 cm, and 0.25 ± 0.11 logMAR at 40 cm, respectively. Despite some minor optical phenomena, 88% of patients would choose the same lens. 63% of patients reported no optical phenomena at all. CS was 1.25 ± 0.41 logCS (photopic), 0.96 ± 0.24 logCS (mesopic), and 0.93 ± 0.24 (mesopic + glare).This nondiffractive wavefront-shaping EDoF IOL provides good VA at far and intermediate distance and functional near VA. It showed good QoV and CS and high spectacle independence for distance and intermediate vision with significantly less optical phenomena than with other EDoF or multifocal IOLs.
TL;DR: In this paper , the authors used fMRI and psychophysics in the same observers to quantify individual differences in V1 cortical magnification and contrast sensitivity at the four polar angle meridians.
Abstract: Abstract A central question in neuroscience is how the organization of cortical maps relates to perception, for which human primary visual cortex (V1) is an ideal model system. V1 nonuniformly samples the retinal image, with greater cortical magnification (surface area per degree of visual field) at the fovea than periphery and at the horizontal than vertical meridian. Moreover, the size and cortical magnification of V1 varies greatly across individuals. Here, we used fMRI and psychophysics in the same observers to quantify individual differences in V1 cortical magnification and contrast sensitivity at the four polar angle meridians. Across observers, the overall size of V1 and localized cortical magnification positively correlated with contrast sensitivity. Moreover, greater cortical magnification and higher contrast sensitivity at the horizontal than the vertical meridian were strongly correlated. These data reveal a link between cortical anatomy and visual perception at the level of individual observer and stimulus location.
TL;DR: In this article , the authors proposed an underwater image restoration method with red channel compensation and blue-green channel restoration, which relies on the hue and attenuation differences between different color channels of the underwater image to estimate the background light.
Abstract: Underwater images often show low contrast, blurring, and color distortion due to the absorption and scattering of light. In contrast to existing underwater image restoration methods, we propose an underwater image restoration method with red channel compensation and blue-green channel restoration. First, a proposed approach relies on the hue and attenuation differences between different color channels of the underwater image to estimate the background light. Then, the red channel is enhanced according to a perfect reflection assumption algorithm. Finally, a new median underwater dark channel prior (MUDCP) is proposed to precisely estimate the blue-green channel transmission map. Experimental results show that our method significantly improves contrast, removes color bias, and preserves more detail than other underwater restoration techniques.
TL;DR: In this article , a novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed to separate a small target from complex backgrounds.
Abstract: Infrared (IR) small target detection in complex backgrounds is one of the key technologies in IR search and tracking applications. Although significant progress has been made over the past few decades, how to separate a small target from complex backgrounds remains a challenging task. In this letter, a novel small target detection method via multidirectional derivative-based weighted contrast measure (MDWCM) is proposed. Initially, multidirectional derivative subbands are quickly obtained by the facet model. Then, an effective division scheme of surrounding area is performed to capture the derivative properties of the target. A new local contrast measure is constructed to simultaneously enhance the target and suppress the background clutter. Third, the MDWCM maps constructed from all derivative subbands are integrated to enhance the robustness of detection. Finally, the small target is extracted by an adaptive segmentation method. The experimental results demonstrate that the proposed algorithm performs favorably compared to other state-of-the-art methods.
TL;DR: In this paper , the research progress of NIR-II PA contrast agents and their applications in biomedicine are reviewed, including inorganic contrast agents, organic contrast agent, and hybrid organic-inorganic contrast agent.
TL;DR: Wang et al. as mentioned in this paper developed a multiscale color restoration method to correct the color cast of underwater images, which is based on light scattering characteristics, and set a 64-block multi-contrast factor histogram stretching to enhance the contrast of the underwater image.
TL;DR: In this paper , the authors present a special feature "Remote Sensing for Vegetation Science" edited by Duccio Rocchini, Hannes Feilhauer, Sebastian Schmidtlein and Jana Müllerová.
Abstract: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2022 The Authors. Applied Vegetation Science published by John Wiley & Sons Ltd on behalf of International Association for Vegetation Science. This article is a part of the Special Feature "Remote Sensing for Vegetation Science" edited by Duccio Rocchini, Hannes Feilhauer, Sebastian Schmidtlein and Jana Müllerová. 1Institute of Geography and Geoecology, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany 2Institute of Botany of the Czech Academy of Sciences, Průhonice, Czech Republic 3Faculty of Environment, Jan Evangelista Purkyně University, Ústí n. L., Czech Republic 4Department of Spatial Sciences, Faculty of Environmental Sciences, University of Life Sciences Prague, PrahaSuchdol, Czech Republic
TL;DR: The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.
Abstract: Photon-counting computed tomography (CT) is a technology that has attracted increasing interest in recent years since, thanks to new-generation detectors, it holds the promise to radically change the clinical use of CT imaging. Photon-counting detectors overcome the major limitations of conventional CT detectors by providing very high spatial resolution without electronic noise, providing a higher contrast-to-noise ratio, and optimizing spectral images. Additionally, photon-counting CT can lead to reduced radiation exposure, reconstruction of higher spatial resolution images, reduction of image artifacts, optimization of the use of contrast agents, and create new opportunities for quantitative imaging. The aim of this review is to briefly explain the technical principles of photon-counting CT and, more extensively, the potential clinical applications of this technology.
TL;DR: In this paper , the authors make health professionals aware of the opportunity to take the lead now in more conscious decisions regarding use of contrast media and give an overview of the different perspectives for action.
Abstract: Contrast media are essential for diagnostic and interventional procedures. Iodinated contrast media are the most commonly used agents, with CT requiring the largest overall quantities. Data show that these iodinated contrast media are found in sewage water, surface water and drinking water in many regions in the world. Because standard drinking water purification techniques only provide poor to moderate removal of iodinated contrast media, these substances pose a problem for drinking water preparation that has not yet been solved. There is a growing body of evidence supporting the negative environmental effects of iodinated contrast media via their breakdown products. The environmental impact of iodinated contrast media can be mitigated by measures focusing on the application of contrast media or the excretion of contrast media. Measures with respect to contrast application include reducing the utilization of contrast media, reducing the waste of contrast media and collecting residues of contrast media at the point of application. The amount of contrast media excreted into the sewage water can be decreased by introducing urine bags and/or special urine collection and waste-water processing techniques in the hospital. To tackle the problem of contrast media in the water system in its entirety, it is necessary for all parties involved to cooperate, from the producer of contrast medium to the consumer of drinking water. This paper aims to make health professionals aware of the opportunity to take the lead now in more conscious decisions regarding use of contrast media and gives an overview of the different perspectives for action.
01 Jun 2022
TL;DR: In this paper , the authors propose a novel category contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks and construct a semantics-aware dictionary with samples from both source and target domains where each target sample is assigned a (pseudo) category label based on the category priors of source samples.
Abstract: Instance contrast for unsupervised representation learning has achieved great success in recent years. In this work, we explore the idea of instance contrastive learning in unsupervised domain adaptation (UDA) and propose a novel Category Contrast technique (CaCo) that introduces semantic priors on top of instance discrimination for visual UDA tasks. By considering instance contrastive learning as a dictionary look-up operation, we construct a semantics-aware dictionary with samples from both source and target domains where each target sample is assigned a (pseudo) category label based on the category priors of source samples. This allows category contrastive learning (between target queries and the category-level dictionary) for category-discriminative yet domain-invariant feature representations: samples of the same category (from either source or target domain) are pulled closer while those of different categories are pushed apart simultaneously. Extensive UDA experiments in multiple visual tasks (e.g., segmentation, classification and detection) show that CaCo achieves superior performance as compared with state-of-the-art methods. The experiments also demonstrate that CaCo is complementary to existing UDA methods and gen-eralizable to other learning setups such as unsupervised model adaptation, open-/partial-set adaptation etc.
TL;DR: In this paper , the authors evaluated the impact of contrast enhancement and different virtual monoenergetic image energies on automatized emphysema quantification with photon-counting detector computed tomography (PCD-CT).
Abstract: Purpose The aim of this study was to evaluate the impact of contrast enhancement and different virtual monoenergetic image energies on automatized emphysema quantification with photon-counting detector computed tomography (PCD-CT). Material and Methods Sixty patients who underwent contrast-enhanced chest CT on a first-generation, clinical dual-source PCD-CT were retrospectively included. Scans were performed in the multienergy (QuantumPlus) mode at 120 kV with weight-adjusted intravenous contrast agent. Virtual noncontrast (VNC) images as well as virtual monoenergetic images (VMIs) from 40 to 80 keV obtained in 10-keV intervals were reconstructed. Computed tomography attenuation was measured in the aorta. Noise was measured in subcutaneous fat and defined as the standard deviation of attenuation. Contrast-to-noise with region of interest in the ascending aorta and signal-to-noise ratio in the subcutaneous fat were calculated. Subjective image quality (and emphysema assessment, lung parenchyma evaluation, and vessel evaluation) was rated by 2 blinded radiologists. Emphysema quantification (with a threshold of −950 HU) was performed by a commercially available software. Virtual noncontrast images served as reference standard for emphysema quantification. Results Noise and contrast-to-noise ratio showed a strong negative correlation (r = −0.98; P < 0.01) to VMI energies. The score of subjective assessment was highest at 70 keV for lung parenchyma and 50 keV for pulmonary vessel evaluation (P < 0.001). The best trade-off for the assessment of emphysema while maintaining reasonable contrast for pulmonary vessel evaluation was determined between 60 and 70 keV. Overall, contrast-enhanced imaging led to significant and systematic underestimation of emphysema as compared with VNC (P < 0.001). This underestimation decreased with increasing VMI-energy (r = 0.98; P = 0.003). Emphysema quantification showed significantly (P < 0.05) increased emphysema volumes with increasing VMI energies, except between 60–70 keV and 70–80 keV. The least difference in emphysema quantification between contrast-enhanced scans and VNC was found at 80 keV. Conclusion Computed tomography emphysema quantification was significantly affected by intravenous contrast administration and VMI-energy level. Virtual monoenergetic image at 80 keV yielded most comparable results to VNC. The best trade-off in qualitative as well as in quantitative image quality evaluation was determined at 60/70 keV.
TL;DR: VNCPC-reconstructions of PCD-CT-angiography datasets have excellent image quality with complete contrast removal and only minimal erroneous subtractions of stent parts/calcifications and could replace TNC-series in almost all cases.
Abstract: The purpose of this study was to evaluate virtual-non contrast reconstructions of Photon-Counting Detector (PCD) CT-angiography datasets using a novel calcium-preserving algorithm (VNCPC) vs. the standard algorithm (VNCConv) for their potential to replace unenhanced acquisitions (TNC) in patients after endovascular aneurysm repair (EVAR). 20 EVAR patients who had undergone CTA (unenhanced and arterial phase) on a novel PCD-CT were included. VNCConv- and VNCPC-series were derived from CTA-datasets and intraluminal signal and noise compared. Three readers evaluated image quality, contrast removal, and removal of calcifications/stent parts and assessed all VNC-series for their suitability to replace TNC-series. Image noise was higher in VNC- than in TNC-series (18.6 ± 5.3 HU, 16.7 ± 7.1 HU, and 14.9 ± 7.1 HU for VNCConv-, VNCPC-, and TNC-series, p = 0.006). Subjective image quality was substantially higher in VNCPC- than VNCConv-series (4.2 ± 0.9 vs. 2.5 ± 0.6; p < 0.001). Aortic contrast removal was complete in all VNC-series. Unlike in VNCConv-reconstructions, only minuscule parts of stents or calcifications were erroneously subtracted in VNCPC-reconstructions. Readers considered 95% of VNCPC-series fully or mostly suited to replace TNC-series; for VNCConv-reconstructions, however, only 75% were considered mostly (and none fully) suited for TNC-replacement. VNCPC-reconstructions of PCD-CT-angiography datasets have excellent image quality with complete contrast removal and only minimal erroneous subtractions of stent parts/calcifications. They could replace TNC-series in almost all cases.
TL;DR: In the United States, over 40% of CT examinations use iodinated contrast agents (ICAs) for better visualization and assessment of pathophysiological processes as discussed by the authors , with shortages potentially persisting through the middle to end of the summer.
Abstract: In the United States, over 40% of CT examinations use iodinated contrast agents (ICAs) for better visualization and assessment of pathophysiological processes [1]. Recent COVID-19 related work slowdowns in Shanghai, China, have affected operations of General Electric (GE) factories and reduced production of Omnipaque (iohexol, GE Healthcare, Milwaukee, Wisconsin) contrast agents, with estimates that the factory will remain at reduced capacity through the end of June 2022, and shortages potentially persisting through the middle to end of the summer [2].
TL;DR: In this article , contrast coding in regression models, including mixed-effect models, determines whether or not model terms should be interpreted as main effects in psycholinguistics, and this is not well-understood.
01 Jun 2022
TL;DR: In this paper , a regional semantic contrast and aggregation (RCA) is proposed to explore rich semantic contexts synergistically among abundant weakly-labeled training data for network learning and inference.
Abstract: Learning semantic segmentation from weakly-labeled (e.g., image tags only) data is challenging since it is hard to infer dense object regions from sparse semantic tags. Despite being broadly studied, most current efforts directly learn from limited semantic annotations carried by individual image or image pairs, and struggle to obtain integral localization maps. Our work alleviates this from a novel perspective, by exploring rich semantic contexts synergistically among abundant weakly-labeled training data for network learning and inference. In particular, we propose regional semantic contrast and aggregation (RCA). RCA is equipped with a regional memory bank to store massive, diverse object patterns appearing in training data, which acts as strong support for exploration of dataset-level semantic structure. Particularly, we propose i) semantic contrast to drive network learning by contrasting massive categorical object regions, leading to a more holistic object pattern understanding, and ii) semantic aggregation to gather diverse relational contexts in the memory to enrich semantic repre-sentations. In this manner, RCA earns a strong capability of fine-grained semantic understanding, and eventually establishes new state-of-the-art results on two popular benchmarks, i.e., PASCAL VOC 2012 and COCO 2014.
TL;DR: In this article , the authors proposed an Optimized VGG-16 architecture using Arithmetic Optimization Algorithm (Optimized VGC-16 using AOA) for AD classification.
TL;DR: The amount of contrast that could be conserved in CT examinations during a global shortage of iodinated contrast media was modeled and there was no standard contrast dosing strategy across centers.
Abstract: Comparison of Strategies to Conserve Iodinated Intravascular Contrast Media for Computed Tomography During a Shortage In April 2022, a global shortage of iodinated contrast media occurred due to a COVID-19–induced supply chain disruption of GE Healthcare in Shanghai, China.1 This shortage is expected to last until at least summer of 2022 and will affect millions of examinations. Approximately 54.4 million diagnostic imaging examinations using contrast are conducted annually in the US, including nearly all angiography and 48% of the 91.4 million computed tomography (CT) scans performed in 2019.2,3 Strategies to minimize patient harm include canceling or delaying tests that have little patient benefit or are not time sensitive. For indicated examinations, alternative tests with similar diagnostic accuracy could be considered. For CT examinations that usually use contrast, several approaches could potentially conserve contrast. For example, unenhanced CT can have similar or only modestly lower diagnostic accuracy as contrast-enhanced CT for many indications. The volume of administered contrast can be reduced by using weight-based dosing,4 lower tube voltage,5 or a combination of these methods. At present, there is no standard contrast dosing strategy across centers.4 Additionally,therearedifferentstrategiesfordeliveringcontrast,and fixed-volume vials (in which the residual is discarded) result in waste compared with multidose vials.4 We modeled the amount of contrast that could be conserved in CT examinations.