To quantitatively evaluate the performance of the most used colormaps in image display using perceptual metrics and to what extent these measures are congruent with the true intensity or uptake of pixels at different levels of defect severity in simulated cardiac images.
Six colormaps, labeled \"Gray\", \"Thermal\", \"Cool\", \"CEqual\", \"Siemens\" and \"S Pet\" extracted from FIJI ImageJ software are included. Colormap data are converted from the red, green, blue color space to CIELAB. Perceptual metrics for measuring \"color difference\" were calculated, including difference (ΔE76) and \"speed\". The pairwise color difference in every two levels or entries is visualized in a 2-dimensional \"heatmap distance matrix\" for each colormap. Curves are plotted for each colormap and compared. In addition, to apply this technique to clinical images, simulated short-axis cardiac slices with incremental defect severity (10% grading) were employed. The circumferential profile curves of true pixel intensity, lightness or luminance, and color difference are plotted simultaneously for each defect severity to visualize the concordance of the three curves in various colormaps.
In 0% defect, all the curves are at the highest level, except for \"s pet\", in that the lightness is not at its maximum value. In the phantom with 10% defect (or 90% of maximum value), discrepancies among curves appear. In \"Siemens\", the ΔE76 drops sharply. In \"Siemens\" colormap, the ΔE76 drops sharply. In 80% defect, ΔE76 curve, in \"gray\" colormap drops more slowly than other curves of other colormaps. In \"s pet\", lightness curve rises paradoxically, although the count intensity and ΔE76 curve match. In 70% defect, again, the curves are in good agreement in \"thermal\", \"Siemens\" and \"cequal\". However, a consistent lag exists in \"gray\". Up to 50% defect, curves maintain their expected pattern, but in defects more severe than 40%, lightness and ΔE76 curves in \"cool\" and \"cequal\" rise paradoxically, and in \"thermal\", they start to slow down in descent. In \"Siemens\", falling pattern of the three curves continues. For \"s pet\" colormap, an erratic pattern of lightness and ΔE76 curves exists.
Of 6 colormaps investigated for estimating defect severity, \"grayscale\" is less favorable than others and \"thermal\" performs slightly better. \"S pet\" or rainbow, which is used traditionally by many practitioners, is strongly discouraged. The \"Siemens\" colormap suffers from decreased discriminating power in the range of mild to moderate/severe. In contrast, the \"cool\" and \"cequal\" colormaps outperform the other colormaps employed in this study to some extent, although they have some shortcomings.
Görüntü ekranında en çok kullanılan renk haritalarının performansını algısal ölçümler kullanarak niceliksel olarak değerlendirmek ve bu ölçümlerin, simüle edilmiş kardiyak görüntülerdeki farklı defekt şiddeti seviyelerindeki piksellerin gerçek yoğunluğu veya alımıyla ne ölçüde uyumlu olduğunu değerlendirmektir.
Çalışmaya FIJI ImageJ yazılımından çıkarılan “Gri”, “Termal”, “Soğuk”, “CEqual”, “Siemens” ve “S Pet” etiketli altı renk haritası dahil edilmiştir. Renk haritası verileri RGB renk uzayından CIELAB’a dönüştürülür. “Renk farkını” ölçmek için “fark” (ΔE76) ve “hız” dahil algısal ölçümler hesaplandı. Her iki seviyedeki veya girişlerdeki ikili renk farkı, her renk haritası için 2 boyutlu bir “ısı haritası uzaklık matrisinde” görselleştirilir. Her renk haritası için eğriler çizilir ve karşılaştırılır. Ek olarak, bu tekniği klinik görüntülere uygulamak için, artan defekt şiddetine (%10 derecelendirme) sahip simüle edilmiş kısa eksenli kalp kesitleri kullanıldı. Gerçek piksel yoğunluğunun, açıklığın veya parlaklığın ve renk farkının çevresel profil eğrileri, çeşitli renk haritalarında üç eğrinin uyumunu görselleştirmek amacıyla her defekt şiddeti için eşzamanlı olarak çizildi.
%0 defektte “s pet” dışında tüm eğriler en yüksek seviyede olup açıklık maksimum değerde değildir. %10 defektli (veya maksimum değerin %90’ı) fantomda eğriler arasında tutarsızlıklar görünür. “Siemens”te ΔE76 keskin bir şekilde düşüyor. “Siemens” renk haritasında ΔE76 keskin bir şekilde düşüyor. %80 defektte, ΔE76 eğrisi, “gri” renk haritasında diğer renk haritalarının diğer eğrilerine göre daha yavaş düşer. “S pet”te, sayım yoğunluğu ve ΔE76 eğrisi eşleşse de açıklık eğrisi paradoksal olarak artar. %70 defektte ise yine “termal”, “Siemens” ve “cequal” eğriler iyi bir uyum içindedir. Ancak “gri” renkte tutarlı bir gecikme mevcuttur. %50’ye kadar defektte, eğriler beklenen şeklini korur, ancak %40’tan daha şiddetli defektlerde, açıklık ve ΔE76 eğrileri “soğuk” ve “cequal”de paradoksal olarak yükselir ve “termal”de alçalırken yavaşlamaya başlar. “Siemens”te üç eğrinin düşme paterni devam eder. “S pet” renk haritası için düzensiz bir açıklık modeli ve ΔE76 eğrileri mevcuttur.
Defekt şiddetini tahmin etmek için incelenen 6 renk haritasından “gri tonlamalı” diğerlerine göre daha az avantajlıdır ve “termal” biraz daha iyi performans göstermektedir. Birçok uygulayıcı tarafından geleneksel olarak kullanılan “s pet” veya gökkuşağı kesinlikle önerilmez. “Siemens” renk haritası, hafif ile orta/şiddetli aralığında ayırt edici gücün azalmasından muzdariptir. Buna karşılık “cool” ve “cequal” renk haritaları, bazı eksiklikleri olsa da, bu çalışmada kullanılan diğer renk haritalarından bir dereceye kadar daha iyi performans göstermektedir.

UNASSIGNED: Among the conferences comprising the Medical Imaging Symposium is the MI104 conference currently titled Image-Guided Procedures, Robotic Interventions, and Modeling, although its name has evolved through at least nine iterations over the last 30 years. Here, we discuss the important role that this forum has presented for researchers in the field during this time.
UNASSIGNED: The origins of the conference are traced from its roots in Image Capture and Display in the late 1980s, and some of the major themes for which the conference and its proceedings have provided a valuable forum are highlighted.
UNASSIGNED: These major themes include image display/visualization, surgical tracking/navigation, surgical robotics, interventional imaging, image registration, and modeling. Exceptional work from the conference is highlighted by summarizing keynote lectures, the top 50 most downloaded proceedings papers over the last 30 years, the most downloaded paper each year, and the papers earning student paper and young scientist awards.
UNASSIGNED: Looking forward and considering the burgeoning technologies, algorithms, and markets related to image-guided and robot-assisted interventions, we anticipate growth and ever increasing quality of the conference as well as increased interaction with sister conferences within the symposium.

Due to the untouchability of online shopping environment, image and text description, as two main ways of product information display, are important indicators for consumers to evaluate products. However, few studies have discussed the synergistic effects of image and text information on consumers. In the present study, in conjunction with the left-right position effect, we examine the expectation that horizontal placement of visual stimuli in different directions has a strong influence on consumers\' product evaluation preferences. This implicit assumption is based on consumers\' unconscious psychological need for closure when processing information. The authors conducted three studies to investigate the relative effects of image information and text statements at different locations in online shopping pages on consumer product evaluations. The results show that: (1) when the evaluation object is a search product, compared with the display mode of left text-right image, the display mode of left image-right text plays a more significant role in consumer product evaluation. The results of experiential products were just the opposite. The way of presenting the text declaration on the left and image on the right has a stronger impact on consumers\' evaluation preference for experiential products (Study 1 and Study 3). (2) The difference in consumers\' evaluation mode of different presentation sequences based on product attributes is driven by their visual information processing fluency (Study 2). These preferences are robust, and it is worth noting that only the order of graphic presentation has no significant influence on consumer product evaluation preference.

Bsoft is a software package primarily developed for processing electron micrographs, with the goal of determining the structures of biologically relevant molecules, molecular assemblies, and parts of cells. However, it incorporates many ways to deal with images, from the mundane to very sophisticated algorithms. This article is an introduction into its use, illustrating that it is an extensive toolbox, for manipulating and understanding images. Bsoft has over 150 programs, allowing the user an infinite number of ways to process images. These programs can be executed on the command line, or through the interactive program called brun. The main visualization program is bshow, providing numerous ways to manipulate and interpret images. The primary aim is to provide the user with powerful capabilities, including processing large numbers of images. An important additional aim is to make it as accessible as possible, making it easier to deal with image formats and features, and enhance productivity.

Two kinds of 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) dyads BDP-OH containing 4-hydroxystyrene groups and BDP-PY bearing pyridinyl units were prepared. In addition, a naphthalene derivative NAP-PY modified by pyridinyl moieties substituent was made. The above three dyads could be used to construct white-light emission (WLE) material by a supramolecular engineering strategy due to their three primary colors of blue, green and red. The supramolecular correlations between the hydroxyl group of BDP-OH and the pyridinyl groups of NAP-PY and BDP-PY were confirmed by 1 H NMR titration, 2D NOESY and FTIR. A fluorescence monitor application was carried out based on the realization of WLE. This work might be useful for designing other WLE supramolecular systems and image display.

OBJECTIVE: The purpose of this study was to establish a novel paradigm to facilitate radiologists\' workflow - combining mutually exclusive CT image properties that emerge from different reconstructions, display settings and organ-dependent spectral evaluation methods into a single context-sensitive imaging by exploiting prior anatomical information.
METHODS: The CT dataset is segmented and classified into different organs, for example, the liver, left and right kidney, spleen, aorta, and left and right lung as well as into the tissue types bone, fat, soft tissue, and vessels using a cascaded three-dimensional fully convolutional neural network (CNN) consisting of two successive 3D U-nets. The binary organ and tissue masks are transformed to tissue-related weighting coefficients that are used to allow individual organ-specific parameter settings in each anatomical region. Exploiting the prior knowledge, we develop a novel paradigm of a context-sensitive (CS) CT imaging consisting of a prior-based spatial resolution (CSR), display (CSD), and dual energy evaluation (CSDE). The CSR locally emphasizes desired image properties. On a per-voxel basis, the reconstruction most suitable for the organ, tissue type, and clinical indication is chosen automatically. Furthermore, an organ-specific windowing and display method is introduced that aims at providing superior image visualization. The CSDE analysis allows to simultaneously evaluate multiple organs and to show organ-specific DE overlays wherever appropriate. The ROIs that are required for a patient-specific calibration of the algorithms are automatically placed into the corresponding anatomical structures. The DE applications are selected and only applied to the specific organs based on the prior knowledge. The approach is evaluated using patient data acquired with a dual source CT system. The final CS images simultaneously link the indication-specific advantages of different parameter settings and result in images combining tissue-related desired image properties.
RESULTS: A comparison with conventionally reconstructed images reveals an improved spatial resolution in highly attenuating objects and in air while the compound image maintains a low noise level in soft tissue. Furthermore, the tissue-related weighting coefficients allow for the combination of varying settings into one novel image display. We are, in principle, able to automate and standardize the spectral analysis of the DE data using prior anatomical information. Each tissue type is evaluated with its corresponding DE application simultaneously.
CONCLUSIONS: This work provides a proof of concept of CS imaging. Since radiologists are not aware of the presented method and the tool is not yet implemented in everyday clinical practice, a comprehensive clinical evaluation in a large cohort might be topic of future research. Nonetheless, the presented method has potential to facilitate workflow in clinical routine and could potentially improve diagnostic accuracy by improving sensitivity for incidental findings. It is a potential step toward the presentation of evermore increasingly complex information in CT and toward improving the radiologists workflow significantly since dealing with multiple CT reconstructions may no longer be necessary. The method can be readily generalized to multienergy data and also to other modalities.

OBJECTIVE: While the performance of displays used for the acquisition and primary interpretation of medical images has been well-characterized, notably absent are publications evaluating and discussing the performance of displays used in Interventional Radiology (IR) suites and Cardiac Catheterization (CC) laboratories. The purpose of this work was to evaluate the performance of these displays and to consider the challenges in implementation of display quality assurance practices in this environment.
METHODS: Ten large format displays used in IR and CC suites were evaluated. A visual inspection of available test patterns was performed followed by a quantitative evaluation of several performance characteristics including luminance ratio, luminance response function, and luminance uniformity. Additionally, the local ambient lighting conditions were evaluated.
RESULTS: Luminance ratios ranged from 243.0 to 1182.1 with a mean value of 500.1 ± 289.2. The maximum deviation between the luminance response function and the DICOM Grayscale Standard Display Function ranged from 11.2% to 38.3% with a mean value of 26.2% ± 10.9%. When evaluating luminance uniformity, the mean maximum luminance deviation was 13.2% ± 3.5%. The mean value of luminance deviation from the median was 7.8% ± 1.0%. Measured values of background illuminance ranged from 29.1 to 310.0 lux with a mean value of 107.6 lux ± 80.4 lux. While no mura or bad pixels were observed during visual inspection, damage including scrapes and scratches as well as smudges was common to most of the displays.
CONCLUSIONS: This work provides much needed data for the characterization of the performance of the large format displays used in IR and CC laboratory suites. These data may be used as a point of comparison when implementing a display QA program.

Clinical specialties have widely varied needs for diagnostic image interpretation, and clinical image and video image consumption. Enterprise viewers are being deployed as part of electronic health record implementations to present the broad spectrum of clinical imaging and multimedia content created in routine medical practice today. This white paper will describe the enterprise viewer use cases, drivers of recent growth, technical considerations, functionality differences between enterprise and specialty viewers, and likely future states. This white paper is aimed at CMIOs and CIOs interested in optimizing the image-enablement of their electronic health record or those who may be struggling with the many clinical image viewers their enterprises may employ today.

Enterprise imaging governance is an emerging need in health enterprises today. This white paper highlights the decision-making body, framework, and process for optimal enterprise imaging governance inclusive of five areas of focus: program governance, technology governance, information governance, clinical governance, and financial governance. It outlines relevant parallels and differences when forming or optimizing imaging governance as compared with other established broad horizontal governance groups, such as for the electronic health record. It is intended for CMIOs and health informatics leaders looking to grow and govern a program to optimally capture, store, index, distribute, view, exchange, and analyze the images of their enterprise.

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