White Light Imaging Research Articles

Diagnostic accuracy of linked color imaging and white light imaging for early gastric cancer and gastrointestinal metaplasia: a systematic review and meta-analysis

BackgroundConventional white light imaging (WLI) frequently misses gastric cancer, resulting in a high rate of undiagnosed cases. This study compares the effectiveness of linked color imaging (LCI) and WLI in detecting early gastric cancer and gastrointestinal metaplasia, aiming to improve clinical diagnostic practices through evidence-based medical insights.MethodsThe QUADAS-2 tool evaluated the quality of the studies. Additionally, methods like Split Component Synthesis (SCS) were utilized to evaluate the diagnostic performance of LCI and WLI.ResultsEleven studies involving a total of 7836 patients were included in the meta-analysis. Comparative analysis revealed that LCI demonstrated a statistically significant superiority over WLI in terms of the detection rates of EGC and GIM (detection rate of EGC: LCI vs WLI, 85% vs. 56.7%, p=0.004, OR 4.78, 95% CI 2.33-9.82, I2 = 71%; detection rate of GIM: LCI vs WLI, 88.9% vs. 40.1%, p=0.0003, OR 9.94, 95% CI 5.59-17.68, I2 = 71%). Additionally, LCI exhibited better sensitivity and specificity for the diagnosis of EGC and GIM compared to WLI. For the entire cohort, the sensitivity of LCI for EGC detection was 80% (95% CI 71%-86%) with a specificity of 82% (95% CI 63%-92%), while for GIM detection, the sensitivity was 87% (95% CI 81%-92%) with a specificity of 85% (95% CI 77%-91%).ConclusionsThe detection efficiency of LCI for EGC and GIM is better than that of WLI, and LCI is recommended as the main screening method for EGC and GIM.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42023452140.

Analysis of clinicopathological factors associate with the visibility of early gastric cancer in endoscopic examination and usefulness of linked color imaging: A multicenter prospective study.

This study investigated clinicopathological factors associated with the visibility of early gastric cancer and the efficacy of linked color imaging. Patients with early gastric cancer who underwent endoscopic treatment between April 2021 and July 2022 were enrolled. All cases underwent white light imaging and linked color imaging. Three experts evaluated lesion visibility using a visual analog scale. A mean score ≥3 on white light imaging was defined as "good visibility", and <3 as "poor visibility". We extracted patient information and endoscopic and pathological data for the lesion and background mucosa, analyzed factors associated with the visibility of early gastric cancer, and compared visibility between white light imaging and linked color imaging. Ninety-seven lesions were analyzed, with good visibility in 49 and poor visibility in 48. Multivariate analysis revealed small lesion size (odds ratio 1.89) and presence of endoscopic intestinal metaplasia (odds ratio 0.49) as significantly associated with the poor visibility of early gastric cancer. Mean visibility score was significantly higher for linked color imaging (P<0.001). Mean score for linked color imaging was significantly higher in the poor visibility group (P<0.001), but not significantly different in the good visibility group (P = 0.292). Mean score was significantly higher with linked color imaging in cases with endoscopic intestinal metaplasia (P = 0.0496) and lesions <20 mm in diameter (<10 mm, P = 0.002; 10-20 mm, P = 0.004). Lesion size and endoscopic intestinal metaplasia are associated with the visibility of early gastric cancer in white light imaging. Linked color imaging improves visibility of gastric cancer with these factors.

Frugal engineering-inspired wearable augmented reality goggle system enables fluorescence-guided cancer surgery

Disparities in surgical outcomes often result from subjective decisions dictated by surgical training, experience, and available resources. To improve outcomes, surgeons have adopted advancements in robotics, endoscopy, and intra-operative imaging including fluorescence-guided surgery (FGS), which highlights tumors and anatomy in real-time. However, technical, economic, and logistic challenges hinder widespread adoption of FGS beyond high-resource centers. To overcome these impediments, we combined laser diodes, Raspberry Pi cameras and computers, off-the-shelf optical components, and 3D-printed parts to make a battery-powered, compact, dual white light and NIR imaging system that has comparable performance to existing bulkier, pricier, and wall-powered technologies. We combined these components with off-the-shelf augmented reality (AR) glasses to create a fully-wearable fluorescence imaging AR Raspberry Pi-based goggle system (FAR-Pi) and validated performance in a pre-clinical cancer surgery model. Novel device design ensures distance-independent coalignment between real and augmented views. As an open-source, affordable, and adaptable system, FAR-Pi is poised to democratize access to FGS and improve health outcomes worldwide.

The Role of Indocyanine Green With Near-Infrared Imaging for the Intraoperative Detection and Enhancement of Endometriosis Lesions: A Narrative Review.

Background: There is a clinical need for improved intraoperative detection of endometriosis, and the use of Indocyanine Green with Near-Infrared Imaging (NIR-ICG) is a novel technique for this purpose. The aim of this review is to determine whether NIR-ICG is an effective tool for endometriosis detection and establish an evidence-based methodology for its use.Methods: This review searches Ovid MEDLINE and Embase through July 2023 and considers primary literature published in English describing the use of NIR-ICG to detect endometriosis intraoperatively. Case studies, video demonstrations and articles describing NIR-ICG used for other surgical roles were not considered. Identified studies were screened independently by two authors, and data was extracted by a single author.Results: NIR-ICG was found to enhance the detection of endometriosis in six out of the nine included studies with additional lesion identification, and to have an unchanged or reduced efficacy compared to current standards in the remaining three. Across all studies there were lesions missed by NIR-ICG which were detected by conventional imaging. A greater duration of time between dye administration and visualisation of lesions was found to be more effective for detection. The ideal ICG protocol proposed from this review is a fixed amount of dye proportional to patient weight prior to surgery (0.25-0.3 mg/kg) followed by a longer waiting time before imaging (10-30 min).Conclusion: NIR-ICG has a possible role to enhance the identification of endometriosis intraoperatively as an adjunct to conventional white light imaging, particularly deeper infiltrating disease. However, substantial further research is required in this field.

Dataset of human skin and fingernails images for non-invasive haemoglobin level assessment

Anaemia, a decrease in total concentration of haemoglobin (Hb) in blood, affects substantial percentage of the population worldwide. Currently, the gold standard for determining the Hb level is the invasive analysis of venous blood. Yet, more and more research groups demonstrate the possibility of non-invasive Hb assessment using white light imaging of tissue sites where Hb is the main chromophore, in particular, fingernails. Despite the promising declarations, non-invasive Hb assessment via RGB-imaging is still poorly used in practice. The main reason is the difficulty in establishing the true accuracy of the methods presented in different works since they are tested on private datasets collected under different experimental conditions. Here we present an open dataset containing RGB images of skin and fingernails for patients with a known level of Hb, thus providing a single benchmark for researchers and engineers in the field, aimed at fostering translation of non-invasive imaging methods to the bedside.

Impact of linked color imaging on the proximal adenoma miss rate: a multicenter tandem randomized controlled trial (the COCORICO trial).

Missed lesions are common during standard colonoscopy and are correlated with post-colonoscopy colorectal cancer. Contrast-enhanced technologies have recently been developed to improve polyp detection. We aimed to evaluate the impact of linked color imaging (LCI) on the proximal adenoma miss rate in routine colonoscopy. This national, multicenter, tandem, randomized trial compared the outcomes of colonoscopy with white-light imaging (WLI) versus LCI for polyp detection in the right colon. Two consecutive examinations of the right colon (upstream of the hepatic flexure) were made with WLI and LCI by the same operator. First-pass examination by WLI or LCI was randomized 1:1 after cecal intubation. According to statistical calculations, 10 endoscopy units had to include approximately 700 patients. The primary outcome was proximal adenoma miss rate. Secondary outcomes were the proximal miss rates for sessile serrated lesions (SSL), advanced adenomas, and polyps. 764 patients were included from 1 January 2020 to 22 December 2022, and 686 patients were randomized (345 WLI first vs. 341 LCI first). Both groups were comparable in terms of demographics and indications. The proximal adenoma miss rate was not significantly higher in the WLI-first group (36.7%) vs. the LCI-first group (31.8%) (estimated mean absolute difference: 4.9% [95%CI -5.2% to 15.0%], P = 0.34). There was also no significant difference in miss rates for SSLs, advanced adenomas, and polyps in the proximal colon. In contrast to previous data, this study does not support the benefit of LCI to the proximal adenoma miss rate in routine colonoscopy.

Multicentre study to assess the performance of an artificial intelligence instrument to support qualitative diagnosis of colorectal polyps

ObjectiveComputer-aided diagnosis (CAD) using artificial intelligence (AI) is expected to support the characterisation of colorectal lesions, which is clinically relevant for efficient colorectal cancer prevention. We conducted this study to...

Modelling non-radially propagating coronal mass ejections and forecasting the time of their arrival at Earth

We present the study of two solar eruptive events observed on December 7 2020 and October 28 2021. Both events were associated with full halo coronal mass ejections (CMEs) and flares. These events were chosen because they show a strong non-radial direction of propagation in the low corona and their main propagation direction observed in the inner heliosphere is not fully aligned with the Sun-Earth line. This characteristic makes them suitable for our study, which aims to inspect how the non-radial direction of propagation in the low corona affects the time of CMEs’ arrival at Earth. We reconstructed the CMEs using SOHO/LASCO and STEREO/COR observations and modelled them with the 3D MHD model EUHFORIA and the cone model for CMEs. In order to compare the accuracy of forecasting the CME and the CME-driven shock arrival time at Earth obtained from different methods, we also used so-called type II bursts, radio signatures of associated shocks, to find the velocities of the CME-driven shocks and forecast the time of their arrival at Earth. Additionally, we estimated the CME arrival time using the 2D CME velocity obtained from the white light images. Our results show that the lowest accuracy of estimated CME Earth arrival times is found when the 2D CME velocity is used (time difference between observed and modelled arrival time, Δt ≈ −29 h and −39 h, for the two studied events, respectively). The velocity of the type II radio bursts provides somewhat better – but still not very accurate – results (Δt ≈ +21 h and −29 h, for the two studied events, respectively). Employing, as an input to EUHFORIA, the CME parameters obtained from the graduated cylindrical shell (GCS) fittings at consequently increasing heights, results in a strongly improved accuracy of the modelled CME and shock arrival time; Δt changes from 20 h to 10 min in the case of the first event, and from 12 h to 30 min in the case of the second one. This improvement shows that when we increased the heights of the GCS reconstruction we accounted for the change in the propagation direction of the studied CMEs, which allowed us to accurately model the CME flank encounter at Earth. Our results show the great importance of the change in the direction of propagation of the CME in the low corona when modelling CMEs and estimating the time of their arrival at Earth.

The simplified depth-predicting score outperforms the depth-predicting score for predicting the depth of invasion in differentiated early gastric cancer patients among nonexpert endoscopists

Aim: Endoscopists utilize depth-predicting score (DPS) and simplified depth-predicting score (S-DPS) to predict the invasion depth of early gastric cancer based on conventional white-light endoscopic features. The effectiveness of these scores has not been fully elucidated among nonexpert endoscopists. This study aimed to compare the ability of DPS and S-DPS to predict invasion depth of differentiated early gastric cancers by nonexpert endoscopists.Participants and Methods: We collected subitem scores of DPS and S-DPS from 19 nonexpert endoscopists for early gastric cancer conventional white-light endoscopy images in the test dataset to predict the invasion depth of the early gastric cancer conventional white-light endoscopy images. Accuracy, specificity, overdiagnosis rate, and underdiagnosis rate were subsequently calculated using the histological invasion depth as the gold standard.Results: Using 3 as the cutoff line, the overall S-DPS diagnostic accuracy for invasion depth was significantly greater than that of DPS [73.86% (69.32%, 75.00%) vs. 67.05% (62.50%, 71.60%), p= 0.005]. The overall S-DPS overdiagnosis rate was significantly lower than that of DPS [7.58% (3.03%, 13.64%) vs. 28.79% (18.18%, 37.88%), p= 0.000]. The overall S-DPS under-diagnosed rate was significantly higher than that of DPS [86.36% (68.18%, 90.91%) vs. 45.45% (31.82%, 59.09%), p=0.000]. The specificity of the S-DPS was significantly greater than that of DPS [92.42% (86.36%, 96.97%) vs. 71.21% (62.12%, 81.82%), p= 0.000].Conclusion: The diagnostic accuracy of the S-DPS was greater than that of the DPS among nonexpert endoscopists. Furthermore, S-DPS is simpler than other methods, making it more conducive to clinical application for nonexpert endoscopists.

Total Solar Eclipse White-light Images as a Benchmark for Potential Field Source Surface Coronal Magnetic Field Models: An In-depth Analysis over a Solar Cycle

Potential field source surface (PFSS) models are widely used to simulate coronal magnetic fields. PFSS models use the observed photospheric magnetic field as the inner boundary condition and assume a perfectly radial field beyond a “source surface” (R ss). At present, total solar eclipse (TSE) white-light images are the only data that delineate the coronal magnetic field from the photosphere out to several solar radii (R ⊙). We utilize a complete solar cycle span of these images between 2008 and 2020 as a benchmark to assess the reliability of PFSS models. For a quantitative assessment, we apply the Rolling Hough Transform to the eclipse data and corresponding PFFS models to measure the difference, Δθ, between the data and model magnetic field lines throughout the corona. We find that the average Δθ, 〈Δθ〉, can be minimized for a given choice of R ss depending on the phase within a solar cycle. In particular, R ss ≈ 1.3 R ⊙ is found to be optimal for solar maximum, while R ss ≈ 3 R ⊙ yields a better match at solar minimum. Regardless, large (〈Δθ〉 > 10°) discrepancies between TSE data and PFSS-generated coronal field lines remain regardless of the choice of source surface. However, implementation of solar-cycle-dependent R ss optimal values does yield more reliable PFSS-generated coronal field lines for use in models and for tracing in situ measurements back to their sources at the Sun.

P50-2 White-light versus linked-color imaging for colorectal adenocarcinoma detection: systematic review and meta-analysis

P50-2 White-light versus linked-color imaging for colorectal adenocarcinoma detection: systematic review and meta-analysis

S915 Comparison of Red Dichromatic Imaging With Standard White Light Imaging in Hemostatic Treatment During Endoscopic Interventions: A Systematic Review and Meta-Analysis

S915 Comparison of Red Dichromatic Imaging With Standard White Light Imaging in Hemostatic Treatment During Endoscopic Interventions: A Systematic Review and Meta-Analysis

Spectrum aided vision enhancer enhances mucosal visualization by hyperspectral imaging in capsule endoscopy.

Narrow-band imaging (NBI) is more efficient in detecting early gastrointestinal cancer than white light imaging (WLI). NBI technology is available only in conventional endoscopy, but unavailable in magnetic-assisted capsule endoscopy (MACE) systems due to MACE's small size and obstacles in image processing issues. MACE is an easy, safe, and convenient tool for both patients and physicians to avoid the disadvantages of conventional endoscopy. Enabling NBI technology in MACE is mandatory. We developed a novel method to improve mucosal visualization using hyperspectral imaging (HSI) known as Spectrum Aided Visual Enhancer (SAVE, Transfer N,Hitspectra Intelligent Technology Co., Kaohsiung, Taiwan). The technique was developed by converting the WLI image captured by MACE to enhance SAVE images. The structural similarity index metric (SSIM) between the WLI MACE images and the enhanced SAVE images was 91%, while the entropy difference between the WLI MACE images and the enhanced SAVE images was only 0.47%. SAVE algorithm can identify the mucosal break on the esophagogastric junction in patients with gastroesophageal reflux disorder. We successfully developed a novel image-enhancing technique, SAVE, in the MACE system, showing close similarity to the NBI from the conventional endoscopy system. The future application of this novel technology in the MACE system can be promising.

Metis Observation of the Onset of Fully Developed Turbulence in the Solar Corona

This Letter reports the first observation of the onset of fully developed turbulence in the solar corona. Long time series of white-light coronal images, acquired by Metis aboard Solar Orbiter at 2 minutes cadence and spanning about 10 hr, were studied to gain insight into the statistical properties of fluctuations in the density of the coronal plasma in the time domain. From pixel-by-pixel spectral frequency analysis in the whole Metis field of view, the scaling exponents of plasma fluctuations were derived. The results show that, over timescales ranging from 1 to 10 hr and corresponding to the photospheric mesogranulation-driven dynamics, the density spectra become shallower moving away from the Sun, resembling a Kolmogorov-like spectrum at 3 R ⊙. According to the latest observation and interpretive work, the observed 5/3 scaling law for density fluctuations is indicative of the onset of fully developed turbulence in the corona. Metis observation-based evidence for a Kolmogorov turbulent form of the fluctuating density spectrum casts light on the evolution of 2D turbulence in the early stages of its upward transport from the low corona.

Protocol for a prospective multicenter randomized controlled trial to evaluate the efficacy of texture and color enhancement imaging (TXI) observation in the detection of colorectal lesions (deTXIon study).

Colonoscopy is the gold standard for detecting and resecting adenomas or early stage cancers to reduce the incidence and mortality rates of colorectal cancer. In a recent observational study, texture and color enhancement imaging (TXI) was reported to improve polyp detection during colonoscopy. This randomized controlled trial involving six Japanese institutions aims to confirm the superiority of TXI over standard white-light imaging (WLI) in detecting colorectal lesions during colonoscopy. During the 1-year study period, 960 patients will be enrolled, with 480 patients in the TXI and WLI groups. The primary endpoint is the mean number of adenomas detected per procedure. The secondary endpoints include adenoma detection rate, advanced adenoma detection rate, polyp detection rate, flat polyp detection rate, depressed lesion detection rate, mean polyps detected per procedure, sessile serrated lesion (SSL) detection rate, mean SSLs detected per procedure and adverse events.

Exploring vision transformers for classifying early Barrett's dysplasia in endoscopic images: A pilot study on white-light and narrow-band imaging.

Various deep learning models, based on convolutional neural network (CNN), have been shown to improve the detection of early esophageal neoplasia in Barrett's esophagus. Vision transformer (ViT), derived from natural language processing, has emerged as the new state-of-the-art for image recognition, outperforming predecessors such as CNN. This pilot study explores the use of ViT to classify the presence or absence of early esophageal neoplasia in endoscopic images of Barrett's esophagus. A BO dataset of 1918 images of Barrett's esophagus from 267 unique patients was used. The images were classified as dysplastic (D-BO) or non-dysplastic (ND-BO). A pretrained vision transformer model, ViTBase16, was used to develop our classifier models. Three ViT models were developed for comparison based on imaging modality: white-light imaging (WLI), narrow-band imaging (NBI), and combined modalities. Performance of each model was evaluated based on accuracy, sensitivity, specificity, confusion matrices, and receiver operating characteristic curves. The ViT models demonstrated the following performance: WLI-ViT (Accuracy: 92%, Sensitivity: 82%, Specificity: 95%), NBI-ViT (Accuracy: 99%, Sensitivity: 97%, Specificity: 99%), and combined modalities-ViT (Accuracy: 93%, Sensitivity: 87%, Specificity: 95%). Combined modalities-ViT showed greater accuracy (94% vs 90%) and sensitivity (80% vs 70%) compared with WLI-ViT when classifying WLI images on a subgroup testing set. ViT exhibited high accuracy in classifying the presence or absence of EON in endoscopic images of Barrett's esophagus. ViT has the potential to be widely applicable to other endoscopic diagnoses of gastrointestinal diseases.

Sunspot Group Detection and Classification by Dual Stream Convolutional Neural Network Method

The automatic detection and analysis of sunspots play a crucial role in understanding solar dynamics and predicting space weather events. This paper proposes a novel method for sunspot group detection and classification called the dual stream Convolutional Neural Network with Attention Mechanism (DSCNN-AM). The network consists of two parallel streams each processing different input data allowing for joint processing of spatial and temporal information while classifying sunspots. It takes in the white light images as well as the corresponding magnetic images that reveal both the optical and magnetic features of sunspots. The extracted features are then fused and processed by fully connected layers to perform detection and classification. The attention mechanism is further integrated to address the “edge dimming” problem which improves the model’s ability to handle sunspots near the edge of the solar disk. The network is trained and tested on the SOLAR-STORM1 data set. The results demonstrate that the DSCNN-AM achieves superior performance compared to existing methods, with a total accuracy exceeding 90%.

435. PREVALENCE OF OESOPHAGEAL PREMALIGNANT LESIONS AMONG PATIENTS REFERRED FOR GASTRO-OESOPHAGEAL-DUODENOSCOPY AT A TERTIARY CARE HOSPITAL IN NORTHERN, SRI LANKA

Abstract Background Oesophageal cancer is the second leading cause of cancer-related mortality in Sri Lanka, where a higher prevalence is found among Tamil-speaking Sri Lankans. Endoscopic screening with Lugol’s chromo endoscopy in high-incidence populations reduces mortality from oesophageal cancer. The objective of this study is to determine the prevalence of premalignant oesophageal lesions - squamous dysplasia and Barrett’s oesophagus and the associated risk factors among Tamil patients undergoing oesophageal-gastro-duodenoscopy (OGD). Methods Consecutive patients undergoing OGD for dyspeptic and gastric acid reflux symptoms were recruited. An interviewer-administered questionnaire was used for data collection. During OGD, the oesophagus was examined under white light imaging, narrow band imaging (NBI), and Lugol’s iodine chromo endoscopy. Biopsies were taken from abnormal mucosa and sent for pathological diagnosis. Statistical software SPSS (V25) was used to analyse the data. Results The study incorporates 410 participants. The majority (61.7%) were females and the median age was 57 (Interquartile range 21) years. On Endoscopy under white light and NBI 11.0%(n=45) had squamous neoplasia. Of them, 16.0% had Low grade dysplasia (LGD), 2.0% had invasive carcinoma and the remaining 82.0% had inflammatory or reactive changes. Endoscopic Barrett’s oesophagus (BO) was found in 17.0% (n=69) and 5.0% (n=19) had both endoscopic and histological confirmation of BO. Of them, 21.0% had LGD. In regression analysis, male gender was at higher risk for BO (OR- 1.251, 95%CI 0.420 – 3.730). Overall, 3.0% of the participants had premalignant lesions. Conclusions The prevalence of premalignant lesions in a symptomatic population was only 3.0%. Further studies with a larger number of targeted population is needed to assess the exact prevalence.

Endoscopic appearances of gastric mucosa in different endoscopic models according to H. pylori infection status.

H. pylori infection has been recognized as a type 1 carcinogen of the gastric malignancy; therefore, early diagnosis and treatment are the corner stone of eradication. Recent findings have also shown that atrophy and intestinal metaplasia remain after successful eradication, which moderately increases the risk of gastric cancer compared with those who have never infected, so the evaluation of gastric mucosa during gastroscopy is important. We aimed to describe and summarize the reliable literature and proposed features of H. pylori infection status and gastritis in research on newly developed endoscopic models that influence clinical practice. In the result, conventional white light endoscopic, image-enhanced endoscopic models, and studies related to the Kyoto classification of gastritis were searched and reviewed. Kyoto classification of gastritis and modified Kyoto classification scoring model for gastritis using conventional white light image (CWLI) endoscopy is an effective tool for evaluating current H. pylori infection status, past infections, eradications, noninfections, and pre-cancerous conditions. This model is widely used, low cost, and time-efficient, and is supported by recent findings. Advanced image-enhanced endoscopic models combined with magnifying endoscopy provide more clear endoscopic features for H. pylori infection status and early gastric cancer. According to H pylori infection status, endoscopic prediction of gastric mucosal surface architecture analysis is possible, which influences clinical management. Endoscopic models might lead us to accurate and early diagnose of H. pylori infection status and may not be effective only for the eradication of H. pylori infection but also in the detection of early gastric cancer status.

A Detailed Analysis of a Magnetic Island Observed by WISPR on Parker Solar Probe

We present the identification and physical analysis of a possible magnetic island feature seen in white-light images observed by the Wide-field Imager for Solar Probe (WISPR) on board the Parker Solar Probe. The island is imaged by WISPR during Parker's second solar encounter on 2019 April 6, when Parker was ∼38 R ⊙ from the Sun's center. We report that the average velocity and acceleration of the feature are approximately 334 km s−1 and −0.64 m s−2. The kinematics of the island feature, coupled with its direction of propagation, indicate that the island is likely entrained in the slow solar wind. The island is elliptical in shape with a density deficit in its center, suggesting the presence of a magnetic guide field. We argue that this feature is consistent with the formation of this island via reconnection in the current sheet of the streamer. The feature's aspect ratio (calculated as the ratio of its minor to major axis) evolves from an elliptical to a more circular shape that approximately doubles during its propagation through WISPR’s field of view. The island is not distinct in other white-light observations from the Solar and Heliospheric Observatory and the Solar Terrestrial Relations Observatory coronagraphs, suggesting that this is a comparatively faint heliospheric feature and that viewing perspective and WISPR’s enhanced sensitivity are key to observing the magnetic island.