Multimodal Imaging Research Articles

Cystic ductal adenocarcinoma of pancreas complicated with neuroendocrine tumor: A case report and review of literature

BACKGROUND Difficulties in making an accurate preoperative diagnosis of cystic pancreatic lesions pose a challenge for radiologists. It would be helpful to report rare cases and review the literature. CASE SUMMARY In the present report, a case of a patient with a pancreatic cystic lesion initially misdiagnosed as a pseudocyst by radiologist was documented, which was later pathologically confirmed as pancreatic ductal adenocarcinoma with neuroendocrine tumor. However, subsequent literature review yielded no previous reports of pancreatic ductal adenocarcinoma with neuroendocrine tumors and cystic lesions. Therefore, literature on the imaging diagnosis of pancreatic cystic lesions was instead reviewed and discussed. CONCLUSION Careful evaluation of the characteristics revealed by multimodal imaging techniques, medical history, laboratory examination data and follow-up observations, is critical to the diagnosis and treatment of pancreatic cystic disease. We provide valuable insights into the diagnosis of pancreatic cystic disease through a rare case report and literature review.

Left ventricular apical aneurysms and outcomes in hypertrophic cardiomyopathy

Abstract Background/Introduction The presence of left ventricular apical aneurysm (LVAA) has been shown to be associated with increased risk of adverse events in patients with hypertrophic cardiomyopathy (HCM). However, the between LVAA size and outcomes is uncertain. Purpose To explore associations between baseline LVAA diameter and progressive changes in LVAA size over time and poor outcomes in HCM patients. Methods Our HCM multimodality imaging database (transthoracic echocardiography (TTE) or cardiac magnetic resonance (CMR)) was retrospectively assessed for studies reporting LVAA, defined as one or more akinetic or dyskinetic apical segments. LVAA size (widest systolic diameter in millimetres in the transverse plane) was recorded at two time points: earliest identification and most recent imaging, with LVAA size progression (mm/year) calculated. Patients’ electronic records were reviewed for composite outcome including stroke, apical thrombus, death, and appropriate therapy from implantable cardioverter defibrillator (ICD). Continuous variables are presented as mean ± standard deviation (SD), categorical data numerically or as percentages. Comparisons between groups were made using Wilcoxon ranks sum and Fischer exact test for continuous and binary variables, respectively. Cox regression was used to assess independent associations with composite outcomes. A p value of <0.05 was considered statistically significant. Results A total of 106 patients were identified with LVAA and clinical follow-up (mean age 64.7±11.4 years, 70% male). Mean follow-up duration was 6.5±3.0 years. Baseline LVAA was confirmed by CMR and TTE in 70 and 30% of cases respectively. Mean LVAA size at baseline was 16.2±7.6 mm (range 6-56 mm). During multivariate Cox analysis, baseline LVAA size in mm was independently associated with the composite outcome (HR 1.04, 95% CIs 1.0 to 1.08, p=0.04). Mean duration between baseline and maximal LVAA size scan was 5.4±3.0 years. Median progression in LVAA size was 1.0 (IQR 0.6 to 1.8) mm per annum (range 0.0 to 8.1 mm). During multivariate analysis, LVAA progression rate in mm per year was independently associated with the composite outcome (HR 1.4 95% CIs 1.11 to 1.7, p=0.002). Conclusion(s) These data support the association between LVAA size and poor outcomes in HCM patients. We also demonstrate the progressive nature of LVAAs and indicate those more rapidly increasing in size are at highest risk of adverse events, with potential implications for management strategies.

Semantic Segmentation of the Prostate Based on Onefold and Joint Multimodal Medical Images Using YOLOv4 and U-Net

Magnetic Resonance Imaging is increasing in importance in prostate cancer diagnosis due to the high accuracy and quality of the examination procedure. However, this process requires a time-consuming analysis of the results. Currently, machine vision is widely used in many areas. It enables automation and support in radiological studies. Successful detection of primary prostate tumors depends on the effective segmentation of the prostate itself. At times, a CT scan may be performed; alternatively, MRI may be the selected option. The data always reach a bottleneck stage. This paper presents the effective training of deep learning models to segment the prostate based on onefold and multimodal medical images. This approach supports the computer-aided diagnosis (CAD) system for radiologists as the first step in cancer exams. A comparison of two approaches designed for prostate segmentation is described. The first combines YOLOv4, the object detection neural network, and U-Net for a semantic segmentation based on onefold modality MRI images. The second presents the same method trained on multimodal images—a CT and MRI mixed dataset. The learning process was carried out in a cloud environment using GPU cards. The experiments are based on data from 120 patients who have undergone MRI and CT examinations. Several metrics evaluated the trained models. In the prostate semantic segmentation process, better results were achieved by mixed MRI with CT datasets. The best model achieved the value of 0.9685 for the Sørensen–Dice coefficient for the threshold value of 0.6.

WaterGPT: Training a Large Language Model to Become a Hydrology Expert

This paper introduces WaterGPT, a language model designed for complex multimodal tasks in hydrology. WaterGPT is applied in three main areas: (1) processing and analyzing data such as images and text in water resources, (2) supporting intelligent decision-making for hydrological tasks, and (3) enabling interdisciplinary information integration and knowledge-based Q&A. The model has achieved promising results. One core aspect of WaterGPT involves the meticulous segmentation of training data for the supervised fine-tuning phase, sourced from real-world data and annotated with high quality using both manual methods and GPT-series model annotations. These data are carefully categorized into four types: knowledge-based, task-oriented, negative samples, and multi-turn dialogues. Additionally, another key component is the development of a multi-agent framework called Water_Agent, which enables WaterGPT to intelligently invoke various tools to solve complex tasks in the field of water resources. This framework handles multimodal data, including text and images, allowing for deep understanding and analysis of complex hydrological environments. Based on this framework, WaterGPT has achieved over a 90% success rate in tasks such as object detection and waterbody extraction. For the waterbody extraction task, using Dice and mIoU metrics, WaterGPT’s performance on high-resolution images from 2013 to 2022 has remained stable, with accuracy exceeding 90%. Moreover, we have constructed a high-quality water resources evaluation dataset, EvalWater, which covers 21 categories and approximately 10,000 questions. Using this dataset, WaterGPT achieved the highest accuracy to date in the field of water resources, reaching 83.09%, which is about 17.83 points higher than GPT-4.

Endolymphatic hydrops and cochlear synaptopathy after noise exposure are distinct sequelae of hair cell stereociliary bundle trauma

Endolymphatic hydrops, increased endolymphatic fluid within the cochlea, is the key pathologic finding in patients with Meniere’s disease, a disease of episodic vertigo, fluctuating hearing loss, tinnitus, and aural fullness. Endolymphatic hydrops also can occur after noise trauma and its presence correlates with cochlear synaptopathy, a form of hearing loss caused by reduced numbers of synapses between hair cells and auditory nerve fibers. Here we tested whether there is a mechanistic link between these two phenomena by using multimodal imaging techniques to analyze the cochleae of transgenic mice exposed to blast and osmotic challenge. In vivo cochlear imaging after blast exposure revealed dynamic increases in endolymph that involved hair cell mechanoelectrical transduction channel block but not the synaptic release of glutamate at the hair cell–auditory nerve synapse. In contrast, ex vivo and in vivo auditory nerve imaging revealed that synaptopathy requires glutamate release from hair cells but not endolymphatic hydrops. Thus, although endolymphatic hydrops and cochlear synaptopathy are both observed after noise exposure, one does not cause the other. They are simply co-existent sequelae that derive from the traumatic stimulation of hair cell stereociliary bundles. Importantly, these data argue that Meniere’s disease derives from hair cell transduction channel blockade.

Intraretinal Microvascular Abnormalities in Eyes with Advanced Stages of Nonproliferative Diabetic Retinopathy: Comparison Between UWF-FFA, CFP, and OCTA-The RICHARD Study.

This study aimed to evaluate intraretinal microvascular abnormalities (IRMA) in eyes with advanced nonproliferative diabetic retinopathy (NPDR) using multimodal approach in co-located areas focusing on central retina (up to 50°) and to look at possible correlations between IRMA and other structural changes, like ischemia and presence of microaneurysms. The RICHARD study (NCT05112445) included 60 eyes from 60 patients with type2 diabetes with moderate-severe NPDR, diabetic retinopathy severity levels 43, 47, and 53 (DRSS). IRMA were defined as capillary tortuosity covering a minimum circular area of 300µm (calculated to correspond to the Early Treatment Diabetic Retinopathy Study standard photo 8A) and were identified using multimodal imaging with distinct fields of view (FoV): color fundus photography (CFP) using a Topcon TRC-50DX camera (Topcon Medical Systems, Japan), Optos California ultra wide field fundus fluorescein angiography (UWF-FFA) (Optos plc, UK), and swept-source optical coherence tomography angiography (SS-OCTA) (PLEX® Elite 9000, ZEISS, USA). Different areas of the retina were examined: central macula (up to 20°) and posterior pole (between 20° and 50°). Multimodal imaging was used to identify IRMA in co-located areas (FoV < 50°) including UWF-FFA, CFP, and SS-OCTA. In eyes with DRSS levels 47 and 53, IRMA were identified in both areas of the retina, while in eyes with DRSS level 43, IRMA were detected only outside of the central macula (FoV > 20°). Our results show that when evaluating the presence of IRMA (FoV < 50°), UWF-FFA detected 203 IRMA, SS-OCTA detected 133 IRMA, and CFP detected 104 IRMA. Our results also show that the presence of IRMA was positively associated with presence of microaneurysms. Identification of IRMA in eyes with advanced NPDR is better achieved by UWF-FFA than CFP and SS-OCTA. A statistically significant correlation was found between the presence of IRMA and the increase in number of microaneurysms. ClinicalTrials.gov, identifier NCT05112445.

Predictive Diagnostic Model for Early Osteoporosis Detection Using Deep Learning and Multimodal Imaging Data: A Systematic Review and Meta-Analysis

Osteoporosis is a common condition that weakens bones, making them more prone to fractures. Early detection is crucial for preventing fractures and improving patients’ quality of life. However, traditional methods, such as Dual-Energy X-ray Absorptiometry (DXA), often struggle to accurately predict fracture risk and may overlook minor changes in bone structure. This study focuses on developing a predictive model for early osteoporosis detection using deep learning algorithms combined with various imaging techniques, including MRI, CT, and High-Resolution Peripheral Quantitative Computed Tomography (HR-pQCT). A systematic review and meta-analysis of studies published between 2014 and 2024 were conducted, examining the use of deep learning models applied to multimodal imaging data. The meta-analysis highlighted differences in the accuracy and effectiveness of various models, and their performance was measured in terms of accuracy, sensitivity, and specificity, following PRISMA guidelines. The results showed that deep learning models outperformed traditional methods in early osteoporosis detection. The use of multiple imaging techniques provided a more detailed assessment of bone health, allowing the models to identify complex patterns that are difficult for human interpretation. These models demonstrated high accuracy and significant potential for improving clinical decision-making. By integrating deep learning with multimodal imaging, this approach offers a promising solution for enhancing the early detection of osteoporosis. The models tested in this study proved to be highly effective, yielding more accurate fracture risk predictions and enabling earlier interventions. This could lead to better patient outcomes and reduced healthcare costs.

Multimodal Optical Imaging of Ex Vivo Fallopian Tubes to Distinguish Early and Occult Tubo-Ovarian Cancers

Background: There are currently no effective screening measures to detect early or occult tubo-ovarian cancers, resulting in late-stage detection and high mortality. This work explores whether an optical imaging catheter can detect early-stage tubo-ovarian cancers or precursor lesions where they originate in the fallopian tubes. Methods: This device collects co-registered optical coherence tomography (OCT) and autofluorescence imaging (AFI). OCT provides three-dimensional assessment of underlying tissue structures; autofluorescence imaging provides functional contrast of endogenous fluorophores. Ex vivo fallopian tubes (n = 28; n = 7 cancer patients) are imaged; we present methods for the calculation of and analyze eleven imaging biomarkers related to fluorescence, optical attenuation, and OCT texture for their potential to detect tubo-ovarian cancers and other lesions of interest. Results: We visualize folded plicae, vessel-like structures, tissue layering, hemosiderin deposits, and regions of fibrotic change. High-grade serous ovarian carcinoma appears as reduced autofluorescence paired with homogenous OCT and reduced mean optical attenuation. Specimens containing cancerous lesions demonstrate a significant increase in median autofluorescence intensity and decrease in Shannon entropy compared to specimens with no lesion. Non-cancerous specimens demonstrate an increase in optical attenuation in the fimbriae when compared to the isthmus or the ampulla. Conclusions: We conclude that this approach shows promise and merits further investigation of its diagnostic potential.

Dissecting neural correlates of theory of mind and executive functions in behavioral variant frontotemporal dementia

Behavioral variant frontotemporal dementia (bvFTD) is characterized by profound and early deficits in social cognition (SC) and executive functions (EF). To date it remains unclear whether deficits of the respective cognitive domains are based on the degeneration of distinct brain regions. In 103 patients with a diagnosis of bvFTD (possible/probable/definite: N = 40/58/5) from the frontotemporal lobar degeneration (FTLD) consortium Germany cohort (age 62.5±9.4 years, gender 38 female/65 male) we applied multimodal structural imaging, i.e. voxel-based morphometry, cortical thickness (CTH) and networks of structural covariance via source based morphometry. We cross-sectionally investigated associations with performance in a modified Reading the Mind in the Eyes Test (RMET; reflective of theory of mind - ToM) and five different tests reflective of EF (i.e. Hamasch-Five-Point Test, semantic and phonemic Fluency, Trail Making Test, Stroop interference). Finally, we investigated the conjunction of RMET correlates with functional networks commonly associated with SC respectively ToM and EF as extracted meta-analytically within the Neurosynth database. RMET performance was mainly associated with gray matter volume (GMV) and CTH within temporal and insular cortical regions and less within the prefrontal cortex (PFC), whereas EF performance was mainly associated with prefrontal regions (GMV and CTH). Overlap of RMET and EF associations was primarily located within the insula, adjacent subcortical structures (i.e. putamen) and the dorsolateral PFC (dlPFC). These patterns were more pronounced after adjustment for the respective other cognitive domain. Corroborative results were obtained in analyses of structural covariance networks. Overlap of RMET with meta-analytically extracted functional networks commonly associated with SC, ToM and EF was again primarily located within the temporal and insular region and the dlPFC. In addition, on a meta-analytical level, strong associations were found for temporal cortical RMET correlates with SC and ToM in particular. These data indicate a temporo-frontal dissociation of bvFTD related disturbances of ToM and EF, with atrophy of the anterior temporal lobe being critically involved in ToM deficits. The consistent overlap within the insular cortex may be attributable to the multimodal and integrative role of this region in socioemotional and cognitive processing.

8-Anilino-1-naphthalenesulfonate-Conjugated Carbon-Coated Ferrite Nanodots for Fluoromagnetic Imaging, Smart Drug Delivery, and Biomolecular Sensing

Background: Superparamagnetic properties and excitation independence have been incorporated into carbon-decorated manganese ferrite nanodots (MnFe@C) to introduce an economical and safer multimodal agent for use in both T1-T2 MRI and fluorescence-based imaging to replace the conventional highly toxic heavy metal contrast agents. Methods: The surface conjugation of 8-anilino-1-naphthalenesulfonate (ANS) to MnFe@C nanodots (ANS-MnFe@C) enhances both longitudinal and transverse MRI relaxation, improves fluorescence for optical imaging, and increases protein detection sensitivity, showing higher multimodal efficacy in terms of molar relaxivity, radiant efficiencies, and fluorescence sensitivity compared to MnFe@C. Results: The band gap energy was determined using Tauc’s equation to be 3.32 eV, while a 72% quantum yield demonstrated that ANS-MnFe@C was highly fluorescent, with the linear range and association constant calculated using the Stern–Volmer relation. The synthesized ANS-MnFe@C demonstrated excellent selectivity and sensitivity for bovine serum albumin (BSA), with a nanomolar detection limit of 367.09 nM and a broad linear range from 0.015 to 0.225 mM. Conclusions: In conclusion, ANS-MnFe@C holds ease of fabrication, good biocompatibility, as assessed in A375 cells, and an effective pH-sensitive doxorubicin release profile to establish anticancer activity in lung cancer cell line (A549), highlighting its potential as an affordable therapeutic agent for multimodal imaging, drug delivery, and protein sensing.

A Multi-Hierarchical Complementary Feature Interaction Network for Accelerated Multi-Modal MR Imaging

Magnetic resonance (MR) imaging is widely used in the clinical field due to its non-invasiveness, but the long scanning time is still a bottleneck for its popularization. Using the complementary information between multi-modal imaging to accelerate imaging provides a novel and effective MR fast imaging solution. However, previous technologies mostly use simple fusion methods and fail to fully utilize their potential sharable knowledge. In this study, we introduced a novel multi-hierarchical complementary feature interaction network (MHCFIN) to realize joint reconstruction of multi-modal MR images with undersampled data and thus accelerate multi-modal imaging. Firstly, multiple attention mechanisms are integrated with a dual-branch encoder–decoder network to represent shared features and complementary features of different modalities. In the decoding stage, the multi-modal feature interaction module (MMFIM) acts as a bridge between the two branches, realizing complementary knowledge transfer between different modalities through cross-level fusion. The single-modal feature fusion module (SMFFM) carries out multi-scale feature representation and optimization of the single modality, preserving better anatomical details. Extensive experiments are conducted under different sampling patterns and acceleration factors. The results show that this proposed method achieves obvious improvement compared with existing state-of-the-art reconstruction methods in both visual quality and quantity.

Transarterial embolization for anterior cranial fossa dural arteriovenous fistula based on multi-modal three-dimensional imaging

Background: Dural arteriovenous fistula (DAVF) in the anterior cranial fossa (ACF) is known to show a high risk of intracranial hemorrhage. Recently, multi-modal fusion imaging with computed tomography angiography, computed tomography venography, and three-dimensional (3D) rotation angiography have been used preoperatively to ensure anatomical safety. We report on endovascular treatment as a first-line approach for ACFDAVF based on the understanding of vascular anatomy obtained from multi-modal fusion imaging. Methods: All patients with ACF-DAVF treated endovascularly as a first-line approach were included in this study. Analyses took into account complications (particularly visual function), immediate angiographic outcomes, and follow-up findings in consecutive patients with ACF-DAVF treated with interventional treatment based on multi-modal fusion imaging. Results: Five patients with ACF-DAVF underwent six sessions of transarterial embolization (TAE) in our institution. The five male patients (mean age, 74.5 years; range, 60–84 years) were treated with liquid embolic agents (Onyx, four procedures; n-butyl 2-cyanoacrylate, two procedures). No difference was seen between preoperative image evaluation and image evaluation during the endovascular procedure, and in all cases, a microcatheter was navigated into a target artery assumed from preoperative multi-modal imaging, allowing treatment completion in a single procedure. In all cases, the shunt disappeared completely and visual function after procedure was maintained. At the last follow-up, all patients showed a modified Rankin scale score of 0 or 1 with no recurrences. Conclusion: Multi-modal fusion imaging facilitates a 3D understanding of the vascular anatomy, allowing TAE as the first-line treatment for ACF-DAVF.

Methodological aspects of correlative, multimodal, multiparametric breast cancer imaging: from data acquisition to image processing for AI-based radioproteomics in a preclinical setting

Preclinical high-field magnetic resonance imaging (MRI) systems offer a diverse array of MRI techniques, providing rich multiparametric MRI (mpMRI) platforms for studying numerous biological parameters. mpMRI platforms prove particularly indispensable when investigating tumors that exhibit profound intratumoral heterogeneity, such as breast cancer. A thoughtful comprehension of the origins of intratumoral heterogeneity is imperative for the judicious assessment of new targeted therapies and treatment interventions. Furthermore, when data from mpMRI are complemented with data from other in vivo imaging modalities, such as positron emission tomography (PET), and correlated with data from ex vivo modalities, such as matrix-assisted laser desorption imaging mass spectrometry (MALDI IMS), the in vivo parameters can be further elucidated at a molecular level and microscopic scale. Nevertheless, extracting meaningful scientific insights from such complex datasets necessitates the utilization of machine learning (ML) approaches to discern region-specific radiomic features. The development of correlative, multimodal imaging (CMI) workflows, such as one incorporating MRI, PET and MALDI IMS, is inherently challenging, given the many technological and methodological challenges related to multimodal data acquisition as well as the physiological limitations of the laboratory mice of the investigation. Standardization efforts in image acquisition and processing are required to increase the reproducibility and translatability of CMI data. To address the challenges of developing standardized CMI workflows and stimulate dialog regarding this area of need, we present a practical workflow to investigate tumor heterogeneity in breast cancer xenografts across various spatial scales. Our workflow entails simultaneous functional MRI and PET acquisitions in living mice, followed by correlation with post-imaging MALDI IMS and histologic data. Additionally, we propose data preprocessing steps for potential ML applications. We illustrate the feasibility of this workflow through two examples, showcasing its effectiveness in comparing in vivo and ex vivo images to evaluate tumor metabolism and hypoxia in mice with breast cancer xenografts.

Structural-informed functional MRI analysis of patients with empathy impairment following stroke.

The underlying functional alterations of brain structural changes among patients with empathy impairment following stroke remain unclear. We sought to investigate functional connectivity changes informed by brain structural abnormalities in multimodal magnetic resonance imaging (MRI) among patients with empathy impairment following stroke. We enrolled people who had experienced their first ischemic stroke, along with healthy controls. We assessed empathy 3 months after stroke using the Chinese version of the Empathy Quotient (EQ). During the acute phase, all patients underwent basic magnetic resonance imaging (MRI), followed by multimodal MRI during follow-up. Our MRI analyses encompassed acute infarction segmentation, volumetric brain measurements, regional quantification of diffusion parameters, and both region-of-interest-based and seed-based functional connectivity assessments. We grouped patients based on the severity of their empathy impairment for comparative analysis. We included 84 patients who had stroke and 22 healthy controls. Patients had lower EQ scores than controls. Patients with low empathy had larger left cortical infarcts (odds ratio [OR] 4.082, 95% confidence interval [CI] 1.183-14.088), more pronounced atrophy in the right cingulate cortex (OR 1.248, 95% CI 1.038-1.502), and lower scores on the Montreal Cognitive Assessment (OR 0.873, 95% CI 0.74-0.947). In addition, the cingulate cortex served as the seed in the seed-based analysis, which showed heightened functional connectivity between the anterior cingulate gyrus and the right superior parietal lobule, specifically in the low-empathy group. We did not evaluate the relationship between specific network involvement and empathy impairment among patients following stroke. Among patients with subacute ischemic stroke, reduced empathy was strongly associated with a more severe cognitive profile and atrophy of the right cingulate cortex. Our subsequent structural-informed functional MRI analysis suggests that the enhanced connectivity between the anterior cingulate gyrus and the superior parietal lobule may function as a compensatory mechanism for this atrophy.

Case report: role of multimodality imaging in diagnostics and follow-up of a giant intramyocardial dissecting hematoma

Case report: role of multimodality imaging in diagnostics and follow-up of a giant intramyocardial dissecting hematoma

Multimodality imaging of right ventricular apical atypical cavernous hemangioma.

Multimodality imaging of right ventricular apical atypical cavernous hemangioma.

Multimodality imaging in recognizing and differentiating cardiac masses in a patient with cancer presenting with NSTEMI

Multimodality imaging in recognizing and differentiating cardiac masses in a patient with cancer presenting with NSTEMI

Super Resolved Single-Cell Spatial Metabolomics from Multimodal Mass Spectrometry Imaging guided by Imaging Mass Cytometry.

Mass spectrometry imaging (MSI) is a powerful technique for spatially resolved analysis of metabolites and other biomolecules within biological tissues. However, the inherent low spatial resolution of MSI often limits its ability to provide detailed cellular-level information. To address this limitation, we propose a guided super-resolution (GSR) approach that leverages high-resolution Imaging Mass Cytometry (IMC) images to enhance the spatial resolution of low-resolution MSI data. By using these detailed IMC images as guides, we improve the resolution of MSI images, creating high-resolution metabolite maps. This enhancement facilitates more precise analysis of cellular structures and tissue architectures, providing deeper insights into super-resolved spatial metabolomics at the single-cell level.

An ensemble deep learning model for medical image fusion with Siamese neural networks and VGG-19.

Multimodal medical image fusion methods, which combine complementary information from many multi-modality medical images, are among the most important and practical approaches in numerous clinical applications. Various conventional image fusion techniques have been developed for multimodality image fusion. Complex procedures for weight map computing, fixed fusion strategy and lack of contextual understanding remain difficult in conventional and machine learning approaches, usually resulting in artefacts that degrade the image quality. This work proposes an efficient hybrid learning model for medical image fusion using pre-trained and non-pre-trained networks i.e. VGG-19 and SNN with stacking ensemble method. The model leveraging the unique capabilities of each architecture, can effectively preserve the detailed information with high visual quality, for numerous combinations of image modalities in image fusion challenges, notably improved contrast, increased resolution, and lower artefacts. Additionally, this ensemble model can be more robust in the fusion of various combinations of source images that are publicly available from Havard-Medical-Image-Fusion Datasets, GitHub. and Kaggle. Our proposed model performance is superior in terms of visual quality and performance metrics to that of the existing fusion methods in literature like PCA+DTCWT, NSCT, DWT, DTCWT+NSCT, GADCT, CNN and VGG-19.

Clinical and Genetic characteristics of patients with Peripheral Retinal Fleck in Koreans.

To describe the clinical and genetic features of Korean patients with peripheral retinal flecks unrelated to aging. A retrospective analysis was conducted on the clinical characteristics of patients with symmetric peripheral retinal flecks. Age-related deposits such as reticular pseudodrusen were excluded, as well as secondary deposits related to intraocular inflammation, tumor, and drug toxicity. Multimodal imaging, electrophysiological examinations, and genetic testing were analyzed. A total of 10 patients (2 males and 8 females) with bilateral peripheral flecks were enrolled in this study. A mean age at diagnosis was 30.5±19.6 years (4-59 years). Within the 10 patients, 6 were genetically confirmed with monogenic retinal disorders. Biallelic pathogenic variants in RDH5 were found in 5 patients, and 1 patient was diagnosed with retinopathy related to Alport syndrome due to a pathogenic variant in COL4A5. Although not genetically confirmed, one case associated with nanophthalmos and another case showing chorioretinal mottling in a carrier of ocular albinism have been identified. In one patient, genetic testing also revealed unknown causes. The mean logMAR initial visual acuity was 0.12±0.18 and 0.07±0.18 in right and left eyes, respectively. Night blindness was reported by 4 patients (40%), with 3 showing decreased or delayed rod response in electroretinogram, particularly those with RDH5 mutations. Differences in the deposit layers and the patterns of flecks were observed on multimodal imaging. In the study population, we observed various causes and clinical differences in the retinal fleck patterns among Koreans, including RDH5-related fundus albipunctatus and Alport syndrome. Despite reports of night blindness symptoms in some cases, all patients demonstrated satisfactory corrected visual acuity.