Modality Imaging Research Articles

Clustered ultra-small iron oxide nanoparticles as potential T1/T2 dual–modal magnetic resonance imaging contrast agents and application to tumor model

Many studies have been conducted on the use of ultra-small iron oxide nanoparticles (USIONs) (d < 3 nm) as potential positive magnetic resonance imaging (MRI)-contrast agents (CAs); however, there is dearth of research on clustered USIONs. In this study, nearly monodispersed clustered USIONs were synthesized using a simple two-step one-pot polyol method. First, USIONs (d = 2.7 nm) were synthesized, and clustered USIONs (d = 27.9 nm) were subsequently synthesized through multiple cross-linking of USIONs with poly(acrylic acid-co-maleic acid) (PAAMA) polymers with many -COOH groups. The clustered PAAMA-USIONs exhibited very weak ferromagnetism owing to the magnetic interaction between superparamagnetic USIONs; this was evidenced by their appreciable r1= 3.9 s‒1mM‒1and high r2/r1ratio of 14.6. Their ability to function as a dual-modal T1/T2MRI-CA in T1-weighted MRI was demonstrated when they simultaneously exhibited positive and negative contrasts in T1-weighted MRI of tumor model mice after intravenous injection. They displayed positive contrasts at the kidneys, bladder, heart, and aorta and negative contrasts at the liver and tumor.&#xD.

Staging in thick cutaneous malignant melanoma: A sequenced approach

BackgroundPatients with thick cutaneous pT4 malignant melanoma are at high risk for metastatic disease and optimal staging is poorly defined. This study examines the frequency and modality of pre-operative staging to provoke thought on optimal staging. MethodsA retrospective study was performed of patients with clinical T4N0 melanoma diagnosed between 2015-2017. The modality (sentinel lymph node biopsy and/or imaging), timing, and findings of staging investigations were assessed. ResultsOne hundred thirty-six patients with pT4a or pT4b cutaneous melanoma and clinically negative regional lymph nodes were included. Forty patients underwent no staging. Thirty-six patients underwent pre-operative imaging, and of these, regional or distant disease was identified in seven patients (19%). Another 36 patients underwent upfront sentinel lymph node biopsy concurrently with wide local excision. A positive sentinel lymph node biopsy was found in eight (22%) of these patients of which two had regional or distant metastatic disease on post-operative imaging. Of 16 who underwent a sentinel lymph node biopsy after negative pre-operative imaging, three had a positive sentinel lymph node biopsy. ConclusionStaging is non-standardized and underutilized in patients with thick melanoma. With evolving systemic treatment options, a well-defined approach to staging, with consideration for pre-operative imaging in pT4 patients, is essential for high-risk patients.

A novel deep learning framework for accurate melanoma diagnosis integrating imaging and genomic data for improved patient outcomes.

Melanoma is one of the most malignant forms of skin cancer, with a high mortality rate in the advanced stages. Therefore, early and accurate detection of melanoma plays an important role in improving patients' prognosis. Biopsy is the traditional method for melanoma diagnosis, but this method lacks reliability. Therefore, it is important to apply new methods to diagnose melanoma effectively. This study presents a new approach to classify melanoma using deep neural networks (DNNs) with combined multiple modal imaging and genomic data, which could potentially provide more reliable diagnosis than current medical methods for melanoma. We built a dataset of dermoscopic images, histopathological slides and genomic profiles. We developed a custom framework composed of two widely established types of neural networks for analysing image data Convolutional Neural Networks (CNNs) and networks that can learn graph structure for analysing genomic data-Graph Neural Networks. We trained and evaluated the proposed framework on this dataset. The developed multi-modal DNN achieved higher accuracy than traditional medical approaches. The mean accuracy of the proposed model was 92.5% with an area under the receiver operating characteristic curve of 0.96, suggesting that the multi-modal DNN approach can detect critical morphologic and molecular features of melanoma beyond the limitations of traditional AI and traditional machine learning approaches. The combination of cutting-edge AI may allow access to a broader range of diagnostic data, which can allow dermatologists to make more accurate decisions and refine treatment strategies. However, the application of the framework will have to be validated at a larger scale and more clinical trials need to be conducted to establish whether this novel diagnostic approach will be more effective and feasible.

AN OUT OF HOSPITAL CARDIAC ARREST SURVIVOR WITH PRESUMED EMBOLIC MYOCARDIAL INFARCTION: ROLE OF MULTI–MODALITY IMAGING

Abstract Background Myocardial infarction with non–obstructive coronary arteries (MINOCA) refers to a puzzling clinical entity that is characterized by symptoms suggestive of acute coronary syndrome, demonstrates troponin elevation with inconclusive coronary angiogram. The underlying pathophysiologic mechanisms include atherosclerotic cause or non–atherosclerotic causes, such as coronary dissection or coronary embolus. A standardized approach that includes multimodality imaging is necessary to determine underlying aetiology and direct appropriate treatment. Case Summary A 57–year–old male was admitted to our hospital after experiencing cardiac arrest after physical exercise (swimming) treated immediately with cardiac pulmonary resuscitation and two defibrillator–delivered electrical shocks for shockable rhythm before return of spontaneous circulation. Relevant abnormal bloods included a hs–Troponin I elevation. Invasive coronary angiography demonstrated non–obstructed coronary arteries. Subsequent cardiac magnetic resonance imaging (MRI) demonstrated akinesia and an area of subendocardic late gadolinium enhancement in the inferior–lateral wall. This pattern was in keeping with an ischaemic cause, as a recent embolic myocardial infarction. Further investigations, including contrast bubble echocardiogram, were performed in order to identify a cause and source of the embolic infarction and led to the diagnosis of tunnel–like patent foramen ovale with right to left shunting, then confirmed on trans–oesophageal echocardiogram. The patient was treated as myocardial infarction most likely due to embolic phenomena in the presence of a PFO. Therefore, a transvenous ICD was implanted for secondary prevention and the patient underwent percutaneous closure of the PFO. Discussion and Conclusion MINOCA can occur in 5–15% of all acute coronary syndromes and represents a working diagnosis. It is important to identify the underlying aetiology of their presentation to appropriately manage them. In particular, where cardiac MRI demonstraded LGE areas ichaemic patterns, myocardial infarction due to secondary embolism should be considered and the next step in clinical management is to identify potential sources of emboli performing further assessments. Multi–modality imaging with cardiac MRI and echocardiogram (transthoracic, contrast bubble and trans–oesophageal) is important in correctly identifying any source of emboli and the diagnosis of any intra–cardiac shunt.

Zirconium-containing nanoscale coordination polymers for positron emission tomography and fluorescence-guided cargo delivery to triple-negative breast tumors

Nanoscale coordination polymer (NCP) is a class of hybrid materials formed by self-assembly of metal ions and organic ligands through coordination. The applications of NCP in biomedicine are quite extensive due to the diversity choice of metal ions and organic ligands. Here we designed Zr-P1 NCP based on Zr4+ selected as metal ion nodes and tetrakis(4-carboxyphenyl) ethylene as bridging ligands. Zr-P1 NCP was modified with functionalized pyrene derived polyethylene glycol (Py-PAA-PEG-Mal) on the surface and further conjugated with cRGD for active targeting of integrin αvβ3 overexpressed in triple-negative breast cancer. Doxorubicin was loaded on Zr-P1 NCP with encapsulation efficiency up to 22 % for the treatment of triple negative breast cancer. 89Zr-P1 NCP can be used for in vivo tumor imaging due to the fluorescence properties resulting from the enhanced aggregation-induced Emission (AIE) behavior of P1 ligands and its positron emission tomography (PET) capability. Cellular evaluation indicated that the functionalized Zr-P1@PEG-RGD presented a good function for tumor cell targeting imaging and doxorubicin could be targeted to triple negative breast cancer when it was loaded onto Zr-P1@PEG-RGD, which corroborated with the in vivo results. In summary, 89Zr-P1@PEG-RGD can serve as a biocompatible nanoplatform for fluorescence and PET image-guided cargo delivery. Statement of SignificanceNanoscale coordination polymer (NCP) is a class of hybrid materials formed by self-assembly of metal ions and organic ligands through coordination. The diversity of available metals and ligand structures upon NCP synthesis plays an advantage in establishing multimodal imaging platforms. Here we designed 89Zr-P1@PEG-RGD NCP based on Zr4+ selected as metal ion nodes and tetrakis(4-carboxyphenyl) ethylene as bridging ligands. 89Zr-P1@PEG-RGD nanomaterials have positron emission tomography (PET) capability due to the incorporation of zirconium-89, which can be used for in vivo tumor imaging with high sensitivity. The chemotherapeutic drug DOX was loaded on Zr-P1 NCP for the treatment of triple-negative breast cancer, and dual modality imaging can provide visual guidance for drug delivery.

Near-infrared-II fluorescence/magnetic resonance double modal imaging of transplanted stem cells using lanthanide co-doped gadolinium oxide nanoparticles.

To ensure maximum therapeutic safety and efficacy of stem cell transplantation, it is essential to observe the kinetics of behavior, accumulation, and engraftment of transplanted stem cells in vivo. However, it is difficult to detect transplanted stem cells with high sensitivity by conventional in vivo imaging technologies. To diagnose the kinetics of transplanted stem cells, we prepared multifunctional nanoparticles, Gd2O3 co-doped with Er3+ and Yb3+ (Gd2O3: Er, Yb-NPs), and developed an in vivo double modal imaging technique with near-infrared-II (NIR-II) fluorescence imaging and magnetic resonance imaging (MRI) of stem cells using Gd2O3: Er, Yb-NPs. Gd2O3: Er, Yb-NPs were transduced into adipose tissue-derived stem cells (ASCs) through a simple incubation process without cytotoxicity under certain concentrations of Gd2O3: Er, Yb-NPs and were found not to affect the morphology of ASCs. ASCs labeled with Gd2O3: Er, Yb-NPs were transplanted subcutaneously onto the backs of mice, and successfully imaged with good contrast using an in vivo NIR-II fluorescence imaging and MRI system. These data suggest that Gd2O3: Er, Yb-NPs may be useful for in vivo double modal imaging with NIR-II fluorescence imaging and MRI of transplanted stem cells.

Research laser scanning damage detection method in f-k domain for laminate composite material

The use of composite plate structures has become increasingly prevalent in industries such as civil engineering, aerospace, high-speed rail, automotive, and wind power. However, the propagation of ultrasonic Lamb waves in these structures is subject to multi-modal and frequency dispersion effects, which, coupled with the complicated anisotropic propagation mechanism of composite materials, makes it challenging to extract damage scatter signals without a health signal as a reference. In this study, we apply the frequency-wavenumber ( f- k) domain filter wavefront modal method to carbon fiber reinforced polymer (CFRP) materials and use a three-dimensional (3D) window function to achieve different modalities and their corresponding damage reflection signals in the absence of a reference dispersion curve. Single modal damage imaging is achieved using a common source method (CSM) by laser vibration scanning experiment. To effectively extract single modalities in laminate material damage detection, a 3D filter window function was constructed. Considering the skew angle correction, it can effectively remove the direct wave signal, so as to realize the imaging of the damage.

Multimodality Imaging in Right Heart Tumors: Proposed Algorithm towards an Appropriate Diagnosis.

A right heart tumor can be identified by transthoracic echocardiography during a routine examination or due to cardiac symptoms. The first step is the assessment by echocardiography, with its multiple techniques, and the obtained information must be judged in a clinical and biological context. The second step comprises one, sometimes even two, of the more complex modality imaging methods. The choice is driven not only by the advantages of each imaging technique but also by local expertise or the preferred imaging modality in the center. This step is followed by staging, follow-up, and/or imaging-guided excision or biopsy, which is performed in selected cases in order to obtain anatomopathological confirmation. In the presence of features suggestive of malignancy or causing hemodynamic impairment, a transvenous biopsy is essential before the more complex imaging modalities (which are still relevant in the staging process). Using a structured imaging approach, it is possible to reach an appropriate diagnosis without a biopsy. Frequently, these imaging techniques have a complementary role, so an integrated imaging approach is recommended. This proposed algorithm for appropriate diagnosis of right heart tumors could serve as a practical guide for clinicians (not only imaging specialists).

The network characteristics in schizophrenia with prominent negative symptoms: a multimodal fusion study

Previous studies on putative neural mechanisms of negative symptoms in schizophrenia mainly used single modal imaging data, and seldom utilized schizophrenia patients with prominent negative symptoms (PNS).This study adopted the multimodal fusion method and recruited a homogeneous sample with PNS. We aimed to identify negative symptoms-related structural and functional neural correlates of schizophrenia. Structural magnetic resonance imaging (sMRI) and resting-state functional MRI (rs-fMRI) were performed in 31 schizophrenia patients with PNS and 33 demographically matched healthy controls.Compared to healthy controls, schizophrenia patients with PNS exhibited significantly altered functional activations in the default mode network (DMN) and had structural gray matter volume (GMV) alterations in the cerebello-thalamo-cortical network. Correlational analyses showed that negative symptoms severity was significantly correlated with the cerebello-thalamo-cortical structural network, but not with the DMN network in schizophrenia patients with PNS.Our findings highlight the important role of the cerebello-thalamo-cortical structural network underpinning the neuropathology of negative symptoms in schizophrenia. Future research should recruit a large sample and schizophrenia patients without PNS, and apply adjustments for multiple comparison, to verify our preliminary findings.

Diagnostic feasibility of middle cerebral artery stenosis or occlusion evaluated by TCCS and CEUS: Repeatability, reproducibility, and diagnostic agreement with DSA

AimThis study aimed to evaluate the feasibility of transcranial color-coded sonography (TCCS) and contrast-enhanced ultrasound (CEUS) in assessing middle cerebral artery (MCA) stem stenosis or occlusion compared to digital subtraction angiography (DSA). MethodsA total of 48 cases including 96 MCAs suspected stem stenosis or obstruction in the MCA were assessed by TCCS, CE-TCCS, and DSA. The diameters of the most severe stenosis (Ds), proximal normal artery (Dn), and diameter stenosis rate of MCA were measured using both the color doppler flow imaging (CDFI) modality of TCCS or CEUS and the CEUS imaging modality. The intraclass correlation coefficients (ICCs) and 95 % confidence intervals (CI) were evaluated, and a weighted Kappa value was used to evaluate the intra-observer agreement, inter-observer agreement, agreement between CDFI modality and DSA stenosis or occlusion, and agreement between CEUS imaging modality and DSA stenosis or occlusion. ResultsThe ICC results indicated excellent repeatability and reproducibility (all ICCs > 0.75; weighted Kappa values >0.81). Compared with DSA, the weighted Kappa values and 95 % CIs of stenosis (the first measurement was taken by two observers) of CDFI modality and CEUS imaging modality were 0.175 (0.041, 0.308) and 0.779 (0.570, 0.988) for observers A and 0.181 (0.046, 0.316) and 0.779 (0.570, 0.988) for observers B respectively. ConclusionThis study indicates that inter- and intra-observer agreements were good for the direct method of measuring percentages of MCA stenosis by TCCS and CEUS. CEUS imaging modality is a new and reliable imaging modality approach to evaluate the MCAs stenosis and occlusion.

RMAP-ResNet: Segmentation of brain tumor OCT images using residual multicore attention pooling networks for intelligent minimally invasive theranostics

Background and objectiveThe occurrence of brain tumors seriously affects patients both mentally and physically, reducing their quality of life. Automatic recognition and segmentation of tumor boundaries are essential for computer-aided diagnosis and treatment. While optical coherence tomography (OCT) has been widely employed in the detection and analysis fundus lesions and vascular segmentation due to its high resolution and real-time imaging advantages, the segmentation of brain tumor OCT images based on deep neural network has not been studied. However, despite the encoding and decoding structure based on deep learning has achieved good results in medical image segmentation, these models still face limitations in accurately segmenting tumor OCT images due to variations in tumor shapes, sizes, and numbers, and the low contrast of OCT images. Accurate segmentation of tumor tissue contours is crucial as the absence of this mechanism hampers the effective application of OCT imaging methods for brain tumor diagnosis and treatment in clinical practice. Therefore, we propose a novel neural network that can accurately segment brain tumors and their residual tissues in OCT images. MethodsIn this study, we propose a novel residual multi-core attention pooling network (RMAP ResNet) for the segmentation of tumorous and its residual tissue. The RMAP module primarily relies on multiple associative effective receptive fields. These fields enable the detection of targets of various sizes. The module also incorporates an attention structure to adjust connection weights, suppressing irrelevant information and emphasizing the characteristics of tumor OCT images. This process restores the contextual dependencies. ResultsWe validated our proposed RMAP ResNet method on the dataset of mouse tumor OCT imaging obtained using the sweep source OCT imaging system. The experimental results demonstrate that RMAP ResNet achieves superior performance in tumor tissue segmentation. The proposed method improves the quality of tumor segmentation for various shapes and sizes, leading to superior results. RMAP ResNet achieved Dice and IoU values of 94.78% and 90.58%, respectively, confirming the effectiveness and advancement of our proposed technology. ConclusionsOur proposed method addresses the crucial issue of the limited use of the OCT imaging modality in tumor resection surgery. It overcomes the challenge of visually identifying the tumor boundary in OCT modality imaging, where physicians using the naked eye may encounter difficulties. This study has potential applications in the fields of surgical navigation and intelligent minimally invasive treatment of brain tumors.

Integrative Bayesian tensor regression for imaging genetics applications.

Identifying biomarkers for Alzheimer's disease with a goal of early detection is a fundamental problem in clinical research. Both medical imaging and genetics have contributed informative biomarkers in literature. To further improve the performance, recently, there is an increasing interest in developing analytic approaches that combine data across modalities such as imaging and genetics. However, there are limited methods in literature that are able to systematically combine high-dimensional voxel-level imaging and genetic data for accurate prediction of clinical outcomes of interest. Existing prediction models that integrate imaging and genetic features often use region level imaging summaries, and they typically do not consider the spatial configurations of the voxels in the image or incorporate the dependence between genes that may compromise prediction ability. We propose a novel integrative Bayesian scalar-on-image regression model for predicting cognitive outcomes based on high-dimensional spatially distributed voxel-level imaging data, along with correlated transcriptomic features. We account for the spatial dependencies in the imaging voxels via a tensor approach that also enables massive dimension reduction to address the curse of dimensionality, and models the dependencies between the transcriptomic features via a Graph-Laplacian prior. We implement this approach via an efficient Markov chain Monte Carlo (MCMC) computation strategy. We apply the proposed method to the analysis of longitudinal ADNI data for predicting cognitive scores at different visits by integrating voxel-level cortical thickness measurements derived from T1w-MRI scans and transcriptomics data. We illustrate that the proposed imaging transcriptomics approach has significant improvements in prediction compared to prediction using a subset of features from only one modality (imaging or genetics), as well as when using imaging and transcriptomics features but ignoring the inherent dependencies between the features. Our analysis is one of the first to conclusively demonstrate the advantages of prediction based on combining voxel-level cortical thickness measurements along with transcriptomics features, while accounting for inherent structural information.

A Dual-Aware deep learning framework for identification of glioma isocitrate dehydrogenase genotype using magnetic resonance amide proton transfer modalities

To propose a Dual-Aware deep learning framework for genotyping of isocitrate dehydrogenase (IDH) in gliomas based on magnetic resonance amide proton transfer (APT) modality data as a means to assist non-invasive diagnosis of gliomas. We collected multimodal magnetic resonance imaging (MRI) imaging data of the brain from 118 cases of gliomas, including 68 wild-type and 50 mutant type cases. The delineation of the ROI of brain glioma was completed in all the cases. APT modality imaging does not require contrast agents, and its signal intensity on tumors is positively correlated with tumor malignancy, and the signal intensity on wild-type IDH is higher than that on mutant IDH. For APT modalities, tumor imaging and derived areas are morphologically variable and lack prominent edge contour characteristics compared with other modalities. Based on these characteristics, we propose the Dual-Aware framework, which introduces the Multi-Aware framework to mine multi-scale features, and the Edge Aware module mines the edge features for automatic genotype identification. The introduction of two types of Aware mechanisms effectively improved the identification rate of the model for glioma IDH genotyping. The accuracy and AUC for each modality data were enhanced, and the best performance was achieved on the APT modality with a prediction accuracy of 83.1% and an AUC of 0.822, suggesting its advantages and effectiveness for identifying glioma IDH genotypes. The proposed deep learning algorithm model constructed based on the image characteristics of the APT modality is effective for glioma IDH genotyping and identification task and may potentially replace the commonly used T1CE modality to avoid contrast agent injection and achieve non- invasive IDH genotyping.

Publication trends in the field of the cornea in the last 4 decades: a bibliometric study.

The objective of this bibliometric study was to identify the top 100 most-cited articles on the cornea published in the English language between 1980 and 2021 using multidimensional citation analysis. The data were obtained from the Thomson Reuters Web of Science Core Collection and the PubMed databases. The top 100 articles in terms of citation number were identified and analyzed. A total of 40,792 articles related to the cornea were retrieved. The 100 most-cited articles were published between 1995 and 2000. The average time since publication was 19.64 ± 5.75years. The mean impact factor of the journals was 10.27 ± 17.14 and the Q category of most journals was Q1. Ophthalmology was the journal with the most published articles (n = 10), which represented level 3 evidence. The three most common topics among the top 100 articles were treatment modality, histopathology, and diagnostic imaging. The most frequently mentioned treatments were related to limbal stem cell failure, crosslinking, and lamellar keratoplasty. We observed a negative correlation between the average number of citations per year and the time passed since publication (r = - 0.629; p = 0.001). Our analysis of the top 100 most-cited articles on the cornea revealed scientific contributions, vital current data related to clinical implementations, and valuable insights into the current developments in ophthalmology. To our knowledge, this is the first study to evaluate the most influential papers on the cornea, and our findings highlight the research quality and latest discoveries and trends in the management cornea diseases.

Self-assembled multifunctional nanotheranostics against circulating tumor clusters in metastatic breast cancer.

Circulating tumor clusters (CTC) disseminating from the primary tumor are responsible for secondary tumor formation where the conventional treatments such as chemotherapy and radiotherapy does not prevent the metastasis at locally advanced stage of breast cancer. In this study, a smart nanotheranostic system has been developed to track and eliminate the CTCs before it can colonize at a new site, which would reduce metastatic progression and increase the five-year survival rate of the breast cancer patients. Targeted multiresponsive (magnetic hyperthermia and pH) nanomicelles incorporated with NIR fluorescent superparamagnetic iron oxide nanoparticles were developed based on self-assembly for dual modal imaging and dual toxicity for spontaneous killing of CTCs in blood stream. A heterogenous tumor clusters model was developed to mimic the CTCs isolated from breast cancer patients. The nanotheranostic system was further evaluated for the targeting property, drug release kinetics, hyperthermia and cytotoxicity against developed CTC model invitro. Invivo model in BALB/c mice equivalent to stage III and IV human metastatic breast cancer was developed to evaluate the biodistribution and therapeutic efficacy of micellar nanotheranostic system. Reduced CTCs in blood stream and low distant organ metastasis after treatment with the nanotheranostic system demonstrates its potential to capture and kill the CTCs that minimize the secondary tumor formation at distant sites.

Off-axis aberrations improve the resolution limits of incoherent imaging.

The presence of off-axis tilt and Petzval curvature, two of the lowest-order off-axis Seidel aberrations, are shown to improve the Fisher information of two-point separation estimation in an incoherent imaging system compared to an aberration-free system. Our results show that the practical localization advantages of modal imaging techniques within the field of quantum-inspired superresolution can be achieved with direct imaging measurement schemes alone.

Unraveling Textural and Chemical Features in Volcanic Rocks Through Advanced Image Processing: A Case Study From the 2019 Paroxysmal Eruptions of Stromboli

AbstractThe Quantitative X‐Ray Map Analyzer software, a new tool for image processing, has been tested on intertwined pumices and scoriae emitted during the two paroxysmal eruptions of Stromboli of 2019, whose textural and compositional heterogeneities reflect the coexistence of low porphyritic shoshonitic‐basalts and high porphyritic shoshonites. The procedure applied was designed to quantitatively document the complex variations in texture and composition of these products, allowing substantial time reduction of analytical and data processing. The procedure utilizes Principal Components Analysis and supervised Maximum Likelihood Classification for multivariate statistical data handling of an array of X‐ray elemental maps acquired at the millimeter scale in thin‐sections. This technique permits the production of high‐contrast colored images, which allow the classification of rock constituents, extrapolating the associated modal abundances and imaging chemical variations within the glass. Results highlight the close interconnection at the microscale of two types of magma in the erupted products, manifested in each processed image by the presence of contiguous areas preserving textural bulk properties typical of the pumice or scoria. The proportion of the two magmas feeding the eruptions is not simply represented by the proportions of scoria and pumice in individual clasts, as both scoria and pumice contain glass with the composition of shoshonitic‐basalts and shoshonites. This method also allows the recognition of important discordances between the textural and chemical features of the two fractions involved, as well as discernment of the compositions of the two interacting magmas at the microscopic scale, even in those micro‐domains showing evidence of intense interaction processes.

Gold-iron oxide nanoparticle: A unique multimodal theranostic approach for thrombosis

Thrombosis is a major concern worldwide as thrombosis-related deaths are attributed to one in five deaths globally. Establishing a clot-targeting theranostic particle for diagnosis and treatment of thrombosis related diseases is crucial to provide rapid life-saving intervention. Herein, we developed activated platelet targeted gold-iron oxide nanoparticles (AuIONP) as a robust and powerful probe facilitating triple imaging modalities, including magnetic resonance imaging (MRI), photoacoustic imaging (PAI), and fluorescence imaging (FLI). Additionally, the antibody tagged AuIONP was utilised to facilitate photothermal thrombolysis, stimulated by the exposure to near-infrared (NIR) laser at 808 nm. In vivo observation of the targeted AuIONP showed a significant increase in MRI T2*-weighted contrast and up to 3-fold more prominent photoacoustic signal and fluorescence intensity compared to the non-targeted group. Upon exposure to NIR laser at 1.5 W/cm2, the localised temperature increase at the clot was observed in the targeted AuIONP treated group, facilitating a significantly higher reduction of thrombus area compared to the non-targeted treated group. Our work offers an innovative theranostic probe with excellent triple modality imaging and thrombolysis.

Role of Cross-sectional Imaging in Evaluation of Parotid Gland Tumours: A Pictorial Review

Radiological evaluation of the parotid gland neoplasms is a major challenge for radiologists, due to the wide variety of imaging features and differential diagnosis. Though Ultrasonography (USG) combined with guided Fine Needle Aspiration Cytology (FNAC) is the primary diagnostic modality, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) play an important role in the evaluation of patients presenting with suspected neoplastic lesions of the parotid gland. Cross-sectional imaging data of seven patients were selected and reviewed in detail. CT and MRI imaging had been done on patients referred to Radiology Department for clinically suspected parotid tumours. All of them underwent surgical excision and histopathological examination postimaging. Benign tumours usually arise from superficial lobe and exhibit strong signal intensity on T2 weighted images with well-defined margins. Lobulated margins with T2 dark rim are characteristic of pleomorphic adenoma. Hyperdense lesion with cystic changes with occasional bilateralism favour Warthin’s tumour. Most of the malignant parotid tumours involve deep lobe and appear as low signal lesion on T2 weighted imaging with ill-defined margins. Locally aggressive features like subcutaneous/deep infiltration strongly suggests malignancy. Cross-sectional imaging feature of carcinoma ex pleomorphic adenoma is variable from focally aggressive to totally aggressive tumourigenesis. Few malignant tumours like high-grade Mucoepidermoid Carcinoma (MEC) and Adenoid Cystic Carcinoma (AdCC) can show tendency towards perineural spread. Although histopathological examination is required for definitive diagnosis, few pathology-specific imaging findings on cross-sectional imaging can help in localising and characterising the parotid lesions and categorising innocuous benign from sinister malignant lesions and thus narrow down the differential diagnosis.

Symbiosis of Conventional Modality and Digital Imaging Technique for Rehabilitating an Ocular Defect

Anophthalmia leads to both physical and social disability which affects the quality and day-to-day functioning of an individual. This article tends to describe the rehabilitation of unilateral anophthalmia by a custom-made eye prosthesis. Stock-made eye prosthesis once served as a boon to prosthodontists, but with the recent advances in the field of technology, custom-made eye prosthesis is preferred more as they cater the demand of the patient in a more socially acceptable manner. Iris positioning has been a major challenge to the prosthodontists since beginning, but with the advent of modern-day technology, things have been made easier and repeatable with commendable accuracy. This article describes a symbiosis of both conventional and digital imaging modality in rehabilitating the left ocular area just by superimposition and comparison of soft- and hard-tissue landmarks manually and digitally.