Multimodal Imaging Analysis Research Articles

Amalric triangular sign: Combined central retinal artery and posterior ciliary artery occlusion presenting with macular edema

A 55-year-old female patient presented with no light perception and features suggestive of central retinal artery occlusion (CRAO) and posterior ciliary artery occlusion. Multimodal imaging analysis revealed retinal thickening, cherry-red spot, pallid optic disc with choroidal infarction (Amalric sign), and macular edema. Carotid Doppler disclosed 70% stenosis of the right common carotid artery. Systemic investigations showed elevated erythrocyte sedimentation rate, total cholesterol, and low-density lipoprotein level. The Amalric triangular sign with CRAO may present with macular edema. Prompt cardiac and carotid evaluation with appropriate management should be done.

Multimodal Imaging Analysis for Brain Clearance: PET Imaging Based Clearance Slope, FASTT2 MRI‐based Parenchyma CSF fraction, and DTI‐based ALPS‐index

AbstractBackgroundGlymphatic clearance is important to maintain brain health, which depends on two interconnected fluid systems: the novel glymphatic system and the traditional well‐characterized CSF system1. There are several imaging tools have been developed to evaluate the glymphatic function in the human brain, however each method providing only a limited perspective into glymphatic system2. A proper imaging tool should consider both circulation pathway of the bulk CSF flow, and a complex PVS network parallel to the microvascular structure. We developed and tested an imaging package including vCSF, CSFF, and DTI‐ALPS, to investigate the ventricular CSF turnover rate, perivascular CSF flow and brain parenchymal CSF fraction.MethodA total of 23 subjects (F=16, M=7, NL=17, MCI=6) age from 63 and 82 years (mean=69.3, std=5.1). PET MK6240 was acquired for lateral ventricle CSF clearance measure3 (vCSF), and MRI FASTT2 and DTI was acquired for CSF fraction4 (CSFF) and diffusion tensor imaging along perivascular spaces5 (DTI‐ALPS) index, respectively. Precisely, vCSF was calculated as the slope of 10 to 30min lateral ventricle TAC normalized by 1‐4min whole brain AUC. CSFF was fitted using three compartment model with 6 TEs = 0, 7.5, 17.5, 67.5, 147.5, 307.5 ms. DTI‐ALPS was calculated by mean(D𝑥_𝑝𝑟𝑜𝑗,D𝑥_𝑎𝑠𝑠𝑜𝑐)/mean (D𝑦_𝑝𝑟𝑜𝑗, D𝑧_𝑎𝑠𝑠𝑜𝑐), where Dx, Dy and Dz are the diffusivity along x‐, y‐ and x‐axis, and the projection, association regions.ResultFigure 4 shows Parenchymal CSFF negative correlates with DTI‐ALPS (r = ‐.51; p=.01 n=23), suggesting link between glymphatic system dysfunction and increased cerebral CSFF. 4 NL subjects with high CSFF and low DTI‐ALPS are amyloid positive. Figure 5 shows ventricular CSF turnover rate (vCSF) negative correlates CSFF (r = ‐.53; p=.01 n=23).ConclusionOur preliminary results showed that ventricular CSF turnover rate (vCSF), perivascular CSF flow (DTI‐ALPS) and brain parenchymal CSF fraction (CSFF) are correlated to each other and indicate the connection between the glymphatic system and the traditional CSF system. The complementary imaging approaches could lead to a much greater understanding of glymphatic clearance, and facilitate a more complete perspective on both glymphatic pathology and function alterations. Reference: 1. JM. Wardlaw,2020. 2. M. Niazi,2018. 3. Y. Li, 2022. 4. L. Zhou, 2021. 5. T. Taoka, 2017

ODP011 Single Nuclei RNA-Sequencing of Frozen Human Adipose Tissue and PET/CT Imaging Coupled With Deep-Learning Reveals Single-Cell Landscape and Transcriptional Dynamics Related to Adipocyte Hypertrophy and in Vivo Dysmetabolism

Abstract Background Adipocyte hypertrophy is associated with glucose and lipid dysmetabolism, ectopic fat accumulation, and metabolic complications independently of the level of obesity [1]. In this study, we aimed to explore potential mechanisms driving the obesity-independent, hypertrophy-associated dysmetabolism at a single-cell resolution and to assess the applicability of single nuclei RNA-sequencing of flash-frozen human subcutaneous adipose tissue (AT) to probe for these metabolic differences. Methods In 40 healthy participants, peri-umbilical subcutaneous adipose tissue biopsies were collected by needle aspiration to determine adipocyte size. Tissue uptake of dietary fat was quantified via oral 18-fluoro-thiaheptadecanoic acid administration with positron emission tomography (PET) [2]. Rates of fatty acid oxidation and esterification were measured using intravenous [11C]-palmitate with PET and multi-compartmental modeling. PET/CT segmentation using deep convolutional neural networks was done with DeepImageTranslator [3,4]. Single nuclei RNA-sequencing was performed in two individuals with AT hypertrophy vs. hyperplasia but matched for the same sex, ethnicity, glucose-tolerance status, BMI, total and percent body fat, and waist circumference. Nuclei were extracted from frozen AT and purified by flow cytometry following a protocol that we have developed and validated. Capture of single nuclei and preparation of RNA libraries were done with 10X Genomics technology. Results Unsupervised clustering identified a number of cell-type markers identical to those previously reported in murine epididymal AT and human AT. We found reduced quantities of fibroadipogenic cells per volume of AT with fat cell hypertrophy. Furthermore, the fraction of DPP4+ fibroblasts is increased with AT hypertrophy. Analyses of RNA velocity shows decreased velocity of adipocyte transformation from a transcriptionally active (TADA) phenotype to mature adipocytes in AT hypertrophy. Furthermore, in AT hypertrophy, adipocytes from the early TADA stage are destined to become insulin-resistant adipocytes, whereas those in AT hyperplasia are developing into an insulin-sensitive phenotype. Metabolic imaging results show that hypertrophied subcutaneous AT is associated with lower subcutaneous and higher visceral AT volume and dietary fatty acid storage capacity. Conclusion We have documented, for the first time, the applicability and consistency of single-nuclei RNA-sequencing of frozen human subcutaneous AT. We uncovered changes in cell clusters and subclusters of adipocytes, fibroblasts, pericytes, endothelial and immune cells as well as differences in transcriptional dynamics and developmental trajectory. [1] Ye RZ, et al. Endocrine reviews. 2022 Feb;43(1): 35-60. [2] Montastier É, Ye RZ, et al. American Journal of Physiology-Endocrinology and Metabolism. 2021 Jun 1;320(6): E1093-106. [3] R. Z. Ye, et al., DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis, SLAS Technology, https://doi.org/10.1016/j.slast.2021.10. 014, [4] Ye EZ, Ye EH, Ye RZ. DeepImageTranslator V2: analysis of multimodal medical images using semantic segmentation maps generated through deep learning. bioRxiv. 2021 Jan 1. Presentation: No date and time listed

Comprehensive Imaging Review of Pleural Fistulas from Diagnosis to Management.

Pleural fistula is an abnormal communication between the pleural cavity and an adjacent structure. The interplay of anatomic and physiologic factors including proximity to various intrathoracic structures, deep pleural recesses, and negative pleural pressures makes the pleura an easy victim of fistulization. Iatrogenic creation followed by necrotizing infections and malignancies are the most common causes. While the overall incidence and size of postsurgical pleural fistulas are decreasing with increased adoption of vascularized flaps for high-risk resections, the smaller fistulas that develop in the setting of post-radiation therapy changes, with necrotizing infections in immunosuppressed patients, and with use of newer antiangiogenic chemotherapies can be challenging to visualize directly. Imaging signs in clinical practice are often subtle and indirect. Multimodality imaging and biochemical pleural fluid analysis can offer important adjunctive information when a diagnosis is only suggested with the first imaging study. Certain pleural fistulas are inconsequential, some spontaneously close with or without diversion of flow or use of positive-pressure ventilation, while others carry a higher risk of complications or recurrence. Estimated fistula size, factors that impair healing, and the possibility of diversion are important considerations when deciding between endoscopic or surgical closure. The authors have tailored this article for a general imager or clinical practitioner and review 10 types of pleural fistulas, ranging from routine to rare, with regard to their etiology, pathophysiology, clinical cues, imaging features, nuances of pleural fluid analysis, and management options available today. ©RSNA, 2022.

Deep multiview learning to identify imaging-driven subtypes in mild cognitive impairment

BackgroundIn Alzheimer’s Diseases (AD) research, multimodal imaging analysis can unveil complementary information from multiple imaging modalities and further our understanding of the disease. One application is to discover disease subtypes using unsupervised clustering. However, existing clustering methods are often applied to input features directly, and could suffer from the curse of dimensionality with high-dimensional multimodal data. The purpose of our study is to identify multimodal imaging-driven subtypes in Mild Cognitive Impairment (MCI) participants using a multiview learning framework based on Deep Generalized Canonical Correlation Analysis (DGCCA), to learn shared latent representation with low dimensions from 3 neuroimaging modalities.ResultsDGCCA applies non-linear transformation to input views using neural networks and is able to learn correlated embeddings with low dimensions that capture more variance than its linear counterpart, generalized CCA (GCCA). We designed experiments to compare DGCCA embeddings with single modality features and GCCA embeddings by generating 2 subtypes from each feature set using unsupervised clustering. In our validation studies, we found that amyloid PET imaging has the most discriminative features compared with structural MRI and FDG PET which DGCCA learns from but not GCCA. DGCCA subtypes show differential measures in 5 cognitive assessments, 6 brain volume measures, and conversion to AD patterns. In addition, DGCCA MCI subtypes confirmed AD genetic markers with strong signals that existing late MCI group did not identify.ConclusionOverall, DGCCA is able to learn effective low dimensional embeddings from multimodal data by learning non-linear projections. MCI subtypes generated from DGCCA embeddings are different from existing early and late MCI groups and show most similarity with those identified by amyloid PET features. In our validation studies, DGCCA subtypes show distinct patterns in cognitive measures, brain volumes, and are able to identify AD genetic markers. These findings indicate the promise of the imaging-driven subtypes and their power in revealing disease structures beyond early and late stage MCI.

Multimodal brain tumor detection using multimodal deep transfer learning

MRI brain image analysis, including brain tumor detection, is a challenging task. MRI images are multimodal, and in recent years, multimodal medical image analysis has gotten more attention. Modes refer to data from multiple sources which are semantically correlated and sometimes provide complementary information to each other. In this paper, modalities in MRI brain images refer to the different planes of view (axial, sagittal, and coronal planes) in which MRI images are taken. In most cases, in the literature on multimodal data analysis, it is assumed that all modalities for all samples are available. While, in medical image analysis, this assumption is not valid, and only some modalities might be available for each sample. The knowledge transfer between and within modalities is considered to tackle this challenge in MRI brain image segmentation. For knowledge transfer, domain adaptation is an important step that deals with the problem of having different distributions between the training and test sets. These challenges have not been considered in recent multimodal brain image analysis studies. This paper proposed a new multimodal deep transfer learning for MRI brain image analysis. The main differences of the proposed approach, with respect to the other multimodal brain image analysis, are 1) proposing a new multimodal feature encoder and 2) proposing a new multimodal adaptation technique to handle the different distribution between the training and test sets. We applied it to IBSR and Figshre brain tumor datasets to evaluate the proposed approach. The results confirm that the proposed approach significantly outperforms the other comparable approaches.

DeepImageTranslator V2: Analysis of Multimodal Medical Images using Semantic Segmentation Maps Generated through Deep Learning

Introduction: Analysis of multimodal medical images often requires the selection of one or many anatomical regions of interest (ROIs) for extraction of useful statistics. This task can prove laborious when a manual approach is used. We have previously developed a user-friendly software tool for image-to-image translation using deep learning. Therefore, we present herein an update to the DeepImageTranslator V2 software with the addition of a tool for multimodal medical image segmentation analysis (hereby referred to as the MMMISA). Methods: The MMMISA was implemented using the Tkinter library; backend computations were implemented using the Pydicom, Numpy, and OpenCV libraries. We tested our software using 4188 slices from whole-body axial 2-deoxy-2-[18F]-fluoroglucose-position emission tomography/ computed tomography scans ([¹⁸F]-FDG-PET/CT) of 10 patients from the American College of Radiology Imaging Network-Head and Neck Squamous Cell Carcinoma (ACRIN-HNSCC) database. Using the deep learning software DeepImageTranslator, a model was trained with 36 randomly selected CT slices and manually labelled semantic segmentation maps. Utilizing the trained model, all the CT scans of the 10 HNSCC patients were segmented with high accuracy. Segmentation maps generated using the deep convolutional network were then used to measure organ specific [¹⁸F]-FDG uptake. We also compared measurements performed using the MMMISA and those made with manually selected ROIs. Results: The MMMISA is a tool that allows user to select ROIs based on deep learning-generated segmentation maps and to compute accurate statistics for these ROIs based on coregistered multimodal images. We found that organ-specific [¹⁸F]-FDG uptake measured using multiple manually selected ROIs is concordant with whole-tissue measurements made with segmentation maps using the MMMISA tool. Doi: 10.28991/HIJ-2022-03-03-07 Full Text: PDF

Deep learning-based multimodal image analysis for cervical cancer detection.

Cervical cancer is the fourth most common cancer in women, and its precise detection plays a critical role in disease treatment and prognosis prediction. Fluorodeoxyglucose positron emission tomography and computed tomography, i.e., FDG-PET/CT and PET/CT, have established roles with superior sensitivity and specificity in most cancer imaging applications. However, a typical FDG-PET/CT analysis involves the time-consuming process of interpreting hundreds of images, and the intense image screening work has greatly hindered clinicians. We propose a computer-aided deep learning-based framework to detect cervical cancer using multimodal medical images to increase the efficiency of clinical diagnosis. This framework has three components: image registration, multimodal image fusion, and lesion object detection. Compared to traditional approaches, our adaptive image fusion method fuses multimodal medical images. We discuss the performance of deep learning in each modality, and we conduct extensive experiments to compare the performance of different image fusion methods with some state-of-the-art (SOTA) object-detection deep learning-based methods in images with different modalities. Compared with PET, which has the highest recognition accuracy in single-modality images, the recognition accuracy of our proposed method on multiple object detection models is improved by an average of 6.06%. And compared with the best results of other multimodal fusion methods, our results have an average improvement of 8.9%.

Riehl's Melanosis: A Multimodality, In Vivo, Real-Time Skin Imaging Study with Cellular Resolution Optical Coherence Tomography and Advanced Skin Diagnosis System in a Tertiary Medical Center.

Background: Riehl’s melanosis is a psychologically devastating hyperpigmentary disorder that typically occurs on the face and neck. The study of Riehl’s melanosis is limited due to its rarity, variable morphology, and lack of noninvasive diagnostic tools. Recent advances in skin imaging analysis and diagnostic systems improve diagnostic accuracy and enable the noninvasive, real-time evaluation of pigmentary disease. A comprehensive study of Riehl’s melanosis clinical morphology with multimodality and in vivo skin imaging systems has yet to be reported. Objectives: To investigate the clinical features and in vivo advanced skin imaging findings of Riehl’s melanosis. Methods: We retrospectively investigated the clinical characteristics, dermoscopic, and histopathological features of Riehl’s melanosis. We further utilized multimodality skin imaging analysis systems, including a cellular resolution optical coherence tomography (OCT) and new skin diagnosis system, to investigate the features of Riehl’s melanosis. In addition, we compared OCT findings with histopathological features and clinical assessment. Results: We evaluated 30 patients with Riehl’s melanosis at a tertiary medical center from 2010 to 2022. The average age was 47.7 ± 12.3 (mean ± SD) years, predominantly female patients (female: n = 23; male: n = 7). Cellular resolution OCT imaging from lesion skin shows increased melanocyte capping, disrupted basement membrane, telangiectatic blood vessels, and melanophages in the dermis. The advanced skin diagnosis system captured subclinical erythema of the skin, highlighting the inflammatory nature of the disease. The results correlated well with histopathological findings. Limitations: This is a single-center, cross-sectional study. Conclusions: We highlight the features of Riehl’s melanosis through a novel cellular resolution OCT and photographic skin diagnosis system. A multimodality skin diagnosis system can serve as a real-time, in vivo, noninvasive method for evaluating pigmentary disorders.

Multimodality imaging and transcriptomics to phenotype mitral valve dystrophy in a unique knock-in Filamin-A rat model.

Degenerative mitral valve dystrophy (MVD) leading to mitral valve prolapse is the most frequent form of MV disease, and there is currently no pharmacological treatment available. The limited understanding of the pathophysiological mechanisms leading to MVD limits our ability to identify therapeutic targets. This study aimed to reveal the main pathophysiological pathways involved in MVD via the multimodality imaging and transcriptomic analysis of the new and unique knock-in (KI) rat model for the FilaminA-P637Q (FlnA-P637Q) mutation associated-MVD. Wild-type (WT) and KI rats were evaluated morphologically, functionally, and histologically between 3-week-old and 3-to-6-month-old based on Doppler echocardiography, 3D micro-computed tomography (microCT), and standard histology. RNA-sequencing and Assay for Transposase-Accessible Chromatin (ATAC-seq) were performed on 3-week-old WT and KI mitral valves and valvular cells, respectively, to highlight the main signalling pathways associated with MVD. Echocardiographic exploration confirmed MV elongation (2.0 ± 0.1 mm vs. 1.8 ± 0.1, P = 0.001), as well as MV thickening and prolapse in KI animals compared to WT at 3 weeks. 3D MV volume quantified by microCT was significantly increased in KI animals (+58% vs. WT, P = 0.02). Histological analyses revealed a myxomatous remodelling in KI MV characterized by proteoglycans accumulation. A persistent phenotype was observed in adult KI rats. Signalling pathways related to extracellular matrix homeostasis, response to molecular stress, epithelial cell migration, endothelial to mesenchymal transition, chemotaxis and immune cell migration, were identified based on RNA-seq analysis. ATAC-seq analysis points to the critical role of transforming growth factor-β and inflammation in the disease. The KI FlnA-P637Q rat model mimics human myxomatous MVD, offering a unique opportunity to decipher pathophysiological mechanisms related to this disease. Extracellular matrix organization, epithelial cell migration, response to mechanical stress, and a central contribution of immune cells are highlighted as the main signalling pathways leading to myxomatous MVD. Our findings pave the road to decipher underlying molecular mechanisms and the specific role of distinct cell populations in this context.

Brain metabolic changes and clinical response to superolateral medial forebrain bundle deep brain stimulation for treatment-resistant depression.

Deep brain stimulation (DBS) to the superolateral branch of the medial forebrain bundle is an efficacious therapy for treatment-resistant depression, providing rapid antidepressant effects. In this study, we use 18F-fluorodeoxyglucose-positron emission tomography (PET) to identify brain metabolic changes over 12 months post-DBS implantation in ten of our patients, compared to baseline. The primary outcome measure was a 50% reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score, which was interpreted as a response. Deterministic fiber tracking was used to individually map the target area; probabilistic tractography was used to identify modulated fiber tracts modeled using the cathodal contacts. Eight of the ten patients included in this study were responders. PET imaging revealed significant decreases in bilateral caudate, mediodorsal thalamus, and dorsal anterior cingulate cortex metabolism that was evident at 6 months and continued to 12 months post surgery. At 12 months post-surgery, significant left ventral prefrontal cortical metabolic decreases were also observed. Right caudate metabolic decrease at 12 months was significantly correlated with mean MADRS reduction. Probabilistic tractography modeling revealed that such metabolic changes lay along cortico-limbic nodes structurally connected to the DBS target site. Such observed metabolic changes following DBS correlated with clinical response provide insights into how future studies can elaborate such data to create biomarkers to predict response, the development of which likely will require multimodal imaging analysis.

3D-Printing to Plan Complex Transcatheter Paravalvular Leaks Closure.

Background: Percutaneous closure of paravalvular leak (PVL) has emerged as an alternative to surgical management in selected cases. Achieving complete PVL occlusion, while respecting prosthesis function remains challenging. A multimodal imaging analysis of PVL morphology before and during the procedure is mandatory to select an appropriate device. We aim to explore the additional value of 3D printing in predicting device related adverse events including mechanical valve leaflet blockade, risk of device embolization and residual shunting. Methods: From the FFPP registries (NCT05089136 and NCT05117359), we included 11 transcatheter PVL closure procedures from three centers for which 3D printed models were produced. Cardiac CT was used for segmentation for 3D printed models (3D-heartmodeling, Caissargues, France). Technology used a laser to fuse very fine powders (TPU Thermoplastic polyurethane) into a final part-laser sintering technology (SLS) with an adapted elasticity. A simulation on 3D printed model was performed using a set of occluders. Results: PVLs were located around aortic prostheses in six cases, mitral prostheses in four cases and tricuspid ring in one case. The device chosen during the simulation on the 3D printed model matched the one implanted in eight cases. In the three other cases, a similar device type was chosen during the procedures but with a different size. A risk of prosthesis leaflet blockade was identified on 3D printed models in four cases. During the procedure, the occluder was removed before release in one case. In another case the device was successfully repositioned and released. In two patients, leaflet impingement was observed post-operatively and surgical device removal had to be performed. Conclusion: In a case-series of complex transcatheter PVL closure procedures, hands-on simulation testing on 3D printed models proved its usefulness to plan and facilitate these challenging procedures.

Bilateral diffuse uveal melanocytic proliferation associated with endometrial carcinoma - multimodal imaging analysis.

Bilateral diffuse uveal melanocytic proliferation associated with endometrial carcinoma - multimodal imaging analysis.

MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method.

Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.

A Survey on the Progress of Computer-Assisted Vascular Intervention

Vascular diseases seriously threaten human health. As a commonly used minimally invasive surgery, vascular interventional diagnosis and treatment faces a variety of clinical burdens, so the support of computer-aided technology is urgently needed. In recent years, the rapid development of extravascular and intravascular medical imaging technology and artificial intelligence methods has promoted the related research of computer-assisted vascular intervention technology. The organic relationship between the basic content and key technologies of computer-assisted vascular intervention and its applications are reviewed and systematically analyzed, including multi-modal medical angiography image analysis, catheter guide wire position tracking, vascular associated lesion characteristics, morphological recognition, and other hot issues, and the relationships between the above contents and surgical planning and surgical navigation are analyzed. It is concluded that the clinical application trend in this field is to combine the integration of diagnosis and treatment with vascular interventional robot technology. Through the analysis of research status and development trends, it is concluded that the challenges faced by various research content are: the accuracy of the segmentation model to quantify the vascular structure and the completeness of topology extraction, intervention path vectorization and accuracy of multimodal information fusion, correlation between vascular anatomical labeling and surgical path navigation, and the level and evaluation method of human-computer interaction in vascular intervention. Analysis shows that in the process of solving these problems, the artificial intelligence diagnosis and treatment technology will continue to be promoted, especially the rapid development and clinical application of robot-assisted vascular intervention.

Multimodal imaging analysis for the impact of retinal peripheral lesions on central neurovascular structure and retinal function in type 2 diabetes with diabetic retinopathy

ObjectivesTo explore the possible role of peripheral lesions (PLs) detected by ultrawide field (UWF) imaging system on central neurovascular structure and retinal function.MethodsNinety-seven diabetic patients were included in this cross-sectional...

Neuroimaging Correlates of Cognitive Deficits in Wilson's Disease.

BackgroundCognitive impairment is common in neurological presentations of Wilson's disease (WD). Various domains can be affected, and subclinical deficits have been reported in patients with hepatic presentations. Associations with imaging abnormalities have not been systematically tested.ObjectiveThe aim was to determine the neuroanatomical basis for cognitive deficits in WD.MethodsWe performed a 16‐item neuropsychological test battery and magnetic resonance brain imaging in 40 patients with WD. The scores for each test were compared between patients with neurological and hepatic presentations and with normative data. Associations with Unified Wilson's Disease Rating Scale neurological examination subscores were examined. Quantitative, whole‐brain, multimodal imaging analyses were used to identify associations with neuroimaging abnormalities in chronically treated stable patients.Results reasoning, executive function, processing speed, calculation, and visuospatial function scores were lower in patients with neurological presentations than in those with hepatic presentations and correlated with neurological examination subscores. Deficits in abstract reasoning and phonemic fluency were associated with lower putamen volumes even after controlling for neurological severity. About half of patients with hepatic presentations had poor performance in memory for faces, cognitive flexibility, or associative learning relative to normative data. These deficits were associated with widespread cortical atrophy and/or white matter diffusion abnormalities.ConclusionsSubtle cognitive deficits in patients with seemingly hepatic presentations represent a distinct neurological phenotype associated with diffuse cortical and white matter pathology. This may precede the classical neurological phenotype characterized by movement disorders and executive dysfunction and be associated with basal ganglia damage. A binary phenotypic classification for WD may no longer be appropriate. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

Carbon Dots in Biomedicine: A Review.

Despite the rapid development of science and technology, the effective treatment of cancer still threatens human life and health. However, the success of cancer treatment is closely related to early diagnosis, identification, and effective treatment. In recent years, with the strengthening of the development and research of nanomaterials for cancer diagnosis and treatment, researchers have found that carbon dots (CDs) have the advantages of wide absorption, excellent biocompatibility, diverse imaging characteristics, and photostability and are widely used in various fields, such as sensing, imaging, and drug/gene transportation. Recently, researchers also discovered that CDs could be used as an effective photosensitizer to generate active oxygen or convert light energy into heat under the stimulation of the external lasers, making them have the effects of photothermal and photodynamic therapy for cancer. In this review, we first outline the single-modal and multimodal imaging analysis of CDs in cancer cells. After introducing diversified imaging functions, we focused on the design and the latest research progress of CDs in phototherapy and introduced in detail the strategies of CDs in phototherapy treatment and the challenges faced by clinical applications. We hope that this overview can provide important insights for researchers and accelerate the pace of research on CDs in imaging-guided phototherapy treatment.

Indolent Nonprogressive Multifocal Choroidal Lymphoid Lesions: A Clinical–Histopathological Correlation

To present the clinicopathologic correlation of indolent nonprogressive multifocal choroidal lesions, clinically presumed to be lymphoid in nature, using multimodal imaging and histopathological analysis of a donor eye. Case study and clinicopathological correlation. A 77-year-old man of Caucasian ancestry with indolent, nonprogressive, multifocal, choroidal infiltration of his right eye, presumed to be lymphocytic in nature based on the appearance of the lesions, was followed up for 19 years. Multimodal imaging, including fundus photography, B-scan ultrasonography, OCT, fluorescein angiography, and indocyanine green angiography, was performed throughout the 19 years of follow-up before the patient's death. The involved eye was preserved 21 hours postmortem and analyzed using standard histopathological and immunohistochemical techniques. Correlation of findings on multimodal imaging with histopathological and immunohistochemical findings in the involved eye. Clinical examination over the course of 19 years showed no deterioration in the visual acuity of the involved eye. Multimodal imaging revealed yellow-orange choroidal lesions that showed no appreciable progression during the 19 years of follow-up. These areas stained minimally on fluorescein angiography. Indocyanine green angiography revealed tortuous choroidal vessels and fluorescence blockage. Enhanced-depth imaging OCT revealed hyporeflective, homogenous choroidal thickening. Light microscopy, histopathology, and immunohistochemistry showed that the lesions were composed of small, mature-appearing B cells that spared the choriocapillaris. The findings were most consistent with extranodal marginal-zone lymphoma of the mucosa-associated lymphoid tissue (MALT). Indolent, nonprogressive, multifocal, choroidal lymphoid lesions in this patient remained confined to the choroid, as determined based on the clinical examination and imaging for almost 2 decades, with no clinical evidence of extension into the retina. Light microscopy, histopathology, and immunohistochemistry postmortem showed that the lesions were composed of small, mature-appearing B cells that spared the choriocapillaris. The findings were consistent with extranodal marginal-zone lymphoma of the MALT. This entity is distinct from more aggressive uveal and choroidal lymphomas and is expected to remain relatively stationary on long-term clinical follow-up, with a good visual prognosis.

Quantitative Chemical Imaging of Bone Tissue for Intraoperative and Diagnostic Applications.

Bone is difficult to image using traditional histopathological methods, leading to challenges in intraoperative pathological evaluation that is critical in guiding surgical treatment, particularly in orthopedic oncology. In this study, we demonstrate that a multimodal quantitative imaging approach that combines stimulated Raman scattering (SRS) microscopy, two-photon fluorescence (TPF) microscopy, and second-harmonic generation (SHG) microscopy can provide useful diagnostic information regarding intact bone tissue fragments from surgical excision or biopsy specimens. We imaged bone samples from 17 patient cases and performed quantitative chemical and morphological analyses of both mineral and organic components of bone. Our main findings show that carbonate content combined with morphometric analysis of bone organic matrix can separate several major classes of bone cancer-associated diagnostic categories with an average accuracy of 92%. This proof-of-principle study demonstrates that quantitative multimodal imaging and machine learning-based analysis of bony tissue can provide crucial diagnostic information for guiding clinical decisions in orthopedic oncology. Moreover, the general methodology of morphological and chemical imaging combined with machine learning can be readily extended to other tissue types for tissue diagnosis in intraoperative and other clinical settings.