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OBJECTIVES: Although neoadjuvant immunochemotherapy has been widely applied in non-small cell lung cancer (NSCLC), predicting treatment response remains a challenge. We used pretreatment multimodal CT to explore deep learning-based immunochemotherapy response image biomarkers. METHODS: This study retrospectively obtained non-contrast enhanced and contrast enhancedbubu CT scans of patients with NSCLC who underwent surgery after receiving neoadjuvant immunochemotherapy at multiple centers between August 2019 and February 2023. Deep learning features were extracted from both non-contrast enhanced and contrast enhanced CT scans to construct the predictive models (LUNAI-uCT model and LUNAI-eCT model), respectively. After the feature fusion of these two types of features, a fused model (LUNAI-fCT model) was constructed. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value. SHapley Additive exPlanations analysis was used to quantify the impact of CT imaging features on model prediction. To gain insights into how our model makes predictions, we employed Gradient-weighted Class Activation Mapping to generate saliency heatmaps. RESULTS: The training and validation datasets included 113 patients from Center A at the 8:2 ratio, and the test dataset included 112 patients (Center B n=73, Center C n=20, Center D n=19). In the test dataset, the LUNAI-uCT, LUNAI-eCT, and LUNAI-fCT models achieved AUCs of 0.762 (95% CI 0.654 to 0.791), 0.797 (95% CI 0.724 to 0.844), and 0.866 (95% CI 0.821 to 0.883), respectively. CONCLUSIONS: By extracting deep learning features from contrast enhanced and non-contrast enhanced CT, we constructed the LUNAI-fCT model as an imaging biomarker, which can non-invasively predict pathological complete response in neoadjuvant immunochemotherapy for NSCLC.
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Carcinoma de Pulmón de Células no Pequeñas , Aprendizaje Profundo , Neoplasias Pulmonares , Terapia Neoadyuvante , Tomografía Computarizada por Rayos X , Humanos , Carcinoma de Pulmón de Células no Pequeñas/tratamiento farmacológico , Carcinoma de Pulmón de Células no Pequeñas/diagnóstico por imagen , Carcinoma de Pulmón de Células no Pequeñas/patología , Neoplasias Pulmonares/tratamiento farmacológico , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Masculino , Femenino , Terapia Neoadyuvante/métodos , Persona de Mediana Edad , Estudios Retrospectivos , Anciano , Tomografía Computarizada por Rayos X/métodos , Inmunoterapia/métodos , Imagen Multimodal/métodosRESUMEN
ABSTRACT: Pulmonary epithelial myoepithelial carcinoma is very rare. We reported the imaging finding of pulmonary epithelial myoepithelial carcinoma of bronchus in a 61-year-old man on 18F-FDG PET/CT. An irregular mass with an SUVmax of 6.36 growing along right upper lobe bronchus and multiple pulmonary nodules around the mass were found on 18F-FDG PET/CT. Postoperative pathology demonstrated the diagnosis of epithelial myoepithelial carcinoma.
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Fluorodesoxiglucosa F18 , Neoplasias Pulmonares , Mioepitelioma , Tomografía Computarizada por Tomografía de Emisión de Positrones , Tomografía Computarizada por Rayos X , Humanos , Masculino , Persona de Mediana Edad , Neoplasias Pulmonares/diagnóstico por imagen , Neoplasias Pulmonares/patología , Mioepitelioma/diagnóstico por imagen , Mioepitelioma/patología , Imagen MultimodalRESUMEN
We present the case of a patient with myocardial infarction due to coronary ectasia. A transthoracic echocardiogram showed a unique image of a cystic-like mass in the right atrium corresponding to the ectatic right coronary artery (arrows), which was confirmed with computed tomography.
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Vasos Coronarios , Ecocardiografía , Imagen Multimodal , Humanos , Imagen Multimodal/métodos , Ecocardiografía/métodos , Dilatación Patológica , Vasos Coronarios/diagnóstico por imagen , Masculino , Tomografía Computarizada por Rayos X/métodos , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/complicaciones , Persona de Mediana Edad , Diagnóstico DiferencialRESUMEN
Purpose: To assess the correspondence between interdigitation zone (IZ) reflectivity, ellipsoid zone (EZ) loss, inner retinal layer reflectivity, patterns of capillary dilation, and telangiectasia in eyes with early macular telangiectasia type 2 (MacTel). Patients and Methods: Twenty-eight eyes of 22 patients with grade 0-2 MacTel (according to the MacTel project classification) and 28 healthy control eyes were included in this study. Multimodal imaging, including optical coherence tomography (OCT) angiography, adaptive optics flood illumination ophthalmoscopy (AO-FIO) and blue light reflectance (BLR), was performed. The EZ, IZ, and outer plexiform layer (OPL) were analyzed on the structural OCT C-scans. The vascular density (VD) was measured on the binarized and skeletonized angiograms of the superficial vascular plexus and deep capillary complex. The vascular diameter index (VDI) was calculated by dividing the binarized VD by the skeletonized VD. Results: On AO-FIO, cone density in the MacTel zone was significantly lower in MacTel eyes than in controls, even in areas located outside the EZ loss (P < 0.001). A distinctive pattern of IZ reflectivity attenuation extended beyond the area of EZ attenuation. The shape and size of a strong OPL hyper-reflectivity corresponded to the MacTel white area (MacTel zone) seen on BLR. Capillary dilation and rarefaction were colocalized with this area, extending beyond visible telangiectasia. The VDI was higher in MacTel eyes than in controls (P < 0.001). Conclusions: These findings suggest that in early MacTel eyes, photoreceptor signal alteration, OPL hyper-reflectivity, and capillary dilation, potentially associated with Müller cell dysfunction, precede the EZ loss.
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Angiografía con Fluoresceína , Oftalmoscopía , Telangiectasia Retiniana , Vasos Retinianos , Tomografía de Coherencia Óptica , Humanos , Tomografía de Coherencia Óptica/métodos , Femenino , Telangiectasia Retiniana/diagnóstico , Telangiectasia Retiniana/fisiopatología , Telangiectasia Retiniana/diagnóstico por imagen , Masculino , Vasos Retinianos/diagnóstico por imagen , Vasos Retinianos/patología , Angiografía con Fluoresceína/métodos , Persona de Mediana Edad , Anciano , Agudeza Visual/fisiología , Fondo de Ojo , Imagen Multimodal , Adulto , Capilares/patología , Capilares/diagnóstico por imagenRESUMEN
Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not fully reflect the complex mechanisms underlying ASD, and combining multiple modalities enables a more comprehensive understanding. Here, we propose, DeepASD, an end-to-end trainable regularized graph learning method for ASD prediction, which incorporates heterogeneous multimodal data and latent inter-patient relationships to better understand the pathogenesis of ASD. DeepASD first learns cross-modal feature representations through a multimodal adversarial-regularized encoder, and then constructs adaptive patient similarity networks by leveraging the representations of each modality. DeepASD exploits inter-patient relationships to boost the ASD diagnosis that is implemented by a classifier compositing of graph neural networks. We apply DeepASD to the benchmarking Autism Brain Imaging Data Exchange (ABIDE) data with four modalities. Experimental results show that the proposed DeepASD outperforms eight state-of-the-art baselines on the benchmarking ABIDE data, showing an improvement of 13.25% in accuracy, 7.69% in AUC-ROC, and 17.10% in specificity. DeepASD holds promise for a more comprehensive insight of the complex mechanisms of ASD, leading to improved diagnosis performance.
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Trastorno del Espectro Autista , Aprendizaje Profundo , Humanos , Trastorno del Espectro Autista/diagnóstico , Redes Neurales de la Computación , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Imagen Multimodal/métodos , Neuroimagen/métodos , Imagen por Resonancia MagnéticaRESUMEN
BACKGROUND: Cardiac rotational parameters in primary symptomatic left ventricular noncompaction (LVNC) with preserved left ventricular ejection fraction (LVEF) are not well understood. We aimed to analyze cardiac rotation measured with cardiac magnetic resonance feature-tracking (CMR-FT) and speckle-tracking echocardiography (Echo-ST) in LVNC morphology subjects with preserved LVEF and different genotypes and healthy controls. METHODS: Our retrospective study included 54 LVNC subjects with preserved LVEF and 54 control individuals. We evaluated functional and rotational parameters with CMR in the total study population and with echocardiography in 39 LVNC and 40 C individuals. All LVNC subjects were genotyped with a 174-gene next-generation sequencing panel and grouped into the subgroups: benign (B), variant of uncertain significance (VUS), and pathogenic (P). RESULTS: In comparison with controls, LVNC subjects had reduced apical rotational degree (p = 0.004) and one-third had negative apical rotation. While the degree of apical rotation was comparable between the three genetic subgroups, they differed significantly in the direction of apical rotation (p<0.001). In contrast to control and B groups, all four studied cardiac rotational patterns were identified in the P and VUS subgroups, namely normal rotation, positive and negative rigid body rotation, and reverse rotation. When the CMR-FT and Echo-ST methods were compared, the direction and pattern of cardiac rotation had moderate to good association (p<0.001) whereas the rotational degrees showed no reasonable correlation or agreement. CONCLUSION: While measuring cardiac rotation using both CMR-FT and Echo-ST methods, subclinical mechanical differences were identified in subjects with LVNC phenotype and preserved LVEF, especially in cases with genetic involvement.
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Ecocardiografía , Imagen Multimodal , Humanos , Masculino , Femenino , Persona de Mediana Edad , Ecocardiografía/métodos , Estudios Retrospectivos , Rotación , Imagen Multimodal/métodos , Adulto , Volumen Sistólico , Función Ventricular Izquierda/fisiología , Imagen por Resonancia Magnética/métodos , Ventrículos Cardíacos/diagnóstico por imagen , Ventrículos Cardíacos/fisiopatología , Anciano , Estudios de Casos y Controles , No Compactación Aislada del Miocardio Ventricular/diagnóstico por imagen , No Compactación Aislada del Miocardio Ventricular/genética , No Compactación Aislada del Miocardio Ventricular/fisiopatologíaRESUMEN
Background: Recurrent pregnancy loss (RPL) frequently links to a prolonged endometrial receptivity (ER) window, leading to the implantation of non-viable embryos. Existing ER assessment methods face challenges in reliability and invasiveness. Radiomics in medical imaging offers a non-invasive solution for ER analysis, but complex, non-linear radiomic-ER relationships in RPL require advanced analysis. Machine learning (ML) provides precision for interpreting these datasets, although research in integrating radiomics with ML for ER evaluation in RPL is limited. Objective: To develop and validate an ML model that employs radiomic features derived from multimodal transvaginal ultrasound images, focusing on improving ER evaluation in RPL. Methods: This retrospective, controlled study analyzed data from 346 unexplained RPL patients and 369 controls. The participants were divided into training and testing cohorts for model development and accuracy validation, respectively. Radiomic features derived from grayscale (GS) and shear wave elastography (SWE) images, obtained during the window of implantation, underwent a comprehensive five-step selection process. Five ML classifiers, each trained on either radiomic, clinical, or combined datasets, were trained for RPL risk stratification. The model demonstrating the highest performance in identifying RPL patients was selected for further validation using the testing cohort. The interpretability of this optimal model was augmented by applying Shapley additive explanations (SHAP) analysis. Results: Analysis of the training cohort (242 RPL, 258 controls) identified nine key radiomic features associated with RPL risk. The extreme gradient boosting (XGBoost) model, combining radiomic and clinical data, demonstrated superior discriminatory ability. This was evidenced by its area under the curve (AUC) score of 0.871, outperforming other ML classifiers. Validation in the testing cohort of 215 subjects (104 RPL, 111 controls) confirmed its accuracy (AUC: 0.844) and consistency. SHAP analysis identified four endometrial SWE features and two GS features, along with clinical variables like age, SAPI, and VI, as key determinants in RPL risk stratification. Conclusion: Integrating ML with radiomics from multimodal endometrial ultrasound during the WOI effectively identifies RPL patients. The XGBoost model, merging radiomic and clinical data, offers a non-invasive, accurate method for RPL management, significantly enhancing diagnosis and treatment.
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Aborto Habitual , Endometrio , Aprendizaje Automático , Humanos , Femenino , Endometrio/diagnóstico por imagen , Adulto , Estudios Retrospectivos , Aborto Habitual/diagnóstico por imagen , Embarazo , Ultrasonografía/métodos , Implantación del Embrión , Estudios de Casos y Controles , Imagen Multimodal/métodos , RadiómicaRESUMEN
Atypical social impairments (i.e., impaired social cognition and social communication) are vital manifestations of autism spectrum disorder (ASD) patients, and the incidence rate of ASD is significantly higher in males than in females. Characterizing the atypical brain patterns underlying social deficits of ASD is significant for understanding the pathogenesis. However, there are no robust imaging biomarkers that are specific to ASD, which may be due to neurobiological complexity and limitations of single-modality research. To describe the multimodal brain patterns related to social deficits in ASD, we highlighted the potential functional role of white matter (WM) and incorporated WM functional activity and gray matter structure into multimodal fusion. Gray matter volume (GMV) and fractional amplitude of low-frequency fluctuations of WM (WM-fALFF) were combined by fusion analysis model adopting the social behavior. Our results revealed multimodal spatial patterns associated with Social Responsiveness Scale multiple scores in ASD. Specifically, GMV exhibited a consistent brain pattern, in which salience network and limbic system were commonly identified associated with all multiple social impairments. More divergent brain patterns in WM-fALFF were explored, suggesting that WM functional activity is more sensitive to ASD's complex social impairments. Moreover, brain regions related to social impairment may be potentially interconnected across modalities. Cross-site validation established the repeatability of our results. Our research findings contribute to understanding the neural mechanisms underlying social disorders in ASD and affirm the feasibility of identifying biomarkers from functional activity in WM.
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Trastorno del Espectro Autista , Sustancia Gris , Imagen por Resonancia Magnética , Imagen Multimodal , Sustancia Blanca , Humanos , Trastorno del Espectro Autista/diagnóstico por imagen , Trastorno del Espectro Autista/fisiopatología , Trastorno del Espectro Autista/patología , Masculino , Sustancia Gris/diagnóstico por imagen , Sustancia Gris/patología , Adulto Joven , Adulto , Sustancia Blanca/diagnóstico por imagen , Sustancia Blanca/patología , Adolescente , Conducta Social , Niño , Neuroimagen/métodos , Encéfalo/diagnóstico por imagen , Encéfalo/patología , Encéfalo/fisiopatologíaRESUMEN
Early-stage diagnosis of liver cancer is challenging, with an overall poor prognosis. The tumor microenvironment of primary liver cancer is complex, exhibiting significant heterogeneity both interpersonally and intratumorally. Therefore, it is of paramount importance to dynamically analyze biological markers in the tumor microenvironment of primary liver cancer in vivo. In recent years, significant progress has been made in the imaging diagnosis and treatment of liver cancer with the development of molecular imaging. Molecular imaging techniques utilize specific nano-imaging probes to evaluate pathological changes of liver cancer at the molecular and cellular levels in real-time. These techniques enable precise imaging to reveal key molecular biomarkers involved in the occurrence and progression of liver cancer, exploring their associations with cancer progression and outcomes. This article focuses on molecular imaging, emphasizing the current research status and latest advancements in the field of liver cancer diagnosis and therapy using techniques such as CT, MRI, optical imaging, PET imaging, and multimodal imaging. It also identifies important future directions and significant challenges for further development.
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Neoplasias Hepáticas , Imagen por Resonancia Magnética , Imagen Molecular , Neoplasias Hepáticas/diagnóstico por imagen , Neoplasias Hepáticas/diagnóstico , Humanos , Imagen Molecular/métodos , Imagen por Resonancia Magnética/métodos , Tomografía de Emisión de Positrones/métodos , Imagen Multimodal/métodos , Tomografía Computarizada por Rayos X/métodos , Microambiente Tumoral , Biomarcadores de TumorRESUMEN
Adipose tissue-derived adult stem (ADAS) cells and extracellular vesicle (EV) therapy offer promising avenues for treating neurodegenerative diseases due to their accessibility and potential for autologous cell transplantation. However, the clinical application of ADAS cells or EVs is limited by the challenge of precisely identifying them in specific regions of interest. This study compares two superparamagnetic iron oxide nanoparticles, differing mainly in size, to determine their efficacy for allowing non-invasive ADAS tracking via MRI/MPI and indirect labeling of EVs. We compared a USPIO (about 5 nm) with an SPIO (Resovist®, about 70 nm). A physicochemical characterization of nanoparticles was conducted using DLS, TEM, MRI, and MPI. ADAS cells were labeled with the two nanoparticles, and their viability was assessed via MTT assay. MRI detected labeled cells, while TEM and Prussian Blue staining were employed to confirm cell uptake. The results revealed that Resovist® exhibited higher transversal relaxivity value than USPIO and, consequently, allows for detection with higher sensitivity by MRI. A 200 µgFe/mL concentration was identified as optimal for ADAS labeling. MPI detected only Resovist®. The findings suggest that Resovist® may offer enhanced detection of ADAS cells and EVs, making it suitable for multimodal imaging. Preliminary results obtained by extracting EVs from ADAS cells labeled with Resovist® indicate that EVs retain the nanoparticles, paving the way to an efficient and multimodal detection of EVs.
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Tejido Adiposo , Células Madre Adultas , Vesículas Extracelulares , Nanopartículas Magnéticas de Óxido de Hierro , Imagen por Resonancia Magnética , Nanopartículas de Magnetita , Vesículas Extracelulares/química , Vesículas Extracelulares/metabolismo , Tejido Adiposo/citología , Humanos , Células Madre Adultas/citología , Células Madre Adultas/metabolismo , Nanopartículas Magnéticas de Óxido de Hierro/química , Imagen por Resonancia Magnética/métodos , Nanopartículas de Magnetita/química , Imagen Multimodal/métodos , Dextranos/química , Medios de Contraste/química , Células CultivadasRESUMEN
Hepatocellular carcinoma (HCC) is a serious health issue due to its low early diagnosis rate, resistance to chemotherapy, and poor five-year survival rate. Therefore, it is crucial to explore novel diagnostic and therapeutic approaches tailored to the characteristics of HCC. Aggregation-induced emission (AIE) is a phenomenon where the luminescence of certain molecules, typically non-luminescent or weakly luminescent in solution, is significantly enhanced upon aggregation. AIE has been extensively applied in bioimaging, biosensors, and therapy. Fluorophore materials based on AIE (AIEgens) have a wide range of application scenarios and potential for clinical translation. This review focuses on recent advances in AIE-based strategies for diagnosing and treating HCC. First, the specific functional mechanism of AIE is described. Next, we summarize recent progress in the application of AIE for multimodal imaging, biosensor detection, and phototherapy. Finally, prospects and challenges for the AIE-based application in the diagnosis and therapy of HCC are discussed.
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Técnicas Biosensibles , Carcinoma Hepatocelular , Neoplasias Hepáticas , Neoplasias Hepáticas/diagnóstico , Neoplasias Hepáticas/diagnóstico por imagen , Neoplasias Hepáticas/terapia , Carcinoma Hepatocelular/diagnóstico , Carcinoma Hepatocelular/terapia , Carcinoma Hepatocelular/diagnóstico por imagen , Humanos , Técnicas Biosensibles/métodos , Colorantes Fluorescentes/química , Animales , Fototerapia , Imagen Óptica/métodos , Imagen Multimodal/métodosRESUMEN
Intravascular ultrasound and optical coherence tomography are used with increasing frequency for the care of coronary patients and in research studies. These imaging tools can identify culprit lesions in acute coronary syndromes, assess coronary stenosis severity, guide percutaneous coronary intervention (PCI), and detect vulnerable plaques and patients. However, they have significant limitations that have stimulated the development of multimodality intracoronary imaging catheters, which provide improvements in assessing vessel wall pathology and guiding PCI. Prototypes combining 2 or even 3 imaging probes with complementary attributes have been developed, and several multimodality systems have already been used in patients, with near-infrared spectroscopy intravascular ultrasound-based studies showing promising results for the identification of high-risk plaques. Moreover, postmortem histology studies have documented that hybrid imaging catheters can enable more accurate characterization of plaque morphology than standalone imaging. This review describes the evolution in the field of hybrid intracoronary imaging; presents the available multimodality catheters; and discusses their potential role in PCI guidance, vulnerable plaque detection, and the assessment of endovascular devices and emerging pharmacotherapies targeting atherosclerosis.
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Enfermedad de la Arteria Coronaria , Vasos Coronarios , Imagen Multimodal , Intervención Coronaria Percutánea , Placa Aterosclerótica , Valor Predictivo de las Pruebas , Tomografía de Coherencia Óptica , Ultrasonografía Intervencional , Humanos , Enfermedad de la Arteria Coronaria/diagnóstico por imagen , Enfermedad de la Arteria Coronaria/terapia , Vasos Coronarios/diagnóstico por imagen , Vasos Coronarios/patología , Intervención Coronaria Percutánea/instrumentación , Diseño de Equipo , Catéteres Cardíacos , Difusión de Innovaciones , Cateterismo Cardíaco/instrumentación , Espectroscopía Infrarroja Corta , AnimalesRESUMEN
A deep understanding of neuron structure and function is crucial for elucidating brain mechanisms, diagnosing and treating diseases. Optical microscopy, pivotal in neuroscience, illuminates neuronal shapes, projections, and electrical activities. To explore the projection of specific functional neurons, scientists have been developing optical-based multimodal imaging strategies to simultaneously capture dynamic in vivo signals and static ex vivo structures from the same neuron. However, the original position of neurons is highly susceptible to displacement during ex vivo imaging, presenting a significant challenge for integrating multimodal information at the single-neuron level. This study introduces a graph-model-based approach for cell image matching, facilitating precise and automated pairing of sparsely labeled neurons across different optical microscopic images. It has been shown that utilizing neuron distribution as a matching feature can mitigate modal differences, the high-order graph model can address scale inconsistency, and the nonlinear iteration can resolve discrepancies in neuron density. This strategy was applied to the connectivity study of the mouse visual cortex, performing cell matching between the two-photon calcium image and the HD-fMOST brain-wide anatomical image sets. Experimental results demonstrate 96.67% precision, 85.29% recall rate, and 90.63% F1 Score, comparable to expert technicians. This study builds a bridge between functional and structural imaging, offering crucial technical support for neuron classification and circuitry analysis.
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Neuronas , Animales , Ratones , Corteza Visual/diagnóstico por imagen , Corteza Visual/fisiología , Microscopía/métodos , Reconocimiento de Normas Patrones Automatizadas , Algoritmos , Imagen Multimodal/métodos , Procesamiento de Imagen Asistido por Computador/métodos , Encéfalo/diagnóstico por imagenRESUMEN
PURPOSE: To investigate the application value of support vector machine (SVM) model based on diffusion-weighted imaging (DWI), dynamic contrast-enhanced (DCE) and amide proton transfer- weighted (APTW) imaging in predicting isocitrate dehydrogenase 1(IDH-1) mutation and Ki-67 expression in glioma. METHODS: The DWI, DCE and APTW images of 309 patients with glioma confirmed by pathology were retrospectively analyzed and divided into the IDH-1 group (IDH-1(+) group and IDH-1(-) group) and Ki-67 group (low expression group (Ki-67 ≤ 10%) and high expression group (Ki-67 > 10%)). All cases were divided into the training set, and validation set according to the ratio of 7:3. The training set was used to select features and establish machine learning models. The SVM model was established with the data after feature selection. Four single sequence models and one combined model were established in IDH-1 group and Ki-67 group. The receiver operator characteristic (ROC) curve was used to evaluate the diagnostic performance of the model. Validation set data was used for further validation. RESULTS: Both in the IDH-1 group and Ki-67 group, the combined model had better predictive efficiency than single sequence model, although the single sequence model had a better predictive efficiency. In the Ki-67 group, the combined model was built from six selected radiomics features, and the AUC were 0.965 and 0.931 in the training and validation sets, respectively. In the IDH-1 group, the combined model was built from four selected radiomics features, and the AUC were 0.997 and 0.967 in the training and validation sets, respectively. CONCLUSION: The radiomics model established by DWI, DCE and APTW images could be used to detect IDH-1 mutation and Ki-67 expression in glioma patients before surgery. The prediction performance of the radiomics model based on the combination sequence was better than that of the single sequence model.
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Neoplasias Encefálicas , Glioma , Isocitrato Deshidrogenasa , Antígeno Ki-67 , Mutación , Máquina de Vectores de Soporte , Humanos , Isocitrato Deshidrogenasa/genética , Glioma/diagnóstico por imagen , Glioma/genética , Glioma/metabolismo , Antígeno Ki-67/metabolismo , Antígeno Ki-67/genética , Persona de Mediana Edad , Femenino , Neoplasias Encefálicas/diagnóstico por imagen , Neoplasias Encefálicas/genética , Neoplasias Encefálicas/metabolismo , Masculino , Estudios Retrospectivos , Adulto , Anciano , Imagen de Difusión por Resonancia Magnética/métodos , Imagen Multimodal , Adulto Joven , Imagen por Resonancia Magnética/métodos , Curva ROC , Medios de ContrasteRESUMEN
Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of Q EN , Q NIQE , Q SD , Q SSEQ and Q TMQI .
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Imagen Multimodal , Redes Neurales de la Computación , Humanos , Imagen Multimodal/métodos , Algoritmos , Imagen por Resonancia Magnética/métodos , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
BACKGROUND: The neurovascular conflict (NVC) at the brainstem exit zone of the facial nerve is considered the primary etiology of primary hemifacial spasm (HFS). Therefore, microvascular decompression (MVD) has become the preferred treatment for HFS. Successful neurovascular decompression can achieve significant therapeutic effects, and accurately identifying the site of compression is crucial for the success of this surgery. Detailed diagnostic neuroimaging plays an important role in accurately identifying the site of compression.The purpose of this study is to explore the feasibility and predictive value of preoperative visualization assessment of the neurovascular relationship in HFS using 3D Slicer software based on multimodal imaging fusion. This aims to reduce the omission of responsible vessels and lower the incidence of postoperative complications, thereby potentially improving the efficacy and safety of the surgery. METHODS: This study retrospectively analyzed 80 patients with HFS who underwent MVD surgery. All patients underwent preoperative cranial MRI scans, including the 3D-FIESTA and the 3D-TOF MRA sequences. Three-dimensional models were reconstructed from the multimodal MRI images using 3D Slicer software. Independent observers, who were blinded to the surgical outcomes, evaluated the neurovascular relationships using both the three-dimensional models and multimodal MRI images. The assessment results were compared with intraoperative findings, and statistical analysis was conducted using SPSS 22.0 software. RESULTS: The agreement between preoperative assessment using the 3D-TOF MRA sequence combined with the 3D-FIESTA sequence and intraoperative findings was represented by a Kappa value of 0.343, while the Kappa value for agreement between three-dimensional reconstruction and intraoperative findings was 0.637. There was a statistically significant difference between the two methods ( X2 = 18.852, P = 0.001 ). The sensitivity and specificity of the 3D-TOF MRA sequence combined with the 3D-FIESTA sequence for evaluating neurovascular relationships were 92.4% and 100%, respectively, while for three-dimensional reconstruction, both were 100%. The Kappa value for agreement between preoperative the 3D-TOF MRA sequence combined with the 3D-FIESTA sequence prediction of offending vessels and intraoperative findings was 0.625, while the Kappa value for agreement between three-dimensional reconstruction and intraoperative findings was 0.938, showing a statistically significant difference ( X2 = 317.798, P = 0.000 ). The Kappa value for agreement between preoperative the 3D-TOF MRA sequence combined with the 3D-FIESTA sequence assessment of the anatomical location of facial nerve involvement in neurovascular compression and intraoperative findings was 0.608, while the Kappa value for agreement between three-dimensional reconstruction and intraoperative findings was 0.918, also showing a statistically significant difference ( X2 = 504.647, P = 0.000 ). CONCLUSIONS: The preoperative visualization assessment of neurovascular relationships in HFS using 3D Slicer software based on multimodal imaging fusion has been demonstrated to be reliable. It is more accurate than combining the 3D-TOF MRA sequence with the 3D-FIESTA sequence and shows higher consistency with intraoperative findings. This method provides guidance for surgical procedures and thereby potentially enhances the efficacy and safety of surgeries to a certain extent.
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Espasmo Hemifacial , Imagen por Resonancia Magnética , Cirugía para Descompresión Microvascular , Imagen Multimodal , Humanos , Espasmo Hemifacial/cirugía , Femenino , Masculino , Persona de Mediana Edad , Estudios Retrospectivos , Cirugía para Descompresión Microvascular/métodos , Adulto , Imagen Multimodal/métodos , Anciano , Imagen por Resonancia Magnética/métodos , Nervio Facial/cirugía , Nervio Facial/diagnóstico por imagen , Cuidados Preoperatorios/métodos , Resultado del Tratamiento , Imagenología Tridimensional/métodosRESUMEN
Simultaneous positron emission tomography (PET)/magnetic resonance imaging provides concurrent information about anatomic, functional, and molecular changes in disease. We are developing a second generation MR-compatible RF-penetrable TOF-PET insert. The insert has a smaller scintillation crystal size and ring diameter compared to clinical whole-body PET scanners, resulting in higher spatial resolution and sensitivity. This paper reports the initial system performance of this full-ring PET insert. The global photopeak energy resolution and global coincidence time resolution, 11.74 ± 0.03 % FWHM and 238.1 ± 0.5 ps FWHM, respectively, are preserved as we scaled up the system to a full ring comprising 12, 288 LYSO-SiPM channels (crystal size: 3.2 × 3.2 × 20 mm3). Throughout a ten-hour experiment, the system performance remained stable, exhibiting a less than 1% change in all measured parameters. In a resolution phantom study, the system successfully resolved all 2.8 mm diameter rods, achieving an average VPR of 0.28 ± 0.08 without TOF and 0.24 ± 0.07 with TOF applied. Moreover, the implementation of TOF in the Hoffman phantom study also enhanced image quality. Initial MR compatibility studies of the full PET ring were performed with it unpowered as a milestone to focus on looking for material and geometry-related artifacts. During all MR studies, the MR body coil functioned as both the transmit and receive coil, and no observable artifacts were detected. As expected, using the body coil also as the RF receiver, MR image signal-to-noise ratio exhibited degradation (â¼30%), so we are developing a high quality receive-only coil that resides inside the PET ring.
Asunto(s)
Encéfalo , Imagen por Resonancia Magnética , Fantasmas de Imagen , Tomografía de Emisión de Positrones , Tomografía de Emisión de Positrones/instrumentación , Imagen por Resonancia Magnética/instrumentación , Encéfalo/diagnóstico por imagen , Ondas de Radio , Imagen Multimodal/instrumentación , Factores de Tiempo , Procesamiento de Imagen Asistido por Computador/métodos , HumanosRESUMEN
The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients' neurological recovery after coma. 32 patients (18-76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient's hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = - 0.74, p < 0.001, 95% CI: - 0.46, - 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.
Asunto(s)
Coma , Imagen Multimodal , Recuperación de la Función , Humanos , Persona de Mediana Edad , Coma/diagnóstico por imagen , Coma/fisiopatología , Adulto , Masculino , Femenino , Anciano , Recuperación de la Función/fisiología , Adolescente , Adulto Joven , Neuroimagen/métodos , Imagen por Resonancia Magnética , Estudios Prospectivos , Encéfalo/diagnóstico por imagen , Encéfalo/fisiopatología , Estudios de Cohortes , Tomografía Computarizada por Tomografía de Emisión de Positrones , Imagen de Difusión por Resonancia MagnéticaRESUMEN
Purpose: Retinitis pigmentosa (RP), the most common inherited retinal disease, is characterized by progressive photoreceptor degeneration. It remains unknown to what extent surviving photoreceptors transduce light and support vision in RP. To address this, we correlated structure and functional measures using adaptive optics scanning laser ophthalmoscopy (AOSLO), adaptive optics microperimetry, and adaptive optics optical coherence tomography (AO-OCT)-based optoretinograms (ORGs). Methods: Four patients with RP were imaged with AOSLO across the visual field covering the transition zone (TZ) of normal to diseased retina. Cone density was estimated in discrete regions spanning the TZ. Visual sensitivity was assessed by measuring increment thresholds for a 3-arcmin stimulus targeted via active eye tracking in AOSLO. ORGs were measured at the same locations using AO-OCT to assess the cones' functional response to a 528 ± 20-nm stimulus. Individual cone outer segment (COS) lengths were measured from AO-OCT in each subject. Results: Cone density was significantly reduced in patients with RP. Density reduction correlated with TZ location in 3 patients with RP, while a fourth had patches of reduced density throughout the retina. ORG amplitude was reduced in regions of normal and reduced cone density in all patients with RP. ORG response and COS length were positively correlated in controls but not in patients with RP. Despite deficits in cone density and ORG, visual sensitivity remained comparable to controls in three of four patients with RP. Conclusions: ORG-based measures of retinal dysfunction may precede deficits in cone structure and visual sensitivity. ORG is a sensitive measure of RP disease status and has significant potential to provide insight into disease progression and treatment efficacy.