Multimodal Imaging Research Articles

Spatial and temporal pattern of structure-function coupling of human brain connectome with development.

Brain structural circuitry shapes a richly patterned functional synchronization, supporting for complex cognitive and behavioural abilities. However, how coupling of structural connectome (SC) and functional connectome (FC) develops and its relationships with cognitive functions and transcriptomic architecture remain unclear. We used multimodal magnetic resonance imaging data from 439 participants aged 5.7-21.9 years to predict functional connectivity by incorporating intracortical and extracortical structural connectivity, characterizing SC-FC coupling. Our findings revealed that SC-FC coupling was strongest in the visual and somatomotor networks, consistent with evolutionary expansion, myelin content, and functional principal gradient. As development progressed, SC-FC coupling exhibited heterogeneous alterations dominated by an increase in cortical regions, broadly distributed across the somatomotor, frontoparietal, dorsal attention, and default mode networks. Moreover, we discovered that SC-FC coupling significantly predicted individual variability in general intelligence, mainly influencing frontoparietal and default mode networks. Finally, our results demonstrated that the heterogeneous development of SC-FC coupling is positively associated with genes in oligodendrocyte-related pathways and negatively associated with astrocyte-related genes. This study offers insight into the maturational principles of SC-FC coupling in typical development.

Multimodal imaging in the diagnosis of bone giant cell tumors: A retrospective study

BACKGROUND Giant cell tumor of bone is a locally aggressive and rarely metastasizing tumor, and also a potential malignant tumor that may develop into a primary malignant giant cell tumor. AIM To evaluate the role of multimodal imaging in the diagnosis of giant cell tumors of bone. METHODS The data of 32 patients with giant cell tumor of bone confirmed by core-needle biopsy or surgical pathology at our hospital between March 2018 and March 2023 were retrospectively selected. All the patients with giant cell tumors of the bone were examined by X-ray, computed tomography (CT) and magnetic resonance imaging (MRI), and 7 of them were examined by positron emission tomography (PET)-CT. RESULTS X-ray imaging can provide overall information on giant cell tumor lesions. CT and MRI can reveal the characteristics of the internal structure of the tumor as well as the adjacent relationships of the tumor, and these methods have unique advantages for diagnosing tumors and determining the scope of surgery. PET-CT can detect small lesions and is highly valuable for identifying benign and malignant tumors to aid in the early diagnosis of metastasis. CONCLUSION Multimodal imaging plays an important role in the diagnosis of giant cell tumor of bone and can provide a reference for the treatment of giant cell tumors.

Multimodal Assessment and Intramodal Comparison of Imaging Techniques for Pediatric Pulmonary Vein Stenosis with Pulmonary Hypertension.

Pulmonary vein stenosis (PVS) is a rare, serious, and progressive disease in the pediatric population. Evaluation is complex and involves multimodality imaging. Diagnosis is important as early treatment to prevent progressive pulmonary hypertension and right ventricular dysfunction is essential. Adult studies have shown good correlation between various imaging modalities; however, there are limited data in children. This is a single-center retrospective pilot study to determine the reliability of measurement of pulmonary vein stenosis and pulmonary hypertension across different imaging modalities-computed tomography angiography (CTA), echocardiography (echo), lung perfusion scan (LPS), and cardiac catheterization (cath). PVS was defined as > 2mmHg by echo and cath and/or 50% reduction in diameter by CTA. Patients had to have an echo, CTA and cath performed within a 1-month timeframe of one another to be included in the study, with LPS data included if testing was completed at initial evaluation. Fifteen total patients were enrolled; 87% were categorized as primary PVS; a condition not directly related to direct injury or prior surgical intervention. Twenty-seven total stenotic pulmonary veins were identified (mean 1.8, range 1-4). CTA had a slightly better agreement with cath than echo in identifying PVS in different vein locations except in the LLPV. Additionally, echo and CTA had excellent sensitivity (91%) and specificity (100%) compared to cath for diagnosis of PH. We conclude that non-invasive imaging of echo and CTA has an acceptable correlation to cardiac catheterization for screening and initial evaluation of PVS and PH, as directly related to PVS, in pediatrics.

Multimodality imaging of acute gynecological emergencies-a pictorial essay.

Gynecological emergencies can often present with a myriad of non-specific signs and symptoms, posing a diagnostic challenge to the clinician. They can be grossly divided into uterine or adnexal pathologies. Uterine pathologies can be secondary to intracavitary accumulation of blood [like in patients with transverse vaginal septum, Robert's uterus, accessory and cavitated uterine mass, unicornuate uterus with contralateral non-communicating functional horn], bleeding per vaginum [like in patients with retained products of conception, enhanced myometrial vascularity], pyometra [secondary to pelvic inflammatory disease, cervical obstruction secondary to benign and malignant causes] or complications of fibroids [like red degeneration, torsion of subserosal fibroid]. The adnexal pathologies can range from ectopic pregnancy in a urine pregnancy test (UPT) positive patient to haemorrhagic ovarian cyst, ovarian torsion, ruptured dermoid cyst and tubo-ovarian abscess in a UPT negative patient. Multimodality imaging including ultrasound (USG), computed tomography (CT) scan and magnetic resonance imaging (MRI) can narrow down the differentials and help in formulating an accurate diagnosis. The objective of this article is to familiarize the readers with multimodality imaging findings in common as well as uncommon acute gynecological emergencies and provide an algorithmic imaging approach for acute gynecological emergencies. USG is typically used as the first line diagnostic modality in diagnosis of acute gynecological emergencies. CT scan & MRI are helpful as a problem-solving tool in acute gynecological emergencies when USG findings are indeterminate.

Resolving spatial response heterogeneity in glioblastoma.

Spatial intratumoral heterogeneity poses a significant challenge for accurate response assessment in glioblastoma. Multimodal imaging coupled with advanced image analysis has the potential to unravel this response heterogeneity. Based on automated tumor segmentation and longitudinal registration with follow-up imaging, we categorized contrast-enhancing voxels of 61 patients with suspected recurrence of glioblastoma into either true tumor progression (TP) or pseudoprogression (PsP). To allow the unbiased analysis of semantically related image regions, adjacent voxels with similar values of cerebral blood volume (CBV), FET-PET, and contrast-enhanced T1w were automatically grouped into supervoxels. We then extracted first-order statistics as well as texture features from each supervoxel. With these features, a Random Forest classifier was trained and validated employing a 10-fold cross-validation scheme. For model evaluation, the area under the receiver operating curve, as well as classification performance metrics were calculated. Our image analysis pipeline enabled reliable spatial assessment of tumor response. The predictive model reached an accuracy of 80.0% and a macro-weighted AUC of 0.875, which takes class imbalance into account, in the hold-out samples from cross-validation on supervoxel level. Analysis of feature importances confirmed the significant role of FET-PET-derived features. Accordingly, TP- and PsP-labeled supervoxels differed significantly in their 10th and 90th percentile, as well as the median of tumor-to-background normalized FET-PET. However, CBV- and T1c-related features also relevantly contributed to the model's performance. Disentangling the intratumoral heterogeneity in glioblastoma holds immense promise for advancing precise local response evaluation and thereby also informing more personalized and localized treatment strategies in the future.

What can go wrong when doing right? A pictorial review of iatrogenic genitourinary complications.

A growing number of treatments for genitourinary diseases can result in various iatrogenic complications. Multimodality imaging in the post-procedural setting is essential for early and accurate diagnosis to limit morbidity and mortality. We review common and uncommon treatment-induced pathologies affecting the genitourinary system via a case-based approach. We illustrate notable complications affecting the kidneys, ureters, bladder, and urethra induced by percutaneous procedures, external beam radiation, immunotherapy, laparoscopic/robotic pelvic surgery, and intravesicular BCG. Finally, we provide guidance on optimal imaging techniques for diagnosis and highlight the role of image-guided interventions for mitigation of complications.

Advancements in Imaging and Therapeutic Options for Dry Age-Related Macular Degeneration and Geographic Atrophy.

Age-related macular degeneration (AMD) is a leading cause of vision loss in the elderly, with dry AMD (d-AMD) leading to geographic atrophy (GA) and significant visual impairment. Multimodal imaging plays a crucial role in d-AMD diagnosis and management, allowing for detailed classification of patient phenotypes and aiding in treatment planning and prognosis determination. Treatment approaches for d-AMD have recently witnessed profound change with the development of specific drugs targeting the complement cascade, with the first anticomplement agents recently approved for GA treatment. Additionally, emerging strategies such as gene therapy and laser treatments may offer potential benefits, though further research is needed to fully establish their efficacy. However, the lack of effective therapies capable of restoring damaged retinal cells remains a major challenge. In the future, genetic treatments aimed at preventing the progression of d-AMD may emerge as a powerful approach. Currently, however, their development is still in the early stages.

Multimodality imaging of caseous mitral annular calcification complicated by possible systemic embolizations.

We demonstrate the case of a man presenting with chest pain in which an initial assessment with echocardiography and, subsequently, with cardiac computed tomography led to a final diagnosis of caseous mitral annular calcification complicated by multiple embolizations.

Multimodality imaging approach in diagnosis and management of post-infarctual evolving left ventricular pseudoaneurysm.

Multimodality imaging approach in diagnosis and management of post-infarctual evolving left ventricular pseudoaneurysm.

Integrative Single-Plaque Analysis Reveals Signature Aβ and Lipid Profiles in the Alzheimer's Brain.

Cerebral accumulation of amyloid-β (Aβ) initiates molecular and cellular cascades that lead to Alzheimer's disease (AD). However, amyloid deposition does not invariably lead to dementia. Amyloid-positive but cognitively unaffected (AP-CU) individuals present widespread amyloid pathology, suggesting that molecular signatures more complex than the total amyloid burden are required to better differentiate AD from AP-CU cases. Motivated by the essential role of Aβ and the key lipid involvement in AD pathogenesis, we applied multimodal mass spectrometry imaging (MSI) and machine learning (ML) to investigate amyloid plaque heterogeneity, regarding Aβ and lipid composition, in AP-CU versus AD brain samples at the single-plaque level. Instead of focusing on a population mean, our analytical approach allowed the investigation of large populations of plaques at the single-plaque level. We found that different (sub)populations of amyloid plaques, differing in Aβ and lipid composition, coexist in the brain samples studied. The integration of MSI data with ML-based feature extraction further revealed that plaque-associated gangliosides GM2 and GM1, as well as Aβ1-38, but not Aβ1-42, are relevant differentiators between the investigated pathologies. The pinpointed differences may guide further fundamental research investigating the role of amyloid plaque heterogeneity in AD pathogenesis/progression and may provide molecular clues for further development of emerging immunotherapies to effectively target toxic amyloid assemblies in AD therapy. Our study exemplifies how an integrative analytical strategy facilitates the unraveling of complex biochemical phenomena, advancing our understanding of AD from an analytical perspective and offering potential avenues for the refinement of diagnostic tools.

The COMBO window: A chronic cranial implant for multiscale circuit interrogation in mice.

Neuroscientists studying the neural correlates of mouse behavior often lack access to the brain-wide activity patterns elicited during a specific task of interest. Fortunately, large-scale imaging is becoming increasingly accessible thanks to modalities such as Ca2+ imaging and functional ultrasound (fUS). However, these and other techniques often involve challenging cranial window procedures and are difficult to combine with other neuroscience tools. We address this need with an open-source 3D-printable cranial implant-the COMBO (ChrOnic Multimodal imaging and Behavioral Observation) window. The COMBO window enables chronic imaging of large portions of the brain in head-fixed mice while preserving orofacial movements. We validate the COMBO window stability using both brain-wide fUS and multisite two-photon imaging. Moreover, we demonstrate how the COMBO window facilitates the combination of optogenetics, fUS, and electrophysiology in the same animals to study the effects of circuit perturbations at both the brain-wide and single-neuron level. Overall, the COMBO window provides a versatile solution for performing multimodal brain recordings in head-fixed mice.

Correlative Super-resolution Optical and Electron Microscopic Imaging of Intracellular Ribosomal RNA by a Terpyridine Iridium(III) Complex.

Ribosomal RNA (rRNA) plays a vital role in binding amino acids together, which dictates the primary structure of a protein. Visualization of its intracellular distribution and dynamics during protein synthesis enables a better understanding of the correlated biological essence. However, appropriate tools targeting live cell rRNA that are capable of multimodal imaging at the nanoscale are still lacking. Here, we rationally designed a series of terpyridine ammonium iridium(III) complexes, one of which is capable of selectively labeling rRNA in living cells. Its metal core and photostable nature allow further super-resolution STED imaging of rRNA found on the rough endoplasmic reticulum at a ∼40 nm resolution that is well correlated under correlative light and electron microscopy (CLEM). Interestingly, the Ir(III) complex demonstrated rRNA dynamics in living cells while boosting protein synthesis at the nanoscale. Our work offers a versatile tool to visualize rRNA synchronously under optical and electron microscopy, which provides a better understanding of rRNA evolution in living systems.

Deep Learning Approaches for Medical Image Processing in the Big Data Era

Diagnostics, therapy planning, and patient monitoring are areas where medical image processing has become indispensable in modern healthcare. The advent of deep learning techniques and the broad usage of big data in healthcare have brought a revolutionary shift in medical picture analysis and interpretation. An overview of deep learning techniques for medical image processing based on big data is given in this article. This article covers the pros and cons of adopting big data for medical imaging, from data storage and analysis to data capture. Beyond that, we take a look at medical image analysis using deep learning algorithms such as recurrent neural network (RNN), Convolutional Neural Network (CNN), and generative adversarial network (GAN), and we highlight its advantages and disadvantages. We also examine recent innovations such as transfer learning, multi-modal imaging fusion, and federated learning, which can improve the accuracy and efficiency of medical image processing systems. Finally, we discuss how medical image processing driven by deep learning could improve clinical decision-making, patient outcomes, and the development of personalized medicine in the era of data-driven healthcare.

A novel fully automatic segmentation and counting system for metastatic lymph nodes on multimodal magnetic resonance imaging: Evaluation and prognostic implications in nasopharyngeal carcinoma

BackgroundThe number of metastatic lymph nodes (MLNs) is crucial for the survival of nasopharyngeal carcinoma (NPC), but manual counting is laborious. This study aims to explore the feasibility and prognostic value of automatic MLNs segmentation and counting. MethodsWe retrospectively enrolled 980 newly diagnosed patients in the primary cohort and 224 patients from two external cohorts. We utilized the nnUnet model for automatic MLNs segmentation on multimodal magnetic resonance imaging. MLNs counting methods, including manual delineation-assisted counting (MDAC) and fully automatic lymph node counting system (AMLNC), were compared with manual evaluation (Gold standard). ResultsIn the internal validation group, the MLNs segmentation results showed acceptable agreement with manual delineation, with a mean Dice coefficient of 0.771. The consistency among three counting methods was as follows 0.778 (Gold vs. AMLNC), 0.638 (Gold vs. MDAC), and 0.739 (AMLNC vs. MDAC). MLNs numbers were categorized into three-category variable (1–4, 5–9, > 9) and two-category variable (<4, ≥ 4) based on the gold standard and AMLNC. These categorical variables demonstrated acceptable discriminating abilities for 5-year overall survival (OS), progression-free, and distant metastasis-free survival. Compared with base prediction model, the model incorporating two-category AMLNC-counting numbers showed improved C-indexes for 5-year OS prediction (0.658 vs. 0.675, P = 0.045). All results have been successfully validated in the external cohort. ConclusionsThe AMLNC system offers a time- and labor-saving approach for fully automatic MLNs segmentation and counting in NPC. MLNs counting using AMLNC demonstrated non-inferior performance in survival discrimination compared to manual detection.

NONEXUDATIVE INTRARETINAL FLUID IN INTERMEDIATE AGE-RELATED MACULAR DEGENERATION.

To describe the occurrence of nonexudative intraretinal fluid (IRF) in intermediate age-related macular degeneration. A retrospective study was designed to include consecutive cases with intermediate age-related macular degeneration associated with IRF. A multimodal imaging approach was used to confirm diagnosis of IRF in intermediate age-related macular degeneration. Multimodal imaging included color fundus photograph, fundus autofluorescence, fluorescein angiography, indocyanine green angiography, optical coherence tomography, and optical coherence tomography angiography. Ten eyes of 10 patients (2 male and 8 female patients, ages 68-80 years) showing IRF in intermediate age-related macular degeneration were included in the study. The mean best-corrected visual acuity was 20/40 Snellen equivalent. Multimodal imaging including fluorescein angiography/indocyanine green angiography and optical coherence tomography demonstrated the absence of macular neovascularization in all cases; optical coherence tomography-angiography did not detect any abnormal flow signal associated with IRF. Seven of 10 patients developed IRF in correspondence of pigment epithelium detachment. Three of 10 patients presented IRF in correspondence of an area of nascent geographic atrophy. Nonexudative intraretinal fluid in intermediate age-related macular degeneration is a novel, distinctive feature that is characterized by the presence of IRF with no evidence of macular neovascular lesions. The authors described different phenotypes of IRF in intermediate age-related macular degeneration. The definite diagnosis of this condition requires further studies with thorough application of multimodal imaging.

Radiologic-Pathologic Correlation of Cardiac Tumors: Updated 2021 WHO Tumor Classification.

Cardiac tumors, although rare, carry high morbidity and mortality rates. They are commonly first identified either at echocardiography or incidentally at thoracoabdominal CT performed for noncardiac indications. Multimodality imaging often helps to determine the cause of these masses. Cardiac tumors comprise a distinct category in the World Health Organization (WHO) classification of tumors. The updated 2021 WHO classification of tumors of the heart incorporates new entities and reclassifies others. In the new classification system, papillary fibroelastoma is recognized as the most common primary cardiac neoplasm. Pseudotumors including thrombi and anatomic variants (eg, crista terminalis, accessory papillary muscles, or coumadin ridge) are the most common intracardiac masses identified at imaging. Cardiac metastases are substantially more common than primary cardiac tumors. Although echocardiography is usually the first examination, cardiac MRI is the modality of choice for the identification and characterization of cardiac masses. Cardiac CT serves as an alternative in patients who cannot tolerate MRI. PET performed with CT or MRI enables metabolic characterization of malignant cardiac masses. Imaging individualized to a particular tumor type and location is crucial for treatment planning. Tumor terminology changes as our understanding of tumor biology and behavior evolves. Familiarity with the updated classification system is important as a guide to radiologic investigation and medical or surgical management. ©RSNA, 2024 Supplemental material is available for this article.

Prior-Guided Adversarial Learning With Hypergraph for Predicting Abnormal Connections in Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by alterations of the brain's structural and functional connectivity during its progressive degenerative processes. Existing auxiliary diagnostic methods have accomplished the classification task, but few of them can accurately evaluate the changing characteristics of brain connectivity. In this work, a prior-guided adversarial learning with hypergraph (PALH) model is proposed to predict abnormal brain connections using triple-modality medical images. Concretely, a prior distribution from anatomical knowledge is estimated to guide multimodal representation learning using an adversarial strategy. Also, the pairwise collaborative discriminator structure is further utilized to narrow the difference in representation distribution. Moreover, the hypergraph perceptual network is developed to effectively fuse the learned representations while establishing high-order relations within and between multimodal images. Experimental results demonstrate that the proposed model outperforms other related methods in analyzing and predicting AD progression. More importantly, the identified abnormal connections are partly consistent with previous neuroscience discoveries. The proposed model can evaluate the characteristics of abnormal brain connections at different stages of AD, which is helpful for cognitive disease study and early treatment.

Deep Learning Model for Estimation of LV Ejection Fraction from Echocardiogram

Heart failure, a leading global cause of death, poses challenges for early prediction of cardiac dysfunction, especially ejection fraction (EF). This study employs Convolutional Neural Networks (CNNs), utilizing ResNet and MobileNet architectures, on the CAMUS dataset with 500 patient records (2CH and 4CH). The goal is to aid healthcare professionals in accurately measuring EF. The CAMUS dataset, comprising multi-modality cardiac imaging and segmentation data, serves as the foundation. The CNN, ResNet, and MobileNet models are fine-tuned through transfer learning and their performance is evaluated based on accuracy. This comparative analysis identifies the model with the best predictive capabilities for EF, showcasing their potential for earlier diagnosis and intervention. Deep learning techniques enhance cardiac healthcare by providing reliable, noninvasive means of predicting heart failure, reducing its impact on patients and healthcare systems.

The mechanism of Sangdantongluo granule in treating post-stroke spasticity based on multimodal fMRI combined with TMS: Study protocol

IntroductionPost-stroke spasticity (PSS) is among the prevalent complications of stroke, greatly affecting motor function recovery and reducing patients' quality of life without timely treatment. Sangdantongluo granule, a modern traditional Chinese patent medicine, has significant clinical efficacy in treating PSS. However, the mechanism of Sangdantongluo granule in treating PSS is still unknown. We designed this study to explore the mechanism of Sangdantongluo granule in treating PSS through multimodal functional magnetic resonance imaging (fMRI) combined with transcranial magnetic stimulation (TMS). Methods and analysisIn a single-center, randomized, double-blind, parallel placebo-controlled study, 60 PSS patients will be recruited in China and randomly assigned to either the experimental or control groups at a ratio of 1:1. For eight weeks, Sangdantongluo granule or placebo will be utilized for intervention. The main outcome is the Modified Ashworth Scale (MAS), the secondary outcome includes the Fugl-Meyer Assessment Scale-upper Extremity (FMA-UE), National Institute of Health Stroke Scale (NIHSS), and Modified Rankin Scale (mRS), the mechanism measure is the changes in cortical excitability and multimodal fMRI at baseline and after eight weeks. Ethics and disseminationThis study was approved by the Ethics Committee of the Affiliated Hospital of Hunan Academy of Traditional Chinese Medicine (approval number: [202364]). Clinical trial registrationChinese Clinical Trial Registry, identifier: ChiCTR2300074793. Registered on 16 August 2023.

Left ventricular hypertrabeculation: clinical interpretation based on multimodality imaging data

Left ventricular hypertrabeculation: clinical interpretation based on multimodality imaging data