Explore the vast range of titles available.

INTRODUCTION This study presents a novel multi‐view learning approach for machine learning (ML)–based Alzheimer's disease (AD) diagnosis. METHODS A diffusion model is proposed to synthesize the fluorodeoxyglucose positron emission tomography (FDG PET) view from the magnetic resonance imaging T1 weighted imaging (MRI T1WI) view and incorporate two synthesis strategies: one‐way synthesis and two‐way synthesis. To assess the utility of the synthesized views, we use multilayer perceptron (MLP)–based classifiers with various combinations of the views. RESULTS The two‐way synthesis achieves state‐of‐the‐art performance with a structural similarity index measure (SSIM) at 0.9380 and a peak‐signal‐to‐noise ratio (PSNR) at 26.47. The one‐way synthesis achieves an SSIM at 0.9282 and a PSNR at 23.83. Both synthesized FDG PET views have shown their effectiveness in improving diagnostic accuracy. DISCUSSION This work supports the notion that ML‐based cross‐domain data synthesis can be a useful approach to improve AD diagnosis by providing additional synthesized disease‐related views for multi‐view learning. Highlights We propose a diffusion model with two strategies to synthesize fluorodeoxyglucose positron emission tomography (FDG PET) from magnetic resonance imaging T1 weighted imaging (MRI T1WI). We raise multi‐view learning with MRl T1Wl and synthesized FDG PET for Alzheimer's disease (AD) diagnosis. We provide a comprehensive experimental comparison for the synthesized FDG PET view. The feasibility of synthesized FDG PET view in AD diagnosis is validated with various experiments. We demonstrate the ability of synthesized FDG PET to enhance the performance of machine learning–based AD diagnosis.

Using a philosophical approach or deductive reasoning, we challenge the dominant clinico-radiological worldview that defines multiple sclerosis (MS) as a focal inflammatory disease of the central nervous system (CNS). We provide a range of evidence to argue that the ‘real MS’ is in fact driven primarily by a smouldering pathological disease process. In natural history studies and clinical trials, relapses and focal activity revealed by magnetic resonance imaging (MRI) in MS patients on placebo or on disease-modifying therapies (DMTs) were found to be poor predictors of long-term disease evolution and were dissociated from disability outcomes. In addition, the progressive accumulation of disability in MS can occur independently of relapse activity from early in the disease course. This scenario is underpinned by a more diffuse smouldering pathological process that may affect the entire CNS. Many putative pathological drivers of smouldering MS can be potentially modified by specific therapeutic strategies, an approach that may have major implications for the management of MS patients. We hypothesise that therapeutically targeting a state of ‘no evident inflammatory disease activity’ (NEIDA) cannot sufficiently prevent disability accumulation in MS, meaning that treatment should also focus on other brain and spinal cord pathological processes contributing to the slow loss of neurological function. This should also be complemented with a holistic approach to the management of other systemic disease processes that have been shown to worsen MS outcomes.

Hyperintensities on T1-weighted magnetic resonance imaging (MRI) in the setting of brain ischemia are usually considered hemorrhagic transformations. Such changes can also be seen due to \"incomplete infarction\" with selective neuronal loss. Arguments regarding the cause of these T1 hyperintensities have shuttled between gemistocytic astrocyte accumulation, tissue calcification and paramagnetic substance deposition. Susceptibility weighted imaging (SWI), a sensitive modality for detecting paramagnetic agents and blood products, has never been used to resolve this issue. The study was aimed to evaluate the SWI signal changes of T1 hyperintense lesion in stroke patients and understand its usefulness in differentiating a hemorrhagic infarct and an incomplete infarct. All the seven patients with infarct, having hyperintensities on T1 weighted MR imaging seen over the last one year were subjected to SWI. In none of the patients SWI failed to show any blooming. By doing SWI for T1-weighted hyperintensities, we can differentiate hemorrhagic infarct and a non-hemorrhagic \"incomplete infarct\". This differentiation will immensely help in planning management strategy and prognostication.

The present study aimed to compare the effectiveness of two abbreviated magnetic resonance imaging (MRI) protocols in assessing the response to neoadjuvant chemoradiotherapy (CRT) in patients with rectal cancer. Data from the examinations of 62 patients with rectal cancer who underwent neoadjuvant CRT and standard contrast-enhanced rectal MRI were retrospectively evaluated. Standard contrast-enhanced T2-weighted imaging (T2-WI), post-contrast T1-weighted imaging (T1-WI) and diffusion-weighted imaging (DWI) MRI, as well as two abbreviated protocols derived from these images, namely protocol AB1 (T2-WI and DWI) and protocol AB2 (post-contrast fat-suppressed (FS) T1-WI and DWI), were assessed. Measurements of lesion length and width, lymph node short-axis length, tumor staging, circumferential resection margin (CRM), presence of extramural venous invasion (EMVI), luminal mucin accumulation (MAIN), mucinous response, mesorectal fascia (MRF) involvement, and MRI-based tumor regression grade (mrTRG) were obtained. The reliability and compatibility of the AB1 and AB2 protocols in the evaluation of tumor response were analyzed. The imaging performed according to the AB1 and AB2 protocols revealed significant decreases in lesion length, width and lymph node size after CRT. These protocols also showed reductions in lymph node positivity, CRM, MRF, EMVI.Furthermore, both protocols were found to be reliable in determining lesion length and width. Additionally, compliance was observed between the protocols in determining lymph node size and positivity, CRM involvement, and EMVI after CRT. In conclusion, the use of abbreviated MRI protocols, specifically T2-WI with DWI sequences or post-contrast FS T1-WI with DWI sequences, is effective for evaluating tumor response in patients with rectal cancer following neoadjuvant CRT. The AB protocols examined in this study yielded similar results in terms of lesion length and width, lymph node positivity, CRM involvement, EMVI, MAIN, and MRF involvement.

ABSTRACT Preeclampsia is a severe pregnancy complication that can cause brain injury, yet early detection of related cognitive deficits remains challenging. Therefore, in order to investigate alterations in choroid plexus volume (CPV) and susceptibility values of the choroid plexus (ChP) obtained from quantitative susceptibility mapping (QSM) in preeclampsia patients, we enrolled 281 participants, comprising 98 nonpregnant healthy controls (NPHC), 85 pregnant healthy controls (PHC), and 98 patients with preeclampsia. All participants were scanned on a 1.5 T MR scanner. The results of clinical characteristics and cognitive tests were collected from all the participants. One‐way ANOVA tests were used to analyze the differences in CPV and susceptibility values of ChP among the three groups. Multiple linear regression analysis was used to find the factors that influenced CPV and its susceptibility values, as well as cognitive decline. Additionally, receiver operating characteristic (ROC) analysis was employed to evaluate the diagnostic performance of the two imaging measures. Preeclampsia patients exhibited smaller CPV and higher susceptibility values compared to the other groups (p < 0.001; p < 0.001). Significant negative correlations were observed between body mass index (BMI), mean arterial pressure and CPV/eTIV (β = −0.100, 95% CI = −0.158 ~ −0.042, p = 0.001; β = −0.022, 95% CI = −0.033 ~ −0.011, p < 0.001). Additionally, significant positive correlations were observed between BMI (β = 0.455, 95% CI = 0.125 ~ 0.786, p = 0.007), mean arterial pressure (β = 0.170, 95% CI = 0.107 ~ 0.232, p < 0.001), hemoglobin (β = 0.152, 95% CI = 0.051 ~ 0.254, p = 0.003) and susceptibility values of ChP. Furthermore, CPV/eTIV and susceptibility values of ChP could be independent contributing factors of scores of TMT. The combination of CPV, susceptibility values of ChP, BMI and gestational week could distinguish preeclampsia from pregnant groups (AUC = 0.787, 95% CI = 0.722–0.853, p < 0.001) as well as distinguish individuals with cognitive decline from preeclampsia patients (AUC = 0.737, 95% CI = 0.621–0.844, p < 0.001). These findings indicate that smaller CPV and higher susceptibility values characterize preeclampsia and may serve as auxiliary indices for its diagnosis and related cognitive decline. Preeclampsia patients exhibit choroid plexus atrophy and iron deposition via high‐resolution T1‐weighted magnetic resonance imaging (T1WI) and quantitative susceptibility mapping, correlating with cognitive decline. Combined choroid plexus volume, susceptibility values, BMI, and gestational week serve as diagnostic biomarkers for preeclampsia and associated cognitive impairment, offering insights into glymphatic dysfunction pathophysiology.

Design of novel nanoplatforms with single imaging elements for dynamic and enhanced T1/T2‐weighted magnetic resonance (MR) imaging of diseases still remains significantly challenging. Here, a facile strategy to synthesize light‐addressable ultrasmall Fe3O4 nanoparticles (NPs) that can form nanoclusters (NCs) under laser irradiation for enhanced and dynamic T1/T2‐weighted MR imaging of inflammatory arthritis is reported. Citric acid‐stabilized ultrasmall Fe3O4 NPs synthesized via a solvothermal approach are linked with both the arthritis targeting ligand folic acid (FA) and light‐addressable unit diazirine (DA) via polyethylene glycol (PEG) spacer. The formed ultrasmall Fe3O4‐PEG‐(DA)‐FA NPs are cytocompatible, display FA‐mediated targeting specificity to arthritis‐associated macrophage cells, and can form NCs upon laser irradiation to have tunable r1 and r2 relaxivities by varying the laser irradiation duration. With these properties owned, the designed Fe3O4‐PEG‐(DA)‐FA NPs can be used for T1‐weighted MR imaging of arthritis without lasers and enhanced dual‐mode T1/T2‐weighted MR imaging of arthritis under laser irradiation due to the formation of NCs that have extended accumulation within the arthritis region and limited intravasation back to the blood circulation. The designed light‐addressable Fe3O4‐PEG‐(DA)‐FA NPs may be used as a promising platform for dynamic and precision T1/T2‐weighted MR imaging of other diseases. Light‐addressable ultrasmall iron oxide nanoparticles (NPs) are designed via linking of targeting ligand folic acid and light‐addressable unit diazirine onto the particle surface. These NPs possess desired cytocompatibility and targeting specificity to arthritis‐associated macrophage cells, and can generate nanoclusters upon laser irradiation to display tunable r1 and r2 relaxivities, thus enabling enhanced dual‐mode T1/T2‐weighted magnetic resonance imaging of inflammatory arthritis.

Increasingly intricate in their multilevel multiscale microarchitecture, metamaterials with unique physical properties are challenging the inherent constraints of natural materials. Their applicability in the nanomedicine field still suffers because nanomedicine requires a maximum size of tens to hundreds of nanometers; however, this size scale has not been achieved in metamaterials. Therefore, “nano‐metamaterials,” a novel class of metamaterials, are introduced, which are rationally designed materials with multilevel microarchitectures and both characteristic sizes and whole sizes at the nanoscale, investing in themselves remarkably unique and significantly enhanced material properties as compared with conventional nanomaterials. Microarchitectural regulation through conventional thermodynamic strategy is limited since the thermodynamic process relies on the frequency‐dependent effective temperature, Teff(ω), which limits the architectural regulation freedom degree. Here, a novel dual‐kinetic control strategy is designed to fabricate nano‐metamaterials by freezing a high‐free energy state in a Teff(ω)‐constant system, where two independent dynamic processes, non‐solvent induced block copolymer (BCP) self‐assembly and osmotically driven self‐emulsification, are regulated simultaneously. Fe3+‐“onion‐like core@porous corona” (Fe3+‐OCPCs) nanoparticles (the products) have not only architectural complexity, porous corona and an onion‐like core but also compositional complexity, Fe3+ chelating BCP assemblies. Furthermore, by using Fe3+‐OCPCs as a model material, a microstructure‐biological performance relationship is manifested in nano‐metamaterials. “Nano‐metamaterials,” a novel class of metamaterials, are introduced, which are rationally designed materials with multilevel microarchitectures and both characteristic sizes and whole sizes at the nanoscale, investing in themselves remarkably unique material properties as compared with conventional nanomaterials. A dual‐kinetic control strategy to fabricate nano‐metamaterials is presented, regulating osmotically driven self‐emulsification and nonsolvent‐induced block copolymer self‐assembly simultaneously.

In vivo detection of H 2 17O produced via metabolic reduction of inhaled 17O-enriched gas is demonstrated using proton magnetic resonance imaging (MRI). Specifically, 1H T 1ρ-weighted MRI, which may be readily implemented on any MRI scanner, is applied as an indirect 17O imaging method to quantitatively monitor the distribution of metabolically produced 17O water (mpH 2 17O) in the rat brain. The delivery of 17O 2 to rats is conducted via a specially designed closed respiration circuit that conserves the expensive gas. Quantitative mapping of H 2 17O performed via 1H T 1ρ-weighted MRI is validated by direct 17O-magnetic resonance spectroscopy. The MRI data show that a steady-state H 2 17O concentration of 25.7 ± 1.66 mM ( n = 4) is achieved in the rat brain within approximately 30 min under the 17O inhalation paradigm used. From the first minute of the mpH 2 17O time courses, cerebral metabolic rate of oxygen (CMRO 2) is estimated to be 2.10 ± 0.44 μmol g −1 min −1 ( n = 4), a value that is consistent with the literature.

ABSTRACT Predicting brain age from T1‐weighted MRI is a promising marker for understanding brain aging and its associated conditions. While deep learning models have shown success in reducing the mean absolute error (MAE) of predicted brain age, concerns about robust and accurate generalization in new data limit their clinical applicability. The large number of trainable parameters, combined with limited medical imaging training data, contributes to this challenge, often resulting in a generalization gap where there is a significant discrepancy between model performance on training data versus unseen data. In this study, we assess a deep model, SFCN‐reg, based on the VGG‐16 architecture, and address the generalization gap through comprehensive preprocessing, extensive data augmentation, and model regularization. Using training data from the UK Biobank, we demonstrate substantial improvements in model performance. Specifically, our approach reduces the generalization MAE by 47% (from 5.25 to 2.79 years) in the Alzheimer's Disease Neuroimaging Initiative dataset and by 12% (from 4.35 to 3.75 years) in the Australian Imaging, Biomarker and Lifestyle dataset. Furthermore, we achieve up to 13% reduction in scan‐rescan error (from 0.80 to 0.70 years) while enhancing the model's robustness to registration errors. Feature importance maps highlight anatomical regions used to predict age. These results highlight the critical role of high‐quality preprocessing and robust training techniques in improving accuracy and narrowing the generalization gap, both necessary steps toward the clinical use of brain age prediction models. Our study makes valuable contributions to neuroimaging research by offering a potential pathway to improve the clinical applicability of deep learning models. We enhanced the robustness of a CNN‐based model for brain age prediction by addressing a key challenge in medical imaging: limited training data often hinders generalization. To overcome this, we refined the MRI preprocessing pipeline and incorporated additional data augmentation and regularization techniques during training. The results showed up to a 47% improvement in brain age prediction accuracy for previously unseen images, a 13% reduction in scan‐rescan error, and increased model robustness against registration errors through a specialized regularization approach. This approach involved freezing the initial layers of the network and introducing spatial dropout to the second‐to‐last convolutional layer halfway through training.

Objective To investigate the value of histogram analysis of T1 mapping and diffusion-weighted imaging (DWI) in predicting the grade, subtype, and proliferative activity of meningioma. Methods This prospective study comprised 69 meningioma patients who underwent preoperative MRI including T1 mapping and DWI. The histogram metrics, including mean, median, maximum, minimum, 10th percentiles (C10), 90th percentiles (C90), kurtosis, skewness, and variance, of T1 and apparent diffusion coefficient (ADC) values were extracted from the whole tumour and peritumoural oedema using FeAture Explorer. The Mann-Whitney U test was used for comparison between low- and high-grade tumours. Receiver operating characteristic (ROC) curve and logistic regression analyses were performed to identify the differential diagnostic performance. The Kruskal-Wallis test was used to further classify meningioma subtypes. Spearman’s rank correlation coefficients were calculated to analyse the correlations between histogram parameters and Ki-67 expression. Results High-grade meningiomas showed significantly higher mean, maximum, C90, and variance of T1 ( p = 0.001–0.009), lower minimum, and C10 of ADC ( p = 0.013–0.028), compared to low-grade meningiomas. For all histogram parameters, the highest individual distinctive power was T1 C90 with an AUC of 0.805. The best diagnostic accuracy was obtained by combining the T1 C90 and ADC C10 with an AUC of 0.864. The histogram parameters differentiated 4/6 pairs of subtype pairs. Significant correlations were identified between Ki-67 and histogram parameters of T1 (C90, mean) and ADC (C10, kurtosis, variance). Conclusion T1 and ADC histogram parameters may represent an in vivo biomarker for predicting the grade, subtype, and proliferative activity of meningioma. Key Points • The histogram parameter based on T1 mapping and DWI is useful to preoperatively evaluate the grade, subtype, and proliferative activity of meningioma. • The combination of T1 C90 and ADC C10 showed the best performance for differentiating low- and high-grade meningiomas.