Advances in the Clinical Study of Nuclear Overhauser Enhancement.

Nuclear overhauser enhancement is a confounding factor arising from the in vivo application of a chemical exchange saturation transfer technique in which two nuclei in close proximity undergo dipole cross-relaxation. Several studies have shown applicability and efficacy of nuclear overhauser enhancement in observing tumors and other lesions in vivo. Thus, this effect could become an emerging molecular imaging research tool for many diseases.

Multimodal data fusion reveals functional and neurochemical correlates of Parkinson's disease.

Background: Neurotransmitter deficits and spatial associations among neurotransmitter distribution, brain activity, and clinical features in Parkinson's disease (PD) remain unclear. Better understanding of neurotransmitter impairments in PD may provide potential therapeutic targets. Therefore, we aimed to investigate the spatial relationship between PD-related patterns and neurotransmitter deficits.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease.

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF).

Neuroimaging biomarkers for detecting schizophrenia: A resting-state functional MRI-based radiomics analysis.

Schizophrenia (SZ) is a common psychiatric disorder that is difficult to accurately diagnose in clinical practice. Quantifiable biomarkers are urgently required to explore the potential physiological mechanism of SZ and improve its diagnostic accuracy. Thus, this study aimed to identify biomarkers that classify SZ patients and healthy control subjects and investigate the potential neural mechanisms of SZ using degree centrality (DC)- and voxel-mirrored homotopic connectivity (VMHC)-based radiomics.

Classification of Parkinson's disease using a region-of-interest- and resting-state functional magnetic resonance imaging-based radiomics approach.

To investigate the value of combining amplitude of low-frequency fluctuations-based radiomics and the support vector machine classifier method in distinguishing patients with Parkinson's disease from healthy controls. A total of 123 patients with Parkinson's disease and 90 healthy controls from three centers with functional and structural MRI images were included in this study. We extracted radiomics features using the Brainnetome 246 atlas from the mean amplitude of low-frequency fluctuations maps.

Radiomics Based on Digital Mammography Helps to Identify Mammographic Masses Suspicious for Cancer.

Objectives: This study aims to build radiomics model of Breast Imaging Reporting and Data System (BI-RADS) category 4 and 5 mammographic masses extracted from digital mammography (DM) for mammographic masses characterization by using a sensitivity threshold similar to that of biopsy.

Materials And Methods: This retrospective study included 288 female patients (age, 52.41 ± 10.

Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis.

Parkinson's disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model.

Magnetic Resonance Imaging of Contrast-Induced Acute Renal Injury and Related Pathological Alterations In Vivo.

Background: The definitive mechanisms of CI-AKI include contrast medium (CM) nephrotoxicity and CM disturbances in renal blood flow, but how the immune system responds to CM has rarely been mentioned in previous studies, and different cell death pathways have not been clearly distinguished.

Aim: To confirm whether MRI detect early CI-AKI and to investigate whether immunity-related responses, pyroptosis, and mitophagy participate in contrast-induced acute renal injury (CI-AKI).

Methods: C57BL/6 mice with CI-AKI were established by tail vein injection of iodixanol 320.

Gadolinium Retention in the Brain of Mother and Pup Mouse: Effect of Pregnancy and Repeated Administration of Gadolinium-Based Contrast Agents.

Background: The association of repeated administration of gadolinium-based contrast agents (GBCAs) with the gadolinium (Gd) retention in the brains of mother and fetus remains unclear.

Purpose: To investigate the effects of pregnancy and repeated administration of GBCAs on Gd retention in the brains of mother and pup mice.

Study Type: Cross-sectional cohort toxicity study.

Capability of intravoxel incoherent motion and diffusion tensor imaging to detect early kidney injury in type 2 diabetes.

Objectives: To prospectively investigate the capability of intravoxel incoherent motion (IVIM) and conventional diffusion tensor imaging (DTI) to identify early kidney function injury in type 2 diabetes.

Methods: Forty-one diabetes patients (normoalbuminuria: n = 27; microalbuminuria: n = 14) and 28 volunteers were recruited. All participants were examined using DTI and IVIM with 3.

The Application of Functional Magnetic Resonance Imaging in Type 2 Diabetes Rats With Contrast-Induced Acute Kidney Injury and the Associated Innate Immune Response.

Aims: Contrast-induced acute kidney injury (CI-AKI) is the third most common in-hospital acquired AKI, and its mechanism is not fully clear. Its morbidity increases among populations with chronic kidney disease (CKD), older age, diabetes mellitus (DM), and so on. Immediate and effective noninvasive diagnostic methods are lacking, so CI-AKI often prolongs hospital stays and increases extra medical costs.

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging.

Schizophrenia (SZ) is a severe psychiatric illness, and it affects around 1% of the general population; however, its reliable diagnosis is challenging. Functional MRI (fMRI) and structural MRI (sMRI) are useful techniques for investigating the functional and structural abnormalities of the human brain, and a growing number of studies have reported that multimodal brain data can improve diagnostic accuracy. Machine learning (ML) is widely used in the diagnosis of neuroscience and neuropsychiatry diseases, and it can obtain high accuracy.

Neuroprotective and Angiogenesis Effects of Levetiracetam Following Ischemic Stroke in Rats.

The present study explored whether levetiracetam (LEV) could protect against experimental brain ischemia and enhance angiogenesis in rats, and investigated the potential mechanisms and . The middle cerebral artery was occluded for 60 min to induce middle cerebral artery occlusion (MCAO). The Morris water maze was used to measure cognitive ability.

Application of Functional Magnetic Resonance Imaging in the Diagnosis of Parkinson's Disease: A Histogram Analysis.

This study aimed to investigate the value of amplitude of low-frequency fluctuation (ALFF)-based histogram analysis in the diagnosis of Parkinson's disease (PD) and to investigate the regions of the most important discriminative features and their contribution to classification discrimination. Patients with PD ( = 59) and healthy controls (HCs; = 41) were identified and divided into a primary set (80 cases, including 48 patients with PD and 32 HCs) and a validation set (20 cases, including 11 patients with PD and nine HCs). The Automated Anatomical Labeling (AAL) 116 atlas was used to extract the histogram features of the regions of interest in the brain.

Histogram analysis of diffusion kurtosis imaging derived maps may distinguish between low and high grade gliomas before surgery.

Objective: To investigate the value of histogram analysis of diffusion kurtosis imaging (DKI) maps in the evaluation of glioma grading.

Methods: A total of 39 glioma patients who underwent preoperative magnetic resonance imaging (MRI) were classified into low-grade (13 cases) and high-grade (26 cases) glioma groups. Parametric DKI maps were derived, and histogram metrics between low- and high-grade gliomas were analysed.