Fluorescent Probes for Disease Diagnosis.

The identification and detection of disease-related biomarkers is essential for early clinical diagnosis, evaluating disease progression, and for the development of therapeutics. Possessing the advantages of high sensitivity and selectivity, fluorescent probes have become effective tools for monitoring disease-related active molecules at the cellular level and in vivo. In this review, we describe current fluorescent probes designed for the detection and quantification of key bioactive molecules associated with common diseases, such as organ damage, inflammation, cancers, cardiovascular diseases, and brain disorders. We emphasize the strategies behind the design of fluorescent probes capable of disease biomarker detection and diagnosis and cover some aspects of combined diagnostic/therapeutic strategies based on regulating disease-related molecules. This review concludes with a discussion of the challenges and outlook for fluorescent probes, highlighting future avenues of research that should enable these probes to achieve accurate detection and identification of disease-related biomarkers for biomedical research and clinical applications.

Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning.

Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson's disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.

A novel generation adversarial network framework with characteristics aggregation and diffusion for brain disease classification and feature selection.

Imaging genetics provides unique insights into the pathological studies of complex brain diseases by integrating the characteristics of multi-level medical data. However, most current imaging genetics research performs incomplete data fusion. Also, there is a lack of effective deep learning methods to analyze neuroimaging and genetic data jointly. Therefore, this paper first constructs the brain region-gene networks to intuitively represent the association pattern of pathogenetic factors. Second, a novel feature information aggregation model is constructed to accurately describe the information aggregation process among brain region nodes and gene nodes. Finally, a deep learning method called feature information aggregation and diffusion generative adversarial network (FIAD-GAN) is proposed to efficiently classify samples and select features. We focus on improving the generator with the proposed convolution and deconvolution operations, with which the interpretability of the deep learning framework has been dramatically improved. The experimental results indicate that FIAD-GAN can not only achieve superior results in various disease classification tasks but also extract brain regions and genes closely related to AD. This work provides a novel method for intelligent clinical decisions. The relevant biomedical discoveries provide a reliable reference and technical basis for the clinical diagnosis, treatment and pathological analysis of disease.

Cytotoxic lesions of the corpus callosum: a systematic review.

OBJECTIVES: Cytotoxic lesions of the corpus callosum (CLOCC) are a common magnetic resonance imaging (MRI) finding associated with various systemic diseases including COVID-19. Although an increasing number of such cases is reported in the literature, there is a lack of systematic evidence summarizing the etiology and neuroimaging findings of these lesions. Thus, the aim of this systematic review was to synthesize the applied nomenclature, neuroimaging and clinical features, and differential diagnoses as well as associated disease entities of CLOCC. MATERIALS AND METHODS: A comprehensive literature search in three biomedical databases identified 441 references, out of which 324 were eligible for a narrative summary including a total of 1353 patients. RESULTS: Our PRISMA-conform systematic review identifies a broad panel of disease entities which are associated with CLOCC, among them toxic/drug-treatment-associated, infectious (viral, bacterial), vascular, metabolic, traumatic, and neoplastic entities in both adult and pediatric individuals. On MRI, CLOCC show typical high T2 signal, low T1 signal, restricted diffusion, and lack of contrast enhancement. The majority of the lesions were reversible within the follow-up period (median follow-up 3 weeks). Interestingly, even though CLOCC were mostly associated with symptoms of the underlying disease, in exceptional cases, CLOCC were associated with callosal neurological symptoms. Of note, employed nomenclature for CLOCC was highly inconsistent. CONCLUSIONS: Our study provides high-level evidence for clinical and imaging features of CLOCC as well as associated disease entities. CLINICAL RELEVANCE STATEMENT: Our study provides high-level evidence on MRI features of CLOCC as well as a comprehensive list of disease entities potentially associated with CLOCC. Together, this will facilitate rigorous diagnostic workup of suspected CLOCC cases. KEY POINTS: • Cytotoxic lesions of the corpus callosum (CLOCC) are a frequent MRI feature associated with various systemic diseases. • Cytotoxic lesions of the corpus callosum show a highly homogenous MRI presentation and temporal dynamics. • This comprehensive overview will benefit (neuro)radiologists during diagnostic workup.

The etiological spectrum of multiple ring-enhancing lesions of the brain: a systematic review of published cases and case series.

OBJECTIVE: Multiple ring-enhancing lesions of the brain are enigmatic neuroimaging abnormality. In this systematic review, we evaluated the etiological spectrum of these lesions. METHODS: This systematic review adhered to the PRISMA guidelines. We searched PubMed, Embase, Scopus, and Google Scholar up until 15 June 2023. We included case reports and case series. Quality evaluation of each case was based on selection, ascertainment, causality, and reporting. The extracted information included demographic characteristics, clinical features, type and number of multiple enhancing brain lesions, diagnostic procedures, final diagnoses, treatments, and patient outcomes. PROTOCOL REGISTRATION: PROSPERO CRD42023437081. RESULTS: We analyzed 156 records representing 161 patients, 60 of whom were immunocompromised. The mean age was 42.6 years, and 67% of patients experienced symptoms for up to 1 month. A higher proportion of immunocompromised patients (42% vs. 30%) exhibited encephalopathy. Chest or CT thorax abnormalities were reported in 27.3% of patients, while CSF abnormalities were found in 31.7%, more frequently among the immunocompromised. Definitive diagnoses were established via brain biopsy, aspiration, or autopsy in 60% of cases, and through CSF examination or other ancillary tests in 40% of cases. Immunocompromised patients had a higher incidence of Toxoplasma gondii infection and CNS lymphoma, while immunocompetent patients had a higher incidence of Mycobacterium tuberculosis infection and immune-mediated and demyelinating disorders. The improvement rate was 74% in immunocompetent patients compared to 52% in the immunocompromised group. CONCLUSION: Multiple ring-enhancing lesions of the brain in immunocompromised patients are more frequently caused by Toxoplasma gondii infections and CNS lymphoma. Conversely, among immunocompetent patients, Mycobacterium tuberculosis infection and immune-related demyelinating conditions are common.

Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.

BACKGROUND: Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice. OBJECTIVE: To assess image quality and diagnostic confidence of AI reconstruction in the pediatric brain on fluid-attenuated inversion recovery (FLAIR) imaging. MATERIALS AND METHODS: This prospective, institutional review board (IRB)-approved study enrolled 50 pediatric patients (median age=12 years, Q1=10 years, Q3=14 years) undergoing clinical brain MRI. T2-weighted (T2W) FLAIR images were reconstructed by both standard clinical and AI reconstruction algorithms (strong denoising). Images were independently rated by two neuroradiologists on a dedicated research picture archiving and communication system (PACS) to indicate whether AI increased, decreased, or had no effect on image quality compared to standard reconstruction. Quantitative analysis of signal intensities was also performed to calculate apparent signal to noise (aSNR) and apparent contrast to noise (aCNR) ratios. RESULTS: AI reconstruction was better than standard in 99% (reader 1, 49/50; reader 2, 50/50) for overall image quality, 99% (reader 1, 49/50; reader 2, 50/50) for subjective SNR, and 98% (reader 1, 49/50; reader 2, 49/50) for diagnostic preference. Quantitative analysis revealed significantly higher gray matter aSNR (30.6±6.5), white matter aSNR (21.4±5.6), and gray-white matter aCNR (7.1±1.6) in AI-reconstructed images compared to standard reconstruction (18±2.7, 14.2±2.8, 4.4±0.8, p<0.001) respectively. CONCLUSION: We conclude that AI reconstruction improved T2W FLAIR image quality in most patients when compared with standard reconstruction in pediatric patients.

Brain magnetic resonance imaging findings in children with neurological complications of coronavirus disease 2019 (Omicron variant): a multicenter retrospective observational study.

BACKGROUND: An increasing rate of encephalopathy associated with coronavirus disease 2019 (COVID-19) has been observed among children. However, the literature on neuroimaging data in children with COVID-19 is limited. OBJECTIVE: To analyze brain magnetic resonance imaging (MRI) of pediatric COVID-19 patients with neurological complications. MATERIALS AND METHODS: This multicenter retrospective observational study analyzed clinical (n=102, 100%) and neuroimaging (n=93, 91.2%) data of 102 children with COVID-19 infections and comorbid acute neurological symptoms. These children were hospitalized at five pediatric intensive care units (PICUs) in China between December 1, 2022, and January 31, 2023. RESULTS: All patients were positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as detected via reverse transcriptase polymerase chain reaction. About 75.7% of the children were infected with the Omicron variant BF.7 strain. Brain MRI was performed 1-12 days following the onset of neurological symptoms, which revealed acute neuroimaging findings in 74.2% (69/93) of cases, including evidence of acute necrotizing encephalopathy (33/69, 47.8%), encephalitis (31/69, 44.9%), reversible splenial lesion syndrome (3/69, 4.3%), reversible posterior leukoencephalopathy (1/69, 1.4%), and hippocampal atrophy (1/69, 1.4%). CONCLUSIONS: Overall, these data highlighted five neuroimaging patterns associated with the outbreak of the SARS-CoV-2 Omicron variant, with acute necrotizing encephalopathy being the most common of these neuroimaging findings. Rarely, the brain MRI of these pediatric COVID-19 patients also demonstrate hippocampal atrophy.

Delayed encephalopathy after acute carbon monoxide poisoning: a case study.

Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a relatively rare inflammatory-associated neurometabolic complication. In this article, we present a case report of a 50-year-old male patient with a history of carbon monoxide poisoning. This acute poisoning, although successfully controlled during a stay in the intensive care unit of a local hospital, later led to persistent neurological symptoms. The patient was then treated in the inpatient unit of the rehabilitation clinic, where cognitive deterioration began to develop 20 days after admission. Subsequent examination using EEG and magnetic resonance imaging confirmed severe encephalopathy later complicated by SARS-CoV-2 infection with fatal consequences due to bronchopneumonia. Because currently there are no approved guidelines for the management of DEACMP, we briefly discuss the existing challenges for future studies, especially the application of rational immunosuppressive therapy already in the acute treatment phase of CO poisoning, which could prevent the development of a severe form of DEACMP.

GNB1 Encephalopathy: Clinical Case Report and Literature Review.

GNB1 encephalopathy is a rare genetic disease caused by pathogenic variants in the G Protein Subunit Beta 1 (GNB1) gene, with only around 68 cases documented worldwide. Although most cases had been caused by de novo germline mutations, in this case, the pathogenic variant was inherited from patient's mother, indicating an autosomal dominant inheritance pattern. The patient presented at 25 years of age with mild developmental delay and cognitive impairment, prominent generalized dystonia, and horizontal nystagmus which are all characterizing symptoms of GNB1 encephalopathy. Electroencephalography (EEG) showed no epileptiform patterns, and magnetic resonance imaging (MRI) revealed hypointensities in globus pallidus and dentate nucleus areas. The main theory for GNB1 encephalopathy pathogenesis is neuronal hyperexcitability caused by impaired ion channel regulation. Due to low specificity of symptoms, diagnosis relies on genetic testing. As there are no standardized GNB1 encephalopathy treatment guidelines, evaluation of different treatment options is based on anecdotal cases. Reviewing different treatment options, deep brain stimulation and intrathecal baclofen pump, as well as some other medications still in preclinical trials, seem to be the most promising.

Inferring the genetic relationship between brain imaging-derived phenotypes and risk of complex diseases by Mendelian randomization and genome-wide colocalization.

Observational studies consistently disclose brain imaging-derived phenotypes (IDPs) as critical markers for early diagnosis of both brain disorders and cardiovascular diseases. However, it remains unclear about the shared genetic landscape between brain IDPs and the risk of brain disorders and cardiovascular diseases, restricting the applications of potential diagnostic techniques through brain IDPs. Here, we reported genetic correlations and putative causal relationships between 921 brain IDPs, 20 brain disorders and six cardiovascular diseases by leveraging their large-scale genome-wide association study (GWAS) summary statistics. Applications of Mendelian randomization (MR) identified significant putative causal effects of multiple region-specific brain IDPs in relation to the increased risks for amyotrophic lateral sclerosis (ALS), major depressive disorder (MDD), autism spectrum disorder (ASD) and schizophrenia (SCZ). We also found brain IDPs specifically from temporal lobe as a putatively causal consequence of hypertension. The genome-wide colocalization analysis identified three genomic regions in which MDD, ASD and SCZ colocalized with the brain IDPs, and two novel SNPs to be associated with ASD, SCZ, and multiple brain IDPs. Furthermore, we identified a list of candidate genes involved in the shared genetics underlying pairs of brain IDPs and MDD, ASD, SCZ, ALS and hypertension. Our results provide novel insights into the genetic relationships between brain disorders and cardiovascular diseases and brain IDP, which may server as clues for using brain IDPs to predict risks of diseases.

Bi-allelic JAM2 Variants Lead to Early-Onset Recessive Primary Familial Brain Calcification.

Primary familial brain calcification (PFBC) is a rare neurodegenerative disorder characterized by a combination of neurological, psychiatric, and cognitive decline associated with calcium deposition on brain imaging. To date, mutations in five genes have been linked to PFBC. However, more than 50% of individuals affected by PFBC have no molecular diagnosis. We report four unrelated families presenting with initial learning difficulties and seizures and later psychiatric symptoms, cerebellar ataxia, extrapyramidal signs, and extensive calcifications on brain imaging. Through a combination of homozygosity mapping and exome sequencing, we mapped this phenotype to chromosome 21q21.3 and identified bi-allelic variants in JAM2. JAM2 encodes for the junctional-adhesion-molecule-2, a key tight-junction protein in blood-brain-barrier permeability. We show that JAM2 variants lead to reduction of JAM2 mRNA expression and absence of JAM2 protein in patient's fibroblasts, consistent with a loss-of-function mechanism. We show that the human phenotype is replicated in the jam2 complete knockout mouse (jam2 KO). Furthermore, neuropathology of jam2 KO mouse showed prominent vacuolation in the cerebral cortex, thalamus, and cerebellum and particularly widespread vacuolation in the midbrain with reactive astrogliosis and neuronal density reduction. The regions of the human brain affected on neuroimaging are similar to the affected brain areas in the myorg PFBC null mouse. Along with JAM3 and OCLN, JAM2 is the third tight-junction gene in which bi-allelic variants are associated with brain calcification, suggesting that defective cell-to-cell adhesion and dysfunction of the movement of solutes through the paracellular spaces in the neurovascular unit is a key mechanism in CNS calcification.

Association of deep phenotyping with diagnostic yield of prenatal exome sequencing for fetal brain abnormalities.

PURPOSE: To evaluate whether deep prenatal phenotyping of fetal brain abnormalities (FBAs) increases diagnostic yield of trio-exome sequencing (ES) compared with standard phenotyping. METHODS: Retrospective exploratory analysis of a multicenter prenatal ES study. Participants were eligible if an FBA was diagnosed and subsequently found to have a normal microarray. Deep phenotyping was defined as phenotype based on targeted ultrasound plus prenatal/postnatal magnetic resonance imaging, autopsy, and/or known phenotypes of other affected family members. Standard phenotyping was based on targeted ultrasound alone. FBAs were categorized by major brain findings on prenatal ultrasound. Cases with positive ES results were compared with those that have negative results by available phenotyping, as well as diagnosed FBAs. RESULTS: A total of 76 trios with FBAs were identified, of which 25 (33%) cases had positive ES results and 51 (67%) had negative results. Individual modalities of deep phenotyping were not associated with diagnostic ES results. The most common FBAs identified were posterior fossa anomalies and midline defects. Neural tube defects were significantly associated with receipt of a negative ES result (0% vs 22%, P = .01). CONCLUSION: Deep phenotyping was not associated with increased diagnostic yield of ES for FBA in this small cohort. Neural tube defects were associated with negative ES results.

Factors associated with MRI success in children cooled for neonatal encephalopathy and controls.

OBJECTIVE: To investigate if an association exists between motion artefacts on brain MRI and comprehension, co-ordination, or hyperactivity scores in children aged 6-8 years, cooled for neonatal encephalopathy (cases) and controls. METHODS: Case children (n = 50) without cerebral palsy were matched with 43 controls for age, sex, and socioeconomic status. Children underwent T1-weighted (T1w), diffusion-weighted image (DWI) brain MRI and cognitive, behavioural, and motor skills assessment. Stepwise multivariable logistic regression assessed associations between unsuccessful MRI and comprehension (including Weschler Intelligence Scale for Children (WISC-IV) verbal comprehension, working memory, processing speed and full-scale IQ), co-ordination (including Movement Assessment Battery for Children (MABC-2) balance, manual dexterity, aiming and catching, and total scores) and hyperactivity (including Strengths and Difficulties Questionnaire (SDQ) hyperactivity and total difficulties scores). RESULTS: Cases had lower odds of completing both T1w and DWIs (OR: 0.31, 95% CI 0.11-0.89). After adjusting for case-status and sex, lower MABC-2 balance score predicted unsuccessful T1w MRI (OR: 0.81, 95% CI 0.67-0.97, p = 0.022). Processing speed was negatively correlated with relative motion on DWI (r = -0.25, p = 0.026) and SDQ total difficulties score was lower for children with successful MRIs (p = 0.049). CONCLUSIONS: Motion artefacts on brain MRI in early school-age children are related to the developmental profile. IMPACT: Children who had moderate/severe neonatal encephalopathy are less likely to have successful MRI scans than matched controls. Motion artefact on MRI is associated with lower MABC-2 balance scores in both children who received therapeutic hypothermia for neonatal encephalopathy and matched controls, after controlling for case-status and sex. Exclusion of children with motion artefacts on brain MRI can introduce sampling bias, which impacts the utility of neuroimaging to understand the brain-behaviour relationship in children with functional impairments.

Neuropsychological, neuroimaging and autopsy findings of butane encephalopathy.

BACKGROUND: Butane is an aliphatic hydrocarbon used in various commercial products. While numerous reports of sudden cardiac-related deaths from butane inhalation have been described, butane-associated acute encephalopathy has rarely been reported. CASE PRESENTATION: A 38-year-old man presented with cognitive dysfunction after butane gas inhalation. Neuropsychological test results showed impairments in verbal and visual memory, and frontal executive function. Diffusion weighted MRI revealed symmetric high-signal changes in the bilateral hippocampus and globus pallidus. FDG-PET demonstrated decreased glucose metabolism in the bilateral precuneus and occipital areas and the left temporal region. At the 8-month follow-up, he showed still significant deficits in memory and frontal functions. Diffuse cortical atrophy with white matter hyperintensities and extensive glucose hypometabolism were detected on follow-up MRI and FDG-PET, respectively. Brain autopsy demonstrated necrosis and cavitary lesions in the globus pallidus. CONCLUSIONS: Only a few cases of butane encephalopathy have been reported to date. Brain lesions associated with butane encephalopathy include lesions in the bilateral thalamus, insula, putamen, and cerebellum. To the best of our knowledge, this is the first report on bilateral hippocampal and globus pallidal involvement in acute butane encephalopathy. The pathophysiology of central nervous system complications induced by butane intoxication is not yet fully understood. However, the direct toxic effects of butane or anoxic injury secondary to cardiac arrest or respiratory depression have been suggested as possible mechanisms of edematous changes in the brain after butane intoxication.

Obstructive sleep apnea: a major risk factor for COVID-19 encephalopathy?

BACKGROUND: This study evaluates the impact of high risk of obstructive sleep apnea (OSA) on coronavirus disease 2019 (COVID-19) acute encephalopathy (AE). METHODS: Between 3/1/2020 and 11/1/2021, 97 consecutive patients were evaluated at the Geneva University Hospitals with a neurological diagnosis of COVID-19 AE. They were divided in two groups depending on the presence or absence of high risk for OSA based on the modified NOSAS score (mNOSAS, respectively ≥ 8 and < 8). We compared patients' characteristics (clinical, biological, brain MRI, EEG, pulmonary CT). The severity of COVID-19 AE relied on the RASS and CAM scores. RESULTS: Most COVID-19 AE patients presented with a high mNOSAS, suggesting high risk of OSA (> 80%). Patients with a high mNOSAS had a more severe form of COVID-19 AE (84.8% versus 27.8%), longer mean duration of COVID-19 AE (27.9 versus 16.9 days), higher mRS at discharge (≥ 3 in 58.2% versus 16.7%), and increased prevalence of brain vessels enhancement (98.1% versus 20.0%). High risk of OSA was associated with a 14 fold increased risk of developing a severe COVID-19 AE (OR = 14.52). DISCUSSION: These observations suggest an association between high risk of OSA and COVID-19 AE severity. High risk of OSA could be a predisposing factor leading to severe COVID-19 AE and consecutive long-term sequalae.

MRI and steroid-responsive encephalopathy associated with autoimmune thyroiditis: first report of conus medullaris involvement and literature review of the known neuroimaging profiles.

BACKGROUND: Steroid-responsive encephalopathy associated with autoimmune thyroiditis (SREAT) is a rare but potentially reversible autoimmune encephalopathy. The most frequent neuroimaging correlates are normal brain MRI or non-specific white matter hyperintensities. METHODS: We present the first description of conus medullaris involvement, also providing an extensive review of MRI patterns described so far. RESULTS: Our results show that in less than 30% of cases, it is possible to find focal SREAT neuroanatomical correlates. Among these, T2w/FLAIR temporal hyperintensities are the most frequent, followed by basal ganglia/thalamic and brainstem involvement, respectively. CONCLUSIONS: Unfortunately, spinal cord investigation is an uncommon practice in the diagnostic approach of encephalopathies, thus neglecting potential pathological lesions of the medulla spinalis. In our opinion, the extension of the MRI study to the cervical, thoracic, and lumbosacral regions may allow finding new, and hopefully specific, anatomical correlates.

The clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) for evaluation of uncooperative patients with brain diseases: comparison with the conventional T2-FLAIR with parallel imaging.

BACKGROUND: Satisfactory magnetic resonance imaging (MRI) of those patients with involuntary head motion due to brain diseases is essential in avoiding missed diagnosis and guiding treatment. PURPOSE: To investigate the clinical feasibility of artificial intelligence-assisted compressed sensing single-shot fluid-attenuated inversion recovery (ACS-SS-FLAIR) in evaluating patients with involuntary head motion due to brain diseases, compared with the conventional T2-FLAIR with parallel imaging (PI-FLAIR). MATERIAL AND METHODS: A total of 33 uncooperative patients with brain disease were prospectively enrolled. Two readers independently reviewed images acquired with ACS-SS-FLAIR and PI-FLAIR at a 3.0-T MR scanner. In the aspects of qualitative evaluation of image quality, overall image quality and lesion conspicuity of ACS-SS-FLAIR and PI-FLAIR were assessed and then statistically compared by paired Wilcoxon rank-sum test. For quantitative evaluation, the relative contrast of lesion-to-cerebral parenchyma were calculated and compared. RESULTS: Overall image quality scores of ACS-SS-FLAIR evaluated by two readers were 2.94 ± 0.24 and 2.91 ± 0.29, respectively, both of which were significantly higher than that of PI-FLAIR, respectively (P < 0.001 and <0.001). Lesion conspicuity scores of were 2.74 ± 0.47 and 2.79 ± 0.44, both of which were significantly higher than that of PI-FLAIR, respectively (P < 0.001 and <0.001). In the quantitative evaluation for image quality, the relative contrast of lesion-to-cerebral parenchyma was 0.34 ± 0.09 in the ACS-SS-FLAIR sequence, significantly larger than that in the PI-FLAIR sequence (P = 0.001). CONCLUSION: The ACS-SS-FLAIR sequence is clinically feasible in the MRI workup of those patients with involuntary head motion due to brain diseases, showing shorter image acquisition time and better image quality compared with conventional PI-FLAIR.

Advanced pediatric neuroimaging.

Advanced magnetic resonance neuroimaging techniques play an important adjunct role to conventional MRI sequences for better depiction and characterization of a variety of brain disorders. In this article we briefly review the basic principles and clinical utility of a select number of these techniques, including clinical functional MRI for presurgical planning, clinical diffusion tensor imaging and related techniques, dynamic susceptibility contrast perfusion imaging using gadolinium injection, and arterial spin labeling perfusion imaging. The article focuses on general principles of clinical MRI acquisition protocols, relevant factors affecting image quality, and a general framework for obtaining images for each of these techniques. We also present relevant advances for acquiring these types of imaging sequences in a clinical setting.

Development of a Nomogram Based on Diffusion-Weighted Imaging and Clinical Information to Predict Delayed Encephalopathy after Acute Carbon Monoxide Poisoning.

BACKGROUND: Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a severe complication that can arise from acute carbon monoxide poisoning (ACOP). This study aims to identify the independent risk factors associated with DEACMP and to develop a nomogram to predict the probability of developing DEACMP. METHODS: The data of patients diagnosed with ACOP between September 2015 and June 2021 were analyzed retrospectively. The patients were divided into the two groups: the DEACMP group and the non-DEACMP group. Univariate analysis and multivariate logistic regression analysis were conducted to identify the independent risk factors for DEACMP. Subsequently, a nomogram was constructed to predict the probability of DEACMP. RESULTS: The study included 122 patients, out of whom 30 (24.6%) developed DEACMP. The multivariate logistic regression analysis revealed that acute high-signal lesions on diffusion-weighted imaging (DWI), duration of carbon monoxide (CO) exposure, and Glasgow Coma Scale (GCS) score were independent risk factors for DEACMP (Odds Ratio = 6.230, 1.323, 0.714, p < 0.05). Based on these indicators, a predictive nomogram was constructed. CONCLUSIONS: This study constructed a nomogram for predicting DEACMP using high-signal lesions on DWI and clinical indicators. The nomogram may serve as a dependable tool to differentiate high-risk patients and enable the provision of personalized treatment to lower the incidence of DEACMP.

Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.

An automated diagnosis system is crucial for helping radiologists identify brain abnormalities efficiently. The convolutional neural network (CNN) algorithm of deep learning has the advantage of automated feature extraction beneficial for an automated diagnosis system. However, several challenges in the CNN-based classifiers of medical images, such as a lack of labeled data and class imbalance problems, can significantly hinder the performance. Meanwhile, the expertise of multiple clinicians may be required to achieve accurate diagnoses, which can be reflected in the use of multiple algorithms. In this paper, we present Deep-Stacked CNN, a deep heterogeneous model based on stacked generalization to harness the advantages of different CNN-based classifiers. The model aims to improve robustness in the task of multi-class brain disease classification when we have no opportunity to train single CNNs on sufficient data. We propose two levels of learning processes to obtain the desired model. At the first level, different pre-trained CNNs fine-tuned via transfer learning will be selected as the base classifiers through several procedures. Each base classifier has a unique expert-like character, which provides diversity to the diagnosis outcomes. At the second level, the base classifiers are stacked together through neural network, representing the meta-learner that best combines their outputs and generates the final prediction. The proposed Deep-Stacked CNN obtained an accuracy of 99.14% when evaluated on the untouched dataset. This model shows its superiority over existing methods in the same domain. It also requires fewer parameters and computations while maintaining outstanding performance.