Pediatric Cranial Ultrasound Revisited: A Comprehensive Review.

ABSTRACT: Cranial ultrasound (CUS) is an indispensable tool in the evaluation of intracranial pathology in premature and term neonates and older infants. Familiarity with standard cranial ultrasound techniques and parameters, normal anatomy, and commonly encountered abnormalities is crucial for providing appropriate care for these patients. This review provides a comprehensive overview of cranial ultrasound in clinical practice.

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.

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.

Incidental findings in MRI of the brain.

This review investigates that there has been an increase in incidental brain MRI findings due to better technology and more scans. These unexpected, asymptomatic anomalies range from harmless to serious, requiring careful clinical and ethical handling. The prevalence of incidental findings with brain MRI is 4.2% and even higher when including white matter hyperintensities. There is a significant variation in this number dependent on the age of the person being scanned and the MRI quality.

Novel findings in a Swedish primary familial brain calcification cohort.

INTRODUCTION: Brain calcifications are frequent findings on imaging. In a small proportion of cases, these calcifications are associated with pathogenic gene variants, hence termed primary familial brain calcification (PFBC). The clinical penetrance is incomplete and phenotypic variability is substantial. This paper aims to characterize a Swedish PFBC cohort including 25 patients: 20 from seven families and five sporadic cases. METHODS: Longitudinal clinical assessment and CT imaging were conducted, abnormalities were assessed using the total calcification score (TCS). Genetic analyses, including a panel of six known PFBC genes, were performed in all index and sporadic cases. Additionally, three patients carrying a novel pathogenic copy number variant in SLC20A2 had their cerebrospinal fluid phosphate (CSF-Pi) levels measured. RESULTS: Among the 25 patients, the majority (76%) displayed varying symptoms during the initial assessment including motor (60%), psychiatric (40%), and/or cognitive abnormalities (24%). Clinical progression was observed in most patients (78.6%), but there was no significant difference in calcification between the first and second scans, with mean scores of 27.3 and 32.8, respectively. In three families and two sporadic cases, pathogenic genetic variants were identified, including a novel finding, in the SLC20A2 gene. In the three tested patients, the CSF-Pi levels were normal. CONCLUSIONS: This report demonstrates the variable expressivity seen in PFBC and includes a novel pathogenic variant in the SLC20A2 gene. In four families and three sporadic cases, no pathogenic variants were found, suggesting that new PFBC genes remain to be discovered.

Insights Into MRI Neuroimaging Patterns of COVID-19 in Children: A Retrospective Comprehensive Analysis.

RATIONALE AND OBJECTIVES: Neurological complications associated with coronavirus disease (COVID-19) have been reported in children; however, data on neuroimaging findings remain limited. This study aimed to comprehensively examine neuroimaging patterns of COVID-19 in children and their relationship with clinical outcomes. MATERIALS AND METHODS: This retrospective cross-sectional study involved reviewing the medical records and MRI scans of 95 children who developed new neurological symptoms within 2-4 weeks of clinical and laboratory confirmation of COVID-19. Patients were categorized into four groups based on guidelines approved by the Centers for Disease Control and Prevention (CDC). Initial brain/spinal MRI was performed. Images were reviewed by three blinded radiologists, and the findings were analyzed and categorized based on the observed patterns in the brain and spinal cord. Follow-up MRI was performed and analyzed to track lesion progression. RESULTS: Encephalopathy was the most common neurological symptom (50.5%). The most common initial MRI involvement patterns were non-confluent multifocal hyperintense white matter (WM) lesions (36.8%) and ischemia (18.9%). Most patients who underwent follow-up MRI (n = 56) showed complete resolution (69.9%); however, some patients developed encephalomalacia and myelomalacia (23.2% and 7.1%, respectively). Non-confluent hyperintense WM lesions were associated with good outcomes (45.9%, P = 0.014), whereas ischemia and hemorrhage were associated with poor outcomes (44.1%, P < 0.001). CONCLUSION: This study revealed diverse neuroimaging patterns in pediatric COVID-19 patients. Non-confluent WM lesions were associated with good outcomes, whereas ischemia and hemorrhage were associated with poorer prognoses. Understanding these patterns is crucial for their early detection, accurate diagnosis, and appropriate management.

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.

Clinical Evaluation of a 2-Minute Ultrafast Brain MR Protocol for Evaluation of Acute Pathology in the Emergency and Inpatient Settings.

BACKGROUND AND PURPOSE: The use of MR imaging in emergency settings has been limited by availability, long scan times, and sensitivity to motion. This study assessed the diagnostic performance of an ultrafast brain MR imaging protocol for evaluation of acute intracranial pathology in the emergency department and inpatient settings. MATERIALS AND METHODS: Sixty-six adult patients who underwent brain MR imaging in the emergency department and inpatient settings were included in the study. All patients underwent both the reference and the ultrafast brain MR protocols. Both brain MR imaging protocols consisted of T1-weighted, T2/T2*-weighted, FLAIR, and DWI sequences. The ultrafast MR images were reconstructed by using a machine-learning assisted framework. All images were reviewed by 2 blinded neuroradiologists. RESULTS: The average acquisition time was 2.1 minutes for the ultrafast brain MR protocol and 10 minutes for the reference brain MR protocol. There was 98.5% agreement on the main clinical diagnosis between the 2 protocols. In head-to-head comparison, the reference protocol was preferred in terms of image noise and geometric distortion (P < .05 for both). The ultrafast ms-EPI protocol was preferred over the reference protocol in terms of reduced motion artifacts (P < .01). Overall diagnostic quality was not significantly different between the 2 protocols (P > .05). CONCLUSIONS: The ultrafast brain MR imaging protocol provides high accuracy for evaluating acute pathology while only requiring a fraction of the scan time. Although there was greater image noise and geometric distortion on the ultrafast brain MR protocol images, there was significant reduction in motion artifacts with similar overall diagnostic quality between the 2 protocols.

Intracranial Epidermoid Cyst: A Volumetric Study of a Surgically Challenging Benign Lesion.

BACKGROUND: Intracranial epidermoid cysts are rare, benign tumors. Nevertheless, the microsurgical removal of these cysts is challenging. This is due to their capacity to adhere to the neurovascular tissue, as well as the associated difficulties in microsurgically peeling off their capsular wall hidden in dead angles. To better understand the rate of recurrence after surgical intervention, we have performed preoperative and postoperative volumetric analysis of epidermoid cysts, allowing the estimation of their growth rate after resection. METHODS: Imaging data from 22 patients diagnosed and surgically treated for an intracranial epidermoid cyst between 2000 and 2022 were retrospectively collected from 2 European neurosurgical centers with microsurgical expertise. Volumetric analysis was performed on magnetic resonance imaging data. RESULTS: Average cyst volume at diagnosis, before any surgery, measured in 12 patients was 28,877.6 ± 10,250.4 mm3 (standard error of the mean [SEM]). Estimated growth rate of incompletely resected epidermoids after surgery was 1,630.05 mm3 ± 729.95 (SEM). Assuming linear growth dynamics and normalizing to postoperative residual volume, the average postoperative growth rate corresponded to 61.5% ± 34.3% (SEM) of the postoperative residual volume per year. We observed signs of recurrence during a radiologic follow-up period of 6.0 ± 2.8 years (standard deviation) in more than 50% of our patients. CONCLUSIONS: Due to their slow-growing nature, epidermoid cysts can often reach a complex multicompartmental size before resection, even in young patients, thus requiring complex approaches with challenging capsular resection, which implies a high risk of nerve and vascular injury per se. Tumor recurrence may be predicted on the basis of postoperative volumetry.

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.

A Young Girl with Acute Encephalopathy Who Presented with Status Epilepticus Following a Flu-Like Illness.

BACKGROUND Acute necrotizing encephalopathy of childhood (ANEC) is a rare form of acute encephalopathy in children that is characterized by rapid and progressive deterioration in the patient's clinical condition. Our aim in reporting the case is to highlight the importance of early diagnoses and aggressive early management of ANEC to achieve a better outcome. We report the case of a 5-year-old girl who presented with acute progressive encephalopathy with bilateral symmetrical thalamic involvement and brainstem lesion proceeded by a flu-like illness. CASE REPORT We report a 5-year-old girl who was previously healthy. She presented with rapid progressive encephalopathy and status epilepticus following a flu-like illness. Her magnetic resonance imaging (MRI) showed significance symmetrical bilateral thalamic enhancement with brainstem lesions, which is consistent with the diagnosis of ANEC of childhood, and her initial electroencephalogram (EEG) showed severe encephalopathy. Despite extensive management of ANEC, the patient showed a poor prognosis. CONCLUSIONS ANEC is a rare, specific, devastating condition in the pediatric population. Early diagnosis and aggressive management should be initiated immediately to avoid the high mortality associated with the condition. Furthermore, to highlight the importance of the clear history of the patient who presented with rapid and progressive deterioration in the level of consciousness and status epilepticus proceeded by viral illness, a rapid neuroradiological images such as brain MRI should be obtained to confirm the diagnosis of this rare condition. We also seek to increase awareness of this disorder among healthcare workers and general practitioners.

Randomizing Human Brain Function Representation for Brain Disease Diagnosis.

Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject's brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.