Imaging Markers for Normal Pressure Hydrocephalus: An Overview

Automated Detection of Hydrocephalus in Pediatric Head Computed Tomography Using VGG 16 CNN Deep Learning Architecture and Based Automated Segmentation Workflow for Ventricular Volume Estimation

Hydrocephalus, particularly congenital hydrocephalus in infants, remains underexplored in deep learning research. While deep learning has been widely applied to medical image analysis, few studies have specifically addressed the automated classification of hydrocephalus. This study proposes a convolutional neural network (CNN) model based on the VGG16 architecture to detect hydrocephalus in infant head CT images. The model integrates an automated method for ventricular volume extraction, applying windowing, histogram equalization, and thresholding techniques to segment the ventricles from surrounding brain structures. Morphological operations refine the segmentation and contours are extracted for visualization and volume measurement. The dataset consists of 105 head CT scans, each with 60 slices covering the ventricular volume, resulting in 6300 slices. Manual segmentation by three trained radiologists served as the reference standard. The automated method showed a high correlation with manual measurements, with R2 values ranging from 0.94 to 0.99. The mean absolute percentage error (MAPE) ranged 3.99 to 11.13%, while the root mean square error (RRMSE) from 4.56 to 13.74%. To improve model robustness, the dataset was preprocessed, normalized, and augmented with rotation, shifting, zooming, and flipping. The VGG16-based CNN used pre-trained convolutional layers with additional fully connected layers for classification, predicting hydrocephalus or normal labels. Performance evaluation using a multi-split strategy (15 independent splits) achieved a mean accuracy of 90.4% ± 1.2%. This study presents an automated approach for ventricular volume extraction and hydrocephalus detection, offering a promising tool for clinical and research applications with high accuracy and reduced observer bias.

Involvement of the Nigrostriatal Pathway in Patients With Idiopathic Normal Pressure Hydrocephalus and Parkinsonism

BACKGROUND AND OBJECTIVES

Idiopathic normal pressure hydrocephalus (iNPH) is characterized by gait disturbance, cognitive decline, and urinary incontinence and may include parkinsonism. The underlying mechanism of parkinsonism in iNPH-whether neurodegenerative or mechanical-remains unclear. This study aimed to assess nigrostriatal integrity in iNPH patients with parkinsonism using dopaminergic transporter imaging (DAT-SPECT) and nigrosome MRI.

METHODS

This prospective study was conducted at the Movement Disorders Clinic, Santa Chiara Hospital, Pisa University, from 2021 to 2023. Inclusion criteria for the iNPH group included the following: (1) clinical diagnosis of probable iNPH per the 2021 Japanese Society Guidelines and (2) parkinsonism per United Kingdom Parkinson's Disease Society Brain Bank criteria. An equal number of patients with Parkinson disease (PD), matched for age and sex, served as a comparison group. All participants underwent DAT-SPECT and 3T MRI within 1 month. Statistical analyses included the Student t test or Fisher-Pitman permutation tests for continuous variables and χ2 tests for categorical variables. Multiple linear regression (adjusted for age and sex) compared DAT binding between groups. Pearson correlation assessed relationships between striatal DAT binding and parkinsonism in patients with iNPH evaluated using the Movement Disorder Society Unified Parkinson's Disease Rating Scale Part III.

RESULTS

A total of 20 patients with iNPH (mean age 75.4 ± 5.1 years, 65% female) and 20 patients with PD (mean age 74 ± 3.7 years, 55% female) were included. Reduced striatal DAT binding was observed in 45% of patients with iNPH, with none exhibiting nigrosome loss. Conversely, all patients with PD showed both reduced DAT binding and nigrosome loss (p < 0.001). After adjusting for age and sex, patients with iNPH exhibited significantly higher putaminal and caudate DAT binding than patients with PD (right putamen: β = -0.644, p < 0.001; left putamen: β = -0.659, p < 0.001; right caudate: β = -0.429, p = 0.006; left caudate: β = -0.391, p = 0.016), with an elevated putaminal/caudate ratio (p = 0.012). In patients with iNPH, striatal DAT binding negatively correlated with motor severity (left: r = -0.626, p = 0.004; right: r = -0.425, p = 0.07).

DISCUSSION

Findings suggest that parkinsonism in iNPH may stem from mechanical disruption of the nigrostriatal pathway rather than neurodegeneration, as indicated by preserved nigrosome integrity despite reduced DAT binding. Limitations include the small sample size and lack of postsurgical follow-up data.

Comprehensive Morphometric Analysis to Identify Key Neuroimaging Biomarkers for the Diagnosis of Adult Hydrocephalus Using Artificial Intelligence

BACKGROUND AND OBJECTIVES

Hydrocephalus involves abnormal cerebrospinal fluid accumulation in brain ventricles. Early and accurate diagnosis is crucial for timely intervention and preventing progressive neurological deterioration. The aim of this study was to identify key neuroimaging biomarkers for the diagnosis of hydrocephalus using artificial intelligence to develop practical and accurate diagnostic tools for neurosurgeons.

METHODS

Fifteen 1-dimensional (1-D) neuroimaging parameters and ventricular volume of adult patients with non-normal pressure hydrocephalus and healthy subjects were measured using manual image processing, and 10 morphometric indices were also calculated. The data set was analyzed using 8 machine, ensemble, and deep learning classifiers to predict hydrocephalus. SHapley Additive exPlanations (SHAP) feature importance analysis identified key neuroimaging diagnostic biomarkers.

RESULTS

Gradient Boosting achieved the highest performance, with an accuracy of 0.94 and an area under the curve of 0.97. SHAP analysis identified ventricular volume as the most important parameter. Given the challenges of measuring volume for clinicians, we identified key 1-D morphometric biomarkers that are easily measurable yet provide similar classifier performance. The results showed that the frontal-temporal horn ratio, modified Evan index, modified cella media index, sagittal maximum lateral ventricle height, and coronal posterior callosal angle are key 1-D diagnostic biomarkers. Notably, higher modified Evan index, modified cella media index, and sagittal maximum lateral ventricle height, and lower frontal-temporal horn ratio and coronal posterior callosal angle values were associated with hydrocephalus prediction. The results also elucidated the relationships between these key 1-D morphometric parameters and ventricular volume, providing potential diagnostic insights.

CONCLUSION

This study highlights the importance of a multifaceted diagnostic approach incorporating 5 easily measurable 1-D neuroimaging biomarkers for neurosurgeons to differentiate non-normal pressure hydrocephalus from healthy subjects. Incorporating our artificial intelligence model, interpreted through SHAP analysis, into routine clinical workflows may transform the diagnostic landscape for hydrocephalus by standardizing diagnosis and overcoming the limitations of visual evaluations, particularly in early stages and challenging cases.

Secondary parkinsonism associated with focal brain lesions

Focal imaging abnormalities in patients with parkinsonism suggest secondary etiology and require a distinctive clinical approach to diagnosis and treatment. We review different entities presenting as secondary parkinsonism associated with structural brain lesions, with emphasis on the clinical course and neuroimaging findings. Secondary parkinsonism may be due to vascular causes, hydrocephalus, space-occupying lesions, metabolic causes (including acquired hepatocerebral degeneration, diabetic uremic encephalopathy, basal ganglia calcifications, osmotic demyelination syndrome), hypoxic-ischaemic brain injury, intoxications (including methanol, carbon monoxide, cyanide, carbon disulfide, manganese poisoning and illicit drugs), infections and immune causes. The onset can vary from acute to chronic. Both uni-and bilateral presentations are possible. Rigidity, bradykinesia and gait abnormalities are more common than rest tremor. Coexisting other movement disorders and additional associated neurological signs may point to the underlying diagnosis. Neuroimaging studies are an essential part in the diagnostic work-up of secondary parkinsonism and may point directly to the underlying etiology. We focus primarily on magnetic resonance imaging to illustrate how structural imaging combined with neurological assessment can lead to diagnosis. It is crucial that typical imaging abnormalities are recognized within the relevant clinical context. Many forms of secondary parkinsonism are reversible with elimination of the specific cause, while some may benefit from symptomatic treatment. This heterogeneous group of acquired disorders has also helped shape our knowledge of Parkinson's disease and basal ganglia pathophysiology, while more recent findings in the field garner support for the network perspective on brain function and neurological disorders.

In Vivo Prevalence of Beta-Amyloid Pathology and Alzheimer's Disease Co-Pathology in Idiopathic Normal-Pressure Hydrocephalus-Association with Neuropsychological Features

Idiopathic normal-pressure hydrocephalus (iNPH) is a clinic-radiological neurological syndrome presenting with cognitive deficits, gait disturbances and urinary incontinence. It often coexists with Alzheimer's disease (AD). Due to the reversible nature of iNPH when promptly treated, a lot of studies have focused on possible biomarkers, among which are cerebrospinal fluid (CSF) biomarkers. The aim of the present study was to determine the rate of beta-amyloid pathology and AD co-pathology by measuring AD CSF biomarkers, namely, amyloid beta with 42 and 40 amino acids (Aβ42), the Aβ42/Aβ40 ratio, total Tau protein (t-Tau) and phosphorylated Tau protein at threonine 181 (p-Tau), in a cohort of iNPH patients, as well as to investigate the possible associations among CSF biomarkers and iNPH neuropsychological profiles. Fifty-three patients with iNPH were included in the present study. CSF Aβ42, Aβ40, t-Tau and p-Tau were measured in duplicate with double-sandwich ELISA assays. The neuropsychological evaluation consisted of the Mini-Mental State Examination, Frontal Assessment Battery, Five-Word Test and CLOX drawing tests 1 and 2. After statistical analysis, we found that amyloid pathology and AD co-pathology are rather common in iNPH patients and that higher values of t-Tau and p-Tau CSF levels, as well as the existence of the AD CSF profile, are associated with more severe memory impairment in the study patients. In conclusion, our study has confirmed that amyloid pathology and AD-co-pathology are rather common in iNPH patients and that CSF markers of AD pathology and t-Tau are associated with a worse memory decline in these patients.

Assessing CT-based Volumetric Analysis via Transfer Learning with MRI and Manual Labels for Idiopathic Normal Pressure Hydrocephalus

Background

Brain computed tomography (CT) is an accessible and commonly utilized technique for assessing brain structure. In cases of idiopathic normal pressure hydrocephalus (iNPH), the presence of ventriculomegaly is often neuroradiologically evaluated by visual rating and manually measuring each image. Previously, we have developed and tested a deep-learning-model that utilizes transfer learning from magnetic resonance imaging (MRI) for CT-based intracranial tissue segmentation. Accordingly, herein we aimed to enhance the segmentation of ventricular cerebrospinal fluid (VCSF) in brain CT scans and assess the performance of automated brain CT volumetrics in iNPH patient diagnostics.

Methods

The development of the model used a two-stage approach. Initially, a 2D U-Net model was trained to predict VCSF segmentations from CT scans, using paired MR-VCSF labels from healthy controls. This model was subsequently refined by incorporating manually segmented lateral CT-VCSF labels from iNPH patients, building on the features learned from the initial U-Net model. The training dataset included 734 CT datasets from healthy controls paired with T1-weighted MRI scans from the Gothenburg H70 Birth Cohort Studies and 62 CT scans from iNPH patients at Uppsala University Hospital. To validate the model's performance across diverse patient populations, external clinical images including scans of 11 iNPH patients from the Universitatsmedizin Rostock, Germany, and 30 iNPH patients from the University of Alabama at Birmingham, United States were used. Further, we obtained three CT-based volumetric measures (CTVMs) related to iNPH.

Results

Our analyses demonstrated strong volumetric correlations (ϱ=0.91, p<0.001) between automatically and manually derived CT-VCSF measurements in iNPH patients. The CTVMs exhibited high accuracy in differentiating iNPH patients from controls in external clinical datasets with an AUC of 0.97 and in the Uppsala University Hospital datasets with an AUC of 0.99.

Discussion

CTVMs derived through deep learning, show potential for assessing and quantifying morphological features in hydrocephalus. Critically, these measures performed comparably to gold-standard neuroradiology assessments in distinguishing iNPH from healthy controls, even in the presence of intraventricular shunt catheters. Accordingly, such an approach may serve to improve the radiological evaluation of iNPH diagnosis/monitoring (i.e., treatment responses). Since CT is much more widely available than MRI, our results have considerable clinical impact.

Structural neuroimaging markers of normal pressure hydrocephalus versus Alzheimer's dementia and Parkinson's disease, and hydrocephalus versus atrophy in chronic TBI-a narrative review

Introduction

Normal Pressure Hydrocephalus (NPH) is a prominent type of reversible dementia that may be treated with shunt surgery, and it is crucial to differentiate it from irreversible degeneration caused by its symptomatic mimics like Alzheimer's Dementia (AD) and Parkinson's Disease (PD). Similarly, it is important to distinguish between (normal pressure) hydrocephalus and irreversible atrophy/degeneration which are among the chronic effects of Traumatic Brain Injury (cTBI), as the former may be reversed through shunt placement. The purpose of this review is to elucidate the structural imaging markers which may be foundational to the development of accurate, noninvasive, and accessible solutions to this problem.

Methods

By searching the PubMed database for keywords related to NPH, AD, PD, and cTBI, we reviewed studies that examined the (1) distinct neuroanatomical markers of degeneration in NPH versus AD and PD, and atrophy versus hydrocephalus in cTBI and (2) computational methods for their (semi-) automatic assessment on Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans.

Results

Structural markers of NPH and those that can distinguish it from AD have been well studied, but only a few studies have explored its structural distinction between PD. The structural implications of cTBI over time have been studied. But neuroanatomical markers that can predict shunt response in patients with either symptomatic idiopathic NPH or post-traumatic hydrocephalus have not been reliably established. MRI-based markers dominate this field of investigation as compared to CT, which is also reflected in the disproportionate number of MRI-based computational methods for their automatic assessment.

Conclusion

Along with an up-to-date literature review on the structural neurodegeneration due to NPH versus AD/PD, and hydrocephalus versus atrophy in cTBI, this article sheds light on the potential of structural imaging markers as (differential) diagnostic aids for the timely recognition of patients with reversible (normal pressure) hydrocephalus, and opportunities to develop computational tools for their objective assessment.

Improve the diagnosis of idiopathic normal pressure hydrocephalus by combining abnormal cortical thickness and ventricular morphometry

Background

The primary imaging markers for idiopathic Normal Pressure Hydrocephalus (iNPH) emphasize morphological measurements within the ventricular system, with no attention given to alterations in brain parenchyma. This study aimed to investigate the potential effectiveness of combining ventricular morphometry and cortical structural measurements as diagnostic biomarkers for iNPH.

Methods

A total of 57 iNPH patients and 55 age-matched healthy controls (HC) were recruited in this study. Firstly, manual measurements of ventricular morphology, including Evans Index (EI), z-Evans Index (z-EI), Cella Media Width (CMW), Callosal Angle (CA), and Callosal Height (CH), were conducted based on MRI scans. Cortical thickness measurements were obtained, and statistical analyses were performed using surface-based morphometric analysis. Secondly, three distinct models were developed using machine learning algorithms, each based on a different input feature: a ventricular morphology model (LVM), a cortical thickness model (CT), and a fusion model (All) incorporating both features. Model performances were assessed using 10-fold cross validation and tested on an independent dataset. Model interpretation utilized Shapley Additive Interpretation (SHAP), providing a visualization of the contribution of each variable in the predictive model. Finally, Spearman correlation coefficients were calculated to evaluate the relationship between imaging biomarkers and clinical symptoms.

Results

iNPH patients exhibited notable differences in cortical thickness compared to HC. This included reduced thickness in the frontal, temporal, and cingulate cortices, along with increased thickness in the supracentral gyrus. The diagnostic performance of the fusion model (All) for iNPH surpassed that of the single-feature models, achieving an average accuracy of 90.43%, sensitivity of 90.00%, specificity of 90.91%, and Matthews correlation coefficient (MCC) of 81.03%. This improvement in accuracy (6.09%), sensitivity (11.67%), and MCC (11.25%) compared to the LVM strategy was significant. Shap analysis revealed the crucial role of cortical thickness in the right isthmus cingulate cortex, emerging as the most influential factor in distinguishing iNPH from HC. Additionally, significant correlations were observed between the typical triad symptoms of iNPH patients and cortical structural alterations.

Conclusion

This study emphasizes the significant role of cortical structure changes in the diagnosis of iNPH, providing a novel insights for assisting clinicians in improving the identification and detection of iNPH.