Wearable MRI and AI improve Alzheimer’s diagnosis with pinpoint accuracy

Revolutionizing dementia care: Discover how AI-powered portable MRI systems are breaking barriers to Alzheimer’s diagnosis, enabling early detection and global accessibility.

Study: Portable low-field magnetic resonance imaging for the evaluation of Alzheimer’s disease. Image credit: illustrissima / Shutterstock

A recent one Nature communications The study optimized portable LF-MRI acquisition and developed a machine learning pipeline to estimate brain morphometry and white matter hyperintensities (WMH) for the diagnosis of Alzheimer’s disease.

Alzheimer’s disease (AD): Pathology and diagnosis

AD is a progressive neurodegenerative disease that affects memory, thinking and behavior. Pathologically, it is characterized by the deposition of amyloid-β (Aβ) and the development of neurofibrillary tangles in the brain. Over time, increased accumulation of these proteins leads to adverse change in brain structure and increased vascular damage, determined through measurable brain atrophy and WMH, respectively.

Typically, the progressive pre-symptomatic stage of AD lasts between 10 and 20 years. This may be why 75% of people with dementia remain undiagnosed for extended periods. The availability of anti-amyloid therapies has increased the urgency for early detection of individuals with AD or mild cognitive impairment (MCI), as early diagnosis enhances treatment benefits.

AD diagnosis is based on cognitive tests, which assess Aβ and phosphorylated tau burden using fluid biomarkers, positron emission tomography (PET) and magnetic resonance imaging (MRI). Clinicians can determine changes in brain structure and integrity from multi-contrast MRI. These imaging markers include generalized and atrophy of the hippocampus, which helps doctors understand the course of disease progression and cognitive decline.

Although neuroimaging greatly aids in the diagnosis and management of AD and MCI, limited local and global accessibility contributes to its underdiagnosis. A previous study highlighted the development of a portable LF-MRI, which could effectively increase accessibility and improve the diagnosis of different neurodegenerative diseases. This study highlighted the safety profile and low-cost point-of-care scanning capabilities of LF-MRI. However, reduced magnetic field strength reduces the signal-to-noise ratio (SNR), affecting image resolution.

About the study

The current study addressed the aforementioned limitation of LF-MRI for diagnosing AD and MCI by developing machine learning tools that can automatically quantify brain morphometry and white matter lesions.

An imaging pipeline was created to aid in the quantification of brain tumors. The refined super-resolution and contrast synthesis technique (LF-SynthSR) was optimized to increase the LF image resolution in the next segmentation (SynthSeg). For example, hippocampal volumes derived from LF-MRI showed close agreement with high-field MRI counterparts, with an Absolute Symmetric Percentage Difference (ASPD) of 2.8% and a Dice similarity coefficient of 0.87. This strategy helped define the optimal LF acquisition parameters for accurate quantification. Enhanced measurement of white matter hyperintensity (WMH) burden (WMH-SynthSeg) using automated segmentation of WMH lesions from T2 fluid-attenuated inversion recovery (FLAIR) images acquired at LF. This study validated LF-SynthSR, SynthSeg, and WMH-SynthSeg using a prospective cohort of patients diagnosed with MCI or AD.

To create an imaging pipeline, participants from three cohorts were enrolled to undergo MRI on a low-field 0.064 T portable MRI with high-field, conventional scanning at a field strength of 1.5–3 T. The first cohort contained twenty healthy subjects ( 10 men and 10 women) with no history of neurological disease or memory disorders.

The second cohort contained 23 participants (11 men and 12 women) who had at least one vascular risk factor. However, none of the participants had neurological complaints or a previous history of memory impairment. The third cohort included 54 subjects (32 men and 22 women) diagnosed with MCI or AD. These participants underwent a LF-MRI imaging protocol that included T1w, T2w, and FLAIR sequences.

Study findings

Although the LF-MRI images did not have sufficient resolution for automatic segmentation with high-field software analysis tools, they were initially highly resolved (SR) on 1 mm isotropic magnetization T1-weighted (T1w) fast-echo gradient (MP). -RAGE)-like images. The study found that isotropic voxel sizes ≤3 mm improved segmentation accuracy, producing ASPD values ​​of less than 5% for hippocampal volumes. In addition, the accuracy of automated segmentation has been improved by improving the LF-SynthSR v2 pipeline, allowing greater usability for low-field imaging applications.

In the first cohort, the accuracy of automated segmentation was assessed by comparing AD-relevant hippocampal, lateral ventricle, and whole brain segmentation volumes generated by the original LF-SynthSR and LF-SynthSR v2 against conventional high-field magnetic resonance imaging (HF ). acquired at 3T.

An improvement in lateral ventricle volume accuracy was achieved comparing LF-SynthSR v2 with LF-SynthSR v1. Image acquisition time varied between 1:53 and 9:48 min, depending on voxel size and array. The study also found that isotropic voxel sizes ≤3 mm improved segmentation accuracy, particularly in the low SNR regime of LF-MRI. The accuracy of brain morphometry was found to be affected by voxel size and geometry. In addition, the LF-SynthSR v2 segmentation pipeline was validated against HF T1w MP-RAGE segments derived from the FreeSurfer ASEG segmentation tool.

WMH lesions due to axonal loss or cerebral small vessel disease were common among patients with cognitive impairment and quantified using WMH-SynthSeg. The use of these findings on FLAIR as hyperintense T2 lesions and the automated quantification of these lesions increased the diagnostic and follow-up capability of AD of LF-MRI.

This study used machine learning to produce WMH lesion volumes (WMHv) from LF-FLAIR images using WMH-SynthSeg. This strategy allowed simultaneous segmentation of WMH T2 FLAIR lesions in addition to previous brain morphometry. WMH volumes were strongly correlated with manual annotations and high-field imaging patterns.

Based on the WMHv generated by WMH-SynthSeg, the machine learning tool was validated as being able to detect patients with MCI, AD and those who are cognitively normal (CN).

conclusions

The current study showed that LF-MRI with machine learning tools can diagnose patients with AD or MCI. In the future, this device could also be evaluated for its ability to detect neurodegenerative tauopathies and vascular dementia. Portability, low cost, and automated assay analysis suggest significant potential for addressing diagnostic disparities worldwide.