A Novel Automated Visual Acuity Test Using a Portable Head-mounted Display.

SIGNIFICANCE: We developed a head-mounted display (HMD) as an automated way of testing visual acuity (VA) to increase workplace efficiency. This study raises its potential utility and advantages, analyzes reasons for its current limitations, and discusses areas of improvement in the development of this device. PURPOSE: Manual VA testing is important but labor-intensive in ophthalmology and optometry clinics. The purpose of this exploratory study is to assess the performance and identify potential limitations of an automated HMD for VA testing. METHODS: Sixty patients from National University Hospital, Singapore, were enrolled in a prospective observational study. The HMD was constructed based on the Snellen chart, with single optotypes displayed at a time. Each subject underwent VA testing of both eyes with the manual Snellen chart tested at 6 m from the subject and the HMD. RESULTS: Fifty-three subjects were included in the final analysis, with an incompletion rate of 11.7% (n = 7). The mean difference in estimated acuity between the HMD and Snellen chart was 0.05 logMAR. However, 95% limits of agreement were large at ±0.33 logMAR. The HMD overestimated vision in patients with poorer visual acuities. In detecting VA worse than 0.30 logMAR (6/12), sensitivity was 63.6% (95% confidence interval, 0.31 to 0.89%), and specificity was 81.0% (95% confidence interval, 0.66 to 0.91%). No significant correlation existed between mean difference and age (r = -0.15, P = .27) or education level (r = 0.04, P = .76). CONCLUSIONS: Advantages of our novel HMD technology include its fully automated nature and its portability. However, the device in its current form is not ready for widespread clinical use primarily because of its low accuracy, which is limited by both technical and user factors. Future studies are needed to improve its accuracy and completion rate and to evaluate for test-retest reliability in a larger population.

Abnormalities of cortical thickness, subcortical shapes, and white matter integrity in subcortical vascular cognitive impairment.

Subcortical vascular cognitive impairment (sVCI) is caused by lacunar infarcts or extensive and/or diffuse lesions in the white matter that may disrupt the white matter circuitry connecting cortical and subcortical regions and result in the degeneration of neurons in these regions. This study used structural magnetic resonance imaging (MRI) and high angular resolution diffusion imaging (HARDI) techniques to examine cortical thickness, subcortical shapes, and white matter integrity in mild vascular cognitive impairment no dementia (VCIND Mild) and moderate-to-severe VCI (MSVCI). Our study found that compared to controls (n = 25), VCIND Mild (n = 25), and MSVCI (n = 30) showed thinner cortex predominantly in the frontal cortex. The cortex in MSVCI was thinner in the parietal and lateral temporal cortices than that in VCIND Mild. Moreover, compared to controls, VCIND Mild and MSVCI showed smaller shapes (i.e., volume reduction) in the thalamus, putamen, and globus pallidus and ventricular enlargement. Finally, compared to controls, VCIND Mild, and MSVCI showed an increased mean diffusivity in the white matter, while decreased generalized fractional anisotropy was only found in the MSVCI subjects. The major axonal bundles involved in the white matter abnormalities were mainly toward the frontal regions, including the internal capsule/corona radiata, uncinate fasciculus, and anterior section of the inferior fronto-occipital fasciculus, and were anatomically connected to the affected cortical and subcortical structures. Our findings suggest that abnormalities in cortical, subcortical, and white matter morphology in sVCI occur in anatomically connected structures, and that abnormalities progress along a similar trajectory from the mild to moderate and severe conditions.

Association of silent lacunar infarct with brain atrophy and cognitive impairment.

OBJECTIVE: Silent lacunar infarct (SLI) is associated with cognitive decline and linked to an increased risk of stroke and dementia. We examined the association of SLI with MRI measures of cortical thickness, subcortical and lateral ventricular shapes and cognition in 285 ethnic Chinese elderly. METHODS: SLI, cortical thickness, shapes of subcortical and ventricular structures were quantified using MRI. The cognitive performance was assessed using comprehensive neuropsychological tests. Linear regression was used to examine associations among SLI, brain measures and cognition. RESULTS: SLI was associated with atrophy in multiple subcortical structures, ventricular enlargement and widespread cortical thinning. Both SLI and atrophy were independently related to poorer performance in attention, memory and language domains. Only SLI was associated with visuomotor speed and executive function, while atrophy mediated the association between SLI and visuoconstruction. CONCLUSIONS: Our findings support a vascular contribution to neurodegeneration and cognitive impairment.

Multi-stage segmentation of white matter hyperintensity, cortical and lacunar infarcts.

Cerebral abnormalities such as white matter hyperintensity (WMH), cortical infarct (CI), and lacunar infarct (LI) are of clinical importance and frequently present in patients with stroke and dementia. Up to date, there are limited algorithms available to automatically delineate these cerebral abnormalities partially due to their complex appearance in MR images. In this paper, we describe an automated multi-stage segmentation approach for labeling the WMH, CI, and LI using multi-modal MR images. We first automatically segment brain tissues (white matter, gray matter, and CSF) based on the T1-weighted image and then identify hyperintense voxels based on the fluid attenuated inversion recovery (FLAIR) image. We finally label the WMH, CI, and LI based on the T1-weighted, T2-weighted, and FLAIR images. The segmentation accuracy is evaluated using a community-based sample of 272 old adults. Our results show that the automated segmentation of the WMH, CI, and LI is comparable with manual labeling in terms of spatial location, volume, and the number of lacunes. Additionally, the WMH volume is highly correlated with the visual grading score based on the Age-Related White Matter Changes (ARWMC) protocol. The evaluations against the manual labeling and ARWMC visual grading suggest that our algorithm provides reasonable segmentation accuracy for the WMH, CI, and LI.

Pseudo-random single photon counting for time-resolved optical measurement.

We report a new time-resolved optical measurement method which combines single photon counting and the spread spectrum time-resolved optical measurement method. A laser diode modulated with pseudo-random bit sequences replaces the short pulse laser used in conventional time-resolved optical systems, while a single photon detector records the pulse sequence in response to the modulated excitation. Periodic cross-correlation is used to retrieve the impulse response. Feasibility of our approach is validated experimentally. A rise time around 150 picoseconds has been achieved with our prototype. Besides high temporal resolution, the new method also affords other benefits such as high photon counting rate, fast data acquisition, portability, and low cost.

Individualized diffeomorphic mapping of brains with large cortical infarcts.

Whole brain mapping of stroke patients with large cortical infarcts is not trivial due to the complexity of infarcts' anatomical location and appearance in magnetic resonance image. In this study, we proposed an individualized diffeomorphic mapping framework for solving this problem. This framework is based on our recent work of large deformation diffeomorphic metric mapping (LDDMM) in Du et al. (2011) and incorporates anatomical features, such as sulcal/gyral curves, cortical surfaces, brain intensity image, and masks of infarcted regions, in order to align a normal brain to the brain of stroke patients. We applied this framework to synthetic data and data of stroke patients and validated the mapping accuracy in terms of the alignment of gyral/sulcal curves, sulcal regions, and brain segmentation. Our results revealed that this framework provided comparable mapping results for stroke patients and healthy controls, suggesting the importance of incorporating individualized anatomical features in whole brain mapping of brains with large cortical infarcts.

Volume reduction in subcortical regions according to severity of Alzheimer's disease.

We investigated whether there exists a hierarchical vulnerability of subcortical structures with respect to the severity of Alzheimer's disease (AD). A total of 236 subjects (179 with AD and 57 with normal cognition) underwent 1.5-T magnetic resonance (MR) imaging. The volumes of the five subcortical structures (amygdala, thalamus, putamen, globus pallidus, and caudate nucleus) and hippocampus were analyzed using a large deformation diffeomorphic metric mapping algorithm. The volume changes were evaluated according to the Clinical Dementia Rating (CDR). Correlation between the volumes of the subcortical structures and scores of the cognitive domain-specific neuropsychological tests were evaluated. Volume loss of the amygdala occurred even in the very mild stage of AD (CDR 0.5), as did volume loss in the hippocampus. Similar reductions in volume occurred in the thalamus and putamen, however during the mild (CDR 1) and moderate (CDR 2) stages of AD, respectively. The globus pallidus and caudate nucleus remained devoid of changes until the moderate stage of AD (p < 0.01). Volume loss in those subcortical structures correlated with the neuropsychological test scores (p < 0.01). Our results suggest that there is a hierarchical vulnerability in subcortical structures according to the clinical severity of AD and that subcortical volume reductions correlate with cognitive impairment.