Retinal lesions caused by eye diseases such as glaucoma and age-related macular degeneration can, over time, eliminate stimulation of parts of the visual cortex. This could lead to degeneration of inactive cortical neuronal tissue, but this has not been established in humans. Here, we used magnetic resonance imaging to assess the effects of prolonged sensory deprivation in human visual cortex. High-resolution anatomical magnetic resonance images were obtained in subjects with foveal (age-related macular degeneration) and peripheral (glaucoma) retinal lesions as well as age-matched controls. Comparison of grey matter between patient and control groups revealed density reductions in the approximate retinal lesion projection zones in visual cortex. This indicates that long-term cortical deprivation, due to retinal lesions acquired later in life, is associated with retinotopic-specific neuronal degeneration of visual cortex. Such degeneration could interfere with therapeutic strategies such as the future application of artificial retinal implants to overcome lesion-induced visual impairment.

MD is associated with degeneration of structures along the visual pathways. A reduction in frontal white matter volume only present in the AMD patients may constitute a neural correlate of previously reported association between AMD and mild cognitive impairment.

POAG adversely affects structures along the full visual pathway, from the optic nerve to the optic radiation. Moreover, MRI in combination with automated morphometry can be used to aid the detection and assessment of glaucomatous damage in the brain.

Neurometabolite alterations were already apparent at onset of both complicated and uncomplicated traumatic brain injury. The ratio of NAA and NAAG has potential to serve as a biomarker reflecting injury severity in a quantifiable manner as it discriminates between the complicated and uncomplicated cases of mTBI.

ObjectivesThis study maps the lipid distributions based on magnetic resonance imaging (MRI) in-and opposed-phase (IOP) sequence and correlates the findings generated from lipid map to histological grading of glioma.MethodsForty histologically proven glioma patients underwent a standard MRI tumour protocol with the addition of IOP sequence. The regions of tumour (solid enhancing, solid non-enhancing, and cystic regions) were delineated using snake model (ITK-SNAP) with reference to structural and diffusion MRI images. The lipid distribution map was constructed based on signal loss ratio (SLR) obtained from the IOP imaging. The mean SLR values of the regions were computed and compared across the different glioma grades.ResultsThe solid enhancing region of glioma had the highest SLR for both Grade II and III. The mean SLR of solid non-enhancing region of tumour demonstrated statistically significant difference between the WHO grades (grades II, III & IV) (mean SLRII = 0.04, mean SLRIII = 0.06, mean SLRIV = 0.08, & p < .01). A strong positive correlation was seen between WHO grades with mean SLR on lipid map of solid non-enhancing (ρ=0.68, p < .01).ConclusionLipid quantification via lipid map provides useful information on lipid landscape in tumour heterogeneity characterisation of glioma. This technique adds to the surgical diagnostic yield by identifying biopsy targets. It can also be used as an adjunct grading tool for glioma as well as to provide information about lipidomics landscape in glioma development.

Skew deviation can be appropriately diagnosed from the nature of the ocular torsion and the vertical deviation, along with the presence of lesion involving posterior paramedian pons and/or medial thalamus.

Segmentation of an acute ischemic stroke from a single modality of a greyscale magnetic resonance imaging (MRI) is an essential and challenging task. Recently, there are several numbers of related works on the automatic segmentation of infarct lesion from the input image and give a high accuracy in extraction of infarct lesion. Still, limited works have been reported in isolating the penumbra tissues and infarct core separately. The segmentation of the penumbra tissues is necessary because that region has the potential to recover. This paper presented an automated segmentation algorithm on diffusion-weighted magnetic resonance imaging (DW-MRI) image utilizing pseudo-colour conversion and K-means clustering techniques. A greyscale image contains only intensity information and often misdiagnosed due to overlap intensity of an image. Colourization is the method of adding colours to greyscale images which allocate luminance or intensity for red, green, and blue channels. The greyscale image is converted to pseudo-colour is to intensify the visual perception and deliver more information. Then, the algorithm segments the region of interest (ROI) using K-means clustering. The result shows the potential of automated segmentation to differentiate between the healthy and lesion tissues with 90.08% in accuracy and 0.89 in dice coefficient. The development of an automated segmentation algorithm was successfully achieved by entirely depending on the computer with minimal interaction.

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