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Parnesh Raniga

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

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YNICL Journal 2021 Journal Article

A prospective cohort study of prodromal Alzheimer’s disease: Prospective Imaging Study of Ageing: Genes, Brain and Behaviour (PISA)

Michelle K. Lupton
Gail A. Robinson
Robert J. Adam
Stephen Rose
Gerard J. Byrne
Olivier Salvado
Nancy A. Pachana
Osvaldo P. Almeida

This prospective cohort study, "Prospective Imaging Study of Ageing: Genes, Brain and Behaviour" (PISA) seeks to characterise the phenotype and natural history of healthy adult Australians at high future risk of Alzheimer's disease (AD). In particular, we are recruiting midlife and older Australians with high and low genetic risk of dementia to discover biological markers of early neuropathology, identify modifiable risk factors, and establish the very earliest phenotypic and neuronal signs of disease onset. PISA utilises genetic prediction to recruit and enrich a prospective cohort and follow them longitudinally. Online surveys and cognitive testing are used to characterise an Australia-wide sample currently totalling over 3800 participants. Participants from a defined at-risk cohort and positive controls (clinical cohort of patients with mild cognitive impairment or early AD) are invited for onsite visits for detailed functional, structural and molecular neuroimaging, lifestyle monitoring, detailed neurocognitive testing, plus blood sample donation. This paper describes recruitment of the PISA cohort, study methodology and baseline demographics.

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YNIMG Journal 2018 Journal Article

Combining images and anatomical knowledge to improve automated vein segmentation in MRI

Phillip G.D. Ward
Nicholas J. Ferris
Parnesh Raniga
David L. Dowe
Amanda C.L. Ng
David G. Barnes
Gary F. Egan

Purpose To improve the accuracy of automated vein segmentation by combining susceptibility-weighted images (SWI), quantitative susceptibility maps (QSM), and a vein atlas to produce a resultant image called a composite vein image (CV image). Method An atlas was constructed in common space from manually traced MRI images from ten volunteers. The composite vein image was derived for each subject as a weighted sum of three inputs; an SWI image, a QSM image and the vein atlas. The weights for each input and each anatomical location, called template priors, were derived by assessing the accuracy of each input over an independent data set. The accuracy of vein segmentations derived automatically from each of the CV image, SWI, and QSM image sets was assessed by comparison with manual tracings. Three different automated vein segmentation techniques were used, and ten performance metrics evaluated. Results Vein segmentations using the CV image were comprehensively better than those derived from SWI or QSM images (mean Cohen's d = 1. 1). Sixty permutations of performance metric, benchmark image, and automated segmentation technique were evaluated. Vein identification improvements that were both large and significant (Cohen's d > 0. 80, p < 0. 05) were found in 77% of the permutations, compared to no improvement in 5%. Conclusion The accuracy of automated vein segmentations derived from the composite vein image was overwhelmingly superior to segmentations derived from SWI or QSM alone.

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YNIMG Journal 2008 Journal Article

Appearance modeling of 11C PiB PET images: Characterizing amyloid deposition in Alzheimer's disease, mild cognitive impairment and healthy aging

Jurgen Fripp
Pierrick Bourgeat
Oscar Acosta
Parnesh Raniga
Marc Modat
Kerryn E Pike
Gareth Jones
Graeme O'Keefe

β-amyloid (Aβ) deposition is one of the neuropathological hallmarks of Alzheimer's disease (AD), Aβ burden can be quantified using 11C PiB PET. Neuropathological studies have shown that the initial plaques are located in the temporal and orbitofrontal cortices, extending later to the cingulate, frontal and parietal cortices (Braak and Braak, 1997). Previous studies have shown an overlap in 11C PiB PET retention between AD, mild cognitive impairment (MCI) patients and normal elderly control (NC) participants. It has also been shown that there is a relationship between Aβ deposition and memory impairment in MCI patients. In this paper we explored the variability seen in 15 AD, 15 MCI and 18 NC by modeling the voxel data from spatially and uptake normalized PiB images using principal component analysis. The first two principal components accounted for 80% of the variability seen in the data, providing a clear separation between AD and NC, and allowing subsequent classification. The MCI cases were distributed along an apparent axis between the AD and NC group, closely aligned with the first principal component axis. The NC cases that were PiB + formed a distinct cluster that was between, but separated from the AD and PiB − NC clusters. The PiB + MCI were found to cluster with the AD cases, and exhibited a similar deposition pattern. The primary principal component score was found to correlate with episodic memory scores and mini mental status examination and it was observed that by varying the first principal component, a change in amyloid deposition could be derived that is similar to the expected progression of amyloid deposition observed from post mortem studies.

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