Showing papers by "Enrico De Vita published in 2016"

NiftyFit: a Software Package for Multi-parametric Model-Fitting of 4D Magnetic Resonance Imaging Data

Abstract: Multi-modal, multi-parametric Magnetic Resonance (MR) Imaging is becoming an increasingly sophisticated tool for neuroimaging. The relationships between parameters estimated from different individual MR modalities have the potential to transform our understanding of brain function, structure, development and disease. This article describes a new software package for such multi-contrast Magnetic Resonance Imaging that provides a unified model-fitting framework. We describe model-fitting functionality for Arterial Spin Labeled MRI, T1 Relaxometry, T2 relaxometry and Diffusion Weighted imaging, providing command line documentation to generate the figures in the manuscript. Software and data (using the nifti file format) used in this article are simultaneously provided for download. We also present some extended applications of the joint model fitting framework applied to diffusion weighted imaging and T2 relaxometry, in order to both improve parameter estimation in these models and generate new parameters that link different MR modalities. NiftyFit is intended as a clear and open-source educational release so that the user may adapt and develop their own functionality as they require.

Optimisation of arterial spin labelling using bayesian experimental design

Abstract: Large-scale neuroimaging studies often use multiple individual imaging contrasts. Due to the finite time available for imaging, there is intense competition for the time allocated to the individual modalities; thus it is crucial to maximise the utility of each method given the resources available. Arterial Spin Labelled (ASL) MRI often forms part of such studies. Measuring perfusion of oxygenated blood in the brain is valuable for several diseases, but quantification using multiple inversion time ASL is time-consuming due to poor SNR and consequently slow acquisitions. Here, we apply Bayesian principles of experimental design to clinical-length ASL acquisitions, resulting in significant improvements to perfusion estimation. Using simulations and experimental data, we validate this approach for a five-minute ASL scan. Our design procedure can be constrained to any chosen scan duration, making it well-suited to improve a variety of ASL implementations. The potential for adaptation to other modalities makes this an attractive method for optimising acquisition in the time-pressured environment of neuroimaging studies.

A novel use of arterial spin labelling MRI to demonstrate focal hypoperfusion in individuals with posterior cortical atrophy: a multimodal imaging study.

Abstract: Posterior cortical atrophy (PCA) is a rare neurodegenerative syndrome, typically due to Alzheimer pathology, characterised by impairments in higher-order visual function and other parieto-occipital skills.1 MRI measures of atrophy and 18F-labelled fluorodeoxyglucose (FDG) positron emission tomography (PET) measures of glucose metabolism typically show posterior cortical deficits broadly mirroring the focal cognitive deficits.1 By contrast, amyloid PET studies demonstrate that fibrillar amyloid is widely deposited across the cortex.2 Arterial spin labelling (ASL) is an MRI methodology that uses endogenous arterial blood water as a tracer to quantify cerebral blood flow (CBF).3 We aimed to assess the ability of ASL to detect patterns of reduced CBF in PCA, and to compare these results with those from other imaging modalities. Five patients fulfilling clinical diagnostic criteria for PCA,4 and five controls attended for three scanning visits usually on consecutive days. On day 1, MRI scans were acquired on a 3 T Siemens TIM Trio scanner with a 32-channel phased array head-coil. Sequences included a sagittal three-dimensional (3D) MPRAGE T1-weighted volumetric scan (acquisition time 9 min 23 s, TE/TR/TI=2.9/2200/900 ms, dimensions 256×256×208, voxel size 1.1×1.1×1.1 mm), and coronal T2 fluid-attenuated inversion recovery (TE/TR/TI=87/9000/2500 ms, voxel size 0.9375×0.9375×5 mm). Perfusion data were acquired using pulsed ASL (FAIR Q2TIPS) with an 8-segment, background-suppressed 3D GRASE imaging readout5 (acquisition time 6 min 40 s, TI1/2=800/2000 ms, voxel size 3.8×3.8×4.0 mm, refocusing pulse flip angle 130°, five repetitions). A set of three saturation recovery images (TR=1,2,5 s) with the same readout module was also acquired to generate tissue M0 and T1 maps for CBF quantification. On day 2, each participant underwent a 10 min PET scan …