M Battiston
UCL Institute of Neurology
SignalAlgorithmParametric statisticsPhysicsBiomedical engineeringWorld Wide WebMagnetic resonance imagingDiffusion MRIArtificial intelligencePattern recognitionChemistryIn vivoGrey matterSpinal cordWhite matterMagnetization transferNuclear medicineComputer scienceNuclear magnetic resonanceMedicineRelaxometryReproducibility
23Publications
6H-index
46Citations
Publications 26
Newest
#1Francesco GrussuH-Index: 13
Last. Shonit PunwaniH-Index: 44
view all 15 authors...
Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI). Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the s...
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#1Julien Cohen-Adad (École Polytechnique de Montréal)H-Index: 51
#2Eva Alonso-Ortiz (École Polytechnique de Montréal)H-Index: 6
Last. Christian Büchel (UHH: University of Hamburg)H-Index: 119
view all 91 authors...
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In a companion paper by Cohen-Adad et al. we introduce the spine generic quantitative MRI protocol that provides valuable metrics for assessing spinal cord macrostructural and microstructural integrity. This protocol was used to acquire a single subject dataset across 19 centers and a multi-subject dataset across 42 centers (for a total of 260 participants), spanning the three main MRI manufacturers: GE, Philips and Siemens. Both datasets are publicly available via git-annex. Data were analysed ...
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#6Dgt Thomas (UCL Institute of Neurology)H-Index: 7
Purpose: To compare different multi-echo combination methods for MRI quantitative susceptibility mapping (QSM), aiming to elucidate, given the current lack of consensus, how to optimally combine multi-echo gradient-recalled echo (GRE) signal phase information, either before or after applying Laplacian-base methods (LBMs) for phase unwrapping or background field removal. Methods: Multi-echo GRE data were simulated in a numerical head phantom, and multi-echo GRE images were acquired at 3 T in 10 h...
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#1Francesco Grussu (UCL: University College London)H-Index: 13
#2M Battiston (UCL: University College London)H-Index: 6
Last. Daniel C. Alexander (UCL: University College London)H-Index: 73
view all 6 authors...
Quantitative Magnetic Resonance Imaging (qMRI) signal model fitting is traditionally performed via non-linear least square (NLLS) estimation. NLLS is slow and its performance can be affected by the presence of different local minima in the fitting objective function. Recently, machine learning techniques, including deep neural networks (DNNs), have been proposed as robust alternatives to NLLS. Here we present a deep learning implementation of qMRI model fitting, which uses DNNs to perform the in...
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#1Francesco Grussu (UCL: University College London)H-Index: 13
#2M Battiston (UCL: University College London)H-Index: 6
Last. Claudia A. M. Wheeler-Kingshott (UNIPV: University of Pavia)H-Index: 66
view all 10 authors...
Abstract Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We pr...
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#1Eva Alonso-Ortiz (École Polytechnique de Montréal)H-Index: 6
#2Charley Gros (École Polytechnique de Montréal)H-Index: 8
Last. M Battiston (UCL: University College London)H-Index: 6
view all 80 authors...
#1Francesco Grussu (UCL: University College London)H-Index: 13
#2Stefano B. Blumberg (UCL: University College London)H-Index: 8
Last. Daniel C. Alexander (UCL: University College London)H-Index: 73
view all 15 authors...
Purpose: We introduce "Select and retrieve via direct upsampling" network (SARDU-Net), a data-driven deep learning framework for model-free quantitative MRI (qMRI) experiment design. Here we provide a practical demonstration of its utility on in vivo joint diffusion-relaxation imaging (DRI) of the prostate. Methods: SARDU-Net selects subsets of informative qMRI measurements within lengthy pilot scans. The algorithm consists of two deep neural networks (DNNs) that are trained jointly end-to-end: ...
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#1Mina Kim (UCL: University College London)H-Index: 4
#2Aaron Kujawa (UCL: University College London)H-Index: 3
Last. Xavier Golay (UCL: University College London)H-Index: 65
view all 12 authors...
PURPOSE: To translate the recently developed PRO-QUEST (Progressive saturation for quantifying exchange rates using saturation times) sequence from preclinical 9.4T to 3T clinical magnetic field strength. METHODS: Numerical simulations were performed to define the optimal saturation flip angles for PRO-QUEST saturation pulses at 3T and demonstrate the effect of a T2 error on the exchange rate (kex ) estimation at various field strengths. Exchange-dependent relaxation rate (Rex ) was measured for...
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