Reducing the number of samples in spatiotemporal dMRI acquisition design

Acquisition time is a major limitation in recovering brain white matter microstructure with diffusion magnetic resonance imaging. The aim of this paper is to bridge the gap between growing demands on spatiotemporal resolution of diffusion signal and the real‐world time limitations. The authors introduce an acquisition scheme that reduces the number of samples under adjustable quality loss.

[1]  P. Basser,et al.  In vivo measurement of axon diameter distribution in the corpus callosum of rat brain. , 2009, Brain : a journal of neurology.

[2]  Jörg Kärger,et al.  The propagator representation of molecular transport in microporous crystallites , 1983 .

[3]  Randeep Bhatia,et al.  Book review: Approximation Algorithms for NP-hard Problems. Edited by Dorit S. Hochbaum (PWS, 1997) , 1998, SIGA.

[4]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[5]  John E. Beasley,et al.  A Genetic Algorithm for the Multidimensional Knapsack Problem , 1998, J. Heuristics.

[6]  R. Deriche,et al.  Regularized, fast, and robust analytical Q‐ball imaging , 2007, Magnetic resonance in medicine.

[7]  Markus Barth,et al.  A time‐efficient acquisition protocol for multipurpose diffusion‐weighted microstructural imaging at 7 Tesla , 2017, Magnetic resonance in medicine.

[8]  Carlo Pierpaoli,et al.  Mean apparent propagator (MAP) MRI: A novel diffusion imaging method for mapping tissue microstructure , 2013, NeuroImage.

[9]  Rachid Deriche,et al.  MAPL: Tissue microstructure estimation using Laplacian-regularized MAP-MRI and its application to HCP data , 2016, NeuroImage.

[10]  Eric Achten,et al.  Optimal Experimental Design for Diffusion Kurtosis Imaging , 2010, IEEE Transactions on Medical Imaging.

[11]  Derek K. Jones,et al.  Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter , 2016, NeuroImage.

[12]  Thomas Bäck,et al.  The zero/one multiple knapsack problem and genetic algorithms , 1994, SAC '94.

[13]  Steen Moeller,et al.  Advances in diffusion MRI acquisition and processing in the Human Connectome Project , 2013, NeuroImage.

[14]  Kawin Setsompop,et al.  Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. , 2013, Journal of magnetic resonance.

[15]  C. Beaulieu,et al.  An in vitro evaluation of the effects of local magnetic‐susceptibility‐induced gradients on anisotropic water diffusion in nerve , 1996, Magnetic resonance in medicine.

[16]  A. Szafer,et al.  An analytical model of restricted diffusion in bovine optic nerve , 1997, Magnetic resonance in medicine.

[17]  Brian Hansen,et al.  Experimentally and computationally fast method for estimation of a mean kurtosis , 2013, Magnetic resonance in medicine.

[18]  Y. Assaf,et al.  Improved precision in CHARMED assessment of white matter through sampling scheme optimization and model parsimony testing , 2014, Magnetic resonance in medicine.

[19]  Y. Cohen,et al.  In vivo and in vitro bi‐exponential diffusion of N ‐acetyl aspartate (NAA) in rat brain: a potential structural probe? , 1998, NMR in biomedicine.

[20]  R. Deriche,et al.  Design of multishell sampling schemes with uniform coverage in diffusion MRI , 2013, Magnetic resonance in medicine.

[21]  Jelle Veraart,et al.  In vivo observation and biophysical interpretation of time-dependent diffusion in human white matter , 2016, NeuroImage.

[22]  Els Fieremans,et al.  Revealing mesoscopic structural universality with diffusion , 2014, Proceedings of the National Academy of Sciences.

[23]  Pierre Weiss,et al.  On the Generation of Sampling Schemes for Magnetic Resonance Imaging , 2016, SIAM J. Imaging Sci..

[24]  M. Budde,et al.  Microstructural organization of axons in the human corpus callosum quantified by diffusion-weighted magnetic resonance spectroscopy of N-acetylaspartate and post-mortem histology , 2014, Brain Structure and Function.

[25]  Pabitra N. Sen,et al.  Time-dependent diffusion coefficient as a probe of geometry , 2004 .

[26]  Rachid Deriche,et al.  A Unifying Framework for Spatial and Temporal Diffusion in Diffusion MRI , 2015, IPMI.

[27]  R. Spillman.  Solving large knapsack problems with a genetic algorithm , 1995, 1995 IEEE International Conference on Systems, Man and Cybernetics. Intelligent Systems for the 21st Century.

[28]  Peter J Basser,et al.  The effect of the diffusion time and pulse gradient duration ratio on the diffraction pattern and the structural information estimated from q-space diffusion MR: experiments and simulations. , 2008, Journal of magnetic resonance.

[29]  Rachid Deriche,et al.  Non‐parametric graphnet‐regularized representation of dMRI in space and time , 2018, Medical Image Anal..

[30]  H. Gudbjartsson,et al.  The rician distribution of noisy mri data , 1995, Magnetic resonance in medicine.

[31]  G J Barker,et al.  Self‐diffusion in CNS tissue by volume‐selective proton NMR , 1994, Magnetic resonance in medicine.

[32]  D. Alexander.  A general framework for experiment design in diffusion MRI and its application in measuring direct tissue‐microstructure features , 2008, Magnetic resonance in medicine.

[33]  Dmitry S. Novikov,et al.  What dominates the time dependence of diffusion transverse to axons: Intra- or extra-axonal water? , 2017, NeuroImage.

[34]  M. Guarini,et al.  Three dimensional k-space trajectory design using genetic algorithms. , 2003, Magnetic resonance imaging.

[35]  Dorit S. Hochba,et al.  Approximation Algorithms for NP-Hard Problems , 1997, SIGA.

[36]  Andrew L. Alexander,et al.  Computation of Diffusion Function Measures in $q$ -Space Using Magnetic Resonance Hybrid Diffusion Imaging , 2008, IEEE Transactions on Medical Imaging.

[37]  P. Basser,et al.  Axcaliber: A method for measuring axon diameter distribution from diffusion MRI , 2008, Magnetic resonance in medicine.

[38]  P. Hagmann,et al.  Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging , 2005, Magnetic resonance in medicine.

[39]  Paul T. Callaghan,et al.  Pulsed-Gradient Spin-Echo NMR for Planar, Cylindrical, and Spherical Pores under Conditions of Wall Relaxation , 1995 .

[40]  Dmitry S. Novikov,et al.  Mesoscopic structure of neuronal tracts from time-dependent diffusion , 2015, NeuroImage.

[41]  Daniel C. Alexander,et al.  NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain , 2012, NeuroImage.

[42]  Daniel C Alexander,et al.  Optimising time-varying gradient orientation for microstructure sensitivity in diffusion-weighted MR. , 2011, Journal of magnetic resonance.

[43]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[44]  Rich Caruana,et al.  Removing the Genetics from the Standard Genetic Algorithm , 1995, ICML.

[45]  Philippe Hantraye,et al.  New paradigm to assess brain cell morphology by diffusion-weighted MR spectroscopy in vivo , 2016, Proceedings of the National Academy of Sciences.

[46]  P. Basser,et al.  New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter , 2004, Magnetic resonance in medicine.

[47]  D. Tuch.  Q‐ball imaging , 2004, Magnetic resonance in medicine.

[48]  L. Wald,et al.  A 64‐channel 3T array coil for accelerated brain MRI , 2013, Magnetic resonance in medicine.

[49]  Tim B. Dyrby,et al.  Orientationally invariant indices of axon diameter and density from diffusion MRI , 2010, NeuroImage.

[50]  Rachid Deriche,et al.  Multi-Spherical Diffusion MRI: Exploring Diffusion Time Using Signal Sparsity , 2016, MICCAI 2016.

[51]  Moshe Sipper,et al.  Evolutionary computation in medicine: an overview , 2000, Artif. Intell. Medicine.

[52]  Daniel C Alexander,et al.  Optimizing gradient waveforms for microstructure sensitivity in diffusion-weighted MR. , 2010, Journal of magnetic resonance.

[53]  Dorit S. Hochbaum,et al.  Approximation Algorithms for NP-Hard Problems , 1996 .

[54]  Rachid Deriche,et al.  Mipy: An Open-Source Framework to improve reproducibility in Brain Microstructure Imaging , 2018 .

[55]  Zbigniew Michalewicz,et al.  Genetic Algorithms for the 0/1 Knapsack Problem , 1994, ISMIS.

[56]  Anne L. Olsen.  Penalty functions and the knapsack problem , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[57]  José Carlos Príncipe,et al.  A Markov Chain Framework for the Simple Genetic Algorithm , 1993, Evolutionary Computation.

[58]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[59]  R. Gruetter,et al.  Investigation of field and diffusion time dependence of the diffusion‐weighted signal at ultrahigh magnetic fields , 2013, NMR in biomedicine.

[60]  Jonathan E. Taylor,et al.  Interpretable whole-brain prediction analysis with GraphNet , 2013, NeuroImage.

[61]  K. Svoboda,et al.  Time-dependent diffusion of water in a biological model system. , 1994, Proceedings of the National Academy of Sciences of the United States of America.