Literature DB >> 28438712

Empirical consideration of the effects of acquisition parameters and analysis model on clinically feasible q-ball imaging.

Kurt G Schilling1, Vishwesh Nath2, Justin A Blaber2, Prasanna Parvathaneni3, Adam W Anderson4, Bennett A Landman5.   

Abstract

Q-ball imaging (QBI) is a popular high angular resolution diffusion imaging (HARDI) technique used to study brain architecture in vivo. Simulation and phantom-based studies suggest that QBI results are affected by the b-value, the number of diffusion weighting directions, and the signal-to-noise ratio (SNR). However, optimal acquisition schemes for QBI in clinical settings are largely undetermined given empirical (observed) imaging considerations. In this study, we acquire a HARDI dataset at five b-values with 11 repetitions on a single subject to investigate the effects of acquisition scheme and subsequent analysis models on the accuracy and precision of measures of tissue composition and fiber orientation derived from clinically feasible QBI at 3T. Clinical feasibility entails short scan protocols - less than 5minutes in the current study - resulting in lower SNR, lower b-values, and fewer diffusion directions than are typical in most QBI protocols with research applications, where time constraints are less prevalent. In agreement with previous studies, we find that the b-value and number of diffusion directions impact the magnitude and variation of QBI indices in both white matter and gray matter regions; however, QBI indices are most heavily dependent on the maximum order of the spherical harmonic (SH) series used to represent the diffusion orientation distribution function (ODF). Specifically, to ensure numerical stability and reduce the occurrence of false peaks and inflated anisotropy, we recommend oversampling by at least 8-12 more diffusion directions than the number of estimated coefficients for a given SH order. In addition, in an equal scan time comparison of QBI accuracy, we find that increasing the directional resolution of the HARDI dataset is preferable to repeating observations; however, our results indicate that as few as 32 directions at a low b-value (1000s/mm2) captures most of the angular information in the q-ball ODF. Our findings provide guidance for determining an optimal acquisition scheme for QBI in the low SNR and low scan time regime, and suggest that care must be taken when choosing the basis functions used to represent the QBI ODF.
Copyright © 2017 Elsevier Inc. All rights reserved.

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Year:  2017        PMID: 28438712      PMCID: PMC5500983          DOI: 10.1016/j.mri.2017.04.007

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  45 in total

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Journal:  Neuroimage       Date:  2002-10       Impact factor: 6.556

2.  Optimal imaging parameters for fiber-orientation estimation in diffusion MRI.

Authors:  Daniel C Alexander; Gareth J Barker
Journal:  Neuroimage       Date:  2005-08-15       Impact factor: 6.556

3.  Optimal acquisition orders of diffusion-weighted MRI measurements.

Authors:  Philip A Cook; Mark Symms; Philip A Boulby; Daniel C Alexander
Journal:  J Magn Reson Imaging       Date:  2007-05       Impact factor: 4.813

4.  Effects of signal-to-noise ratio on the accuracy and reproducibility of diffusion tensor imaging-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5 T.

Authors:  Jonathan A D Farrell; Bennett A Landman; Craig K Jones; Seth A Smith; Jerry L Prince; Peter C M van Zijl; Susumu Mori
Journal:  J Magn Reson Imaging       Date:  2007-09       Impact factor: 4.813

5.  Evaluation of the accuracy and angular resolution of q-ball imaging.

Authors:  Kuan-Hung Cho; Chun-Hung Yeh; Jacques-Donald Tournier; Yi-Ping Chao; Jyh-Horng Chen; Ching-Po Lin
Journal:  Neuroimage       Date:  2008-04-09       Impact factor: 6.556

6.  Resolving crossing fibres using constrained spherical deconvolution: validation using diffusion-weighted imaging phantom data.

Authors:  J-Donald Tournier; Chun-Hung Yeh; Fernando Calamante; Kuan-Hung Cho; Alan Connelly; Ching-Po Lin
Journal:  Neuroimage       Date:  2008-05-09       Impact factor: 6.556

7.  Optimization of diffusion spectrum imaging and q-ball imaging on clinical MRI system.

Authors:  Li-Wei Kuo; Jyh-Horng Chen; Van Jay Wedeen; Wen-Yih Isaac Tseng
Journal:  Neuroimage       Date:  2008-02-26       Impact factor: 6.556

8.  White matter changes after stroke in type 2 diabetic rats measured by diffusion magnetic resonance imaging.

Authors:  Guangliang Ding; Jieli Chen; Michael Chopp; Lian Li; Tao Yan; Esmaeil Davoodi-Bojd; Qingjiang Li; Siamak Pn Davarani; Quan Jiang
Journal:  J Cereb Blood Flow Metab       Date:  2015-12-18       Impact factor: 6.200

9.  Identifying preoperative language tracts and predicting postoperative functional recovery using HARDI q-ball fiber tractography in patients with gliomas.

Authors:  Eduardo Caverzasi; Shawn L Hervey-Jumper; Kesshi M Jordan; Iryna V Lobach; Jing Li; Valentina Panara; Caroline A Racine; Vanitha Sankaranarayanan; Bagrat Amirbekian; Nico Papinutto; Mitchel S Berger; Roland G Henry
Journal:  J Neurosurg       Date:  2015-12-11       Impact factor: 5.115

10.  The DTI Challenge: Toward Standardized Evaluation of Diffusion Tensor Imaging Tractography for Neurosurgery.

Authors:  Sonia Pujol; William Wells; Carlo Pierpaoli; Caroline Brun; James Gee; Guang Cheng; Baba Vemuri; Olivier Commowick; Sylvain Prima; Aymeric Stamm; Maged Goubran; Ali Khan; Terry Peters; Peter Neher; Klaus H Maier-Hein; Yundi Shi; Antonio Tristan-Vega; Gopalkrishna Veni; Ross Whitaker; Martin Styner; Carl-Fredrik Westin; Sylvain Gouttard; Isaiah Norton; Laurent Chauvin; Hatsuho Mamata; Guido Gerig; Arya Nabavi; Alexandra Golby; Ron Kikinis
Journal:  J Neuroimaging       Date:  2015-08-11       Impact factor: 2.486

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  4 in total

1.  Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge.

Authors:  Vishwesh Nath; Kurt G Schilling; Prasanna Parvathaneni; Yuankai Huo; Justin A Blaber; Allison E Hainline; Muhamed Barakovic; David Romascano; Jonathan Rafael-Patino; Matteo Frigo; Gabriel Girard; Jean-Philippe Thiran; Alessandro Daducci; Matt Rowe; Paulo Rodrigues; Vesna Prčkovska; Dogu B Aydogan; Wei Sun; Yonggang Shi; William A Parker; Abdol A Ould Ismail; Ragini Verma; Ryan P Cabeen; Arthur W Toga; Allen T Newton; Jakob Wasserthal; Peter Neher; Klaus Maier-Hein; Giovanni Savini; Fulvia Palesi; Enrico Kaden; Ye Wu; Jianzhong He; Yuanjing Feng; Michael Paquette; Francois Rheault; Jasmeen Sidhu; Catherine Lebel; Alexander Leemans; Maxime Descoteaux; Tim B Dyrby; Hakmook Kang; Bennett A Landman
Journal:  J Magn Reson Imaging       Date:  2019-06-09       Impact factor: 4.813

2.  Empirical estimation of intravoxel structure with persistent angular structure and Q-ball models of diffusion weighted MRI.

Authors:  Vishwesh Nath; Kurt G Schilling; Prasanna Parvathaneni; Justin Blaber; Allison E Hainline; Zhaohua Ding; Adam Anderson; Bennett A Landman
Journal:  J Med Imaging (Bellingham)       Date:  2018-03-06

3.  Histological validation of diffusion MRI fiber orientation distributions and dispersion.

Authors:  Kurt G Schilling; Vaibhav Janve; Yurui Gao; Iwona Stepniewska; Bennett A Landman; Adam W Anderson
Journal:  Neuroimage       Date:  2017-10-23       Impact factor: 6.556

4.  A Comparative Study of Diffusion Fiber Reconstruction Models for Pyramidal Tract Branches.

Authors:  Xinjun Suo; Lining Guo; Dianxun Fu; Hao Ding; Yihong Li; Wen Qin
Journal:  Front Neurosci       Date:  2021-12-09       Impact factor: 4.677

  4 in total

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