Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI.

MRI harmonization Pulseq dMRI open MRI open-source reproducibility vendor-agnostic

Journal

Magnetic resonance in medicine
ISSN: 1522-2594
Titre abrégé: Magn Reson Med
Pays: United States
ID NLM: 8505245

Informations de publication

Date de publication:
12 Mar 2024
Historique:
revised: 31 01 2024
received: 07 12 2023
accepted: 05 02 2024
medline: 12 3 2024
pubmed: 12 3 2024
entrez: 12 3 2024
Statut: aheadofprint

Résumé

To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform. We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences. The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.

Identifiants

pubmed: 38469671
doi: 10.1002/mrm.30062
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Subventions

Organisme : NIMH NIH HHS
ID : R01MH116173
Pays : United States
Organisme : NIMH NIH HHS
ID : R01MH125860
Pays : United States
Organisme : NIMH NIH HHS
ID : K01MH117346
Pays : United States
Organisme : NIBIB NIH HHS
ID : R01EB032378
Pays : United States

Informations de copyright

© 2024 International Society for Magnetic Resonance in Medicine.

Références

Johansen-Berg H, Behrens TEJ. Diffusion MRI: from Quantitative Measurement to In Vivo Neuroanatomy. Academic Press; 2013.
Le Bihan D, Mangin JF, Poupon C, et al. Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging. 2001;5422:534-546.
De Santis S, Bastiani M, Droby A, et al. Characterizing microstructural tissue properties in multiple sclerosis with diffusion MRI at 7 T and 3 T: the impact of the experimental design. Neuroscience. 2019;403:17-26.
Tae WS, Ham BJ, Pyun SB, Kang SH, Kim BJ. Current clinical applications of diffusion-tensor imaging in neurological disorders. J Clin Neurol. 2018;14:129-140.
Beer AL, Plank T, Greenlee MW. Aging and central vision loss: relationship between the cortical macro-structure and micro-structure. Neuroimage. 2020;212:116670.
Koller K, Rudrapatna U, Chamberland M, et al. MICRA: microstructural image compilation with repeated acquisitions. Neuroimage. 2021;225:117406.
Van Essen DC, Smith SM, Barch DM, Behrens TEJ, Yacoub E, Ugurbil K. The WU-Minn human connectome project: an overview. Neuroimage. 2013;80:62-79.
Froeling M, Tax CMW, Vos SB, Luijten PR, Leemans A. “MASSIVE” brain dataset: multiple acquisitions for standardization of structural imaging validation and evaluation. Magn Reson Med. 2017;77:1797-1809.
Magnotta VA, Matsui JT, Liu D, et al. MultiCenter reliability of diffusion tensor imaging. Brain Connect. 2012;2:345-355.
Helmer KG, Chou M-C, Preciado RI, et al. Multi-site study of diffusion metric variability: characterizing the effects of site, vendor, field strength, and echo time using the histogram distance. Proc SPIE Int Soc Opt Eng. 2016;9788:97881G.
Vollmar C, O'Muircheartaigh J, Barker GJ, et al. Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0T scanners. Neuroimage. 2010;51:1384-1394.
Palacios EM, Martin AJ, Boss MA, et al. Toward precision and reproducibility of diffusion tensor imaging: a multicenter diffusion phantom and traveling volunteer study. Am J Neuroradiol. 2017;38:537-545.
Zhu T, Hu R, Qiu X, et al. Quantification of accuracy and precision of multi-center DTI measurements: a diffusion phantom and human brain study. Neuroimage. 2011;56:1398-1411.
Mirzaalian H, Ning L, Savadjiev P, et al. Inter-site and inter-scanner diffusion MRI data harmonization. Neuroimage. 2016;135:311-323.
Cetin Karayumak S, Bouix S, Ning L, et al. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. Neuroimage. 2019;184:180-200.
Warrington S, Ntata A, Mougin O, et al. A resource for development and comparison of multimodal brain 3 T MRI harmonisation approaches. Imaging Neuroscience. 2023:1-27.
De Luca A, Karayumak SC, Leemans A, et al. Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH). Neuroimage. 2022;259:119439.
Fortin JP, Parker D, Tunç B, et al. Harmonization of multi-site diffusion tensor imaging data. Neuroimage. 2017;161:149-170.
Pinto MS, Paolella R, Billiet T, et al. Harmonization of brain diffusion MRI: concepts and methods. Front Neurosci. 2020;14:14.
Jochimsen TH, Von Mengershausen M. ODIN - object-oriented development Interface for NMR. J Magn Reson. 2004;170:67-78.
Magland JF, Li C, Langham MC, Wehrli FW. Pulse sequence programming in a dynamic visual environment: SequenceTree. Magn Reson Med. 2016;75:257-265.
Layton KJ, Kroboth S, Jia F, et al. Pulseq: a rapid and hardware-independent pulse sequence prototyping framework. Magn Reson Med. 2017;77:1544-1552.
Nielsen JF, Noll DC. TOPPE: a framework for rapid prototyping of MR pulse sequences. Magn Reson Med. 2018;79:3128-3134.
Cordes C, Konstandin S, Porter D, Günther M. Portable and platform-independent MR pulse sequence programs. Magn Reson Med. 2020;83:1277-1290.
Karakuzu A, Biswas L, Cohen-Adad J, Stikov N. Vendor-neutral sequences and fully transparent workflows improve inter-vendor reproducibility of quantitative MRI. Magn Reson Med. 2022;88:1212-1228.
Ravi K, Geethanath S, Vaughan J. PyPulseq: a python package for MRI pulse sequence design. J Open Source Softw. 2019;4:1725.
Herz K, Mueller S, Perlman O, et al. Pulseq-CEST: towards multi-site multi-vendor compatibility and reproducibility of CEST experiments using an open-source sequence standard. Magn Reson Med. 2021;86:1845-1858.
Tong G, Gaspar AS, Qian E, et al. A framework for validating open-source pulse sequences. Magn Reson Imaging. 2022;87:7-18.
Hennig J, Barghoorn A, Zhang S, Zaitsev M. Single shot spiral TSE with annulated segmentation. Magn Reson Med. 2022;88:651-662.
Gaspar AS, Silva NA, Price AN, Ferreira AM, Nunes RG. Open-source myocardial T1 mapping with simultaneous multi-slice acceleration: combining an auto-calibrated blipped-bSSFP readout with VERSE-MB pulses. Magn Reson Med. 2023;90:539-551.
Dang HN, Endres J, Weinmüller S, et al. MR-zero meets RARE MRI: joint optimization of refocusing flip angles and neural networks to minimize T2-induced blurring in spin echo sequences. Magn Reson Med. 2023;90:1345-1362.
Matheson GJ. We need to talk about reliability: making better use of test-retest studies for study design and interpretation. PeerJ. 2019;7:e6918.
Lin LI-K. A concordance correlation coefficient to evaluate reproducibility. Biometrics. 1989;45:255-268.
Reischauer C, Staempfli P, Jaermann T, Boesiger P. Construction of a temperature-controlled diffusion phantom for quality control of diffusion measurements. J Magn Reson Imaging. 2009;29:692-698.
Griswold MA, Jakob PM, Heidemann RM, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med. 2002;47:1202-1210.
Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med. 1995;34:65-73.
Teipel SJ, Reuter S, Stieltjes B, et al. Multicenter stability of diffusion tensor imaging measures: a European clinical and physical phantom study. Psychiatry Res. 2011;194:363-371.
Brant-Zawadzki M, Gillan GD, Nitz WR. MP RAGE: a three-dimensional, T1-weighted, gradient-echo sequence-initial experience in the brain. Radiology. 1992;182:769-775.
Inati SJ, Naegele JD, Zwart NR, et al. ISMRM raw data format: a proposed standard for MRI raw datasets. Magn Reson Med. 2017;77:411-421.
Lei T, Udupa JK. Gibbs ringing artifact and spatial correlation in MRI. Medical Imaging 2003. Physics of Medical Imaging; 2003:961.
Gorgolewski KJ, Auer T, Calhoun VD, et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data. 2016;3:160044.
Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23:S208-S219.
Andersson JLR, Sotiropoulos SN. An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage. 2016;125:1063-1078.
Andersson JLR, Skare S, Ashburner J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage. 2003;20:870-888.
Fischl B. FreeSurfer. Neuroimage. 2012;62:774-781.
Avants BB, Tustison N, Johnson H. Advanced Normalization Tools (ANTS) Release 2.x. 2014.
Aja-Fernández S, Pieciak T, Vegas-Sánchez-Ferrero G. Spatially variant noise estimation in MRI: a homomorphic approach. Med Image Anal. 2015;20:184-197.
Farrell JAD, Landman BA, Jones CK, et al. 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 1.5T. J Magn Reson Imaging. 2007;26:756-767.
Polders DL, Leemans A, Hendrikse J, Donahue MJ, Luijten PR, Hoogduin JM. Signal to noise ratio and uncertainty in diffusion tensor imaging at 1.5, 3.0, and 7.0 Tesla. J Magn Reson Imaging. 2011;33:1456-1463.
Lee Y, Callaghan MF, Acosta-Cabronero J, Lutti A, Nagy Z. Establishing intra- and inter-vendor reproducibility of T1 relaxation time measurements with 3T MRI. Magn Reson Med. 2019;81:454-465.
Xue H, Inati S, Sørensen TS, Kellman P, Hansen MS. Distributed MRI reconstruction using Gadgetron-based cloud computing. Magn Reson Med. 2015;73:1015-1025.
Hansen MS, Sørensen TS. Gadgetron: an open source framework for medical image reconstruction. Magn Reson Med. 2013;69:1768-1776.
Zhang H, Schneider T, Wheeler-Kingshott CA, Alexander DC. NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage. 2012;61:1000-1016.
Jensen JH, Helpern JA, Ramani A, Lu H, Kaczynski K. Diffusional kurtosis imaging: the quantification of non-Gaussian water diffusion by means of magnetic resonance imaging. Magn Reson Med. 2005;53:1432-1440.
Setsompop K, Gagoski BA, Polimeni JR, Witzel T, Wedeen VJ, Wald LL. Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn Reson Med. 2012;67:1210-1224.

Auteurs

Qiang Liu (Q)

Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
School of Biomedical Engineering, Southern Medical University, Guangzhou, China.

Lipeng Ning (L)

Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Imam Ahmed Shaik (IA)

Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Congyu Liao (C)

Department of Radiology, Stanford University, Stanford, California, USA.

Borjan Gagoski (B)

Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts, USA.

Berkin Bilgic (B)

Department of Radiology, Harvard Medical School, Boston, Massachusetts, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.
Harvard/MIT Health Sciences and Technology, Cambridge, Massachusetts, USA.

William Grissom (W)

Department of Biomedical Engineering, Case School of Medicine, Case Western Reserve University, Cleveland, Ohio, USA.

Jon-Fredrik Nielsen (JF)

Functional MRI Laboratory, Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.

Maxim Zaitsev (M)

Division of Medical Physics, Department of Diagnostic and Interventional Radiology, University Medical Center Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.

Yogesh Rathi (Y)

Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.

Classifications MeSH