Connectome 2.0: Performance evaluation and initial in vivo human brain diffusion MRI results
Presented During:
Thursday, June 27, 2024: 11:30 AM - 12:45 PM
COEX
Room:
Hall D 2
Poster No:
2354
Submission Type:
Abstract Submission
Authors:
Gabriel Ramos Llorden1,2, Peter Dietz3, Mathias Davids1,2, Hong-Hsi Lee1,2, Yixin Ma1,2, Mirsad Mahmutovic4, Alina Scholz4, Hansol Lee1,2, Chiara Maffei1,2, Anastasia Yendiki1,2, Berkin Bilgic1,2, John E. Kirsch1,2, Daniel J. Park1, Bryan Clifford5, Wei-Ching Lo5, Stefan Stocker3, Jasmine Fischer3, Gudrun Ruyters3, Manuela Roesler3, Elmar Rummert3, Andreas Krug3, Andreas Potthast3, Thomas Benner3, Rebecca Ramb3, Peter Basser6, Thomas Witzel7, Lawrence L. Wald1,2, Bruce Rosen1,2, Boris Keil4,8, Susie Huang1,2
Institutions:
1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, 2Harvard Medical School, Boston, MA, 3Siemens Healthineers, Erlangen, Germany, 4Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Science, Giessen, Germany, 5Siemens Medical Solutions USA, Boston, MA, 6Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, Maryland, 7Q Bio Inc, San Carlos, CA, 8Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany
First Author:
Gabriel Ramos Llorden
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Co-Author(s):
Mathias Davids
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Hong-Hsi Lee, MD PhD
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Yixin Ma
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Mirsad Mahmutovic
Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Science
Giessen, Germany
Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Science
Giessen, Germany
Alina Scholz
Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Science
Giessen, Germany
Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Science
Giessen, Germany
Hansol Lee, PhD
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Chiara Maffei
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Anastasia Yendiki
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Berkin Bilgic
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
John E. Kirsch
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Daniel J. Park
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Charlestown, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital
Charlestown, MA
Peter Basser
Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH
Bethesda, Maryland
Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH
Bethesda, Maryland
Lawrence L. Wald
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Bruce Rosen, MD, PhD
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Boris Keil
Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Science|Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg
Giessen, Germany|Marburg, Germany
Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Science|Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg
Giessen, Germany|Marburg, Germany
Susie Huang, MD PhD
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital|Harvard Medical School
Charlestown, MA|Boston, MA
Introduction:
We present the Connectome 2.0 scanner[1], developed for next-generation human connectomics and microstructure imaging of human brain circuits across scales. Here, we report on the hardware's design, construction, and evaluation, and initial results for in vivo human brain diffusion MRI.
Methods:
Scanner and gradient coil design
The Connectome 2.0 system (Fig. 1a) (MAGNETOM Connectom.X, Siemens Healthineers, Erlangen, Germany) was designed on a 3T scanner platform and targeted in vivo human brain imaging using a maximum gradient strength (Gmax) of 500 mT/m and maximum slew rate (SRmax) of 600 T/m/s. The gradient coil is designed as an asymmetric head gradient following the stepped geometry of the Siemens 7T Impulse head gradient coil (Gmax=200 mT/m, SRmax=900 T/m/s),[2] but modified to include double-primary layers in all axes to achieve Gmax=500 mT/m. The gradient coil design was iterated upon using peripheral nerve stimulation (PNS) modeling to raise the PNS thresholds and maximize the usable gradient parameter space.[3,4]
Radiofrequency (RF) coils design
A 72-channel in vivo head receive coil[5] and a 64-channel ex vivo whole brain coil[6] were designed and constructed for high-sensitivity diffusion MRI acquisitions. Each receive array was outfitted with its own dedicated local transmit coil and 16-channel 19F clip-on field probe system for concurrent field monitoring. [7]
The Connectome 2.0 system (Fig. 1a) (MAGNETOM Connectom.X, Siemens Healthineers, Erlangen, Germany) was designed on a 3T scanner platform and targeted in vivo human brain imaging using a maximum gradient strength (Gmax) of 500 mT/m and maximum slew rate (SRmax) of 600 T/m/s. The gradient coil is designed as an asymmetric head gradient following the stepped geometry of the Siemens 7T Impulse head gradient coil (Gmax=200 mT/m, SRmax=900 T/m/s),[2] but modified to include double-primary layers in all axes to achieve Gmax=500 mT/m. The gradient coil design was iterated upon using peripheral nerve stimulation (PNS) modeling to raise the PNS thresholds and maximize the usable gradient parameter space.[3,4]
Radiofrequency (RF) coils design
A 72-channel in vivo head receive coil[5] and a 64-channel ex vivo whole brain coil[6] were designed and constructed for high-sensitivity diffusion MRI acquisitions. Each receive array was outfitted with its own dedicated local transmit coil and 16-channel 19F clip-on field probe system for concurrent field monitoring. [7]
Results:
Gradient system:
(a) Geometry: The stepped design with shoulder cutouts and outer gradient coil diameter of 81 cm provides access for subjects of varying sizes. The inner gradient coil diameter is 44 cm, and the free bore diameter is 40 cm.
(b) Gradient performance: The scanner achieves Gmax=500 mT/m and SRmax=600 T/m/s driven by 2 GPAs (1200A/2250V) per axis compared to the 4 GPAs (900A/2000V) of the original Connectome scanner.[8] The gradient coil has higher efficiency (0.42 mT/m/A) and lower inductance per axis than the original Connectome gradient coil (X=2250, Y=2450, Z=1800μH) with direct current resistance of 0.28 Ω.
(c) Non-linearity: Maximum field linearity deviation in a 20cm sphere is X=6.7%, Y=8.2%, and Z=11.7%.
(d) Cooling: The gradient coil incorporates direct liquid cooling technology to maximize thermal energy transfer and dissipation.
(e) Active E-shims include 1st and 2nd-order harmonics. The gradient achieves 2.4-4.2x greater PNS thresholds than the Connectome 1.0 gradient coil.
RF coils
The 72-channel in vivo coil provides a 1.5x improvement in SNR in the peripheral regions of the brain compared to a standard 32-channel head coil. The 64-channel ex vivo coil outperforms the 72-channel in vivo coil by 1.73x in average SNR.
Improvements in SNR for high b-value diffusion imaging:
The stronger gradients of the Connectome 2.0 scanner enable significant reductions in diffusion time ∆ and pulse width δ compared to Connectome 1.0, resulting in shorter TE by up to 50% (Fig. 1b). SNR gains are at least 4-fold compared to a state-of-the-art clinical scanner and up to double compared to Connectome 1.0 (Fig. 1c) at high b-values (Fig. 1d).
In vivo human brain imaging
High-resolution tractography was performed on a healthy volunteer (34F) on the Connectome 2.0 scanner using Connectome 2.0 and Connectome 1.0 protocols (see bottom of Fig. 2). Whole-brain tractograms were generated, seeding local probabilistic tractography in every voxel within a white matter mask.[9] SNR of Connectome 2.0 enables the delineation of fine fiber tracts like the mammillo-tegmental tract, which could not be seen with a matched protocol using Connectome 1.0 parameters. [10]
(a) Geometry: The stepped design with shoulder cutouts and outer gradient coil diameter of 81 cm provides access for subjects of varying sizes. The inner gradient coil diameter is 44 cm, and the free bore diameter is 40 cm.
(b) Gradient performance: The scanner achieves Gmax=500 mT/m and SRmax=600 T/m/s driven by 2 GPAs (1200A/2250V) per axis compared to the 4 GPAs (900A/2000V) of the original Connectome scanner.[8] The gradient coil has higher efficiency (0.42 mT/m/A) and lower inductance per axis than the original Connectome gradient coil (X=2250, Y=2450, Z=1800μH) with direct current resistance of 0.28 Ω.
(c) Non-linearity: Maximum field linearity deviation in a 20cm sphere is X=6.7%, Y=8.2%, and Z=11.7%.
(d) Cooling: The gradient coil incorporates direct liquid cooling technology to maximize thermal energy transfer and dissipation.
(e) Active E-shims include 1st and 2nd-order harmonics. The gradient achieves 2.4-4.2x greater PNS thresholds than the Connectome 1.0 gradient coil.
RF coils
The 72-channel in vivo coil provides a 1.5x improvement in SNR in the peripheral regions of the brain compared to a standard 32-channel head coil. The 64-channel ex vivo coil outperforms the 72-channel in vivo coil by 1.73x in average SNR.
Improvements in SNR for high b-value diffusion imaging:
The stronger gradients of the Connectome 2.0 scanner enable significant reductions in diffusion time ∆ and pulse width δ compared to Connectome 1.0, resulting in shorter TE by up to 50% (Fig. 1b). SNR gains are at least 4-fold compared to a state-of-the-art clinical scanner and up to double compared to Connectome 1.0 (Fig. 1c) at high b-values (Fig. 1d).
In vivo human brain imaging
High-resolution tractography was performed on a healthy volunteer (34F) on the Connectome 2.0 scanner using Connectome 2.0 and Connectome 1.0 protocols (see bottom of Fig. 2). Whole-brain tractograms were generated, seeding local probabilistic tractography in every voxel within a white matter mask.[9] SNR of Connectome 2.0 enables the delineation of fine fiber tracts like the mammillo-tegmental tract, which could not be seen with a matched protocol using Connectome 1.0 parameters. [10]
·a, Connectome 2.0 at MGH with the 72-channel in vivo coil. b, Minimum achievable TE at a given b-value. c, SNR gain with respect to Connectome 1.0 at a given b-value. d, In vivo DWIs at high-b value.
·High-spatial resolution diffusion MRI acquired at 1 mm isotropic resolution at b=1000 and 2500 s/mm2. Closeups of the midline sagittal view showing diencephalic and brainstem pathways.
Conclusions:
The Connectome 2.0 scanner equipped with Gmax=500 mT/m and SRmax=600 T/m/s and the latest RF coil technology achieves unprecedented sensitivity and resolution for mesoscale diffusion MRI in the living human brain.
Novel Imaging Acquisition Methods:
Diffusion MRI 1
Imaging Methods Other 2
Keywords:
MRI
MRI PHYSICS
Other - MIcrostructure, Hardware, Diffusion MRI
1|2Indicates the priority used for review
Provide references using author date format
[4] Davids M. (2020), ‘Optimization of MRI gradient coils with explicit peripheral nerve stimulation constraints’, IEEE Transactions on Medical Imaging 40.1 (2020): 129-142.
[2] Feinberg D.A. (2023), ‘Next-generation MRI scanner designed for ultra-high-resolution human brain imaging at 7 Tesla’, Nature Methods https://doi.org/10.1038/s41592-023-02068-7
[10] Maffei C. (2024), ‘Visualization of fine white matter bundles in the living human brain with diffusion MRI using 500 mT/m gradient strength’, Submitted to the 2024 Annual Meeting of the International Society for Magnetic Resonance in Medicine.
[5] Mahmutovic M. (2024), ‘A 72-channel Head Coil with an Integrated 16-Channel Field Camera for the Connectome 2.0 Scanner’, Submitted to the 2024 Annual Meeting of the International Society for Magnetic Resonance in Medicine.
[1] Huang S.Y (2021), ‘Connectome 2.0: Developing the next-generation ultra-high gradient strength human MRI scanner for bridging studies of the micro-, meso- and macro-connectome’, Neuroimage. 2021 Nov;243:118530.
[6] Scholz A. (2023), ‘Design of a 64-channel ex vivo Brain Rx Array Coil with field monitoring and temperature control for DWI at 3T’, Proceedings of the International Society of Magnetic Resonance in Medicine (2023) 0215
[8] Setsompop K. (2013), ‘Pushing the limits of in vivo diffusion MRI for the Human Connectome Project’, Neuroimage 80 (2013): 220-233.
[9] Tournier J.-D. (2010), ‘Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions’, Proceedings of the International Society of Magnetic Resonance in Medicine 18, (2010) 1670
[7] Wilm B.J. (2015) , ’Diffusion MRI with concurrent magnetic field monitoring’, Magnetic Resonance in Medicine 2015;74(4):925-933. doi:10.1002/MRM.25827