7T in-vivo MRI at 350μm iso. res. using multi echo T2* imaging with flow artifact mitigation

Poster No:

2052 

Submission Type:

Abstract Submission 

Authors:

Omer Faruk Gulban1, Laurentius Huber1, Benedikt Poser1, Kendrick Kay2, Martin Havlicek1, Federico De Martino1, Dimo Ivanov1

Institutions:

1Maastricht University, Maastricht, N/A, 2University of Minnesota, Minneapolis, MN

First Author:

Omer Faruk Gulban  
Maastricht University
Maastricht, N/A

Co-Author(s):

Laurentius Huber, Ph.D.  
Maastricht University
Maastricht, N/A
Benedikt Poser  
Maastricht University
Maastricht, N/A
Kendrick Kay  
University of Minnesota
Minneapolis, MN
Martin Havlicek  
Maastricht University
Maastricht, N/A
Federico De Martino  
Maastricht University
Maastricht, N/A
Dimo Ivanov  
Maastricht University
Maastricht, N/A

Introduction:

Spatial misencoding of the vascular signal due to flow is an imaging artifact presenting a significant challenge for in vivo MRI[1] at high resolutions (≤0.5mm). In this artifact, the signal of flowing spins appears to move away from their true location[2] which can lead to incorrect (quantitative) T2* estimations. This artifact is especially problematic for high resolution in-vivo MRhistology[3]. Although flow compensation is available [4], there is no method for multiple echoes (>2). Here we propose a flow artifact mitigation method for multi-echo gradient recalled echo (ME-GRE) images after applying 90° rotations in their phase-encoding direction to show intracortical details at 350μm iso. res. acquired under 1hour scanning time.

Methods:

We acquired data from one volunteer (28, f) at 7 Tesla (Siemens) using a 32-ch. head coil & 3D ME-GRE with bipolar readout (350μm isotropic resolution;TR=30ms;TE1-6=[4-25]ms;α=11°;14min.,coverage=201.6×201.6×36.4mm^3). 4 ME-GRE images were acquired in 1 session. We changed the phase-encoding direction by 90° in each acquisition. The 90°-changes were introduced in order to control the direction of the flow artifact[2] which appears as spatial signal shift along the vector component of the flow direction on readout axis.

We also acquired a T1 image using 3D MP2RAGE[5] (650μm iso.; TR/TE/TI1/TI2=5000/2.46/900/2750ms; α1/α2=5°/3°; FOV=208×208×156mm3).

We considered 3 sources of artifacts while improving signal-to-noise ratio (SNR): (i) head motion across acquisitions, (ii) spatial distortions due to readout directions, (iii) spatial misencoding of the vascular signal due to flow.

The distortion introduced by bipolar readouts[6] was corrected by registering odd and even echoes with the same readout polarity. Then, the images with the same phase-encoding axes (R-L & L-R [Mx] or A-P & P-A[My]) were motion corrected and averaged to increase SNR. Keeping the data with different phase encoding axes (Mx,My) separate was deliberate in order to avoid mixing different flow artifacts.

To mitigate the flow artifacts, we used compositing (piecing together a new image by using parts of multiple other images). Since the artifact induced by the misencoding of vascular flow is different across images with 90° phase-encoding direction difference, it is possible to compose a new set of echoes by selecting the signal of each voxel from a set that is not affected by the artifact (a voxel affected by the artifact in Mx, will not be affected in My). For voxels not affected in either Mx or My, phase-encoding axis, the signal was averaged. Then, we estimated quantitative T2* by fitting a mono-exponential decay.

Results:

Fig1 shows evolution of the flow artifact across echoes in images acquired with 90° phase-encoding direction difference. Note the different direction of vascular signal displacement across images with 90° phase-encoding direction difference. Also notice the effect of flow artifact mitigation. See that the flow artifact (i.e. the bright arterial signal moving across echoes) is mostly gone.

Fig2 shows stria of Gennari together with other intracortical details and pial vessels, which are clearly visible in our quantitative T2* images.
Supporting Image: fig1_before_after_artifact.png
   ·It is easier to see the flow artifact in video format. See https://youtu.be/bFnvwbNabmw for the video version of this figure.
Supporting Image: fig2_layers.png
   ·Quantitative T2* images (350 micron isotropic resolution) showing the stria of Gennari and layers&vessels of auditory cortex. Please see the caption embedded into the figure for further details.
 

Conclusions:

Accounting for the spatial misencoding of the vascular signal due to flow[1, 2] is highly relevant for high resolution in vivo MRI because this artifact can prevent accurate gray matter T2* estimates. By mitigating this artifact, we show more accurate quantitative T2* estimates in gray matter, white matter, vessels, and cerebrospinal fluid. T2* estimates of all these tissues that are less affected by partial voluming at 350μm iso. resolution are essential for future high resolution fMRI hemodynamic signal modeling[7, 8, 9].

Modeling and Analysis Methods:

Exploratory Modeling and Artifact Removal
Methods Development 2

Novel Imaging Acquisition Methods:

Anatomical MRI 1

Keywords:

Blood
Cortex
Cortical Columns
Cortical Layers
HIGH FIELD MR
MRI
MRI PHYSICS
Myelin
STRUCTURAL MRI
Other - Vessels

1|2Indicates the priority used for review

My abstract is being submitted as a Software Demonstration.

No

Please indicate below if your study was a "resting state" or "task-activation” study.

Other

Healthy subjects only or patients (note that patient studies may also involve healthy subjects):

Healthy subjects

Was any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.

Yes

Was any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.

Not applicable

Please indicate which methods were used in your research:

Structural MRI

For human MRI, what field strength scanner do you use?

7T

Which processing packages did you use for your study?

Other, Please list  -   ITK-SNAP, Nibabel, Numpy

Provide references using author date format

[1] Wehrli, F. W. (1990). Time-of-flight effects in MR imaging of flow. Magnetic Resonance in Medicine, 14(2), 187–193.

[2] Larson, T. C., Kelly, W. M., Ehman, R. L., & Wehrli, F. W. (1990). Spatial misregistration of vascular flow during MR imaging of the CNS: cause and clinical significance. AJR. American Journal of Roentgenology, 155(5), 1117–1124.

[3] Weiskopf, N., Mohammadi, S., Lutti, A., & Callaghan, M. F. (2015). Advances in MRI-based computational neuroanatomy: from morphometry to in-vivo histology. Current Opinion in Neurology, 28(4), 313–322.

[4] Deistung, A., Dittrich, E., Sedlacik, J., Rauscher, A., & Reichenbach, J. R. (2009). ToF-SWI: simultaneous time of flight and fully flow compensated susceptibility weighted imaging. Journal of Magnetic Resonance Imaging : JMRI, 29(6), 1478–1484.

[5] Marques, J. P., Kober, T., Krueger, G., van der Zwaag, W., Van de Moortele, P.-F., & Gruetter, R. (2010). MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. NeuroImage, 49(2), 1271–1281.

[6] Cohen-Adad, J. (2014). What can we learn from T2* maps of the cortex? NeuroImage, 93 Pt 2, 189–200.

[7] Uludağ, K., Müller-Bierl, B., & Uğurbil, K. (2009). An integrative model for neuronal activity-induced signal changes for gradient and spin echo functional imaging. NeuroImage, 48(1), 150–165.

[8] Havlicek, M., & Uludag, K. (2019). A dynamical model of the laminar BOLD response. NeuroImage, 116209.

[9] Petridou, N., & Siero, J. C. W. (2019). Laminar fMRI: What can the time domain tell us? NeuroImage, 197(February 2017), 761–771.