Andrew Dupuis1, Yong Chen1, Michael Hansen2, Kelvin Chow3, Dan Ma1, Mark Griswold1, and Rasim Boyacioglu1
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Microsoft Research, Redmond, WA, United States, 3Siemens Medical Solutions USA, Inc., Chicago, IL, United States
Synopsis
Keywords: MR Fingerprinting/Synthetic MR, Quantitative Imaging, Reproducibility, MNI, Automated, Gadgetron, FIRE
We
evaluate the reproducibility of 3D-MRF versus clinical-standard MPRAGE
and TSE acquisitions for ten subjects across three acquisition sets on
two scanner via a fully-automated registration and regional analysis
framework. T1 and T2 quantitative maps from 3D-MRF were found to be
highly reproducible (T1+/-4.6%, T2+/-6.3%) across scanners and sessions,
with no significant difference between repeating a scan immediately on
the same scanner, repeating a scan after repositioning the subject and
reshimming, and repeating a scan on a different scanner entirely. The
same is not true for MPRAGE (+/-12.4%) or TSE (+/-28.8%) acquisitions.
Purpose
Magnetic
Resonance Fingerprinting (MRF) has been shown to reliably and
reproducibly quantify T1 and T2 in both phantom and in-vivo studies
using a 2D acquisition with offline reconstruction and manually drawn
ROIs. However, the intensive computation needed for MRF reconstruction
has historically necessitated manually performing offline
reconstructions, making comparison against clinical standard imaging
difficult and limiting the utility of well-established post-processing
tools like FSL.
In this work, we evaluate a fully-automatic
online 3D-MRF reconstruction, as well as a cross-modality registration
and analysis system allowing for diret comparison of 3D-MRF versus
clinical standard MPRAGE and TSE acquisitions. We use this system to
perform an intrascanner and interscanner reproducibility study to
determine whether in-vivo 3D-MRF reproducibility meets or exceeds that
of MPRAGE and TSE. Additionally, we evaluate whether the reproducibility
of T1/T2 maps across sessions and scanners is stable enough to
potentially eliminate scanner dependency as a differentiation between
MRF datsets.Methods
Ten
healthy volunteers were imaged over two sessions on different days, with
each session occuring on one of two 3T scanners on different software
versions (MAGNETOM Vida, VA20 and VA31, Siemens Healthcare, Erlangen,
Germany). Each session consisted of three “sets” of acquisitions , with
each set consisting of three “series” of images: 3D-MRF FISP with a B1
mapping prescan, 3D-MPRAGE, and multislice 2D-TSE. All were acquired
with a field of view of 250x250x150mm3 and a spatial resolution of
1x1x2.5mm3. First, an “original” image set was acquired for each
subject, followed immediately by a “repetition” set. Subjects were
removed from the scanner, asked to stand up, and then sent back into the
bore. A new localizer was acquired, followed by the final “reposition”
set. The procedure was then repeated on the second scanner (a maximum of
7 days apart).
TSE and MPRAGE datasets were processed online by
each scanner’s ‘product’ reconstruction. 3D-MRF was reconstructed
online via a custom Gadgetron Kubernetes cluster using the FIRE
interface prototype. Quantitative maps were returned to the scanner via
FIRE. All images were then exported to header-complete DICOM sets.
A
custom fully-automated registration and region extraction pipeline was
used for analysis. Figure 1 details the registration flow used in this
study. Images were first converted to NIFTI using dcm2niix. Synthetic
MPRAGE and TSE contrasts were generated for MRF acquisitions, then all
images were linearly registered to the first scanner’s “original” MPRAGE
image series via FLIRT. Finally, the “original” MPRAGE set was
registered to MNI-152 via FNIRT. All transformation and warp matrices
were saved.
Atlas label images were generated for each
atlas/subject/scanner/set combination and uploaded to Azure. Twelve
well-defined, relatively homogenous regions from the Harvard-Oxford
subcortical atlas were selected for the purpose of comparison.
Bland-Altman plots were then generated comparing the reproducibility of
regional means across subjects, scanners, and repetitions. Results
60
imaging sets were acquired for 10 healthy subjects, constituting ~1.4TB
of raw data. Data were converted to ISMRMD format for future analysis.
All raw data, DICOMs, NIFTI-converted images, linear and nonlinear
transformation matrices, warped label maps, and atlas-label voxel
dictionaries were uploaded to Azure Blob storage for validation and
retrospective analysis. Access via a Python API is available upon
request to encourage further investigation and statistical analysis
using these data.
Figures 2, 3, and 4 compare the
reproducibility of 3D-MRF, TSE, and MPRAGE acquisitions using regional
means compared between immediate repetitions, subject repositions on the
same scanner, and multiple repetitions on two scanners respectively.
Voxelwise T1/T2 and T1w/T2w ratios for regions are included since they
are a common pseudoquantitative biomarker reliant on inter/intrasession
repeatability. Figure 5 summarizes the mean percent differences and
standard deviations of 3D-MRF, TSE, and MPRAGE across all three
comparisons. Note that the standard deviation of the MPRAGE and TSE data
sets was substantially larger than either of the MRF maps.Discussion
The
consistent biases and standard deviations of T1 and T2 values in
Figure 5 indicate that in-vivo 3D-MRF is reproducible whether a scan is
repeated immediately (T1: 1.25±4.33%, T2: 1.88±6.18%), with a reposition of the subject on the same scanner (T1: 0.43±4.49%, T2: 1.31±4.88%), or on a different scanner on a different day (T1: -0.57±4.90%, T2: -3.28±5.34%).
Of note, the standard deviations observed are the same magnitude as one
“step” within the MRF dictionary used during reconstruction. The T1/T2
ratio calculations showed similar stability.
MPRAGE, TSE , and
the resulting T1w/T2w ratios do not exhibit the same reproducibility as
3D-MRF. Although MPRAGE and TSE are qualitative acquisitions and
therefore not expected to produce reliable quantitative output, the
highly unpredictable differences observed across repetitions (MPRAGE:
6.56±14.21%, TSE: -0.43±23.11%), sessions (MPRAGE: -0.98±12.67%, TSE: -2.25±26.87%) and scanners (MPRAGE: 1.10±13.75%, TSE: 3.74±32.30%) casts doubt on the reliability of metrics like T1w/T2w ratio, even for contrasts acquired within the same scan session.
The
apparent reproducibility of in-vivo 3D-MRF offers multiple
opportunities: intrascanner and interscanner variations are negligible
such that data from many sessions, scanners, and sites can potentially
be treated as a single dataset for inference or other deep learning
processing . Similarly, intrasubject cross-scanner comparisons are
likely valid. Finally, the reliability of the resulting maps suggests
that synthetic contrast generation based on MRF maps may allow for
system- and session-agnostic T1/T2 weighted contrasts with
reproducibility exceeding the current clinical standard.Acknowledgements
This work was supported by Siemens Healthineers and Microsoft CorporationReferences
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