Andrew Dupuis1, Yong Chen2, Mark A Griswold1,2, and Rasim Boyacioglu2
1Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States, 2Radiology, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
Synopsis
Motivation:
Magnetic Resonance Fingerprinting relies on the stable performance of
vendor hardware and software through-time. However, the effects of
software upgrades on the repeatability of quantitative protocols
require evaluation.
Goal(s): Measure changes to MRF-derived T1 and T2 values resulting from a vendor-provided software upgrade.
Approach:
8 healthy volunteers were imaged before and after a software upgrade.
Regional T1 and T2 changes were evaluated using correlation plots and
MNI-derived regional means.
Results:
The evaluated sequence was similarly repeatable in both the
intra-version test-retest case (T1: -0.49+/- 3.85%, T2: -0.53+/-7.59%)
and pre- to post-upgrade (T1: -0.01+/-4.23% , T2: -2.10+/-8.44%).
Impact:
Showing similar repeatability of 3D MRF maps before and after vendor
software upgrade establishes performance reliability. The automated
analysis pipeline does not require qualitative images and can be used to
test MRF reproducibility in various stages of longitudinal studies.
Introduction
Clinical
or large scale research deployment of Magnetic Resonance Fingerprinting
(MRF) based techniques relies on the stability of the measurement
system and protocol, regardless of changes to scanner hardware and
software [1,2]. However, logs of changes made by a vendor to scanner
software defaults, research APIs, or hardware configurations are largely
unavailable, which may result in affected relaxation maps [3]. We
perform a precision comparison of a clinically deployed 3D-MRF sequence
[4] before and after upgrading to a new scanner software baseline.Methods
11
healthy volunteers were imaged after obtaining consent according to our
institutional IRB using a 3T scanner hardware before a software upgrade
(VA30 to VA50, MAGNETOM Vida, Siemens). Data from 8 volunteers, which
returned for the second scan and did not have motion artifacts, were
retained. All data was acquired using mrftools[cite] and stored in the
ISMRM-RD format, as well as reconstructed online using the FIRE
prototype interface [5] with additional acquisition timing details (TR,
TE, Flip Angle, RF Phase, and Readout ID) logged to user-defined fields
within the acquisition header. A hash of these acquisition headers was
used to verify that sequence timing and acquisition settings remained
consistent at runtime, after the software upgrade. Raw data structure
was also characterized to ensure data arrays were populated in the same
manner.
Volunteers were scanned twice during each session, on
each scanner version to establish the test-retest versus
pre/post-upgrade repeatability of the imaging method. The repeatability
of both overall correlation and brain region mean was tested for both
before and after the software update to ensure the repeatability was
truly scanner software agnostic. All MRF datasets were registered
linearly to the pre-upgrade initial acquisition using a synthetic MPRAGE
lookup table [cite abstract?] and the FLIRT image registration toolbox
[6,7]. After the registration, a single nonlinear registration was
performed to generate voxel masks for brain regions based on the MNI-152
Harvard-Oxford Subcortical Structural Atlas. Figure 1 depicts the
pipeline used for image registration and evaluation.
Results
When
we ran the sequence for the new version that was cross-compatible for
multiple previous versions, image quality was noticeably degraded. After
working with the vendor to pinpoint the change made in the software
upgrade, the issue was resolved but the study had to pause for two
months. 32 imaging sets of data from 8 volunteers were converted to
ISMRM-RD format for future analysis. Figure 2 demonstrates the
correlation consistency of aggregate T1 and T2 values across the target
population between pre- and post-upgrade data. Figures 3 and 4 compare
the reproducibility of 3D-MRF acquisitions using regional means compared
between immediate repetitions and multiple repetitions on two scanners
respectively. Figure 5 summarizes the mean percent differences and
standard deviations of 3D-MRF before and after the upgrade: in the
intra-version test-retest case within the same scan session, -0.49+/-
3.85% variability was observed in T1, with -0.53+/-7.59% variability in
T2. In the cross-version case, -0.01+/-4.23% variability was observed in
T1, with -2.10+/-8.44% variability in T2. Discussion
The
repeatability of the examined 3D-MRF sequence was found to be very
similar whether an examination was repeated immediately or repeated more
than 2 months after a software version upgrade across all subjects.
Good correlation was seen in both T1 and T2 values from all examined
brain regions. Importantly, the proposed pipeline does not require
qualitative images for registration between imaging sets or non-linear
registration to MNI space which typically is based on T1-weighted images
such as MPRAGE.. The pipeline can be deployed at various stages of
longitudinal studies to confirm baseline repeatability and
reproducibility after hardware or software updates. Confirmation of
reproducibility after software or hardware upgrades is inherently
important for confidence in data acquisition, an attribute that may have
been taken for granted or assumed in previous updates.
While
identical sequence source code on previous software versions from our
vendor (VA20/VA30/VA31; Siemens) on multiple hardware platforms
(Vida/Skyra/Aera; Siemens) had yielded identical headers and raw data
formatting, initial compilation of the same codebase for the VA50
platform did not yield the same hash or raw data behaviors. This
difference in headers is due to API changes within the vendor’s
acquisition, raw data management, and image reconstruction systems. With
vendor assistance, modifications to the sequence were made until
sequence hash and raw data structure matched the VA31 system,
emphasizing the importance of cooperation between researchers and
vendors to ensure consistent performance across software upgrades.Acknowledgements
No acknowledgement found.References
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