Yiming Wang1, Limin Zhou1, Durga Udayakumar1,2, and Ananth J. Madhuranthakam1,2
1Radiology, UT Southwestern Medical Center, Dallas, TX, United States, 2Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX, United States
Synopsis
Arterial spin labelling
(ASL) is a non-invasive and non-contrast perfusion imaging technique that can
serve as an imaging biomarker to assess tissue blood flow characteristics. A
perfusion phantom is valuable to evaluate newly developed ASL sequences, test
consistency and to compare sequence reproducibility across various scanners. In
this study, we performed a longitudinal assessment of the reproducibility and repeatability
of perfusion measurement using 2D pCASL sequence with 20 PLDs over 5 weeks
duration with a previously developed 3D printed perfusion phantom. Intra-class
correlation coefficient (ICC) of measured perfusion and T1 are 0.96 and 0.94
respectively, indicating good reproducibility and repeatability.
Purpose
Arterial
spin labelling (ASL) is a non-contrast perfusion imaging method that quantifies
the blood flow which is one of the key physiological parameters. Perfusion
abnormalities are associated with pathophysiological process such as cancer and
neurodegenerative diseases. However, without a validated standard, it would be
challenging to interpret the quantitative readouts. Hence a well calibrated and
validated perfusion measures are highly desirable for efficient and safe
clinical implementation of the ASL sequence [1, 2]. Perfusion phantom can
enable ground truth evaluations such as quality control and cross-scanner
comparison of ASL sequences, which will ensure consistency of longitudinal ASL measurements.
Previously, we have developed a 3D-printed perfusion phantom that mimics the perfusion
through human tissue with branching of arterial vessels [3]. Thus, the purpose
of this study was to assess the perfusion phantom’s reproducibility and
repeatability by longitudinally acquiring quantitative ASL measures using a multi-PLD
2D pCASL sequence over a period of 5 weeks. Methods
Phantom
structure: The
perfusion phantom is connected to tubes through two interfaces on either side
of the main unit, one serves as input, and the other as output. (Fig. 1a, b) Distilled
water is circulated by a peristaltic pump placed outside the scanner room,
which is connected to the phantom by extended tubes. The phantom first distributes
the incoming flow of water into smaller channels through its 3D-printed branches
mimicking capillaries. Water then enters a small port next to a chamber that
contains a sponge. (Fig. 1b) The sponge absorbed the water and mimics body
tissue mass. The branches and the small port ensure that the sponge is
sufficiently perfused, before water exits the phantom through the output structures
that mirror the input flow setup.
MR
Imaging: A multiple
post-label delay (PLD) 2D pCASL sequence was used for imaging, using a single
shot turbo spin echo (SShTSE) acquisition. Parameters were: label duration = 1
s, 20 evenly-spaced PLDs ranging from 0.2 to 7.8 s, at 0.4 s increments. Other
imaging parameters were: TR = 3/3.4/3.8/…/10.6 s, TE = 36 ms, TSE factor = 38,
echo spacing = 4.9 ms, FOV = 100 x 100 mm2, acquired resolution = 1.6 x 1.6
mm2 , slice thickness = 10 mm, matrix = 64x64, scan time = 4:30 mins.
M0 images were acquired for quantification with same parameters except for a
single PLD = 6 s, a single TR = 8 s, and scan time = 8 s. Sequences were run at
a pump flow rate of 400 mL/min for this reproducibility study, and were
repeated for 3 times per session. In this ongoing study, one session is run each
week and the data over a period of 5 weeks is presented in this abstract.
Data
Analysis: Polygon
ROI covering most of the perfusion was drawn on the perfusion difference image that
has the highest perfusion among the 20 PLD images. The ROI was then copied to
other PLD perfusion weighted images, followed by the calculation of their mean signal
intensities within the ROI. The mean signal intensities of the perfusion
difference images were normalized by the mean signal intensity of the M0 image within
the same ROI. Normalized signal intensities of the 20 PLDs were then fitted into
a general kinetic model for perfusion and T1 quantification, using nonlinear
regression analysis with least squares method on GraphPad Prism 7.04 (GraphPad
Software, San Diego, CA) [4]. Perfusion unit was converted from ms-1
to mL/100g/min according to the kinetic model, using an assumed labeling
efficiency of 0.9 for pCASL [4]. Mean and standard deviation of the quantified
perfusion and T1 values from each week’s 3 runs of 2D pCASL were calculated. The
reliability among the 3 runs of 2D pCASL measurements were estimated using intra-class
correlation coefficient (ICC), for both the quantified perfusion and T1. ICC
estimates and their 95% confidence interval (CI) were calculated using SPSS statistics
version 25.0 (IBM, Armonk, NY) based on a single-measurement,
absolute-agreement, 2-way mixed-effects model. Results
Fig. 2a-c
show the perfusion weighted images across 20 PLDs from 3 runs of 2D pCASL in
one session, and Fig. 2d shows images from 1 run of 2D pCASL of another session.
Signal intensities (open circle) and their regression analysis results (solid
line) across 20 PLDs are shown for 3 runs of 2D pCASL in 1 session (Fig. 3a), and
for all 5 sessions across 5 weeks (Fig. 3b), showing overall highly reproducible
signal intensities and regression analyses. Mean values of R2 across
all 15 regression analyses was 0.984, with a standard deviation of 0.003,
indicating good reproducibility. Mean values and standard deviations of the quantified
perfusion values and T1s from the 3 runs of 2D pCASL were consistent over the 5
weeks (Table 1) with a strong ICC (Table 2). Conclusions
2D pCASL quantitative measurements with multiple
PLDs in a previously-developed 3D-printed perfusion phantom showed high reproducibility
and repeatability, indicating its reliable use as a quality control phantom for
ASL measurements. Future works will include studying the phantom’s performance
at different pump flow rates and determining an appropriate inflow range for
its optimal performance. Acknowledgements
This work
was partly supported by the NIH/NCI grant U01CA207091.References
[1] Alsop, DC et al.
Radiology 1998; 208: 410-416
[2] Chiribiri,
A et al. MRM 2013; 69(3): 698-707
[3] Greer, JS et al. ISMRM 2017: 3805
[4] Buxton, RB et al, MRM 1998; 40(3): 383-396.