Sander Brinkhof1, Razmara Nizak2, Vitaliy Khlebnikov3, Dennis Klomp3, Bennie ten Haken4, Jeanine J Prompers3, and Daniel Saris2,5
1University Medical Center Utrecht, Utrecht, Netherlands, 2Orthopaedics, University Medical Center Utrecht, Utrecht, 3Radiology, University Medical Center Utrecht, Utrecht, Netherlands, 4Faculty of Science and Technology, University of Twente, Enschede, Netherlands, 5MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, Netherlands
Synopsis
The purpose of this study was to assess the
sensitivity of 3D gagCEST at 7T in cartilage repair patients with respect to
healthy volunteers. Six healthy volunteers were scanned for stability
assessments and five patients with cartilage defects were included to assess clinical
applicability of the gagCEST sequence. The mean GAG effect size in healthy
controls is 10.2 %, which is three times higher than the coefficient of
variation in the stability assessments. The results of this study demonstrate
the stability of 3D gagCEST at 7T and the results from patients with cartilage
defects indicate a correlation with clinical findings.
Purpose
The molecular composition of cartilage
changes after trauma and during the course of degenerative diseases such as
osteoarthritis, and is relevant for cartilage repair. Of particular interest
are changes in the amount of glycosaminoglycan (GAG) in articular cartilage,
which can be monitored directly in vivo with glycosaminoglycan chemical
exchange saturation transfer (gagCEST) 1–3. The purpose of this study was to assess the sensitivity of new 3D gagCEST
sequence at 7 T in cartilage repair patients with respect to healthy
volunteers.Methods
Experiments were carried out on a 7.0 T
whole body scanner (Achieva; Philips Healthcare, Best, Netherlands) using a wrap-around
32-channel receiver knee coil and a volume transmit coil (MR Coils BV,
Zaltbommel, the Netherlands). The 3D gagCEST sequence as implemented in this
work was a pseudo-steady state 4 spoiled gradient echo sequence with a pre-saturation pulse train of
25 ms sinc-shaped pulses (2 µT, N=20, duty cycle 62.5%), readout (sagittal
orientation, TR = 2.75 ms, TE = 1.4 ms, FA = 5, FOV = 140 x 150 x 135, TFE factor
of 370, SENSE factor 2, five shots and inter-shot T1 recovery time = 2s, k-space
center-weighted acquisition), two dummy scans, scan time 6min:59s. The sequence
was preliminary optimized through Bloch-McConnell simulations for maximum
sensitivity to GAG effect size.
CEST images were acquired at 17 saturation
offsets from -3 ppm to 3 ppm (± 333 ppm for normalization). The CEST
datasets were analyzed in MATLAB (version 2015b, the Mathworks inc., Natick,
MA, USA) using a Lorentzian fit with a three-pool model (water, GAG and magnetization
transfer) 5, after which the area under the curve from 0.6 ppm to 1.2 ppm was
used to calculate the GAG effect. Figure 1A shows the location of the region of
interest (ROI) in the trochlea, used for both the stability analyses and
calculation of GAG effect in healthy controls (age 24 to 29, four males and two
females). Figure 1B shows CEST spectrum in the ROI. Stability measurements were
taken by repeating the same 0 ppm offset nine times and stability was assessed with
coefficient of variation.
Five patients undergoing arthroscopy for
repair of a femoral cartilage defect were included in this study to assess the
clinical value of the new gagCEST sequence, i.e. whether it can discriminate
between healthy and damaged cartilage. The cartilage was manually segmented,
after which gagCEST MRI values were assigned to each voxel resulting in a 3D
colormap of the cartilage depicting gagCEST values. Results
Table 1 summarizes signal stability
assessments in vivo, the average CV was 3.2%. Subject three showed a higher CV,
which is related to the motion artifacts which were clearly seen in the dynamic
series. Table 2 shows the GAG effect size in healthy controls, which was on
average 10.2% (±
4%).
3D segmentations of the cartilage are used
to assess the gagCEST values within the whole knee in patients. An example of a
3D segmentation of the cartilage with GAG effect values in each voxel is shown
in Figure 2. This specific patient had a defect on the medial side of his knee
(ICRS grade IV) and some blistering and small defects on the lateral side and
trochlea (ICRS grade I/II). The results of cartilage repair patients are
summarized in Table 3, which show that the GAG effect is always significantly higher
was (p < 0.05, related-samples Wilcoxon Signed Rank Test) in healthy
cartilage compared to damaged cartilage. Discussion
The results of this study demonstrate the
stability and clinical applicability of the new 3D gagCEST at 7 T. If we use
the area under the curve of a range around the expected GAG effect, which is commonly
used in literature 1,6,7, we find a GAG effect between 5.0% and 16.3% in healthy cartilage. These
results are acquired within a clinically feasible scan time of seven minutes.
The gagCEST results from patients with cartilage defects indicate a correlation
with clinical findings. Acknowledgements
No acknowledgement found.References
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