Marcus Raudner1, Tom Hilbert2,3,4, Tobias Kober2,3,4, Vladimir Juras1, Ewald Moser5, Claudia Kronnerwetter1, David Stelzeneder6, and Siegfried Trattnig1
1High Field MR Centre, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria, 2Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland, 3Department of Radiology, University Hospital (CHUV), Lausanne, Switzerland, 4LTS5, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 5Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria, 6Department of Orthopaedics, Medical University of Vienna, Vienna, Austria
Synopsis
The quantitative measurement of the T2 relaxation
time has been shown to be a useful tool for radiological diagnosis. However, the
use of quantitative MRI (qMRI) in clinical routine is often hindered due to
long acquisition times. Here, we assess T2 parameters in the lumbar and
cervical spine as well as the knee using GRAPPATINI, a model-based accelerated
T2 mapping sequence. Additionally, synthetic T2-weighted (T2w) images are
derived from the quantitative maps. The T2 maps and synthetic T2w images are
compared to conventional T2w and T2 mapping sequences, yielding an overall 5.8-fold time-saving.
Purpose
T2 mapping offers great potential as non-invasive
tool in musculoskeletal applications since T2 relaxation times differ between
healthy and diseased cartilage. This has already been shown in
studies assessing knee cartilage and intervertebral discs (IVDs)2–6. However, the long acquisition times of conventional
Carr-Purcell-Meiboom-Gill (CPMG) sequences prohibits the broad
clinical application of T2 mapping. Therefore, intermediate-weighted (IW), T2w
and T1w TSE sequences are used for morphological evaluation of the knee7 and the IVD8 providing only qualitative images for
diagnosis.
GRAPPATINI1 is a model-based iterative algorithm to
reconstruct highly-undersampled k-space data and offers a T2 map and multiple
synthetic T2w images with arbitrary echo times directly reconstructed on the
scanner. This enables fast T2 mapping acquisition embedded in a clinical
workflow. Here we compare GRAPPATINI to a fully sampled CPMG sequence and TSE
images acquired with a clinical protocol in a healthy volunteer through the
assessment of knee cartilage as well as lumbar and cervical IVDs.Methods
After written and oral consent was
obtained, one healthy volunteer (age 22 years, female) was scanned at 3T
(Magnetom Prisma, Siemens Healthcare, Erlangen, Germany). GRAPPATINI was used to acquire 10-fold undersampled 2D k-space data. For comparison, standard fully-sampled CPMG T2 mapping and
a clinical TSE protocol were acquired. A dedicated 15-channel knee coil,
64-channel head/neck coil and an 8-channel spine coil in conjunction with a
32-channel body coil were used as receivers. T2 maps
were acquired from CPMG data using the MapIt product software installed on the
scanner. T2 map and synthetic contrasts from GRAPPATINI were directly reconstructed
on the scanner hardware. TSE and synthetic contrasts
were compared at identical TE regarding their respective signal-to-noise ratio (SNR) and
contrast-to-noise ratio (CNR). An overview of the employed sequence parameters
is shown in figure 2.
T2
values in regions-of-interest (ROI) were assessed using JiveX (Visus Technology
Transfer GmbH, Germany) and analyzed via paired t-tests and calculation
of Pearson correlation coefficients.
Knee cartilage T2
values were aggregated from anterior, central and posterior femoral
cartilage as well as patellar and tibial regions. IVDs were compared in the regions of the anterior and posterior annulus
fibrosus (AF) as well as the nucleus pulposus (NP). ROIs were manually drawn
and copy-pasted between maps.Results
Figure 1 shows a side-by-side comparison
of T2w TSE and synthetic images with a color-coded overlay of the T2 maps
derived via CPMG and GRAPPATINI. SNR was
compared for bone, cartilage and muscle and CNR for cartilage-bone,
cartilage-muscle and cartilage-fluid. Comparing these parameters for the
synthetic conrasts and the T2w TSE images, SNR and CNR correlated significantly
(r=.962,p<.001 and r=.995,p<.001) and were not significantly
different (p=.221 and p=.329).
The mean T2 values in CPMG and
GRAPPATINI maps correlated significantly in the lumbar spine (r=.984,p<.001), cervical spine (r=.838,p<.001) and the knee (r=.928,p<.001). This shows strong coherence of both methods of relaxation time
measurement. For an overview of the assessed mean values see Figure 3.
Mean values of the respective areas differed
significantly in the knee (p=.010) and the cervical (p<.001), but not
the lumbar spine (p=.112).Discussion
The strong Pearson
correlation coefficients of CPMG and GRAPPATINI suggest that this prototype
sequence could potentially replace the fully sampled standard approach. High
k-space undersampling for T2 mapping allows for a significant shortening in
scan time required in the knee (2:14min vs. 12:54min) and the cervical and
lumbar spine (2:32min vs. 13:38min) while preserving data quality and
accuracy of generated T2 maps.
GRAPPATINI tends to overestimate in areas
with low T2 values because the algorithm requires at least a factor of three
times ∆TE for an accurate reconstruction. However, CPMG and a typical mono-exponential model tend to overestimate
T2 in areas with higher values, whereas GRAPPATINI has already been shown to be
very close to a single-echo reference in these T2 value areas.1 For this preliminary assessment of the cervical spine,
GRAPPATINI suffered from continuous overestimation due to image artifacts, but
still provided a highly significant correlation to CPMG-based T2 values.Conclusion
GRAPPATINI makes
way for accurate assessment of T2 relaxation times and additionally provides
synthetic T2w TSE-like contrasts comparable to conventional
morphological sequences. Using GRAPPATINI, quantitative (T2 mapping) and
qualitative (synthetic T2w TSE) assessment of anatomical regions is possible in
less than 3 minutes at identical spatial resolution. Acquiring
quantitative high-resolution images within clinically feasible scan times might
increase the value of T2 mapping and encourages early diagnosis of disease based on quantitative, automated analysis. This is
a promising outlook since broad clinical application of qMRI may allow establishing
a database of various normative ranges to detect changes in early stages of disease.Acknowledgements
This work was
supported by the Austrian Science Fund (FWF) KLI541-B30References
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