Lea Behrendt1,2, Andreas Voskrebenzev1,2, Cristian Crisosto Gonzalez1,2, Marcel Gutberlet1,2, Helen Marshall3, Anna-Maria Dittrich4, Laurie Smith3, Paul Hughes3, Jim Wild3, and Jens Vogel-Claussen1,2
1Institute of Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany, 2Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), German Center for Lung Research (DZL), Hannover, Germany, 3University of Sheffield, Sheffield, United Kingdom, 4Paediatric Pneumology and Neonatology, Hannover Medical School, Hannover, Germany
Synopsis
Dynamic contrast enhanced (DCE) MRI is an established
technique for measurement of lung perfusion, but requires the administration of
contrast agents and a breath hold. Thus, methods for contrast agent free
assessment of lung perfusion in free breathing, like phase resolved functional
lung (PREFUL) MRI, are desirable. Therefore, in this dual center and dual
vendor feasibility study, we validated PREFUL MRI against DCE MRI in patients
with CF. Perfusion defect percentage (QDP) maps of both methods were
calculated, showing an overlap of 61% for the whole lung. Further, a strong
correlation between QDPPREFUL and QDPDCE was found
(r=0.70, p=0.005).
Introduction
Dynamic contrast enhanced (DCE) MRI is an established technique
for assessment of lung perfusion1,2, but requires the administration
of gadolinium-based intravenous contrast agents. These are reported to cause
side effects like nephrogenic systemic sclerosis in patients with renal failure3.
Furthermore, a breath-hold is required for DCE data acquisition. Therefore,
validation of free-breathing contrast agent free 1H MRI postprocessing
techniques like phase resolved functional lung (PREFUL) MRI4 is
desirable. PREFUL MRI is based on the reconstruction of one respiratory and
cardiac cycle to gain dynamic ventilation and perfusion information from one
data set. A previous study already validated PREFUL MRI with DCE MRI in patients
with chronic obstructive pulmonary disease (COPD)5. However, no feasibility
study of PREFUL MRI across different centers was conducted. Therefore, we validated
PREFUL MRI against DCE MRI in patients with cystic fibrosis (CF) in a dual
center study using scanners from two different vendors.Method
14 CF patients were included in this study (Center 1:
9 patients, Center 2: 5 patients). All patients underwent PREFUL and DCE MRI in
the same imaging session.
Center 1: Data acquisition was performed on a 1.5T
scanner (Siemens Avanto, Siemens Healthcare, Erlangen, Germany) using a spoiled
gradient echo sequence (Field of view (FoV) = 500 x 500mm2, matrix
size = 128 x 128 (interpolated to 256 x 256), TE = 0.82ms – 0.88ms, TR = 3,
flip angle 3°, temporal resolution 192ms, slice thickness 15mm) for PREFUL MRI
and a 3D time-resolved angiography with stochastic trajectories (TWIST)
sequence (FoV = 450 x 366mm2, matrix size 256 x 146, TE = 0.80ms –
0.86ms, TR = 2.37ms – 2.47ms, temporal resolution 1.1s – 1.3s, slice thickness 5mm)
for DCE MRI.
Center 2: Data was acquired on a 1.5T scanner (Signa
HDxt, GE Healthcare, Milwaukee, WI) using a spoiled gradient echo sequence (FoV
= 480 x 480mm2, matrix size = 128 x 128 (interpolated to 256 x 256),
TE = 0.8ms, TR = 2.5ms, flip angle 4°, temporal resolution 374ms, slice thickness
15mm) for PREFUL MRI and a 3D gradient echo sequence with view sharing (TRICKS)
and parallel imaging (R=2) (FoV = 480 x 480mm2, matrix size 120 x
80, TE = 0.69ms, TR = 2.1ms, temporal resolution 0.6s, slice thickness 10mm)
for DCE MRI.
For PREFUL MRI four coronal slices, (two posterior
slices, one tracheal slice centered at the aortic arch, one anterior slice)
were acquired. Due to thinner slice thickness of DCE MRI compared to PREFUL
MRI, compound DCE slices were calculated. PREFUL images were registered to DCE
images. For further analysis, a perfusion-weighted PREFUL phase of the
reconstructed PREFUL cardiac cycle and a corresponding perfusion-weighted DCE
phase of the contrast agent pass of the DCE data set was selected. Then,
perfusion defect percentage (QDP) maps for every slice and the whole lung were
calculated using an individual threshold for each slice as previously descibed5.
Values below this threshold were identified as perfusion defect. To assess the
agreement between QDP-maps, the Dice coefficients of the defect and healthy
regions were calculated on a voxel by voxel level. Furthermore, the spatial
overlap between PREFUL and DCE QDP maps (i.e. percentage of voxels labeled as
perfusion defect or healthy tissue with both methods) was computed and compared
using a paired two-sided Wilcoxon test. The agreement of QDP was further
assessed by Bland Altman analysis and Pearson correlation.Results
Figure 1 shows representative perfusion-weighted
PREFUL and DCE maps for both centers, the corresponding QDP-maps and maps
illustrating the spatial overlap of the QDP-maps, with good visual agreement.
In Table 1 QDPPREFUL and QDPDCE are compared for all
slices. A median spatial overlap of 61% was found for the whole lung. Using
Pearson correlation, a correlation coefficient of r = 0.70 (p = 0.005) was
obtained between QDPPREFUL and QDPDCE for the whole lung.
Bland Altman analysis for the whole lung is shown in Figure 2 (mean difference
-5.4%, 95% confidence interval of mean difference [-12.8%-2.0%], mean difference ± 1.96*standard deviation [-37.5%-26.7%]).Discussion
In accordance with a recent study5, good
spatial agreement was found comparing QDP-maps. Minor differences between QDPDCE
and QDPPREFUL could be explained by the lower resolution of PREFUL
compared to DCE causing widening and blurring of vessels, which may contribute
to imprecise estimation of perfused areas. In addition, the threshold used to
calculate QDP-maps impacts the QDP values.
Further, in this study data acquisition was performed with
different temporal and spatial resolution across centers and imaging methods.
Therefore, depending on the resolution, widening and blurring of vessels as
well as partial volume effects are differently pronounced, affecting the
calculated QDP-maps.Conclusion
In this dual center study feasibility of PREFUL MRI
was shown and validated with DCE MRI using scanners from two different vendors.
This is an important step towards clinical implementation of PREFUL MRI.Acknowledgements
No acknowledgement found.References
-
Ingrisch M, Dietrich O, Attenberger UI, et al. Quantitative pulmonary perfusion
magnetic resonance imaging: influence of temporal resolution and
signal-to-noise ratio. Invest Radiol 2010;45:7-14.
- Kaireit T, Sorrentino SA, Renne J, et al. Functional lung MRI for regional
monitoring of patients with cystic fibrosis. PLoS One 2017;12: e0187483
- Kanda
T, Ishii K, Kawaguchi H, Kitajima K, Takenaka D. High signal intensity in the
dentate nucleus and globus pallidus on unenhanced T1-weighted MR images:
relationship with increasing cumulative dose of a gadolinium-based contrast
material. Radiology 2014;270:834-841.
- Voskrebenzev A, Gutberlet M, Klimeš F, et al. Feasibility of quantitative
regional ventilation and perfusion mapping with phase-resolved functional lung
(PREFUL) MRI in healthy volunteers and COPD, CTEPH, and CF patients. Magn Reson
Med 2018;79:2306-2314.
- Kaireit TF, Voskrebenzev A, Gutberlet M, et al. Comparison of quantitative
regional perfusion-weighted phase resolved functional lung (PREFUL) MRI with
dynamic gadolinium-enhanced regional pulmonary perfusion MRI in COPD patients.
J Magn Reson Imaging 2019;49:1122-1132.