Lale Umutlu1, Jasmin Jaeger1, Matthias Nittka2, Stephan Kannengiesser 2, Josef Pfeuffer 2, Gregor Koerzdoerfer 2, Rainer Kirsch2, Florian Meise2, Harald Quick3,4, Vikas Gulani5, Mark Griswold5, Ken Herrmann6, Marcel Gratz3,4, and Michael Forsting1
1Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany, 2Siemens Healthcare GmbH, Erlangen, Germany, 3University Duisburg-Essen, Erwin L. Hahn Institute for MR Imaging, Essen, Germany, 4High Field and Hybrid MR Imaging, University Hospital Essen, Germany, 5Case Western Reserve University, Cleveland, OH, 6Department of Nuclear Medicine, University Hospital Essen, Essen, Germany
Synopsis
The successful
implementation of integrated PET/MR systems has enabled a unique platform for
simultaneous multi-parametric imaging comprising morphologic, functional and
metabolic features of pathologic tissue. MR Fingerprinting has been recently
presented as a robust and fast framework for
simultaneous accurate quantification of multiple MR tissue properties.
The results of our study, combining tissue characterization based on PET/MR
imaging and MR Fingerprinting, indicate a correlation between tumor grading and
changes in tissue features, demonstrating the high diagnostic potential of this
novel approach for multi-parametric tissue characterization.
Introduction
11C-Methionine PET imaging has been shown to provide a
high detection rate of brain tumors as well as improved assessment of potential
tumor recurrences. With the successful introduction of integrated PET/MR
systems into scientific and clinical imaging, a new platform for simultaneous
multiparametric assessment of morphologic, functional and metabolic features of
brain tumors has been established (1). As an additional parametric feature,
in-vivo relaxometry offers valuable information on tissue characterization. Magnetic
Resonance Fingerprinting (MRF) has been shown to provide simultaneous
quantification of multiple tissue properties, with initial demonstration in
relaxometry (2,3). The aim of this study was to investigate a novel approach of multiparametric
tissue characterization of brain tumors based on morphologic, functional,
metabolic and relaxometric properties, pulling together quantitative data from
different imaging modalities. Methods
Imaging: 10 patients (2 f, 3 m; mean age 45.2, age range
25-62) with suspected brain tumors or suspected tumor relapse were enrolled in
this trial. 8 of these 10 patients showed tumor / tumor relapse, 2
post-therapeutic changes. Examinations were performed on an integrated 3-Tesla PET/MR
system (Biograph mMR, Siemens Healthcare, Erlangen, Germany). PET and MR
imaging was obtained simultaneously in one bed position (20 minutes) after injection of 11C-Methionine
(mean ± SD, 752.1 MBq; ± 254.2 MBq). The (PET)MRI protocol included the
following sequences: (1) T1-weighted TIRM (repetition time [TR], 2000 ms; echo
time [TE], 13 ms; slice thickness (ST), 5 mm), (2) 3D-FLAIR (TR 5000 ms, TE 395
ms, ST 1 mm), (3) diffusion-weighted imaging (TR 7900 ms; TE 101 ms; b-values:
0,1.000 s/mm2 ; ST 5mm), (4) susceptibility-weighted imaging (TR 26
ms, TE 20 ms, ST 2 mm), (5) post contrast 3D-MPRAGE imaging (0.5 mmol/kg bw
gadoterate meglumine; Dotarem, Guerbet) (TR 1790 ms, TE 2.67 ms, ST 1 mm), (6)
single-voxel spectroscopy (TR 2000 ms, TE 135 ms, ST 15 x 15 x15mm3).
MR Fingerprinting was performed on a 3-Tesla MR
scanner (MAGNETOM Skyra, Siemens Healthcare) utilizing a prototype
implementation of the MRF technique (1). Imaging was acquired through the representative
solid areas of the tumors, and quantitative T1 and T2 maps were generated.
Multiple regions of interest (ROIs) were manually outlined at predetermined
locations of the tumors, and T1 and T2 relaxation values at these sites were
extracted. Each slice acquisition amounted to a total of approx. 40 seconds.
Evaluation: Data analysis was
performed by an experienced radiologist and a nuclear medicine physician. To quantify the metabolic activity of
11C-Methionine-avid lesions, maximum standardized uptake values (SUVmax)
were determined by drawing a 3-dimensional isocontour on PET/MR images. In
addition, the ratio to uptake in normal
brain parenchyma of the contralateral normal cortex was calculated (T/N ratio).
ADC values were calculated based on ROI analysis. Relaxation times of the
suspicious lesions were measured by manually placing regions of interest in the
calculated parametric maps. Results
The following tumor entities were detected in the
investigated patient cohort: (1) primary low-grade glioma grade 2 (n=1), (2)
relapse of a low-grade glioma grade 2 (n=1), (3) primary astrocytoma grade 3
(n=1), (4) multifocal astrocytoma grade 3-4 (n=1), (5) primary glioblastoma
(n=3), (6) relapse of glioblastoma (n=1). An increase in malignancy from
low-grade (grade 1-2) to high-grade glioma (grade 3,4) was associated with
increasing / positive PET uptake, contrast enhancement, increasing values in
Choline and Choline/Creatine as well as increasing T1 and T2 values. While
low-grade astrocytoma did not show any pathologic tracer uptake or contrast enhancement,
grade 3 gliomas showed increasing tracer uptake and increasing T1 and T2 values
in MRF, respectively (mean values grade 3 tumors: SUVmax:1.8 T/N:
1.3; T1: 1442/ T2: 122.9; mean values grade 4 tumors: SUVmax: 3.4
T/N: 2.1; T1: 1752/ T2: 93.8). In one patient, the diagnosis based solely on
the MRI data was altered from low-grade astrocytoma (grade 2; no contrast
enhancement) to high-grade astrocytoma (grade 3) due to PET uptake, showing
correspondingly increased T1 and T2 relaxation values. Discussion & Conclusion
This study presents preliminary results of an enhanced
multi-parametric approach to tissue characterization of brain tumors. The
results of this novel approach of multi-parametric PET/MR imaging of brain
tumors including MR Fingerprinting demonstrate corresponding changes in tissue
property based on the combined assessment of morphologic, functional, metabolic and
relaxometric properties, indicating a correlation to the grading of brain
tumors and leveraging tissue characterization to a unique level of
multi-parametric assessment. Acknowledgements
No acknowledgement found.References
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