A Comparison of 7 Tesla MR Spectroscopic Imaging and 3 Tesla MR Fingerprinting for Tumor Localization in Glioma Patients.
Philipp LazenPedro Lima CardosoSukrit SharmaCornelius CadrienThomas Roetzer-PejrimovskyJulia FurtnerBernhard StrasserLukas HingerlAlexandra LipkaMatthias PreusserWolfgang MarikWolfgang BognerGeorg WidhalmKarl RoesslerSiegfried TrattnigGilbert J HangelPublished in: Cancers (2024)
This paper investigated the correlation between magnetic resonance spectroscopic imaging (MRSI) and magnetic resonance fingerprinting (MRF) in glioma patients by comparing neuro-oncological markers obtained from MRSI to T1/T2 maps from MRF. Data from 12 consenting patients with gliomas were analyzed by defining hotspots for T1, T2, and various metabolic ratios, and comparing them using Sørensen-Dice similarity coefficients (DSCs) and the distances between their centers of intensity (COIDs). The median DSCs between MRF and the tumor segmentation were 0.73 (T1) and 0.79 (T2). The DSCs between MRSI and MRF were the highest for Gln/tNAA (T1: 0.75, T2: 0.80, tumor: 0.78), followed by Gly/tNAA (T1: 0.57, T2: 0.62, tumor: 0.54) and tCho/tNAA (T1: 0.61, T2: 0.58, tumor: 0.45). The median values in the tumor hotspot were T1 = 1724 ms, T2 = 86 ms, Gln/tNAA = 0.61, Gly/tNAA = 0.28, Ins/tNAA = 1.15, and tCho/tNAA = 0.48, and, in the peritumoral region, were T1 = 1756 ms, T2 = 102 ms, Gln/tNAA = 0.38, Gly/tNAA = 0.20, Ins/tNAA = 1.06, and tCho/tNAA = 0.38, and, in the NAWM, were T1 = 950 ms, T2 = 43 ms, Gln/tNAA = 0.16, Gly/tNAA = 0.07, Ins/tNAA = 0.54, and tCho/tNAA = 0.20. The results of this study constitute the first comparison of 7T MRSI and 3T MRF, showing a good correspondence between these methods.
Keyphrases
- magnetic resonance
- mass spectrometry
- multiple sclerosis
- ms ms
- end stage renal disease
- ejection fraction
- newly diagnosed
- high resolution
- chronic kidney disease
- prognostic factors
- computed tomography
- magnetic resonance imaging
- high grade
- rectal cancer
- photodynamic therapy
- molecular dynamics simulations
- single molecule
- minimally invasive
- patient reported
- convolutional neural network
- atomic force microscopy