Accelerated Simultaneous T 2 and T 2 * Mapping of Multiple Sclerosis Lesions Using Compressed Sensing Reconstruction of Radial RARE-EPI MRI.
Carl J J HerrmannLudger StarkeJason M MillwardJoseph KuchlingFriedemann PaulThoralf NiendorfPublished in: Tomography (Ann Arbor, Mich.) (2023)
(1) Background: Radial RARE-EPI MRI facilitates simultaneous T 2 and T 2 * mapping (2in1-RARE-EPI). With modest undersampling (R = 2), the speed gain of 2in1-RARE-EPI relative to Multi-Spin-Echo and Multi-Gradient-Recalled-Echo references is limited. Further reduction in scan time is crucial for clinical studies investigating T 2 and T 2 * as imaging biomarkers. We demonstrate the feasibility of further acceleration, utilizing compressed sensing (CS) reconstruction of highly undersampled 2in1-RARE-EPI. (2) Methods: Two-fold radially-undersampled 2in1-RARE-EPI data from phantoms, healthy volunteers ( n = 3), and multiple sclerosis patients ( n = 4) were used as references, and undersampled (R extra = 1-12, effective undersampling R eff = 2-24). For each echo time, images were reconstructed using CS-reconstruction. For T 2 (RARE module) and T 2 * mapping (EPI module), a linear least-square fit was applied to the images. T 2 and T 2 * from CS-reconstruction of undersampled data were benchmarked against values from CS-reconstruction of the reference data. (3) Results: We demonstrate accelerated simultaneous T 2 and T 2 * mapping using undersampled 2in1-RARE-EPI with CS-reconstruction is feasible. For R extra = 6 (TA = 01:39 min), the overall MAPE was ≤8% (T 2 *) and ≤4% (T 2 ); for R extra = 12 (TA = 01:06 min), the overall MAPE was <13% (T 2 *) and <5% (T 2 ). (4) Conclusion: Substantial reductions in scan time are achievable for simultaneous T 2 and T 2 * mapping of the brain using highly undersampled 2in1-RARE-EPI with CS-reconstruction.
Keyphrases
- multiple sclerosis
- high resolution
- contrast enhanced
- magnetic resonance
- magnetic resonance imaging
- electronic health record
- high density
- diffusion weighted imaging
- deep learning
- end stage renal disease
- chronic kidney disease
- white matter
- mass spectrometry
- convolutional neural network
- optical coherence tomography
- prognostic factors
- machine learning
- artificial intelligence
- functional connectivity
- patient reported outcomes