Accelerated white matter lesion analysis based on simultaneous T₁ and T₂^∗ quantification using magnetic resonance fingerprinting and deep learning

Abstract

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Lesion probability mapping in MS patients using a regression network on MR Fingerprinting

“…Additionally, the network was able to perform the tasks of reconstruction, denoising, distortion correction, and segmentation within a single architecture with promising accuracy. T 1 and T 2 * maps as well as the NAWM-and GM-probability maps showed good agreement as also previously reported [16] with a mean relative error of 5.2% for T 1 and T 2 * and mean dice coefficients of higher than 0.9 for NAWM and GM. The use of a single network is advantageous due to the faster and simpler reconstruction compared with several networks for the different processing steps although the accuracy seemed to be slightly compromised.…”

“…As previously reported [16], an MRF sequence based on echo-planar imaging was acquired across 6 healthy subjects and 18 patient with WM lesions at a 3T scanner (Magnetom Skyra, Siemens Healthineers; site 1) and 24 patient with WM lesions at a 3T scanner (Magnetom Prisma, Siemens Healthineers; site 2). The sequence parameters for both scanners were FOV = 240 x 240 mm, in-plane resolution = 1 × 1mm 2 , slice thickness = 2 mm, GRAPPA factor = 3, partial Fourier = 5/8, varying flip angle α (34-86 • ), TE (16-76.5 ms), TR (3530 -6370 ms).…”

“…[10,11] Rieger et al proposed an MRF method to quantify T 1 and T 2 * with an echo-planar imaging (EPI) readout, [12] which showed promising results in renal and neural applications. [13][14][15][16] The fact that only conventional undersampling factors lead to only slightly corrupted magnitude data reduces the time for reconstruction and increases its robustness. However, a major drawback of MRF is the tradeoff between reconstruction time and accuracy.…”

“…[27][28][29][30][31][32] In previous work, a CNN was used for the denoising, distortion correction, reconstruction, and generation of NAWM and gray matter (GM) probability maps yielding results comparable to conventional methods in a fraction of time. [16] The proposed architecture combined several post-processing tasks, making the application fast and easy. However, the WM lesions have to be segmented for further analysis, which is always time-consuming and suffers from high intra-and inter-observer variabilities.…”

“…[37] This could provide more information about lesion geometry, structure, and changes similar to lesion probability mapping. [2,3,38] In this work, we use the U-net as previously reported [16] to predict WM lesion probability maps by training the CNN with the manual annotated binary lesion masks while combining several processing steps.…”