Hongyu Li^{1}, Mingrui Yang^{2}, Jeehun Kim^{2}, Ruiying Liu^{1}, Chaoyi Zhang^{1}, Peizhou Huang^{1}, Sunil Kumar Gaire^{1}, Dong Liang^{3}, Xiaojuan Li^{2}, and Leslie Ying^{1}

^{1}Department of Biomedical Engineering, Department of Electrical Engineering, The State University of New York at Buffalo, Buffalo, NY, United States, ^{2}Program of Advanced Musculoskeletal Imaging (PAMI), Cleveland Clinic, Cleveland, OH, United States, ^{3}Paul C. Lauterbur Research Center for Biomedical Imaging, Medical AI research center, SIAT, CAS, Shenzhen, China

This abstract presents a deep learning method to generate T1rho and T2 relaxation maps simultaneously within one scan. The method uses 3D deep convolutional neural networks to exploit the nonlinear relationship between and within the combined subsampled T1rho and T2-weighted images and the combined T1rho and T2 maps, bypassing conventional fitting models. Compare with separated trained relaxation maps, this new method also exploits the autocorrelation and cross-correlation between subsampled echoes. Experiments show that the proposed method is capable of generating T1rho and T2 maps simultaneously from only 3 subsampled echoes within one scan with quantification results comparable to reference maps.

Ten sets of knee data were collected at a 3T MR scanner (Prisma, Siemens Healthineers) with a 1Tx/15Rx knee coil (QED), using a magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots (MAPSS) T1ρ and T2 quantification sequences (time of spin-lock [TSLs] of 0, 10, 20, 30, 40, 50, 60, 70ms, spin-lock frequency 500Hz, Preparation TEs of 0, 9.7, 21.3, 32.9, 44.5, 56.1, 67.6, 79.2 ms, matrix size 160×320×8×24 [PE×FE×Echo×Slice], FOV 14cm, and slice thickness 4mm). Among these data, 8 sets were used to train the proposed MSCNN and 2 for testing. For echo subsampling, the 1st, 3rd and 6th echoes are selected. For images within selected echoes, 2D Poisson random sampling was used with an acceleration factor of 2 for further reduction. Hardware specification: i9 7980XE; 64 GB; GPU 2x NVIDIA Quadro P6000. The training takes around 15 hours. It takes only 0.08 seconds to generate a complete set of combined T1rho and T2 maps using the learned network while it takes around 30 min to get both maps using conventional exponential decay fitting.

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Fig.1. Flowchart of conventional exponential
model fitting and Model Skipped Convolutional Neural Network (MSCNN).

Fig.2. 3D convolutional kernels used in MSCNN.

Fig.3. T1rho/T2 maps from 3 echoes using MSCNN with a combined reduction
factor of 10.66, and the reference T1rho/T2 maps from two separate scans, each
with 8 fully sampled echoes.