Saunders Adam M, Kim Michael E, Gao Chenyu, Remedios Lucas W, Krishnan Aravind R, Schilling Kurt G, O'Grady Kristin P, Smith Seth A, Landman Bennett A
Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States.
Department of Computer Science, Vanderbilt University, Nashville, TN, United States.
Magn Reson Imaging. 2025 Apr;117:110322. doi: 10.1016/j.mri.2025.110322. Epub 2025 Jan 3.
While typical qualitative T1-weighted magnetic resonance images reflect scanner and protocol differences, quantitative T1 mapping aims to measure T1 independent of these effects. Changes in T1 in the brain reflect structural changes in brain tissue. Magnetization-prepared two rapid acquisition gradient echo (MP2RAGE) is an acquisition protocol that allows for efficient T1 mapping with a much lower scan time per slab compared to multi-TI inversion recovery (IR) protocols. We collect and register B1-corrected MP2RAGE acquisitions with an additional inversion time (MP3RAGE) alongside multi-TI selective inversion recovery acquisitions for four subjects. We use a maximum a posteriori (MAP) T1 estimation method for both MP2RAGE and compare to typical point estimate MP2RAGE T1 mapping, finding no bias from MAP MP2RAGE but a sensitivity to B inhomogeneities with MAP MP3RAGE. We demonstrate a tissue-dependent bias between MAP MP2RAGE T1 estimates and the multi-TI inversion recovery T1 values. To correct this bias, we train a patch-based ResNet-18 to calibrate the MAP MP2RAGE T1 estimates to the multi-TI IR T1 values. Across four folds, our network reduces the RMSE significantly (white matter: from 0.30 ± 0.01 s to 0.11 ± 0.02 s, subcortical gray matter: from 0.26 ± 0.02 s to 0.10 ± 0.02 s, cortical gray matter: from 0.36 ± 0.02 s to 0.17 ± 0.03 s). Using limited paired training data from both sequences, we can reduce the error between quantitative imaging methods and calibrate to one of the protocols with a neural network.
虽然典型的定性T1加权磁共振图像反映了扫描仪和协议差异，但定量T1映射旨在测量不受这些影响的T1。大脑中T1的变化反映了脑组织的结构变化。磁化准备的双快速采集梯度回波（MP2RAGE）是一种采集协议，与多TI反转恢复（IR）协议相比，它允许以更低的每层扫描时间进行高效的T1映射。我们收集并配准了四名受试者的经B1校正的MP2RAGE采集数据以及额外反转时间（MP3RAGE）的采集数据，同时还收集了多TI选择性反转恢复采集数据。我们对MP2RAGE均使用最大后验（MAP）T1估计方法，并与典型的点估计MP2RAGE T1映射进行比较，发现MAP MP2RAGE没有偏差，但MAP MP3RAGE对B不均匀性敏感。我们证明了MAP MP2RAGE T1估计值与多TI反转恢复T1值之间存在组织依赖性偏差。为了纠正这种偏差，我们训练了一个基于补丁的ResNet-18，将MAP MP2RAGE T1估计值校准为多TI IR T1值。在四个折叠中，我们的网络显著降低了均方根误差（白质：从0.30±0.01秒降至0.11±0.02秒，皮质下灰质：从0.26±0.02秒降至0.10±0.02秒，皮质灰质：从0.36±0.02秒降至0.17±0.03秒）。使用来自两个序列的有限配对训练数据，我们可以减少定量成像方法之间的误差，并通过神经网络校准到其中一个协议。
