Clinical Sciences Lund, Lund University, Lund, Sweden.
Department of Information Technology, Uppsala University, Uppsala, Sweden.
J Magn Reson. 2021 Jul;328:106991. doi: 10.1016/j.jmr.2021.106991. Epub 2021 Apr 23.
Diffusion MRI uses magnetic field gradients to sensitize the signal to the random motion of spins. In addition to the prescribed gradient waveforms, background field gradients contribute to the diffusion weighting and thereby cause an error in the measured signal and consequent parameterization. The most prominent contribution to the error comes from so-called 'cross-terms.' In this work we present a novel gradient waveform design that enables diffusion encoding that cancels such cross-terms and yields a more accurate measurement. This is achieved by numerical optimization that maximizes encoding efficiency with a simultaneous constraint on the 'cross-term sensitivity' (c = 0). We found that the optimized cross-term-compensated waveforms were superior to previous cross-term-compensated designs for a wide range of waveform types that yield linear, planar, and spherical b-tensor encoding. The efficacy of the proposed design was also demonstrated in practical experiments using a clinical MRI system. The sensitivity to cross-terms was evaluated in a water phantom with a folded surface which provoked strong internal field gradients. In every comparison, the cross-term-compensated waveforms were robust to the effects of background gradients, whereas conventional designs were not. We also propose a method to measure background gradients from diffusion-weighted data, and show that cross-term-compensated waveforms produce parameters that are markedly less dependent on the background compared to non-compensated designs. Finally, we also used simulations to show that the proposed cross-term compensation was robust to background gradients in the interval 0 to 3 mT/m, whereas non-compensated designs were impacted in terms of a severe signal and parameter bias. In conclusion, we have proposed and demonstrated a waveform design that yields efficient cross-term compensation and facilitates accurate diffusion MRI in the presence of static background gradients regardless of their amplitude and direction. The optimization framework is compatible with arbitrary spin-echo sequence timing and RF events, b-tensor shapes, suppression of concomitant gradient effects and motion encoding, and is shared in open source.
扩散 MRI 使用磁场梯度使自旋的随机运动对信号敏感。除了规定的梯度波形外，背景场梯度也会对扩散加权产生影响，从而导致测量信号出现误差，并对后续参数化产生影响。最显著的误差来源是所谓的“交叉项”。在这项工作中，我们提出了一种新的梯度波形设计，该设计能够实现扩散编码，从而消除这些交叉项，并获得更准确的测量结果。这是通过数值优化实现的，该优化通过同时限制“交叉项灵敏度”（c=0）最大化编码效率。我们发现，优化后的交叉项补偿波形在广泛的波形类型下表现优于之前的交叉项补偿设计，这些波形类型可以产生线性、平面和球形 b 张量编码。该设计的有效性还在使用临床 MRI 系统的实际实验中得到了验证。在一个具有折叠表面的水模体中评估了对交叉项的敏感性，该表面会引起强烈的内部场梯度。在每一次比较中，交叉项补偿波形都对背景梯度的影响具有鲁棒性，而传统设计则不然。我们还提出了一种从扩散加权数据中测量背景梯度的方法，并表明与非补偿设计相比，交叉项补偿波形产生的参数对背景的依赖性明显较小。最后，我们还使用模拟来证明所提出的交叉项补偿在背景梯度为 0 到 3 mT/m 的范围内具有鲁棒性，而未补偿的设计则会受到严重的信号和参数偏差的影响。总之，我们提出并验证了一种波形设计，该设计能够实现高效的交叉项补偿，并在存在静态背景梯度的情况下促进准确的扩散 MRI，无论其幅度和方向如何。该优化框架与任意自旋回波序列定时和 RF 事件、b 张量形状、伴随梯度效应和运动编码的抑制兼容，并在开源中共享。
