Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.

中文翻译：

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利用扩散 MRI 深度学习评估发育中大脑的微观结构：新生儿和胎儿研究


弥散加权磁共振成像（dMRI）广泛用于评估大脑白质。纤维取向分布函数（FOD）是表示白质纤维的取向和密度的常用方法。然而，使用标准的 FOD 计算方法，准确的估计需要大量的测量数据，而这些测量数据通常无法用于新生儿和胎儿。我们建议通过使用深度学习方法将多达六个扩散加权测量值映射到目标 FOD 来克服这一限制。为了训练模型，我们使用通过多壳高角分辨率测量计算出的 FOD 作为目标。广泛的定量评估表明，新的深度学习方法使用明显更少的测量，取得了与约束球形反卷积等标准方法和两种最先进的深度学习方法相当或更好的结果。对于具有一根和两根纤维的体素，我们的方法在纤维数量方面的一致率分别为 77.5% 和 22.2%，比其他深度学习方法高 3% 和 5.4%，角度误差为 10 °和20°，比其他深度学习方法低6°和5°。为了确定评估我们方法性能的基线，我们使用密集采样的新生儿数据计算一致性指标。此外，我们在新生儿和胎儿的两个临床外部数据集上证明了新的深度学习方法在扫描仪、采集协议和解剖学方面的通用性。我们使用死后组织学数据验证了胎儿 FOD，这是通过深度学习首次成功估计的。 我们的结果表明，深度学习在通过体内 dMRI 测量计算发育中大脑的纤维取向密度方面具有优势，但由于采集时间有限，这些测量通常非常有限。我们的研究结果还强调了 dMRI 在探测发育中的大脑微观结构方面的内在局限性。