Kebiri Hamza, Gholipour Ali, Lin Rizhong, Vasung Lana, Calixto Camilo, Krsnik Željka, Karimi Davood, Bach Cuadra Meritxell
CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
Med Image Anal. 2024 Jul;95:103186. doi: 10.1016/j.media.2024.103186. Epub 2024 Apr 25.
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.
扩散加权磁共振成像（dMRI）被广泛用于评估脑白质。纤维方向分布函数（FODs）是表示白质纤维方向和密度的常用方法。然而，使用标准的FOD计算方法时，准确估计需要大量测量，而新生儿和胎儿通常无法获得这么多测量数据。我们建议通过使用深度学习方法将少至六个扩散加权测量映射到目标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得到的FOD作为目标。广泛的定量评估表明，这种新的深度学习方法使用的测量数据显著减少，与约束球面反卷积等标准方法以及两种先进的深度学习方法相比，取得了相当或更优的结果。对于分别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符合率分别为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符合指标。此外，我们在两个新生儿和胎儿的临床外部数据集上展示了这种新的深度学习方法在不同扫描仪、采集协议和解剖结构上的通用性。我们使用死后组织学数据验证了首次通过深度学习成功估计的胎儿FOD。我们的结果显示了深度学习在从体内dMRI测量计算发育中大脑的纤维方向密度方面的优势，由于采集时间受限，体内dMRI测量往往非常有限。我们的发现还突出了dMRI在探测发育中大脑微观结构方面的内在局限性。
