SUFFICIENT：一种用于高分辨率三维各向同性胎儿脑MRI重建的扫描特定无监督深度学习框架
SUFFICIENT: A scan-specific unsupervised deep learning framework for high-resolution 3D isotropic fetal brain MRI reconstruction
AI总结：
提出一种扫描特定的无监督深度学习框架SUFFICIENT，通过迭代SVR和SRR实现高分辨率各向同性胎儿脑MRI重建，无需外部训练数据，在模拟和临床数据上优于现有方法。
摘要：
从运动伪影污染的二维切片中高质量重建三维胎儿脑MRI对于临床诊断至关重要。可靠的基于切片到体素配准（SVR）的运动校正和超分辨率重建（SRR）方法是必不可少的。与传统方法相比，深度学习（DL）在增强SVR和SRR方面显示出潜力。然而，它需要大规模的外部训练数据集，这些数据集对于临床胎儿MRI难以获取。为了解决这个问题，我们提出了一种无监督的迭代SVR-SRR框架，用于各向同性高分辨率（HR）体素重建。具体而言，SVR被公式化为一个函数，将二维切片和三维目标体素映射到刚性变换矩阵，该矩阵将切片对齐到目标体素中的底层位置。该函数由卷积神经网络参数化，通过最小化预测位置处的体素切片与输入切片之间的差异来训练。在SRR中，嵌入在深度图像先验框架内的解码网络与全面的图像退化模型相结合，以生成高分辨率体素。深度图像先验框架提供了局部一致性先验，以指导HR体素的重建。通过执行前向退化模型，通过最小化预测切片与观测切片之间的损失来优化HR体素。在大幅度运动污染的模拟数据和临床数据上进行的综合实验表明，所提出的框架优于最先进的胎儿脑重建框架。
英文摘要：
High-quality 3D fetal brain MRI reconstruction from motion-corrupted 2D slices is crucial for clinical diagnosis. Reliable slice-to-volume registration (SVR)-based motion correction and super-resolution reconstruction (SRR) methods are essential. Deep learning (DL) has demonstrated potential in enhancing SVR and SRR when compared to conventional methods. However, it requires large-scale external training datasets, which are difficult to obtain for clinical fetal MRI. To address this issue, we propose an unsupervised iterative SVR-SRR framework for isotropic HR volume reconstruction. Specifically, SVR is formulated as a function mapping a 2D slice and a 3D target volume to a rigid transformation matrix, which aligns the slice to the underlying location in the target volume. The function is parameterized by a convolutional neural network, which is trained by minimizing the difference between the volume slicing at the predicted position and the input slice. In SRR, a decoding network embedded within a deep image prior framework is incorporated with a comprehensive image degradation model to produce the high-resolution (HR) volume. The deep image prior framework offers a local consistency prior to guide the reconstruction of HR volumes. By performing a forward degradation model, the HR volume is optimized by minimizing loss between predicted slices and the observed slices. Comprehensive experiments conducted on large-magnitude motion-corrupted simulation data and clinical data demonstrate the superior performance of the proposed framework over state-of-the-art fetal brain reconstruction frameworks.
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