发表机构
Department of Radiology, Lausanne University Hospital and University of Lausanne; CIBM Center for Biomedical Imaging; Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM) and TUM University Hospital; Department of Computing, Imperial College London; Munich Center for Machine Learning (MCML); Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London; Institut de Neurosciences de la Timone, UMR 7289, CNRS, Aix-Marseille Université; Department Woman-Mother-Child, Lausanne University Hospital（放射科，洛桑大学医院和洛桑大学; 生物医学成像中心; 人工智能在医疗和医学中的 chair，慕尼黑技术大学（TUM）和慕尼黑大学医院; 计算系，伦敦帝国理工学院; 慕尼黑机器学习中心; 生物医学工程系，伦敦国王学院生物医学工程与成像科学学校; Timone 神经科学研究所，UMR 7289，CNRS，阿维尼昂-马赛大学; 妇女-母亲-儿童部门，洛桑大学医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对现有胎儿脑切片到体积重建方法在非临床回波时间受限问题，提出PRIME-SVR隐式神经表示框架，通过全连接网络建模、估计采集退化，利用正则化加强耦合，实现自监督重建，提升了重建质量并加速定量成像。
AI 中文摘要
切片到体积重建（SVR）是从多个方向采集的运动受损二维MRI切片堆栈中获取高分辨率（HR）三维胎儿脑体积的标准方法。现有SVR方法仅针对临床范围的回波时间（TE）进行了优化和验证，限制了其在非临床TE下的使用，且与定量T2映射不兼容。我们提出了PRIME-SVR，这是第一个用于多回波MRI联合HR重建的隐式神经表示（INR）框架。一个全连接网络对从空间坐标到跨TE信号强度的连续函数进行建模，另一个网络估计特定切片的采集退化。通过基于Bloch方程的正则化来强制跨TE相干性，惩罚与预期T2衰减的偏差，并采用自适应加权来加强对退化堆栈的耦合。该方法是完全自监督的。我们在来自两个中心、两个供应商和两个场强（1.5T和0.55T）的39个体内胎儿采集数据（13名受试者x 3个TE）上验证了PRIME-SVR。与现有技术相比，PRIME-SVR将重建清晰度提高了47%，解剖学准确性提高了30%，跨TE结构一致性提高了14%。它能够在以前SVR无法达到的晚期TE进行重建，在0.55T下生成了第一个各向同性0.8mm的T2映射，以及第一个基于INR的SVR衍生的T2映射。PRIME-SVR还通过减少多TE重建所需的数据来加速定量成像，将采集时间从15分钟缩短到10分钟，同时在白质和深部灰质中保持T2准确性在1.7%以内，对于高质量采集，采集时间缩短到5分钟，平均T2误差为2.3%。
英文摘要
Slice-to-volume reconstruction (SVR) is the standard method for obtaining high-resolution (HR) 3D fetal brain volumes from motion-corrupted 2D MRI slice stacks acquired in multiple orientations. Existing SVR methods are optimized and validated only for clinical-range echo times (TEs), limiting their use at non-clinical TEs and making them incompatible with quantitative T2 mapping, a protocol- and center-independent biomarker of fetal brain maturation requiring HR reconstructions across multiple TEs. We present PRIME-SVR, the first implicit neural representation (INR) framework for joint HR reconstruction from multi-echo MRI. A single fully connected network models a continuous function from spatial coordinates to signal intensities across TEs, while a second network estimates slice-specific acquisition degradations. Cross-TE coherence is enforced via a Bloch equation-derived regularization penalizing deviations from expected T2 decay, with adaptive weighting that strengthens coupling for degraded stacks. The method is fully self-supervised. We validate PRIME-SVR on 39 in vivo fetal acquisitions (13 subjects x 3 TEs) from two centers, two vendors, and two field strengths (1.5 T and 0.55 T). Compared to state-of-the-art SVR, PRIME-SVR improves reconstruction sharpness by 47%, anatomical accuracy by 30%, and cross-TE structural consistency by 14%. It enables reconstruction at late TEs previously inaccessible to SVR, yielding the first 0.8 mm isotropic T2 maps at 0.55 T and the first T2 maps derived from INR-based SVR. PRIME-SVR also accelerates quantitative imaging by reducing the data needed for multi-TE reconstruction, cutting acquisition from 15 to 10 minutes while keeping T2 accuracy within 1.7% in white and deep gray matter, or to 5 minutes with a mean T2 error of 2.3% for high-quality acquisitions.
