Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the
microstructure of the rapidly-developing fetal brain. However, fetal motion
during scans and its interaction with magnetic field inhomogeneities result in
artifacts and data scattering across spatial and angular domains. The effects
of those artifacts are more pronounced in high-angular resolution fetal dMRI,
where signal-to-noise ratio is very low. Those effects lead to biased estimates
and compromise the consistency and reliability of dMRI analysis. This work
presents HAITCH, the first and the only publicly available tool to correct and
reconstruct multi-shell high-angular resolution fetal dMRI data. HAITCH offers
several technical advances that include a blip-reversed dual-echo acquisition
for dynamic distortion correction, advanced motion correction for model-free
and robust reconstruction, optimized multi-shell design for enhanced
information capture and increased tolerance to motion, and outlier detection
for improved reconstruction fidelity. The framework is open-source, flexible,
and can be used to process any type of fetal dMRI data including single-echo or
single-shell acquisitions, but is most effective when used with multi-shell
multi-echo fetal dMRI data that cannot be processed with any of the existing
tools. Validation experiments on real fetal dMRI scans demonstrate significant
improvements and accurate correction across diverse fetal ages and motion
levels. HAITCH successfully removes artifacts and reconstructs high-fidelity
fetal dMRI data suitable for advanced diffusion modeling, including fiber
orientation distribution function estimation. These advancements pave the way
for more reliable analysis of the fetal brain microstructure and tractography
under challenging imaging conditions.

HAITCH 是第一个也是唯一一个公开可用的工具，用于纠正和重建多壳高角分辨率的胎儿弥散磁共振成像数据，能够消除伪迹并重建适用于高级扩散建模的胎儿 dMRI 数据，为在具有挑战性的成像条件下更可靠地分析胎儿大脑微结构和胼胝体成像铺平了道路。

HAITCH: 胎儿多壳扩散加权 MRI 的形变和运动校正框架

HAITCH: A Framework for Distortion and Motion Correction in Fetal  Multi-Shell Diffusion-Weighted MRI

The quality of fetal MRI is significantly affected by unpredictable and
substantial fetal motion, leading to the introduction of artifacts even when
fast acquisition sequences are employed. The development of 3D real-time fetal
pose estimation approaches on volumetric EPI fetal MRI opens up a promising
avenue for fetal motion monitoring and prediction. Challenges arise in fetal
pose estimation due to limited number of real scanned fetal MR training images,
hindering model generalization when the acquired fetal MRI lacks adequate pose.
In this study, we introduce FetalDiffusion, a novel approach utilizing a
conditional diffusion model to generate 3D synthetic fetal MRI with
controllable pose. Additionally, an auxiliary pose-level loss is adopted to
enhance model performance. Our work demonstrates the success of this proposed
model by producing high-quality synthetic fetal MRI images with accurate and
recognizable fetal poses, comparing favorably with in-vivo real fetal MRI.
Furthermore, we show that the integration of synthetic fetal MR images enhances
the fetal pose estimation model's performance, particularly when the number of
available real scanned data is limited resulting in 15.4% increase in PCK and
50.2% reduced in mean error. All experiments are done on a single 32GB V100
GPU. Our method holds promise for improving real-time tracking models, thereby
addressing fetal motion issues more effectively.

该研究提出了一种使用条件扩散模型生成可控姿态的三维合成胎儿磁共振成像方法，并采用辅助姿态级损失来增强模型性能。实验证明，该方法生成的高质量合成胎儿磁共振成像能够准确识别胎儿姿态，并与真实胎儿磁共振成像比较具有竞争优势。此外，通过集成合成胎儿磁共振成像，可以提高胎儿姿态估计模型的性能，尤其适用于数据有限的情况下，相对于真实扫描数据，能够提高 15.4％的 PCK 值和减少 50.2％的平均误差。该方法有望更有效地解决胎儿运动问题，从而提高实时追踪模型的性能。