Time-efficient combined morphologic and quantitative joint MRI based on clinical image contrasts – An exploratory in-situ study of standardized cartilage defects
paper_authors: Teresa Lemainque, Nicola Pridöhl, Shuo Zhang, Marc Huppertz, Manuel Post, Can Yüksel, Masami Yoneyama, Andreas Prescher, Christiane Kuhl, Daniel Truhn, Sven Nebelung
results: 研究发现，在创建损伤后，软骨中的刺激时间增加了，而bone texture和软骨恢复时间也发生了变化。但是，MIXTURE序列和参照序列之间的差异不是很大。Abstract
OBJECTIVES: Quantitative MRI techniques such as T2 and T1$\rho$ mapping are beneficial in evaluating cartilage and meniscus. We aimed to evaluate the MIXTURE (Multi-Interleaved X-prepared Turbo-Spin Echo with IntUitive RElaxometry) sequences that provide morphologic images with clinical turbo spin-echo (TSE) contrasts and additional parameter maps versus reference TSE sequences in an in-situ model of human cartilage defects. MATERIALS AND METHODS: Prospectively, standardized cartilage defects of 8mm, 5mm, and 3mm diameter were created in the lateral femora of 10 human cadaveric knee specimens (81$\pm$10 years, nine male/one female). Using a clinical 3T MRI scanner and knee coil, MIXTURE sequences combining (i) proton-density weighted fat-saturated (PD-w FS) images and T2 maps and (ii) T1-weighted images and T1$\rho$ maps were acquired before and after defect creation, alongside the corresponding 2D TSE and 3D TSE reference sequences. Defect delineability, bone texture, and cartilage relaxation times were quantified. Inter-sequence comparisons were made using appropriate parametric and non-parametric tests. RESULTS: Overall, defect delineability and texture features were not significantly different between the MIXTURE and reference sequences. After defect creation, relaxation times increased significantly in the central femur (for T2) and all regions combined (for T1$\rho$). CONCLUSION: MIXTURE sequences permit time-efficient simultaneous morphologic and quantitative joint assessment based on clinical image contrasts. While providing T2 or T1$\rho$ maps in clinically feasible scan time, morphologic image features, i.e., cartilage defect delineability and bone texture, were comparable between MIXTURE and corresponding reference sequences.
摘要
目标：量化MRI技术，如T2和T1ρ图像，有助于评估软骨和股骨。我们想要评估MIXTURE（多元排序X准备扩散螺旋共振成像）序列，它们提供了辐射学像和临床旋转普朗共振（TSE）对比的形态图像，以及附加参数图像，与参照TSE序列进行比较。材料和方法：我们使用了一台临床3T MRI仪器和膝关节磁共振器，在人体腓骨上创造了标准化软骨损伤（半径8毫米，3毫米和5毫米）。使用MIXTURE序列，我们获得了杂合PD-wFS图像和T2图像，以及T1-weighted图像和T1ρ图像，并与相应的2D TSE和3D TSE参照序列一起进行了收集。我们评估了损伤定义性、骨Texture和软骨弹性时间。我们使用了适当的 Parametric 和非 Parametric 测试进行对比。结果：总的来说，损伤定义性和Texture特征没有显著差异 междуMIXTURE和参照序列。在中部腓骨（T2）和所有区域（T1ρ）中，创伤后弹性时间明显增加。结论：MIXTURE序列允许在临床可行的扫描时间内同时进行形态和量化骨关节评估，基于临床图像对比的辐射学图像。虽然提供了T2或T1ρ图像，但形态图像特征，例如软骨损伤定义性和骨Texture，与MIXTURE序列和相应的参照序列相比没有显著差异。
paper_authors: Yang Gao, Zhuang Xiong, Shanshan Shan, Yin Liu, Pengfei Rong, Min Li, Alan H Wilman, G. Bruce Pike, Feng Liu, Hongfu Sun for:* 这项研究旨在解决深度学习（DL）扫描仪磁场方向变化的限制，以提高Quantitative susceptibility mapping（QSM）重构问题的精度和稳定性。methods:* 提出了一种Orientation-Adaptive Latent Feature Editing（OA-LFE）模块，可以学习探测探针方向向量的编码，并将其直接integrated into deep networks的 latent features中。results:* 在 simulated和实验室人脑 Dataset上，与多种已有的 QSM重构框架进行了比较，并证明了iQSM+可以在不同的探针方向下重构QSM图像，并且图像的准确性得到了显著改进。Abstract
Quantitative susceptibility mapping (QSM) is a post-processing technique for deriving tissue magnetic susceptibility distribution from MRI phase measurements. Deep learning (DL) algorithms hold great potential for solving the ill-posed QSM reconstruction problem. However, a significant challenge facing current DL-QSM approaches is their limited adaptability to magnetic dipole field orientation variations during training and testing. In this work, we propose a novel Orientation-Adaptive Latent Feature Editing (OA-LFE) module to learn the encoding of acquisition orientation vectors and seamlessly integrate them into the latent features of deep networks. Importantly, it can be directly Plug-and-Play (PnP) into various existing DL-QSM architectures, enabling reconstructions of QSM from arbitrary magnetic dipole orientations. Its effectiveness is demonstrated by combining the OA-LFE module into our previously proposed phase-to-susceptibility single-step instant QSM (iQSM) network, which was initially tailored for pure-axial acquisitions. The proposed OA-LFE-empowered iQSM, which we refer to as iQSM+, is trained in a self-supervised manner on a specially-designed simulation brain dataset. Comprehensive experiments are conducted on simulated and in vivo human brain datasets, encompassing subjects ranging from healthy individuals to those with pathological conditions. These experiments involve various MRI platforms (3T and 7T) and aim to compare our proposed iQSM+ against several established QSM reconstruction frameworks, including the original iQSM. The iQSM+ yields QSM images with significantly improved accuracies and mitigates artifacts, surpassing other state-of-the-art DL-QSM algorithms.
摘要
量子感测图像（QSM）是一种后处理技术，用于从MRI阶段测量结果中提取组织磁矩分布。深度学习（DL）算法在解决QSM重建问题上具有巨大潜力。然而，目前的DL-QSM方法面临的主要挑战是在训练和测试过程中磁 dipôle场方向的变化不能适应。在这种情况下，我们提出了一种新的 Orientación-Adaptive Latent Feature Editing（OA-LFE）模块，用于学习获取训练和测试过程中磁 dipôle场方向的编码。这种模块可以 direct Plug-and-Play（PnP）地 integrating into various existing DL-QSM architectures，以便从任意磁 dipôle场方向重建QSM图像。我们通过将OA-LFE模块与我们之前提出的Single-step instant QSM（iQSM）网络结合，并将其称为iQSM+。iQSM+网络在自我超vised的方式下在特制的MRI大脑数据集上进行训练。我们对于这些数据集进行了丰富的实验，包括使用3T和7T的MRI平台，并与其他已知QSM重建框架进行比较。iQSM+网络对于不同的MRI平台和疾病 condition进行了广泛的应用，并且可以减少artifacts并提高QSM图像的准确性，胜过其他当前state-of-the-art DL-QSM算法。