Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
Magn Reson Med. 2021 Aug;86(2):625-636. doi: 10.1002/mrm.28767. Epub 2021 Mar 25.
To develop and evaluate a novel method for reconstruction of high-quality sodium MR images from noisy, limited k-space data.
A novel reconstruction method was developed for reconstruction of high-quality sodium images from noisy, limited k-space data. This method is based on a novel image model that contains a motion-compensated generalized series model and a sparse model. The motion-compensated generalized series model enables effective use of anatomical information from a proton image for denoising and resolution enhancement of sodium data, whereas the sparse model enables high-resolution reconstruction of sodium-dependent novel features. The underlying model estimation problems were solved efficiently using convex optimization algorithms.
The proposed method has been evaluated using both simulation and experimental data obtained from phantoms, healthy human volunteers, and tumor patients. Results showed a substantial improvement in spatial resolution and SNR over state-of-the-art reconstruction methods, including compressed sensing and anatomically constrained reconstruction methods. Quantitative tissue sodium concentration maps were obtained from both healthy volunteers and brain tumor patients. These tissue sodium concentration maps showed improved lesion fidelity and allowed accurate interrogation of small targets.
A new method has been developed to obtain high-resolution sodium images with good SNR at 3 T. The proposed method makes effective use of anatomical prior information for denoising, while using a sparse model synergistically to recover sodium-dependent novel features. Experimental results have been obtained to demonstrate the feasibility of achieving high-quality tissue sodium concentration maps and their potential for improved detection of spatially heterogeneous responses of tumor to treatment.
开发并评估一种从噪声有限的 k 空间数据重建高质量钠磁共振图像的新方法。
开发了一种从噪声有限的 k 空间数据重建高质量钠图像的新方法。该方法基于一种新的图像模型，该模型包含运动补偿广义级数模型和稀疏模型。运动补偿广义级数模型能够有效地利用质子图像中的解剖学信息来对钠数据进行去噪和分辨率增强，而稀疏模型能够实现对钠依赖的新特征的高分辨率重建。使用凸优化算法有效地解决了基础模型估计问题。
使用模拟数据和来自体模、健康志愿者和肿瘤患者的实验数据对所提出的方法进行了评估。结果表明，与包括压缩感知和解剖约束重建方法在内的现有重建方法相比，该方法在空间分辨率和 SNR 方面有了显著提高。从健康志愿者和脑肿瘤患者中获得了定量的组织钠浓度图。这些组织钠浓度图提高了病变的逼真度，并能够准确探测到小目标。
已经开发出一种在 3T 下获得具有良好 SNR 的高分辨率钠图像的新方法。该方法有效地利用解剖学先验信息进行去噪，同时协同使用稀疏模型来恢复钠依赖的新特征。已经获得了实验结果，以证明实现高质量组织钠浓度图的可行性及其在提高对肿瘤治疗的空间异质性反应的检测能力方面的潜力。
