Fujita Naoto, Yokosawa Suguru, Shirai Toru, Terada Yasuhiko
Institute of Pure and Applied Physics, University of Tsukuba, Tsukuba, Japan.
FUJIFILM Corporation, Medical Systems Research and Development Center, Minato-ku, Tokyo, Japan.
Int J Comput Assist Radiol Surg. 2025 May 3. doi: 10.1007/s11548-025-03356-7.
Quantitative magnetic resonance imaging (qMRI) enables imaging of physical parameters related to the nuclear spin of protons in tissue, and is poised to revolutionize clinical research. However, improving the accuracy and clinical relevance of qMRI is essential for its practical implementation. This requires significantly reducing the currently lengthy acquisition times to enable clinical examinations and provide an environment where clinical accuracy and reliability can be verified. Deep learning (DL) has shown promise in significantly reducing imaging time and improving image quality in recent years. This study introduces a novel approach, quantitative deep cascade of convolutional network (qDC-CNN), as a framework for accelerated quantitative parameter mapping, offering a potential solution to this challenge. This work aims to verify that the proposed model outperforms the competing methods.
The proposed qDC-CNN is an integrated deep-learning framework combining an unrolled image reconstruction network and a fully connected neural network for parameter estimation. Training and testing utilized simulated multi-slice multi-echo (MSME) datasets generated from the BrainWeb database. The reconstruction error with ground truth was evaluated using normalized root mean squared error (NRMSE) and compared with conventional DL-based methods. Two validation experiments were performed: (Experiment 1) assessment of acceleration factor (AF) dependency (AF = 5, 10, 20) with fixed 16 echoes, and (Experiment 2) evaluation of the impact of reducing contrast images (16, 8, 4 images).
In most cases, the NRMSE values of S0 and T2 estimated from the proposed qDC-CNN were within 10%. In particular, the NRMSE values of T2 were much smaller than those of the conventional methods.
The proposed model had significantly smaller reconstruction errors than the conventional models. The proposed method can be applied to other qMRI sequences and has the flexibility to replace the image reconstruction module to improve performance.
定量磁共振成像（qMRI）能够对与组织中质子核自旋相关的物理参数进行成像，有望彻底改变临床研究。然而，提高qMRI的准确性和临床相关性对于其实际应用至关重要。这需要大幅缩短目前冗长的采集时间，以便进行临床检查，并提供一个能够验证临床准确性和可靠性的环境。近年来，深度学习（DL）在显著缩短成像时间和提高图像质量方面显示出了前景。本研究引入了一种新方法，即定量深度卷积网络级联（qDC-CNN），作为加速定量参数映射的框架，为这一挑战提供了潜在的解决方案。这项工作旨在验证所提出的模型优于竞争方法。
所提出的qDC-CNN是一个集成的深度学习框架，它结合了一个展开的图像重建网络和一个用于参数估计的全连接神经网络。训练和测试使用了从BrainWeb数据库生成的模拟多切片多回波（MSME）数据集。使用归一化均方根误差（NRMSE）评估与真实值的重建误差，并与传统的基于DL的方法进行比较。进行了两项验证实验：（实验1）在固定16个回波的情况下评估加速因子（AF）依赖性（AF = 5、10、20），以及（实验2）评估减少对比图像数量（16、8、4幅图像）的影响。
在大多数情况下，从所提出的qDC-CNN估计的S0和T2的NRMSE值在10%以内。特别是，T2的NRMSE值比传统方法小得多。
所提出的模型比传统模型具有显著更小的重建误差。所提出的方法可以应用于其他qMRI序列，并且具有替换图像重建模块以提高性能的灵活性。
