Advanced Technology for Medicine & Signals, National School of Engineering of Sfax (ENIS), Sfax University, Sfax, Tunisia.
Higher Institute of Computer Science and Multimedia of Gabes, Gabes University, Zrig Eddakhlania, Tunisia.
Med Biol Eng Comput. 2021 Jan;59(1):85-106. doi: 10.1007/s11517-020-02285-8. Epub 2020 Nov 24.
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) could be considered a challenged task since it could be designed as an efficient technique for fast MRI acquisition which could be highly beneficial for several clinical routines. In fact, it could grant better scan quality by reducing motion artifacts amount as well as the contrast washout effect. It offers also the possibility to reduce the exploration cost and the patient's anxiety. Recently, Deep Learning Neuronal Network (DL) has been suggested in order to reconstruct MRI scans with conserving the structural details and improving parallel imaging-based fast MRI. In this paper, we propose Deep Convolutional Encoder-Decoder architecture for CS-MRI reconstruction. Such architecture bridges the gap between the non-learning techniques, using data from only one image, and approaches using large training data. The proposed approach is based on autoencoder architecture divided into two parts: an encoder and a decoder. The encoder as well as the decoder has essentially three convolutional blocks. The proposed architecture has been evaluated through two databases: Hammersmith dataset (for the normal scans) and MICCAI 2018 (for pathological MRI). Moreover, we extend our model to cope with noisy pathological MRI scans. The normalized mean square error (NMSE), the peak-to-noise ratio (PSNR), and the structural similarity index (SSIM) have been adopted as evaluation metrics in order to evaluate the proposed architecture performance and to make a comparative study with the state-of-the-art reconstruction algorithms. The higher PSNR and SSIM values as well as the lowest NMSE values could attest that the proposed architecture offers better reconstruction and preserves textural image details. Furthermore, the running time is about 0.8 s, which is suitable for real-time processing. Such results could encourage the neurologist to adopt it in their clinical routines. Graphical abstract.
压缩感知磁共振成像（CS-MRI）可以被认为是一项具有挑战性的任务，因为它可以设计为一种用于快速 MRI 采集的有效技术，这对于许多临床常规非常有益。事实上，它可以通过减少运动伪影的数量以及对比度洗脱效应来提高扫描质量。它还提供了降低探索成本和患者焦虑的可能性。最近，深度学习神经网络（DL）被提出用于重建 MRI 扫描，以保持结构细节并改善基于并行成像的快速 MRI。在本文中，我们提出了一种用于 CS-MRI 重建的深度卷积编解码器架构。这种架构弥合了非学习技术（仅使用一张图像的数据）和使用大量训练数据的方法之间的差距。所提出的方法基于自动编码器架构，分为两部分：编码器和解码器。编码器和解码器本质上都有三个卷积块。所提出的架构已经通过两个数据库进行了评估：哈默史密斯数据集（用于正常扫描）和 MICCAI 2018 数据集（用于病理性 MRI）。此外，我们扩展了我们的模型以处理有噪声的病理性 MRI 扫描。归一化均方误差（NMSE）、峰值信噪比（PSNR）和结构相似性指数（SSIM）被用作评估指标，以评估所提出架构的性能，并与最先进的重建算法进行比较研究。较高的 PSNR 和 SSIM 值以及较低的 NMSE 值可以证明所提出的架构提供了更好的重建效果，并保留了纹理图像的细节。此外，运行时间约为 0.8s，适用于实时处理。这些结果可以鼓励神经科医生在他们的临床常规中采用它。
