This paper proposes to learn analysis transform network for dynamic magnetic resonance imaging (LANTERN) with small dataset. Integrating the strength of CS-MRI and deep learning, the proposed framework is highlighted in three components: (i) The spatial and temporal domains are sparsely constrained by using adaptively trained CNN. (ii) We introduce an end-to-end framework to learn the parameters in LANTERN to solve the difficulty of parameter selection in traditional methods. (iii) Compared to existing deep learning reconstruction methods, our reconstruction accuracy is better when the amount of data is limited. Our model is able to fully exploit the redundancy in spatial and temporal of dynamic MR images. We performed quantitative and qualitative analysis of cardiac datasets at different acceleration factors (2x-11x) and different undersampling modes. In comparison with state-of-the-art methods, extensive experiments show that our method achieves consistent better reconstruction performance on the MRI reconstruction in terms of three quantitative metrics (PSNR, SSIM and HFEN) under different undersamling patterns and acceleration factors.
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging
Deepwise Healthcare（深睿医疗）；Sir Run Run Shaw Hospital, Zhejiang University School of Medicine（浙江大学医学院附属邵逸夫医院）；Ningbo Medical Center Lihuili Hospital（宁波医疗中心李惠利医院）；The University of Hong Kong（香港大学）
Multiple Inputs and Mixwd data for Alzheimer's Disease Classification Based on 3D Vision Transformer
Universidad de Salamanca（萨拉曼卡大学）；Universidad Surcolombiana（南哥伦比亚大学）；Corporación Universitaria Autónoma de Nariño（纳里尼奥自治大学）；Mohammed VI Polytechnic University（穆罕默德六世理工大学）
Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging
Tsinghua University（清华大学）；Shenzhen Institute of Future Media Technology（深圳未来媒体技术研究院）；Westlake University（西湖大学）；ETH Zürich（苏黎世联邦理工学院）；University of Würzburg（维尔茨堡大学）
TranSMS: Transformers for Super-Resolution Calibration in Magnetic Particle Imaging
磁性粒子成像（MPI）在高时空分辨率下为磁性纳米粒子（MNP）提供了出色的对比度。MPI中的一个常见程序开始于校准扫描以测量系统矩阵（SM），然后用于建立逆问题以在后续扫描期间重建MNP分布的图像。该校准使得重建能够灵敏地考虑各种系统缺陷。然而，耗时的SM测量必须在系统特性的显著变化下重复进行。在这里，我们介绍了一种新的深度学习方法，用于加速MPI校准，该方法基于Transformers for SM super-resolution（TranSMS）。低分辨率SM测量使用大的MNP样本来执行，以提高信噪比效率，并且高分辨率SM通过基于模型的深度学习来超分辨。TranSMS利用Vision Transformer模块捕获低分辨率输入图像中的上下文关系，利用密集卷积模块定位高分辨率图像特征，利用数据一致性模块确保测量保真度。模拟和实验数据的演示表明，TransSMS显着提高SM恢复和MPI重建高达64倍的加速在二维成像。
Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamics
School of Computer Science and Electronic Engineering, University of Essex, UK（计算机科学与电子工程学院，埃塞克斯大学）；School of Life Sciences, University of Essex, UK（生命科学学院，埃塞克斯大学）
