Chin-Cheng Chan,Justin P Haldar
Chin-Cheng Chan
For the past several decades, it has been popular to reconstruct Fourier imaging data using model-based approaches that can easily incorporate physical constraints and advanced regularization/machine learning priors. The most common modelin...
Cagla D Bahadir,Alan Q Wang,Adrian V Dalca et al.
Cagla D Bahadir et al.
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to achieve accelerated scan times. CS-MRI presents two fundamental problems: (1) where to sample and (2) how to reconstruct an under-sampled scan. In this paper, we ...
2D Ultrasound Elasticity Imaging of Abdominal Aortic Aneurysms Using Deep Neural Networks [0.03%]
基于深度神经网络的腹主动脉瘤二维超声弹性成像研究
Utsav Ratna Tuladhar,Richard Simon,Doran Mix et al.
Utsav Ratna Tuladhar et al.
Abdominal aortic aneurysms (AAA) pose a significant clinical risk due to their potential for rupture, which is often asymptomatic but can be fatal. Although maximum diameter is commonly used for risk assessment, diameter alone is insufficie...
Scan-Adaptive MRI Undersampling Using Neighbor-based Optimization (SUNO) [0.03%]
基于邻居优化的自适应MRI欠采样（SUNO）方法
Siddhant Gautam,Angqi Li,Nicole Seiberlich et al.
Siddhant Gautam et al.
Accelerated MRI aims to reduce scan time by acquiring data more efficiently, for example, through optimized pulse sequences or readouts that increase k -space coverage per excitation (e.g., echo planar imaging), or by collecting partial k -...
Rodrigo A Lobos,Xiaokai Wang,Rex T L Fung et al.
Rodrigo A Lobos et al.
The partially separable functions (PSF) model is commonly adopted in dynamic MRI reconstruction, as is the underlying signal model in many reconstruction methods including the ones relying on low-rank assumptions. Even though the PSF model ...
A Convergent Generalized Krylov Subspace Method for Compressed Sensing MRI Reconstruction with Gradient-Driven Denoisers [0.03%]
基于梯度驱动降噪的压缩感知磁共振图像重构的广义Krylov子空间方法
Tao Hong,Umberto Villa,Jeffrey A Fessler
Tao Hong
Model-based reconstruction plays a key role in compressed sensing (CS) MRI, as it incorporates effective image regularizers to improve the quality of reconstruction. The Plug-and-Play and Regularization-by-Denoising frameworks leverage adva...
IE-GADCI: An End-to-End Incoherence-Enhanced Generative Adversarial Deep Compressive Imaging [0.03%]
基于对抗生成网络的端到端模型在压缩感知磁共振成像中的应用研究
Kangning Zhang,Yifei Sun,Varun Yelluru et al.
Kangning Zhang et al.
Single-pixel imaging (SPI) within the framework of compressive sensing (CS) is a powerful technique that enables image acquisition at sub-Nyquist sampling rates by leveraging the sparse latent representations of the object scenes. As a cost...
Using Randomized Nyström Preconditioners to Accelerate Variational Image Reconstruction [0.03%]
利用随机Nyström加权矩阵加速变分图像恢复
Tao Hong,Zhaoyi Xu,Jason Hu et al.
Tao Hong et al.
Model-based iterative reconstruction plays a key role in solving inverse problems. However, the associated minimization problems are generally large-scale, nonsmooth, and sometimes even nonconvex, which present challenges in designing effic...
Convergent Complex Quasi-Newton Proximal Methods for Gradient-Driven Denoisers in Compressed Sensing MRI Reconstruction [0.03%]
收敛复拟牛顿 proximal 方法在压缩感知 MRI 重建中的梯度去噪器中的应用
Tao Hong,Zhaoyi Xu,Se Young Chun et al.
Tao Hong et al.
In compressed sensing (CS) MRI, model-based methods are pivotal to achieving accurate reconstruction. One of the main challenges in model-based methods is finding an effective prior to describe the statistical distribution of the target ima...
An Efficient Algorithm for Spatial-Spectral Partial Volume Compartment Mapping with Applications to Multicomponent Diffusion and Relaxation MRI [0.03%]
有效的空间光谱部分体积包层映射算法及其在多组分扩散和弛豫MRI中的应用
Yunsong Liu,Debdut Mandal,Congyu Liao et al.
Yunsong Liu et al.
We introduce a new algorithm to solve a regularized spatial-spectral image estimation problem. Our approach is based on the linearized alternating directions method of multipliers (LADMM), which is a variation of the popular ADMM algorithm....