The accurate measurement of 3D cardiac function is an important task in the analysis of cardiac magnetic resonance (MR) images. However, short-axis image acquisitions with thick slices are commonly used in clinical practice due to constraints of acquisition time, signal-to-noise ratio and patient ...
Super-resolution method for MR images based on multi-resolution CNN Specifically, a novel 3D multi-resolution analysis multi-modality SR reconstruction network for MRI is built, which takes full advantage of structural ... L Kang,L Kang,G Liu,... - 《Biomedical Signal Processing & Control》 ...
Our proposed method applies the CS theory to Super Resolution (SR) single Magnetic Resonance Imaging (MRI). We first use a LR image generated by applying a Gaussian filter on the original image (for k-space under-sampling) and then generate the HR image by using CS theory. The formulation...
superresolution techniques have been previously applied to increase image resolution in functional MRI (fMRI) [6] and Diffusion Tensor Imaging (DTI) studies [7]. Unfortunately, most of such techniques are based on the acquisition of multiple low...
Brain MRI Image Super-Resolution Reconstruction: A Systematic Review 2024, IEEE Access View full text Improving anisotropy resolution of computed tomography and annotation using 3D super-resolution network Biomedical Signal Processing and Control, Volume 82, 2023, Article 104590 ...
In general, super-resolution reconstruction methods based on deep learning are better than traditional methods, but these methods also have some problems. First, in the data processing, the output of the convolution layer is called the feature map, which has a one-to-one relationship with each ...
A comparison study is presented of two methods for MRI reconstruction using super-resolution techniques which combine multiple multi-slice stacks into a single high- resolution 3-D image volume. Sampling configurations are compared involving stacks with parallel orientations at different sub-pixel offset...
This work introduces a model-based super-resolution reconstruction (SRR) technique for achieving high-resolution diffusion-weighted MRI. Diffusion-weighted imaging (DWI) is a key technique for investigating white matter non-invasively. However, due to hardware and imaging time constraints, the technique...
Single Image Super-Resolution (SISR) which aims to recover a high resolution (HR) image from a low-resolution (LR) image has a wide range of medical applications. In this paper, we present a novel...doi:10.1007/978-981-10-7554-4_42Liu, Jia...
deep-learningimage-processingmrisuper-resolutioninverse-problemscomputational-imagingcomputed-tomographyplug-and-playdeblurringdiffusion-modelsunfoldeddeep-equilibrium-models UpdatedFeb 3, 2025 Python js3611/Deep-MRI-Reconstruction Star331 Code Issues Pull requests ...