Deep ADMM-Net for Compressive Sensing MRI
Authors: yan yang, Jian Sun, Huibin Li, Zongben Xu
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on MRI image reconstruction under different sampling ratios in k-space demonstrate that it significantly improves the baseline ADMM algorithm and achieves high reconstruction accuracies with fast computational speed. |
| Researcher Affiliation | Academia | Yan Yang Xi an Jiaotong University yangyan92@stu.xjtu.edu.cn Jian Sun Xi an Jiaotong University jiansun@mail.xjtu.edu.cn Huibin Li Xi an Jiaotong University huibinli@mail.xjtu.edu.cn Zongben Xu Xi an Jiaotong University zbxu@mail.xjtu.edu.cn |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-sourcing the code for the methodology described. |
| Open Datasets | Yes | We train and test ADMM-Net on brain and chest MR images2. For each dataset, we randomly take 100 images for training and 50 images for testing. ADMM-Net is separately learned for each sampling ratio. The reconstruction accuracies are reported as the average NMSE and Peak Signalto-Noise Ratio (PSNR) over the test images. The sampling pattern in k-space is the commonly used pseudo radial sampling. All experiments are performed on a desktop with Intel core i7-4790k CPU. 2CAF Project: https://masi.vuse.vanderbilt.edu/workshop2013/index.php/Segmentation_Challenge_Details |
| Dataset Splits | No | The paper states, "For each dataset, we randomly take 100 images for training and 50 images for testing." but does not mention a validation set split or provide details on cross-validation. |
| Hardware Specification | Yes | All experiments are performed on a desktop with Intel core i7-4790k CPU. |
| Software Dependencies | No | The paper mentions "L-BFGS1" with a URL and "Rice Wavelet Toolbox: http://dsp.rice.edu/software/rice-wavelet-toolbox" but does not provide specific version numbers for these or other general software dependencies. |
| Experiment Setup | Yes | We choose normalized mean square error (NMSE) as the loss function in network training. We learn the parameters by minimizing the loss w.r.t. them using L-BFGS. We initialize the network parameters Θ according to the ADMM solver of the following baseline CS-MRI model. In this model, we set Dl as a DCT basis and impose l1-norm regularization in the DCT transform space. In the nonlinear transform layer, we uniformly choose 101 positions located within [-1,1]. The sampling pattern in k-space is the commonly used pseudo radial sampling. |