kssigari/LLF

Penalized PET reconstruction using deep learning prior and local linear fitting (TMI 2018)

This code is related to this paper:
Penalized PET reconstruction using deep learning prior and local linear fitting, TMI 2018
(https://ieeexplore.ieee.org/document/8354909/)
This code can be used for all-types of Siemens scanners, such as HR+, HRRT, Biograph.
You can change ParamSetting.m
The data should be arc-corrected.
Details:
Linux/Windows available
We provide pre-compiled Siemens-type projector and backprojector, Parallel computing based on OpenMP is used. "libomp" should be linked. First you can try Demo_OPOSEM.m, if you see errors please check this:
1.1) Linux: https://www.mathworks.com/matlabcentral/answers/125117-openmp-mex-files-static-tls-problem
1.2) Windows: I have not seen errors yet, but if you see errors, please let me know.

+extra) The Geometric parameters are in ParamSetting.m This code can be used for HR+, Biograph as well. You can change ParamSetting.m, The sinogram should be arc-corrected! (Please study: Michellogram and arc-correction)

Sinograms in Data folder We provide one clinical data for test. The scanner is the high-resolution research tomograph (HRRT) dedicated for brain studies, Siemens. We provide full data (4800 sec), and downsampled data for 4x, 6x, 8x, 10x.

Please download the "Data" folder: https://www.dropbox.com/sh/33kqnvbbclhvscr/AACAj0_qmCZby_yjKZjuCdLia?dl=0

Demo examples

4.1 OPOSEM (ordinary poisson ordered subsets expectation maximization)
4.2 OS-SQS+Non local means penalty (ordered subsets separable quadratic surrogates): Non-local means implementation is clearly explained in this paper: Kim et al. "Low-dose CT reconstruction using spatially encoded nonlocal penalty", Medical Physics.
4.3 Proposed method: OS-SQS + DnCNN + local linear fitting (LLF)
+4.4 OS-SART + Quadratic penalty (for researchers)

Install Caffe version 1
Please install Caffe with Matlab option on.
First install CPU version, and if it works, then try to install GPU version.
GPU version is more complicated. So if you just want to compare with your results and you are not a Caffe user, I highly recommend to install CPU version. But computational time will be slow.

These are pre-trained outputs: "DnCNN_6ds_iter_100000.caffemodel" "DnCNN_6ds_iter_100000.solverstate"
The network is: "DnCNN_deploy_test.prototxt"

After installation Caffe v1, please open "bin/DnCNN_prior.m" and "bin/DnCNN_prior_grad.m" and then change this option:

caffe.set_mode_gpu();
gpu_id = 0;
caffe.set_device(gpu_id);
if you use CPU or another GPU number, change this: ex) caffe.set_mode_cpu(); or gpu_id = 2;

Enjoy,

Kyungsang