这个Project是用Matlab完成图像的模型重建

BIM 252/EEC 205 Computational Imaging Winter 2020

PROJECT

The focus of this project is on reconstruction methods. Therefore, we use simulated data to

avoid any requirement of imaging modality specific knowledge (so the competition is fair to

everyone). Specifically, the data were generated by the following Matlab codes:

load train_phantom % get the phantom image variable x

angle = [0:10:179];

ybar = radon(x,angle);

% add Poisson noise

y = poissrnd(ybar);

The following codes perform reconstruction using iradon and calculate the RMSE, which will be

used as the baseline value (see performance score calculation below).

% reconstruct using iradon

fbp = iradon(y,angle,’linear’,’ram-lak’,1,115);

% now compute RMSE

rmse = sqrt(mean((x(:)-fbp(:)).^2))

The following code can be used to generate a projection matrix H to be used in a model-based

iterative reconstruction method:

H = gen_sys_N(115, angle);

There are four data files: two for training and two for testing

train_phantom.mat contains a brain phantom image ‘x’ and its projection ‘y’

train_ct.mat contains a CT image ‘x’ and its projection ‘y’.

These two files are training data with ground truth images (shown in figure below) so

that you can use them to tune your reconstruction methods.

For the deep learning method, you will need to get training data by yourself. You can

use the phantom() function in matlab with random parameters or use online resources.

test_phantom.mat

test_ct.mat

These two files are testing data that will be used to evaluated the performance of your

methods. They contain only the noisy projection ‘y’ without the ground truth image. You will

submit your reconstructed images of the two testing data in ONE .mat file. The RMSE will be

calculated for you. The .mat file should contains 4 variables named as follows:

recon_phantom_mb : model-based reconstruction of test phantom

recon_phantom_dl : deep-learning reconstruction of test phantom

recon_ct_mb : model-based reconstruction of test CT image

recon_ct_dl : deep-learning reconstruction of test CT image

Each variable should be a 115×115 array. Please save them in a mat file with filename ‘{your last

name}_recon.mat’ (without {}) and upload it to Canvas.

It is very important that you following the above naming instructions so that we can calculate

your RMSE performance score which counts 50% of your project grade!

Figure. The brain phantom image (left) and the real CT image (right). The images used in

the project are downsampled to 115×115 to reduce computation time.

Grading

The project will be graded based on your RMSE performance on the testing data (50%) and

written report (50%).

The RMSE performance score is calculated based on the reduction of RMSE over the baseline

method, which is the reconstruction by iradon.m in MATLAB. The method achieves the lowest

RMSE will receive a full score of 100, and score of other methods will be calculated by

performance score = ���� ���� − ����6789:;

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× 100

The performance score will be calculated for every test dataset and reconstruction method

combination (4 combinations in total) separately and the average will be used as the final score.

Please note that only the RMSE of the testing data will be considered in the calculation of

performance score.

The written report should include a method section, a result section, and a discussion section.

The method section should describe the model-based reconstruction algorithm and deeplearning

based reconstruction methods and the rationale of your choice, and any procedure

that you used to optimize the performance.

The result section should include at least your (i) reconstructed images of the training data, (ii)

reconstructed image of the testing data, and (iii) the RMSE of the training data reconstruction,

for BOTH model-based reconstruction and deep-learning based reconstruction. You can also

include any intermediate results that you used to fine tune your method.

The discussion section should compare the two methods that you studied, and discuss their

respective pros and cons, as well as any limitations of your study.