gistlib - nmr sequences contain noise as a cnn input and the corresponding clean nmr sequences as the output in matlab in matlab

One way to remove noise in NMR sequences is by using Convolutional Neural Networks (CNNs). Here's how you can use a CNN to clean NMR sequences in Matlab:

Load the NMR data and preprocess it according to your requirements. Usually, NMR data is acquired as a 1D signal, and you might need to reshape it into a 2D matrix that resembles an image.


main.m
% load the nmr data
nmr_data = load('nmr_data.mat');

% reshape the nmr data into an image-like matrix
nmr_image = reshape(nmr_data, [nx, ny]);
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Create a CNN architecture that suits your needs. A CNN typically consists of convolution layers, activation functions, pooling layers, and fully connected layers. You can use MATLAB's Deep Learning Toolbox to create a CNN layer-by-layer.


main.m
% create a CNN architecture
layers = [
    % convolution layer with 32 filters and a 3x3 kernel
    convolution2dLayer(3, 32, 'Padding', 1)
    reluLayer()
    % max pooling layer with a 2x2 pool size
    maxPooling2dLayer(2, 'Stride', 2)
    % 2nd convolution layer with 64 filters and a 3x3 kernel
    convolution2dLayer(3, 64, 'Padding', 1)
    reluLayer()
    % max pooling layer with a 2x2 pool size
    maxPooling2dLayer(2, 'Stride', 2)
    % 3rd convolution layer with 128 filters and a 3x3 kernel
    convolution2dLayer(3, 128, 'Padding', 1)
    reluLayer()
    % up-sampling layer with a 2x2 scale factor
    transposedConv2dLayer(2, 128, 'Stride', 2)
    % last convolution layer with 1 filter and 3x3 kernel
    convolution2dLayer(3, 1, 'Padding', 1)
    % regression layer
    regressionLayer()
];
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Split the NMR data into training and testing subsets. We'll use the noisy NMR data as CNN input and the clean NMR data as CNN output.


main.m
% split the data into training and testing subsets
train_data_percent = 0.8;
train_data_len = round(train_data_percent * length(nmr_image));
train_data = nmr_image(1:train_data_len, :);
test_data = nmr_image(train_data_len+1:end, :);

% create the corresponding clean data
clean_train_data = clean_my_data(train_data);
clean_test_data = clean_my_data(test_data);
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In the code above, clean_my_data() is a function that cleans the NMR data. You can write this function based on your NMR data characteristics and your preferred type of noise removal technique.

Train the CNN using the noisy NMR data as input and the clean NMR data as output.


main.m
% set the training options
options = trainingOptions('adam', ...
                          'MaxEpochs', 10, ...
                          'MiniBatchSize', 64, ...
                          'InitialLearnRate', 1e-3);

% train the CNN
net = trainNetwork(train_data, clean_train_data, layers, options);
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Test the CNN performance on the test dataset.


main.m
predicted_clean = predict(net, test_data);
mse = immse(predicted_clean, clean_test_data);
fprintf('MSE between predicted and clean NMR data: %f\n', mse);
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In the code above, immse() is a built-in Matlab function that calculates the Mean Squared Error (MSE) between two images. You can use other performance metrics as well, depending on your preference.

That's it! You can now use your trained CNN to clean NMR data in a variety of applications.

nmr sequences contain noise as a cnn input and the corresponding clean nmr sequences as the output in matlab in matlab

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