MRI T1 Map production from multiple Flip Angles

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Grace 2024-7-5

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评论： Umar 2024-7-16

I've edited a MATLAB code that produces a T1 map from two flip angles. I have MRI slices that are 5x170 slices for 5 different flip angles (2,10,11,12,13). I converted them into .nii files and performed the matlab code which worked. It displays 2 rows of T1 maps for each set of angles. The first row of images represent the T1 values from the initial iteration, which are essentially the initial guesses of the T1 relaxation times. These values are based on the raw data without the iterative refinement process applied. The second row of images represent the T1 values after the iterative refinement process has been completed (after 10 iterations). These values are more accurate and have been corrected for residual T1 saturation effects through the iterative process. However, my T1 maps that came out have a lot of noise especially in the background and don't look like a normal T1 map, can anyone help me understand why this might be?

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Answer 1

Umar 2024-7-5

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Hi Grace,

The high noise levels in the T1 maps could be due to several factors such as Inadequate preprocessing of MRI slices before T1 map generation can introduce noise, the iterative process used to refine T1 values may not be converging effectively, leading to inaccurate results and initial guesses for T1 relaxation times might be too far from the true values, affecting the refinement process.

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Grace 2024-7-7

Hi Umar,

Thanks for the reply.

What pre-processing techniques might you advise to use? I have used McFLIRT to register my slices to the first slices to remove movement. I tried using the BET tool but it only acknowledged my first FA and disregarded the rest. As for the iterative process, i am correcting for residual t1 saturation effects using equations derived from the bloch equations over 10 iterations. Ive tried increasing hem number of iterations but the processing tim increases dramatically.

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Answer 2

Umar 2024-7-7

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Hi Grace,

In your MRI data pre-processing workflow, considering the challenges you've encountered, here are some tailored recommendations for optimizing your approach:

Since you mentioned that the BET tool only acknowledged your first FA image and disregarded the rest, you may want to explore alternative brain extraction methods such as FSL's FAST or FreeSurfer's recon-all. These tools offer robust brain extraction capabilities that can handle variations across different slices more effectively.

While correcting for residual t1 saturation effects using equations derived from Bloch equations is a sound approach, the issue of processing time increasing dramatically with each iteration is a common concern. To mitigate this, you could consider implementing parallel processing techniques or utilizing high-performance computing resources to expedite the iterative process without compromising accuracy.

To streamline your pre-processing pipeline further, you may benefit from fine-tuning parameters in McFLIRT for motion correction to achieve optimal registration results. Additionally, exploring advanced denoising techniques like ICA-AROMA or spatial/temporal filtering methods can enhance the quality of your data while reducing processing time. Please bear in mind that it is essential to validate each step of your pre-processing pipeline by visually inspecting intermediate results and conducting quality control checks to ensure the accuracy and reliability of your processed data. This includes assessing registration quality, brain extraction completeness, and the impact of iterative corrections on image integrity.

Hopefully, by incorporating these recommendations into your pre-processing workflow and leveraging advanced tools and optimization strategies, you can enhance the efficiency and effectiveness of your MRI data analysis while maintaining high-quality results. Remember to adapt these suggestions to your specific dataset characteristics and research objectives for optimal outcomes.

Also, try exploring emerging pre-processing techniques such as deep learning-based approaches or multi-modal fusion methods could offer novel solutions to address complex challenges in MRI data analysis. Collaborating with experts in the field or seeking guidance from specialized forums and resources can also provide valuable insights and support for refining your pre-processing strategies. Continuously staying informed about advancements in neuroimaging methodologies can empower you to leverage cutting-edge techniques for improved research outcomes.

Let me know if you need further assistance.

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Grace 2024-7-16

Hi Umar,

Wow! Thanks for the comprehensive overview. I was working on a timeline that would include a lot of what you mentioned post processing. Initially however, I am just aiming on obtaining a simple T1 map without the background noise. I have tried adding a mask with a threshold value of 60 which is the slightly higher pixel value of the background (dark) pixels in the images (from the histrogram of averaged slices). I applied this threshold to each T1 map in the iteration but get out the attached T1 maps. This is the code i used:

% Create a mask to ignore background pixels

threshold = 60; % Set an appropriate threshold value

meanImage = mean(IMG0, 4); % Average across all flip angles

mask = meanImage > threshold;

...

% The FOR loop iteratively corrects for residual T1 saturation effects

for iter = 1:10

T1satCorrect = 1 + T1 ./ TR .* (FArad(iPD) .^ 2) / 2;

T1satCorrect(isnan(T1satCorrect)) = eps;

PD = IMG0(:,:,:,iPD) / FArad(iPD) ./ T1satCorrect;

PD(~mask) = NaN; % Apply the mask to PD

T1 = (1 ./ ((IMG0(:,:,:,iELSE) ./ reshape(FArad(iELSE), [1, 1, 1, length(iELSE)])) ./ PD) - 1) ./ ...

reshape(FArad(iELSE) .^ 2, [1, 1, 1, length(iELSE)]) * 2 * TR;

T1(~mask) = NaN; % Apply the mask to T1

T1i(:,:,:,:,iter) = T1;

end

I am struggling to see what is going wrong.

Umar 2024-7-16

Hi Grace,

No problem, glad to help you out. It sounds like you are trying to perform a series of calculations involving MRI-related parameters and applying masks to the results. Well, to address the issue of background noise in the T1 maps you are facing, you need to make a modification in the mask application. Instead of applying the mask directly to PD and T1 matrices, the mask should be used to filter out unwanted values during the calculations. Because if you apply the mask after the calculations for PD and T1, it will make sure thatbonly the relevant pixels are considered in the T1 map generation process, effectively removing background noise. This adjustment should help in obtaining cleaner T1 maps without unwanted artifacts.

Please let me know if you have any further questions.

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