IDEAL MRI sequences

I was given MRI images of the liver of patients, taken with LMS IDEAL optimized low flip. I need to calculate the pdff. I have 72 images per patient with echotime ranging from 1.2ms to 11.2 ms. How do I identify which is the fat, water OP, IP image?
Thank you so much. and yes I am relative new to MRI imaging.


To calculate PDFF you’ll need to perform fitting across all echoes to estimate PDFF and R2* values. ‘Signal Intensity Fat Fraction’ would be calculated using IP and OP from a 2 point DIXON acquisition for example.

There are more details around fitting LMS IDEAL in the publication below.

Thank you so much UCLjames.
Would you mind If I contacted you via email? I have some follow up questions.
Thank you.

Thank you for your reply. I am new to MRI (it should be obvious, by now ) and I have a little more than a week to get some PDFF values from this set of images.
I have some follow up questions
1- For each echo (total of 6 echo times) I have 12 images: 6 magnitudes and 6 (I believe) phases (funny looking ones): Do I just average the 6 images together?
2 - Is each image supposed to be preprocessed some how (within -echo, withing image type) ?
3 - IDEAL is used on the phase images. In the paper, it is not clear how the magnitude images were combined with phase processes images
Thank you everyone for the help

Hi Gianluca,

Its quite ambitious to try to write / test / deploy this type of analysis code if you’re new to PDFF in such a short time scale.

Regards processing, it’s not just a matter of taking an average across echoes / images. You will need to calculate values by fitting across echoes. There are both complex and magnitude only approaches that can be used - a number of papers provide details on these calculations and the advantages of each technique.

I’d suggest you try some of the open source code out there to begin with such as the ismrm fat-water toolbox, or FattyRiot. Links to FattyRiot code are below.

Does anyone else have any recommendations for open source code for PDFF?

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