NIH: Rapid whole-brain high-resolution myelin water fraction mapping from extremely under-sampled MRI data using deep neural network, 2023

Now published - link here

Preprint
Rapid whole-brain high-resolution myelin water fraction mapping from extremely under-sampled magnetic resonance imaging data using deep neural network
Zhaoyuan Gong, Nikkita Khattar, Matthew Kiely, Curtis Triebswetter, Mustapha Bouhrara

Changes in myelination are a cardinal feature of brain development and the pathophysiology of several cerebral diseases, including multiple sclerosis and dementias. Advanced magnetic resonance imaging (MRI) methods have been developed to probe myelin content through the measurement of myelin water fraction (MWF). However, the prolonged data acquisition and post-processing times of current MWF mapping methods pose substantial hurdles to their clinical implementation.

Recently, fast steady-state MRI sequences have been implemented to produce high spatial resolution whole-brain MWF mapping within 20 min. Despite the subsequent significant advances in the inversion algorithm to derive MWF maps from steady-state MRI, the high-dimensional nature of such inversion does not permit further reduction of the acquisition time by data under-sampling.

In this work, we present an unprecedented reduction in the computation (~30 s) and the acquisition time (~ 7 min) required for whole-brain high-resolution MWF mapping through a new Neural Network (NN)-based approach, named: Relaxometry of Extremely Under-SamplEd Data (NN-REUSED). Our analyses demonstrate virtually similar accuracy and precision in derived MWF values using the NN-REUSED approach as compared to results derived from the fully-sampled reference method.

The reduction in the acquisition and computation times represents a breakthrough toward clinically practical MWF mapping.

Link

 

Clinical MRI of the brain basically looks at signal from the hydrogen atoms of water molecules, and how this is affected by the relevant tissue structure and composition.

MRI loves its acronyms and I think many of them are backronyms, where the "cool" sounding term was coined and then an explanation hammered into it. I would like to suggest that medical physicists and radiologists are much better at this than certain well known BPS researchers.

And yes there is indeed a rapper called Despot that should really style himself as "MC Despot" in my view. Anyhow

 

Here they show the dramatic reduction in white matter myelin content in a patient with Alzheimer disease vs HC. Top row is the conventional technique (BMC-mcDESPOT). Bottom row is the authors' new technique. The acquisition time (patient on magnet time) is the same - 21 minutes, but note the processing time: 30 seconds vs 30 hours. As proposed, this technique would be feasible for clinical practice and large-n research.

 

For more background, see Magnetic Resonance of Myelin Water: An in vivo Marker for Myelin (2016, Brain Plasticity). A few quotes —

An aside —

And in relation to the "all tests are normal" and in particular normal clinical MRI brain imaging for ME, note the following in relation to the normal appearing white matter in multiple sclerosis —

Definitely want to see the fast technique in the thread's preprint evaluating ME/LC. Finally this comment on concussion, noting the neurological symptom overlap with ME —

 

For current state-of-the-art see —
So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging (2020, paywall, sci-hub)

Also —
Using myelin water imaging to link underlying pathology to clinical function in multiple sclerosis: A scoping review (2022)
Macromolecular Proton Fraction as a Myelin Biomarker: Principles, Validation, and Applications (2022)
Mechanisms of myelin repair, MRI techniques and therapeutic opportunities in multiple sclerosis (2021)

 

Published as —

REUSED: A deep neural network method for rapid whole-brain high-resolution myelin water fraction mapping from extremely under-sampled MRI (2023)
Gong; Khattar; Kiely; Triebswetter; Bouhrara

Changes in myelination are a cardinal feature of brain development and the pathophysiology of several central nervous system diseases, including multiple sclerosis and dementias. Advanced magnetic resonance imaging (MRI) methods have been developed to probe myelin content through the measurement of myelin water fraction (MWF).

However, the prolonged data acquisition and post-processing times of current MWF mapping methods pose substantial hurdles to their clinical implementation. Recently, fast steady-state MRI sequences have been implemented to produce high-spatial resolution whole-brain MWF mapping within ~20 min. Despite the subsequent significant advances in the inversion algorithm to derive MWF maps from steady-state MRI, the highdimensional nature of such inversion does not permit further reduction of the acquisition time by data undersampling.

In this work, we present an unprecedented reduction in the computation (~30 s) and the acquisition time (~7 min) required for whole-brain high-resolution MWF mapping through a new Neural Network (NN)based approach, named NN-Relaxometry of Extremely Under-SamplEd Data (NN-REUSED). Our analyses demonstrate virtually similar accuracy and precision in derived MWF values using NN-REUSED compared to results derived from the fully sampled reference method. The reduction in the acquisition and computation times represents a breakthrough toward clinically practical MWF mapping.

Link (Computerized Medical Imaging and Graphics)