So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging

Lee, Jong-Ho; Hyun, Jae‐Won; Lee, Jieun; Choi, Eun‐Jung; Shin, Hyeong-Geol; Min, Kyeongseon; Nam, Yoonho; Kim, Ho Jin; Oh, Se‐Hong

doi:10.1002/jmri.27059

Cited by 80 publications

(87 citation statements)

References 129 publications

(190 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The MWF-reading linear correlations could be driven by marked group differences of MWF between good and poor readers, although such an MWF-reading relationship appears to exist within each group (e.g., right thalamus MWF vs. WRAT III in good readers; Figure 5), granted such an assessment is beyond the scope of our small sample size. Our pilot study provides compelling evidence for larger replication studies of myelin in reading and dyslexia using faster MWF imaging techniques yielding more extensive data collection over the whole brain (Alonso-Ortiz et al, 2015;Lee et al, 2020). That said, our average white matter MWF was 0.10 in the good readers, which fits with a very recent report of 0.10-0.11 in adults using the same type of MRI pulse sequence (Ocklenburg et al, 2019).…”

Section: Limitations and Future Directionssupporting

confidence: 85%

Myelin Water Imaging Demonstrates Lower Brain Myelination in Children and Adolescents With Poor Reading Ability

Beaulieu

Yip

Low

et al. 2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

Magnetic resonance imaging (MRI) provides a means to non-invasively investigate the neurological links with dyslexia, a learning disability that affects one's ability to read. Most previous brain MRI studies of dyslexia and reading skill have used structural or diffusion imaging to reveal regional brain abnormalities. However, volumetric and diffusion MRI lack specificity in their interpretation at the microstructural level. Myelin is a critical neural component for brain function and plasticity, and as such, deficits in myelin may impact reading ability. MRI can estimate myelin using myelin water fraction (MWF) imaging, which is based on evaluation of the proportion of short T2 myelin-associated water from multiexponential T2 relaxation analysis, but has not yet been applied to the study of reading or dyslexia. In this study, MWF MRI, intelligence, and reading assessments were acquired in 20 participants aged 10-18 years with a wide range of reading ability to investigate the relationship between reading ability and myelination. Group comparisons showed markedly lower MWF by 16-69% in poor readers relative to good readers in the left and right thalamus, as well as the left posterior limb of the internal capsule, left/right anterior limb of the internal capsule, left/right centrum semiovale, and splenium of the corpus callosum. MWF over the entire group also correlated positively with three different reading scores in the bilateral thalamus as well as white matter, including the splenium of the corpus callosum, left posterior limb of the internal capsule, left anterior limb of the internal capsule, and left centrum semiovale. MWF imaging from T2 relaxation suggests that myelination, particularly in the bilateral thalamus, splenium, and left hemisphere white matter, plays a role in reading abilities. Myelin water imaging thus provides a potentially valuable in vivo imaging tool for the study of dyslexia and its remediation.

show abstract

Section: Limitations and Future Directionssupporting

confidence: 85%

Myelin Water Imaging Demonstrates Lower Brain Myelination in Children and Adolescents With Poor Reading Ability

Beaulieu

Yip

Low

et al. 2020

Front. Hum. Neurosci.

View full text Add to dashboard Cite

show abstract

“…[36,37] In further work, this has to be compared with conventional methods for assessing these components such as the myelin or extracellular water. [43,45,46] This study has some limitations. Because the lesions were manually segmented, there is a large amount of variation in the annotation, which was also evident in the relatively high intra-observer variability (0.68).…”

Section: Discussionmentioning

confidence: 94%

Lesion probability mapping in MS patients using a regression network on MR Fingerprinting

Hermann

Golla

Martínez

et al. 2021

Preprint

View full text Add to dashboard Cite

Purpose To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to T 1 , T 2 * , NAWM, and GM- probability maps. Methods We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected T 1 and T 2 * maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps. Results WM lesions were predicted with a dice coefficient of 0.61±0.09 and a lesion detection rate of 0.85±0.25 for a threshold of 33%. The network jointly enabled accurate T 1 and T 2 * times with relative deviations of 5.2% and 5.1% and average dice coefficients of 0.92±0.04 and 0.91±0.03 for NAWM and GM after binarizing with a threshold of 80%. Conclusion DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.

show abstract

“…[ 36 , 37 ] In further work, this has to be compared with conventional methods for assessing these components such as the myelin or extracellular water. [ 43 , 45 , 46 ]…”

Section: Discussionmentioning

confidence: 99%

Lesion probability mapping in MS patients using a regression network on MR fingerprinting

et al. 2021

View full text Add to dashboard Cite

Background To develop a regression neural network for the reconstruction of lesion probability maps on Magnetic Resonance Fingerprinting using echo-planar imaging (MRF-EPI) in addition to $$T_1$$ T 1 , $${T_2}^*$$ T 2 ∗ , NAWM, and GM- probability maps. Methods We performed MRF-EPI measurements in 42 patients with multiple sclerosis and 6 healthy volunteers along two sites. A U-net was trained to reconstruct the denoised and distortion corrected $$T_1$$ T 1 and $${T_2}^*$$ T 2 ∗ maps, and to additionally generate NAWM-, GM-, and WM lesion probability maps. Results WM lesions were predicted with a dice coefficient of $$0.61\pm 0.09$$ 0.61 ± 0.09 and a lesion detection rate of $$0.85\pm 0.25$$ 0.85 ± 0.25 for a threshold of 33%. The network jointly enabled accurate $$T_1$$ T 1 and $${T_2}^*$$ T 2 ∗ times with relative deviations of 5.2% and 5.1% and average dice coefficients of $$0.92\pm 0.04$$ 0.92 ± 0.04 and $$0.91\pm 0.03$$ 0.91 ± 0.03 for NAWM and GM after binarizing with a threshold of 80%. Conclusion DL is a promising tool for the prediction of lesion probability maps in a fraction of time. These might be of clinical interest for the WM lesion analysis in MS patients.

show abstract

So You Want to Image Myelin Using MRI: An Overview and Practical Guide for Myelin Water Imaging

Cited by 80 publications

References 129 publications

Myelin Water Imaging Demonstrates Lower Brain Myelination in Children and Adolescents With Poor Reading Ability

Myelin Water Imaging Demonstrates Lower Brain Myelination in Children and Adolescents With Poor Reading Ability

Lesion probability mapping in MS patients using a regression network on MR Fingerprinting

Lesion probability mapping in MS patients using a regression network on MR fingerprinting

Contact Info

Product

Resources

About