Magnetic susceptibility imaging has evolved significantly since the introduction of susceptibility-weighted imaging (SWI), which was initially developed for clinical applications such as detecting microhemorrhages. In the mid-2000s, advancements in high-resolution phase imaging led to the development of quantitative susceptibility mapping (QSM), a method that allows for the quantitative assessment of magnetic susceptibility in the brain. QSM has opened up new possibilities for neuroimaging, particularly in the quantification of iron in deep brain structures, a biomarker associated with various neurological conditions. While QSM has provided valuable insights, it has primarily been limited to measuring a combined signal from different sources of magnetic susceptibility. In the brain, iron and myelin are the two dominant contributors, each with opposite magnetic properties iron being paramagnetic and myelin being diamagnetic. This limitation has motivated the need for more refined techniques to disentangle these distinct sources of susceptibility. In this presentation, I will introduce a technique developed in our lab called susceptibility source separation. This method provides a breakthrough by allowing the separate quantification of myelin and iron in the brain. Through susceptibility source separation, we can now obtain high-resolution maps that independently profile the distribution of myelin and iron. This is particularly valuable for studying the brain, where myelin and iron play crucial roles in both healthy brain function and the progression of neurological diseases. I will discuss the underlying physics that enables this separation, including the biophysical modeling and algorithm employed. The validation of the method has been demonstrated through various approaches including histology, showing its accuracy in separating the signals of myelin and iron. The clinical potential of this technique is significant, with early studies indicating its applicability to diseases such as multiple sclerosis, where both iron deposition and myelin degradation are key pathological features. In addition, the method holds promise for more accurate mapping of cortical myelination, which could improve our understanding of a variety of neurodegenerative diseases and brain aging.
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