Synopsis
Recent applications of microstructure imaging in clinical studies are introduced. The reported results show microstructure imaging holds promise to provide clinically valuable information, though further validation and optimization are necessary to be really used in the clinics.
1. Sensitivity or specificity
Diffusion MRI (dMRI) signal reflects many microstructure properties; distributions of cell sizes, shapes, and density, orientation distribution, demyelination, membrane permeability, etc. Since first demonstrated in acute cerebral infarction [1], this unique sensitivity of dMRI remarkably changed our image diagnosis, providing useful image contrasts in many diseases [2-4], though, for most cases, the exact pathological substrates behind the signal abnormalities have not been completely elucidated. The sensitivity to subtle abnormalities that cannot be detected by macroscopic imaging also brought insights into mechanisms of many neuropsychological diseases [5-7].
Though specificity is one of the goal of microstructure imaging, it is much more difficult to achieve than sensitivity. Specificity requires modeling (covered by the next talk) [8-11]. The validity of model is currently under active researches [12-14], and it is yet not very clear what microstructure properties can be exactly recovered from clinically feasible acquisition. Besides, in pathology, the true model might be different from normal tissue. Nonetheless, simplified models proposed to suit clinical needs [15-17] have been applied in diseases and suggested the derived indices are of potential utility. The decision whether to employ models or not relies on what one wants to achieve in the particular work. Here, results from both approaches are described, as most of clinical applications, whether using models or representations [8], seem primarily aiming at practical utility, ability to discriminate clinically relevant conditions.
2. Applications in clinical researches
Demyelinating diseases
In multiple sclerosis (MS), axonal damages in the normal appearing white matter (NAWM) are present early in the disease and independent contributors to the progression of symptoms [18]. Indices of axon density estimated from dMRI have been shown sensitivity to such occult pathology in the patients [19-22]. In a recent study, axonal water fraction of the corticospinal tract was shown to predict clinical worsening at 1-year follow-up [23]. Comparison with neuromyelitis optica spectrum disorder (NMOSD), another demyelinating disease, is also emerging [24], aiming to characterize NAWM pathology in NMOSD that is likely to be different from that of MS [25,26].
Parkinson’s disease
In Parkinson’s disease (PD), Neurite Orientation Dispersion and Density Imaging (NODDI) [16] successfully demonstrated retrograde axonal degeneration of the nigro-striatal pathway [27], in agreement with histology [28]. In addition, combining NODDI with myelin-sensitive magnetization transfer imaging [29] showed propagation of degeneration in the cerebral white matter with disease progression and that axonal degeneration precedes demyelination [30].
Correlation with clinical outcome
Many studies reported correlations between dMRI parameters and neurocognitive functions in both normal and diseased populations. A few examples that have relevance to short-term outcome and therapeutic interventions are introduced below. In temporal lobe epilepsy, DTI and DKI parameters have shown sensitivity to the extra-hippocampal damage, which is related to post-surgical seizure control [31,32]. A recent work on CO intoxication [33] showed that DKI parameters at 1 week after the event predicted delayed neuropsychological impairment. In patients with meningiomas, consistency of the mass is valuable information for surgeons and prediction by MRI has been sought [34]. FA and MD showed correlation with consistency of meningiomas [35]. Further Investigation using multiple diffusion encoding (MDE) will be interesting, as it enables more specific decomposition, microscopic anisotropy and isotropic diffusion variance, and has been tested in brain tumors [36]. Clinically feasible MDE protocols have been recently proposed [37,38].
3. What can microstructure imaging bring to clinical practice?
Clinical imaging is limited in terms of available hardware and scan time. However, even when the acquisitions required for advanced models are not very feasible, knowledge from microstructure imaging may have implications to improve clinical imaging. NODDI showed good demarcation of focal cortical dysplasia (FCD) [39], indicating we can improve detection by emphasizing certain aspects of the measured signal. Similarly, it is probably worth to consider if we can find some practically useful metrics of time-dependent diffusion within the clinically available range of t [40]. Of course, as shown in a study of prostate cancer [41], more comprehensive understanding of the underlying model will make such processes more efficient.Acknowledgements
No acknowledgement found.References
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