![]() The heat map of standardized kurtosis values derived from optimal QSI (myelin map) was then created. For this purpose, animal studies were first performed to optimize the acquisition protocol of a non-Gaussian QSI metric. In the current study, we aimed to refine QSI protocols to enable their clinical application and to visualize myelin signals in a clinical setting. Quantitation of non-Gaussianity for water diffusion by q-space diffusional MRI (QSI) renders biological diffusion barriers such as myelin sheaths however, the time-consuming nature of this method hinders its clinical application. Whole brain fiber-tractography from 26 sparsely-sampled DWIs (left), the results from fully-sampled DWIs (center), and results from our framework (right).White matter abnormalities in the CNS have been reported recently in various neurological and psychiatric disorders. Under fully synthetic settings (0 DWIs), our result displays improved fidelity compared to direct B0, T2, T1 to scalar translation. As the number of DWIs decreases, model-fitting results show strong quality degradation and qDL-2D produces inaccurate structural details (see yellow insets), whereas our method preserves microstructure with high fidelity (see external capsule/red arrows). NODDI (ODI) and DKI (RK) scalar maps are estimated by different methods from arbitrarily downsampled DWIs. Quantitative microstructural estimation quality under varying DWI acquisition budgets as measured by SSIM (higher is better). visualizes a test slice where artifacts (red arrows) appear in the reference DWI, which are corrected by the generated DWIs from our methods. shows translation results with a standard DWI slice in the test set as the reference II. All potential input channels (B0, T1, T2) are visualized on the left. Synthesized DWI images (right) compared to reference image (middle) for different gradient vectors in q-space.ĭWI synthesis results under various model configurations: A uses B0 as model input constrained by L1 loss only B, C, D include additive adversarial components on top of A, while C and D take additional T2/T1 image as model input. Merged with arbitrarily downsampled DWIs, various microstructural indices can be calculated with diffusion model fitting. Once trained, the generator is able to synthesize DWIs along gradients in q-space (blue dots). Our q-space conditioned translation framework. Across several recent methodologies, the proposed approach yields improved DWI synthesis accuracy and fidelity with enhanced downstream utility as quantified by the accuracy of scalar microstructure indices estimated from the synthesized images. Moreover, this approach enables the downstream estimation of high-quality microstructural maps from arbitrarily subsampled DWIs, which may be particularly important in cases with sparsely sampled DWIs. Our translation network linearly modulates its internal representations conditioned on continuous Q-space information, removing the need for fixed sampling schemes. We propose a generative adversarial translation framework for high-quality DW image estimation with arbitrary Q-space sampling given commonly acquired structural images. Further, such approaches restrict direct downstream estimations, such as tractography, from arbitrarily sampled DWIs unless we use model fitting methods spherical harmonics, for example. However, they implicitly make unrealistic assumptions of static Q-space sampling during reconstruction. ![]() Recent deep learning approaches for Diffusion MRI modeling circumvent the requirement of densely-sampled diffusion-weighted images (DWIs) by directly predicting microstructural indices from sparsely-sampled DWIs supervised with fully-sampled DWIs.
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