Computational diffusion MRI : MICCAI Workshop最新文献

Superficial white matter (SWM) plays an important role in functioning of the human brain, and it contains a large amount of cortico-cortical connections. However, the difficulties of generating complete and reliable U-fibers make SWM-related analysis lag behind relatively matured Deep white matter (DWM) analysis. With the aid of some newly proposed surface-based SWM tractography algorithms, we have developed a specialized SWM filtering method based on a symmetric variational autoencoder (VAE). In this work, we first demonstrate the advantage of the spherical representation and generate these spherical tracts using the triangular mesh and the registered spherical surface. We then introduce the Filtering via symmetric Autoencoder for Spherical Superficial White Matter tractography (FASSt) framework with a novel symmetric weights module to perform the filtering task in a latent space. We evaluate and compare our method with the state-of-the-art clustering-based method on diffusion MRI data from Human Connectome Project (HCP). The results show that our proposed method outperform these clustering methods and achieves excellent performance in groupwise consistency and topographic regularity.

Quantitative diffusion weighted MRI in the abdomen provides important markers of disease, however significant limitations exist for its accurate computation. One such limitation is the low signal-to-noise ratio, particularly at high diffusion b-values. To address this, multiple diffusion directional images can be collected at each b-value and geometrically averaged, which invariably leads to longer scan time, blurring due to motion and other artifacts. We propose a novel parameter estimation technique based on self supervised diffusion denoising probabilistic model that can effectively denoise diffusion weighted images and work on single diffusion gradient direction images. Our source code is made available at https://github.com/quin-med-harvard-edu/ssDDPM.

Susceptibility-induced distortion is a common artifact in diffusion MRI (dMRI), which deforms the dMRI locally and poses significant challenges in connectivity analysis. While various methods were proposed to correct the distortion, residual distortions often persist at varying degrees across brain regions and subjects. Generating a voxel-level residual distortion severity map can thus be a valuable tool to better inform downstream connectivity analysis. To fill this current gap in dMRI analysis, we propose a supervised deep-learning network to predict a severity map of residual distortion. The training process is supervised using the structural similarity index measure (SSIM) of the fiber orientation distribution (FOD) in two opposite phase encoding (PE) directions. Only b0 images and related outputs from the distortion correction methods are needed as inputs in the testing process. The proposed method is applicable in large-scale datasets such as the UK Biobank, Adolescent Brain Cognitive Development (ABCD), and other emerging studies that only have complete dMRI data in one PE direction but acquires b0 images in both PEs. In our experiments, we trained the proposed model using the Lifespan Human Connectome Project Aging (HCP-Aging) dataset $(n=662)$ and apply the trained model to data $(n=1330)$ from UK Biobank. Our results show low training, validation, and test errors, and the severity map correlates excellently with an FOD integrity measure in both HCP-Aging and UK Biobank data. The proposed method is also highly efficient and can generate the severity map in around 1 second for each subject.

Fitting of the multicompartment biophysical model of white matter is an ill-posed optimization problem. One approach to make it computationally tractable is through Orientation Distribution Function (ODF) Fingerprinting. However, the accuracy of this method relies solely on ODF dictionary generation mechanisms which either sample the microstructure parameters on a multidimensional grid or draw them randomly with a uniform distribution. In this paper, we propose a stepwise stochastic adaptation mechanism to generate ODF dictionaries tailored specifically to the diffusion-weighted images in hand. The results we obtained on a diffusion phantom and in vivo human brain images show that our reconstructed diffusivities are less noisy and the separation of a free water fraction is more pronounced than for the prior (uniform) distribution of ODF dictionaries.

Traumatic brain injury (TBI) is a serious condition, potentially causing seizures and other lifelong disabilities. Patients who experience at least one seizure one week after TBI (late seizure) are at high risk for lifelong complications of TBI, such as post-traumatic epilepsy (PTE). Identifying which TBI patients are at risk of developing seizures remains a challenge. Although magnetic resonance imaging (MRI) methods that probe structural and functional alterations after TBI are promising for biomarker detection, physical deformations following moderate-severe TBI present problems for standard processing of neuroimaging data, complicating the search for biomarkers. In this work, we consider a prediction task to identify which TBI patients will develop late seizures, using fractional anisotropy (FA) features from white matter tracts in diffusion-weighted MRI (dMRI). To understand how best to account for brain lesions and deformations, four preprocessing strategies are applied to dMRI, including the novel application of a lesion normalization technique to dMRI. The pipeline involving the lesion normalization technique provides the best prediction performance, with a mean accuracy of 0.819 and a mean area under the curve of 0.785. Finally, following statistical analyses of selected features, we recommend the dMRI alterations of a certain white matter tract as a potential biomarker.