Keywords: Tractography, Tractography & Fibre Modelling, Multimodal, Microscopy, structural connectivity, diffusion, machine learning

Motivation: Joint modelling of diffusion MRI and microscopy can leverage their complementary strengths to improve the estimation of fibre orientations. Ideally, these benefits would extend beyond the few datasets where dMRI and microscopy are acquired in the same brain to improve orientation estimates in in-vivo data.

Goal(s): To translate the unique properties of joint dMRI-microscopy data modelling to benefit in-vivo dMRI datasets.

Approach: We construct a domain adaptation adversarial network that can estimate microscopy-informed FODs from single-shell in-vivo dMRI.

Results: Tractography performed using network-derived FODs show improved tracking in grey matter, bottleneck regions, superficial white matter fibres, and long-range structural connectivity.

Impact: Our microscopy-informed neural network improves fibre orientation estimation from in-vivo single-shell dMRI datasets. We demonstrate improvements in fibre tracking that may enable more precise and detailed detection of connectivity, with a broad range of applications in basic and clinical neuroscience.

Keywords: Microstructure, Microstructure

Motivation: In-vivo brain microstructure can be estimated using diffusion MRI. However, most approaches do not quantify estimates reliability, although crucial for interpreting the results, and consider every voxel independently, leading to high uncertainties.

Goal(s): Our goal is to develop a new framework to efficiently estimate tissue microstructure and improve data fitting quality.

Approach: We propose Hierarchical-µGUIDE, a Bayesian method that estimates posterior distributions, by combining simulation-based inference with a hierarchical structure.

Results: Hierarchical-µGUIDE bypasses the high computational and time cost of conventional Bayesian approaches. Sharper microstructure parameter maps that preserve tissue heterogeneity are obtained, along with a tissue parcellation that segments an epileptic lesion.

Impact: The proposed Bayesian framework improves single-subject inference for clinical diagnosis, by efficiently estimating posterior distributions, reducing estimates uncertainty, and learning a tissue parcellation. This works unlocks the possibility to apply hierarchical Bayesian methods taylored for microstructure estimation to large datasets.

Keywords: Diffusion Reconstruction, Diffusion/other diffusion imaging techniques, gSlider, deep-learning, self-supervised, AI/ML Image Reconstruction

Motivation: gSlider utilizes radio-frequency encoding to acquire high and isotropic resolution brain diffusion-MRI with high SNR. However, this comes at the cost of prolonged acquisition time, which also increases the sensitivity to motion.

Goal(s): This work proposes gSlider Network (gNET) to accelerate gSlider from acquisitions with jointly subsampled RF- and q-space.

Approach: The self-supervised model was trained and tested on a 1mm³ resolution BUDA-gSlider dataset (T_acq = 32 min). FSL and the DIMOND self-supervised were used to estimate the diffusion parameters.

Results: gNET achieved an acceleration factor of R=2 and, when combined with DIMOND, reached a total R=4-fold (T_acq = 8 min).

Impact: gNET facilitates super-resolution dMRI by reducing the acquisition time by 4-fold with high fidelity. Its application may propel new discoveries in the neuroscientific field and the clinical translation of the gSlider framework.

Keywords: DWI/DTI/DKI, Diffusion/other diffusion imaging techniques, Denoising, Self-Supervised Learning, Spatio-Angular Domain

Motivation: Diffusion MRI (DMRI) suffers from heavy noise. The noise issue reduces the accuracy and reliability of the derived diffusion metrics.

Goal(s): Existing Deep Learning (DL) methods for DMRI denoising usually rely on training with paired noisy-clean data, which are unavailable in a clinical setting. Therefore, we propose a self-supervised DL denoising method, called Spatio-Angular Noise2Noise, for DMRI denoising.

Approach: We stem from the fact that a network trained with paired noisy data can capture the essential information of underlying clean data for noise reduction.

Results: Extensive experiments on simulated and real datasets demonstrate the superiority of SAN2N over existing DMRI denoising methods.

Impact: SAN2N can reduce the noise effectively and improve the quality of fiber ODFs and tractography.

Keywords: DWI/DTI/DKI, Brain, denoise

Motivation: Since the noisy magnitude MR data generally follows Rician distribution, using the noisy images and network’s output to construct unsupervised learning’s loss function for denoising will lead to a biased estimation, especially for DW images which suffers from the lower SNR. 

Goal(s): To address the noise bias issue.

Approach: We proposed two noise-correction loss functions for unsupervised denoising of DW images, based on DIP and the characteristics of Rician distribution.

Results: The experimental results on simulated and in-vivo data demonstrated that the proposed loss functions effectively corrected the signal-dependent noise bias and improved the accuracy of unsupervised learning-based DW images denoising method. 

Impact: Firstly, we proposed two noise-correction loss functions and validate their effectiveness in denoising DW images. Secondly, the proposed loss functions are not limited to DW images and can be directly applied to other modality MR images.

Keywords: Analysis/Processing, Signal Representations, AI, transformers, synthetic data, parameter estimation, IVIM

Motivation: Conventional model-fitting approaches neglect spatial information. Recent work showed promise in using convolutional neural networks (CNNs) trained on spatially-correlated synthetic data. However, the convergence rate remained suboptimal, and the spatial extent was limited.

Goal(s): To improve estimator performance by utilizing transformer networks and training on larger receptive-fields.  

Approach: Transformers with self-attention and neighborhood-attention with increased receptive-field were trained on spatially-correlated synthetic data (IVIM), and evaluated quantitatively using novel fractal-noise maps and in-vivo scans.

Results: Transformers excelled in integrating spatial information over CNNs. The application of larger receptive-fields with neighborhood-attention effectively leveraged correlated signal information from nearby voxels, leading to improved estimator performance.

Impact: The improved parameter estimation from neighborhood-attention models trained on synthetic data brings challenging ill-posed signal analysis problems, like IVIM, closer to clinical implementation. Additionally, the novel fractal-noise maps provide spatially-correlated ground truths, permitting new approaches to quantitative medical image analysis. 

Keywords: Microstructure, Diffusion/other diffusion imaging techniques, Diffusion analysis and visualization, biomarkers, cortical architecture, non-invasive virtual histology

Motivation: Advanced diffusion MRI (dMRI) has enabled noninvasive assessment of cortical measures conventionally only available from neuropathology. Analytical dMRI models are limited by restrictive model assumptions. 

Goal(s): In this study, we develop Diffusion-MRI based Estimation of Cortical Architecture using Machine-learning (DECAM), a translational framework of “noninvasive neuropathology” that can quantify cortical architecture based on dMRI. 

Approach: DECAM incorporates cortical label vectors to address the challenge of achieving perfect MRI-histology registration in primate brains due to their complex morphology. 

Results: By providing high-fidelity, reproducible whole-brain soma density maps validated with histology, DECAM paves the way for data-driven noninvasive histology for potential applications such as Alzheimer’s.

Impact: DECAM is the first translational framework and robust pipeline that addresses the challenge of estimating high-fidelity whole-brain soma density in primate brains with complex morphology. DECAM paves the way for data-driven noninvasive histology for potential applications such as Alzheimer’s.

Keywords: DWI/DTI/DKI, Diffusion Tensor Imaging, Deep Learning

Motivation: Existing methods tend to suffer from Rician noise, leading to detail loss during the reconstruction of DTI-derived parametric maps. This issue becomes particularly pronounced when sparsely sampled q-space data are used.

Goal(s): Our goal was to facilitate fast and high-fidelity estimation of DTI metrics.

Approach: We propose a novel SVD-based regularizer, which can effectively preserve fine details while suppressing noise during network training.

Results: Experimental results consistently demonstrate that the proposed method estimates DTI parameter maps with finer details, outperforming current state-of-the-art methods.

Impact: The proposed method may facilitate fast and high-fidelity DTI with a newly designed SVD-based regularizer, and it has a potential to become a practical tool in clinical and neuroscientific applications.

Keywords: Diffusion Reconstruction, Diffusion Tensor Imaging

Motivation: High-quality DTI requires numerous DWIs, extending scan times; however, despite deep learning's advances in reconstructing DTI with fewer DWIs, its adaptability across various gradient protocols remains limited, challenging its clinical application.

Goal(s): Our aim is to enable consistent, high-quality DTI reconstructions from fewer DWIs across different gradient schemes, enhancing adaptability in various clinical environments.

Approach: We employ self-supervised contrastive learning to extract and preserve key features between datasets derived from the same data with different gradient sampling methods.

Results: Our method reliably enhanced diffusion tensor maps from reduced DWIs across various gradient sampling schemes, outperforming both conventional methods and state-of-the-art deep learning model.

Impact: Our method creates high-quality DTI from fewer DWIs, reducing scan times and easing patient burden, while showing consistent performance across various gradient sampling schemes, ensuring high adaptability and ease of use in diverse clinical settings.