Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.

Shiv Nadar University

Diffusion tensor imaging (DTI) is a non-invasive technique for analyzing the movement of water in the brain. However, the precision of measurements required for tracking white matter pathways can lead to long scan times, which can be challenging for some patient populations such as pediatric patients. To address this issue, researchers have been experimenting with deep learning techniques for faster estimation of DTI parameters, which are helpful in neurological diagnosis, of diffusion-weighted images. Our proposed solution is a transformer neural network-based approach for fast estimation of diffusion tensor parameters using sparse measurements. While there have been attempts to address this problem, our proposed model handles both scalable and generalized estimation of DTI parameters using multiple sparse measurements. Through experimentation on the Human Connectome Project (HCP) Young Adult benchmark dataset, our proposed model demonstrated state-of-the-art results in terms of fractional anisotropy (FA), axial diffusivity (AD), and mean diffusivity (MD) when compared to traditional linear least square (LLS) fitting and 3D U-Net model with input size (3D U-Net16).

Computer Science and Engineering

(SoE) School of Engineering

Neural Computing and Applications

SwinDTI: Swin transformer based generalized fast estimation of diffusion tensor parameters from sparse data

Alzheimer disease is one of the most common neuro-degenerative diseases, with an estimated 6.2 million cases in the United States. This research article investigates the potential of Transformer-based deep learning techniques to accelerate the processing of diffusion tensor imaging (DTI) measures and improve the early diagnosis of Alzheimer disease (AD) using sparse data. Diffusion Weighted Imaging (DWI) is a time-consuming process, with each diffusion direction taking between 2-5 minutes, and at least 40 diffusion directions are needed for routine clinical diagnosis, which needs scanning duration exceeding 3 hours for each patient. By leveraging the attention mechanism, our proposed model generates quantitative measures of fractional anisotropy (FA), axial diffusivity (AxD), and mean diffusivity (MD) using 5 and 21 diffusion directions, making it useful for clinical diagnosis through reduced scanning time of more than half. Our experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset demonstrate that our proposed model outperforms the traditional linear least square method, achieving accurate quantitative measurement of FA, AxD, and MD scores for early diagnosis of AD patients from healthy controls using sparse diffusion directions. Our analysis highlights the potential of Swin-Transformer attention-based deep learning framework to improve the early diagnosis and treatment of Alzheimer’s disease.

Proceedings of Machine Learning Research

Early Diagnosis of Alzheimer through Swin-Transformer-Based Deep Learning Framework using Sparse Diffusion Measures

Frontotemporal dementia (FTD) is a devastating neurodegenerative disorder that primarily affects the frontal and temporal lobes of the brain, leading to cognitive decline and behavioral changes. Early and accurate diagnosis of FTD is crucial for initiating timely interventions and providing appropriate care to patients. In the opinion of the experts, about 12-22 persons out of the population of 100,000 persons experience FTD. That means between 1.2 million and 1.8 million people have it worldwide. This research paper proposes a novel deep learning framework that utilizes sparse diffusion measures extracted from neuroimaging data to aid in the early diagnosis of Frontotemporal Dementia. The proposed model leverages the power of deep learning techniques to automatically learn relevant features from the data and effectively distinguish between healthy individuals and those with FTD. The experimental results demonstrate the promising potential of the proposed approach in improving FTD diagnosis and paving the way for future research in this area.