RESEARCH
Multiparametric MRI in Biopsy-Naive Prostate Cancer
Current challenges of using serum prostate-specific antigen (PSA) level-based screening, issues of overdiagnosis with random biopsy, and multifocality and molecular heterogeneity of prostate cancer (PCa) can be addressed by integrating pre-biopsy multiparametric MR imaging (mpMRI) approaches into the diagnostic workup. We previously showed the superiority of mpMRI to TRUS-guided systematic biopsy for detecting clinically significant prostate cancer. Nevertheless, the early stages of incorporating mpMRI and targeted biopsy are underway, and there are various potential sources of errors associated with the use of mpMRI and software-based fusion biopsy devices. These errors can lead to the possibility of clinically significant diseases being overlooked or missed.
We thus aim to use advanced MR methods eg. Hybrid Multi-dimensional MRI to non-invasively measure prostatic tissue composition and to guide prostate biopsy. This will be the first study of its kind in the Asian population.
Integrating Radiomics, Metabolomics, and Radiogenomics with Deep Learning for Renal Tumor Analysis
Introducing a groundbreaking project that combines the power of radiomics, metabolomics, and radiogenomics signatures with state-of-the-art deep learning techniques to revolutionize the field of renal tumor analysis. Our innovative approach aims to extract comprehensive data from medical imaging, metabolite profiles, and genomic information to uncover hidden patterns and insights in renal tumors.
By integrating deep learning algorithms, we can unlock the full potential of these multi-dimensional datasets, enabling more accurate diagnosis, personalized treatment strategies, and improved patient outcomes. The synergy of radiomics, metabolomics, and radiogenomics holds tremendous promise for enhancing our understanding of renal tumors and guiding clinical decision-making.
With this project, we aim to push the boundaries of medical imaging and computational analysis, paving the way for a new era in renal tumor research and patient care.
Assessing Morphometric, Metabolic, and Tractographic Alterations in Alzheimer's Disease using mpMRI with Integrated Transfer Learning
Our lab has also started exploring the potential of MR methods in various Alzheimer's disease (AD). AD is a progressive neurodegenerative disorder characterized by cognitive decline and memory loss. This study aims to investigate the specific cortical and subcortical changes, metabolic changes, and tractography changes in AD using Multiparametric MRI (mpMRI) techniques and Transfer Learning techniques. mpMRI can provide quantitative measurements of the brain's microstructure, which can reveal subtle changes in the brain's organization and function as transfer learning focuses on leveraging knowledge gained from one task to improve performance on a related task.
mpMRI scans will be performed using a 1.5T/3T MRI scanner. Cognitive function will be assessed using standardized neuropsychological tests. In house modified, Freesurfer, LC Model, DSI studio and other software will be used for comprehensive analysis of mpmRI of AD. The study is expected to provide insights into the specific alterations that occur in cortical and subcortical structures such as reduced hippocampal, entorhinal cortex volumes, and cortical thickness. Further, we expect changes in FA values in different regions of brain particularly in hippocampus in AD. We also expect metabolic alterations NAA/myo-inositol in AD. These alterations can be used as biomarkers for AD diagnosis and monitoring disease progression.
Furthermore, incorporating transfer learning enhances the accuracy and predictive power of the analysis, and can offer a cutting-edge approach to understanding the intricacies of the disease progression. This combined approach can offer a holistic understanding of AD, paving the way for enhanced diagnostic procedures and potential therapeutic interventions.
Traumatic Brain Injuries: Advanced Diffusion MRI Techniques and AI Methods
India faces significant challenges in managing Traumatic Brain Injury (TBI), with current diagnostic tools like CT scans and Diffusion Tensor Imaging (DTI) demonstrating limitations in specificity and accurate assessment of TBI including assessment of crossing fiber tracts in TBI, highlighting a need for more advanced mathematical and diffusion approaches
To address this, our pioneering approach integrates advanced multicompartment diffusion-MRI models (NODDI, DKI, etc) and groundbreaking AI/ML methods (FSRCNN, SRDenseNet) to enhance the resolution of MRI scans. This circumvents the need for costly high-field machinery, establishing a novel benchmark in TBI diagnostics.
Advancing Breast Cancer Diagnosis and Treatment through AI and mpMRI Techniques
Breast cancer is the most frequently diagnosed cancer, accounting for 11.7% of all new cancers diagnosed in 2020. Breast cancer is a major healthcare challenge around the world, necessitating early detection and effective treatment strategies to improve patient outcomes. Recent advancements in artificial intelligence (AI) based methods (eg. radiomics, deep, and machine learning) have shown potential to improve the diagnostic accuracy in improved detection and characterization of breast lesions.
We evaluate the potential of mpMRI methods for suspicious clinical symptoms and equivocal findings on mammography and detection of additional tumor foci in the ipsilateral and contralateral breast.
Design and Development of Computer Vision-based Screening Tool for Automatic Diagnosis of Osteoporosis (Co-Investigator)
Osteoporosis is a chronic disease characterized by progressive bone loss and changes in microstructural levels, resulting in increasing bone weakness and fragility. The main objective of this research project is to develop a “Computer Vision based Screening Tool for Automatic Diagnosis of Osteoporosis” to assess the bone health condition and its severity using artificial intelligence (AI) based methods. This device will be economical, user friendly and it will not require any expert or specialized manpower to operate it.
The resulting prototype shall be practically usable and affordable, making it well suited for rapid medical screening at primary health care centers.
Other areas:
Beside these, we have also initiated a preliminary work on emerging MR methods (MRS, rfMRI, etc) in neurological diseases and NAFLD.