Magnetic Resonance Imaging (MRI) is one of the greatest inventions, which enable non-invasive imaging of internal structure and functions of humans. It’s being widely used for screening, diagnosis, treatment planning, and response assessment of various diseases.

It’s amazing to see that ~60% of our body consists of water!

It’s amazing to see that protons of water exhibit magnetic properties!

It’s amazing to see that inspite of having so many tiny magnets, we are diamagnetic! Thanks to randomness 😊.

It’s amazing to see how a strong magnet creates bulk magnetization or polarization; RF coils generate and detect MRI signal; gradient coils encode spatial localization and fill K-space; Fourier Transform of K-space generates MR image!

Research Interests:

Our group works on development of methods, techniques and software for medical image(MRI, CT) processing/analysis and evaluating their potential applications in disease diagnosis, treatment planning and response assessment.

Applications of Machine Learning/Deep Learning in Healthcare based upon Medical Imaging, particularly Multi-parametric MRI

• Diagnosis, grading/classification, treatment monitoring (Diseases: Cancer/tumor, Stroke, Osteoarthritis (OA), etc.)
• Development of Computer-Aided Diagnosis (CAD) systems

Development of quantitative methods, techniques and software tools in the field of MRI, particularly in:

• Perfusion-MRI, for mapping hemodynamic and physiological information
• CEST-MRI, for high resolution in vivo molecular mapping (CrCEST, GagCEST, GluCEST)
• Susceptibility-Weighted-Imaging (SWI), for micro-vascular/angiogenesis imaging
• Diffusion weighted MRI
• Relaxometry; T1, T2, T1rho mapping
• B0 and B1 field inhomogeneity
• Multiparametric MRI

Medical Image Processing

• Segmentation and classification of tissues
• Image registration
• Mitigation of noise and artifacts

Mathematical modeling

Professional Experience:

Education: