We mostly work on Magnetic Resonance Imaging (MRI) and a
bit on X-Ray Computed Tomography (CT). They are two very
different imaging modalities and the challenges are different as
well. But the underlying signal processing techniques are
MRI is a versatile imaging modality. It is safe and can
produce very quality images. But the main hindrance in its
widespread application is its slow data acquisition time. So how
do we tackle this?
The only way to speed up the scan time is to acquire less
data. Now the question arises - how do we reconstruct the image
from less data? Compressed Sensing (CS) has played an integral
part in answering this questions in the last half a decade. CS
reconstructs MR images by exploiting their spatial redundancies
(for static MRI) or spatio-temporal redundancies (for dynamic
We work on almost all the different MRI problems; but most
notably on multi-channel parallel MRI and dynamic MRI. We have
devised robust techniques for parallel MRI with a fitting name -
CaLM (Calibration Less Multi-channel) MRI. It performs better
than commercial (GRAPPA) and state-of-the-art (l1 SPIRiT)
methods; but its major advantage stems from the fact that it
does not require any calibration or parameter estimation.
In dynamic MRI, the challenge is to reduce the scan time
in order to improve temporal resolution. The best reconstruction
methods fuse multiple approaches of sparse and low-rank recovery
to achieve this goal. We too have some results worth noticing.
In CT imaging, the problem is different. CT is harmful
owing to its ionizing radiation. How can we make it safer? - We
got to reduce the subject's exposure to ionizing radiation
without compromising on image quality. The only way this can be
achieved is by reducing the amount of data we usually acquire.
Again CS comes to the aid. The approach here is similar to MRI -
even though the goals are different.