I am interested in biomedical Image and signal processing, combined with different machine learning techniques to aid in clinical diagnosis and treatment procedures of various diseases.
At VUIIS, I am trying to analyze the intrinsic functional connectivity of the spinal cord of non-human primates, using the Local field Potential signal . In particular, I am trying to do data-driven analysis for analyzing the intrinsic functional connectivity of the spinal cord.
Also, in another project I am involved in applying machine learning methods to differentiate between different stages of Alzeihmer's using White matter resting state connectivity obtained using fMRI.
A Sengupta, S Agarwal, PK Gupta, S Ahlawat, R Patir, RK Gupta, A Singh. On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI.European journal of radiology 106, 199-20
A Sengupta, RK Gupta, A Singh.Evaluation of B 1 inhomogeneity effect on DCE-MRI data analysis of brain tumor patients at 3T.Journal of translational medicine 15 (1), 242
Anirban Sengupta, Anandh K. Ramaniharan,Rakesh K Gupta, Sumeet Agarwal.Glioma Grading Using a Machine?Learning Framework Based on Optimized Features Obtained From T1 Perfusion MRI and Volumes of Tumor Components. Journal of Magnetic Resonance Imaging. https://doi.org/10.1002/jmri.26704
Sengupta A, Sahoo P, Gupta P, Gupta RK, Singh A. T1 mapping using 3-point FSE and multi-flip-angle methods and effect of B1 field inhomogeneity on T1 in human brain on 3T MRI scanner. ESMRMB Annual Scientific Meeting 2016, Vienna. PN-202
Sengupta A, Gupta RK, Agarwal S and Singh A. A Machine Learning Based Approach for Fast T1 estimation with Improved Accuracy. ISMRM: 25th Annual Meeting & Exhibition-2017, Honolulu, USA, (Proc.Intl.Soc.Mag.Reson.Med. 25(2017), Page Nu-3994).