Med.uth.edu
CURRICULUM VITAE
February 28, 2018
NAME: Sushmita Datta, Ph.D.
PRESENT TITLE: Assistant Professor of Diagnostic and Interventional Imaging
ADDRESS: 3014 Pennfield Park CT
Katy, TX 77494
BUSINESS Department of Diagnostic and Interventional Imaging
ADDRESS: The University of Texas-Houston Health Science Center, Medical School,
MSE R166
6431 Fannin Street
Houston, Texas 77030
CITIZENSHIP: USA
UNDERGRADUATE Maharshi Dayanand Saraswati University, Ajmer
EDUCATION: Rajasthan, India
1990-1993 B.Sc.
GRADUATE Indian Institute of Technology Kanpur, India
EDUCATION: 1995-2003 Ph.D., Mathematics
Indian Institute of Technology Kanpur, India
1993-1995 M.Sc., Mathematics
ACADEMIC & Assistant Professor
ADMINISTRATIVE Diagnostic and Interventional Imaging
APPOINTMENTS: Medical School
University of Texas-Houston Health Science Center
2008-Present
Post-doctoral Fellow
Department of Diagnostic and Interventional Imaging
Medical School
University of Texas-Houston Health Science Center
2003-2008
CERTIFICATION: SAS Certified Base Programmer for SAS 9
SAS Certified Advanced Programmer for SAS 9
PROFESSIONAL International Society of Magnetic Resonance in Medicine
ORGANIZATIONS: (ISMRM)
2006 – present
EDITORIAL Reviewer, Abstracts for IEEE Engineering in Medicine and Biology
POSITIONS: Society Conference 2009 – 2011
Reviewer, Abstracts for ISMRM Conference 2010 - present
Reviewer, Radiology
2010 – present
Reviewer, NeuroImage: Clinical
2013 – present
AWARDS: Radiology Editor’s Recognition Award for reviewing with Distinction (2011, 2012)
MENTOR:
RESEARCH ASSOCIATES:
Xiaojun Sun, 2008 - Present
Priya Goel, 2009 - 2012
Shakuntala Kondraganti 2017 - Present
SUMMER STUDENTS:
Dante M. Soares, Summer 2009
T. Davis Staewen, Summer 2010
Avyay Reddy, Summer 2016
CONTRIBUTION TO SCIENCE:
I have led the development of automated segmentation package, MRIAP (Magnetic Resonance Imaging Automated Processing). The comprehensive software package verifies the images for quality and performs all the pre-processing that includes image registration, intensity non-uniformities correction, semi-automated brain extraction, segmentation of T2 hyperintense lesions, T1 hypointense lesions (black holes), and T1 hyperintense lesions (Gd-enhanced lesions, normal tissues including gray matter, white matter, and cerebrospinal fluid. The software package also includes semi-automated techniques for reducing false classifications by an operator. In addition, it provides various tools for analyzing the data by addressing the issues associated with the images that violate the protocol. The MRIAP software package is used for various clinical trials led by Dr. Lublin and Dr. Jerry Wolinsky. I first developed the automated identification and quantification of black holes in MS and published the work in NeuroImage.
Further, I developed an automated technique for the identification of lesion growth and regression using images from two timepoints. This significantly helps monitor changes in lesions and to evaluate treatment efficacy in time. I have developed methodology for automated brain extraction using T2 weighted images that is fast and robust. The technique provides better estimation of intracranial brain by retaining extracellular CSF. I also published work on the improvement of the classification of whole brain by addressing the intensity variation between cerebellum and cerebral cortex refining the cerebellum GM-WM classification. I was first in reporting the intensity difference of cerebellum from cerebral cortex and published the work in JMRI. Other groups reported similar observations thereafter. In addition, I developed the automated classification of three-dimensional MR images for T2-hyperintense lesions and normal tissues by incorporating anatomical knowledge and exploiting the differential intensities between the cerebellum and cerebral tissues.
Later, I analyzed the cohort of CombiRx trial for regional atrophy by applying tensor-based morphometry (TBM). I also analyzed the same cohort for disease prediction using cortical thickness measurements and the results suggest that the change in cortical thickness in first six months could predict cortical thinning in three years. Automated techniques are developed for the identification of the pathological components of Glioblastoma on FLAIR images and T1 post-contrast images by applying grayscale morphological reconstructions techniques. The developed techniques take into account the intensity variation, contextual information within, and surrounding pathology using multi-modal images.
Currently, I am working on the identification and classification of infarcts in patients with stroke and TBI. Following infarct extraction, the remaining brain is classified into normal tissues. The classification techniques are applied in on-going clinical trials led by Dr. Narayana sponsored by SanBio. In addition, I am involved in data management for acquired images arriving from different centers and the results that are being provided to vendors associated with the trials.
CURRENT GRANT SUPPORT:
Image Analysis Center (Narayana/Savitz) 6/15/15 – 6/30/18 6.0 Calendar
SanBio, Inc. Annual DC $255,984
Major Goals: Provide MRI analysis on the traumatic brain injured patients treated with stem cells as a part of phase 2 clinical trials.
Image Analysis Center (Narayana/Savitz) 11/09/15 – 10/31/2018 6.0 Calendar
SanBio, Inc. Annual DC $495,123
Major Goals: Provide MRI analysis on the Stroke patients treated with stem cells as a part of phase 2 clinical trials.
PAST GRANT SUPPORT:
Narayana, Ponnada (Principal Investigator)
NIH/NINDS
Lesion Activity and Atrophy in Multiple Sclerosis: Analysis of Multi-center MRI
Grant # 5 R01 NS078244-03 (Narayana)
9/1/2012 - 5/31/2017
Total support (DC only) $329,176
Kramer, Larry (Principal Investigator)
NSBRI
SPACE-COT:Studying the Physiology of head tilt
Grant # NCC 9-58-73
05/01/2015 - 04/30/2016
Total support (DC only) $69,213
Wolinsky, Jerry (Principal Investigator)
Sanofi-Aventis
Study Efc6260: Teriflu (HMR1726/EFC6260)
Grant # HMR1726/EFC6260
10/01/2007 to 12/15/2014
Narayana, Ponnada (Principal Investigator)
NIH/NIBIB
Automated MR Image Analysis in MS: Identification of a Surrogate
Grant # 2 R01 EB02095
9/15/02 – 2/29/13
Total support $2,658,860.390
Lublin, Fred (Principal Investigator)
NIH/NINDS
Combination Therapy in Multiple Sclerosis
Grant # U01 NS045719-01A1
2/1/2003 – 11/30/2012
Total Support (DC only) $10,073,501
FUTURE GRANT SUPPORT PLANS:
Automated classification of Glioblastoma using multi-modal images.
Texture analysis of MR images for automated identification of diffuse white matter in MS.
PUBLICATIONS
A. ABSTRACTS:
1. Datta, S., Gupta, R.K., Rao, S.B., Kaliprasad, V.S.N., and Rathore, R.K.S. Segmentation of Lesions in MRI using Cubic Splines. National Symposium on Magnetic Resonance and Biomolecular Structure and Function, TIFR India, January 17-20, 2000.
2. Rao, S.B., Gupta, R.K., Kaliprasad, V.S.N., Datta, S., and Rathore, R.K.S. A Robust Method for Tissue Parameters Estimation in MR Imaging. National Symposium on Magnetic Resonance and Biomolecular Structure and Function, TIFR India, January 17-20, 2000.
3. Rathore, R.K.S., Gupta, R.K., Kaliprasad, V.S.N., Rao, S.B., and Datta, S. Iterative Sharpening of the Resolution in Magnetic Resonance Imaging. Proc. Scientific Meeting of ISMRM at Colorado, USA, April 2000.
4. Datta, S., Rao, S.B., Rathore, R.K.S., Gupta, R.K., and Verma, R. A Segmentation Method Based on Maximum Likelihood Estimation for Quantitation in MR Images. Symposium on Spatially Resolved Magnetic Resonance & 7th NMRS Symposium, Chennai, February 7-10, 2001.
5. Rao, S.B., Datta, S., Rathore, R.K.S., Gupta, R.K., and Verma, R. A Numerical Study of Errors in Tissue Parameter Estimation. Symposium on Spatially Resolved Magnetic Resonance & 7th NMRS Symposium, Chennai, February 7-10, 2001.
6. Rathore, R.K.S., Datta, S., Gupta, R.K., Rao, S.B., and Verma, R. An MLE Based Segmentation Method for Quantitation in MR Images. Proc. 9th Scientific Meeting of ISMRM at Glasgow, April 2001.
7. He, R., Datta, S., Sajja, B.R., Mehta, M., and Narayana, P.A. Adaptive FCM with contextual constrains for segmentation of multi-spectral MRI. IEEE EMBS International Conference, San Francisco, USA, September 2004.
8. Sajja, B.R., Datta, S., He, R., and Narayana, P.A. A Unified Approach for Lesion Segmentation on MRI of Multiple Sclerosis. IEEE EMBS International Conference, San Francisco, USA, September 2004.
9. Datta, S., Sajja, B.R., He, R., and Narayana, P.A. Automatic Segmentation of Black Holes in Multiple Sclerosis on MR Images. Proc.13th Scientific Meeting of ISMRM at Miami, USA, May 2005.
10. Sajja, B.R., Datta, S., He, R., and Narayana, P.A. Minimization of False MS Lesion Classifications on MR Images: Quantitative Validation. Proc. 13th Scientific Meeting of ISMRM at Miami, USA, May 2005.
11. Datta, S., Sajja, B.R., He, R., Wolinsky, J.S., and Narayana, P.A. Segmentation of Gray- And White Matter on MR Brain Images in Multiple Sclerosis. Proc. 14th Scientific Meeting of ISMRM at Seattle, USA, May 2006.
12. Mogatadakala, K.V., Datta, S., Poonawalla, A.H., Hasan, K.M., Wolinsky, J.S., and Narayana, P.A. Identification of Abnormal White Matter in Multiple Sclerosis. Proc. 14th Scientific Meeting of ISMRM at Seattle, USA, May 2006.
13. He, R., Datta, S., Sajja, B.R., and Narayana, P.A. Histogram-Based Acceleration of EM Algorithm for Segmentation of Multi-Spectral MRI with Contextual Constraints. Proc. 14th Scientific Meeting of ISMRM at Seattle, USA, May 2006.
14. Bhat, H.V., Sajja, B.R., Datta, S., and Narayana, P.A. Fast Analysis of 1H Magnetic Resonance Spectroscopic Imaging Data: An Artificial Neural Network Based Approach. Proc. 14th Scientific Meeting of ISMRM at Seattle, USA, May 2006.
15. Sajja, B.R., Datta, S., He, R., and Narayana, P.A. Fast Segmentation of MR Brain at 3T using Phase Sensitive Inversion Recovery Images. Proc. 15th Scientific Meeting of ISMRM at Berlin, Germany, May 2007.
16. Poonawalla, A.H., Datta, S., Nelson, F., Wolinsky, J.S., and Narayana, P.A. High Resolution 3D MRI of Cortical Lesions in Multiple Sclerosis. Proc. 15th Scientific Meeting of ISMRM at Berlin, Germany, May 2007.
17. Datta, S., Sajja, B.R., He, R., Dieber, J.M., Wolinsky, J.S., and Narayana P.A. Segmentation of MR Brain Images with Intensity Correction and Partial Volume Averaging. Proc. 16th Scientific Meeting of ISMRM at Toronto, Canada, May 2008.
18. Tao, G., He, R., Datta, S., and Narayana, P.A. Inverse Consistent Geometric Flow based Nonlinear Registration Driven by Mutual Information. Proc. 16th Scientific Meeting of ISMRM at Toronto, Canada, May 2008.
19. Tao, G., Datta, S., He, R., and Narayana, P.A. Mutual Information Driven Inverse Consistent Nonlinear Registration. IEEE EMBS International Conference, Vancouver, Canada, August 2008.
20. He, R., Datta, S., Tao, G., and Narayana, P.A. Information Measures-Based Intensity Standardization of MRI. IEEE EMBS International Conference, Vancouver, Canada, August 2008.
21. Poonawalla, A.H., Datta, S., Juneja, V., Nelson, F., Wolinsky, J.S., Cutter, G., and Narayana, P.A. Inverse dependence between patient population and correlation of composite MRI scores with EDSS in Multiple Sclerosis. Proc. 17th Scientific Meeting of ISMRM at Hawaii, USA, April 2009.
22. Poonawalla, A.H., Datta, S., Juneja, V., Nelson, F., Wolinsky, J.S., Cutter, G., and Narayana, P.A. Improved correlation of composite MRI scores with EDSS in Multiple Sclerosis. Proc. 17th Scientific Meeting of ISMRM at Hawaii, USA, April 2009.
23. Tao, G., Datta, S., He, R., and Narayana, P.A. Atrophy and Shape Changes in Deep Gray Matter in Multiple Sclerosis: A Tensor Based Morphometry. Proc. 17th Scientific Meeting of ISMRM at Hawaii, USA, April 2009.
24. Hasan, K.M., Kamali, A., Iftikhar, A., Datta, S., Nelson, F., Wolinsky, J.S., and Narayana, P.A. Diffusion Tensor Tractogrpahy Quantification of Wallerian Degeneration of the Uncinate Fasciculus in Multiple Sclerosis. Proc. 17th Scientific Meeting of ISMRM at Hawaii, USA, April 2009.
25. Datta, S., Wolinsky, J.S., and Narayana, P.A. Knowledge-Driven Automated Segmentation of Cortical Lesions on MR Brain Images in MS. Proc. 18th Scientific Meeting of ISMRM at Stockton, Sweden, May 2010.
26. Datta, S., Sun, X., and Narayana, P.A. Cerebellar GM-WM segmentation accuracy in assessing brain atrophy. Proc. 19th Scientific Meeting of ISMRM at Montreal, Quebec, Canada, May, 2011.
27. Datta, S., Sun, X., Shukla, K., and Narayana, P.A. Improved segmentation with high-resolution 3D MR images in MS. Proc. 17th Annual Meeting of the Organization of human Brain Mapping at Quebec City, Canada, June 2011.
28. Datta, S., Sun, X., Shukla, K., and Narayana, P.A. Improving lesion classification using an empirical knowledge of false classifications in multiple sclerosis. Proc. 20th Scientific Meeting of ISMRM at Melbourne, Australia, May 2012.
29. Datta, S., and Narayana, P.A. Automated Identification of Lesion Activity in Multiple Sclerosis. 2012 BMES Annual Meeting at Atlanta, USA, October 2012.
30. Datta, S., Staewen, T.D., Goel, P., Cofield, S.S., Cutter, G.R., Lublin, F.D., Wolinsky, J.S., and Narayana, P.A. Regional Gray Matter Atrophy in Multiple Sclerosis using Tensor Based Morphometry: A Multi-Center Study. Proc. 21st Scientific Meeting of ISMRM at Salt Lake City, USA, April, 2013.
31. Datta, S., Staewen, T.D., Cofield, S.S., Cutter, G.R., Lublin, F.D., Wolinsky, J.S., and Narayana, P.A; CombiRx Investigators Group. How Important is Lesion In-painting on Gray Matter Atrophy in Multiple Sclerosis? Proc. 22nd Scientific Meeting of ISMRM, Milan, Italy, May, 2014.
32. Narayana, P.A., Datta, S., Staewen, T.D., Cofield, S.S., Cutter, G.R., Lublin, F.D., and Wolinsky, J.S; CombiRx Investigators Group. Temporal changes in regional atrophy in a large, relapsing multiple sclerosis cohort. Proc. 22nd Scientific Meeting of ISMRM, Milan, Italy, May, 2014.
33. Datta, S., Govindarajan, K.A., Cofield, S.S., Cutter, G.R., Lublin, F.D., Wolinsky, J.S., and Narayana, P.A. Prediction of disease course in multiple sclerosis using cortical thinning measurements at baseline. Proc. 23rd Scientific Meeting of ISMRM, Toronto, Canada, June, 2015.
34. Datta, S., Zhu, J-J., Riascos, R., and Narayana, P.A. Morphological reconstruction based automated segmentation of glioblastoma tumor volume on multi-modal MRI images. 20th Annual meeting of Society for Neuro-Oncology, San Antonio, Texas, November 19-22, 2015.
35. Datta, S., Zhu, J-J., Riascos-Castaneda, R.F., and Narayana, P.A. Automated extraction of glioblastoma tumor sub-components using multi-modal MRI. 24th Annual meeting of ISMRM, Singapore, May 2016.
B. REFEREED ORIGINAL ARTICLES IN JOURNALS:
1. Gupta, R.K., Husain, N., Kathuria, M.K., Datta, S., Rathore, R.K.S., and Husain, M. Magnetization Transfer MR Imaging Correlation with Histopathology in Intracranial Tuberculomas. Clinical Radiology 56: 656-663, 2001.
2. Datta, S., Sajja, B.R., He, R., Wolinsky, J.S., Gupta, R.K., and Narayana, P.A. Narayana, Segmentation and quantification of black holes in Multiple Sclerosis. Neuroimage 29: 467-474, 2006.
3. Sajja, B.R., Datta, S., He, R., Mehta, M., Gupta, R.K., Wolinsky, J.S., and Narayana, P.A. Unified Approach for Multiple Sclerosis Lesion Segmentation on Brain MRI. Annals of Biomedical Engg. 34: 142-151, 2006.
4. Datta, S., Sajja, B.R., He, R., Gupta, R.K., Wolinsky, J.S., and Narayana, P.A. Segmentation of gadolinium-enhanced lesions on MRI in multiple sclerosis. Journal of Magnetic Resonance Imaging 25: 932-937, 2007.
5. He, R., Datta, S., Sajja, B.R., and Narayana, P.A. Generalized Fuzzy Clustering for Segmentation of Multi-Spectral Magnetic Resonance Images. Computerized Medical Imaging and Graphics 32: 353-356, 2008.
6. He, R., Sajja, B.R., Datta, S., and Narayana, P.A. Volume and Shape in Feature Space on Adaptive FCM in MRI Segmentation. Annals of Biomedical Engg. 36:1580-1593, 2008.
7. Datta, S., Tao, G., He, R., Wolinsky, J.S., and Narayana, P.A. Improved Cerebellar Tissue Classification on Magnetic Resonance Images of Brain. Journal of Magnetic Resonance Imaging 29:1035-1042, 2009.
8. Tao, G., Datta, S., He, R., Nelson, F., Wolinsky, J.S., and Narayana, P.A. Deep Gray Matter Atrophy in Multiple Sclerosis: A Tensor Based Morphometry. Journal of the Neurological Sciences 282:39-46, 2009.
9. Tao, G., He, R., Datta, S., and Narayana, P.A. Symmetric inverse consistent nonlinear registration driven by mutual information. Computer Methods and Programs in Biomedicine 95:105-115, 2009.
10. Narayana, P.A., Datta, S., Tao, G., Steinberg, J.L., and Moeller, F.G. Effect of Cocaine on Structural Changes in Brain: MRI Volumetry using Tensor-Based Morphometry. Drug and Alcohol Dependence 111:191-199, 2010.
11. Poonawalla, A.H., Datta, S., Juneja, V., Nelson, F., Wolinsky, J.S., Cutter, G., and Narayana, P.A. Composite MRI Scores Improve Correlation with EDSS in Multiple Sclerosis. Multiple Sclerosis 16:1117-1125, 2010.
12. Yu, X., Zhang, Y., Lasky, R.E., Datta, S., Parikh, N.A., and Narayana, P.A. Comprehensive Brain MRI Segmentation in High Risk Preterm Newborns. PLoS One 5:e13874, 2010.
13. Datta, S. and Narayana, P.A. Automated brain extraction from T2-weighted magnetic resonance images. Journal of Magnetic Resonance Imaging 33::822-829, 2011.
14. Nelson, F., Datta, S., Garcia, N., Rozario, N.L., Perez, F., Cutter, G., Narayana, P.A., and Wolinsky, J.S. Intracortical lesions by 3T magnetic resonance imaging and correlation with cognitive impairment in multiple sclerosis. Multiple Sclerosis 17:1122-1129, 2011.
15. Hasan, K.M., Walimuni, I.S., Abid, H., Datta, S., Wolinsky, J.S., and Narayana, P.A. Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls: Implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS. Journal of the Neurological Sciences 314:99-109, 2012.
16. Datta, S., and Narayana, P.A. A comprehensive approach to the segmentation of multichannel three-dimensional MR brain images in multiple sclerosis. Neuroimage: Clinical 2:184-196, 2013.
17. Narayana, P.A., Govindarajan, K.A., Goel, P., Datta, S., Lincoln, J.A., Cofield, S.S., Cutter, G.R., Lublin, F.D., and Wolinsky, J.S. Regional cortical thickness in relapsing remitting multiple sclerosis: A multi-center study. Neuroimage: Clinical 2:120-131, 2013.
18. Wolinsky, J.S., Narayana, P.A., Nelson, F., Datta, S., O'Connor, P., Confavreux, C., Comi, G., Kappos, L., Olsson, T.P., Truffinet, P., Wang, L., Miller, A., Freedman, M.S.; for the Teriflunomide Multiple Sclerosis Oral (TEMSO) Trial Group. Magnetic resonance imaging outcomes from a phase III trial of teriflunomide. Multiple Sclerosis Journal 19:1310-1319, 2013.
19. Nelson, F., Poonawalla, A., Datta, S., Wolinsky, J., Narayana, P. Is 3D MPRAGE better than the combination DIR/PSIR for cortical lesion detection at 3T MRI? Multiple Sclerosis and Related Disorders 3:253:257, 2014.
20. Narayana, P.A., Zhou, Y., Hasan, K.M., Datta, S., Sun, X., and Wolinsky, J.S. Hypoperfusion and T1-hypointense lesions in white matter in multiple sclerosis. Multiple Sclerosis Journal 20:365-373, 2014.
21. Datta, S., Staewen, T.D., Cofield, S.S., Cutter, G.R., Lublin, F.D., Wolinsky, J.S., and Narayana, P.A. Regional gray matter atrophy in relapsing remitting multiple sclerosis: Baseline analysis of multi-center data. Multiple Sclerosis and Related Disorders 4:124-136, 2015.
22. Govindarajan, K.A., Datta, S., Hasan, K.M., Choi, S., Rahbar, M.H., Cofield, S.S., Cutter, G.R., Lublin, F.D., Wolinsky, J.S., Narayana, P.A.; MRI Analysis Center at Houston, The CombiRx Investigators Group. Effect of in-painting on cortical thickness measurements in multiple sclerosis: A large cohort study. Hum Brain Mapp 36:3749-3760, 2015.
23. Murray, K.O., Nolam, M.S., Ronca, S.E., Datta, S., Govindarajan, K.A., Narayana, P., Salazar, L., Woods, S.P., Hasbun, R. The neurocognitive and MRI outcomes of West Nile virus infection: Preliminary Analysis Using an External Control Group. Frontiers in Neurology (accepted).
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