Angel Pineda

My research interests are in medical inverse problems. My current research focuses on statistical methods and data analysis in magnetic resonance imaging and x-ray computed tomography. Most medical imaging tasks can be viewed as the estimation of a parameter (such as the size of a tumor) or detection of a lesion under various types of uncertainty. My work tries to optimize performance of medical imaging systems on these tasks.

My current emphasis is to accelerate magnetic resonance imaging (MRI) with evaluation of how the acceleration affects the clinical tasks. In this work, we use statistical detection of signals as a way of optimizing the methods to accelerate MRI. The project combines psychophysical studies of humans detecting tumors with mathematical models to develop model observers that can predict human performance in the detection tasks. These observers are then used to optimize both the acquisition and reconstruction of undersampled MRI images. The reconstruction methods include compressed sensing and neural networks which introduce interesting questions in optimization and machine learning. This work is done in collaboration with student researchers at Manhattan College and researchers at the University of Iowa, University of Southern California and University of California, Santa Barbara. This project is funded by NIH R-15 grant 1R15EB029172-01 .

1. Pineda AR, Miedema H , Lingala SG, Nayak KS, “Optimizing constrained reconstruction in magnetic resonance imaging for signal detection”, Physics in Medicine and Biology, 66, 2021, 145014.
2. Scarinci M, Encarnacion M, Pineda AR, Evans LS, “Visualization of Xylary Rings of Stems of Artemisia tridentata spp. Wyomingensis.”, Universal Journal of Applied Mathematics, 5, 2017, 28-33.
3. Caceres L, de la Pena JA, Pineda AR, Di Prisco C, Solotar, A, "Mathematics in Latin America and the Caribbean: So Much Happening, So Much to Do", Notices of the American Mathematical Society, 61, 2014, 1052-1055.
4. Kwembe TA, Leonard K, Pineda AR, "Academic Year Undergraduate Research: the CURM Model", Involve, 7, 2014, 383-394.
5. Baek J, Pineda AR, Pelc NJ, "To Bin or Not to Bin?, The Effect of CT System Limiting Resolution in Noise and Detectability", Physics in Medicine and Biology, 58, 2013, 1433-1446.
6. Pineda AR, Tward DJ, Gonzalez A, Siewerdsen JH "Beyond Noise-Power in 3D Computed Tomography:The Local NPS and Off-Diagonal Elements of the Fourier Covariance Matrix", Medical Physics, 39, 2012, 3240-3252.
7. Reeder SB, Bice EK,Yu H, Hernando D, Pineda AR, "On the Performance of T2* Correction Methods for Quantification of Hepatic Fat Content", Magnetic Resonance in Medicine. 67, 2012, 389-404.
8. Reeder SB, McKenzie CA, Pineda AR, Yu H, Brau AC, Shimakawa A, Hargreaves BA, Gold GE, Brittain JH, "Water-Fat Separation with IDEAL Gradient Echo Imaging", Journal of Magnetic Resonance Imaging, 25, 2007, 644-652. (*One of 25 papers chosen for the 25th Anniversary Edition of the Journal*)
9. Pineda AR, Yoon S, Paik DS, Fahrig R, "Optimization of a Tomosynthesis System for the Detection of Lung Nodules", Medical Physics, 33, 2006, 1372-1379.
10. Pineda AR, Reeder SB, Wen Z, Pelc NJ, "Cramer-Rao Bounds in 3-Point Decomposition of Water and Fat", Magnetic Resonance in Medicine, 54, 2005, 625-635.
11. Reeder SB, Pineda AR, Wen Z, Shimakawa A, Yu H, Gold GE, Beaulieu CH, Pelc NJ, "Iterative Decomposition of Water and Fat with Echo Asymmetry and Least Squares Estimation (IDEAL):Application with Fast-Spin Echo Imaging", Magnetic Resonance in Medicine, 54, 2005, 636-644.
12. Pineda AR, Barrett HH, "Figures of Merit for Detectors in Digital Radiography. I. Flat Background and Deterministic Blurring", Medical Physics, 31, 2004, 348-358.
13. Mason T, Pineda AR,Wofsy C, Goldstein B, "Effective Rate Models for the Analysis of Transport-Dependent Biosensor Data", Mathematical Biosciences, 159, 1999, 123-144.
14. Pineda AR, Root RG, "Mathematical Modeling of a Radially Inhomogeneous Plate under Load and Tension", Journal of Applied Mechanics, 64, 1997, 233-237.

Previous Academic Positions:

• 2013-2016 Associate Professor of Mathematics, CSU, Fullerton (CSUF)
• 2007-2013 Assistant Professor of Mathematics, CSU, Fullerton (CSUF)

Society Memberships:

• Society of Industrial and Applied Mathematics (SIAM)
• Mathematical Association of America (MAA)
• Association for Women in Mathematics (AWM)
• International Society for Magnetic Resonance in Medicine (ISMRM)
• International Society for Optics and Photonics (SPIE) 

Service:

• Comission for Developing Countries, International Mathematical Union: http://www.mathunion.org/cdc/

Selected Honors & Awards:

• 2011 CSU, Fullerton Teacher Scholar Award for Mentoring Student Research, awarded by the College of Science and Mathematics
• 2008-09 Mathematical Association of America Dolciani-Halloran Project NExT Fellow
• 2004-06 American Lung Association Senior Research Training Fellowship
• 1998-99 University of Arizona Michael A. Cusanovich Research Fellowship
• 1995 Volunteer of the Year, Lafayette College
• 1995 Phi Beta Kappa
• 1994 Tau Beta Pi
• 1992 Sigma Xi

Selected Grants:

• NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) 1R15EB029172-01, “Optimizing Acquisition and Reconstruction of Under-sampled MRI for Signal Detection”, Role: PI, 2020-2023, Funding: $395,210.

  Research grant involving undergraduate and graduate students using
  data science and statistics to improve magnetic resonance imaging.

• 2014-15 NSF DMS 1345012 Role: PI (with S. Annin at CSUF) Funding: $600,146 MCTP: Graduate Readiness and Access in Mathematics (GRAM)
  Comprehensive mentoring program to prepare underrepresented math students to be successful in graduate studies.
• 2010-11 NSF (through CURM at BYU) Role: PI 
  Separating Chemical Species in Magnetic Resonance Imaging
  The grant funds research with four undergraduates at CSUF and faculty development as a research mentor.
• 2007-10 NIH 1R01CA112163 Role: Co-PI at CSUF (PI: Jeffrey H. Siewerdsen at JHU)
  Image Science for the New X-ray: Taking NEQ to Task
  Explored non-stationarity in CT reconstructions and it's connection to the Fourier Covariance Matrix.
• Summer 2008 GE Healthcare Technologies Role: PI (Co-PI: William Gearhart) 
  Understanding the Mathematics of HYPR-type Algorithms
  The grant funds the work of CSUF students in working to analyze a new imaging method for GE.