Joyoni Deydey

Associate Professor

Ph.D. in Electrical and Computer Engineering, 1999 - Carnegie Mellon University

Louisiana State University
Department of Physics & Astronomy
459-A Nicholson Hall, Tower Dr.
Baton Rouge, LA 70803-4001


  • Fall: MedP 7111: Advanced Medical Imaging Physics
  • Spring: MedP 4111: Introduction to Medical Imaging
  • Spring: (Co-Instructor): Radiological Physics for Residents (LSU Health Sc)

Research Grants

  • NIH NIBIB Trail-blazer Award 1-R21-EB029026-01A1 (PI: Dey, J)  Breast Cancer Detection and Imaging using Analyzer-less X-ray Interferometry, July 2020 - March 2023 (~3 years), Direct: $400K, Total: $524,584
  • NSF EPSCOR RII Track 4 (PI: Dey, J)  Neutron Imaging Interferometry for Non-Destructive Testing, Feb 2020 - Jan 2021 (2 years), Direct: $155,262, Total: $227,680.  Award # 1929150 


  • J. Dey and S.J. Glick, "SPECT Camera Design", Patent No., US 8,519,351 B2, Aug 27, 2013. 
  • J. Dey, N. Bhusal, L. Butler, J. P. Dowling, K. Ham, V. Singh, "Phase Contrast X-ray Interferometry" US Patent No., 10872708 , Dec 22, 2020. 



  • Jingzhu Xu  (Graduated, May 2020). Received Coates Research Scholar Award, 2019, Physics & Astronomy for her dissertation topic, $5000. 


  • Hunter Meyer (Current)
  • Bryce Smith (Current)
  • Sydney Carr (Current)
  • Lacey Medlock (Current)
  • Ivan Hidrovo (Graduated, Summer 2022)
  • Elizabeth Park (Graduated, Summer 2022)
  • Hanif Soysal (Graduated, Summer 2019)
  • Narayan Bhusal (Graduated, December 2018)  


  • Bryce Smith (2019-2020, Summer 2020)
  • Ivan Hidrovo (2016, 2017, 2018 )
  • Megan Chesal 2016, 2017, 2018)
  • David Sanchez (REU, Summer 2017)


  • Research Interest in Medical Imaging, Image Processing and Deep Learning: Interferometric Imaging, Imaging system design and optimization reducing patient dose, imaging time; Image reconstruction with physical modeling; Deep-learning for oncological prediction and staging; Mathematical (pde) models of tumor growth and treatment; Segmentation and registration. My research focuses on new systems and algorithms designed to help large patient populations with new imaging advances: for example, new methods of imaging, higher resolution and sensitivity systems for lowering the dose to patients, better quality diagnostic images, more efficient acquisition etc.; pathological quantification algorithms; predictions for oncology; correction of motion artifacts for better diagnosis.

Current Projects Include


  • Neutron Interferometry: Neutrons show wave-particle duality. They manifest interference effects similar to X-rays and visible light.  We wish to analyze and maximize the neutron interferometric beamline performance with simulations and experiments. For this purpose, we are building an analytical and Monte-Carlo based simulator which will be validated experimentally at National Institute of Standards and Technology (NIST) Center for Neutron Research (NCNR). Neutrons interact relatively weakly with metal compared to X-rays. This is useful for imaging bone-metal joints, where X-ray imaging would lead to strong metal artifacts. The research team will perform biomedical imaging, such as non-invasive ex-vivo imaging of bone-implant interface at NCNR. In collaboration with scientists at the NIST Center for Neutron Research, Gaithersburg, MD. 
  • Phase-Contrast X-ray interferometry not only provides attenuation images provided by conventional CT but also scatter and phase images, affording higher soft-tissue contrast in images compared to conventional CT. We are investigating a novel modulated phase grating (MPG) system to bring Phase Contrast X-ray a step closer to the clinic. In collaboration with Professor Les Butler (Dept of Chemistry, LSU), Dr. Kyungmin Ham (CAMD, LSU).

Example Relevant Publications/Patents/Disclosures

System Design and Optimization

  • Optimizing a Novel Gamma Camera design for Cardiac SPECT: We are investigating a high-sensitive and/or high-resolution gamma-camera design with a system of Ellipsoid detectors with pinhole collimation for Cardiac SPECT, which is an important modality for assessing myocardial perfusion with millions of patients undergoing nuclear cardiology scans per year. In course of our research, we have built a comprehensive multi-pinhole system simulator and iterative reconstruction.

Example Relevant Publications/Patents/Disclosures

Deep-learning applications to Imaging

Building Bioanalytical Tools using Mathematical Models

  • Tumor progress and disease treatment monitoring by extracting biophysical-model-parameters from images: Oncology Imaging is performed using modalities including CT, MRI, FDG-PET, SPECT etc. Applying realistic mathematical models to serial-images of tumors extracts biologically relevant information from images, such as cell-motility, growth-rate etc. We are investigating a new mathematical Finite Element tumor-model where effects of the necrotic core are considered in addition to cell-motility, growth, apoptosis and migration. We also acquired 6 preclinical serial SPECT/CT datasets and fitted an existing ode compartmental volume model. In collaboration with J. M. Mathis, PhD, (Dept of Comparative Biomedical Sciences, School of Vet Medicine, LSU) and S. W. Walker, PhD (Dept of Mathematics, LSU).

Example Relevant Publications/Patents/Disclosures

Example Relevant Publications/Patents/Disclosures