Hybrid model-based and data-driven imaging for high-resolution cellular tomography
Official title: Hybrid Model-Based and Data-Driven Frameworks for High-Resolution Tomographic Imaging
Funded at University of Calif-Lawrenc Berkeley Lab · NIH-11398044 · Data updated 2026-11-12
A new imaging approach blends advanced math and learning methods to produce crisper images of biomolecules inside cells.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Calif-Lawrenc Berkeley Lab NIH-funded |
| Lab location | 1 site (Berkeley, United States) |
| Project start | September 15, 2023 |
| Project end | August 31, 2028 |
| Funding year | 2026 |
| Award (this year) | $505,225 |
| NIH project number | 5R35GM150982-04 |
| Project ID | NIH-11398044 on NIH RePORTER |
Who could benefit from this research
✓Could help
Not applicable to patients directly; this is a technology development project for high-resolution imaging in biology.
✕Less likely to help
Individuals will not directly receive patient care through this grant.
What this research studies
This project aims to improve cellular tomography by combining automated 3D alignment with regularized reconstructions using iterative and convex optimization techniques. It also introduces model-based and data-driven algorithms powered by machine learning to handle challenges like radiation damage and artifacts. The goal is to achieve higher resolution in reconstructed images of cells, organelles, and molecular machines in their native state. Researchers will test these methods on cryo-electron and X-ray tomography data to visualize structure-function relationships more clearly.
Why it matters
Potential benefit: If successful, patients could benefit indirectly through faster and more accurate imaging techniques that aid in diagnosing and understanding diseases at the cellular level.
How similar studies have gone: Previous imaging methods have improved with better alignment and reconstruction, but this project seeks further gains using joint optimization and learning.
Where this research is happening
-
University of Calif-Lawrenc Berkeley Lab
Berkeley, United States Active
Researchers
- Principal investigator: Pande, Kanupriya — University of Calif-Lawrenc Berkeley Lab
- Study coordinator: Pande, Kanupriya
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.