Home > Press > Device invented by UCLA professor uses deep learning and photonic time stretch to analyze 36 million images per second
UCLA’s Claire Lifan Chen and Ata Mahjoubfar with the new device, which can image cancer cells extremely quickly and without damaging blood samples. Credit: Tunde Akinloye/CNSI |
Abstract:
Scientists at the California NanoSystems Institute at UCLA have developed a new technique for identifying cancer cells in blood samples faster and more accurately than the current standard methods.
In one common approach to testing for cancer, doctors add biochemicals to blood samples. Those biochemicals attach biological “labels” to the cancer cells, and those labels enable instruments to detect and identify them. However, the biochemicals can damage the cells and render the samples unusable for future analyses.
There are other current techniques that don’t use labeling but can be inaccurate because they identify cancer cells based only on one physical characteristic.
The new technique images cells without destroying them and can identify 16 physical characteristics — including size, granularity and biomass — instead of just one. It combines two components that were invented at UCLA: a photonic time stretch microscope, which is capable of quickly imaging cells in blood samples, and a deep learning computer program that identifies cancer cells with over 95 percent accuracy.
Deep learning is a form of artificial intelligence that uses complex algorithms to extract meaning from data with the goal of achieving accurate decision making.
The study, which was published in the journal Nature Scientific Reports, was led by Barham Jalali, professor and Northrop-Grumman Optoelectronics Chair in electrical engineering; Claire Lifan Chen, a UCLA doctoral student; and Ata Mahjoubfar, a UCLA postdoctoral fellow.
Photonic time stretch was invented by Jalali, and he holds a patent for the technology. The new microscope is just one of many possible applications; it works by taking pictures of flowing blood cells using laser bursts in the way that a camera uses a flash. This process happens so quickly — in nanoseconds, or billionths of a second — that the images would be too weak to be detected and too fast to be digitized by normal instrumentation.
The new microscope overcomes those challenges using specially designed optics that boost the clarity of the images and simultaneously slow them enough to be detected and digitized at a rate of 36 million images per second. It then uses deep learning to distinguish cancer cells from healthy white blood cells.
“Each frame is slowed down in time and optically amplified so it can be digitized,” Mahjoubfar said. “This lets us perform fast cell imaging that the artificial intelligence component can distinguish.”
Normally, taking pictures in such minuscule periods of time would require intense illumination, which could destroy live cells. The UCLA approach also eliminates that problem.
“The photonic time stretch technique allows us to identify rogue cells in a short time with low-level illumination,” Chen said.
The researchers write in the paper that the system could lead to data-driven diagnoses by cells’ physical characteristics, which could allow quicker and earlier diagnoses of cancer, for example, and better understanding of the tumor-specific gene expression in cells, which could facilitate new treatments for disease.
The study’s other authors were Li-Chia Tai, Ian Blaby and Allen Huang of UCLA, and Kayvan Niazi of NantBio. The research was supported by NantWorks, LLC, the parent company of NantBio.
####
For more information, please click here
Contacts:
Shaun Mason, CNSI
310-794-5346
Copyright © UCLA
If you have a comment, please Contact us.Issuers of news releases, not 7th Wave, Inc. or Nanotechnology Now, are solely responsible for the accuracy of the content.
Related Links |
Related News Press |
News and information
Simulating magnetization in a Heisenberg quantum spin chain April 5th, 2024
NRL charters Navy’s quantum inertial navigation path to reduce drift April 5th, 2024
Discovery points path to flash-like memory for storing qubits: Rice find could hasten development of nonvolatile quantum memory April 5th, 2024
Good as gold - improving infectious disease testing with gold nanoparticles April 5th, 2024
Cancer
New micromaterial releases nanoparticles that selectively destroy cancer cells April 5th, 2024
Imaging
Nanoscale CL thermometry with lanthanide-doped heavy-metal oxide in TEM March 8th, 2024
The USTC realizes In situ electron paramagnetic resonance spectroscopy using single nanodiamond sensors November 3rd, 2023
Possible Futures
Discovery points path to flash-like memory for storing qubits: Rice find could hasten development of nonvolatile quantum memory April 5th, 2024
With VECSELs towards the quantum internet Fraunhofer: IAF achieves record output power with VECSEL for quantum frequency converters April 5th, 2024
Announcements
NRL charters Navy’s quantum inertial navigation path to reduce drift April 5th, 2024
Discovery points path to flash-like memory for storing qubits: Rice find could hasten development of nonvolatile quantum memory April 5th, 2024
Interviews/Book Reviews/Essays/Reports/Podcasts/Journals/White papers/Posters
Simulating magnetization in a Heisenberg quantum spin chain April 5th, 2024
Discovery points path to flash-like memory for storing qubits: Rice find could hasten development of nonvolatile quantum memory April 5th, 2024
Tools
Ferroelectrically modulate the Fermi level of graphene oxide to enhance SERS response November 3rd, 2023
The USTC realizes In situ electron paramagnetic resonance spectroscopy using single nanodiamond sensors November 3rd, 2023
The latest news from around the world, FREE | ||
Premium Products | ||
Only the news you want to read!
Learn More |
||
Full-service, expert consulting
Learn More |
||