UCLA Engineers Build World’s Fastest Camera to Detect Cancer Cells

July 7, 2012, By Sanjeev Ramachandran

What is the important part of treating cancer? The ability to distinguish and isolate cancer cells from among a large population of assorted cells. But this is not easy as it sounds. However, a new optical microscope developed by UCLA engineers is trying to make things easier.

They have managed to integrate the photonic time-stretch camera technology with advanced microfluidics and real-time image processing in order to classify cells in blood samples.

The new blood-screening technology has a throughput of 100,000 cells per second, about 100 times higher than conventional imaging-based blood analyzers.

UCLA Engineers Build World’s Fastest Camera to Detect Cancer Cells

Achieving good statistical accuracy requires an automated, high-throughput instrument that can examine millions of cells in a reasonably short time.

Bahram Jalali, from UCLA Henry Samueli School of Engineering and Applied Science says that to catch these elusive cells, the camera must be able to capture and digitally process millions of images continuously at a very high frame rate.

Conventional CCD and CMOS cameras are not fast and sensitive enough. It takes time to read the data from the array of pixels, and they become less sensitive to light at high speed.

So he teamed up with Dino Di Carlo, a UCLA associate professor of bioengineering, with expertise in optics and high-speed electronics, microfluidics, and biotechnology, to develop a high-throughput flow-through optical microscope with the ability to detect rare cells with sensitivity of one part per million in real time.

The research demonstrates real-time identification of rare breast cancer cells in blood with a record low false-positive rate of one cell in a million.

The results were obtained by mixing cancer cells grown in a laboratory with blood in various proportions to emulate real-life patient blood.

The technology has taken us one step closer to statistically accurate early detection of cancer and for monitoring the efficiency of drug and radiation therapy. The technology is also potentially useful for urine analysis, water quality monitoring and related applications

The team is currently performing clinical tests in collaboration with clinicians to further validate the clinical utility of the technology.

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