Noninvasive imaging method can penetrate deeper into living tissue

 Metabolic imaging is a noninvasive method that enables clinicians and scientists to study living cells using laser light, which can help them assess disease progression and treatment responses.

But light scatters when it shines into biological tissue, limiting how deep it can penetrate and hampering the resolution of captured images.

Now, MIT researchers have developed a new technique that more than doubles the usual depth limit of metabolic imaging. Their method also boosts imaging speeds, yielding richer and more detailed images.

This new technique does not require tissue to be preprocessed, such as by cutting it or staining it with dyes. Instead, a specialized laser illuminates deep into the tissue, causing certain intrinsic molecules within the cells and tissues to emit light. This eliminates the need to alter the tissue, providing a more natural and accurate representation of its structure and function.

The researchers achieved this by adaptively customizing the laser light for deep tissues. Using a recently developed fiber shaper -- a device they control by bending it -- they can tune the color and pulses of light to minimize scattering and maximize the signal as the light travels deeper into the tissue. This allows them to see much further into living tissue and capture clearer images.

Greater penetration depth, faster speeds, and higher resolution make this method particularly well-suited for demanding imaging applications like cancer research, tissue engineering, drug discovery, and the study of immune responses.

"This work shows a significant improvement in terms of depth penetration for label-free metabolic imaging. It opens new avenues for studying and exploring metabolic dynamics deep in living biosystems," says Sixian You, assistant professor in the Department of Electrical Engineering and Computer Science (EECS), a member of the Research Laboratory for Electronics, and senior author of a paper on this imaging technique.

She is joined on the paper by lead author Kunzan Liu, an EECS graduate student; Tong Qiu, an MIT postdoc; Honghao Cao, an EECS graduate student; Fan Wang, professor of brain and cognitive sciences; Roger Kamm, the Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering; Linda Griffith, the School of Engineering Professor of Teaching Innovation in the Department of Biological Engineering; and other MIT colleagues. The research will appear in Science Advances.

Source: ScienceDaily