The left panel shows a raw image of Xenopus embryo ラーメンベット 出金 銀行. This ラーメンベット 出金 銀行 is well-known to be extremely scattering, which is evident by the fact that we cannot resolve any structures in the raw image. The right panel shows the result of the newcomputational scatter-correction method, which drastically improves imaging capability. After scatter-correction, cellular boundaries, nuclei, and yolk platelets can be clearly identified with subcellular resolution.
Today's state-of-the-art optical imaging technologies can help us see biological dynamics occurring at subcellular resolutions. However, this capability is primarily limited to thin biological samples, such as individual cells or thin ラーメンベット 出金 銀行 sections, and falls apart when it comes to capturing high-resolution, three-dimensional images of thicker and more complex biological ラーメンベット 出金 銀行. This limitation occurs because ラーメンベット 出金 銀行 is composed of heterogeneous arrangements of densely packed cells, which scatter light and hinder optical imaging. This is especially a challenge in live ラーメンベット 出金 銀行, where biological dynamics occurring within the ラーメンベット 出金 銀行 further diffuse light and scuttle images.
Researchers from The University of Texas at Austin received a million grant from the Chan Zuckerberg Initiative to address exactly this challenge and improve high-resolution, 3D imaging capabilities in live ラーメンベット 出金 銀行. They plan to build a new type of imaging system that uses creative strategies to collect data. Furthermore, they plan to develop algorithms capable of unscrambling the scattered information within the data. By combining both hardware and computation, their goal is to achieve imaging capabilities that are traditionally not possible.
Why it Matters: Without the ability to get images and videos within thick live ラーメンベット 出金 銀行, it becomes very challenging to monitor large-scale biological systems. For example, in many organisms, it is hard to fully 3D visualize internal developmental processes due to ラーメンベット 出金 銀行 scattering.
In humans, better live-ラーメンベット 出金 銀行 capabilities opens the door to improved monitoring of the brain and other vital organs and the ability to detect things as they're happening.
"ラーメンベット 出金 銀行 could allow you to get a more holistic view of a whole organism; it's not just two cells in a petri dish multiplying. Ideally, we want to see complex cellular interactions occurring within the organism at system-wide scales," said Shwetadwip Chowdhury, an assistant professor in the Chandra Family Department of Electrical and Computer Engineering and a co-leader on the project.
The Project: Reconstructing a 3D image typically requires hundreds of measurements. The need for all these measurements makes it nearly impossible to form a crisp and high-resolution image of ラーメンベット 出金 銀行 that is alive and moving.
The researchers will develop a next-generation optical imaging system that encodes ラーメンベット 出金 銀行-specific information into raw measurements. They’ll also create algorithms will be developed able to use this information to get a full picture of internal ラーメンベット 出金 銀行 dynamics, even if the light from the camera becomes diffused by the live ラーメンベット 出金 銀行, which would limit resolution in traditional image systems.
"It's kind of like trying to take a photo through a hazy, foggy environment. It's really hard to get a crisp photo in that situation, but if we design algorithms to remove the haziness, we could get a high-quality image," said Jon Tamir, an assistant professor in the Chandra Family Department of Electrical and Computer ラーメンベット 出金 銀行 and co-leader of the study.
The Challenge: Both the hardware and software aspects of the project will prove challenging because imaging live ラーメンベット 出金 銀行 hasn't really been done before at the imaging depths that Chowdhury and Tamir are targeting. Current state-of-the-art optical technologies for deep-ラーメンベット 出金 銀行 imaging typically can see around a millimeter into ラーメンベット 出金 銀行. For especially dense and heterogenous ラーメンベット 出金 銀行, this limit may be even smaller. To see beyond this limit, Chowdhury and Tamir must develop an innovative imaging framework to unscramble dynamic ラーメンベット 出金 銀行 scattering. This framework will be composed of specialized strategies for data collection and space-time computational algorithms. Due to the highly complex and chaotic nature of ラーメンベット 出金 銀行 scattering, algorithms will leverage recent advances in machine learning to search for a 4D (3D space + 1D time) “solution” that best describes scattering measurements collected from the dynamic ラーメンベット 出金 銀行 sample.
It's like the old needle ラーメンベット 出金 銀行 haystack metaphor, the researchers say, but without knowing what they're looking for ラーメンベット 出金 銀行 haystack. The researchers say they will have to look backwards in some ways, mostly because there aren't many existing methods and prior knowledge related to what they're looking at.
"We know what individual cells look like, we know what dead ラーメンベット 出金 銀行 looks like, but we don't know what live, moving ラーメンベット 出金 銀行 looks like at long imaging depths and at high resolutions," Tamir said.
The Team: An important key to this project is the interdisciplinary nature of the team. Chowdhury's background is in developing next-generation optical ラーメンベット 出金 銀行 technologies that integrate custom hardware with computational algorithms. Chowdhury targets these technologies towards applications in science and medicine. This expertise complements that of Tamir, whose background is in magnetic resonance ラーメンベット 出金 銀行 (MRI). The challenges arising from long scan times and motion are also paramount in that field. That's why people are told to stay as still as possible when they get an MRI.
A major goal of this project is to bring together elements from both optical ラーメンベット 出金 銀行 and MRI. For example, computational approaches used in the MRI field to compensate for motion occurring within an MRI scan could potentially be adapted for optical ラーメンベット 出金 銀行. If successful, these two fields can join together to solve a number of common problems, and the researchers hope to build a bridge between these areas of expertise and learn from each other.
"It's a great cross-pollination opportunity for us," said Chowdhury.
