TL;DR:
- DEEP-squared, an innovative microscopy technique, offers faster and clearer imaging of living brain tissue.
- It builds upon the DEEP technique by incorporating machine learning to reduce the number of required images from hundreds to just 32.
- The collaborative effort between biologists, physicists, and computer scientists has driven the success of DEEP-squared.
- This advancement signifies the potential for cross-disciplinary cooperation to revolutionize microscopy technology and expand our understanding of organ imaging.
Main AI News:
In the relentless pursuit of imaging the intricate depths of a living brain, scientists have embarked on an extraordinary journey marked by innovation and collaboration. The quest for clearer, faster, and more efficient imaging techniques has led to remarkable breakthroughs, and at the forefront of this transformative endeavor is DEEP-squared – a groundbreaking advancement that promises to redefine the way we observe the inner workings of the brain.
In 2021, Dushan Wadduwage, a distinguished scientist at the FAS Center for Advanced Imaging, introduced DEEP, or De-scattering with Excitation Patterning, a pioneering microscopy technique featured in Science Advances. DEEP employed a computational algorithm that utilized multiple pre-encoded excitation patterns to illuminate multiple points on brain tissue simultaneously. The resulting images rivaled the quality of those obtained through conventional two-photon microscopy, yet required only a fraction of the images – mere hundreds compared to the typical hundreds of thousands. With DEEP, scientists could delve as deep as 300 microns into live mouse brains.
However, Wadduwage was not content with ‘good enough.’ Could DEEP produce pristine images with only tens of images? This question sparked the next phase of innovation.
In a recent publication in Light: Science and Applications, Wadduwage harnessed the power of machine learning to push the boundaries of the DEEP technique even further. Collaborating with co-authors, he trained a neural network-driven algorithm using AI on multiple sets of images. This innovative approach enabled them to reconstruct exceptionally clear images with just 32 scattered images, a significant reduction from the initial 256 images reported in their earlier work. This enhanced method was aptly named DEEP-squared: Deep learning powered de-scattering with excitation patterning.
The journey toward DEEP-squared began with the collection of images from typical two-photon point-scanning microscopy, serving as the “ground-truth.” The DEEP microscope, guided by the principles of physics, developed a computational model for image formation, which was then employed to simulate scattered input images. Through iterative training, the DEEP-squared AI model produced reconstructed images that remarkably resembled the ground-truth reference. Subsequently, these cutting-edge techniques were employed to capture vivid images of blood vessels within a mouse brain.
Wadduwage emphasized the interdisciplinary nature of this endeavor, where biologists conducting animal experiments, physicists constructing optical systems, and computer scientists developing algorithms seamlessly collaborated to craft this innovative solution. The fusion of expertise from various domains has propelled DEEP-squared to the forefront of microscopy technology, offering a glimpse into the limitless potential of cross-disciplinary cooperation.
As we look to the future, Wadduwage envisions further advancements in DEEP-squared, including improvements in instrument design to acquire data even more rapidly. This is a testament to the enduring commitment of scientists to continually refine and enhance their tools to unveil the mysteries of the brain.
Source: Medical Xpress
Conclusion:
DEEP-squared represents a groundbreaking leap in the field of high-quality organ imaging. Its capacity to provide clearer and faster results with significantly fewer images showcases the power of interdisciplinary collaboration in driving technological advancements. This innovation has the potential to reshape the market by enabling more efficient and detailed organ imaging, with far-reaching implications for medical research and diagnostics.
