Saturday, June 29, 2024
For centuries, microscopes have been limited by the capabilities of their optical lenses, offering either high resolution with a small field of view or low resolution with a larger field of view.
In 2013, engineers introduced Fourier ptychographic microscopy (FPM), blending traditional microscope sensing with computer algorithms to produce sharper, deeper images over larger areas.
FPM quickly gained traction for its ability to capture high-resolution images while maintaining a broad field of view using affordable equipment.
Recently, a new technique has emerged that surpasses FPM by delivering clearer images with fewer measurements.
Described in a Nature Communications paper, this method has potential applications in biomedical imaging, digital pathology, and drug screening.
The new technique, Angular Ptychographic Imaging with Closed-form method (APIC), improves upon FPM by eliminating its major drawback: the need for iterative adjustments to achieve an optimal solution.
APIC solves a linear equation to identify and correct optical aberrations, yielding clear images over large fields of view without iteration.
Bioengineering, and Medical Engineering, the researchers found a way to remove the iterative nature of the FPM algorithm. APIC directly solves for aberrations, ensuring accurate and clear images.
"They can now solve for the high-resolution complex field in a closed-form manner, eliminating the need for iteration." This guarantees that we see the true details of a sample.
Like FPM, APIC measures both the intensity and phase of light, with phase information crucial for correcting aberrations. APIC's analytical solution replaces FPM's trial-and-error approach, making it faster, more accurate, and leveraging deeper insights into the optical system.
APIC also allows researchers to obtain clear images over a large field of view without frequent refocusing. FPM required refocusing if the sample height varied, but APIC eliminates this need, speeding up the process and reducing human error during image stitching.
"They developed a framework to correct aberrations and improve resolution."
Yang emphasizes APIC's role in optimizing image data for AI applications. "AI can outperform expert pathologists in predicting metastatic progression from histopathology slides of lung cancer patients."
The paper, "High-resolution, large field-of-view label-free imaging via aberration-corrected, closed-form complex field reconstruction," was published in Nature Communications on June 3, supported by research funding.
Source: caltech.edu
