The field of biomedical imaging has seen significant advancements in recent years, particularly in the area of 3D quantitative phase imaging (QPI). QPI is a label-free technique that allows for the imaging of transparent living organisms and cells without the need for contrast agents or dyes. This enables researchers to study biological samples in their natural state, without inducing any damage or interference. It provides high-resolution 3D refractive index (RI) distribution inside the samples, which is valuable information for various applications in hematology, neurology, and immunology, aiding in disease diagnosis and infection detection. In an effort to improve the imaging capabilities of QPI, researchers have developed a new algorithm for recovering the 3D refractive index distribution of biological samples that exhibit multiple types of light scattering. This article delves into the details of this new algorithm and its potential impact on the field of 3D imaging.
While 3D imaging techniques have proven to be effective in studying thick biological samples, they often face challenges in achieving both high-speed acquisition and high resolution. This limitation has prompted the development of intensity diffraction tomography (IDT) approaches, which are label-free phase tomography techniques that can be integrated with a standard microscope. Two methods of IDT, namely annular IDT (aIDT) and multiplexed IDT (mIDT), have been recently developed to address these challenges. aIDT utilizes an LED ring that matches the numerical aperture of the objective, while mIDT involves the use of multiple LEDs to illuminate the sample simultaneously.
One of the key issues faced by researchers working with aIDT and mIDT was that existing IDT reconstruction algorithms were not well-suited for their new approaches, primarily due to the use of high-NA objectives. Consequently, the research team decided to develop a new algorithm that could effectively handle the complexities of their imaging techniques. They devised an algorithm based on a multiple scattering model using the split-step non-paraxial (SSNP) method, which has proven successful in overcoming similar limitations in optical diffraction tomography.
The researchers tested their new algorithm on various biological samples, including buccal epithelial cells and a multi-scattering live C. elegans embryo. The application of the algorithm to aIDT allowed for the discrimination of cells at different depths, reconstruction of cell boundaries and membranes, and visualization of native bacteria surrounding the cells. Furthermore, when applied to the C. elegans embryo using mIDT, the resulting reconstructed images revealed intricate details of the worm’s folding patterns. Notably, the single-depth cross-section provided morphological information about the cells’ outlines, the buccal cavity, and the tail of the worm.
The successful application of the new algorithm in conjunction with aIDT and mIDT demonstrates its potential for enhancing the quality of 3D imaging in the study of live biological samples. By extending the SSNP method to IDT, the researchers were able to achieve high-quality images with a large field of view. This advancement opens up opportunities for further research in the fields of hematology, neurology, and immunology, where understanding the 3D refractive index distribution of biological samples is crucial for diagnosing diseases and identifying infections. Additionally, this breakthrough could inspire the development of more sophisticated algorithms and techniques for 3D imaging, driving innovation in the field of biomedical imaging.
The development of a new algorithm for recovering the 3D refractive index distribution of biological samples exhibiting multiple types of light scattering marks a significant advancement in the field of 3D imaging techniques for live specimens. The algorithm, based on the split-step non-paraxial method, overcomes the limitations faced by existing IDT reconstruction algorithms, allowing for high-speed acquisition and high-resolution imaging. The successful application of the algorithm with aIDT and mIDT demonstrates its potential for facilitating research in various biomedical disciplines and improving disease diagnosis. As the field continues to evolve, these advancements in 3D imaging techniques hold promise for uncovering new insights in the intricate world of live biological samples.
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