Restored interlaced volumetric imaging increases image quality and scanning speed during intravital imaging in living mice. Issue 5 (2nd March 2020)

• Record Type: Journal Article
• Authors: Sogabe, Maina; Ohzeki, Masayuki; Fujimoto, Koji; Sehara‐Fujisawa, Atsuko; Nishimura, Satoshi
• Is Part Of: Journal of biophotonics. Volume 13:Issue 5(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-02
• Subjects: image reconstruction -- intravital imaging -- motion artifacts -- two‐photon microscopy -- vascular imaging; Photonics -- Periodicals; Optical materials -- Periodicals; Optics -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3605
• DOI: 10.1002/jbio.201960204
• Languages: English
• ISSNs: 1864-063X

Abstract

Abstract: Dynamic intravital imaging is essential for revealing ongoing biological phenomena within living organisms and is influenced primarily by several factors: motion artifacts, optical properties and spatial resolution. Conventional imaging quality within a volume, however, is degraded by involuntary movements and trades off between the imaged volume, imaging speed and quality. To balance such trade‐offs incurred by two‐photon excitation microscopy during intravital imaging, we developed a unique combination of interlaced scanning and a simple image restoration algorithm based on biological signal sparsity and a graph Laplacian matrix. This method increases the scanning speed by a factor of four for a field size of 212 μm × 106 μm × 130 μm, and significantly improves the quality of four‐dimensional dynamic volumetric data by preventing irregular artifacts due to the movement observed with conventional methods. Our data suggest this method is robust enough to be applied to multiple types of soft tissue. Abstract : Intravital imaging in living mice is a still evolving technique that involves balanced trade‐offs among requirements for a large 3D volume, fast scanning speed and high image quality. Our proposed method, rIVI meets all of these requirements simultaneously by employing a combination of interlaced scanning and a simple image restoration algorithm based on biological signal sparsity and improves image quality compared with conventional imaging.