title: Superresolution microscope image reconstruction by spatiotemporal object decomposition and association: application in resolving t-tubule structure in skeletal muscle.
abstract: One key factor that limits resolution of single-molecule superresolution microscopy relates to the localization accuracy of the activated emitters, which is usually deteriorated by two factors. One originates from the background noise due to out-of-focus signals, sample auto-fluorescence, and camera acquisition noise; and the other is due to the low photon count of emitters at a single frame. With fast acquisition rate, the activated emitters can last multiple frames before they transiently switch off or permanently bleach. Effectively incorporating the temporal information of these emitters is critical to improve the spatial resolution. However, majority of the existing reconstruction algorithms locate the emitters frame by frame, discarding or underusing the temporal information. Here we present a new image reconstruction algorithm based on tracklets, short trajectories of the same objects. We improve the localization accuracy by associating the same emitters from multiple frames to form tracklets and by aggregating signals to enhance the signal to noise ratio. We also introduce a weighted mean-shift algorithm (WMS) to automatically detect the number of modes (emitters) in overlapping regions of tracklets so that not only well-separated single emitters but also individual emitters within multi-emitter groups can be identified and tracked. In combination with a maximum likelihood estimator method (MLE), we are able to resolve low to medium density of overlapping emitters with improved localization accuracy. We evaluate the performance of our method with both synthetic and experimental data, and show that the tracklet-based reconstruction is superior in localization accuracy, particularly for weak signals embedded in a strong background. Using this method, for the first time, we resolve the transverse tubule structure of the mammalian skeletal muscle.
authors.forename: Mingzhai
authors.initials: M
authors.lastname: Sun
authors.forename: Jiaqing
authors.initials: J
authors.lastname: Huang
authors.forename: Filiz
authors.initials: F
authors.lastname: Bunyak
authors.forename: Kristyn
authors.initials: K
authors.lastname: Gumpper
authors.forename: Gejing
authors.initials: G
authors.lastname: De
authors.forename: Matthew
authors.initials: M
authors.lastname: Sermersheim
authors.forename: George
authors.initials: G
authors.lastname: Liu
authors.forename: Pei-Hui
authors.initials: PH
authors.lastname: Lin
authors.forename: Kannappan
authors.initials: K
authors.lastname: Palaniappan
authors.forename: Jianjie
authors.initials: J
authors.lastname: Ma
mesh_terms: D000465:Algorithms; D000818:Animals; D002478:Cells, Cultured; D007091:Image Processing, Computer-Assisted; D018482:Muscle, Skeletal / Q000166:cytology*; D036103:Nanotechnology / Q000379:methods*; D017785:Photons; D059629:Signal-To-Noise Ratio