PMID28650477 Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy
 Tobias NÃ¶bauer, Oliver Skocek, Alejandro J PernÃaAndrade, Lukas Weilguny, Francisca MartÃnez Traub, Maxim I Molodtsov & Alipasha Vaziri
 Cellscale imaging at video rates of hundreds of GCaMP6 labeled neurons with lightfield imaging followed by computationallyefficient deconvolution and iterative demixing based on nonnegative factorization in space and time.


 Utilized a hybrid lightfield and 2p microscope, but didn't use the latter to inform the SID algorithm.
 Algorithm:
 Remove motion artifacts
 Time iteration:
 Compute the standard deviation versus time (subtract mean over time, measure standard deviance)
 Deconvolve standard deviation image using RichardsonLucy algo, with nonnegativity, sparsity constraints, and a simulated PSF.
 Yields hotspots of activity, putative neurons.
 These neuron lcoations are convolved with the PSF, thereby estimating its ballistic image on the LFM.
 This is converted to a binary mask of pixels which contribute information to the activity of a given neuron, a 'footprint'
 Form a matrix of these footprints, p * n, $S_0$ (p pixels, n neurons)
 Also get the corresponding image data $Y$ , p * t, (t time)
 Solve: minimize over T $ Y  ST_2$ subject to $T \geq 0$
 That is, find a nonnegative matrix of temporal components $T$ which predicts data $Y$ from masks $S$ .
 Space iteration:
 Start with the masks again, $S$ , find all sets $O^k$ of spatially overlapping components $s_i$ (e.g. where footprints overlap)
 Extract the corresponding data columns $t_i$ of T (from temporal step above) from $O^k$ to yield $T^k$ . Each column corresponds to temporal data corresponding to the spatial overlap sets. (additively?)
 Also get the data matrix $Y^k$ that is image data in the overlapping regions in the same way.
 Minimize over $S^k$ $ Y^k  S^k T^k_2$
 Subject to $S^k >= 0$
 That is, solve over the footprints $S^k$ to best predict the data from the corresponding temporal components $T^k$ .
 They also impose spatial constraints on this nonnegative least squares problem (not explained).
 This process repeats.
 allegedly 1000x better than existing deconvolution / blind source segmentation algorithms, such as those used in CaImAn
