Figure 7. Motion correction of multispectral 3D imaging of labelled immune cells in a murine lymph node.

tdTomato labelled B cells (red), Kaede labelled OT2 T cells (green) and subcapsular sinus macrophages labelled with Alexa 680 (magenta) imaged in a 150 μm z-stack through the inguinal lymph node (SHG signal from fibrillar capsule, blue) over 30 min. (A) One time point rendered volumetrically. (B–D) Temporally colour-coded (blue, early time points; red, late time points) orthogonal projections of time series (with spectral channels merged) with (B) no correction, (C) Imaris drift correction and (D) motion correction with Galene. Inset, expanded view of region highlighted in white. (E) Correlation between the stationary collagen and macrophage signal for each volume in the sequence and the reference volume for each package. (F) Average correlation for each correction package. Results show mean ± SEM, calculated per time point.

Figure 7—figure supplement 1. Algorithmic optimisation for realignment of three dimensional data.

(A,B) Structure of Hessian matrix used during optimisaton in the Lucas–Kanade framework in the parameter formulation used by (A) Greenberg and Kerr (2009) and (B) in this work for the 3D (x, y, z) case with 10 realignment points in total. Grey and white checkerbox indicate elements of the Hessian Matrix. Blue squares indicate matrix elements associated with the second realignment position, red squares indicate matrix elements associated with the third realignment position and yellow squares indicate matrix elements associated with both the second and third realignment position. (C) Time required for realignment of the 512 × 512 × 51 3D intensity data shown in Figure 7 using 15 realignment points per z position (2295 parameters in total) and 51 time points using (i) conventional factorisation, (iii) banded factorisation and (iii) banded factorisation with GPU computation of the error function and Jacobian.