## 5 Dynamic FF-OCT  
In this section, we will briefly review D-FF-OCT, including details of available contrasts and structures that can be revealed, and we present a few potential applications. We developed dynamic full-field OCT (D-FF-OCT), inspired by the work on quasi elastic dynamic light scattering and other dynamic measurements to measure biological activity [33; 108; 109; 110; 111; 112; 113]. We therefore wondered whether the intracellular movements and vibrations can be important enough to modulate the optical path length of our interferometer by themselves, even without reference piezo modulation. Interestingly, we found out that in fresh biological samples, calculating the standard deviation of the interferometric signal over time gives a substantially different contrast in comparison with the regular FF-OCT signal. We discovered later that this dynamic signal was related to cellular metabolism [39]. Interestingly, D-FF-OCT can access motility contrast in 3D, taking advantage of the FF-OCT low coherence to only select the phase variations at a given plane. Now, what we generally name D-FF-OCT is a set of mathematical techniques to analyze the temporal fluctuations of the FF-OCT signal. Trying to understand and quantify these fluctuations, we were inspired and influenced by similar measurements performed with more standard OCT [4; 114; 115; 116], other interferometric techniques [33; 111], or general dynamic techniques [117]. The new contrast revealed by D-FF-OCT originates from a different interpretation of the temporal fluctuations of the signal. In regular FF-OCT, the temporal fluctuations are averaged out to increase the signal-to-noise ratio of strongly backscattering structures. On the contrary, in the first versions of D-FF-OCT [39], we emphasized the amplitude of fluctuations of the signal by calculating the standard deviation, as described previously. This amplitude of fluctuations not only depends on the backscattering strength, but also on the scatterers dynamics inside a given voxel. An interesting layout of D-FF-OCT is to remove the piezo modulation, since the incoherent light varies less, and more slowly than the interference term. In such a configuration, the backscattering amplitude and the phase fluctuation amplitude are coupled in the final image. However, we found out that D-FF-OCT images calculated from four phase signals are often less contrasted, so we often stop the piezo movement and only acquire direct images. To extract more information from intracellular dynamics, we now try to emphasize not only the amplitude of fluctuations, but also the timescales of variations. We measured the interferometric signal autocorrelation to extract two characteristic times, and could measure the metabolic-related effects of a drug that depolymerizes F-actin [40]. Similarly, we also emphasize fluctuation timescales by calculating the Fourier transform of the time signal at a given pixel, or set of pixels. Then, we integrate the signals between different frequency bands to measure the dynamic signal originating from different timescales. We displayed the values by using an HSV color space, with the central frequency as the hue, the spectral width as the saturation and the amplitude of fluctuations as the value for each pixel of an image. This method is illustrated in Figure 5B,E, and enables segregation of cell populations based not only on their shapes but also on their fluctuation dynamics. We can distinguish cells with highly dynamic membranes, such as red blood cells [118] or lymphocytes [119] from slowly varying cells. Finally, both our group and [PERSON] et al. [120] also used singular value decomposition (SVD) to better separate large structures that are slowly varying from smaller structures that are varying more rapidly. SVD can also eliminate slow movements from all the samples, similarly to what was obtained in ultrasound [121].