## 5 Dynamic FF-OCT
Although the location of cell bodies can often be inferred from the FF-OCT image in regions with low signal, D-FF-OCT can distinguish between a cell and a local oedema (which appears black in D-FF-OCT) for example, and gives a better precision of the cell contour. For example, in Figure 6 and in the cortex region of coronal brain slices, the position of neuron somas can be inferred from the FF-OCT image, but the dynamic image can better reveal individual neuron shapes and sizes, from which the neuron type might be inferred (pyramidal or stellate neurons). Ultimately, the identification of cortical layers can be performed in situ. In addition, D-FF-OCT may play an important role as a diagnostic tool as it can detect nuclei in large cells (Figures 5E,F and 6). Nuclear size and shape is an important biomarker of cancer, with higher nuclear to cytoplasmic ratios in tumor cells [122; 123]. The nuclei mainly appear black in D-FF-OCT, even though the inner dynamics of the nuclei are significant and usually faster than cytoplasm dynamics [110]. Our hypothesis for the dark appearance of the nuclei is that nuclei are more densely packed than cytoplasm, and appear somehow homogeneous at a 1 \(\upmu\)m resolution. Inside a homogenous nucleus, there should be no backscattering except on its contour and no dynamics to image, which can explain why nuclei appear black in both FF-OCT and D-FF-OCT. Interestingly, imaging cells with higher NA objectives, and with a transverse resolution below 500 nm, enables us to recover some dynamic signal inside nuclei (See Figure 5F). Figure 5: Dynamic FF-OCT accessible contrasts. (**A,B**) Same field of view of an ex vivo subcutaneous pancreatic tumor respectively with FF-OCT and D-FF-OCT. Panel A shows a fibrous environment dense in collagen fibers without specific cues of cancerous cells. On the other hand, panel B identifies slowly varying cell bodies (in blue), and rapidly varying immune cells (bright white cells); (**C,D**) Zoom corresponding to the white areas of panels A and B respectively; (**E**) D-FF-OCT image of a fresh rat liver with 10X 0.3 NA objectives, based on a frequency analysis to compute the color. D-FF-OCT can reveal hepatocyte cytoplasms, and nuclei (black region at the center of cells), organized along stationary capillaries, characterized by the presence of rapidly varying red blood cells trapped inside; (**F**) D-FF-OCT image of a similar fresh rat liver with 40X 0.8 NA objectives. The increased resolution allows to better capture intracellular dynamics, and to capture some dynamic signal inside the nucleus; (**G**) High resolution image of red blood cells trapped in a capillary of a fresh rat retina, acquired with the 40X 0.8 objectives. The better resolution allows to capture the enhanced dynamics localized at the red blood cells membrane. Another interesting D-FF-OCT feature for diagnosis is its ability to detect red blood cells and immune cells (See Figure 5B,D,E). Indeed, in ex vivo tissues, the red blood cells are trapped inside the capillaries, and can be imaged even at low frame rates. Due to their strong membrane fluctuations [118; 124], they produce an intense and fast dynamic signal (See Figure 5E,G) on their edges. Similarly, immune cells are known for their intense motility as they are constantly exploring their environment and changing their shapes, as their protrusions are looking for abnormal markers to suppress [119]. Interestingly, the nature of the immune cell might be inferred from their motility coefficient [119]. Firstly, D-FF-OCT can therefore detect immune cells infiltration inside the tumors, which seems to be correlated with the tumor grade [125; 126]. Secondly, FF-OCT can usually detect capillaries, but cannot make the difference between a blood vessel and a lymphatic vessel. Thanks to the additional information from D-FF-OCT, and by detecting either red blood cells, or lymphocytes in a vessel, a more accurate angiogram and lymphogram could be established in cancerous tissues.