## 5 Dynamic FF-OCT
Interestingly, correlations between an increase of the blood vessel density and tumor grades have been well established for a long time ([127; 128] among many others). Similar results with the lymphangiogram have been obtained [129]. Quite generally, denser angiograms and lymphangiograms increase the risk of metastasis. D-FF-OCT is promising, when compared with digital holography techniques [33], dynamic light scattering techniques [110], or quantitative phase imaging [124; 130; 131], as it provides optical sectioning, and can access a motility-based contrast in 3D in scattering tissues with a subcellular resolution. Additionally, compared with dynamic OCT techniques [116; 132], we take advantage of the higher spatial resolution to capture subcellular variations, which further enable the cells to be revealed. To our knowledge, the contrast revealed by D-FF-OCT is therefore quite unique. Similar dynamic measurements in OCT mainly focus on the blood flow velocity [133; 134; 116], even with high transverse resolution [135] similar to ours, but, to our knowledge, they never directly show dynamic signals coming from intracellular dynamics. We are wondering whether it comes from a loss in the signal collection due to the use of lower NA illumination, from a difference in the algorithms used (if only blood flow was expected), or from a difference in the exposure time per pixel. In terms of multimodality, D-FF-OCT is attractive, as it does not require any modification from a regular FF-OCT. It can be associated with any other dual modality system to provide three different contrasts [41], as will be pointed out in the next section. D-FF-OCT is highly complementary to standard FF-OCT especially in fibrous environments, as fibers are highly backscattering structures  
Figure 6: D-FF-OCT as a histology tool for the brain. This figure presents a combination of FF-OCT (**Panel A**) and D-FF-OCT (**Panel B**) in a thick coronal section of fresh ex vivo rat brain. This image has been acquired at a few dozen microns below the section surface and presents the first cellular layers of the cortex. It reveals both myelinated axons in FF-OCT, as well as the neuronal cell bodies in D-FF-OCT. **Panel C** shows a frequency analysis performed in D-FF-OCT, which reveals highly dynamic nuclei in red, neuronal cytoplas with intermediate dynamics (mostly in green) and mostly static myelin fibers in blue. The scale bar represents 60 μm. and often hide cells entangled inside the matrix. However, fibers are usually still, while cells have intracellular activity that produces a highly dynamic signal. Hence, D-FF-OCT can easily reveal these cells, and suppress the stationary signal from the fibers. Nevertheless, one should keep in mind that D-FF-OCT significantly lowers the acquisition rates of FF-OCT microscopy. Indeed, even though each image is acquired at the camera frame rate, significant cellular dynamics occur in the second timescale, which is not very compressible because it is mainly sample dependent. In practice, our fastest D-FF-OCT measurements were at one to two seconds per plane that therefore expand to several minutes for a 3D volume. As for penetration depth, D-FF-OCT shows similar features to FF-OCT, except that the D-FF-OCT signal is often lost before the FF-OCT signal when going in depth, mainly because D-FF-OCT is generally weaker. Nevertheless, we succeeded in imaging D-FF-OCT signals at 100 \(\upmu\)m in brain slices, or even through the entire retina, going through up to 200 \(\upmu\)m.