Multiphoton excitation fluorescence imaging is widely used in the field of biomedical optics and has become one of the most important research tools due to its low invasiveness, strong penetration, high signal-to-noise ratio and high spatial resolution. Photodamage in biological tissues can be caused if excessive photon density or laser power is applied during imaging.

While signal-to-noise ratio determines the lower limit of laser power that can be used in multiphoton imaging, photodamage delineates its upper boarders. For in-vivo label-free imaging, due to the small cross section of endogenous fluorophores (≈10^-2 GM), the tunability range of laser power between nondestructive imaging and photodamage is very narrow. Therefore, a reasonable laser power is required to ensure that multiphoton imaging with sufficient information can be obtained, and that the cells or biological tissues remain functionally active after long time irradiation. Reducing photodamage and optimizing imaging parameters is one of the major challenges in multiphoton imaging. Photodamage studies are essential to the optimization of imaging parameters.

Photodamage can be intuitively understood by the concept of ionization penalty in multiphoton bioimaging (Fig. 1). Ionization penalty occurs at irradiance of about 2×10¹² W/cm², where, for a single fluorescence photon emitted from fluorophores, a free electron is produced from water. The chemicals arising from ionization of water molecules can be detrimental to biomolecules and tissues.

The underlying mechanisms behind photodamage can be generally divided into photochemical and photothermal effects, as illustrated in Fig. 2. For photochemical effects, they can be further divided into UV-A like photo-oxidation effect and plasma-mediated chemical effect that is wavelength-independent.

The severity of photodamage is related to the laser parameters and optical parameters of biotissue. To evaluate the cause of photodamage, the state-of-the-art numerical tools are summarized, including a refined multi-rate-equation model to simulate free electron energy spectrum [Eqs. (4) and (5)], linear and nonlinear heating leading to temperature rise by a single laser pulse [Eqs. (8) and (9)], and heat accumulation by pulse series [Eqs. (11)-(14)].

Recent research progresses of photodamage in different tissues and at different wavelengths are analyzed. For two-photon imaging of non-pigmented tissues such as murine intestinal mucosa (Fig. 4), nondestructive imaging can be achieved at average power P_avg≈20 mW, with typical laser parameters of repetition rate f_PRF≈80 MHz, wavelength λ≈800 nm and pulse duration τ_L≈100 fs. Photodamage occurs when ≥2 times imaging power at focus is used. In contrast, for pigmented tissue such as murine retina (Fig. 7), photodamage occurs at average power as low as 3.5 mW with f_PRF≈80 MHz. Simulation results show that photodamage in non-pigmented tissue is mediated with laser-induced low density plasmas (Fig. 6), whereas photodamage in pigmented retina is mainly driven by heating (Fig. 8).

For three-photon imaging of deep murine brain tissue (≈1 mm) using typical laser parameters λ≈1.3 μm, f_PRF≈1 MHz, a non-zero chance of photodamage is observed for laser power P_avg≥150 mW (Fig. 9). Immunostaining as well as Monte-Carlo simulation results indicate that linear absorption and heating are likely to cause the photodamage. However, multi-rate-equation modeling shows that laser-induced plasma-mediated effects may be involved as well (Fig. 10).

In this review, we analyze photodamage in pigmented and non-pigmented tissues in multiphoton imaging. We come to the conclusion that photochemical effects are dominant in two-photon imaging of pigment-free tissues, while photothermal effects play a leading role in two-photon imaging of pigmented tissues. For three-photon imaging of deep murine brain tissue, photodamage is likely to arise from synergistic photochemical and photothermal effects.

Fully using the photon budget without photodamage is still a big challenge in multiphoton imaging. Traditional optimization model is time-demanding. Recently, with the booming development of machine learning, it has been applied to the optimization research of super-resolution optical microscopy. Subsequent combination of photodamage threshold database and machine learning can be a new direction to achieve online, automatic optimization of imaging parameters for multiphoton imaging.