F-mode ultraviolet photoacoustic remote sensing for label-free virtual H&E histopathology using a single excitation wavelength

Abstract

Photoacoustic remote sensing (PARS) is a novel all-optical imaging modality that allows for non-contact detection of initial photoacoustic pressures. Using 266-nm excitation pulses, ultraviolet PARS (UV-PARS) has previously demonstrated imaging contrast for cell nuclei in histological samples with &lt;<!-- 400 n m resolution. In prior PARS-based imaging schemes, the signal amplitude at an interrogation point was determined by the maximum deflection from the DC scattering signal in response to a pulsed excitation. This method, however, does not take into consideration additional information encoded in the frequency domain of the recorded PARS signals. Here, we present a frequency domain technique called F-mode PARS that can be used to generate images with nuclear and cytoplasmic enhanced contrast, enabling label-free virtual hematoxylin-and-eosin-like microscopy, using only a single excitation wavelength. With F-mode processing, we have been able to demonstrate contrast-to-noise ratios of up to 38 dB between cell nuclei and surrounding cytoplasm, which represents up to a 25-dB improvement over previous implementations of UV-PARS systems.

An Ultraviolet‐Transparent Ultrasound Transducer Enables High‐Resolution Label‐Free Photoacoustic Histopathology

“…[24][25][26] A recent advancement in non-contact imaging, known as UV photoacoustic remote sensing (UV-PARS), has emerged with a reflection-mode configuration, enabling histopathology of animal and human tissues without a water coupling medium. [27][28][29] The UV-PARS achieves high-resolution imaging using high-NA objectives, unlike the UV-PAM which requires partially distinct optical and acoustic paths. However, the optical heterogeneity in biological tissues induces light scattering only in the excitation beam of the UV-PAM, while the UV-PARS suffers from pronounced light scattering in both the excitation and detection beams.…”

Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy

“…1 c. UV-PARS was first demonstrated by Haven et al 12 for cell nuclei imaging and further improved in subsequent reports 13 , 14 . Integration of near-infrared (NIR) scattering microscopy further enabled simultaneous acquisition of complementary virtual eosin contrast co-registered with the UV-PARS images 15 – 17 , simplifying the complexity of previous dual-contrast approaches 18 , 19 . The NIR scattering was later upgraded to 266 nm pulsed UV scattering along with a pulse peak sample-and-hold detection circuit to achieve improved resolution in the virtual eosin data 20 utilizing the existing excitation laser source.…”

K-Means for Noise-Insensitive Multi-Dimensional Feature Learning

“…Conventionally, in PAM and PARS [2], [19]- [21], [24]- [26], only a scalar amplitude is extracted from each TD signal, accomplished by either using a Hilbert transform [27] to find an envelope of the signal, from which the difference between maximum and minimum values is computed, or by directly computing the difference between maximum and minimum of the raw TD signal itself. Recently, other methods have been developed to extract additional information related to the frequency content of the signals, as a means of inferring information related to the imaged target [28]- [30]. This work takes a different approach by proposing an unsupervised (clustering) approach to learn time-domain features that relate to the underlying target.…”