SIMPA: an open-source toolkit for simulation and image processing for photonics and acoustics

Abstract

. Significance: Optical and acoustic imaging techniques enable noninvasive visualisation of structural and functional properties of tissue. The quantification of measurements, however, remains challenging due to the inverse problems that must be solved. Emerging data-driven approaches are promising, but they rely heavily on the presence of high-quality simulations across a range of wavelengths due to the lack of ground truth knowledge of tissue acoustical and optical properties in realistic settings. Aim: To facilitate this process, we present the open-source simulation and image processing for photonics and acoustics (SIMPA) Python toolkit. SIMPA is being developed according to modern software design standards. Approach: SIMPA enables the use of computational forward models, data processing algorithms, and digital device twins to simulate realistic images within a single pipeline. SIMPA’s module implementations can be seamlessly exchanged as SIMPA abstracts from the concrete implementation of each forward model and builds the simulation pipeline in a modular fashion. Furthermore, SIMPA provides comprehensive libraries of biological structures, such as vessels, as well as optical and acoustic properties and other functionalities for the generation of realistic tissue models. Results: To showcase the capabilities of SIMPA, we show examples in the context of photoacoustic imaging: the diversity of creatable tissue models, the customisability of a simulation pipeline, and the degree of realism of the simulations. Conclusions: SIMPA is an open-source toolkit that can be used to simulate optical and acoustic imaging modalities. The code is available at: https://github.com/IMSY-DKFZ/simpa , and all of the examples and experiments in this paper can be reproduced using the code available at: https://github.com/IMSY-DKFZ/simpa_paper_experiments .

Quantification of vascular networks in photoacoustic mesoscopy

“…To test the accuracy of the segmentation pipelines, the L -nets were then used to simulate in vivo photoacoustic vascular networks embedded in muscle tissue using the Simulation and Image Processing for Photoacoustic Imaging (SIMPA) python package (SIMPA v0.1.1, https://github.com/CAMI-DKFZ/simpa ) [47] and the k-Wave MATLAB toolbox (k-Wave v1.3, MATLAB v2020b, MathWorks, Natick, MA, USA) [48] . Planar illumination of the L -nets on the XY plane was achieved using Monte-Carlo eXtreme (MCX v2020, 1.8) simulation on the L -net computational grid of size 10.24 × 10.24 × 2.80 mm 3 with 20 µm isotropic resolution.…”

Photoacoustic image synthesis with generative adversarial networks

“… Generation of optical parameter images: Based on the geometrical information, the remaining (here optical) parameter images are generated (typically also in a probabilistic manner) as described in Section 2.3.4 . Generation of PAT images: The optical parameter images were used as the input of our simulation pipeline [36] . …”

Special Section Guest Editorial: Introduction to the Special Section Celebrating 30 years of Open Source Monte Carlo Codes in Biomedical Optics

“…New open-source MC simulators are presented by Hayakawa et al., 7 Gröhl et al., 8 and Zhang and Fang, 9 offering user-friendly interfaces and versatile MC simulations in 3D heterogeneous media for general purpose use or modality-specific applications, such as photoacoustic imaging.…”

“…Gröhl et al. 8 used Python architecture to generate photoacoustic images using a built-in library of biological structures, as well as the above-mentioned work by Hänninen et al. 14 …”

“…Next to the code mentioned above for MC simulations of polarized light propagation in turbid media, 10 two approaches to simulate photoacoustic images are presented. Gröhl et al 8 used Python architecture to generate photoacoustic images using a built-in library of biological structures, as well as the above-mentioned work by Hänninen et al 14 Simulation of time-domain diffuse correlation spectroscopy provides a challenging environment, as stochastic photon path simulations need to be paired with the wave character of light to predict the speckle intensity fluctuations correlated with blood flow. This code is made publicly available by Cheng et al 17 It would be interesting to see a convergence of that code with the work by Fu and Richards 5 mentioned above.…”