{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/7"}],"enrichment":{"capability":"AOT_biomaps is a Python library for Acousto-Optic Tomography image reconstruction, providing tomographic algorithms (MLEM, PDHG, LS, DEPIERRO, MAPEM, LBFGS) with CPU and GPU implementations, plus acoustic simulation, optical modeling, and signal processing.","skillfed_tags":["biomedical-imaging","gpu-accelerated","tomography"],"use_cases":["Reconstruct 3D biomedical images from acousto-optic tomography sensor data using iterative algorithms like MLEM or PDHG.","Simulate acoustic wave propagation and optical interactions in heterogeneous tissue models for AOT experiment design.","Accelerate tomographic reconstruction on GPU hardware to reduce computation time from minutes to seconds.","Process and visualize 2D/3D tomographic datasets with built-in filtering, backprojection, and Radon transform tools.","Benchmark different sparse matrix formats (CSR, SELL-C-sigma) for memory efficiency in large-scale reconstruction problems."],"what_it_does":"AOT_biomaps is a specialized library for Acousto-Optic Tomography, a biomedical imaging modality that combines acoustic and optical physics. It implements multiple tomographic reconstruction algorithms (MLEM, PDHG, LS, DEPIERRO, MAPEM, LBFGS) with both CPU (NumPy) and GPU (CuPy) backends, allowing automatic fallback between implementations. The library also provides acoustic wave simulation, optical media modeling, signal processing utilities, and 2D/3D visualization.\n\nThe package is designed for research in biomedical imaging and combines advanced reconstruction mathematics with practical optimization for sparse matrix operations. It depends on numpy, scipy, tqdm, matplotlib, and optionally cupy-cuda12x for GPU acceleration and k-wave-python for acoustic simulation. Installation is straightforward via pip, with a CPU-only mode available for systems without CUDA.","worth_installing":"Yes, if you are working on acousto-optic tomography research or biomedical imaging reconstruction. The library is actively maintained, has low install friction, offers both CPU and GPU paths, and implements multiple state-of-the-art algorithms. However, verify the license status before use in proprietary work, and confirm that k-wave-python is available if acoustic simulation is required. Not relevant for general-purpose image processing or non-AOT applications."},"id":"aot-biomaps","links":{"html":"https://skillfed.io/packages/aot-biomaps","md":"https://skillfed.io/packages/aot-biomaps.md","pypi":"https://pypi.org/project/aot-biomaps/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-29","license_spdx":null,"license_treatment":"unclear","name":"aot-biomaps","python_support":"supports_current","summary":"Acousto-Optic Tomography Reconstruction Library"},"popularity":{"monthly_downloads":124301,"position":11876,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"2.9.905"}