# Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removal

arXiv: https://arxiv.org/abs/1808.10848 · HTML: https://mlsanity.com/paper/1808.10848/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Aug 2018.

## Built on by (most cited first, 30 of 57)

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- [Rotate to Scan: UNet-like Mamba with Triplet SSM Module for Medical Image Segmentation](https://mlsanity.com/paper/2403.17701/) (2024)
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- [Fourier Neural Operator Networks: A Fast and General Solver for the Photoacoustic Wave Equation](https://mlsanity.com/paper/2108.09374/) (2021)
- [IC-U-Net: A U-Net-based Denoising Autoencoder Using Mixtures of Independent Components for Automatic EEG Artifact Removal](https://mlsanity.com/paper/2111.10026/) (2021)
- [A Comprehensive Review of U-Net and Its Variants: Advances and Applications in Medical Image Segmentation](https://mlsanity.com/paper/2502.06895/) (2025)
- [Cross-modality Attention Adapter: A Glioma Segmentation Fine-tuning Method for SAM Using Multimodal Brain MR Images](https://mlsanity.com/paper/2307.01124/) (2023)
- [Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomography](https://mlsanity.com/paper/2204.06270/) (2022)
- [Application of belief functions to medical image segmentation: A review](https://mlsanity.com/paper/2205.01733/) (2022)
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- [AS-Net: Fast Photoacoustic Reconstruction with Multi-feature Fusion from Sparse Data](https://mlsanity.com/paper/2101.08934/) (2021)
- [Small but Mighty: Enhancing 3D Point Clouds Semantic Segmentation with U-Next Framework](https://mlsanity.com/paper/2304.00749/) (2023)
- [CLASH: Complementary Learning with Neural Architecture Search for Gait Recognition](https://mlsanity.com/paper/2407.03632/) (2024)
- [A Lightweight Structure Aimed to Utilize Spatial Correlation for Sparse-View CT Reconstruction](https://mlsanity.com/paper/2101.07613/) (2021)
- [R2U3D: Recurrent Residual 3D U-Net for Lung Segmentation](https://mlsanity.com/paper/2105.02290/) (2021)
- [On Learning the Invisible in Photoacoustic Tomography with Flat Directionally Sensitive Detector](https://mlsanity.com/paper/2204.10001/) (2022)
- [Enhancing Weakly Supervised 3D Medical Image Segmentation through Probabilistic-aware Learning](https://mlsanity.com/paper/2403.02566/) (2024)
- [EFCNet: Every Feature Counts for Small Medical Object Segmentation](https://mlsanity.com/paper/2406.18201/) (2024)
- [Dense Dilated UNet: Deep Learning for 3D Photoacoustic Tomography Image Reconstruction](https://mlsanity.com/paper/2104.03130/) (2021)
- [EAR-U-Net: EfficientNet and attention-based residual U-Net for automatic liver segmentation in CT](https://mlsanity.com/paper/2110.01014/) (2021)

## Abstract (arXiv)

Photoacoustic imaging is an emerging imaging modality that is based upon the photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure waves are measured by an array of detectors and used to reconstruct an image of the initial pressure distribution. A common challenge faced in PAT is that the measured acoustic waves can only be sparsely sampled. Reconstructing sparsely sampled data using standard methods results in severe artifacts that obscure information within the image. We propose a modified convolutional neural network (CNN) architecture termed Fully Dense UNet (FD-UNet) for removing artifacts from 2D PAT images reconstructed from sparse data and compare the proposed CNN with the standard UNet in terms of reconstructed image quality.