mlsanityData to 7 Oct 2026

Diffusion assisted image reconstruction in optoacoustic tomography

arXiv 2404.10239 ↗First posted 16 Apr 2024eess.IVcited by 1

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

In this paper we consider the problem of acoustic inversion in the context of the optoacoustic tomography image reconstruction problem. By leveraging the ability of the recently proposed diffusion models for image generative tasks among others, we devise an image reconstruction architecture based on a conditional diffusion process. The scheme makes use of an initial image reconstruction, which is preprocessed by an autoencoder to generate an adequate representation. This representation is used as conditional information in a generative diffusion process. Although the computational requirements for training and implementing the architecture are not low, several design choices discussed in the work were made to keep them manageable. Numerical results show that the conditional information allows to properly bias the parameters of the diffusion model to improve the quality of the initial reconstructed image, eliminating artifacts or even reconstructing finer details of the ground-truth image that are not recoverable by the initial image reconstruction method. We also tested the proposal under experimental conditions and the obtained results were in line with those corresponding to the numerical simulations. Improvements in image quality up to 17 % in terms of peak signal-to-noise ratio were observed.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 25 of its 59 references that are in the map. First posted on arXiv 16 Apr 2024.

Builds on

25 of 59 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023SPIRiT-Diffusion: Self-Consistency Driven Diffusion Model for Accelerated MRI11 Apr
  2. 2021Solving Inverse Problems in Medical Imaging with Score-Based Generative Models15 Nov

Other references · 23

  1. 2017Attention Is All You Needcited by 59,932
  2. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  3. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  4. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  5. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  6. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  7. 2020Denoising Diffusion Implicit Modelscited by 9,655
  8. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  9. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,005
  10. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,736
  11. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,155
  12. 2023Score-Based Diffusion Models as Principled Priors for Inverse Imagingcited by 126
11 more
  1. 2018Fully Dense UNet for 2D Sparse Photoacoustic Tomography Artifact Removalcited by 56
  2. 2017Model based learning for accelerated, limited-view 3D photoacoustic tomographycited by 45
  3. 2023Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Datacited by 40
  4. 2019Solving Bayesian Inverse Problems via Variational Autoencoderscited by 32
  5. 2020Deep Learning in Photoacoustic Tomography: Current approaches and future directionscited by 25
  6. 2022RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imagingcited by 12
  7. 2022j-Wave: An open-source differentiable wave simulatorcited by 9
  8. 2022Utilizing variational autoencoders in the Bayesian inverse problem of photoacoustic tomographycited by 5
  9. 2021On the robustness of model-based algorithms for photoacoustic tomography: comparison between time and frequency domainscited by 2
  10. 2023Invariant Representations in Deep Learning for Optoacoustic Imagingcited by 2
  11. 2022Combining band-frequency separation and deep neural networks for optoacoustic imagingcited by 1

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Using Spatial Diffusions for Optoacoustic Tomography Image Reconstructioncited by 0

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