Speeding up Photoacoustic Imaging using Diffusion Models

• URL: http://arxiv.org/abs/2312.08834v1
• Date: Thu, 14 Dec 2023 11:34:27 GMT
• Title: Speeding up Photoacoustic Imaging using Diffusion Models
• Authors: Irem Loc and Mehmet Burcin Unlu
• Abstract summary: Photoacoustic Microscopy (PAM) integrates optical and acoustic imaging, offering enhanced penetration depth for detecting optical-absorbing components in tissues. With speed limitations imposed by laser pulse repetition rates, the potential role of computational methods is highlighted in accelerating PAM imaging. We are proposing a novel and highly adaptable DiffPam algorithm that utilizes diffusion models for speeding up the PAM imaging process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Background: Photoacoustic Microscopy (PAM) integrates optical and acoustic imaging, offering enhanced penetration depth for detecting optical-absorbing components in tissues. Nonetheless, challenges arise in scanning large areas with high spatial resolution. With speed limitations imposed by laser pulse repetition rates, the potential role of computational methods is highlighted in accelerating PAM imaging. Purpose: We are proposing a novel and highly adaptable DiffPam algorithm that utilizes diffusion models for speeding up the photoacoustic imaging process. Method: We leveraged a diffusion model trained exclusively on natural images, comparing its performance with an in-domain trained U-Net model using a dataset focused on PAM images of mice brain microvasculature. Results: Our findings indicate that DiffPam achieves comparable performance to a dedicated U-Net model, without the need for a large dataset or training a deep learning model. The study also introduces the efficacy of shortened diffusion processes for reducing computing time without compromising accuracy. Conclusion: This study underscores the significance of DiffPam as a practical algorithm for reconstructing undersampled PAM images, particularly for researchers with limited AI expertise and computational resources.

Related papers

• Resolution Enhancement of Under-sampled Photoacoustic Microscopy Images using Implicit Neural Representations [4.672315172406826]
  Acoustic-Resolution Photoacoustic Microscopy (AR-PAM) is promising for subcutaneous vascular imaging. Traditional deconvolution methods use the Point Spreadvolution (PSF) to improve resolution. We propose an approach based on Implicit Neural Representations (INR) Our method learns a continuous mapping from spatial coordinates to initial acoustic pressure, overcoming the limitations of discrete imaging.
  arXiv  Detail & Related papers  (2024-10-15T00:44:57Z)
• One Step Diffusion-based Super-Resolution with Time-Aware Distillation [60.262651082672235]
  Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. Recent techniques have been devised to enhance the sampling efficiency of diffusion-based SR models via knowledge distillation. We propose a time-aware diffusion distillation method, named TAD-SR, to accomplish effective and efficient image super-resolution.
  arXiv  Detail & Related papers  (2024-08-14T11:47:22Z)
• LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion [23.729378821117123]
  Denoising Diffusion Probabilistic Model (DDPM) holds promise for low-light image enhancement in medical field. DDPMs are computationally demanding and slow, limiting their practical medical applications. We propose a lightweight DDPM, dubbed LighTDiff, to capture global structural information using low-resolution images.
  arXiv  Detail & Related papers  (2024-05-17T05:31:19Z)
• ACDMSR: Accelerated Conditional Diffusion Models for Single Image Super-Resolution [84.73658185158222]
  We propose a diffusion model-based super-resolution method called ACDMSR. Our method adapts the standard diffusion model to perform super-resolution through a deterministic iterative denoising process. Our approach generates more visually realistic counterparts for low-resolution images, emphasizing its effectiveness in practical scenarios.
  arXiv  Detail & Related papers  (2023-07-03T06:49:04Z)
• Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction [0.0]
  We propose a few-shot supervised learning framework for optical coherence tomography ( OCT) noise reduction. This framework offers a dramatic increase in training speed and requires only a single image, or part of an image, and a corresponding speckle suppressed ground truth. Our results demonstrate significant potential for improving sample complexity, generalization, and time efficiency.
  arXiv  Detail & Related papers  (2023-06-13T19:46:40Z)
• Low-Light Image Enhancement with Wavelet-based Diffusion Models [50.632343822790006]
  Diffusion models have achieved promising results in image restoration tasks, yet suffer from time-consuming, excessive computational resource consumption, and unstable restoration. We propose a robust and efficient Diffusion-based Low-Light image enhancement approach, dubbed DiffLL.
  arXiv  Detail & Related papers  (2023-06-01T03:08:28Z)
• Diffusion Probabilistic Model Made Slim [128.2227518929644]
  We introduce a customized design for slim diffusion probabilistic models (DPM) for light-weight image synthesis. We achieve 8-18x computational complexity reduction as compared to the latent diffusion models on a series of conditional and unconditional image generation tasks.
  arXiv  Detail & Related papers  (2022-11-27T16:27:28Z)
• ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning [47.68307909984442]
  Single Image Super-Resolution (SISR) is a technique aimed to obtain high-resolution (HR) details from one single low-resolution input image. Deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts.
  arXiv  Detail & Related papers  (2021-02-25T14:52:23Z)
• Photoacoustic Microscopy with Sparse Data Enabled by Convolutional Neural Networks for Fast Imaging [0.9786690381850356]
  Photoacoustic microscopy (PAM) has been a promising biomedical imaging technology in recent years. Reducing sampling density can naturally shorten image acquisition time, which is at the cost of image quality. We propose a method using convolutional neural networks (CNNs) to improve the quality of sparse PAM images.
  arXiv  Detail & Related papers  (2020-06-08T05:49:32Z)
• Reconstructing undersampled photoacoustic microscopy images using deep learning [11.74890470096844]
  We propose a novel application of deep learning principles to reconstruct undersampled PAM images. Our results have collectively demonstrated the robust performance of our model to reconstruct PAM images with as few as 2% of the original pixels.
  arXiv  Detail & Related papers  (2020-05-30T12:39:52Z)
• Multifold Acceleration of Diffusion MRI via Slice-Interleaved Diffusion Encoding (SIDE) [50.65891535040752]
  We propose a diffusion encoding scheme, called Slice-Interleaved Diffusion. SIDE, that interleaves each diffusion-weighted (DW) image volume with slices encoded with different diffusion gradients. We also present a method based on deep learning for effective reconstruction of DW images from the highly slice-undersampled data.
  arXiv  Detail & Related papers  (2020-02-25T14:48:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.