Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction

• URL: http://arxiv.org/abs/2409.06198v1
• Date: Tue, 10 Sep 2024 03:57:31 GMT
• Title: Deep kernel representations of latent space features for low-dose PET-MR imaging robust to variable dose reduction
• Authors: Cameron Dennis Pain, Yasmeen George, Alex Fornito, Gary Egan, Zhaolin Chen,
• Abstract summary: Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising means of incorporating prior information into the image reconstruction problem to produce quantitatively accurate images from compromised signal. We present a method which explicitly models deep latent space features using a robust kernel representation, providing robust performance on previously unseen dose reduction factors.
• Score: 0.09362267584678274
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-dose positron emission tomography (PET) image reconstruction methods have potential to significantly improve PET as an imaging modality. Deep learning provides a promising means of incorporating prior information into the image reconstruction problem to produce quantitatively accurate images from compromised signal. Deep learning-based methods for low-dose PET are generally poorly conditioned and perform unreliably on images with features not present in the training distribution. We present a method which explicitly models deep latent space features using a robust kernel representation, providing robust performance on previously unseen dose reduction factors. Additional constraints on the information content of deep latent features allow for tuning in-distribution accuracy and generalisability. Tests with out-of-distribution dose reduction factors ranging from $\times 10$ to $\times 1000$ and with both paired and unpaired MR, demonstrate significantly improved performance relative to conventional deep-learning methods trained using the same data. Code:https://github.com/cameronPain

Related papers

• 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)
• Diffusion Transformer Model With Compact Prior for Low-dose PET Reconstruction [7.320877150436869]
  We propose a diffusion transformer model (DTM) guided by joint compact prior (JCP) to enhance the reconstruction quality of low-dose PET imaging. DTM combines the powerful distribution mapping abilities of diffusion models with the capacity of transformers to capture long-range dependencies. Our approach not only reduces radiation exposure risks but also provides a more reliable PET imaging tool for early disease detection and patient management.
  arXiv  Detail & Related papers  (2024-07-01T03:54:43Z)
• Partitioned Hankel-based Diffusion Models for Few-shot Low-dose CT Reconstruction [10.158713017984345]
  We propose a few-shot low-dose CT reconstruction method using Partitioned Hankel-based Diffusion (PHD) models. In the iterative reconstruction stage, an iterative differential equation solver is employed along with data consistency constraints to update the acquired projection data. The results approximate those of normaldose counterparts, validating PHD model as an effective and practical model for reducing artifacts and noise while preserving image quality.
  arXiv  Detail & Related papers  (2024-05-27T13:44:53Z)
• Rotational Augmented Noise2Inverse for Low-dose Computed Tomography Reconstruction [83.73429628413773]
  Supervised deep learning methods have shown the ability to remove noise in images but require accurate ground truth. We propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN) Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented Noise2Inverse (RAN2I) method keeps better image quality over a different range of sampling angles.
  arXiv  Detail & Related papers  (2023-12-19T22:40:51Z)
• LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models [54.93010869546011]
  We propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images. Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules. We observe different roles of UNet denoising and decoder reconstruction in the latent diffusion model, which inspires us to decompose the low-light image enhancement task into latent-space low-frequency content generation and decoding-phase high-frequency detail maintenance.
  arXiv  Detail & Related papers  (2023-12-02T04:31:51Z)
• PET Synthesis via Self-supervised Adaptive Residual Estimation Generative Adversarial Network [14.381830012670969]
  Recent methods to generate high-quality PET images from low-dose counterparts have been reported to be state-of-the-art for low-to-high image recovery methods. To address these issues, we developed a self-supervised adaptive residual estimation generative adversarial network (SS-AEGAN) SS-AEGAN consistently outperformed the state-of-the-art synthesis methods with various dose reduction factors.
  arXiv  Detail & Related papers  (2023-10-24T06:43:56Z)
• ArSDM: Colonoscopy Images Synthesis with Adaptive Refinement Semantic Diffusion Models [69.9178140563928]
  Colonoscopy analysis is essential for assisting clinical diagnosis and treatment. The scarcity of annotated data limits the effectiveness and generalization of existing methods. We propose an Adaptive Refinement Semantic Diffusion Model (ArSDM) to generate colonoscopy images that benefit the downstream tasks.
  arXiv  Detail & Related papers  (2023-09-03T07:55:46Z)
• Contrastive Diffusion Model with Auxiliary Guidance for Coarse-to-Fine PET Reconstruction [62.29541106695824]
  This paper presents a coarse-to-fine PET reconstruction framework that consists of a coarse prediction module (CPM) and an iterative refinement module (IRM) By delegating most of the computational overhead to the CPM, the overall sampling speed of our method can be significantly improved. Two additional strategies, i.e., an auxiliary guidance strategy and a contrastive diffusion strategy, are proposed and integrated into the reconstruction process.
  arXiv  Detail & Related papers  (2023-08-20T04:10:36Z)
• 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)
• Self-Supervised Pre-Training for Deep Image Prior-Based Robust PET Image Denoising [0.5999777817331317]
  Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration. We propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance.
  arXiv  Detail & Related papers  (2023-02-27T06:55:00Z)

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.