List-Mode PET Image Reconstruction Using Deep Image Prior
- URL: http://arxiv.org/abs/2204.13404v1
- Date: Thu, 28 Apr 2022 10:44:33 GMT
- Title: List-Mode PET Image Reconstruction Using Deep Image Prior
- Authors: Kibo Ote, Fumio Hashimoto, Yuya Onishi, Takashi Isobe, Yasuomi Ouchi
- Abstract summary: List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners.
Deep learning is one possible solution to enhance the quality of PET image reconstruction.
In this study, we propose a novel list-mode PET image reconstruction method using an unsupervised CNN called deep image prior.
- Score: 3.6427817678422016
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: List-mode positron emission tomography (PET) image reconstruction is an
important tool for PET scanners with many lines-of-response (LORs) and
additional information such as time-of-flight and depth-of-interaction. Deep
learning is one possible solution to enhance the quality of PET image
reconstruction. However, the application of deep learning techniques to
list-mode PET image reconstruction have not been progressed because list data
is a sequence of bit codes and unsuitable for processing by convolutional
neural networks (CNN). In this study, we propose a novel list-mode PET image
reconstruction method using an unsupervised CNN called deep image prior (DIP)
and a framework of alternating direction method of multipliers. The proposed
list-mode DIP reconstruction (LM-DIPRecon) method alternatively iterates
regularized list-mode dynamic row action maximum likelihood algorithm
(LM-DRAMA) and magnetic resonance imaging conditioned DIP (MR-DIP). We
evaluated LM-DIPRecon using both simulation and clinical data, and it achieved
sharper images and better tradeoff curves between contrast and noise than the
LM-DRAMA and MR-DIP. These results indicated that the LM-DIPRecon is useful for
quantitative PET imaging with limited events. In addition, as list data has
finer temporal information than dynamic sinograms, list-mode deep image prior
reconstruction is expected to be useful for 4D PET imaging and motion
correction.
Related papers
- Deep unrolled primal dual network for TOF-PET list-mode image reconstruction [8.288813766151279]
Time-of-flight (TOF) information provides more accurate location data for annihilation photons.
Deep learning algorithms have shown promising results in PET image reconstruction.
In this study, we propose a deep unrolled dual network for TOFPET list-mode reconstruction.
arXiv Detail & Related papers (2024-10-15T00:17:47Z) - Deep Learning Based Speckle Filtering for Polarimetric SAR Images. Application to Sentinel-1 [51.404644401997736]
We propose a complete framework to remove speckle in polarimetric SAR images using a convolutional neural network.
Experiments show that the proposed approach offers exceptional results in both speckle reduction and resolution preservation.
arXiv Detail & Related papers (2024-08-28T10:07:17Z) - 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) - Disruptive Autoencoders: Leveraging Low-level features for 3D Medical
Image Pre-training [51.16994853817024]
This work focuses on designing an effective pre-training framework for 3D radiology images.
We introduce Disruptive Autoencoders, a pre-training framework that attempts to reconstruct the original image from disruptions created by a combination of local masking and low-level perturbations.
The proposed pre-training framework is tested across multiple downstream tasks and achieves state-of-the-art performance.
arXiv Detail & Related papers (2023-07-31T17:59:42Z) - Fully 3D Implementation of the End-to-end Deep Image Prior-based PET
Image Reconstruction Using Block Iterative Algorithm [0.0]
Deep image prior (DIP) has attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction.
We present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method.
arXiv Detail & Related papers (2022-12-22T16:25:58Z) - Is Deep Image Prior in Need of a Good Education? [57.3399060347311]
Deep image prior was introduced as an effective prior for image reconstruction.
Despite its impressive reconstructive properties, the approach is slow when compared to learned or traditional reconstruction techniques.
We develop a two-stage learning paradigm to address the computational challenge.
arXiv Detail & Related papers (2021-11-23T15:08:26Z) - Direct PET Image Reconstruction Incorporating Deep Image Prior and a
Forward Projection Model [0.0]
Convolutional neural networks (CNNs) have recently achieved remarkable performance in positron emission tomography (PET) image reconstruction.
We propose an unsupervised direct PET image reconstruction method that incorporates a deep image prior framework.
Our proposed method incorporates a forward projection model with a loss function to achieve unsupervised direct PET image reconstruction from sinograms.
arXiv Detail & Related papers (2021-09-02T08:07:58Z) - Direct Reconstruction of Linear Parametric Images from Dynamic PET Using
Nonlocal Deep Image Prior [13.747210115485487]
Direct reconstruction methods have been developed to estimate parametric images directly from the measured PET sinograms.
Due to limited counts received, signal-to-noise-ratio (SNR) and resolution of parametric images produced by direct reconstruction frameworks are still limited.
Recently supervised deep learning methods have been successfully applied to medical imaging denoising/reconstruction when large number of high-quality training labels are available.
arXiv Detail & Related papers (2021-06-18T21:30:22Z) - Adaptive Gradient Balancing for UndersampledMRI Reconstruction and
Image-to-Image Translation [60.663499381212425]
We enhance the image quality by using a Wasserstein Generative Adversarial Network combined with a novel Adaptive Gradient Balancing technique.
In MRI, our method minimizes artifacts, while maintaining a high-quality reconstruction that produces sharper images than other techniques.
arXiv Detail & Related papers (2021-04-05T13:05:22Z) - BP-DIP: A Backprojection based Deep Image Prior [49.375539602228415]
We propose two image restoration approaches: (i) Deep Image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the degraded image; and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works.
We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time.
arXiv Detail & Related papers (2020-03-11T17:09:12Z) - FastPET: Near Real-Time PET Reconstruction from Histo-Images Using a
Neural Network [0.0]
This paper proposes FastPET, a novel direct reconstruction convolutional neural network that is architecturally simple, memory space efficient.
FastPET operates on a histo-image representation of the raw data enabling it to reconstruct 3D image volumes 67x faster than Ordered subsets Expectation Maximization (OSEM)
The results show that not only are the reconstructions very fast, but the images are high quality and lower noise than iterative reconstructions.
arXiv Detail & Related papers (2020-02-11T20:32:47Z)
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.