Learned Discrepancy Reconstruction and Benchmark Dataset for Magnetic Particle Imaging

• URL: http://arxiv.org/abs/2501.05583v1
• Date: Thu, 09 Jan 2025 21:21:06 GMT
• Title: Learned Discrepancy Reconstruction and Benchmark Dataset for Magnetic Particle Imaging
• Authors: Meira Iske, Hannes Albers, Tobias Knopp, Tobias Kluth,
• Abstract summary: Magnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles.<n>One key challenge in the MPI image reconstruction task arises from its underlying noise model.<n>We introduce the Learned Discrepancy Approach, a novel learning-based reconstruction method for inverse problems.
• Score: 3.7898596546142818
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Magnetic Particle Imaging (MPI) is an emerging imaging modality based on the magnetic response of superparamagnetic iron oxide nanoparticles to achieve high-resolution and real-time imaging without harmful radiation. One key challenge in the MPI image reconstruction task arises from its underlying noise model, which does not fulfill the implicit Gaussian assumptions that are made when applying traditional reconstruction approaches. To address this challenge, we introduce the Learned Discrepancy Approach, a novel learning-based reconstruction method for inverse problems that includes a learned discrepancy function. It enhances traditional techniques by incorporating an invertible neural network to explicitly model problem-specific noise distributions. This approach does not rely on implicit Gaussian noise assumptions, making it especially suited to handle the sophisticated noise model in MPI and also applicable to other inverse problems. To further advance MPI reconstruction techniques, we introduce the MPI-MNIST dataset - a large collection of simulated MPI measurements derived from the MNIST dataset of handwritten digits. The dataset includes noise-perturbed measurements generated from state-of-the-art model-based system matrices and measurements of a preclinical MPI scanner device. This provides a realistic and flexible environment for algorithm testing. Validated against the MPI-MNIST dataset, our method demonstrates significant improvements in reconstruction quality in terms of structural similarity when compared to classical reconstruction techniques.

Related papers

• CryoGS: Gaussian Splatting for Cryo-EM Homogeneous Reconstruction [55.2480439325792]
  cryogenic electron microscopy (cryo-EM) facilitates the determination of macromolecular structures at near-atomic resolution.<n>The core computational task in single-particle cryo-EM is to reconstruct the 3D electrostatic potential of a molecule.<n>We introduce cryoGS, a GMM-based method that integrates Gaussian splatting with the physics of cryo-EM image formation.
  arXiv  Detail & Related papers  (2025-08-06T23:24:43Z)
• Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model [105.95160543743984]
  We propose a deep learning procedure called Dual-Energy Decomposition Model-based Diffusion (DEcomp-MoD) for quantitative material decomposition.<n>We show that DEcomp-MoD outperform state-of-the-art unsupervised score-based model and supervised deep learning networks.
  arXiv  Detail & Related papers  (2025-07-24T01:00:06Z)
• Fast Trajectory-Independent Model-Based Reconstruction Algorithm for Multi-Dimensional Magnetic Particle Imaging [1.7614751781649953]
  2D Particleoidal Imaging (MPI) is a promising technique for visualizing the distribution of superparamagnetic nanoparticles.<n>MPI reconstruction relies on either time-consuming distribution system matrix or model-based simulation of the forward operator.<n>In this paper we present the first reconstruction on real trajectory-independent MPI data with a model-based MPI reconstruction.
  arXiv  Detail & Related papers  (2025-05-28T19:13:46Z)
• Efficient Noise Calculation in Deep Learning-based MRI Reconstructions [23.3078469067914]
  Noise analyses are central to MRI reconstruction for providing an explicit measure of solution fidelity.<n>Deep learning (DL)-based reconstruction methods have often overlooked noise propagation due to inherent analytical and computational challenges.<n>This work proposes a theoretically grounded, memory-efficient technique to calculate voxel-wise variance for quantifying uncertainty due to acquisition noise.
  arXiv  Detail & Related papers  (2025-05-04T06:28:06Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• MDiff-FMT: Morphology-aware Diffusion Model for Fluorescence Molecular Tomography with Small-scale Datasets [29.920562899648985]
  Fluorescence molecular tomography (FMT) is a sensitive optical imaging technology widely used in biomedical research. The inverse problem poses a huge challenge to FMT reconstruction. We report for the first time a morphology-aware diffusion model, MDiff-FMT, based on denoising diffusion probabilistic model (DDPM) to achieve high-fidelity morphological reconstruction.
  arXiv  Detail & Related papers  (2024-10-09T10:41:31Z)
• Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI [76.60362295758596]
  Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information. We propose to resolve these artifacts by lifting the 2D MRI reconstruction problem to 3D, introducing an additional "spectral" dimension to model this off-resonance.
  arXiv  Detail & Related papers  (2023-11-22T05:44:51Z)
• Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data [2.308563547164654]
  Regularization by neural style transfer (RNST) reconstructs high-quality images from noisy, low-quality inputs across varying image styles.<n>We validate RNST on clinical MRI scans, demonstrating its ability to significantly improve image quality.
  arXiv  Detail & Related papers  (2023-08-21T18:26:35Z)
• On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis [58.634791552376235]
  Deep Learning (DL) models have achieved state-of-the-art performance in diagnosing multiple diseases using reconstructed images as input. DL models are sensitive to varying artifacts as it leads to changes in the input data distribution between the training and testing phases. We propose to use other normalization techniques, such as Group Normalization and Layer Normalization, to inject robustness into model performance against varying image artifacts.
  arXiv  Detail & Related papers  (2023-06-23T03:09:03Z)
• Optimization-Based Deep learning methods for Magnetic Resonance Imaging Reconstruction and Synthesis [0.0]
  This dissertation aims to provide advanced nonsmooth variational models (Magnetic Resonance Image) MRI reconstruction, efficient learnable image reconstruction algorithms, and deep learning methods for MRI reconstruction and synthesis. The first part introduces a novel based deep neural network whose architecture is inspired by proximal gradient descent for a variational model. The second part is a substantial extension of the preliminary work in the first part by solving the calibration-free fast pMRI reconstruction problem in a discrete-time optimal framework. The third part aims at developing a generalizable Magnetic Resonance Imaging (MRI) reconstruction method in the metalearning framework.
  arXiv  Detail & Related papers  (2023-03-02T18:59:44Z)
• Pixelated Reconstruction of Foreground Density and Background Surface Brightness in Gravitational Lensing Systems using Recurrent Inference Machines [116.33694183176617]
  We use a neural network based on the Recurrent Inference Machine to reconstruct an undistorted image of the background source and the lens mass density distribution as pixelated maps. When compared to more traditional parametric models, the proposed method is significantly more expressive and can reconstruct complex mass distributions.
  arXiv  Detail & Related papers  (2023-01-10T19:00:12Z)
• Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance [7.544757765701024]
  This paper investigates how generative models, trained on ground-truth images, can be used changesas priors for inverse problems. We utilize variational autoencoders (VAEs) that generate not only an image but also a covariance uncertainty matrix for each image. We compare our proposed learned regularization against other unlearned regularization approaches and unsupervised and supervised deep learning methods.
  arXiv  Detail & Related papers  (2022-10-26T09:51:49Z)
• Stable Deep MRI Reconstruction using Generative Priors [13.400444194036101]
  We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only. The results demonstrate competitive performance, on par with state-of-the-art end-to-end deep learning methods.
  arXiv  Detail & Related papers  (2022-10-25T08:34:29Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Denoising Diffusion Restoration Models [110.1244240726802]
  Denoising Diffusion Restoration Models (DDRM) is an efficient, unsupervised posterior sampling method. We demonstrate DDRM's versatility on several image datasets for super-resolution, deblurring, inpainting, and colorization.
  arXiv  Detail & Related papers  (2022-01-27T20:19:07Z)
• Reference-based Magnetic Resonance Image Reconstruction Using Texture Transforme [86.6394254676369]
  We propose a novel Texture Transformer Module (TTM) for accelerated MRI reconstruction. We formulate the under-sampled data and reference data as queries and keys in a transformer. The proposed TTM can be stacked on prior MRI reconstruction approaches to further improve their performance.
  arXiv  Detail & Related papers  (2021-11-18T03:06:25Z)
• Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset [2.2366638308792735]
  MPI has a continuously increasing number of potential medical applications. One prerequisite for successful performance in these applications is a proper solution to the image reconstruction problem. We investigate a novel reconstruction approach based on a deep image prior, which builds on representing the solution by a deep neural network.
  arXiv  Detail & Related papers  (2020-07-03T10:13:10Z)

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.