Active MRI Acquisition with Diffusion Guided Bayesian Experimental Design

• URL: http://arxiv.org/abs/2506.16237v1
• Date: Thu, 19 Jun 2025 11:48:30 GMT
• Title: Active MRI Acquisition with Diffusion Guided Bayesian Experimental Design
• Authors: Jacopo Iollo, Geoffroy Oudoumanessah, Carole Lartizien, Michel Dojat, Florence Forbes,
• Abstract summary: Key challenge in maximizing the benefits of Magnetic Resonance Imaging (MRI) in clinical settings is to accelerate acquisition times without significantly degrading image quality.<n>This objective requires a balance between under-sampling the raw k-space measurements for faster acquisitions and gathering sufficient raw information for high-fidelity image reconstruction and analysis tasks.<n>We propose to use sequential Bayesian experimental design (BED) to provide an adaptive and task-dependent selection of the most informative measurements.
• Score: 1.3142127084199051
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: A key challenge in maximizing the benefits of Magnetic Resonance Imaging (MRI) in clinical settings is to accelerate acquisition times without significantly degrading image quality. This objective requires a balance between under-sampling the raw k-space measurements for faster acquisitions and gathering sufficient raw information for high-fidelity image reconstruction and analysis tasks. To achieve this balance, we propose to use sequential Bayesian experimental design (BED) to provide an adaptive and task-dependent selection of the most informative measurements. Measurements are sequentially augmented with new samples selected to maximize information gain on a posterior distribution over target images. Selection is performed via a gradient-based optimization of a design parameter that defines a subsampling pattern. In this work, we introduce a new active BED procedure that leverages diffusion-based generative models to handle the high dimensionality of the images and employs stochastic optimization to select among a variety of patterns while meeting the acquisition process constraints and budget. So doing, we show how our setting can optimize, not only standard image reconstruction, but also any associated image analysis task. The versatility and performance of our approach are demonstrated on several MRI acquisitions.

Related papers

• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  We propose a unified MRI reconstruction model robust to various measurement undersampling patterns and image resolutions.<n>Our model improves SSIM by 11% and PSNR by 4 dB over a state-of-the-art CNN (End-to-End VarNet) with 600$times$ faster inference than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling [34.50287066865267]
  Compressed Sensing (CS) facilitates rapid image acquisition by selecting a small subset of measurements sufficient for high-fidelity reconstruction. Adaptive CS seeks to further enhance this process by dynamically choosing future measurements based on information gleaned from data that is already acquired. We propose AdaSense, a novel Adaptive CS approach that leverages zero-shot posterior sampling with pre-trained diffusion models.
  arXiv  Detail & Related papers  (2024-07-11T07:56:17Z)
• Inter-slice Super-resolution of Magnetic Resonance Images by Pre-training and Self-supervised Fine-tuning [49.197385954021456]
  In clinical practice, 2D magnetic resonance (MR) sequences are widely adopted. While individual 2D slices can be stacked to form a 3D volume, the relatively large slice spacing can pose challenges for visualization and subsequent analysis tasks. To reduce slice spacing, deep-learning-based super-resolution techniques are widely investigated. Most current solutions require a substantial number of paired high-resolution and low-resolution images for supervised training, which are typically unavailable in real-world scenarios.
  arXiv  Detail & Related papers  (2024-06-10T02:20:26Z)
• Efficient MRI Parallel Imaging Reconstruction by K-Space Rendering via Generalized Implicit Neural Representation [16.63720411275398]
  This study presents a generalized implicit neural representation (INR)-based framework for MRI PI reconstruction.<n>The proposed method overcomes limitations by leveraging prior knowledge of voxel-specific features.<n>Experiments on publicly available MRI datasets demonstrate the superior performance of the proposed method.
  arXiv  Detail & Related papers  (2023-09-12T09:07:03Z)
• Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
  Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels. They are not widely adopted by general users due to their substantial storage requirements. We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
  arXiv  Detail & Related papers  (2023-06-21T06:59:07Z)
• Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection [2.662628670752034]
  We present a new paradigm for experimental design that simultaneously optimize the design (set of image channels) and trains a machine-learning model to execute a user-specified image-analysis task. Results show substantial improvement over classical experimental design, two recent application-specific methods within the new paradigm, and state-of-the-art approaches in supervised feature selection.
  arXiv  Detail & Related papers  (2022-10-13T10:36:24Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• Multi-modal Aggregation Network for Fast MR Imaging [85.25000133194762]
  We propose a novel Multi-modal Aggregation Network, named MANet, which is capable of discovering complementary representations from a fully sampled auxiliary modality. In our MANet, the representations from the fully sampled auxiliary and undersampled target modalities are learned independently through a specific network. Our MANet follows a hybrid domain learning framework, which allows it to simultaneously recover the frequency signal in the $k$-space domain.
  arXiv  Detail & Related papers  (2021-10-15T13:16:59Z)
• Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks [2.7716102039510564]
  We propose to change the focus from the quality of the reconstructed image to the quality of the downstream image analysis outcome. We find the optimal undersampling patterns in $textitk$-space that maximize target value functions of interest in commonplace medical vision problems.
  arXiv  Detail & Related papers  (2021-08-10T20:48:47Z)
• End-to-End Sequential Sampling and Reconstruction for MR Imaging [37.29958197193658]
  We propose a framework that learns a sequential sampling policy simultaneously with a reconstruction strategy. Our proposed method outperforms the current state-of-the-art learned k-space sampling baseline on up to 96.96% of test samples.
  arXiv  Detail & Related papers  (2021-05-13T17:56:18Z)
• Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI [30.838990115880197]
  Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. We propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality.
  arXiv  Detail & Related papers  (2020-04-22T12:50:03Z)
• Hyperspectral-Multispectral Image Fusion with Weighted LASSO [68.04032419397677]
  We propose an approach for fusing hyperspectral and multispectral images to provide high-quality hyperspectral output. We demonstrate that the proposed sparse fusion and reconstruction provides quantitatively superior results when compared to existing methods on publicly available images.
  arXiv  Detail & Related papers  (2020-03-15T23:07:56Z)

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.