Reconstruction as a service: a data space for off-site image reconstruction in magnetic particle imaging

• URL: http://arxiv.org/abs/2401.05987v1
• Date: Thu, 11 Jan 2024 15:42:52 GMT
• Title: Reconstruction as a service: a data space for off-site image reconstruction in magnetic particle imaging
• Authors: Anselm von Gladiss, Amir Shayan Ahmadian, Jan J\"urjens
• Abstract summary: Magnetic particle imaging (MPI) is an emerging medical imaging modality which offers a unique combination of high temporal and spatial resolution, sensitivity and biocompatibility. For system-matrix (SM) based image reconstruction in MPI, a huge amount of calibration data needs to be acquired prior to reconstruction in a time-consuming procedure. Due to its size, handling the calibration data can be challenging. We propose a data space aimed at improving the efficiency of SM-based image reconstruction in MPI.
• Score: 1.3566464121222226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Magnetic particle imaging (MPI) is an emerging medical imaging modality which offers a unique combination of high temporal and spatial resolution, sensitivity and biocompatibility. For system-matrix (SM) based image reconstruction in MPI, a huge amount of calibration data needs to be acquired prior to reconstruction in a time-consuming procedure. Conventionally, the data is recorded on-site inside the scanning device, which significantly limits the time that the scanning device is available for patient care in a clinical setting. Due to its size, handling the calibration data can be challenging. To solve these issues of recording and handling the data, data spaces could be used, as it has been shown that the calibration data can be measured in dedicated devices off-site. We propose a data space aimed at improving the efficiency of SM-based image reconstruction in MPI. The data space consists of imaging facilities, calibration data providers and reconstruction experts. Its specifications follow the reference architecture model of international data spaces (IDS). Use-cases of image reconstruction in MPI are formulated. The stakeholders and tasks are listed and mapped to the terminology of IDS. The signal chain in MPI is analysed to identify a minimum information model which is used by the data space.

Related papers

• Estimating Task-based Performance Bounds for Accelerated MRI Image Reconstruction Methods by Use of Learned-Ideal Observers [7.765750378590293]
  The performance of the ideal observer (IO) acting on imaging measurements has long been advocated as a figure-of-merit to guide the optimization of imaging systems. estimation of IO performance can provide valuable guidance when designing under-sampled data-acquisition techniques.
  arXiv  Detail & Related papers  (2025-01-16T01:09:30Z)
• 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. 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)
• MRGen: Segmentation Data Engine For Underrepresented MRI Modalities [59.61465292965639]
  Training medical image segmentation models for rare yet clinically significant imaging modalities is challenging due to the scarcity of annotated data. This paper investigates leveraging generative models to synthesize training data, to train segmentation models for underrepresented modalities.
  arXiv  Detail & Related papers  (2024-12-04T16:34:22Z)
• A Unified Model for Compressed Sensing MRI Across Undersampling Patterns [69.19631302047569]
  Deep neural networks have shown great potential for reconstructing high-fidelity images from undersampled measurements. Our model is based on neural operators, a discretization-agnostic architecture. Our inference speed is also 1,400x faster than diffusion methods.
  arXiv  Detail & Related papers  (2024-10-05T20:03:57Z)
• Sequential-Scanning Dual-Energy CT Imaging Using High Temporal Resolution Image Reconstruction and Error-Compensated Material Basis Image Generation [6.361772490498643]
  We developed sequential-scanning imaging using high temporal resolution image reconstruction and error-compensated material basis image generation. Results demonstrated the improvement of quantification accuracy and image quality using ACCELERATION.
  arXiv  Detail & Related papers  (2024-08-27T03:09:39Z)
• NeRF Solves Undersampled MRI Reconstruction [1.3597551064547502]
  This article presents a novel undersampled magnetic resonance imaging (MRI) technique that leverages the concept of Neural Radiance Field (NeRF) With radial undersampling, the corresponding imaging problem can be reformulated into an image modeling task from sparse-view rendered data. A multi-layer perceptron, which is designed to output an image intensity from a spatial coordinate, learns the MR physics-driven rendering relation between given measurement data and desired image.
  arXiv  Detail & Related papers  (2024-02-20T18:37:42Z)
• 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)
• CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction [62.61209705638161]
  There has been growing interest in deep learning-based CMR imaging algorithms. Deep learning methods require large training datasets. This dataset includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects.
  arXiv  Detail & Related papers  (2023-09-19T15:14:42Z)
• Generalized Implicit Neural Representation for Efficient MRI Parallel Imaging Reconstruction [16.63720411275398]
  This study presents a generalized implicit neural representation (INR)-based framework for MRI PI reconstruction. The framework's INR model treats fully sampled MR images as a continuous function of spatial coordinates and prior voxel-specific features. Experiments on publicly available MRI datasets demonstrate the superior performance of the proposed method in reconstructing images at multiple acceleration factors.
  arXiv  Detail & Related papers  (2023-09-12T09:07:03Z)
• Learned Interferometric Imaging for the SPIDER Instrument [5.65707814923407]
  We present two data-driven approaches for reconstructing images from measurements made by the SPIDER instrument. Deep learning is used to learn prior information from training data, increasing the reconstruction quality and significantly reducing computation time. We show that these methods can also be applied in domains where training data is scarce, such as astronomical imaging.
  arXiv  Detail & Related papers  (2023-01-24T19:00:03Z)
• Attentive Symmetric Autoencoder for Brain MRI Segmentation [56.02577247523737]
  We propose a novel Attentive Symmetric Auto-encoder based on Vision Transformer (ViT) for 3D brain MRI segmentation tasks. In the pre-training stage, the proposed auto-encoder pays more attention to reconstruct the informative patches according to the gradient metrics. Experimental results show that our proposed attentive symmetric auto-encoder outperforms the state-of-the-art self-supervised learning methods and medical image segmentation models.
  arXiv  Detail & Related papers  (2022-09-19T09:43:19Z)
• Engineering AI Tools for Systematic and Scalable Quality Assessment in Magnetic Resonance Imaging [0.0]
  Building a big MRI data repository has multiple challenges related to privacy, data size, DICOM format, logistics, and non-standardized images. Not only building the data repository is difficult, but using data pooled from the repository is also challenging. This position paper describes challenges in constructing a large MRI data repository and using data downloaded from such data repositories in various aspects.
  arXiv  Detail & Related papers  (2021-12-02T22:47:16Z)
• Multi-Modal MRI Reconstruction with Spatial Alignment Network [51.74078260367654]
  In clinical practice, magnetic resonance imaging (MRI) with multiple contrasts is usually acquired in a single study. Recent researches demonstrate that, considering the redundancy between different contrasts or modalities, a target MRI modality under-sampled in the k-space can be better reconstructed with the helps from a fully-sampled sequence. In this paper, we integrate the spatial alignment network with reconstruction, to improve the quality of the reconstructed target modality.
  arXiv  Detail & Related papers  (2021-08-12T08:46:35Z)
• Multi-institutional Collaborations for Improving Deep Learning-based Magnetic Resonance Image Reconstruction Using Federated Learning [62.17532253489087]
  Deep learning methods have been shown to produce superior performance on MR image reconstruction. These methods require large amounts of data which is difficult to collect and share due to the high cost of acquisition and medical data privacy regulations. We propose a federated learning (FL) based solution in which we take advantage of the MR data available at different institutions while preserving patients' privacy.
  arXiv  Detail & Related papers  (2021-03-03T03:04:40Z)

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.