Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI

• URL: http://arxiv.org/abs/2201.12535v1
• Date: Sat, 29 Jan 2022 09:06:04 GMT
• Title: Validation and Generalizability of Self-Supervised Image Reconstruction Methods for Undersampled MRI
• Authors: Thomas Yu, Tom Hilbert, Gian Franco Piredda, Arun Joseph, Gabriele Bonanno, Salim Zenkhri, Patrick Omoumi, Meritxell Bach Cuadra, Erick Jorge Canales-Rodr\'iguez, Tobias Kober, Jean-Philippe Thiran
• Abstract summary: Two self-supervised algorithms based on self-supervised denoising and neural network image priors were investigated. Their generalizability was tested with prospectively under-sampled data from experimental conditions different to the training.
• Score: 4.832984894979636
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: To investigate aspects of the validation of self-supervised algorithms for reconstruction of undersampled MR images: quantitative evaluation of prospective reconstructions, potential differences between prospective and retrospective reconstructions, suitability of commonly used quantitative metrics, and generalizability. Theory and Methods: Two self-supervised algorithms based on self-supervised denoising and neural network image priors were investigated. These methods are compared to a least squares fitting and a compressed sensing reconstruction using in-vivo and phantom data. Their generalizability was tested with prospectively under-sampled data from experimental conditions different to the training. Results: Prospective reconstructions can exhibit significant distortion relative to retrospective reconstructions/ground truth. Pixel-wise quantitative metrics may not capture differences in perceptual quality accurately, in contrast to a perceptual metric. All methods showed potential for generalization; generalizability is more affected by changes in anatomy/contrast than other changes. No-reference image metrics correspond well with human rating of image quality for studying generalizability. Compressed Sensing and learned denoising perform similarly well on all data. Conclusion: Self-supervised methods show promising results for accelerating image reconstruction in clinical routines. Nonetheless, more work is required to investigate standardized methods to validate reconstruction algorithms for future clinical use.

Related papers

• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• 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)
• Solving Inverse Problems in Medical Imaging with Score-Based Generative Models [87.48867245544106]
  Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) Existing solutions based on machine learning typically train a model to directly map measurements to medical images. We propose a fully unsupervised technique for inverse problem solving, leveraging the recently introduced score-based generative models.
  arXiv  Detail & Related papers  (2021-11-15T05:41:12Z)
• 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)
• Evaluation of Complexity Measures for Deep Learning Generalization in Medical Image Analysis [77.34726150561087]
  PAC-Bayes flatness-based and path norm-based measures produce the most consistent explanation for the combination of models and data. We also investigate the use of multi-task classification and segmentation approach for breast images.
  arXiv  Detail & Related papers  (2021-03-04T20:58:22Z)
• Joint reconstruction and bias field correction for undersampled MR imaging [7.409376558513677]
  Undersampling the k-space in MRI allows saving precious acquisition time, yet results in an ill-posed inversion problem. Deep learning schemes are susceptible to differences between the training data and the image to be reconstructed at test time. In this work, we address the sensitivity of the reconstruction problem to the bias field and propose to model it explicitly in the reconstruction.
  arXiv  Detail & Related papers  (2020-07-26T12:58:34Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)
• Learning Global and Local Features of Normal Brain Anatomy for Unsupervised Abnormality Detection [35.15777802684473]
  We demonstrate an unsupervised learning framework for pixel-wise abnormality detection in brain magnetic resonance imaging. Our concept is as follows: If an image reconstruction network can faithfully reproduce the global features of normal anatomy, then the abnormal lesions in unseen images can be identified. The results show that the area under the receiver operating characteristics curve values for metastatic brain tumors, extracranial metastatic tumors, postoperative cavities, and structural changes are 0.78, 0.61, 0.91, and 0.60, respectively.
  arXiv  Detail & Related papers  (2020-05-26T08:46:32Z)
• Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images [70.13735569016752]
  We investigate the robustness of deep learning in CT image reconstruction by showing false negative and false positive lesion cases. We propose a data consistent reconstruction (DCR) method to improve their image quality, which combines the advantages of compressed sensing and deep learning. The efficacy of the proposed method is demonstrated in cone-beam CT with truncated data, limited-angle data and sparse-view data, respectively.
  arXiv  Detail & Related papers  (2020-05-20T13:30:49Z)
• Unsupervised Lesion Detection via Image Restoration with a Normative Prior [6.495883501989547]
  We propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions pixel-wise using MAP estimation. Experiments with gliomas and stroke lesions in brain MRI show that the proposed approach outperforms the state-of-the-art unsupervised methods by a substantial margin.
  arXiv  Detail & Related papers  (2020-04-30T18:03:18Z)

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.