Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy

• URL: http://arxiv.org/abs/2105.07961v1
• Date: Mon, 17 May 2021 15:42:28 GMT
• Title: Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy
• Authors: Alan Q. Wang, Aaron K. LaViolette, Leo Moon, Chris Xu, and Mert R. Sabuncu
• Abstract summary: We propose a method of jointly optimizing both sensing and reconstruction end-to-end under a total measurement constraint. We train our model on a rich dataset of confocal, two-photon, and wide-field microscopy images.
• Score: 9.210673747947165
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image. Much work has gone into optimizing the sensing and reconstruction portions separately. We propose a method of jointly optimizing both sensing and reconstruction end-to-end under a total measurement constraint, enabling learning of the optimal sensing scheme concurrently with the parameters of a neural network-based reconstruction network. We train our model on a rich dataset of confocal, two-photon, and wide-field microscopy images comprising of a variety of biological samples. We show that our method outperforms several baseline sensing schemes and a regularized regression reconstruction algorithm.

Related papers

• 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)
• Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning [0.6990493129893112]
  Technological developments to increase the speed of acquisition often results in systems with a narrower spectral bandwidth, and hence a lower axial resolution. Traditionally, image-processing-based techniques have been utilized to reconstruct subsampled OCT data. In this study, we simulate reduced axial scan (A-scan) resolution by Gaussian windowing in the spectral domain and investigate the use of a learning-based approach for image feature reconstruction.
  arXiv  Detail & Related papers  (2023-01-02T02:18:32Z)
• OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing [62.993663757843464]
  Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. No standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings.
  arXiv  Detail & Related papers  (2022-06-17T08:11:26Z)
• Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks [46.751292014516025]
  Deep neural networks have been applied to reconstruct undersampled k-space data and have shown improved reconstruction performance. This work proposes a novel framework to learn k-space sampling trajectories by considering it as an Ordinary Differential Equation (ODE) problem. Experiments were conducted on different in-viv datasets (textite.g., brain and knee images) acquired with different sequences.
  arXiv  Detail & Related papers  (2022-04-05T20:28:42Z)
• 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)
• An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities [5.536263246814308]
  We develop a novel calibration-free fast parallel MRI (pMRI) reconstruction method incorporate with discrete-time optimal control framework. We propose to recover both magnitude and phase information by taking advantage of structured multiplayer convolutional networks in image and Fourier spaces.
  arXiv  Detail & Related papers  (2021-09-20T06:42:42Z)
• 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)
• 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)
• Deep Parallel MRI Reconstruction Network Without Coil Sensitivities [4.559089047554929]
  We propose a novel deep neural network architecture by mapping the robust proximal gradient scheme for fast image reconstruction in parallel MRI (pMRI) with regularization function trained from data. The proposed network learns to adaptively combine the multi-coil images from incomplete pMRI data into a single image with homogeneous contrast, which is then passed to a nonlinear encoder to efficiently extract sparse features of the image.
  arXiv  Detail & Related papers  (2020-08-04T08:39:36Z)
• Kullback-Leibler Divergence-Based Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation [152.609322951917]
  We come up with a Kullback-Leibler (KL) divergence-based Fuzzy C-Means (FCM) algorithm by incorporating a tight wavelet frame transform and a morphological reconstruction operation. The proposed algorithm works well and comes with better segmentation performance than other comparative algorithms.
  arXiv  Detail & Related papers  (2020-02-21T05:19:10Z)

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.