MR spectroscopic image reconstruction using structural information from anatomical MR images

2003 ◽  
Author(s):  
Thomas S. Denney, Jr. ◽  
Stanley J. Reeves
Keyword(s):  
Image Reconstruction ◽  
Structural Information ◽  
Mr Images

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

2019 ◽  
Vol 17 (01) ◽  
pp. 1950003
Author(s):  
Apurba Roy ◽  
Santi P. Maity
Keyword(s):  
Image Reconstruction ◽  
Linear Prediction ◽  
Random Noise ◽  
Curvelet Transform ◽  
Conditional Entropy ◽  
Mr Images ◽  
Recursive Filtering ◽  
Entropy Maximization ◽  
Residual Noise ◽  
Incomplete Measurement

In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.

An Efficient Light-Weight Network for Fast Reconstruction on MR Images

2021 ◽  
Vol 17 ◽  
Author(s):  
Bowen Zhen ◽  
Yingjie Zheng ◽  
Bensheng Qiu
Keyword(s):  
Feature Extraction ◽  
Image Reconstruction ◽  
Data Consistency ◽  
Training Procedure ◽  
Light Weight ◽  
Mr Images ◽  
High Quality ◽  
Mr Image ◽  
Generalization Ability ◽  
Mr Image Reconstruction

Background: In recent years, deep learning (DL) algorithms have emerged in endlessly and achieved impressive performance, which makes it possible to accelerate magnetic resonance (MR) image reconstruction with DL instead of compressed sensing (CS) methods. However, a DL-based MR image reconstruction method has always suffered from its heavy learning parameters and poor generalization ability so far. Therefore, an efficient light-weight network is still in desperate need of fast MR image reconstruction. Methods: We propose an efficient and light-weight MR reconstruction network (named RecNet) that uses a Convolutional Neural Network (CNN) to fast reconstruct high-quality MR images. Specifically, the network is composed of cascade modules, and each cascade module is further divided into feature extraction blocks and a data consistency layer. The feature extraction block can not only effectively extract the features of MR images, but also do not introduce too many parameters for the whole network. To stabilize the training procedure, the correction information of image frequency is adopted in the data consistency (DC) layer. Results: We have evaluated RecNet on a public dataset and the results show that the image quality reconstructed by RecNet is the best on the peak a signal-to-noise ratio (PSNR) and structural similarity index (SSIM) evaluation standards. In addition, the pre-trained RecNet can also reconstruct high-quality MR images on an unseen dataset. Conclusion: The results demonstrate that the RecNet has superior reconstruction ability in various metrics than comparative methods. The RecNet can quickly generate high-quality MR images in fewer parameters. Furthermore, the RecNet has an excellent generalization ability on pathological images and different sampling rates data.

The NCEM One-Angstrom Microscope Project Reaches 0.89Å Resolution

2000 ◽  
Vol 6 (S2) ◽  
pp. 1192-1193 ◽  
Author(s):  
Michael A. O'Keefe
Keyword(s):  
Electron Microscopy ◽  
Transmission Electron Microscopy ◽  
Image Reconstruction ◽  
Structural Information ◽  
Computer Software ◽  
Image Simulation ◽  
Resolution Limit ◽  
Transmission Electron ◽  
Better Than

Transmission electron microscopy to a resolution of 0.89Å has been achieved at the National Center for Electron Microscopy and is available to electron microscopists who have a requirement for this level of resolution. Development of this capability commenced in 1993, when the National Center for Electron Microscopy agreed to fund a proposal for a unique facility, a one- Ångstrom microscope (OÅM).2 The OÅM project provides materials scientists with transmission electron microscopy at a resolution better than one Angstrom by exploiting the significantly higher information limit of a FEG-TEM over its Scherzer resolution limit. To turn the misphased information beyond the Scherzer limit into useful resolution, the OÅM requires extensive image reconstruction. One method chosen was reconstruction from off-axis holograms; another was reconstruction from focal series of underfocused images. The OÅM is then properly a combination of a FEG-TEM (a CM300FEG-UT) together with computer software able to generate sub-Ångstrom images from experimental images obtained on the FEG-TEM.Before the advent of the OÅM, NCEM microscopists relied on image simulation to obtain structural information beyond the TEM resolution limit.

scholarly journals Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Di Zhao ◽  
Yanhu Huang ◽  
Feng Zhao ◽  
Binyi Qin ◽  
Jincun Zheng
Keyword(s):  
Deep Learning ◽  
Image Reconstruction ◽  
Structural Information ◽  
Reference Image ◽  
Training Procedure ◽  
Mr Image ◽  
Image Prior ◽  
Clinical Scenarios ◽  
Deep Image ◽  
Mr Image Reconstruction

Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, high-quality patient-based datasets for network training. In this paper, we propose a novel deep learning-based method for undersampled MR image reconstruction that does not require pre-training procedure and pre-training datasets. The proposed reference-driven method using wavelet sparsity-constrained deep image prior (RWS-DIP) is based on the DIP framework and thereby reduces the dependence on datasets. Moreover, RWS-DIP explores and introduces structure and sparsity priors into network learning to improve the efficiency of learning. By employing a high-resolution reference image as the network input, RWS-DIP incorporates structural information into network. RWS-DIP also uses the wavelet sparsity to further enrich the implicit regularization of traditional DIP by formulating the training of network parameters as a constrained optimization problem, which is solved using the alternating direction method of multipliers (ADMM) algorithm. Experiments on in vivo MR scans have demonstrated that the RWS-DIP method can reconstruct MR images more accurately and preserve features and textures from undersampled k -space measurements.

Incorporation of structural CT and MR images in PET image reconstruction

1991 ◽  
Author(s):  
Chin-Tu Chen ◽  
Xiaolong Ouyang ◽  
Caesar Ordonez ◽  
Xiaoping Hu ◽  
Wing H. Wong ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Mr Images ◽  
Pet Image Reconstruction
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