scholarly journals Second-Order Regression-Based MR Image Upsampling

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Hu ◽  
Xi Wu ◽  
Jiliu Zhou
Keyword(s):  
Mapping Function ◽  
Zero Order ◽  
Point Estimation ◽  
Acquisition Time ◽  
Mr Images ◽  
Mr Image ◽  
Data Acquisition Time ◽  
Visual Artifacts ◽  
Magnetic Resonance Imaging Mri ◽  
Voxel Intensity

The spatial resolution of magnetic resonance imaging (MRI) is often limited due to several reasons, including a short data acquisition time. Several advanced interpolation-based image upsampling algorithms have been developed to increase the resolution of MR images. These methods estimate the voxel intensity in a high-resolution (HR) image by a weighted combination of voxels in the original low-resolution (LR) MR image. As these methods fall into the zero-order point estimation framework, they only include a local constant approximation of the image voxel and hence cannot fully represent the underlying image structure(s). To this end, we extend the existing zero-order point estimation to higher orders of regression, allowing us to approximate a mapping function between local LR-HR image patches by a polynomial function. Extensive experiments on open-access MR image datasets and actual clinical MR images demonstrate that our algorithm can maintain sharp edges and preserve fine details, while the current state-of-the-art algorithms remain prone to some visual artifacts such as blurring and staircasing artifacts.

Fasu-Net: Fast Alzheimer’s Disease Screening with Undersampled MRI Using Convolutional Neural Networks

2021 ◽  
Vol 11 (8) ◽  
pp. 2211-2221
Author(s):  
Yuanbo Xie ◽  
Haitao Jiang ◽  
Hongwei Du ◽  
Jinzhang Xu ◽  
Bensheng Qiu
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Structural Mri ◽  
Potential Method ◽  
Disease Screening ◽  
Acquisition Time ◽  
Mr Images ◽  
Brain Mr Images ◽  
Magnetic Resonance Imaging Mri ◽  
Questionnaire Method

Alzheimer’s Disease (AD) is a progressive and irreversible neurodegenerative condition, which results in dementia. Mild Cognitive Impairment (MCI) is an intermediate state between normal aging and AD. Instead of traditional questionnaire method, magnetic resonance imaging (MRI) can be used by radiologists to diagnose and screening AD recently, but long acquisition time is not conducive to screening AD and MCI. To solve this problem, we develop a Fasu-Net (Fast Alzheimer’s disease Screening neural network with Undersampled MRI) for AD and MCI clinical classification. The network uses undersampled structural MRI with a shorter acquisition time to improve the screening and diagnosis efficiency of AD. For achieving the best classification result, three axial planes of brain MR images were feed into the Fasu-Net with transfer learning method. The experiment results on undersampled 3D T1-weighted images database (ADNI) show that in the AD versus MCI versus HC (Healthy Controls) classification, the Fasu-Net achieved the accuracy of 91.41%, thus can be a potential method for fast clinical screening of AD.

scholarly journals Performance Evaluation of Total Variation based Compressed Sensing MRI for Different Sampling Patterns

2019 ◽  
Vol 8 (11) ◽  
pp. 148-154
Keyword(s):  
Magnetic Resonance ◽  
Information Acquisition ◽  
Variable Density ◽  
Human Anatomy ◽  
Acquisition Time ◽  
Radial Sampling ◽  
Data Acquisition Time ◽  
Quantitative Analyses ◽  
Magnetic Resonance Imaging Mri ◽  
Acquisition Method

Magnetic Resonance Imaging (MRI) has been utilized broadly for clinical purposes to portray human anatomy due to its non-intrusive nature. The information acquisition method in MRI naturally picks up encoded signals (Fourier transformed) instead of pixel values and is called k-space information. Sparse reconstruction techniques can be executed in MRI for producing an image from fewer measurements. Compressive sensing (CS) technique samples the signals at a rate lower than traditional Nyquist’s rate and thereby reduces the data acquisition time in MRI. This paper investigates a new proposed sampling scheme along with radial sampling and 1D Cartesian variable density sampling. For various sampling percentages, subjective and quantitative analyses are carried out on the reconstructed Magnetic Resonance image. Experimental results depicts that the high sampling density near the center of k-space gives a better reconstruction of compressing sensing MRI.

scholarly journals Combined Similarity to Reference Image with Joint Sparsifying Transform for Longitudinal Compressive Sensing MRI

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Ruirui Kang ◽  
Gangrong Qu ◽  
Bin Cao ◽  
Long Yan
Keyword(s):  
Compressive Sensing ◽  
Acquisition Time ◽  
Reference Image ◽  
Mr Images ◽  
Mr Image ◽  
Iterative Shrinkage ◽  
Novel Method ◽  
Thresholding Algorithm ◽  
Iterative Shrinkage Thresholding ◽  
Better Than

It is challenging to save acquisition time and reconstruct a medical magnetic resonance (MR) image with important details and features from its compressive measurements. In this paper, a novel method is proposed for longitudinal compressive sensing (LCS) MR imaging (MRI), where the similarity between reference and acquired image is combined with joint sparsifying transform. Furthermore, the joint sparsifying transform with the wavelet and the Contourlet can efficiently represent both isotropic and anisotropic features and the objective function is solved by extended smooth-based monotone version of the fast iterative shrinkage thresholding algorithm (SFISTA). The experiment results demonstrate that the existing regularization model obtains better performance with less acquisition time and recovers both edges and fine details of MR images, much better than the existing regularization model based on the similarity and the wavelet transform for LCS-MRI.

scholarly journals Assessing MRI susceptibility artefact through an indicator of image distortion

2016 ◽  
Vol 2 (1) ◽  
pp. 427-431 ◽  
Author(s):  
Alfredo Illanes ◽  
Johannes W. Krug ◽  
Michael Friebe
Keyword(s):  
Medical Devices ◽  
Observer Variability ◽  
Resonance Imaging ◽  
Mr Images ◽  
Mr Image ◽  
Automated Algorithm ◽  
Susceptibility Artefact ◽  
Magnetic Resonance Imaging Mri ◽  
Study Participants ◽  
Automatic Algorithm

AbstractSusceptibility artefacts in magnetic resonance imaging (MRI) caused by medical devices can result in a severe degradation of the MR image quality. The quantification of susceptibility artefacts is regulated by the ASTM standard which defines a manual method to assess the size of an artefact. This means that the estimated artefact size can be user dependent. To cope with this problem, we propose an algorithm to automatically quantify the size of such susceptibility artefacts. The algorithm is based on the analysis of a 3D surface generated from the 2D MR images. The results obtained by the automatic algorithm were compared to the manual measurements performed by study participants. The results show that the automatic and manual measurements follow the same trend. The clear advantage of the automated algorithm is the absence of the inter- and intra-observer variability. In addition, the algorithm also detects the slice containing the largest artefact which was not the case for the manual measurements.

An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI Reconstruction

2019 ◽  
Vol 15 (3) ◽  
pp. 281-291 ◽  
Author(s):  
Mei Sun ◽  
Jinxu Tao ◽  
Zhongfu Ye ◽  
Bensheng Qiu ◽  
Jinzhang Xu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Low Rank ◽  
Mr Images ◽  
Scanning Time ◽  
Previous State ◽  
Rank Approximation ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
Rank Constraints

Background: In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction. </P><P> Discussion: Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction. Methods: Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation. </P><P> Results & Conclusion: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.

T2-weighted spin-echo pulse sequence with variable repetition and echo times for reduction of MR image acquisition time.

Radiology ◽  
1991 ◽  
Vol 180 (2) ◽  
pp. 551-556 ◽  
Author(s):  
R K Butts ◽  
F Farzaneh ◽  
S J Riederer ◽  
J N Rydberg ◽  
R C Grimm
Keyword(s):  
Pulse Sequence ◽  
Spin Echo ◽  
Image Acquisition ◽  
Acquisition Time ◽  
Mr Image ◽  
Image Acquisition Time ◽  
Echo Pulse

scholarly journals Automated Head Tissue Modelling Based on Structural Magnetic Resonance Images for Electroencephalographic Source Reconstruction

2021 ◽  
Author(s):  
Gaia Amaranta Taberna ◽  
Jessica Samogin ◽  
Dante Mantini
Keyword(s):  
Magnetic Resonance ◽  
Brain Structures ◽  
Magnetic Resonance Images ◽  
Head Model ◽  
Source Reconstruction ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Eeg Recordings ◽  
Tissue Modelling

AbstractIn the last years, technological advancements for the analysis of electroencephalography (EEG) recordings have permitted to investigate neural activity and connectivity in the human brain with unprecedented precision and reliability. A crucial element for accurate EEG source reconstruction is the construction of a realistic head model, incorporating information on electrode positions and head tissue distribution. In this paper, we introduce MR-TIM, a toolbox for head tissue modelling from structural magnetic resonance (MR) images. The toolbox consists of three modules: 1) image pre-processing – the raw MR image is denoised and prepared for further analyses; 2) tissue probability mapping – template tissue probability maps (TPMs) in individual space are generated from the MR image; 3) tissue segmentation – information from all the TPMs is integrated such that each voxel in the MR image is assigned to a specific tissue. MR-TIM generates highly realistic 3D masks, five of which are associated with brain structures (brain and cerebellar grey matter, brain and cerebellar white matter, and brainstem) and the remaining seven with other head tissues (cerebrospinal fluid, spongy and compact bones, eyes, muscle, fat and skin). Our validation, conducted on MR images collected in healthy volunteers and patients as well as an MR template image from an open-source repository, demonstrates that MR-TIM is more accurate than alternative approaches for whole-head tissue segmentation. We hope that MR-TIM, by yielding an increased precision in head modelling, will contribute to a more widespread use of EEG as a brain imaging technique.

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