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.

Joint Application of Magnetic Resonance Imaging and Biochemical Biomarkers in Diagnosis of Multiple Sclerosis

2020 ◽  
Vol 27 (39) ◽  
pp. 6703-6726
Author(s):  
Fatemeh Momeni ◽  
Amir B. Ghaemmaghami ◽  
Majid Nejati ◽  
Mohammad Hossein Pourhanifeh ◽  
Laleh Shiri Sichani ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Magnetic Resonance ◽  
Autoimmune Disorder ◽  
Human Anatomy ◽  
Diagnostic Tools ◽  
Resonance Imaging ◽  
Biochemical Biomarkers ◽  
Joint Application ◽  
Magnetic Resonance Imaging Mri

Multiple Sclerosis (MS), an autoimmune disorder associated with spinal cord and brain, chiefly affects the white matter. Regarding the complexity as well as heterogenic etiology of this disease, the treatment of MS has been a challenging issue up to now. Researchers are working to develop new therapeutic strategies and drugs as complementary therapies. MS diagnosis significantly depends on the findings of Magnetic Resonance Imaging (MRI) examination. In this imaging technique, gadolinium is used as a contrast agent to reveal active plaques intending to destroy the bloodbrain barrier. It also detects plaques that are not correlated with the neurological symptoms. It has been attempted to determine biomarkers related to different dimensions of MS in various organizational hierarchy levels of the human anatomy (i.e., cells, proteins, RNA, and DNA). These biomarkers are appropriate diagnostic tools for MS diagnosis. In this review, we summarized the application of MRI and biochemical biomarkers to monitor MS patients. Moreover, we highlighted the joint application of MRI and biomarkers for the diagnosis of MS subjects.

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.

scholarly journals Chaotic Compressed Sensing and Its Application to Magnetic Resonance Imaging

2014 ◽  
Vol 3 (3-4) ◽  
Author(s):  
Nguyen Linh-Trung ◽  
Truong Minh-Chinh ◽  
Tan Tran-Duc ◽  
Ha Vu Le ◽  
Minh Ngoc Do
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Compressed Sensing ◽  
Acquisition Time ◽  
Parallel Mri ◽  
Resonance Imaging ◽  
Measurement Matrix ◽  
Fast Mri ◽  
Mri Scans ◽  
Magnetic Resonance Imaging Mri

Fast image acquisition in magnetic resonance imaging (MRI) is important, due to the need to find ways that help relieve patient’s stress during MRI scans. Methods for fast MRI have been proposed, most notably among them are pMRI (parallel MRI), SWIFT (SWeep Imaging with Fourier Transformation), and compressed sensing (CS) based MRI. Although it promises to significantly reduce acquisition time, applying CS to MRI leads to difficulties with hardware design because of the randomness nature of the measurement matrix used by the conventional CS methods. In this paper, we propose a novel method that combines the above-mentioned three approaches for fast MRI by designing a compound measurement matrix from a series of single measurement matrices corresponding to pMRI, SWIFT, and CS. In our method, the CS measurement matrix is designed to be deterministic via chaotic systems. This chaotic compressed sensing (CCS) measurement matrix, while retaining most features of the random CS matrix, is simpler to realize in hardware. Several compound measurement matrices have been constructed and examined in this work, including CCS-MRI, CCS-pMRI, CCS-SWIFT, and CCS-pSWIFT. Simulation results showed that the proposed method allows an increase in the speed of the MRI acquisition process while not compromising the quality of the acquired MR images.

scholarly journals Variable Density Sampling Trajectories for Compressive Sensed MRI

2020 ◽  
Vol 9 (3) ◽  
pp. 1264-1267
Keyword(s):  
Signal To Noise Ratio ◽  
Sampling Theorem ◽  
Variable Density ◽  
Acquisition Time ◽  
Sparse Signals ◽  
Body Parts ◽  
The Novel ◽  
Scanning Time ◽  
High Resolution Images ◽  
Magnetic Resonance Imaging Mri

Diagnosis of diseases require high resolution images of human body parts. Magnetic Resonance Imaging (MRI) is a popular technology commonly used for this purpose. In addition to having several benefits, this technology has few shortcomings also. One of them is its high scanning time. In MRI acquisition of image is based on the principle of traditional sampling theorem. The novel sampling theory called as Compressive Sensing (CS) which allows the reconstruction of sparse signals from undersampled data. The application of CS onto MRI will drastically reduce the acquisition time and hence scanning time. In this manuscript analysis and application of CS on to MRI is demonstrated. Simulations are carried out using Variable Density Sampling trajectories (VDS). Then a comparative study is made in terms of Signal to Noise Ratio (SNR) and execution time based on the result obtained.

scholarly journals Implementing magnetic resonance imaging into clinical routine screening in patients with psychosis?

2017 ◽  
Vol 211 (4) ◽  
pp. 192-193 ◽  
Author(s):  
Stefan Borgwardt ◽  
André Schmidt
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
First Episode ◽  
Resonance Imaging ◽  
Psychotic Illness ◽  
Episode Psychosis ◽  
Neurobiological Substrates ◽  
Quantitative Analyses ◽  
Magnetic Resonance Imaging Mri ◽  
Indispensable Tool

SummaryIn this issue, Falkenberg et al explore the practicability of magnetic resonance imaging (MRI) as part of the initial clinical assessment in patients with first-episode psychosis and the prevalence, nature and clinical significance of radiological abnormalities in these patients. They provide evidence for the use of MRI data to detect gross brain abnormalities. In addition, improvements in quantitative analyses makes MRI an indispensable tool to elucidate the neurobiological substrates that might underlie primary (or idiopathic) psychotic illness.

scholarly journals Autonomous Magnetic Resonance Imaging

2020 ◽  
Author(s):  
Keerthi Sravan Ravi ◽  
Sairam Geethanath
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Signal To Noise Ratio ◽  
Pulse Sequences ◽  
User Interaction ◽  
Time Constraints ◽  
Acquisition Time ◽  
Resonance Imaging ◽  
Image Reconstructions ◽  
Magnetic Resonance Imaging Mri

AbstractAccess to Magnetic Resonance Imaging (MRI) across developing countries from being prohibitive to scarcely available. For example, eleven countries in Africa have no scanners. One critical limitation is the absence of skilled manpower required for MRI usage. Some of these challenges can be mitigated using autonomous MRI (AMRI) operation. In this work, we demonstrate AMRI to simplify MRI workflow by separating the required intelligence and user interaction from the acquisition hardware. AMRI consists of three components: user node, cloud and scanner. The user node voice interacts with the user and presents the image reconstructions at the end of the AMRI exam. The cloud generates pulse sequences and performs image reconstructions while the scanner acquires the raw data. An AMRI exam is a custom brain screen protocol comprising of one T1-, T2- and T2*-weighted exams. A neural network is trained to incorporate Intelligent Slice Planning (ISP) at the start of the AMRI exam. A Look Up Table was designed to perform intelligent protocolling by optimising for contrast value while satisfying signal to noise ratio and acquisition time constraints. Data were acquired from four healthy volunteers for three experiments with different acquisition time constraints to demonstrate standard and self-administered AMRI. The source code is available online. AMRI achieved an average SNR of 22.86 ± 0.89 dB across all experiments with similar contrast. Experiment #3 (33.66% shorter table time than experiment #1) yielded a SNR of 21.84 ± 6.36 dB compared to 23.48 ± 7.95 dB for experiment #1. AMRI can potentially enable multiple scenarios to facilitate rapid prototyping and research and streamline radiological workflow. We believe we have demonstrated the first Autonomous MRI of the brain.

scholarly journals Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Cancers ◽  
2021 ◽  
Vol 13 (19) ◽  
pp. 4742
Author(s):  
Hao Ding ◽  
Carlos Velasco ◽  
Huihui Ye ◽  
Thomas Lindner ◽  
Matthew Grech-Sollars ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Response Assessment ◽  
Response Monitoring ◽  
Clinical Settings ◽  
Full Potential ◽  
Cancer Management ◽  
Non Invasive ◽  
Quantitative Analyses ◽  
Magnetic Resonance Imaging Mri ◽  
The Moment

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians’ judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment.

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