scholarly journals Experimental testing on phantom image for improved MRI compressed sensing

2020 ◽  
Vol 12 (1) ◽  
pp. 18-21
Author(s):  
Aleksandar Kamilovski
Keyword(s):  
Compressed Sensing ◽  
Wavelet Transforms ◽  
Parallel Imaging ◽  
Experimental Testing ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Experimental Tests ◽  
Reconstruction Method ◽  
Test Image ◽  
Phantom Image

This paper presents a possible way for improving the techniques of compressed sensing and parallel imaging techniques for brain MRI. Experimental tests have been performed over a phantom test image. An exclusive elliptical sampling mask has been generated, which in combination with double-density wavelet transforms offers improvement over the standard approach. Additional tests undertaken as part of this research propose the usage of nonlinear reconstruction method, generated elliptical sampling mask and double-density wavelet transform for application of compressed sensing to brain MRI. An assessment of the results for diagnostic usage has been done by a specialist of radiology.

scholarly journals A novel framework for compressed sensing brain MRI edge detection and de-noising

2021 ◽  
Author(s):  
Rouzbeh Zamyadi
Keyword(s):  
Compressed Sensing ◽  
Edge Detection ◽  
Noise Reduction ◽  
Wavelet Transforms ◽  
Brain Mri ◽  
Qualitative Evaluation ◽  
Visual Quality ◽  
Edge Preservation ◽  
Image Reconstruction Techniques ◽  
Detection Techniques

In this thesis a novel edge detection technique is developed that employs compressed sensing image reconstruction techniques. The ability of compressed sensing noise reduction is combined with wavelet transforms, acting both as a sparsifying transform as well as an edge detection media. The proposed design was implemented and simulated on a brain phantom. The simulation results were provided for a variety of different sets of variables, and the differences were explained. The results obtained are compared with other edge detection techniques already in use. One important comparison criteria is the visual quality of images; according to which the proposed technique presents improved noise reduction and edge preservation. In addition to qualitative evaluation a method of quantitative measurement based on structural content is also utilized. It is found that the values for such a measure of the proposed method is 1.0755, 1.0174 and 0.5590 for Gaussian, Speckle, and Salt & Pepper noise types respectively. These results indicate that this novel method also improves edge preservation, while the visual quality inspection indicates how much noise has been suppressed.

scholarly journals A novel framework for compressed sensing brain MRI edge detection and de-noising

2021 ◽  
Author(s):  
Rouzbeh Zamyadi
Keyword(s):  
Compressed Sensing ◽  
Edge Detection ◽  
Noise Reduction ◽  
Wavelet Transforms ◽  
Brain Mri ◽  
Qualitative Evaluation ◽  
Visual Quality ◽  
Edge Preservation ◽  
Image Reconstruction Techniques ◽  
Detection Techniques

In this thesis a novel edge detection technique is developed that employs compressed sensing image reconstruction techniques. The ability of compressed sensing noise reduction is combined with wavelet transforms, acting both as a sparsifying transform as well as an edge detection media. The proposed design was implemented and simulated on a brain phantom. The simulation results were provided for a variety of different sets of variables, and the differences were explained. The results obtained are compared with other edge detection techniques already in use. One important comparison criteria is the visual quality of images; according to which the proposed technique presents improved noise reduction and edge preservation. In addition to qualitative evaluation a method of quantitative measurement based on structural content is also utilized. It is found that the values for such a measure of the proposed method is 1.0755, 1.0174 and 0.5590 for Gaussian, Speckle, and Salt & Pepper noise types respectively. These results indicate that this novel method also improves edge preservation, while the visual quality inspection indicates how much noise has been suppressed.

scholarly journals Enhancement of MRI Imaging by Combining Ramp Sampling and Parallel Imaging Techniques in Cerebral Tumors

2019 ◽  
Vol 16 (1) ◽  
pp. 221-236
Author(s):  
Ehab A Hegazy
Keyword(s):  
Frontal Lobe ◽  
Parallel Imaging ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Occipital Lobe ◽  
Geometric Distortion ◽  
Mri Imaging ◽  
New Techniques ◽  
Reduction Percentage ◽  
Parallel Technique

Our aim was to decrease geometric distortion artifact in DWI on brain by using different MR techniques such as ramp sampling and parallel imaging. The study was done on 15 patients, They were subject to routine brain MRI examination for different symptoms, examination was done on 1.5 T scanner, patients were divided to three group five patient in each group, geometric distortion in frontal and occipital lobe measured in the routine acquired DWI and after used the interested strategy compared to anatomical T2-FSE acquired in the routine examination then reduction percentage for each strategy was calculated.  Application of parallel technique imaging showed significant artifact reduction    in frontal lobe with approximately 54.36 % and about 34.8% in occipital lobe, while ramp sampling showed reduction with approximately 19% in frontal lobe and reduced artifact by 41.58%   in the occipital lobe.   Finally we conclude that using new techniques such as ramp sampling and parallel imaging    used with EPI data acquisition   reduced geometric distortion artifact and increased geometric resolution.

scholarly journals Effect of MRI acquisition acceleration via compressed sensing and parallel imaging on brain volumetry

2021 ◽  
Author(s):  
Michael Dieckmeyer ◽  
Abhijit Guha Roy ◽  
Jyotirmay Senapati ◽  
Christian Wachinger ◽  
Lioba Grundl ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Parallel Imaging ◽  
Brain Mri ◽  
Intraclass Correlation ◽  
Contrast Ratio ◽  
Brain Parenchyma ◽  
Repeated Measurements ◽  
Systematic Bias ◽  
Brain Volumetry ◽  
Brain Measurements

Abstract Objectives To investigate the effect of compressed SENSE (CS), an acceleration technique combining parallel imaging and compressed sensing, on potential bias and precision of brain volumetry and evaluate it in the context of normative brain volumetry. Materials and methods In total, 171 scans from scan-rescan experiments on three healthy subjects were analyzed. Each subject received 3D-T1-weighted brain MRI scans at increasing degrees of acceleration (CS-factor = 1/4/8/12/16/20/32). Single-scan acquisition times ranged from 00:41 min (CS-factor = 32) to 21:52 min (CS-factor = 1). Brain segmentation and volumetry was performed using two different software tools: md.brain, a proprietary software based on voxel-based morphometry, and FreeSurfer, an open-source software based on surface-based morphometry. Four sub-volumes were analyzed: brain parenchyma (BP), total gray matter, total white matter, and cerebrospinal fluid (CSF). Coefficient of variation (CoV) of the repeated measurements as a measure of intra-subject reliability was calculated. Intraclass correlation coefficient (ICC) with regard to increasing CS-factor was calculated as another measure of reliability. Noise-to-contrast ratio as a measure of image quality was calculated for each dataset to analyze the association between acceleration factor, noise and volumetric brain measurements. Results For all sub-volumes, there is a systematic bias proportional to the CS-factor which is dependent on the utilized software and subvolume. Measured volumes deviated significantly from the reference standard (CS-factor = 1), e.g. ranging from 1 to 13% for BP. The CS-induced systematic bias is driven by increased image noise. Except for CSF, reliability of brain volumetry remains high, demonstrated by low CoV (< 1% for CS-factor up to 20) and good to excellent ICC for CS-factor up to 12. Conclusion CS-acceleration has a systematic biasing effect on volumetric brain measurements.

On-Site Experimental Testing to Study the Vibration of Composite Floors

2014 ◽  
Vol 601 ◽  
pp. 231-234
Author(s):  
Cristian Lucian Ghindea ◽  
Dan Cretu ◽  
Monica Popescu ◽  
Radu Cruciat ◽  
Elena Tulei
Keyword(s):  
Dynamic Characteristics ◽  
Experimental Testing ◽  
Concrete Slab ◽  
Human Perception ◽  
Experimental Tests ◽  
International Standards ◽  
Structural Element ◽  
Element Analysis ◽  
Floor Slabs ◽  
Composite Floors

As a general trend, in order to reduce material consumption or to reduce the mass of the structures, composite floor slabs solutions are used to achieve large spans floor slabs. This solutions led to floors sensitive to vibrations induced generally by human activities. As a verification of the design concepts of the composite floors, usually, it is recommended a further examination of the floor after completion by experimental tests. Although the experimental values of the dynamic response of the floor are uniquely determined, the processing can take two directions of evaluation. The first direction consist in determining the dynamic characteristics of the floor and their comparison with the design values. Another way that can be followed in the processing of the experimental results is to consider the human perception and comfort to the vibration on floors. The paper aims to present a case study on a composite floor, with steel beams and concrete slab, tested on-site. Both aspects of data processing are analyzed, in terms of the structural element, and in terms of the effect on human perception and comfort. Experimentally obtained values for the dynamic characteristics of the floor are compared with numerical values from finite element analysis, while the second type of characteristic values are compared with various human comfort threshold values found in international standards.

Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing

2013 ◽  
Vol 71 (2) ◽  
pp. 645-660 ◽  
Author(s):  
Huajun She ◽  
Rong-Rong Chen ◽  
Dong Liang ◽  
Edward V. R. DiBella ◽  
Leslie Ying
Keyword(s):  
Compressed Sensing ◽  
Parallel Imaging ◽  
Imaging Reconstruction

U mỡ trong xương bướm: một trường hợp hiếm gặp

2021 ◽  
Author(s):  
Thu Hien Trinh Thi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Rare Case ◽  
Imaging Techniques ◽  
Brain Mri ◽  
Brain Magnetic Resonance Imaging ◽  
Sphenoid Bone ◽  
Resonance Imaging ◽  
Large Tumor ◽  
Intraosseous Lipoma

TÓM TẮT U mỡ trong xương là khối u lành tính hiếm gặp, thường gặp ở các xương dẹt, hiếm gặp ở xương nền sọ, đặc biệt là xương bướm. Trong đa số các trường hợp, u mỡ trong xương bướm thường được phát hiện tình cờ qua chụp cắt lớp vi tinh (CLVT) hoặc cộng hưởng từ (CHT) sọ não. Đây là một khối u phát triển chậm, ít gây ra triệu chứng, một số trường hợp gây triệu chứng khi khối u to chèn ép vào cấu trúc lân cận như tuyến yên hoặc dây thần kinh thị. Trong bài này, chúng tôi báo cáo một trường hợp u mỡ trong xương bướm không triệu chứng được phát hiện tình cờ và được chẩn đoán dựa vào phim chụp cộng hưởng từ sọ não. Bệnh nhân được khuyến nghị theo dõi định kỳ bằng cộng hưởng từ mà không phải tiến hành bất kỳ phương pháp điều trị nào. Từ khóa: U mỡ, xương bướm, MRI, cộng hưởng từ sọ não, chẩn đoán hình ảnh. ABSTRACT INTRAOSSEOUS LIPOMA OF SPHENOID BONE: A RARE CASE REVIEW Intraosseous lipoma is very rare, usually benign tumor of flat bones. The incidence of an intraosseous lipomalocated basal skull bones is extremely rare, especially in sphenoid bone. Radiological imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT) are used to detect the intraosseous lipoma by accident. These tumors are slow growing and usually asymptomatic, in some cases causing symptoms when the large tumor presses on nearby structures such as pituitary gland or the optic nerve. We present a rare case of lipomaof the sphenoid bone discovered incidentally with brain magnetic resonance imaging. The patient has been followed-up by magnetic resonance imaging without the need for surgery. Keywords: Intraosseous lipoma, sphenoid bone, MRI, brain MRI, diagnostic radiology

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