scholarly journals 3D Data Denoising via Nonlocal Means Filter by Using Parallel GPU Strategies

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Salvatore Cuomo ◽  
Pasquale De Michele ◽  
Francesco Piccialli
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Resonance Imaging ◽  
Nonlocal Means ◽  
Research Fields ◽  
3D Data ◽  
Algorithm Mapping ◽  
Magnetic Resonance Imaging Mri ◽  
Gpu Architecture ◽  
High Computational Complexity

Nonlocal Means (NLM) algorithm is widely considered as a state-of-the-art denoising filter in many research fields. Its high computational complexity leads researchers to the development of parallel programming approaches and the use of massively parallel architectures such as the GPUs. In the recent years, the GPU devices had led to achieving reasonable running times by filtering, slice-by-slice, and 3D datasets with a 2D NLM algorithm. In our approach we design and implement a fully 3D NonLocal Means parallel approach, adopting different algorithm mapping strategies on GPU architecture and multi-GPU framework, in order to demonstrate its high applicability and scalability. The experimental results we obtained encourage the usability of our approach in a large spectrum of applicative scenarios such as magnetic resonance imaging (MRI) or video sequence denoising.

Magnetic resonance imaging of catalytically relevant processes

2019 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexandra I. Svyatova ◽  
Kirill V. Kovtunov ◽  
Igor V. Koptyug
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
State Of The Art ◽  
Chemical Processes ◽  
Resonance Imaging ◽  
Heterogeneous Catalytic ◽  
Heterogeneous Catalytic Processes ◽  
Hyperpolarized Mri ◽  
Magnetic Resonance Imaging Mri ◽  
Low Sensitivity

Abstract The main aim of this article is to provide a state-of-the-art review of the magnetic resonance imaging (MRI) utilization in heterogeneous catalysis. MRI is capable to provide very useful information about both living and nonliving objects in a noninvasive way. The studies of an internal heterogeneous reactor structure by MRI help to understand the mass transport and chemical processes inside the working catalytic reactor that can significantly improve its efficiency. However, one of the serious disadvantages of MRI is low sensitivity, and this obstacle dramatically limits possible MRI application. Fortunately, there are hyperpolarization methods that eliminate this problem. Parahydrogen-induced polarization approach, for instance, can increase the nuclear magnetic resonance signal intensity by four to five orders of magnitude; moreover, the obtained polarization can be stored in long-lived spin states and then transferred into an observable signal in MRI. An in-depth account of the studies on both thermal and hyperpolarized MRI for the investigation of heterogeneous catalytic processes is provided in this review as part of the special issue emphasizing the research performed to date in Russia/USSR.

scholarly journals Automated Multimodal image fusion for brain tumor detection

2021 ◽  
Vol 3 (1) ◽  
pp. 68-82
Author(s):  
Harpreet Kaur ◽  
◽  
Deepika Koundal ◽  
Virendar Kadyan ◽  
Navneet Kaur ◽  
...  
Keyword(s):  
Image Fusion ◽  
State Of The Art ◽  
Signal To Noise Ratio ◽  
Visual Image ◽  
Fusion Rule ◽  
Resonance Imaging ◽  
Signal To Noise ◽  
Multiple Images ◽  
Fused Image ◽  
Magnetic Resonance Imaging Mri

In medical domain, various multimodalities such as Computer tomography (CT) and Magnetic resonance imaging (MRI) are integrated into a resultant fused image. Image fusion (IF) is a method by which vital information can be preserved by extracting all important information from the multiple images into the resultant fused image. The analytical and visual image quality can be enhanced by the integration of different images. In this paper, a new algorithm has been proposed on the basis of guided filter with new fusion rule for the fusion of different imaging modalities such as MRI and Fluorodeoxyglucose images of brain for the detection of tumor. The performance of the proposed method has been evaluated and compared with state-of-the-art image fusion techniques using various qualitative as well as quantitative evaluation metrics. From the results, it has been observed that more information has achieved on edges and content visibility is also high as compared to the other techniques which makes it more suitable for real applications. The experimental results are evaluated on the basis of with-reference and without-references metric such as standard deviation, entropy, peak signal to noise ratio, mutual information etc.

QuadrupletNet: A Novel Local Descriptor for Brain Tumor Detection and Segmentation

2020 ◽  
Vol 10 (7) ◽  
pp. 1763-1768
Author(s):  
Jun Jiang ◽  
Jing Jin ◽  
Binluo Wang ◽  
Jinming Wang ◽  
Tiaojuan Ren ◽  
...  
Keyword(s):  
Brain Tumor ◽  
State Of The Art ◽  
Tumor Detection ◽  
Segmentation Method ◽  
Resonance Imaging ◽  
Brain Tumor Surgery ◽  
Local Descriptor ◽  
Mri Brain ◽  
Triplet Loss ◽  
Magnetic Resonance Imaging Mri

Brain tumor detection and segmentation from Magnetic Resonance Imaging (MRI) images is being one of the emerging fields in the biomedicine. A formidable undertaking in brain tumor surgery, medical care, treatment programme and quantitative assessment of MRI images is to precisely diagnose its location and extent. Recently, the convolutional neural network (CNN) based detection and segmentation method on brain tumor MRI images is being one of the emerging fields in the medical imaging as an automatic clinic treatment and evaluation solution. In this article, we put forward a brand new quadruplet loss in CNN framework, which achieves higher accuracy in brain tumor detection and segmentation than other pairwise loss and triplet loss methods. By applying the proposed quadruplet loss to the original L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g., the Triplet loss, as indicated in experiments on Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, and on our own collected MRI brain tumor datasets (named MBTD).

Magnetic Resonance Imaging of the Brain in Children With Headache

2011 ◽  
Vol 50 (12) ◽  
pp. 1134-1139 ◽  
Author(s):  
Phillipp Fridolin Streibert ◽  
Werner Piroth ◽  
Michael Mansour ◽  
Patrick Haage ◽  
Thorsten Langer ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Clinical Relevance ◽  
State Of The Art ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

The aim of this study was to evaluate the frequency of abnormal findings in magnetic resonance imaging (MRI) in children with headache, the clinical relevance of these findings, and whether more sophisticated technologies also result in more relevant abnormal findings. The MRIs of 1004 children with age ranging from 1 to 17 years were retrospectively analyzed. Children who were investigated with established sequences (n = 419) were compared with those examined with state-of-the-art MRI acquisition technology (n = 585). In 216/1004 investigations, MRI was performed because of headache (74/216 with established sequences, 142/216 with state-of-the-art acquisition technology). In 114/216 (52.8%) patients with headache, the MRI was abnormal with relevant findings in 23/114 patients and findings without clinical relevance in 91/114 children. A higher incidence of abnormal findings than in previous reports was found but there was only limited clinical gain of information using modern sequences in children with headache.

scholarly journals Functional MRI of the kidney: tools for translational studies of pathophysiology of renal disease

2006 ◽  
Vol 290 (5) ◽  
pp. F958-F974 ◽  
Author(s):  
Pottumarthi V. Prasad
Keyword(s):  
Animal Models ◽  
Renal Disease ◽  
Functional Mri ◽  
State Of The Art ◽  
Functional Information ◽  
Resonance Imaging ◽  
Ischemic Renal Disease ◽  
Translational Studies ◽  
Magnetic Resonance Imaging Mri ◽  
Do So

Magnetic resonance imaging (MRI) provides exquisite anatomic detail of various organs and is capable of providing additional functional information. This combination allows for comprehensive diagnostic evaluation of pathologies such as ischemic renal disease. Noninvasive MRI techniques could facilitate translation of many studies performed in controlled animal models using technologies that are invasive to humans. Such a translation is being recognized as essential because many proposed interventions and drugs that prove efficacious in animal models fail to do so in humans. In this article, we review the state-of-the-art functional MRI technique as applied to the kidneys.

scholarly journals Improving the Speed of MRI with Artificial Intelligence

2020 ◽  
Vol 24 (01) ◽  
pp. 012-020 ◽  
Author(s):  
Patricia M. Johnson ◽  
Michael P. Recht ◽  
Florian Knoll
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
Future Perspectives ◽  
Resonance Imaging ◽  
The Past ◽  
Tremendous Progress ◽  
Accelerated Mri ◽  
Magnetic Resonance Imaging Mri ◽  
Image Modality ◽  
Resolution Data

AbstractMagnetic resonance imaging (MRI) is a leading image modality for the assessment of musculoskeletal (MSK) injuries and disorders. A significant drawback, however, is the lengthy data acquisition. This issue has motivated the development of methods to improve the speed of MRI. The field of artificial intelligence (AI) for accelerated MRI, although in its infancy, has seen tremendous progress over the past 3 years. Promising approaches include deep learning methods for reconstructing undersampled MRI data and generating high-resolution from low-resolution data. Preliminary studies show the promise of the variational network, a state-of-the-art technique, to generalize to many different anatomical regions and achieve comparable diagnostic accuracy as conventional methods. This article discusses the state-of-the-art methods, considerations for clinical applicability, followed by future perspectives for the field.

scholarly journals Multi-Sectional Views Textural Based SVM for MS Lesion Segmentation in Multi-Channels MRIs

2012 ◽  
Vol 6 (1) ◽  
pp. 56-72 ◽  
Author(s):  
Bassem A Abdullah ◽  
Akmal A Younis ◽  
Nigel M John
Keyword(s):  
State Of The Art ◽  
Automatic Segmentation ◽  
Brain Magnetic Resonance Imaging ◽  
The Other ◽  
Support Vector ◽  
Textural Features ◽  
Lesion Segmentation ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
A New Technique

In this paper, a new technique is proposed for automatic segmentation of multiple sclerosis (MS) lesions from brain magnetic resonance imaging (MRI) data. The technique uses a trained support vector machine (SVM) to discriminate between the blocks in regions of MS lesions and the blocks in non-MS lesion regions mainly based on the textural features with aid of the other features. The classification is done on each of the axial, sagittal and coronal sectional brain view independently and the resultant segmentations are aggregated to provide more accurate output segmentation. The main contribution of the proposed technique described in this paper is the use of textural features to detect MS lesions in a fully automated approach that does not rely on manually delineating the MS lesions. In addition, the technique introduces the concept of the multi-sectional view segmentation to produce verified segmentation. The proposed textural-based SVM technique was evaluated using three simulated datasets and more than fifty real MRI datasets. The results were compared with state of the art methods. The obtained results indicate that the proposed method would be viable for use in clinical practice for the detection of MS lesions in MRI.

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