Improved Diagnostic Process of Multiple Sclerosis Using Automated Detection and Selection Process in Magnetic Resonance Imaging

Malka, Dror; Vegerhof, Adi; Cohen, Eran; Rayhshtat, Mark; Libenson, Alex; Shalev, Maya Aviv; Zalevsky, Zeev

doi:10.3390/app7080831

Cited by 9 publications

(7 citation statements)

References 19 publications

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“…In the imaging process, neither ionization radiation nor a contrast agent can be used to obtain clear images with high contrast. The application of MRI in medical diagnosis can be roughly summarized into seven aspects: (1) tumor diagnosis and evaluation [1], (2) vascular display [2], (3) dynamic joint observation and detection of small lesions [3], (4) diagnosis and tracking of stroke [4], (5) cardiac imaging and cardiac function assessment, (6) surgery and interventional therapy [5].…”

Section: Introductionmentioning

confidence: 99%

Fe/Mn Multilayer Nanowires as High-Performance T1-T2 Dual Modal MRI Contrast Agents

Cao

et al. 2021

Materials

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A lot of nanomaterials are using T1-T2 dual mode magnetic resonance (MR) contrast agents (CAs), but multilayer nanowire (NW) with iron (Fe) and manganese (Mn) as T1-T2 dual modal CAs has not been reported yet. Herein, we synthesized a Fe/Mn multilayer NW with an adjustable Fe layer, as T1-T2 dual-mode CAs. The relaxation performance of Fe/Mn multilayer NW was studied at 1.5 T. Results show that, when the length of the Fe layer is about 10 nm and the Mn is about 5 nm, the r1 value (21.8 mM−1s−1) and r2 value (74.8 mM−1s−1) of the Fe/Mn multilayer NW are higher than that of Mn NW (3.7 mM−1s−1) and Fe NW (59.3 mM−1s−1), respectively. We predict that our Fe/Mn multilayer NW could be used as T1-T2 dual mode MRI CAs in the near future.

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Section: Introductionmentioning

confidence: 99%

Fe/Mn Multilayer Nanowires as High-Performance T1-T2 Dual Modal MRI Contrast Agents

Cao

et al. 2021

Materials

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“…The inflammatory demyelination of the CNS is the principal cause of the common neurological disorder multiple sclerosis (MS) [8,19,20,22,30], which affects the central nervous system [31] and is characterized by lesions produced in the white matter (WM) [32] of the brain and affects nearly 2.5 million people worldwide, especially young adults (ages 18-35 years) [4,30,31].…”

Section: Introductionmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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“…Recently, the learning-based method has been widely used in all kinds of fields [28][29][30], such as optical, image processing and also the Electronics Design Automation (EDA) field, especially timing analysis, and has shown great potential [31][32][33]. Das et al [31] build a model that still focuses the cell delay model by a learning-based method that comprehensively captures process, voltage, and temperature, along with input slew and output load, but it is not suited for path delay variation prediction directly.…”

Section: Introductionmentioning

confidence: 99%

Novel Prediction Framework for Path Delay Variation Based on Learning Method

et al. 2020

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Path delay variation becomes a serious concern in advanced technology, especially for multi-corner conditions. Plenty of timing analysis methods have been proposed to solve the issue of path delay variation, but they mainly focus on every single corner and are based on a characterized timing library, which neglects the correlation among multiple corners, resulting in a high characterization effort for all required corners. Here, a novel prediction framework is proposed for path delay variation by employing a learning-based method using back propagation (BP) regression. It can be used to solve the issue of path delay variation prediction under a single corner. Moreover, for multi-corner conditions, the proposed framework can be further expanded to predict corners that are not included in the training set. Experimental results show that the proposed model outperforms the traditional Advanced On-Chip Variation (AOCV) method with 1.4X improvement for the prediction of path delay variation for single corners. Additionally, while predicting new corners, the maximum error is 4.59%, which is less than current state-of-the-art works.

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Improved Diagnostic Process of Multiple Sclerosis Using Automated Detection and Selection Process in Magnetic Resonance Imaging

Cited by 9 publications

References 19 publications

Fe/Mn Multilayer Nanowires as High-Performance T1-T2 Dual Modal MRI Contrast Agents

Fe/Mn Multilayer Nanowires as High-Performance T1-T2 Dual Modal MRI Contrast Agents

MR Images, Brain Lesions, and Deep Learning

Novel Prediction Framework for Path Delay Variation Based on Learning Method

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