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Статті в журналах з теми "Artificial magnetic resonance"

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Автор: Grafiati

Опубліковано: 10 грудня 2022

Оновлено: 28 січня 2023

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1

Belyaev, Boris A., Andrey V. Izotov, Alexander A. Leksikov, Alexey M. Serzhantov, Konstantin V. Lemberg, and Platon N. Solovev. "Thin Magnetic Films with Artificial Texture on Substrate: Microwave Properties." Solid State Phenomena 215 (April 2014): 233–36. http://dx.doi.org/10.4028/www.scientific.net/ssp.215.233.

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Using the scanning spectrometer of ferromagnetic resonance (FMR) the experimental dependences of the resonance field and FMR line width of thin permalloy magnetic films, which were deposited in vacuum on the substrate with an artificial texture, were obtained. The texture was produced by putting parallel grooves using a diamond cutter on glass substrates with period from 5 to 100 μm. It was found that the presence of the texture led to a considerable increase of the resonance field and FMR line width, when the external field was directed orthogonal to the grooves. On the base of a numerical micromagnetic simulation the explanation of the nature of observable in thin magnetic films effects was given.

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2

Hill, Charles E., Luca Biasiolli, Matthew D. Robson, Vicente Grau, and Michael Pavlides. "Emerging artificial intelligence applications in liver magnetic resonance imaging." World Journal of Gastroenterology 27, no. 40 (October 28, 2021): 6825–43. http://dx.doi.org/10.3748/wjg.v27.i40.6825.

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3

Solomou, Aikaterini, Anastasios Apostolos, and Nikolaos Ntoulias. "Artificial Intelligence in Magnetic Resonance Imaging: A Feasible Practice?" Journal of Medical Imaging and Radiation Sciences 51, no. 3 (September 2020): 501–2. http://dx.doi.org/10.1016/j.jmir.2020.04.010.

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4

Seetharam, Karthik, and Stamatios Lerakis. "Cardiac magnetic resonance imaging: the future is bright." F1000Research 8 (September 13, 2019): 1636. http://dx.doi.org/10.12688/f1000research.19721.1.

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Over the last 15 years, cardiovascular magnetic resonance (CMR) imaging has progressively evolved to become an indispensable tool in cardiology. It is a non-invasive technique that enables objective and functional assessment of myocardial tissue. Recent innovations in magnetic resonance imaging scanner technology and parallel imaging techniques have facilitated the generation of T1 and T2 parametric mapping to explore tissue characteristics. The emergence of strain imaging has enabled cardiologists to evaluate cardiac function beyond conventional metrics. Significant progress in computer processing capabilities and cloud infrastructure has supported the growth of artificial intelligence in CMR imaging. In this review article, we describe recent advances in T1/T2 mapping, myocardial strain, and artificial intelligence in CMR imaging.

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5

Szarf, Gilberto, and Cesar H. Nomura. "APLICAÇÃO DA INTELIGÊNCIA ARTIFICIAL EM IMAGEM CARDIOVASCULAR: EM TOMOGRAFIA COMPUTADORIZADA E RMN." Revista da Sociedade de Cardiologia do Estado de São Paulo 32, no. 1 (January 15, 2022): 27–30. http://dx.doi.org/10.29381/0103-8559/2022320127-30.

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Ao longo dos últimos anos, foram desenvolvidos conhecimentos relacionados à aplicação de IA em imagens médicas. O resultado disso é que hoje temos algoritmos sendo desenvolvidos para pesquisa e outros disponíveis para serem incorporados em nossa prática. Este artigo oferece uma visão relacionada às possíveis aplicações de IA que podem auxiliar ao longo da jornada dos pacientes para os quais foi solicitada uma tomografia computadorizada ou uma ressonância magnética do coração. Perspectivas futuras também são alvo de comentários.

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6

Ionescu, Daniela, and Gabriela Apreotesei. "Wave absorption control in the new designed photonic metamaterials with artificial opal." MATEC Web of Conferences 178 (2018): 04004. http://dx.doi.org/10.1051/matecconf/201817804004.

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Photonic metamaterials consisting of artificial opal with magnetic inclusions were considered, used in controllable microwave electronic devices. The analyzed structures consist of matrices of SiO2 nanospheres (diameter 200 - 400 nm) with included clusters of ferrite spinels (MnxCo0.6-xZn0.4Fe2O4, NixCo0.6-xZn0.4Fe2O4, LaxCo0.6-xZn0.4Fe2O4, NdxCo0.6-xZn0.4Fe2O4) in interspherical nanospacing (4 ÷ 7% concentration). The ellipsoidal clusters are polycrystalline, with spatial dimensions of 20 – 30 nm and grains of 5 – 12 nm. A controlled wave absorption was obtained in these high inductivity structures. Evolution of the wave attenuation coefficient, α[dB/m], in function of the applied magnetic field and particle inclusion size, for different content of the magnetic ions in the ferrite inclusion, have been determined at frequencies around the samples ferromagnetic resonance, by structural simulation. The test configuration was: sample inside the rectangular waveguide, mode TE10, in the frequency range 24 ÷ 40 GHz. The polarizing magnetic field for the ferrites was tested in the range of 0 ÷ 20 kOe and minimized by modifying the structure. The metamaterial design optimization was realized, controllable by different parameters at structure level. The ferromagnetic resonance influence on the control process was pointed out and also the particular results and effects which can be induced by the resonant behavior.

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7

Cau, Riccardo, Valeria Cherchi, Giulio Micheletti, Michele Porcu, Lorenzo Mannelli, Pierpaolo Bassareo, Jasjit S. Suri, and Luca Saba. "Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging." Journal of Thoracic Imaging 36, no. 3 (March 24, 2021): 142–48. http://dx.doi.org/10.1097/rti.0000000000000584.

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8

Calivà, Francesco, Nikan K. Namiri, Maureen Dubreuil, Valentina Pedoia, Eugene Ozhinsky, and Sharmila Majumdar. "Studying osteoarthritis with artificial intelligence applied to magnetic resonance imaging." Nature Reviews Rheumatology 18, no. 2 (November 30, 2021): 112–21. http://dx.doi.org/10.1038/s41584-021-00719-7.

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9

Murphy, Matthew C., Armando Manduca, Joshua D. Trzasko, Kevin J. Glaser, John Huston, and Richard L. Ehman. "Artificial neural networks for stiffness estimation in magnetic resonance elastography." Magnetic Resonance in Medicine 80, no. 1 (November 28, 2017): 351–60. http://dx.doi.org/10.1002/mrm.27019.

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10

Rajini N, Hema. "Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN." International research journal of engineering, IT & scientific research 3, no. 1 (January 31, 2017): 36–44. http://dx.doi.org/10.21744/irjeis.v3n1.895.

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A brain tumor classification system has been designed and developed. This work presents a new approach to the automated classification of astrocytoma, medulloblastoma, glioma, glioblastoma multiforme and craniopharyngioma type of brain tumors based on first order statistics and gray level co-occurrence matrix, in magnetic resonance images. The magnetic resonance feature image used for the tumor detection consists of T2-weighted magnetic resonance images for each axial slice through the head. To remove the unwanted noises in the magnetic resonance image, median filtering is used. First order statistics and gray level co-occurrence matrix-based features are extracted. Finally, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks are used to classify the brain tumor images. The application of the proposed method for tracking tumor is demonstrated to help pathologists distinguish its type of tumor. A classification with an accuracy of 89%, 90%, 91% and 95% has been obtained by, k-nearest neighbor, artificial neural network, support vector machine and convolutional neural networks.

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11

Wei, Jianqiang, Chunman Zhang, Liujia Ma, and Chunrui Zhang. "Artificial Intelligence Algorithm-Based Intraoperative Magnetic Resonance Navigation for Glioma Resection." Contrast Media & Molecular Imaging 2022 (March 4, 2022): 1–8. http://dx.doi.org/10.1155/2022/4147970.

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The study aimed to analyze the application value of artificial intelligence algorithm-based intraoperative magnetic resonance imaging (iMRI) in neurosurgical glioma resection. 108 patients with glioma in a hospital were selected and divided into the experimental group (intraoperative magnetic resonance assisted glioma resection) and the control group (conventional surgical experience resection), with 54 patients in each group. After the resection, the tumor resection rate, NIHSS (National Institute of Health Stroke Scale) score, Karnofsky score, and postoperative intracranial infection were calculated in the two groups. The results revealed that the average tumor resection rate in the experimental group was significantly higher than that in the control group (P < 0.05). There was no significant difference in Karnofsky score before and after the operation in the experimental group (P > 0.05). There was no significant difference in NIHSS score between the experimental group and the control group after resection (P > 0.05). The number of patients with postoperative neurological deficits in the experimental group was smaller than that in the control group. In addition, there was no significant difference in infection rates between the two groups after glioma resection (P > 0.05). In summary, intraoperative magnetic resonance navigation on the basis of a segmentation dictionary learning algorithm has great clinical value in neurosurgical glioma resection. It can maximize the removal of tumors and ensure the integrity of neurological function while avoiding an increased risk of postoperative infection, which is of great significance for the treatment of glioma.

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12

Liu, Shi Yang, Zhi Fang Lin, and S. T. Chui. "Controlling Electromagnetic Wave Based on Magnetic Metamaterials." Advances in Science and Technology 77 (September 2012): 237–45. http://dx.doi.org/10.4028/www.scientific.net/ast.77.237.

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Metamaterials are composite artificial materials composed of sub-wavelength resonant building blocks designed with state-of-the-art configurations, which exhibit novel and unique electromagnetic (EM) properties. The building blocks are usually made of metallic material, where surface plasmon polaritons can be excited at the interface, leading to many interesting and promising phenomena and applications. However, some drawbacks are accompanied such as intrinsic loss, narrow working frequency, and tunability limitation. We have designed a class of metamaterials composed of building blocks made of ferrite materials, it is accordingly termed magnetic metamaterials (MM). It is demonstrated that with the MM we can construct a magnetically tunable negative index material with the impedance matched to the air. The excitation of the magnetic surface plasmon (MSP) resonance can also be observed in the MM. Due to the MSP resonance and the time reversal symmetry breaking nature of the MM, the unidirectional waveguiding of the EM wave is demonstrated.

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13

Zhang, Li Wei, Yuan Cheng Lou, Yu Huan Zhao, Qin Wang, Wen Tao Qiao, and Li Xin Li. "Subwavelength Plasmon Microcavity Based on the Indefinite Metamaterial Waveguide." Advanced Materials Research 217-218 (March 2011): 1392–97. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.1392.

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The resonance properties of surface plasmon in the IMM/dielectric/IMM waveguide are theoretically studied by using finite-difference time-domain technique, where the claddings are indefinite metamaterials (IMMs). From the dispersion relation, it is found that the IMM/dielectric/IMM waveguide supports TE polarized surface plasmon if IMM is always-cutoff with negative permeability. For an IMM/dielectric/IMM waveguide with a finite length, a subwavelength plasmon microcavity can be formed by Fabry-Perot resonance. At the resonant frequency, the magnetic field is maximized at the dielectric core entrance and exit, the electromagnetic energy is strongly concentrated around the dielectric core. When an artificial magnetic resonator is put at the core entrance and the resonance frequency is tuned to the plasmon microcavity mode, Rabi splitting can appear because of the strong coupling between this resonator and the cavity mode.

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14

Zheng, Zhiyan, Ruixuan He, Cuijun Lin, and Chunyu Huang. "Multimodal Magnetic Resonance Imaging to Diagnose Knee Osteoarthritis under Artificial Intelligence." Computational Intelligence and Neuroscience 2022 (June 23, 2022): 1–9. http://dx.doi.org/10.1155/2022/6488889.

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This work aimed to investigate the application value of the multimodal magnetic resonance imaging (MRI) algorithm based on the low-rank decomposition denoising (LRDD) in the diagnosis of knee osteoarthritis (KOA), so as to offer a better examination method in the clinic. Seventy-eight patients with KOA were selected as the research objects, and they all underwent T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), fat suppression T2WI (SE-T2WI), and fat saturation T2WI (FS-T2WI). All obtained images were processed by using the I-LRDD algorithm. According to the degree of articular cartilage lesions under arthroscopy, the patients were divided into a group I, a group II, a group III, and a group IV. The sensitivity, specificity, accuracy, and consistency of KOA diagnosis of T1WI, T2WI, SE-T2WI, and FS-T2WI were analyzed by referring to the results of arthroscopy. The results showed that the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) of the I-LRDD algorithm used in this work were higher than those of image block priori denoising (IBPD) and LRDD, and the time consumption was lower than that of IBDP and LRDD ( p < 0.05). The sensitivity, specificity, accuracy, and consistency (Kappa value) of multimodal MRI in the diagnosis of KOA were 88.61%, 85.3%, 87.37%, and 0.73%, respectively, which were higher than those of T1WI, T2WI, SE-T2WI, and FS-T2WI. The sensitivity, specificity, accuracy, and consistency of multimodal MRI in diagnosing lesions in group IV were 95%, 96.10%, 95.88%, and 0.70%, respectively, which were much higher than those in groups I, II, and III ( p < 0.05). In conclusion, the LRDD algorithm shows a good image processing efficacy, and the multimodal MRI showed a good diagnosis effect on KOA, which was worthy of promotion clinically.

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15

Umanska, Anna, Dmytro Melnychuk, Sergey Melnychuk, and Liliya Kalachniuk. "Magnetic Resonance and Coronarographic Study of Rat Heart During Artificial Hypobiosis." Problems of Cryobiology and Cryomedicine 29, no. 2 (June 25, 2019): 181. http://dx.doi.org/10.15407/cryo29.02.181.

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16

JOSEPH, PETER M., YUJI YUASA, HAROLD L. KUNDEL, BISWANATH MUKHERJI, and HENRY A. SLOVITER. "Magnetic Resonance Imaging of Fluorine in Rats Infused with Artificial Blood." Investigative Radiology 20, no. 5 (August 1985): 504–5. http://dx.doi.org/10.1097/00004424-198508000-00011.

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17

Tandel, Gopal S., Antonella Balestrieri, Tanay Jujaray, Narender N. Khanna, Luca Saba, and Jasjit S. Suri. "Multiclass magnetic resonance imaging brain tumor classification using artificial intelligence paradigm." Computers in Biology and Medicine 122 (July 2020): 103804. http://dx.doi.org/10.1016/j.compbiomed.2020.103804.

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18

Shaikh, Aasef G. "A trail of artificial vestibular stimulation: electricity, heat, and magnet." Journal of Neurophysiology 108, no. 1 (July 1, 2012): 1–4. http://dx.doi.org/10.1152/jn.01169.2011.

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The interaction between the magnetic field of a magnetic resonance imaging (MRI) machine and ion currents within the inner-ear endolymph results in a Lorentz force. This force produces a pressure that pushes on the cupula within the semicircular canals causing nystagmus and vertigo. Here I discuss several implications of this unique and noninvasive way to stimulate the vestibular system in experimental neurophysiology and clinical neurology.

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19

Huang, Bing, Yun Huang, Xin Ma, and Yuequn Chen. "Intelligent Algorithm-Based Magnetic Resonance for Evaluating the Effect of Platelet-Rich Plasma in the Treatment of Intractable Pain of Knee Arthritis." Contrast Media & Molecular Imaging 2022 (May 26, 2022): 1–11. http://dx.doi.org/10.1155/2022/9223928.

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The application of intelligent algorithms in the treatment of intractable pain of patients with platelet-rich plasma (PRP) knee osteoarthritis by magnetic resonance was investigated. The automatic diagnosis of magnetic resonance knee osteoarthritis was established with multiple intelligent algorithms, including gray projection algorithm, adaptive binarization algorithm, and active shape model (ASM). The difference between automatic magnetic resonance detection indexes of the patients with knee osteoarthritis and artificial measurement results was analyzed. The included patients received PRP treatment. Knee osteoarthritis MRI osteoarthritis knee scores (KOA MOAKS) and Western Ontario and McMaster Universities arthritis index (WOMAC) before and after treatment were compared. The results showed that the results of knee osteoarthritis scores, inferior angle of femur, superior angle of tibia, and tibiofemoral angle (TFA) by automatic magnetic resonance diagnostic model were entirely consistent with artificial detection results. After the treatment, the total scores of knee lateral area, interior area, central area, and patellar area were all remarkably lower than those before the treatment ( P < 0.05 ). After the treatment, knee KOA MOAKS scores and WOMAC scores were both lower than those before the treatment ( P < 0.05 ). Visual analogue scale (VAS) scores 1 week, 2 weeks, and 3 weeks after the treatment were decreased compared with those before the treatment ( P < 0.05 ). Relevant studies indicated that intelligent algorithm-based automatic magnetic resonance diagnostic knee osteoarthritis model showed good utilization values, which could provide the reference and basis for the treatment of the patients with knee osteoarthritis.

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20

Curiale, Ariel H., Facundo Cabrera, Pablo Jimenez, Jorgelina Medus, Germán Mato, and Matías Calandrelli. "Detection of Fibrosis in Cine Magnetic Resonance Images Using Artificial Intelligence Techniques." Revista Argentina de Cardiologia 90, no. 2 (April 17, 2022): 130–33. http://dx.doi.org/10.7775/rac.v90.i2.20504.

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Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the lesions etiology

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21

Menhardt, Wido, and Karl-Heinrich Schmidt. "Computer vision on magnetic resonance images." Pattern Recognition Letters 8, no. 2 (September 1988): 73–85. http://dx.doi.org/10.1016/0167-8655(88)90049-9.

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22

Argentiero, Adriana, Giuseppe Muscogiuri, Mark G. Rabbat, Chiara Martini, Nicolò Soldato, Paolo Basile, Andrea Baggiano, et al. "The Applications of Artificial Intelligence in Cardiovascular Magnetic Resonance—A Comprehensive Review." Journal of Clinical Medicine 11, no. 10 (May 19, 2022): 2866. http://dx.doi.org/10.3390/jcm11102866.

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Cardiovascular disease remains an integral field on which new research in both the biomedical and technological fields is based, as it remains the leading cause of mortality and morbidity worldwide. However, despite the progress of cardiac imaging techniques, the heart remains a challenging organ to study. Artificial intelligence (AI) has emerged as one of the major innovations in the field of diagnostic imaging, with a dramatic impact on cardiovascular magnetic resonance imaging (CMR). AI will be increasingly present in the medical world, with strong potential for greater diagnostic efficiency and accuracy. Regarding the use of AI in image acquisition and reconstruction, the main role was to reduce the time of image acquisition and analysis, one of the biggest challenges concerning magnetic resonance; moreover, it has been seen to play a role in the automatic correction of artifacts. The use of these techniques in image segmentation has allowed automatic and accurate quantification of the volumes and masses of the left and right ventricles, with occasional need for manual correction. Furthermore, AI can be a useful tool to directly help the clinician in the diagnosis and derivation of prognostic information of cardiovascular diseases. This review addresses the applications and future prospects of AI in CMR imaging, from image acquisition and reconstruction to image segmentation, tissue characterization, diagnostic evaluation, and prognostication.

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23

Nelson, Chris R., Jessica Ekberg, and Kent Fridell. "Prostate Cancer Detection in Screening Using Magnetic Resonance Imaging and Artificial Intelligence." Open Artificial Intelligence Journal 6, no. 1 (March 20, 2020): 1–11. http://dx.doi.org/10.2174/1874061802006010001.

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Background: Prostate cancer is a leading cause of death among men who do not participate in a screening programme. MRI forms a possible alternative for prostate analysis of a higher level of sensitivity than the PSA test or biopsy. Magnetic resonance is a non-invasive method and magnetic resonance tomography produces a large amount of data. If a screening programme were implemented, a dramatic increase in radiologist workload and patient waiting time will follow. Computer Aided-Diagnose (CAD) could assist radiologists to decrease reading times and cost, and increase diagnostic effectiveness. CAD mimics radiologist and imaging guidelines to detect prostate cancer. Aim: The purpose of this study was to analyse and describe current research in MRI prostate examination with the aid of CAD. The aim was to determine if CAD systems form a reliable method for use in prostate screening. Methods: This study was conducted as a systematic literature review of current scientific articles. Selection of articles was carried out using the “Preferred Reporting Items for Systematic Reviews and for Meta-Analysis” (PRISMA). Summaries were created from reviewed articles and were then categorised into relevant data for results. Results: CAD has shown that its capability concerning sensitivity or specificity is higher than a radiologist. A CAD system can reach a peak sensitivity of 100% and two CAD systems showed a specificity of 100%. CAD systems are highly specialised and chiefly focus on the peripheral zone, which could mean missing cancer in the transition zone. CAD systems can segment the prostate with the same effectiveness as a radiologist. Conclusion: When CAD analysed clinically-significant tumours with a Gleason score greater than 6, CAD outperformed radiologists. However, their focus on the peripheral zone would require the use of more than one CAD system to analyse the entire prostate.

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24

Zhang, Yudong, Lenan Wu, and Shuihua Wang. "MAGNETIC RESONANCE BRAIN IMAGE CLASSIFICATION BY AN IMPROVED ARTIFICIAL BEE COLONY ALGORITHM." Progress In Electromagnetics Research 116 (2011): 65–79. http://dx.doi.org/10.2528/pier11031709.

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25

Enriquez, José S., Yan Chu, Shivanand Pudakalakatti, Kang Lin Hsieh, Duncan Salmon, Prasanta Dutta, Niki Zacharias Millward, et al. "Hyperpolarized Magnetic Resonance and Artificial Intelligence: Frontiers of Imaging in Pancreatic Cancer." JMIR Medical Informatics 9, no. 6 (June 17, 2021): e26601. http://dx.doi.org/10.2196/26601.

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Background There is an unmet need for noninvasive imaging markers that can help identify the aggressive subtype(s) of pancreatic ductal adenocarcinoma (PDAC) at diagnosis and at an earlier time point, and evaluate the efficacy of therapy prior to tumor reduction. In the past few years, there have been two major developments with potential for a significant impact in establishing imaging biomarkers for PDAC and pancreatic cancer premalignancy: (1) hyperpolarized metabolic (HP)-magnetic resonance (MR), which increases the sensitivity of conventional MR by over 10,000-fold, enabling real-time metabolic measurements; and (2) applications of artificial intelligence (AI). Objective Our objective of this review was to discuss these two exciting but independent developments (HP-MR and AI) in the realm of PDAC imaging and detection from the available literature to date. Methods A systematic review following the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines was performed. Studies addressing the utilization of HP-MR and/or AI for early detection, assessment of aggressiveness, and interrogating the early efficacy of therapy in patients with PDAC cited in recent clinical guidelines were extracted from the PubMed and Google Scholar databases. The studies were reviewed following predefined exclusion and inclusion criteria, and grouped based on the utilization of HP-MR and/or AI in PDAC diagnosis. Results Part of the goal of this review was to highlight the knowledge gap of early detection in pancreatic cancer by any imaging modality, and to emphasize how AI and HP-MR can address this critical gap. We reviewed every paper published on HP-MR applications in PDAC, including six preclinical studies and one clinical trial. We also reviewed several HP-MR–related articles describing new probes with many functional applications in PDAC. On the AI side, we reviewed all existing papers that met our inclusion criteria on AI applications for evaluating computed tomography (CT) and MR images in PDAC. With the emergence of AI and its unique capability to learn across multimodal data, along with sensitive metabolic imaging using HP-MR, this knowledge gap in PDAC can be adequately addressed. CT is an accessible and widespread imaging modality worldwide as it is affordable; because of this reason alone, most of the data discussed are based on CT imaging datasets. Although there were relatively few MR-related papers included in this review, we believe that with rapid adoption of MR imaging and HP-MR, more clinical data on pancreatic cancer imaging will be available in the near future. Conclusions Integration of AI, HP-MR, and multimodal imaging information in pancreatic cancer may lead to the development of real-time biomarkers of early detection, assessing aggressiveness, and interrogating early efficacy of therapy in PDAC.

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Boscolo Galazzo, Ilaria, Federica Cruciani, Lorenza Brusini, Ahmed Salih, Petia Radeva, Silvia Francesca Storti, and Gloria Menegaz. "Explainable Artificial Intelligence for Magnetic Resonance Imaging Aging Brainprints: Grounds and challenges." IEEE Signal Processing Magazine 39, no. 2 (March 2022): 99–116. http://dx.doi.org/10.1109/msp.2021.3126573.

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27

Powell, Kerrington, Myung S. Kim, Alyson Haslam, and Vinay Prasad. "Artificial intelligence and magnetic resonance imaging may not make cancer screening better." Journal of Cancer Policy 31 (March 2022): 100314. http://dx.doi.org/10.1016/j.jcpo.2021.100314.

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28

Driscoll, T., G. O. Andreev, D. N. Basov, S. Palit, Tong Ren, Jack Mock, Sang-Yeon Cho, Nan Marie Jokerst, and D. R. Smith. "Quantitative investigation of a terahertz artificial magnetic resonance using oblique angle spectroscopy." Applied Physics Letters 90, no. 9 (February 26, 2007): 092508. http://dx.doi.org/10.1063/1.2679766.

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29

Bhat, Himanshu, Balasrinivasa Rao Sajja, and Ponnada A. Narayana. "Fast quantification of proton magnetic resonance spectroscopic imaging with artificial neural networks." Journal of Magnetic Resonance 183, no. 1 (November 2006): 110–22. http://dx.doi.org/10.1016/j.jmr.2006.08.004.

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Abdul Jameel, Abdul Gani, Vincent Van Oudenhoven, Abdul-Hamid Emwas, and S. Mani Sarathy. "Predicting Octane Number Using Nuclear Magnetic Resonance Spectroscopy and Artificial Neural Networks." Energy & Fuels 32, no. 5 (April 17, 2018): 6309–29. http://dx.doi.org/10.1021/acs.energyfuels.8b00556.

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Scott, Jonathan M., Arvin Arani, Armando Manduca, Kiaran P. McGee, Joshua D. Trzasko, John Huston, Richard L. Ehman, and Matthew C. Murphy. "Artificial neural networks for magnetic resonance elastography stiffness estimation in inhomogeneous materials." Medical Image Analysis 63 (July 2020): 101710. http://dx.doi.org/10.1016/j.media.2020.101710.

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MAZOV, L. S. "STRIPES, PSEUDOGAP AND SC-SDW RESONANCE IN HTSC PEROVSKITES." International Journal of Modern Physics B 14, no. 29n31 (December 20, 2000): 3577–83. http://dx.doi.org/10.1142/s0217979200003654.

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The significant role of spin stripes in HTSC system is emphasized. The evidence for spin density wave (SDW) nature of pseudogap is presented. The dependence of CuO2 lattice modulation amplitude on the temperature and applied magnetic field is studied. The indication to possible SDW-SC resonance is obtained. The possible amplification of Tc with using of artificial magnetic stripes with taking into account their dynamic nature is proposed. An example of twofold amplification of Tc in thin single-crystalline film of La-based cuprate is presented.

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Phoenix, V. R., W. M. Holmes, and B. Ramanan. "Magnetic resonance imaging (MRI) of heavy-metal transport and fate in an artificial biofilm." Mineralogical Magazine 72, no. 1 (February 2008): 483–86. http://dx.doi.org/10.1180/minmag.2008.072.1.483.

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AbstractUnlike planktonic systems, reaction rates in biofilms are often limited by mass transport, which controls the rate of supply of contaminants into the biofilm matrix. To help understand this phenomenon, we investigated the potential of magnetic resonance imaging (MRI) to spatially quantify copper transport and fate in biofilms. For this initial study we utilized an artificial biofilm composed of a 50:50 mix of bacteria and agar. MRI successfully mapped Cu2+ uptake into the artificial biofilm by mapping T2 relaxation rates. A calibration protocol was used to convert T2 values into actual copper concentrations. Immobilization rates in the artificial biofilm were slow compared to the rapid equilibration of planktonic systems. Even after 36 h, the copper front had migrated only 3 mm into the artificial biofilm and at this distance from the copper source, concentrations were very low. This slow equilibration is a result of (1) the time it takes copper to diffuse over such distances and (2) the adsorption of copper onto cell surfaces, which further impedes copper diffusion. The success of this trial run indicates MRI could be used to quantitatively map heavy metal transport and immobilization in natural biofilms.

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34

Chiusano, Gabriele, Alessandra Staglianò, Curzio Basso, and Alessandro Verri. "Unsupervised tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging." Artificial Intelligence in Medicine 61, no. 1 (May 2014): 53–61. http://dx.doi.org/10.1016/j.artmed.2014.02.001.

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35

González-Villà, Sandra, Arnau Oliver, Sergi Valverde, Liping Wang, Reyer Zwiggelaar, and Xavier Lladó. "A review on brain structures segmentation in magnetic resonance imaging." Artificial Intelligence in Medicine 73 (October 2016): 45–69. http://dx.doi.org/10.1016/j.artmed.2016.09.001.

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36

Kehtarnavaz, N., M. Chung, L. A. Hayman, and R. E. Wendt III. "Magnetic Resonance Image Segmentation by Contextual Fuzzy Clustering." Journal of Intelligent and Fuzzy Systems 1, no. 4 (1993): 295–305. http://dx.doi.org/10.3233/ifs-1993-1404.

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37

Bhalodiya, Jayendra M., Sarah N. Lim Choi Keung, and Theodoros N. Arvanitis. "Magnetic resonance image-based brain tumour segmentation methods: A systematic review." DIGITAL HEALTH 8 (January 2022): 205520762210741. http://dx.doi.org/10.1177/20552076221074122.

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Background Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development. Purpose To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score). Statistical tests We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation. Conclusion U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available.

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Wang, Shuihua, Yin Zhang, Tianmin Zhan, Preetha Phillips, Yudong Zhang, Ge Liu, Siyuan Lu, and Xueyan Wu. "PATHOLOGICAL BRAIN DETECTION BY ARTIFICIAL INTELLIGENCE IN MAGNETIC RESONANCE IMAGING SCANNING (INVITED REVIEW)." Progress In Electromagnetics Research 156 (2016): 105–33. http://dx.doi.org/10.2528/pier16070801.

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39

Zhao, Wanlu, Desheng Zhang, and Xinjian Mao. "Application of Artificial Intelligence in Radiotherapy of Nasopharyngeal Carcinoma with Magnetic Resonance Imaging." Journal of Healthcare Engineering 2022 (February 2, 2022): 1–10. http://dx.doi.org/10.1155/2022/4132989.

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The value of automatic organ-at-risk outlining software for radiotherapy is based on artificial intelligence technology in clinical applications. The accuracy of automatic segmentation of organs at risk (OARs) in radiotherapy for nasopharyngeal carcinoma was investigated. In the automatic segmentation model which is proposed in this paper, after CT scans and manual segmentation by physicians, CT images of 147 nasopharyngeal cancer patients and their corresponding outlined OARs structures were selected and grouped into a training set (115 cases), a validation set (12 cases), and a test set (20 cases) by complete randomization. Adaptive histogram equalization is used to preprocess the CT images. End-to-end training is utilized to improve modeling efficiency and an improved network based on 3D Unet (AUnet) is implemented to introduce organ size as prior knowledge into the convolutional kernel size design to enable the network to adaptively extract features from organs of different sizes, thus improving the performance of the model. The DSC (Dice Similarity Coefficient) coefficients and Hausdorff (HD) distances of automatic and manual segmentation are compared to verify the effectiveness of the AUnet network. The mean DSC and HD of the test set were 0.86 ± 0.02 and 4.0 ± 2.0 mm, respectively. Except for optic nerve and optic cross, there was no statistical difference between AUnet and manual segmentation results ( P > 0.05). With the introduction of the adaptive mechanism, AUnet can achieve automatic segmentation of the endangered organs of nasopharyngeal carcinoma based on CT images more accurately, which can substantially improve the efficiency and consistency of segmentation of doctors in clinical applications.

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Meyer-Bäse, Anke, Lia Morra, Uwe Meyer-Bäse, and Katja Pinker. "Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging." Contrast Media & Molecular Imaging 2020 (August 28, 2020): 1–18. http://dx.doi.org/10.1155/2020/6805710.

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Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.

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41

Anker, Lawrence S., and Peter C. Jurs. "Prediction of carbon-13 nuclear magnetic resonance chemical shifts by artificial neural networks." Analytical Chemistry 64, no. 10 (May 15, 1992): 1157–64. http://dx.doi.org/10.1021/ac00034a015.

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42

Scardino, E., G. Villa, G. Bonomo, D. V. Matei, F. Verweij, B. Rocco, R. Varela, and O. de Cobelli. "Magnetic resonance imaging combined with artificial erection for local staging of penile cancer." Urology 63, no. 6 (June 2004): 1158–62. http://dx.doi.org/10.1016/j.urology.2004.01.008.

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43

Liu, Qiang, Shao Qing Wang, Dong Yue Yu, and Guang Ju Liang. "Retrospective Research: Analysis of Liver ³¹P Magnetic Resonance Spectroscopy Combined with Support Vector Machine." Applied Mechanics and Materials 220-223 (November 2012): 2936–40. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.2936.

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Abstract. Support vector machine is in the statistical learning theory developed on the basis of a new kind of machine learning method, field in pattern recognition in a wide range of applications. Artificial intelligence technology has been widely used in medical field. Among them, the support vector machine (SVM) technology can mass of data for feature vector extraction.31P(Phosphorus-31) magnetic resonance imaging in clinical spectrum analysis, facing mass data, can use the support vector machine (SVM) of31P magnetic resonance spectroscopy data modeling, used in liver disease classification of common nodules, this experiment set up three research object: hepatocellular carcinoma (HCC), liver cirrhosis and normal liver tissue. Through the kernel function based on polynomial and RBF kernel function of support vector machine classifier carries on the comparison, and get three liver classification recognition rate. Experiments show that based on31P magnetic resonance spectroscopy data of support vector machine (SVM) model can be classified to living liver diagnostic forecast, so as to improve the31P magnetic resonance spectroscopy on HCC diagnosis accuracy rate.

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44

Chaddad, Ahmad, Michael J. Kucharczyk, Abbas Cheddad, Sharon E. Clarke, Lama Hassan, Shuxue Ding, Saima Rathore, et al. "Magnetic Resonance Imaging Based Radiomic Models of Prostate Cancer: A Narrative Review." Cancers 13, no. 3 (February 1, 2021): 552. http://dx.doi.org/10.3390/cancers13030552.

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The management of prostate cancer (PCa) is dependent on biomarkers of biological aggression. This includes an invasive biopsy to facilitate a histopathological assessment of the tumor’s grade. This review explores the technical processes of applying magnetic resonance imaging based radiomic models to the evaluation of PCa. By exploring how a deep radiomics approach further optimizes the prediction of a PCa’s grade group, it will be clear how this integration of artificial intelligence mitigates existing major technological challenges faced by a traditional radiomic model: image acquisition, small data sets, image processing, labeling/segmentation, informative features, predicting molecular features and incorporating predictive models. Other potential impacts of artificial intelligence on the personalized treatment of PCa will also be discussed. The role of deep radiomics analysis-a deep texture analysis, which extracts features from convolutional neural networks layers, will be highlighted. Existing clinical work and upcoming clinical trials will be reviewed, directing investigators to pertinent future directions in the field. For future progress to result in clinical translation, the field will likely require multi-institutional collaboration in producing prospectively populated and expertly labeled imaging libraries.

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45

Vasilyak, L. M., O. D. Volpyan, A. I. Kuzmichev, Yu A. Obod, V. Ya Pecherkin, and P. A. Privalov. "Resonant reflection of plane microwave electromagnetic waves by the linear dielectric-ring structures." Industrial laboratory. Diagnostics of materials 88, no. 2 (February 22, 2022): 49–53. http://dx.doi.org/10.26896/1028-6861-2022-88-2-49-53.

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Artificial materials with negative magnetic and dielectric permittivity have unique electrodynamic properties that are not present in natural materials. We present the results of studying of the main magnetic LC resonance induced by a plane electromagnetic wave of GHz range in the linear structures of subwavelength dielectric ring elements with a high relative permittivity. The dielectric constant of the ring material (capacitor ceramics) is 160. Resonant scattering on the main magnetic mode and wave properties of linear structures consisting of subwavelength dielectric elements in the form of flat thin rings were studied. A single ring or ring structures were arranged in such a way that the vectors of the electric and magnetic fields of a plane incident electromagnetic wave were parallel to the plane of the ring, whereas the wave vector was perpendicular to the plane of the ring. Linear structures consisting of two or three rings were oriented along the magnetic vector of the incident wave. The magnetic field probe was placed on the line of the axis of symmetry of the ring and structures relative to the wave vector at the side of the structures most distant from the antenna. The spectra of transmitted radiation were measured during resonant excitation of magnetic fields in a system of dielectric rings in the near (distance — 2 mm) and remote (distance — 30 mm) zones from the ring. It is shown that in the near wave zone, splitting of the resonant frequency occurs due to mutual inductance and interaction of the rings. As the number of rings increases, the number of additional peaks also increases. A bandwidth of ~200 MHz with an amplitude 25 dB greater than the amplitude of the incident electromagnetic wave in the specified spectrum appears between the split levels. In the far zone, the transmitted radiation at the resonance frequency for a single ring practically does not change due to the splitting of this resonance frequency due to the interaction of the rings in the structure. The results obtained can be used in the development of new materials.

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Ahmed, Ahmed Shihab. "ON-Line MRI Image Selection and Tumor Classification using Artificial Neural Network." Ibn AL- Haitham Journal For Pure and Applied Sciences 33, no. 1 (January 20, 2020): 162. http://dx.doi.org/10.30526/33.1.2363.

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When soft tissue planning is important, usually, the Magnetic Resonance Imaging (MRI) is a medical imaging technique of selection. In this work, we show a modern method for automated diagnosis depending on a magnetic resonance images classification of the MRI. The presented technique has two main stages; features extraction and classification. We obtained the features corresponding to MRI images implementing Discrete Wavelet Transformation (DWT), inverse and forward, and textural properties, like rotation invariant texture features based on Gabor filtering, and evaluate the meaning of every property in the classification. The classifier is according to Feed Forward Back Propagation Artificial Neural Network (FP-ANN) in the classification stage. The properties thereafter derived to be implemented to teach a neural network based binary classifier that will be automatically able to conclude whether the image is that of a pathological, suffering from brain lesion, or a normal brain. The proposed algorithm obtained the sensitivity of 97.50%, specificity of 82.86% and accuracy of 94.3% for clinical Brain MRI database. This outcome proofs that the presented algorithm is robust and effective compared with other recent techniques.

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47

Carrara, Enrico A., Franco Pagliari, and Claudio Nicolini. "Neural networks for the peak-picking of nuclear magnetic resonance spectra." Neural Networks 6, no. 7 (January 1993): 1023–32. http://dx.doi.org/10.1016/s0893-6080(09)80012-9.

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48

Dewan, Raimi, M. K. A. Rahim, Mohamad Rijal Hamid, and M. F. M. Yusoff. "Analysis of Wideband Antenna Performance over Dual Band Artificial Magnetic Conductor (AMC) Ground Plane." Applied Mechanics and Materials 735 (February 2015): 273–77. http://dx.doi.org/10.4028/www.scientific.net/amm.735.273.

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A Coplanar Waveguide (CPW) wideband antenna operates from 2.69 GH to 6.27 GHz which act as reference antenna (RA) has been designed. A Dual Band AMC (DBAMC) unit cells have been proposed to operate at 2.45 GHz and 5.8 GHz. AMC is a metamaterial which mimics the behavior of zero reflection phase of Perfect Magnetic Conductor (PMC) at resonance frequency which not naturally existed in nature. Subsequently the antenna is incorporated with AMC unit cell, herein referred as Antenna with Dual Band AMC (ADBAMC). The DBAMC succesfully excites additional resonance at 2.45 GHz outside the initial operating range of standalone CPW wideband antenna. Incorporation of DBAMC to antenna achieves back lobe suppression at 2.45 GHz and 5.8 GHz. The overall average gain of AMC incorporated antenna is improved from 2.69 to 6.29 GHz as opposed to the standalone reference CPW wideband antenna. Study of surface current is also presented and discussed.

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Jiang, Nijie, Hong Xie, Jiao Lin, Yun Wang, and Yanan Yin. "Diagnosis and Nursing Intervention of Gynecological Ovarian Endometriosis with Magnetic Resonance Imaging under Artificial Intelligence Algorithm." Computational Intelligence and Neuroscience 2022 (June 11, 2022): 1–10. http://dx.doi.org/10.1155/2022/3123310.

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This research was aimed to study the application value of the magnetic resonance imaging (MRI) diagnosis under artificial intelligence algorithms and the effect of nursing intervention on patients with gynecological ovarian endometriosis. 116 patients with ovarian endometriosis were randomly divided into a control group (routine nursing) and an experimental group (comprehensive nursing), with 58 cases in each group. The artificial intelligence fuzzy C-means (FCM) clustering algorithm was proposed and used in the MRI diagnosis of ovarian endometriosis. The application value of the FCM algorithm was evaluated through the accuracy, Dice, sensitivity, and specificity of the imaging diagnosis, and the nursing satisfaction and the incidence of adverse reactions were used to evaluate the effect of nursing intervention. The results showed that, compared with the traditional hard C-means (HCM) algorithm, the artificial intelligence FCM algorithm gave a significantly higher partition coefficient, and its partition entropy and running time were significantly reduced, with significant differences ( P < 0.05 ). The average values of Dice, sensitivity, and specificity of patients’ MRI images were 0.77, 0.73, and 0.72, respectively, which were processed by the traditional HCM algorithm, while those values obtained by the improved artificial intelligence FCM algorithm were 0.92, 0.90, and 0.93, respectively; all the values were significantly improved ( P < 0.05 ). In addition, the accuracy of MRI diagnosis based on the artificial intelligence FCM algorithm was 94.32 ± 3.05%, which was significantly higher than the 81.39 ± 3.11% under the HCM algorithm ( P < 0.05 ). The overall nursing satisfaction of the experimental group was 96.5%, which was significantly better than the 87.9% of the control group ( P < 0.05 ). The incidence of postoperative adverse reactions in the experimental group (7.9%) was markedly lower than that in the control group (24.1%), with a significant difference ( P < 0.05 ). In short, MRI images under the artificial intelligence FCM algorithm could greatly improve the clinical diagnosis of ovarian endometriosis, and the comprehensive nursing intervention would also improve the prognosis and recovery of patients.

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Behrends, Volker, Benedikt Geier, Huw D. Williams, and Jacob G. Bundy. "Direct Assessment of Metabolite Utilization by Pseudomonas aeruginosa during Growth on Artificial Sputum Medium." Applied and Environmental Microbiology 79, no. 7 (January 25, 2013): 2467–70. http://dx.doi.org/10.1128/aem.03609-12.

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ABSTRACTWe grewPseudomonas aeruginosain LB and artificial sputum medium (ASM) (filtered and unfiltered) and quantified metabolite utilization and excretion by nuclear magnetic resonance (NMR) spectroscopy (metabolic footprinting or extracellular metabolomics). Utilization rates were similar between media, but there were differences in excretion—e.g., acetate was produced only in unfiltered ASM.

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