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Journal articles on the topic 'Magnetic Resonance Imaging data'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Lowe, Mark J., and James A. Sorenson. "Spatially filtering functional magnetic resonance imaging data." Magnetic Resonance in Medicine 37, no. 5 (May 1997): 723–29. http://dx.doi.org/10.1002/mrm.1910370514.

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2

Nguyen, Hoai-Thu, Sylvain Grange, Benjamin Leporq, Magalie Viallon, Pierre Croisille, and Thomas Grenier. "Impact of Distortion on Local Radiomic Analysis of Quadriceps Based on Quantitative Magnetic Resonance Imaging Data." International Journal of Pharma Medicine and Biological Sciences 10, no. 2 (April 2021): 49–54. http://dx.doi.org/10.18178/ijpmbs.10.2.49-54.

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3

Tsirmpas, Charalampos, Kostas Giokas, Dimitra Iliopoulou, and Dimitris Koutsouris. "Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy Cloud Computing Framework." International Journal of Reliable and Quality E-Healthcare 1, no. 4 (October 2012): 1–12. http://dx.doi.org/10.4018/ijrqeh.2012100101.

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Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) are two non-invasive techniques that are increasingly being used to identify and quantify biochemical markers associated with certain diseases, e.g., choline in the case of cancer. The associating of MRI/MRS images, patient’s electronic health record, genome information, and environmental factors increase the precision of diagnosis and treatment. The authors present a collaboration framework based on Cloud Computing which allows analysis of MRI/MRS data based on advanced mathematical tools, advanced combination, and link discovery between different data types, so as to increase the precision and consequently avoid non-appropriate therapy and treatment plans.

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4

Tardivon, Anne A., Alexandra Athanasiou, Fabienne Thibault, and Carl El Khoury. "Breast imaging and reporting data system (BIRADS): Magnetic resonance imaging." European Journal of Radiology 61, no. 2 (February 2007): 212–15. http://dx.doi.org/10.1016/j.ejrad.2006.08.036.

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5

Kent, Daniel. "Correction: Data in Review on Magnetic Resonance Imaging." Annals of Internal Medicine 109, no. 5 (September 1, 1988): 438. http://dx.doi.org/10.7326/0003-4819-109-5-438_2.

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6

Pei, Haonan, Dong Cui, Weifang Cao, Yongxin Guo, and Qing Jiao. "Development of Magnetic Resonance Imaging Data Arrangement Toolbox." Journal of Medical Imaging and Health Informatics 7, no. 7 (November 1, 2017): 1607–10. http://dx.doi.org/10.1166/jmihi.2017.2173.

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7

Ney, Derek, Elliot K. Fishman, and Leonard Dickens. "Interactive multidimensional display of magnetic resonance imaging data." Journal of Digital Imaging 3, no. 4 (November 1990): 254–60. http://dx.doi.org/10.1007/bf03168123.

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8

Frank, Lawrence R., Richard B. Buxton, and Eric C. Wong. "Probabilistic analysis of functional magnetic resonance imaging data." Magnetic Resonance in Medicine 39, no. 1 (January 1998): 132–48. http://dx.doi.org/10.1002/mrm.1910390120.

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9

Hoda, Syed A., and Alexander J. Swistel. "“Data! Data! Data!”: Charting the Course for Mammary Magnetic Resonance Imaging." Breast Journal 20, no. 5 (September 2014): 451–52. http://dx.doi.org/10.1111/tbj.12325.

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10

Chen, Yongsheng, E. Mark Haacke, and Jun Li. "Peripheral nerve magnetic resonance imaging." F1000Research 8 (October 28, 2019): 1803. http://dx.doi.org/10.12688/f1000research.19695.1.

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Magnetic resonance imaging (MRI) has been used extensively in revealing pathological changes in the central nervous system. However, to date, MRI is very much underutilized in evaluating the peripheral nervous system (PNS). This underutilization is generally due to two perceived weaknesses in MRI: first, the need for very high resolution to image the small structures within the peripheral nerves to visualize morphological changes; second, the lack of normative data in MRI of the PNS and this makes reliable interpretation of the data difficult. This article reviews current state-of-the-art capabilities in in vivo MRI of human peripheral nerves. It aims to identify areas where progress has been made and those that still require further improvement. In particular, with many new therapies on the horizon, this review addresses how MRI can be used to provide non-invasive and objective biomarkers in the evaluation of peripheral neuropathies. Although a number of techniques are available in diagnosing and tracking pathologies in the PNS, those techniques typically target the distal peripheral nerves, and distal nerves may be completely degenerated during the patient’s first clinic visit. These techniques may also not be able to access the proximal nerves deeply embedded in the tissue. Peripheral nerve MRI would be an alternative to circumvent these problems. In order to address the pressing clinical needs, this review closes with a clinical protocol at 3T that will allow high-resolution, high-contrast, quantitative MRI of the proximal peripheral nerves.

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11

Voss, Henning U., Jonathan P. Dyke, Karsten Tabelow, Nicholas D. Schiff, and Douglas J. Ballon. "Magnetic resonance advection imaging of cerebrovascular pulse dynamics." Journal of Cerebral Blood Flow & Metabolism 37, no. 4 (July 20, 2016): 1223–35. http://dx.doi.org/10.1177/0271678x16651449.

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We analyze the pulsatile signal component of dynamic echo planar imaging data from the brain by modeling the dependence between local temporal and spatial signal variability. The resulting magnetic resonance advection imaging maps depict the location of major arteries. Color direction maps allow for visualization of the direction of blood vessels. The potential significance of magnetic resonance advection imaging maps is demonstrated on a functional magnetic resonance imaging data set of 19 healthy subjects. A comparison with the here introduced pulse coherence maps, in which the echo planar imaging signal is correlated with a cardiac pulse signal, shows that the magnetic resonance advection imaging approach results in a better spatial definition without the need for a pulse reference. In addition, it is shown that magnetic resonance advection imaging velocities can be estimates of pulse wave velocities if certain requirements are met, which are specified. Although for this application magnetic resonance advection imaging velocities are not quantitative estimates of pulse wave velocities, they clearly depict local pulsatile dynamics. Magnetic resonance advection imaging can be applied to existing dynamic echo planar imaging data sets with sufficient spatiotemporal resolution. It is discussed whether magnetic resonance advection imaging might have the potential to evolve into a biomarker for the health of the cerebrovascular system.

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12

Frollo, Ivan, Peter Andris, Jiri Pribil, Tomas Dermek, and Daniel Gogola. "Soft Magnetic Material Testing Using Magnetic Resonance Imaging." Advanced Materials Research 740 (August 2013): 618–23. http://dx.doi.org/10.4028/www.scientific.net/amr.740.618.

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Soft magnetic field samples were placed into the homogenous magnetic field of an imager based on nuclear magnetic resonance. Several samples made of a soft magnetic material (cut from a data disc) were tested. Theoretical computations on a magnetic double layer were performed. For experimental verification an MRI 0.178 Testa ESAOTE Opera imager was used. For our experiments a homogeneous circular holder (reference medium) - a container filled with doped water - was designed. The resultant image corresponds to the magnetic field variations in the vicinity of the samples. For data detection classical gradient-echo and spin-echo imaging methods, susceptible to magnetic field inhomogeneities, were used. Experiments proved that the proposed method is perspective for soft magnetic material testing using magnetic resonance imaging methods (MRI).

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13

Kemik, Kerem, and Emel Ada. "Data Analysis and Reliability in Functional Magnetic Resonance Imaging." Türk Radyoloji Dergisi/Turkish Journal of Radiology 37, no. 3 (May 7, 2019): 39–46. http://dx.doi.org/10.5152/turkjradiol.2019.95919.

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14

Prior, F. W., B. Brunsden, C. Hildebolt, T. S. Nolan, M. Pringle, S. N. Vaishnavi, and L. J. Larson-Prior. "Facial Recognition From Volume-Rendered Magnetic Resonance Imaging Data." IEEE Transactions on Information Technology in Biomedicine 13, no. 1 (January 2009): 5–9. http://dx.doi.org/10.1109/titb.2008.2003335.

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15

Nielsen, Thomas, Kim Mouridsen, Ross J. Maxwell, Hans Stødkilde-Jørgensen, Leif Østergaard, and Michael R. Horsman. "Segmentation of dynamic contrast enhanced magnetic resonance imaging data." Acta Oncologica 47, no. 7 (January 2008): 1265–70. http://dx.doi.org/10.1080/02841860802277489.

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16

Suponenkovs, Artjoms, Zigurds Markovics, and Ardis Platkajis. "Computer Analysis of Knee by Magnetic Resonance Imaging Data." Procedia Computer Science 104 (2017): 354–61. http://dx.doi.org/10.1016/j.procs.2017.01.145.

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17

Anderson, David, Bruce Golden, Edward Wasil, and Hao Zhang. "Predicting prostate cancer risk using magnetic resonance imaging data." Information Systems and e-Business Management 13, no. 4 (February 7, 2014): 599–608. http://dx.doi.org/10.1007/s10257-014-0239-2.

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18

Cervantes, Hernán, Carlos A. C. Jousseph, and Said R. Rabbani. "Functional Magnetic Resonance Imaging Data Analysis by Autoregressive Estimator." Applied Magnetic Resonance 43, no. 3 (June 29, 2012): 321–30. http://dx.doi.org/10.1007/s00723-012-0371-4.

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19

Vidya Shankar, Rohini, John C. Chang, Houchun H. Hu, and Vikram D. Kodibagkar. "Fast data acquisition techniques in magnetic resonance spectroscopic imaging." NMR in Biomedicine 32, no. 3 (January 14, 2019): e4046. http://dx.doi.org/10.1002/nbm.4046.

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20

Khaliq, Amir A., Ijaz M. Qureshi, and Jawad A. Shah. "Unmixing functional magnetic resonance imaging data using matrix factorization." International Journal of Imaging Systems and Technology 22, no. 4 (November 1, 2012): 195–99. http://dx.doi.org/10.1002/ima.22022.

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21

Zhang, Linlin, Michele Guindani, and Marina Vannucci. "Bayesian models for functional magnetic resonance imaging data analysis." Wiley Interdisciplinary Reviews: Computational Statistics 7, no. 1 (November 4, 2014): 21–41. http://dx.doi.org/10.1002/wics.1339.

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22

Briola, Chiara. "Magnetic Resonance Imaging and Magnetic Resonance Imaging Cholangiopancreatography of the Pancreas in Small Animals." Veterinary Sciences 9, no. 8 (July 23, 2022): 378. http://dx.doi.org/10.3390/vetsci9080378.

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Magnetic resonance imaging (MRI) and MR cholangiopancreatography (MRCP) have emerged as non-invasive diagnostic techniques for the diagnosis of pancreatic and pancreatic duct disorders in humans. The number of studies focused on MR and MRCP for pancreatic disease in small animals is very limited. MR has been described for the evaluation of insulinoma in dogs and to investigate pancreatitis in cats. The studies were based on a standard protocol with T2 weighted (w) fast recovery fast spin-echo (FRFSE) with and without fat suppression, T1w FSE pre-contrast and T1w FSE post-contrast with and without fat suppression. MRCP after secretin stimulation has been described in cats to assess the pancreatic ductal system, taking advantage of pulse sequences heavily T2w as rapid acquisition with rapid enhancement (RARE), fast-recovery fast spin-echo (FRFSE) sequences and single-shot fast spin-echo (SSFSE) sequences. In addition to the standard protocol, fast spoiled gradient recalled echo pulse sequences (fSPGR) and volume interpolated 3D gradient-echo T1w pulse sequences pre and post-contrast have also been used in cats, reaching the goal of assessing the biliary tree and the pancreatic duct with the same sequence and in multiple planes. Despite the small amount of data, the results show potential, and the most recent technical innovations, in particular, focused on diffusion MRI and fast acquisition, further support the need for continued evaluation of MRI as an effective instrument for the investigation of pancreatic disease.

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23

Tardivon, Anne A., Alexandra Athanasiou, Fabienne Thibault, and Carl El Khoury. "Breast imaging and reporting data system (BIRADS) magnetic resonance imaging illustrated cases." European Journal of Radiology 61, no. 2 (February 2007): 216–23. http://dx.doi.org/10.1016/j.ejrad.2006.08.037.

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24

Lee, Karen A. "Breast Imaging Reporting and Data System Category 3 for Magnetic Resonance Imaging." Topics in Magnetic Resonance Imaging 23, no. 6 (December 2014): 337–44. http://dx.doi.org/10.1097/rmr.0000000000000037.

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25

Yokoo, Patricia, Gabriel Lucca de Oliveira Salvador, Jesus José André Quintana Castillo, Ana Carolina Nicoletti Basso, Rafael Sarmento do Amaral, Rafael Olinto Pelaez de Campos, Renan Arrais Ykeda Barreto, Oscar Fernando Ghattas Orozco, Alexandre Cavalheiro Cavalli, and Mauricio Zaparolli. "Prostate imaging reporting and data system correlation with Gleason score: Pathological aspects of magnetic resonance imaging findings." Urologia Journal 86, no. 4 (July 14, 2019): 189–96. http://dx.doi.org/10.1177/0391560319858482.

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Objective: Prostate cancer has a high prevalence and mortality, being the most diagnosed urologic cancer. Prostatic magnetic resonance imaging showed high sensitivity in the detection of clinically significant neoplasia and agreement with the Gleason score. Therefore, we attempted to evaluate the diagnostic accuracy of the prostate imaging reporting and data system, using biopsy and prostatectomy as the reference standard. The secondary goal of correlating prostatic magnetic resonance imaging findings and anatomopathological samples is obtained. Materials and Methods: We retrospectively analyzed seventy-nine 1.5 Tesla prostatic magnetic resonance imaging scans in patients aged 31 to 86 years, performed at the Clinical Hospital of the Federal University of Paraná between January 2015 and February 2018. Results: Considering all 79 patients, prostatic magnetic resonance imaging was able to diagnose tumor in 47 patients (59.4%). Considering the peripheral zone, the prostatic magnetic resonance imaging had a sensitivity of 75.0% (95% confidence interval: 52.1%–98.0%), specificity of 89.5% (95% confidence interval: 66.0%–100%), 94.4% positive predictive value (95% confidence interval: 71.0%–100%), 66.7% negative predictive value (95% confidence interval: 43.0%–69.0%), 83.8% Positive Likelihood Ratio (PVR) (95% confidence interval: 60.0%–100%), 27.9% Negative Likelihood Ratio (RVN) (95% confidence interval: 5.0%–50.0 %), and accuracy of 86.3% (95% confidence interval: 63.0%–100%). The receiver operating characteristic curve obtained demonstrated the sensitivity variation according to the prostate imaging reporting and data system score of the patients, obtaining an area under the curve of 84.8 for a prostate imaging reporting and data system cutoff of 3. Conclusion: The use of the prostate imaging reporting and data system score is useful for the screening and classification of prostate cancer, due to its easy reproducibility, even in a population with an unknown prostate cancer prevalence, which can be easily correlated with biopsy studies and/or radical prostatectomy.

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26

Webb, Megan E., Farnaz Amoozegar, and Ashley D. Harris. "Magnetic Resonance Imaging in Pediatric Migraine." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 46, no. 6 (July 16, 2019): 653–65. http://dx.doi.org/10.1017/cjn.2019.243.

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ABSTRACT:This literature review provides an overview of the research using magnetic resonance imaging (MRI) in pediatric migraine and compares findings with the adult migraine literature. A literature search using PubMed was conducted using all relevant sources up to February 2019. Using MRI methods to categorize and explain pediatric migraine in comparison with adult migraine is important, in order to recognize and appreciate the differences between the two entities, both clinically and physiologically. We aim to demonstrate the differences and similarities between pediatric and adult migraine using data from white matter and gray matter structural studies, cerebral perfusion, metabolites, and functional MRI (fMRI) studies, including task-based and resting-state blood oxygen level-dependent studies. By doing this we identify areas that need further research, as well as possible areas where intervention could alter outcomes.

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27

Liu, Zhuqing, Andreas J. Bartsch, Veronica J. Berrocal, and Timothy D. Johnson. "A mixed-effects, spatially varying coefficients model with application to multi-resolution functional magnetic resonance imaging data." Statistical Methods in Medical Research 28, no. 4 (January 15, 2018): 1203–15. http://dx.doi.org/10.1177/0962280217752378.

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Spatial resolution plays an important role in functional magnetic resonance imaging studies as the signal-to-noise ratio increases linearly with voxel volume. In scientific studies, where functional magnetic resonance imaging is widely used, the standard spatial resolution typically used is relatively low which ensures a relatively high signal-to-noise ratio. However, for pre-surgical functional magnetic resonance imaging analysis, where spatial accuracy is paramount, high-resolution functional magnetic resonance imaging may play an important role with its greater spatial resolution. High spatial resolution comes at the cost of a smaller signal-to-noise ratio. This begs the question as to whether we can leverage the higher signal-to-noise ratio of a standard functional magnetic resonance imaging study with the greater spatial accuracy of a high-resolution functional magnetic resonance imaging study in a pre-operative patient. To answer this question, we propose to regress the statistic image from a high resolution scan onto the statistic image obtained from a standard resolution scan using a mixed-effects model with spatially varying coefficients. We evaluate our model via simulation studies and we compare its performance with a recently proposed model that operates at a single spatial resolution. We apply and compare the two models on data from a patient awaiting tumor resection. Both simulation study results and the real data analysis demonstrate that our newly proposed model indeed leverages the larger signal-to-noise ratio of the standard spatial resolution scan while maintaining the advantages of the high spatial resolution scan.

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28

Huang, Chao, Liang Shan, H. Cecil Charles, Wolfgang Wirth, Marc Niethammer, and Hongtu Zhu. "Diseased Region Detection of Longitudinal Knee Magnetic Resonance Imaging Data." IEEE Transactions on Medical Imaging 34, no. 9 (September 2015): 1914–27. http://dx.doi.org/10.1109/tmi.2015.2415675.

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29

Ke, Qiao, Jiangshe Zhang, Wei Wei, Robertas Damasevicius, and Marcin Wozniak. "Adaptive Independent Subspace Analysis of Brain Magnetic Resonance Imaging Data." IEEE Access 7 (2019): 12252–61. http://dx.doi.org/10.1109/access.2019.2893496.

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30

Brown, Emery N., and Marlene Behrmann. "Controversy in statistical analysis of functional magnetic resonance imaging data." Proceedings of the National Academy of Sciences 114, no. 17 (April 18, 2017): E3368—E3369. http://dx.doi.org/10.1073/pnas.1705513114.

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31

Tagaris, G. A., W. Richter, S. G. Kim, and A. P. Georgopoulos. "Box-Jenkins intervention analysis of functional magnetic resonance imaging data." Neuroscience Research 27, no. 3 (March 1997): 289–94. http://dx.doi.org/10.1016/s0168-0102(97)01154-1.

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32

Reza, Syed M. S., Manar D. Samad, Zeina A. Shboul, Karra A. Jones, and Khan M. Iftekharuddin. "Glioma grading using structural magnetic resonance imaging and molecular data." Journal of Medical Imaging 6, no. 02 (April 24, 2019): 1. http://dx.doi.org/10.1117/1.jmi.6.2.024501.

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33

Moratal, David, W. Thomas Dixon, Senthil Ramamurthy, Stamatios Lerakis, W. James Parks, and Marijn E. Brummer. "Optimal sampling for “Noquist” reduced-data cine magnetic resonance imaging." Medical Physics 40, no. 1 (December 26, 2012): 012302. http://dx.doi.org/10.1118/1.4770270.

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34

Kullmann, Walter H., and Manfred Fuchs. "Integrated imaging of neuromagnetic reconstructions and morphological magnetic resonance data." Clinical Physics and Physiological Measurement 12, A (January 1, 1991): 37–41. http://dx.doi.org/10.1088/0143-0815/12/a/008.

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35

Ardekani, Babak A. "Comments on ?probabilistic analysis of functional magnetic resonance imaging data?" Magnetic Resonance in Medicine 41, no. 6 (June 1999): 1279. http://dx.doi.org/10.1002/(sici)1522-2594(199906)41:6<1279::aid-mrm28>3.0.co;2-u.

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36

Thomas, Jonathan B., Lisa Jong, J. David Spence, Bruce A. Wasserman, Brian K. Rutt, and David A. Steinman. "Anthropometric data for magnetic resonance imaging of the carotid bifurcation." Journal of Magnetic Resonance Imaging 21, no. 6 (2005): 845–49. http://dx.doi.org/10.1002/jmri.20317.

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37

Hunjan, Sandeep, Elfar Adalsteinsson, Dong-Hyun Kim, Griffith R. Harsh, Arthur L. Boyer, Daniel Spielman, and Lei Xing. "Quality assurance of magnetic resonance spectroscopic imaging–derived metabolic data." International Journal of Radiation Oncology*Biology*Physics 57, no. 4 (November 2003): 1159–73. http://dx.doi.org/10.1016/s0360-3016(03)01564-5.

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38

Tilea, B., C. Alberti, C. Adamsbaum, P. Armoogum, J. F. Oury, D. Cabrol, G. Sebag, G. Kalifa, and C. Garel. "Cerebral biometry in fetal magnetic resonance imaging: new reference data." Ultrasound in Obstetrics and Gynecology 33, no. 2 (February 2009): 173–81. http://dx.doi.org/10.1002/uog.6276.

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39

Panta, Om Biju, Yagya Raj Pathak, and Dan Bahadur Karki. "Magnetic Resonance Imaging Findings in Spondylodiscitis." Journal of Nepal Health Research Council 15, no. 3 (January 1, 2018): 217–21. http://dx.doi.org/10.3126/jnhrc.v15i3.18843.

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Background: Magnetic Resonance Imaging is the imaging modality of choice for imaging spinal infection due to its high sensitivity and specificity. This study aims to study the magnetic resonance imaging changes in patients with spondylodiscitis.Methods: The study was a retrospective study carried in a multimodality imaging centre in Kathmandu. Magnetic resonance imaging records and clinical record of 3 years duration were reviewed and patients with clinical and radiological diagnosis of spondylodiscitis were included in the study. Three radiologists interpreted Magnetic Resonance Imaging with mutual consensus in disputed issues. Data analysis was done with Statistical Package for Social Sciences21.0.Results: A total of 52 patients were included in the study. The mean age of the patients was 43.9 ± 17.6 years. Spondylodiscitis involved lumbar spine in 26(50%) case, cervical and thoracic spine in 13(25%) cases each. Multiple IV discs were involved in 24(46.2%) cases, which was most common in cervical spine and least common in thoracic spine. Only one vertebral end plate was involved in 16(30.8%) cases. Epidural collection was seen in 23(44.2%) cases and paravertebral collection was noted in 63(33.5%) cases. Statistical significant difference in region of spine involved (p=0.02) and epidural collection (p=0.04) was noted between genders.Conclusions: Lumbar spine was the most common level involved with spondylodiscitis, perivertebral enhancing soft tissue was present in all cases, and involvement of disc and the endplates were the most common pattern.

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40

Patel, V. A., B. S. Oberman, T. T. Zacharia, and H. Isildak. "Magnetic resonance imaging findings in Ménière's disease." Journal of Laryngology & Otology 131, no. 7 (June 6, 2017): 602–7. http://dx.doi.org/10.1017/s0022215117001086.

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AbstractObjectives:To identify and evaluate cranial magnetic resonance imaging findings associated with Ménière's disease.Methods:Seventy-eight patients with a documented diagnosis of Ménière's disease and 35 controls underwent 1.5 T or 3 T magnetic resonance imaging of the brain. Patients also underwent otological, vestibular and audiometric examinations.Results:Lack of visualisation of the left and right vestibular aqueducts was identified as statistically significant amongst Ménière's disease patients (left,p= 0.0001, odds ratio = 0.02; right,p= 0.0004, odds ratio = 0.03). Both vestibular aqueducts were of abnormal size in the Ménière's disease group, albeit with left-sided significance (left,p= 0.008, odds ratio = 10.91; right,p= 0.49, odds ratio = 2.47).Conclusion:Lack of vestibular aqueduct visualisation on magnetic resonance imaging was statistically significant in Ménière's disease patients compared to the general population. The study findings suggest that magnetic resonance imaging can be useful to rule out retrocochlear pathology and provide radiological data to support the clinical diagnosis of Ménière's disease.

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Abdelmoula, Walid M., Michael S. Regan, Begona G. C. Lopez, Elizabeth C. Randall, Sean Lawler, Ann C. Mladek, Michal O. Nowicki, et al. "Automatic 3D Nonlinear Registration of Mass Spectrometry Imaging and Magnetic Resonance Imaging Data." Analytical Chemistry 91, no. 9 (April 2019): 6206–16. http://dx.doi.org/10.1021/acs.analchem.9b00854.

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42

Winnek Douglas, A. "5337102 Instant printer for volumetric imaging of data from magnetic resonance imaging apparatus." Magnetic Resonance Imaging 13, no. 5 (January 1995): XI. http://dx.doi.org/10.1016/0730-725x(95)98027-n.

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43

Tokuda, Yukiko, Keiko Kuriyama, Atsushi Nakamoto, Soomi Choi, Kenji Yutani, Yuki Kunitomi, Takashi Haneda, et al. "Evaluation of Suspicious Nipple Discharge by Magnetic Resonance Mammography Based on Breast Imaging Reporting and Data System Magnetic Resonance Imaging Descriptors." Journal of Computer Assisted Tomography 33, no. 1 (January 2009): 58–62. http://dx.doi.org/10.1097/rct.0b013e3181671ad2.

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44

Scarabino, T., A. Bertolino, M. Burroni, T. Popolizio, J. Duyn, D. R. Weinberger, and U. Salvolini. "White Matter Lesions in Phenylketonuria: Evaluation with Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy." Rivista di Neuroradiologia 16, no. 2 (April 2003): 251–61. http://dx.doi.org/10.1177/197140090301600204.

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Phenylketonuria (PKU) is a congenital metabolic autosomic recessive disease, caused by a deficit in the liver of phenyl-alanine hydroxylase, the enzyme responsible for conversion of phenyl-alanine (PHE) into tyrosine. Reduction of this enzymatic activity is responsible for increased phenyl-alanine in blood and tissues and, above all, in brain. Accumulation of PHE causes neural damage which produces a typical clinical picture with mental retardation, psychiatric symptoms and epilepsy. It is now possible to diagnose this disease early (with neonatal screening), before irreversible clinical symptoms reflecting central nervous system injury appear. Early diagnosis allows timely onset of therapy (the only possible) consisting of a special diet with reduced intake of PHE (integrated with a mix of aminoacids) whose objective is to keep levels of PHE low in the blood (3–6 mg/dl). Magnetic Resonance Imaging (MRI) is the elective diagnostic tool to evaluate in vivo the involvement of the brain in PKU. Previous MRI morphological studies in patients with PKU have reported various focal symmetrical lesions in periventricular white matter (especially parieto-occipital) of patients with PKU with PHE blood values higher than 10 mg/dl. These lesions, whose importance is not yet clear, seem to represent a reversible structural alteration of myelin, since they regress if blood PHE decreases. Proton magnetic resonance spectroscopy (1H-MRS) can measure in vivo brain metabolites which could help determine the nature of white matter lesions. In particular, changes in NAA (a marker of neuronal integrity) or mI (a potential astrocytic marker) could point to possible neurochemical dysfunction, whereas Cho levels may parallel the degree of the tissue myelination. The purpose of the present study was to evaluate morphologically and biochemically the regional specificity of white matter lesions with structural MRI and with 1H-MRSI. The study included 12 patients with PKU ten to 42 years of age. All patients underwent structural MRI scans while eight of them were also studied with 1H-MRSI. Structural MRI lesions in white matter were analyzed both qualitatively (signal intensity) and quantitatively (location and extension). 1H-MRSI metabolites were measured as the ratio of the area under each peak: NAA/Cr, NAA/Cho, Cho/Cr. Analysis of location and extension of the lesion on structural MRI data showed limited involvement of parieto-occipital white matter in three cases (with isointense or vaguely hypointense lesions in T1, and moderately hyperintense lesions in T2); medium involvement in six cases (with fairly hypointense or isointense lesions in T1, fairly or moderately hyperintense lesions in T2); serious involvement in three cases (with isointense or fairly hypointense lesions in T1, and fairly hyperintense lesions in T2). As for 1H-MRSI data, ANOVA showed a significant reduction of NAA/Cho and increase in Cho/Cr in white matter lesions, but no change in NAA/Cr. No correlation was found between clinical parameters and morphological or spectroscopic data. In conclusion, our morphological MRI data confirmed the presence of multiple signal alterations, focal and symmetrical, in deep periventricular white matter (especially posterior), with occasional involvement of subcortical white matter. However, these lesions do not seem to be strongly predictive of clinical outcome. 1H-MRSI data suggest increased Cho levels in white matter lesions. Since Cho is thought to reflect membrane turnover, these data may support the demyelinating nature of lesions, consistent with earlier post mortem studies.

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Palesh, Mohammad, Sten Fredrikson, Hamidreza Jamshidi, Pia Maria Jonsson, and Goran Tomson. "Diffusion of magnetic resonance imaging in Iran." International Journal of Technology Assessment in Health Care 23, no. 2 (April 2007): 278–85. http://dx.doi.org/10.1017/s0266462307070377.

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Objectives:The aim of this article is to describe the diffusion of magnetic resonance imaging (MRI) in Iran, including regional variations during the period of 1990 to 2005 and international comparisons.Methods:Data on the diffusion of MRI were obtained from the Medical Equipment Office of the Ministry of Health (MOH) and, using self-administered questionnaires, from forty-one universities specializing in medical sciences. Data were gathered from the year of first purchase up to mid-2005. Information for international comparisons was obtained from the Organization for Economic Cooperation and Development health data of 2006.Results:Iran purchased its first MRI unit in 1990. Since then, the number of MRI units has increased remarkably. The diffusion curve of MRI in Iran follows an S-shaped curve with a very slow speed in the period of 1991–95. Accelerated adoption occurred later coinciding with a significant influence from the private sector, especially from 1999. Iran had ninety-three MRI units in 2005, and the number of MRI units per million in the population was 1.36.Conclusions:The number of MRI units in provinces is not in direct proportion to the number of their inhabitants. Rational adoption and equitable diffusion of MRI may require the MOH and regulatory bodies to improve their ability in health technology assessment and integrate it into the policy making regarding adoption, diffusion, and utilization of health technologies.

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Tiwari, Yash V., Jianfei Lu, Qiang Shen, Bianca Cerqueira, and Timothy Q. Duong. "Magnetic resonance imaging of blood–brain barrier permeability in ischemic stroke using diffusion-weighted arterial spin labeling in rats." Journal of Cerebral Blood Flow & Metabolism 37, no. 8 (January 1, 2016): 2706–15. http://dx.doi.org/10.1177/0271678x16673385.

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Diffusion-weighted arterial spin labeling magnetic resonance imaging has recently been proposed to quantify the rate of water exchange (Kw) across the blood–brain barrier in humans. This study aimed to evaluate the blood–brain barrier disruption in transient (60 min) ischemic stroke using Kw magnetic resonance imaging with cross-validation by dynamic contrast-enhanced magnetic resonance imaging and Evans blue histology in the same rats. The major findings were: (i) at 90 min after stroke (30 min after reperfusion), group Kw magnetic resonance imaging data showed no significant blood–brain barrier permeability changes, although a few animals showed slightly abnormal Kw. Dynamic contrast-enhanced magnetic resonance imaging confirmed this finding in the same animals. (ii) At two days after stroke, Kw magnetic resonance imaging revealed significant blood–brain barrier disruption. Regions with abnormal Kw showed substantial overlap with regions of hyperintense T2 (vasogenic edema) and hyperperfusion. Dynamic contrast-enhanced magnetic resonance imaging and Evans blue histology confirmed these findings in the same animals. The Kw values in the normal contralesional hemisphere and the ipsilesional ischemic core two days after stroke were: 363 ± 17 and 261 ± 18 min−1, respectively (P < 0.05, n = 9). Kw magnetic resonance imaging is sensitive to blood–brain barrier permeability changes in stroke, consistent with dynamic contrast-enhanced magnetic resonance imaging and Evans blue extravasation. Kw magnetic resonance imaging offers advantages over existing techniques because contrast agent is not needed and repeated measurements can be made for longitudinal monitoring or averaging.

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Stanitski, Carl L. "Correlation of Arthroscopic and Clinical Examinations With Magnetic Resonance Imaging Findings of Injured Knees in Children and Adolescents." American Journal of Sports Medicine 26, no. 1 (January 1998): 2–6. http://dx.doi.org/10.1177/03635465980260012001.

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This study evaluated the correlation among clinical diagnosis, magnetic resonance imaging reports, and arthroscopic findings in 28 patients aged 8 to 17 years (average, 14.4) with knee injuries. Meniscal, anterior cruciate ligament, and articular surface injuries were evaluated. A highly positive correlation (78.5%) was found between clinical and arthroscopic findings. A highly negative correlation was found between arthroscopic and magnetic resonance imaging findings (78.5%) and between clinical and magnetic resonance imaging findings (75%). In this series, accuracy, positive predictive value, negative predictive value, sensitivity, and specificity data were much more favorable from clinical examination than from magnetic resonance imaging. Overall, magnetic resonance imaging diagnoses added little guidance to patient management and at times provided spurious information.

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Wang, Z. X., S. L. Chen, Q. Q. Wang, B. Liu, J. Zhu, and J. Shen. "The performance of magnetic resonance imaging in the detection of triangular fibrocartilage complex injury: a meta-analysis." Journal of Hand Surgery (European Volume) 40, no. 5 (January 19, 2015): 477–84. http://dx.doi.org/10.1177/1753193414567425.

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The aim of this study was to evaluate the accuracy of magnetic resonance imaging in the detection of triangular fibrocartilage complex injury through a meta-analysis. A comprehensive literature search was conducted before 1 April 2014. All studies comparing magnetic resonance imaging results with arthroscopy or open surgery findings were reviewed, and 25 studies that satisfied the eligibility criteria were included. Data were pooled to yield pooled sensitivity and specificity, which were respectively 0.83 and 0.82. In detection of central and peripheral tears, magnetic resonance imaging had respectively a pooled sensitivity of 0.90 and 0.88 and a pooled specificity of 0.97 and 0.97. Six high-quality studies using Ringler’s recommended magnetic resonance imaging parameters were selected for analysis to determine whether optimal imaging protocols yielded better results. The pooled sensitivity and specificity of these six studies were 0.92 and 0.82, respectively. The overall accuracy of magnetic resonance imaging was acceptable. For peripheral tears, the pooled data showed a relatively high accuracy. Magnetic resonance imaging with appropriate parameters are an ideal method for diagnosing different types of triangular fibrocartilage complex tears. Level of Evidence: Diagnostic Level III

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Khan, Laila, Rukhsana Aziz, and Tahira Nishtar. "MRI findings in pregnancy related neurological complications." Journal of the Pakistan Medical Association 72, no. 12 (November 15, 2022): 2448–51. http://dx.doi.org/10.47391/jpma.5135.

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Objectives: To describe the various neurological complications that occur in pregnancy and puerperium using magnetic resonance imaging as the diagnostic tool. Method: The prospective study was conducted at the Radiology Department of Lady Reading Hospital, Peshawar, Pakistan, from June 2018 to June 2019, and comprised pregnant and puerperium patients presenting with neurological symptoms who were referred for magnetic resonance imaging. Clinical records of the patients were reviewed for risk factors and neurological symptomatology. Imaging was done using a 1.5Tesla machine. Departmental routine imaging protocols for magnetic resonance imaging brain and magnetic resonance venography were used. Data was analysed using SPSS 23. Results: There were 60 pregnant women with a mean age of 25.85±5.1 years (range: 17-40 years). Magnetic resonance imaging showed posterior reversible encephalopathy syndrome in 20(33.3%) patients and haemorrhagic infarct in 18(30%), while 9(15%) were found to be normal. Magnetic resonance venography exhibited dural sinus thrombosis in 19(31.7%) patients. Conclusion: Magnetic resonance imaging was found to play a vital role in early diagnosis of pregnancy-related neurological complications. Key Words: MRI, Neurological complications, MRV, PRES, DST.

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Yamada, Noriaki, Kazuya Hoshino, Tadash Sugiyama, and Hiroyuki Matsuura. "4656426 Nuclear magnetic resonance data processing method." Magnetic Resonance Imaging 5, no. 5 (January 1987): V. http://dx.doi.org/10.1016/0730-725x(87)90149-4.

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