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Showing 1 to 12 of 31 entries
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Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis.

Abdominal radiology (New York)

Jiang H, Chen J, Gao R, Huang Z, Wu M, Song B.
PMID: 27678393
Abdom Radiol (NY). 2017 Feb;42(2):490-501. doi: 10.1007/s00261-016-0913-6.

PURPOSE: A meta-analysis was performed to assess the diagnostic performance of diffusion-weighted imaging (DWI) in liver fibrosis (LF) staging.METHODS: We conducted a comprehensive literature search to identify relevant articles. Diagnostic data were extracted for each METAVIR fibrosis stage (F0-F4)....

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Jiang H, Chen J, Gao R, et al. Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdom Radiol (NY). 2017;42(2):490-501doi: 10.1007/s00261-016-0913-6.
Jiang, H., Chen, J., Gao, R., Huang, Z., Wu, M., & Song, B. (2017). Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdominal radiology (New York), 42(2), 490-501. https://doi.org/10.1007/s00261-016-0913-6
Jiang, Hanyu, et al. "Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis." Abdominal radiology (New York) vol. 42,2 (2017): 490-501. doi: https://doi.org/10.1007/s00261-016-0913-6
Jiang H, Chen J, Gao R, Huang Z, Wu M, Song B. Liver fibrosis staging with diffusion-weighted imaging: a systematic review and meta-analysis. Abdom Radiol (NY). 2017 Feb;42(2):490-501. doi: 10.1007/s00261-016-0913-6. PMID: 27678393.
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Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI.

NeuroImage

Tax CMW, Dela Haije T, Fuster A, Westin CF, Viergever MA, Florack L, Leemans A.
PMID: 27456538
Neuroimage. 2016 Nov 15;142:260-279. doi: 10.1016/j.neuroimage.2016.07.042. Epub 2016 Jul 25.

The question whether our brain pathways adhere to a geometric grid structure has been a popular topic of debate in the diffusion imaging and neuroscience societies. Wedeen et al. (2012a, b) proposed that the brain's white matter is organized...

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Tax CMW, Dela Haije T, Fuster A, et al. Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI. Neuroimage. 2016;142:260-279doi: 10.1016/j.neuroimage.2016.07.042.
Tax, C. M. W., Dela Haije, T., Fuster, A., Westin, C. F., Viergever, M. A., Florack, L., & Leemans, A. (2016). Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI. NeuroImage, 142260-279. https://doi.org/10.1016/j.neuroimage.2016.07.042
Tax, Chantal M W, et al. "Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI." NeuroImage vol. 142 (2016): 260-279. doi: https://doi.org/10.1016/j.neuroimage.2016.07.042
Tax CMW, Dela Haije T, Fuster A, Westin CF, Viergever MA, Florack L, Leemans A. Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI. Neuroimage. 2016 Nov 15;142:260-279. doi: 10.1016/j.neuroimage.2016.07.042. Epub 2016 Jul 25. PMID: 27456538; PMCID: PMC5400114.
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[Fiber tractography in MR-DTI].

Nihon Igaku Hoshasen Gakkai zasshi. Nippon acta radiologica

Masuya Y.
PMID: 12833602
Nihon Igaku Hoshasen Gakkai Zasshi. 2003 May;63(5):8-12.

No abstract available.

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Masuya Y. [Fiber tractography in MR-DTI]. Nihon Igaku Hoshasen Gakkai Zasshi. 2003;63(5):8-12
Masuya, Y. (2003). [Fiber tractography in MR-DTI]. Nihon Igaku Hoshasen Gakkai zasshi. Nippon acta radiologica, 63(5), 8-12.
Masuya, Yoshitaka. "[Fiber tractography in MR-DTI]." Nihon Igaku Hoshasen Gakkai zasshi. Nippon acta radiologica vol. 63,5 (2003): 8-12.
Masuya Y. [Fiber tractography in MR-DTI]. Nihon Igaku Hoshasen Gakkai Zasshi. 2003 May;63(5):8-12. Japanese. PMID: 12833602.
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Evaluating contextual processing in diffusion MRI: application to optic radiation reconstruction for epilepsy surgery.

PloS one

Tax CM, Duits R, Vilanova A, ter Haar Romeny BM, Hofman P, Wagner L, Leemans A, Ossenblok P.
PMID: 25077946
PLoS One. 2014 Jul 31;9(7):e101524. doi: 10.1371/journal.pone.0101524. eCollection 2014.

Diffusion MRI and tractography allow for investigation of the architectural configuration of white matter in vivo, offering new avenues for applications like presurgical planning. Despite the promising outlook, there are many pitfalls that complicate its use for (clinical) application....

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Tax CM, Duits R, Vilanova A, et al. Evaluating contextual processing in diffusion MRI: application to optic radiation reconstruction for epilepsy surgery. PLoS One. 2014;9(7):e101524doi: 10.1371/journal.pone.0101524.
Tax, C. M., Duits, R., Vilanova, A., ter Haar Romeny, B. M., Hofman, P., Wagner, L., Leemans, A., & Ossenblok, P. (2014). Evaluating contextual processing in diffusion MRI: application to optic radiation reconstruction for epilepsy surgery. PloS one, 9(7), e101524. https://doi.org/10.1371/journal.pone.0101524
Tax, Chantal M W, et al. "Evaluating contextual processing in diffusion MRI: application to optic radiation reconstruction for epilepsy surgery." PloS one vol. 9,7 (2014): e101524. doi: https://doi.org/10.1371/journal.pone.0101524
Tax CM, Duits R, Vilanova A, ter Haar Romeny BM, Hofman P, Wagner L, Leemans A, Ossenblok P. Evaluating contextual processing in diffusion MRI: application to optic radiation reconstruction for epilepsy surgery. PLoS One. 2014 Jul 31;9(7):e101524. doi: 10.1371/journal.pone.0101524. eCollection 2014. PMID: 25077946; PMCID: PMC4117467.
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Diffusion MR Imaging in the Head and Neck: Principles and Applications.

Neuroimaging clinics of North America

Norris CD, Quick SE, Parker JG, Koontz NA.
PMID: 32600630
Neuroimaging Clin N Am. 2020 Aug;30(3):261-282. doi: 10.1016/j.nic.2020.04.001. Epub 2020 Jun 11.

Diffusion imaging is a functional MR imaging tool that creates tissue contrast representative of the random, microscopic translational motion of water molecules within human body tissues. Long considered a cornerstone MR imaging sequence for brain imaging, diffusion-weighted imaging (DWI)...

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Norris CD, Quick SE, Parker JG, et al. Diffusion MR Imaging in the Head and Neck: Principles and Applications. Neuroimaging Clin N Am. 2020;30(3):261-282doi: 10.1016/j.nic.2020.04.001.
Norris, C. D., Quick, S. E., Parker, J. G., & Koontz, N. A. (2020). Diffusion MR Imaging in the Head and Neck: Principles and Applications. Neuroimaging clinics of North America, 30(3), 261-282. https://doi.org/10.1016/j.nic.2020.04.001
Norris, Carrie D, et al. "Diffusion MR Imaging in the Head and Neck: Principles and Applications." Neuroimaging clinics of North America vol. 30,3 (2020): 261-282. doi: https://doi.org/10.1016/j.nic.2020.04.001
Norris CD, Quick SE, Parker JG, Koontz NA. Diffusion MR Imaging in the Head and Neck: Principles and Applications. Neuroimaging Clin N Am. 2020 Aug;30(3):261-282. doi: 10.1016/j.nic.2020.04.001. Epub 2020 Jun 11. PMID: 32600630.
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Comparison of q-Space Reconstruction Methods for Undersampled Diffusion Spectrum Imaging Data.

Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine

Varela-Mattatall GE, Koch A, Stirnberg R, Chabert S, Uribe S, Tejos C, Stöcker T, Irarrazaval P.
PMID: 31080210
Magn Reson Med Sci. 2020 May 01;19(2):108-118. doi: 10.2463/mrms.mp.2019-0015. Epub 2019 May 10.

PURPOSE: To compare different q-space reconstruction methods for undersampled diffusion spectrum imaging data.MATERIALS AND METHODS: We compared the quality of three methods: Mean Apparent Propagator (MAP); Compressed Sensing using Identity (CSI) and Compressed Sensing using Dictionary (CSD) with simulated...

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Varela-Mattatall GE, Koch A, Stirnberg R, et al. Comparison of q-Space Reconstruction Methods for Undersampled Diffusion Spectrum Imaging Data. Magn Reson Med Sci. 2019;19(2):108-118doi: 10.2463/mrms.mp.2019-0015.
Varela-Mattatall, G. E., Koch, A., Stirnberg, R., Chabert, S., Uribe, S., Tejos, C., Stöcker, T., & Irarrazaval, P. (2020). Comparison of q-Space Reconstruction Methods for Undersampled Diffusion Spectrum Imaging Data. Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine, 19(2), 108-118. https://doi.org/10.2463/mrms.mp.2019-0015
Varela-Mattatall, Gabriel E, et al. "Comparison of q-Space Reconstruction Methods for Undersampled Diffusion Spectrum Imaging Data." Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine vol. 19,2 (2020): 108-118. doi: https://doi.org/10.2463/mrms.mp.2019-0015
Varela-Mattatall GE, Koch A, Stirnberg R, Chabert S, Uribe S, Tejos C, Stöcker T, Irarrazaval P. Comparison of q-Space Reconstruction Methods for Undersampled Diffusion Spectrum Imaging Data. Magn Reson Med Sci. 2020 May 01;19(2):108-118. doi: 10.2463/mrms.mp.2019-0015. Epub 2019 May 10. PMID: 31080210; PMCID: PMC7232027.
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Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer.

Cancer imaging : the official publication of the International Cancer Imaging Society

Xu L, Zhang G, Shi B, Liu Y, Zou T, Yan W, Xiao Y, Xue H, Feng F, Lei J, Jin Z, Sun H.
PMID: 31864408
Cancer Imaging. 2019 Dec 21;19(1):90. doi: 10.1186/s40644-019-0274-9.

PURPOSE: To compare the diagnostic accuracy of biparametric MRI (bpMRI) and multiparametric MRI (mpMRI) for prostate cancer (PCa) and clinically significant prostate cancer (csPCa) and to explore the application value of dynamic contrast-enhanced (DCE) MRI in prostate imaging.METHODS AND...

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Xu L, Zhang G, Shi B, et al. Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer. Cancer Imaging. 2019;19(1):90doi: 10.1186/s40644-019-0274-9.
Xu, L., Zhang, G., Shi, B., Liu, Y., Zou, T., Yan, W., Xiao, Y., Xue, H., Feng, F., Lei, J., Jin, Z., & Sun, H. (2019). Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer. Cancer imaging : the official publication of the International Cancer Imaging Society, 19(1), 90. https://doi.org/10.1186/s40644-019-0274-9
Xu, Lili, et al. "Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer." Cancer imaging : the official publication of the International Cancer Imaging Society vol. 19,1 (2019): 90. doi: https://doi.org/10.1186/s40644-019-0274-9
Xu L, Zhang G, Shi B, Liu Y, Zou T, Yan W, Xiao Y, Xue H, Feng F, Lei J, Jin Z, Sun H. Comparison of biparametric and multiparametric MRI in the diagnosis of prostate cancer. Cancer Imaging. 2019 Dec 21;19(1):90. doi: 10.1186/s40644-019-0274-9. PMID: 31864408; PMCID: PMC6925429.
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Quantitative Whole-Body Diffusion-Weighted MR Imaging.

Magnetic resonance imaging clinics of North America

Donners R, Blackledge M, Tunariu N, Messiou C, Merkle EM, Koh DM.
PMID: 30316462
Magn Reson Imaging Clin N Am. 2018 Nov;26(4):479-494. doi: 10.1016/j.mric.2018.06.002. Epub 2018 Sep 15.

Whole-body diffusion-weighted MRI has emerged as a powerful diagnostic tool for disease detection and staging mainly used in systemic bone disease. The large field-of-view functional imaging technique highlights cellular tumor and suppresses normal tissue signal, allowing quantification of an...

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Donners R, Blackledge M, Tunariu N, et al. Quantitative Whole-Body Diffusion-Weighted MR Imaging. Magn Reson Imaging Clin N Am. 2018;26(4):479-494doi: 10.1016/j.mric.2018.06.002.
Donners, R., Blackledge, M., Tunariu, N., Messiou, C., Merkle, E. M., & Koh, D. M. (2018). Quantitative Whole-Body Diffusion-Weighted MR Imaging. Magnetic resonance imaging clinics of North America, 26(4), 479-494. https://doi.org/10.1016/j.mric.2018.06.002
Donners, Ricardo, et al. "Quantitative Whole-Body Diffusion-Weighted MR Imaging." Magnetic resonance imaging clinics of North America vol. 26,4 (2018): 479-494. doi: https://doi.org/10.1016/j.mric.2018.06.002
Donners R, Blackledge M, Tunariu N, Messiou C, Merkle EM, Koh DM. Quantitative Whole-Body Diffusion-Weighted MR Imaging. Magn Reson Imaging Clin N Am. 2018 Nov;26(4):479-494. doi: 10.1016/j.mric.2018.06.002. Epub 2018 Sep 15. PMID: 30316462.
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Multi-fiber tractography visualizations for diffusion MRI data.

PloS one

Vos SB, Viergever MA, Leemans A.
PMID: 24282597
PLoS One. 2013 Nov 25;8(11):e81453. doi: 10.1371/journal.pone.0081453. eCollection 2013.

In recent years, several new diffusion MRI approaches have been proposed to explore microstructural properties of the white matter, such as Q-ball imaging and spherical deconvolution-based techniques to estimate the orientation distribution function. These methods can describe the estimated...

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Vos SB, Viergever MA, Leemans A. Multi-fiber tractography visualizations for diffusion MRI data. PLoS One. 2013;8(11):e81453doi: 10.1371/journal.pone.0081453.
Vos, S. B., Viergever, M. A., & Leemans, A. (2013). Multi-fiber tractography visualizations for diffusion MRI data. PloS one, 8(11), e81453. https://doi.org/10.1371/journal.pone.0081453
Vos, Sjoerd B, et al. "Multi-fiber tractography visualizations for diffusion MRI data." PloS one vol. 8,11 (2013): e81453. doi: https://doi.org/10.1371/journal.pone.0081453
Vos SB, Viergever MA, Leemans A. Multi-fiber tractography visualizations for diffusion MRI data. PLoS One. 2013 Nov 25;8(11):e81453. doi: 10.1371/journal.pone.0081453. eCollection 2013. PMID: 24282597; PMCID: PMC3839966.
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Quantifying the effect of tissue deformation on diffusion-weighted MRI: a mathematical model and an efficient simulation framework applied to cardiac diffusion imaging.

Physics in medicine and biology

Mekkaoui I, Moulin K, Croisille P, Pousin J, Viallon M.
PMID: 27385441
Phys Med Biol. 2016 Aug 07;61(15):5662-86. doi: 10.1088/0031-9155/61/15/5662. Epub 2016 Jul 07.

Cardiac motion presents a major challenge in diffusion weighted MRI, often leading to large signal losses that necessitate repeated measurements. The diffusion process in the myocardium is difficult to investigate because of the unqualified sensitivity of diffusion measurements to...

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Mekkaoui I, Moulin K, Croisille P, et al. Quantifying the effect of tissue deformation on diffusion-weighted MRI: a mathematical model and an efficient simulation framework applied to cardiac diffusion imaging. Phys Med Biol. 2016;61(15):5662-86doi: 10.1088/0031-9155/61/15/5662.
Mekkaoui, I., Moulin, K., Croisille, P., Pousin, J., & Viallon, M. (2016). Quantifying the effect of tissue deformation on diffusion-weighted MRI: a mathematical model and an efficient simulation framework applied to cardiac diffusion imaging. Physics in medicine and biology, 61(15), 5662-86. https://doi.org/10.1088/0031-9155/61/15/5662
Mekkaoui, Imen, et al. "Quantifying the effect of tissue deformation on diffusion-weighted MRI: a mathematical model and an efficient simulation framework applied to cardiac diffusion imaging." Physics in medicine and biology vol. 61,15 (2016): 5662-86. doi: https://doi.org/10.1088/0031-9155/61/15/5662
Mekkaoui I, Moulin K, Croisille P, Pousin J, Viallon M. Quantifying the effect of tissue deformation on diffusion-weighted MRI: a mathematical model and an efficient simulation framework applied to cardiac diffusion imaging. Phys Med Biol. 2016 Aug 07;61(15):5662-86. doi: 10.1088/0031-9155/61/15/5662. Epub 2016 Jul 07. PMID: 27385441.
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Diffusion-weighted magnetic resonance imaging for tumour response assessment: why, when and how?.

Cancer imaging : the official publication of the International Cancer Imaging Society

Afaq A, Andreou A, Koh DM.
PMID: 20880779
Cancer Imaging. 2010 Oct 04;10:S179-88. doi: 10.1102/1470-7330.2010.9032.

Diffusion-weighted magnetic resonance imaging (DWI) is increasingly being used to assess tumour response to a variety of anticancer treatments. The technique is quick to perform without the need for administration of exogenous contrast medium, and enables the apparent diffusion...

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Afaq A, Andreou A, Koh DM. Diffusion-weighted magnetic resonance imaging for tumour response assessment: why, when and how?. Cancer Imaging. 2010;10:S179-88doi: 10.1102/1470-7330.2010.9032.
Afaq, A., Andreou, A., & Koh, D. M. (2010). Diffusion-weighted magnetic resonance imaging for tumour response assessment: why, when and how?. Cancer imaging : the official publication of the International Cancer Imaging Society, 10S179-88. https://doi.org/10.1102/1470-7330.2010.9032
Afaq, A, et al. "Diffusion-weighted magnetic resonance imaging for tumour response assessment: why, when and how?." Cancer imaging : the official publication of the International Cancer Imaging Society vol. 10 (2010): S179-88. doi: https://doi.org/10.1102/1470-7330.2010.9032
Afaq A, Andreou A, Koh DM. Diffusion-weighted magnetic resonance imaging for tumour response assessment: why, when and how?. Cancer Imaging. 2010 Oct 04;10:S179-88. doi: 10.1102/1470-7330.2010.9032. PMID: 20880779; PMCID: PMC2967137.
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The difference of apparent diffusion coefficient in the middle cerebellar peduncle among parkinsonian syndromes: Evidence from a meta-analysis.

Journal of the neurological sciences

Sako W, Murakami N, Izumi Y, Kaji R.
PMID: 27000228
J Neurol Sci. 2016 Apr 15;363:90-4. doi: 10.1016/j.jns.2016.02.034. Epub 2016 Feb 16.

The measurement of middle cerebellar peduncle (MCP) width allows for differential diagnosis between Parkinson's disease (PD) and multiple system atrophy with predominant parkinsonian features (MSA-P). However, it remains controversial whether apparent diffusion coefficient (ADC) value in the MCP of...

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Sako W, Murakami N, Izumi Y, et al. The difference of apparent diffusion coefficient in the middle cerebellar peduncle among parkinsonian syndromes: Evidence from a meta-analysis. J Neurol Sci. 2016;363:90-4doi: 10.1016/j.jns.2016.02.034.
Sako, W., Murakami, N., Izumi, Y., & Kaji, R. (2016). The difference of apparent diffusion coefficient in the middle cerebellar peduncle among parkinsonian syndromes: Evidence from a meta-analysis. Journal of the neurological sciences, 36390-4. https://doi.org/10.1016/j.jns.2016.02.034
Sako, Wataru, et al. "The difference of apparent diffusion coefficient in the middle cerebellar peduncle among parkinsonian syndromes: Evidence from a meta-analysis." Journal of the neurological sciences vol. 363 (2016): 90-4. doi: https://doi.org/10.1016/j.jns.2016.02.034
Sako W, Murakami N, Izumi Y, Kaji R. The difference of apparent diffusion coefficient in the middle cerebellar peduncle among parkinsonian syndromes: Evidence from a meta-analysis. J Neurol Sci. 2016 Apr 15;363:90-4. doi: 10.1016/j.jns.2016.02.034. Epub 2016 Feb 16. PMID: 27000228.
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Showing 1 to 12 of 31 entries