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Showing 1 to 12 of 132 entries
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Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging.

Frontiers in aging neuroscience

Liu S, Cai W, Pujol S, Kikinis R, Feng DD.
PMID: 26941639
Front Aging Neurosci. 2016 Feb 23;8:23. doi: 10.3389/fnagi.2016.00023. eCollection 2016.

The research on staging of pre-symptomatic and prodromal phase of neurological disorders, e.g., Alzheimer's disease (AD), is essential for prevention of dementia. New strategies for AD staging with a focus on early detection, are demanded to optimize potential efficacy...

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Liu S, Cai W, Pujol S, et al. Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci. 2016;8:23doi: 10.3389/fnagi.2016.00023.
Liu, S., Cai, W., Pujol, S., Kikinis, R., Feng, D. D. (2016). Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Frontiers in aging neuroscience, 823. https://doi.org/10.3389/fnagi.2016.00023
Liu, Sidong, et al. "Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging." Frontiers in aging neuroscience vol. 8 (2016): 23. doi: https://doi.org/10.3389/fnagi.2016.00023
Liu S, Cai W, Pujol S, Kikinis R, Feng DD. Cross-View Neuroimage Pattern Analysis in Alzheimer's Disease Staging. Front Aging Neurosci. 2016 Feb 23;8:23. doi: 10.3389/fnagi.2016.00023. eCollection 2016. PMID: 26941639; PMCID: PMC4763344.
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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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A Kalman Filtering Perspective for Multiatlas Segmentation.

SIAM journal on imaging sciences

Gao Y, Zhu L, Cates J, MacLeod RS, Bouix S, Tannenbaum A.
PMID: 26807162
SIAM J Imaging Sci. 2015;8(2):1007-1029. doi: 10.1137/130933423. Epub 2015 Apr 30.

In multiatlas segmentation, one typically registers several atlases to the novel image, and their respective segmented label images are transformed and fused to form the final segmentation. In this work, we provide a new dynamical system perspective for multiatlas...

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Gao Y, Zhu L, Cates J, et al. A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM J Imaging Sci. 2015;8(2):1007-1029doi: 10.1137/130933423.
Gao, Y., Zhu, L., Cates, J., MacLeod, R. S., Bouix, S., & Tannenbaum, A. (2015). A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM journal on imaging sciences, 8(2), 1007-1029. https://doi.org/10.1137/130933423
Gao, Yi, et al. "A Kalman Filtering Perspective for Multiatlas Segmentation." SIAM journal on imaging sciences vol. 8,2 (2015): 1007-1029. doi: https://doi.org/10.1137/130933423
Gao Y, Zhu L, Cates J, MacLeod RS, Bouix S, Tannenbaum A. A Kalman Filtering Perspective for Multiatlas Segmentation. SIAM J Imaging Sci. 2015;8(2):1007-1029. doi: 10.1137/130933423. Epub 2015 Apr 30. PMID: 26807162; PMCID: PMC4722821.
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Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport.

Proceedings of SPIE--the International Society for Optical Engineering

Gao Y, Zhu LJ, Bouix S, Tannenbaum A.
PMID: 25302008
Proc SPIE Int Soc Opt Eng. 2014 Mar 21;9034:90342X. doi: 10.1117/12.2043282.

Longitudinal analysis of medical imaging data has become central to the study of many disorders. Unfortunately, various constraints (study design, patient availability, technological limitations) restrict the acquisition of data to only a few time points, limiting the study of...

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Gao Y, Zhu LJ, Bouix S, et al. Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport. Proc SPIE Int Soc Opt Eng. 2014;9034:90342Xdoi: 10.1117/12.2043282.
Gao, Y., Zhu, L. J., Bouix, S., & Tannenbaum, A. (2014). Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport. Proceedings of SPIE--the International Society for Optical Engineering, 903490342X. https://doi.org/10.1117/12.2043282
Gao, Yi, et al. "Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport." Proceedings of SPIE--the International Society for Optical Engineering vol. 9034 (2014): 90342X. doi: https://doi.org/10.1117/12.2043282
Gao Y, Zhu LJ, Bouix S, Tannenbaum A. Interpolation of Longitudinal Shape and Image Data via Optimal Mass Transport. Proc SPIE Int Soc Opt Eng. 2014 Mar 21;9034:90342X. doi: 10.1117/12.2043282. PMID: 25302008; PMCID: PMC4187117.
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Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery.

Proceedings of SPIE--the International Society for Optical Engineering

Gao Y, Zhu L, Norton I, Agar NY, Tannenbaum A.
PMID: 25302009
Proc SPIE Int Soc Opt Eng. 2014 Mar 12;9036:90360D. doi: 10.1117/12.2043273.

Desorption electrospray ionization mass spectrometry (DESI-MS) provides a highly sensitive imaging technique for differentiating normal and cancerous tissue at the molecular level. This can be very useful, especially under intra-operative conditions where the surgeon has to make crucial decision...

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Gao Y, Zhu L, Norton I, et al. Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery. Proc SPIE Int Soc Opt Eng. 2014;9036:90360Ddoi: 10.1117/12.2043273.
Gao, Y., Zhu, L., Norton, I., Agar, N. Y., & Tannenbaum, A. (2014). Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery. Proceedings of SPIE--the International Society for Optical Engineering, 903690360D. https://doi.org/10.1117/12.2043273
Gao, Yi, et al. "Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery." Proceedings of SPIE--the International Society for Optical Engineering vol. 9036 (2014): 90360D. doi: https://doi.org/10.1117/12.2043273
Gao Y, Zhu L, Norton I, Agar NY, Tannenbaum A. Reconstruction and Feature Selection for Desorption Electrospray Ionization Mass Spectroscopy Imagery. Proc SPIE Int Soc Opt Eng. 2014 Mar 12;9036:90360D. doi: 10.1117/12.2043273. PMID: 25302009; PMCID: PMC4187386.
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Automated dispersion and orientation analysis for carbon nanotube reinforced polymer composites.

Nanotechnology

Gao Y, Li Z, Lin Z, Zhu L, Tannenbaum A, Bouix S, Wong CP.
PMID: 23060008
Nanotechnology. 2012 Nov 02;23(43):435706. doi: 10.1088/0957-4484/23/43/435706. Epub 2012 Oct 11.

The properties of carbon nanotube (CNT)/polymer composites are strongly dependent on the dispersion and orientation of CNTs in the host matrix. Quantification of the dispersion and orientation of CNTs by means of microstructure observation and image analysis has been...

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Gao Y, Li Z, Lin Z, et al. Automated dispersion and orientation analysis for carbon nanotube reinforced polymer composites. Nanotechnology. 2012;23(43):435706doi: 10.1088/0957-4484/23/43/435706.
Gao, Y., Li, Z., Lin, Z., Zhu, L., Tannenbaum, A., Bouix, S., & Wong, C. P. (2012). Automated dispersion and orientation analysis for carbon nanotube reinforced polymer composites. Nanotechnology, 23(43), 435706. https://doi.org/10.1088/0957-4484/23/43/435706
Gao, Yi, et al. "Automated dispersion and orientation analysis for carbon nanotube reinforced polymer composites." Nanotechnology vol. 23,43 (2012): 435706. doi: https://doi.org/10.1088/0957-4484/23/43/435706
Gao Y, Li Z, Lin Z, Zhu L, Tannenbaum A, Bouix S, Wong CP. Automated dispersion and orientation analysis for carbon nanotube reinforced polymer composites. Nanotechnology. 2012 Nov 02;23(43):435706. doi: 10.1088/0957-4484/23/43/435706. Epub 2012 Oct 11. PMID: 23060008; PMCID: PMC3492886.
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DIFFEOMORPHIC POINT SET REGISTRATION USING NON-STATIONARY MIXTURE MODELS.

Proceedings. IEEE International Symposium on Biomedical Imaging

Wassermann D, Ross J, Washko G, Westin CF, Estépar RS.
PMID: 24419463
Proc IEEE Int Symp Biomed Imaging. 2013; doi: 10.1109/ISBI.2013.6556656.

This paper investigates a diffeomorphic point-set registration based on non-stationary mixture models. The goal is to improve the non-linear registration of anatomical structures by representing each point as a general non-stationary kernel that provides information about the shape of...

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Wassermann D, Ross J, Washko G, et al. DIFFEOMORPHIC POINT SET REGISTRATION USING NON-STATIONARY MIXTURE MODELS. Proc IEEE Int Symp Biomed Imaging. 2013;doi: 10.1109/ISBI.2013.6556656.
Wassermann, D., Ross, J., Washko, G., Westin, C. F., & Estépar, R. S. (2013). DIFFEOMORPHIC POINT SET REGISTRATION USING NON-STATIONARY MIXTURE MODELS. Proceedings. IEEE International Symposium on Biomedical Imaging, . https://doi.org/10.1109/ISBI.2013.6556656
Wassermann, D, et al. "DIFFEOMORPHIC POINT SET REGISTRATION USING NON-STATIONARY MIXTURE MODELS." Proceedings. IEEE International Symposium on Biomedical Imaging vol. (2013). doi: https://doi.org/10.1109/ISBI.2013.6556656
Wassermann D, Ross J, Washko G, Westin CF, Estépar RS. DIFFEOMORPHIC POINT SET REGISTRATION USING NON-STATIONARY MIXTURE MODELS. Proc IEEE Int Symp Biomed Imaging. 2013; doi: 10.1109/ISBI.2013.6556656. PMID: 24419463; PMCID: PMC3886289.
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Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

Zhang F, Hoffmann N, Karayumak SC, Rathi Y, Golby AJ, O'Donnell LJ.
PMID: 32558816
Med Image Comput Comput Assist Interv. 2019 Oct;11766:599-608. doi: 10.1007/978-3-030-32248-9_67. Epub 2019 Oct 10.

We present a deep learning tractography segmentation method that allows fast and consistent white matter fiber tract identification across healthy and disease populations and across multiple diffusion MRI (dMRI) acquisitions. We create a large-scale training tractography dataset of 1...

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Zhang F, Hoffmann N, Karayumak SC, et al. Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions. Med Image Comput Comput Assist Interv. 2019;11766:599-608doi: 10.1007/978-3-030-32248-9_67.
Zhang, F., Hoffmann, N., Karayumak, S. C., Rathi, Y., Golby, A. J., & O'Donnell, L. J. (2019). Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions. Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 11766599-608. https://doi.org/10.1007/978-3-030-32248-9_67
Zhang, Fan, et al. "Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions." Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention vol. 11766 (2019): 599-608. doi: https://doi.org/10.1007/978-3-030-32248-9_67
Zhang F, Hoffmann N, Karayumak SC, Rathi Y, Golby AJ, O'Donnell LJ. Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions. Med Image Comput Comput Assist Interv. 2019 Oct;11766:599-608. doi: 10.1007/978-3-030-32248-9_67. Epub 2019 Oct 10. PMID: 32558816; PMCID: PMC7301958.
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Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type.

NeuroImage. Clinical

Brabec J, Szczepankiewicz F, Lennartsson F, Englund E, Pebdani H, Bengzon J, Knutsson L, Westin CF, Sundgren PC, Nilsson M.
PMID: 34922122
Neuroimage Clin. 2021 Dec 13;33:102912. doi: 10.1016/j.nicl.2021.102912. Epub 2021 Dec 13.

BACKGROUND: Preoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach.PURPOSE: To investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type.MATERIALS AND...

Cite
Brabec J, Szczepankiewicz F, Lennartsson F, et al. Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. Neuroimage Clin. 2021;33:102912doi: 10.1016/j.nicl.2021.102912.
Brabec, J., Szczepankiewicz, F., Lennartsson, F., Englund, E., Pebdani, H., Bengzon, J., Knutsson, L., Westin, C. F., Sundgren, P. C., & Nilsson, M. (2021). Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. NeuroImage. Clinical, 33102912. https://doi.org/10.1016/j.nicl.2021.102912
Brabec, Jan, et al. "Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type." NeuroImage. Clinical vol. 33 (2021): 102912. doi: https://doi.org/10.1016/j.nicl.2021.102912
Brabec J, Szczepankiewicz F, Lennartsson F, Englund E, Pebdani H, Bengzon J, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. Neuroimage Clin. 2021 Dec 13;33:102912. doi: 10.1016/j.nicl.2021.102912. Epub 2021 Dec 13. PMID: 34922122; PMCID: PMC8688887.
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White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis.

Molecular psychiatry

Di Biase MA, Cetin-Karayumak S, Lyall AE, Zalesky A, Cho KIK, Zhang F, Kubicki M, Rathi Y, Lyons MG, Bouix S, Billah T, Anticevic A, Schleifer C, Adkinson BD, Ji JL, Tamayo Z, Addington J, Bearden CE, Cornblatt BA, Keshavan MS, Mathalon DH, McGlashan TH, Perkins DO, Cadenhead KS, Tsuang MT, Woods SW, Stone WS, Shenton ME, Cannon TD, Pasternak O.
PMID: 34024906
Mol Psychiatry. 2021 Nov;26(11):6833-6844. doi: 10.1038/s41380-021-01128-8. Epub 2021 May 24.

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of...

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Di Biase MA, Cetin-Karayumak S, Lyall AE, et al. White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis. Mol Psychiatry. 2021;26(11):6833-6844doi: 10.1038/s41380-021-01128-8.
Di Biase, M. A., Cetin-Karayumak, S., Lyall, A. E., Zalesky, A., Cho, K. I. K., Zhang, F., Kubicki, M., Rathi, Y., Lyons, M. G., Bouix, S., Billah, T., Anticevic, A., Schleifer, C., Adkinson, B. D., Ji, J. L., Tamayo, Z., Addington, J., Bearden, C. E., Cornblatt, B. A., Keshavan, M. S., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Cadenhead, K. S., Tsuang, M. T., Woods, S. W., Stone, W. S., Shenton, M. E., Cannon, T. D., & Pasternak, O. (2021). White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis. Molecular psychiatry, 26(11), 6833-6844. https://doi.org/10.1038/s41380-021-01128-8
Di Biase, Maria A, et al. "White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis." Molecular psychiatry vol. 26,11 (2021): 6833-6844. doi: https://doi.org/10.1038/s41380-021-01128-8
Di Biase MA, Cetin-Karayumak S, Lyall AE, Zalesky A, Cho KIK, Zhang F, Kubicki M, Rathi Y, Lyons MG, Bouix S, Billah T, Anticevic A, Schleifer C, Adkinson BD, Ji JL, Tamayo Z, Addington J, Bearden CE, Cornblatt BA, Keshavan MS, Mathalon DH, McGlashan TH, Perkins DO, Cadenhead KS, Tsuang MT, Woods SW, Stone WS, Shenton ME, Cannon TD, Pasternak O. White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis. Mol Psychiatry. 2021 Nov;26(11):6833-6844. doi: 10.1038/s41380-021-01128-8. Epub 2021 May 24. PMID: 34024906; PMCID: PMC8611104.
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White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis.

Molecular psychiatry

Di Biase MA, Cetin-Karayumak S, Lyall AE, Zalesky A, Cho KIK, Zhang F, Kubicki M, Rathi Y, Lyons MG, Bouix S, Billah T, Anticevic A, Schleifer C, Adkinson BD, Ji JL, Tamayo Z, Addington J, Bearden CE, Cornblatt BA, Keshavan MS, Mathalon DH, McGlashan TH, Perkins DO, Cadenhead KS, Tsuang MT, Woods SW, Stone WS, Shenton ME, Cannon TD, Pasternak O.
PMID: 34024906
Mol Psychiatry. 2021 Nov;26(11):6833-6844. doi: 10.1038/s41380-021-01128-8. Epub 2021 May 24.

Subtle alterations in white matter microstructure are observed in youth at clinical high risk (CHR) for psychosis. However, the timing of these changes and their relationships to the emergence of psychosis remain unclear. Here, we track the evolution of...

Cite
Di Biase MA, Cetin-Karayumak S, Lyall AE, et al. White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis. Mol Psychiatry. 2021;26(11):6833-6844doi: 10.1038/s41380-021-01128-8.
Di Biase, M. A., Cetin-Karayumak, S., Lyall, A. E., Zalesky, A., Cho, K. I. K., Zhang, F., Kubicki, M., Rathi, Y., Lyons, M. G., Bouix, S., Billah, T., Anticevic, A., Schleifer, C., Adkinson, B. D., Ji, J. L., Tamayo, Z., Addington, J., Bearden, C. E., Cornblatt, B. A., Keshavan, M. S., Mathalon, D. H., McGlashan, T. H., Perkins, D. O., Cadenhead, K. S., Tsuang, M. T., Woods, S. W., Stone, W. S., Shenton, M. E., Cannon, T. D., & Pasternak, O. (2021). White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis. Molecular psychiatry, 26(11), 6833-6844. https://doi.org/10.1038/s41380-021-01128-8
Di Biase, Maria A, et al. "White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis." Molecular psychiatry vol. 26,11 (2021): 6833-6844. doi: https://doi.org/10.1038/s41380-021-01128-8
Di Biase MA, Cetin-Karayumak S, Lyall AE, Zalesky A, Cho KIK, Zhang F, Kubicki M, Rathi Y, Lyons MG, Bouix S, Billah T, Anticevic A, Schleifer C, Adkinson BD, Ji JL, Tamayo Z, Addington J, Bearden CE, Cornblatt BA, Keshavan MS, Mathalon DH, McGlashan TH, Perkins DO, Cadenhead KS, Tsuang MT, Woods SW, Stone WS, Shenton ME, Cannon TD, Pasternak O. White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis. Mol Psychiatry. 2021 Nov;26(11):6833-6844. doi: 10.1038/s41380-021-01128-8. Epub 2021 May 24. PMID: 34024906; PMCID: PMC8611104.
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Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type.

NeuroImage. Clinical

Brabec J, Szczepankiewicz F, Lennartsson F, Englund E, Pebdani H, Bengzon J, Knutsson L, Westin CF, Sundgren PC, Nilsson M.
PMID: 34922122
Neuroimage Clin. 2021 Dec 13;33:102912. doi: 10.1016/j.nicl.2021.102912. Epub 2021 Dec 13.

BACKGROUND: Preoperative radiological assessment of meningioma characteristics is of value for pre- and post-operative patient management, counselling, and surgical approach.PURPOSE: To investigate whether tensor-valued diffusion MRI can add to the preoperative prediction of meningioma consistency, grade and type.MATERIALS AND...

Cite
Brabec J, Szczepankiewicz F, Lennartsson F, et al. Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. Neuroimage Clin. 2021;33:102912doi: 10.1016/j.nicl.2021.102912.
Brabec, J., Szczepankiewicz, F., Lennartsson, F., Englund, E., Pebdani, H., Bengzon, J., Knutsson, L., Westin, C. F., Sundgren, P. C., & Nilsson, M. (2021). Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. NeuroImage. Clinical, 33102912. https://doi.org/10.1016/j.nicl.2021.102912
Brabec, Jan, et al. "Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type." NeuroImage. Clinical vol. 33 (2021): 102912. doi: https://doi.org/10.1016/j.nicl.2021.102912
Brabec J, Szczepankiewicz F, Lennartsson F, Englund E, Pebdani H, Bengzon J, Knutsson L, Westin CF, Sundgren PC, Nilsson M. Histogram analysis of tensor-valued diffusion MRI in meningiomas: Relation to consistency, histological grade and type. Neuroimage Clin. 2021 Dec 13;33:102912. doi: 10.1016/j.nicl.2021.102912. Epub 2021 Dec 13. PMID: 34922122; PMCID: PMC8688887.
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Showing 1 to 12 of 132 entries