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Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis.

Current opinion in neurology

Rovaris M, Filippi M.
PMID: 10499178
Curr Opin Neurol. 1999 Jun;12(3):337-44. doi: 10.1097/00019052-199906000-00012.

Although the correlations between magnetic resonance findings and long-term disease evolution range from poor to moderate, conventional precontrast and postcontrast magnetic resonance imaging provides sensitive and reliable measures to monitor multiple sclerosis activity over time. New pulse sequences with...

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Rovaris M, Filippi M. Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis. Curr Opin Neurol. 1999;12(3):337-44doi: 10.1097/00019052-199906000-00012.
Rovaris, M., & Filippi, M. (1999). Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis. Current opinion in neurology, 12(3), 337-44. https://doi.org/10.1097/00019052-199906000-00012
Rovaris, M, and Filippi, M. "Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis." Current opinion in neurology vol. 12,3 (1999): 337-44. doi: https://doi.org/10.1097/00019052-199906000-00012
Rovaris M, Filippi M. Magnetic resonance techniques to monitor disease evolution and treatment trial outcomes in multiple sclerosis. Curr Opin Neurol. 1999 Jun;12(3):337-44. doi: 10.1097/00019052-199906000-00012. PMID: 10499178.
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White matter hyperintensities in patients with Parkinson's disease: A systematic review and meta-analysis.

Journal of the neurological sciences

Butt A, Kamtchum-Tatuene J, Khan K, Shuaib A, Jickling GC, Miyasaki JM, Smith EE, Camicioli R.
PMID: 33975191
J Neurol Sci. 2021 Jul 15;426:117481. doi: 10.1016/j.jns.2021.117481. Epub 2021 May 04.

INTRODUCTION: Mechanisms driving neurodegeneration in Parkinson's disease (PD) are unclear and neurovascular dysfunction may be a contributing factor. White matter hyperintensities (WMH) are commonly found on brain MRI in patients with PD. It is controversial if they are more...

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Butt A, Kamtchum-Tatuene J, Khan K, et al. White matter hyperintensities in patients with Parkinson's disease: A systematic review and meta-analysis. J Neurol Sci. 2021;426:117481doi: 10.1016/j.jns.2021.117481.
Butt, A., Kamtchum-Tatuene, J., Khan, K., Shuaib, A., Jickling, G. C., Miyasaki, J. M., Smith, E. E., & Camicioli, R. (2021). White matter hyperintensities in patients with Parkinson's disease: A systematic review and meta-analysis. Journal of the neurological sciences, 426117481. https://doi.org/10.1016/j.jns.2021.117481
Butt, Asif, et al. "White matter hyperintensities in patients with Parkinson's disease: A systematic review and meta-analysis." Journal of the neurological sciences vol. 426 (2021): 117481. doi: https://doi.org/10.1016/j.jns.2021.117481
Butt A, Kamtchum-Tatuene J, Khan K, Shuaib A, Jickling GC, Miyasaki JM, Smith EE, Camicioli R. White matter hyperintensities in patients with Parkinson's disease: A systematic review and meta-analysis. J Neurol Sci. 2021 Jul 15;426:117481. doi: 10.1016/j.jns.2021.117481. Epub 2021 May 04. PMID: 33975191.
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[The diagnostic value of version 2.1 prostate imaging reporting and data system for prostate transitional zone lesions].

Zhonghua yi xue za zhi

Yang S, Zhang CY, Zhang YY, Tan SX, Wei CG, Shen XH, Shen JK.
PMID: 33333685
Zhonghua Yi Xue Za Zhi. 2020 Dec 08;100(45):3609-3613. doi: 10.3760/cma.j.cn112137-20200506-01442.

No abstract available.

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Yang S, Zhang CY, Zhang YY, et al. [The diagnostic value of version 2.1 prostate imaging reporting and data system for prostate transitional zone lesions]. Zhonghua Yi Xue Za Zhi. 2020;100(45):3609-3613doi: 10.3760/cma.j.cn112137-20200506-01442.
Yang, S., Zhang, C. Y., Zhang, Y. Y., Tan, S. X., Wei, C. G., Shen, X. H., & Shen, J. K. (2020). [The diagnostic value of version 2.1 prostate imaging reporting and data system for prostate transitional zone lesions]. Zhonghua yi xue za zhi, 100(45), 3609-3613. https://doi.org/10.3760/cma.j.cn112137-20200506-01442
Yang, S, et al. "[The diagnostic value of version 2.1 prostate imaging reporting and data system for prostate transitional zone lesions]." Zhonghua yi xue za zhi vol. 100,45 (2020): 3609-3613. doi: https://doi.org/10.3760/cma.j.cn112137-20200506-01442
Yang S, Zhang CY, Zhang YY, Tan SX, Wei CG, Shen XH, Shen JK. [The diagnostic value of version 2.1 prostate imaging reporting and data system for prostate transitional zone lesions]. Zhonghua Yi Xue Za Zhi. 2020 Dec 08;100(45):3609-3613. doi: 10.3760/cma.j.cn112137-20200506-01442. Chinese. PMID: 33333685.
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Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data.

Medical physics

Wright M, Dietz B, Yip E, Yun J, Gabos Z, Fallone BG, Wachowicz K.
PMID: 34528275
Med Phys. 2021 Nov;48(11):6724-6739. doi: 10.1002/mp.15238. Epub 2021 Sep 27.

PURPOSE: A rapid real-time 2D accelerated method was developed for magnetic resonance imaging (MRI) using principal component analysis (PCA) in the temporal domain. This method employs a moving window of previous dynamic frames to reconstruct the current, real-time frame...

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Wright M, Dietz B, Yip E, et al. Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data. Med Phys. 2021;48(11):6724-6739doi: 10.1002/mp.15238.
Wright, M., Dietz, B., Yip, E., Yun, J., Gabos, Z., Fallone, B. G., & Wachowicz, K. (2021). Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data. Medical physics, 48(11), 6724-6739. https://doi.org/10.1002/mp.15238
Wright, Mark, et al. "Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data." Medical physics vol. 48,11 (2021): 6724-6739. doi: https://doi.org/10.1002/mp.15238
Wright M, Dietz B, Yip E, Yun J, Gabos Z, Fallone BG, Wachowicz K. Time domain principal component analysis for rapid, real-time 2D MRI reconstruction from undersampled data. Med Phys. 2021 Nov;48(11):6724-6739. doi: 10.1002/mp.15238. Epub 2021 Sep 27. PMID: 34528275.
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Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting.

Investigative radiology

Kiselev VG, Körzdörfer G, Gall P.
PMID: 33186141
Invest Radiol. 2021 Jan;56(1):1-9. doi: 10.1097/RLI.0000000000000738.

Quantitative magnetic resonance imaging (MRI) is a long-standing challenge. We advocate that the origin of the problem is the simplification applied in commonly used models of the MRI signal relation to the target parameters of biological tissues. Two research...

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Kiselev VG, Körzdörfer G, Gall P. Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting. Invest Radiol. 2021;56(1):1-9doi: 10.1097/RLI.0000000000000738.
Kiselev, V. G., Körzdörfer, G., & Gall, P. (2021). Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting. Investigative radiology, 56(1), 1-9. https://doi.org/10.1097/RLI.0000000000000738
Kiselev, Valerij G, et al. "Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting." Investigative radiology vol. 56,1 (2021): 1-9. doi: https://doi.org/10.1097/RLI.0000000000000738
Kiselev VG, Körzdörfer G, Gall P. Toward Quantification: Microstructure and Magnetic Resonance Fingerprinting. Invest Radiol. 2021 Jan;56(1):1-9. doi: 10.1097/RLI.0000000000000738. PMID: 33186141.
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Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow.

Physics in medicine and biology

Gustafsson CJ, Swärd J, Adalbjörnsson SI, Jakobsson A, Olsson LE.
PMID: 33179610
Phys Med Biol. 2020 Nov 12;65(22):225011. doi: 10.1088/1361-6560/abb0f9.

Identification of prostate gold fiducial markers in magnetic resonance imaging (MRI) images is challenging when CT images are not available, due to misclassifications from intra-prostatic calcifications. It is also a time consuming task and automated identification methods have been...

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Gustafsson CJ, Swärd J, Adalbjörnsson SI, et al. Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow. Phys Med Biol. 2020;65(22):225011doi: 10.1088/1361-6560/abb0f9.
Gustafsson, C. J., Swärd, J., Adalbjörnsson, S. I., Jakobsson, A., & Olsson, L. E. (2020). Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow. Physics in medicine and biology, 65(22), 225011. https://doi.org/10.1088/1361-6560/abb0f9
Gustafsson, Christian Jamtheim, et al. "Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow." Physics in medicine and biology vol. 65,22 (2020): 225011. doi: https://doi.org/10.1088/1361-6560/abb0f9
Gustafsson CJ, Swärd J, Adalbjörnsson SI, Jakobsson A, Olsson LE. Development and evaluation of a deep learning based artificial intelligence for automatic identification of gold fiducial markers in an MRI-only prostate radiotherapy workflow. Phys Med Biol. 2020 Nov 12;65(22):225011. doi: 10.1088/1361-6560/abb0f9. PMID: 33179610.
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De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology.

Journal of medical Internet research

Jeong YU, Yoo S, Kim YH, Shim WH.
PMID: 33208302
J Med Internet Res. 2020 Dec 10;22(12):e22739. doi: 10.2196/22739.

BACKGROUND: High-resolution medical images that include facial regions can be used to recognize the subject's face when reconstructing 3-dimensional (3D)-rendered images from 2-dimensional (2D) sequential images, which might constitute a risk of infringement of personal information when sharing data....

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Jeong YU, Yoo S, Kim YH, et al. De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology. J Med Internet Res. 2020;22(12):e22739doi: 10.2196/22739.
Jeong, Y. U., Yoo, S., Kim, Y. H., & Shim, W. H. (2020). De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology. Journal of medical Internet research, 22(12), e22739. https://doi.org/10.2196/22739
Jeong, Yeon Uk, et al. "De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology." Journal of medical Internet research vol. 22,12 (2020): e22739. doi: https://doi.org/10.2196/22739
Jeong YU, Yoo S, Kim YH, Shim WH. De-Identification of Facial Features in Magnetic Resonance Images: Software Development Using Deep Learning Technology. J Med Internet Res. 2020 Dec 10;22(12):e22739. doi: 10.2196/22739. PMID: 33208302; PMCID: PMC7759440.
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Determination of skeletal tumor extent: is an isotropic T1-weighted 3D sequence adequate?.

European radiology

Luna R, Fritz J, Del Grande F, Ahlawat S, Fayad LM.
PMID: 33179165
Eur Radiol. 2021 May;31(5):3138-3146. doi: 10.1007/s00330-020-07394-4. Epub 2020 Nov 12.

OBJECTIVES: To test the hypothesis that an accelerated, T1-weighted 3D CAIPIRINHA SPACE sequence with isotropic voxel size offers a similar performance to conventional T1-weighted 2D TSE (turbo spin echo) for the evaluation of bone tumor extent and characteristics.METHODS: Thirty-four...

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Luna R, Fritz J, Del Grande F, et al. Determination of skeletal tumor extent: is an isotropic T1-weighted 3D sequence adequate?. Eur Radiol. 2020;31(5):3138-3146doi: 10.1007/s00330-020-07394-4.
Luna, R., Fritz, J., Del Grande, F., Ahlawat, S., & Fayad, L. M. (2021). Determination of skeletal tumor extent: is an isotropic T1-weighted 3D sequence adequate?. European radiology, 31(5), 3138-3146. https://doi.org/10.1007/s00330-020-07394-4
Luna, Rodrigo, et al. "Determination of skeletal tumor extent: is an isotropic T1-weighted 3D sequence adequate?." European radiology vol. 31,5 (2021): 3138-3146. doi: https://doi.org/10.1007/s00330-020-07394-4
Luna R, Fritz J, Del Grande F, Ahlawat S, Fayad LM. Determination of skeletal tumor extent: is an isotropic T1-weighted 3D sequence adequate?. Eur Radiol. 2021 May;31(5):3138-3146. doi: 10.1007/s00330-020-07394-4. Epub 2020 Nov 12. PMID: 33179165.
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Magnetic resonance imaging of multiple sclerosis.

Journal of neuroimaging : official journal of the American Society of Neuroimaging

Filippi M, Tortorella C, Rovaris M.
PMID: 12380475
J Neuroimaging. 2002 Oct;12(4):289-301. doi: 10.1111/j.1552-6569.2002.tb00136.x.

Although conventional magnetic resonance imaging (cMRI) is widely used for diagnosing multiple sclerosis (MS) and monitoring disease activity and evolution, the correlation between cMRI and clinical findings is far from strict. Among the reasons for this "clinical-MRI paradox," a...

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Filippi M, Tortorella C, Rovaris M. Magnetic resonance imaging of multiple sclerosis. J Neuroimaging. 2002;12(4):289-301doi: 10.1111/j.1552-6569.2002.tb00136.x.
Filippi, M., Tortorella, C., & Rovaris, M. (2002). Magnetic resonance imaging of multiple sclerosis. Journal of neuroimaging : official journal of the American Society of Neuroimaging, 12(4), 289-301. https://doi.org/10.1111/j.1552-6569.2002.tb00136.x
Filippi, Massimo, et al. "Magnetic resonance imaging of multiple sclerosis." Journal of neuroimaging : official journal of the American Society of Neuroimaging vol. 12,4 (2002): 289-301. doi: https://doi.org/10.1111/j.1552-6569.2002.tb00136.x
Filippi M, Tortorella C, Rovaris M. Magnetic resonance imaging of multiple sclerosis. J Neuroimaging. 2002 Oct;12(4):289-301. doi: 10.1111/j.1552-6569.2002.tb00136.x. PMID: 12380475.
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Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI.

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

Yu N, Duan H, Yang C, Yu Y, Dang S.
PMID: 34930463
Cancer Imaging. 2021 Dec 20;21(1):68. doi: 10.1186/s40644-021-00441-3.

PURPOSE: To determine whether the pulmonary MR imaging with free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence can detect lung lesions and display lesion profiles with an accuracy comparable to that of computed tomography (CT), which is the...

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Yu N, Duan H, Yang C, et al. Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI. Cancer Imaging. 2021;21(1):68doi: 10.1186/s40644-021-00441-3.
Yu, N., Duan, H., Yang, C., Yu, Y., & Dang, S. (2021). Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI. Cancer imaging : the official publication of the International Cancer Imaging Society, 21(1), 68. https://doi.org/10.1186/s40644-021-00441-3
Yu, Nan, et al. "Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI." Cancer imaging : the official publication of the International Cancer Imaging Society vol. 21,1 (2021): 68. doi: https://doi.org/10.1186/s40644-021-00441-3
Yu N, Duan H, Yang C, Yu Y, Dang S. Free-breathing radial 3D fat-suppressed T1-weighted gradient echo (r-VIBE) sequence for assessment of pulmonary lesions: a prospective comparison of CT and MRI. Cancer Imaging. 2021 Dec 20;21(1):68. doi: 10.1186/s40644-021-00441-3. PMID: 34930463.
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Subtyping relapsing-remitting multiple sclerosis using structural MRI.

Journal of neurology

Zhuo Z, Li Y, Duan Y, Cao G, Zheng F, Ding J, Tian D, Wang X, Wang J, Zhang X, Li K, Zhou F, Huang M, Li Y, Li H, Zeng C, Zhang N, Sun J, Yu C, Han X, Haller S, Barkhof F, Shi F, Liu Y.
PMID: 33387013
J Neurol. 2021 May;268(5):1808-1817. doi: 10.1007/s00415-020-10376-7. Epub 2021 Jan 02.

BACKGROUND AND PURPOSE: Subtyping relapsing-remitting multiple sclerosis (RRMS) patients may help predict disease progression and triage patients for treatment. We aimed to subtype RRMS patients by structural MRI and investigate their clinical significances.METHODS: 155 relapse-remitting MS (RRMS) and 210...

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Zhuo Z, Li Y, Duan Y, et al. Subtyping relapsing-remitting multiple sclerosis using structural MRI. J Neurol. 2021;268(5):1808-1817doi: 10.1007/s00415-020-10376-7.
Zhuo, Z., Li, Y., Duan, Y., Cao, G., Zheng, F., Ding, J., Tian, D., Wang, X., Wang, J., Zhang, X., Li, K., Zhou, F., Huang, M., Li, Y., Li, H., Zeng, C., Zhang, N., Sun, J., Yu, C., Han, X., Haller, S., Barkhof, F., Shi, F., & Liu, Y. (2021). Subtyping relapsing-remitting multiple sclerosis using structural MRI. Journal of neurology, 268(5), 1808-1817. https://doi.org/10.1007/s00415-020-10376-7
Zhuo, Zhizheng, et al. "Subtyping relapsing-remitting multiple sclerosis using structural MRI." Journal of neurology vol. 268,5 (2021): 1808-1817. doi: https://doi.org/10.1007/s00415-020-10376-7
Zhuo Z, Li Y, Duan Y, Cao G, Zheng F, Ding J, Tian D, Wang X, Wang J, Zhang X, Li K, Zhou F, Huang M, Li Y, Li H, Zeng C, Zhang N, Sun J, Yu C, Han X, Haller S, Barkhof F, Shi F, Liu Y. Subtyping relapsing-remitting multiple sclerosis using structural MRI. J Neurol. 2021 May;268(5):1808-1817. doi: 10.1007/s00415-020-10376-7. Epub 2021 Jan 02. PMID: 33387013.
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CT and MRI Image Fusion Error: An Analysis of Co-Registration Error Using Commercially Available Deep Brain Stimulation Surgical Planning Software.

Stereotactic and functional neurosurgery

Burke JF, Tanzillo D, Starr PA, Lim DA, Larson PS.
PMID: 33535219
Stereotact Funct Neurosurg. 2021;99(3):196-202. doi: 10.1159/000511114. Epub 2021 Feb 03.

INTRODUCTION: During deep brain stimulation (DBS) surgery, computed tomography (CT) and magnetic resonance imaging (MRI) scans need to be co-registered or fused. Image fusion is associated with the error that can distort the location of anatomical structures. Co-registration in...

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Burke JF, Tanzillo D, Starr PA, et al. CT and MRI Image Fusion Error: An Analysis of Co-Registration Error Using Commercially Available Deep Brain Stimulation Surgical Planning Software. Stereotact Funct Neurosurg. 2021;99(3):196-202doi: 10.1159/000511114.
Burke, J. F., Tanzillo, D., Starr, P. A., Lim, D. A., & Larson, P. S. (2021). CT and MRI Image Fusion Error: An Analysis of Co-Registration Error Using Commercially Available Deep Brain Stimulation Surgical Planning Software. Stereotactic and functional neurosurgery, 99(3), 196-202. https://doi.org/10.1159/000511114
Burke, John F, et al. "CT and MRI Image Fusion Error: An Analysis of Co-Registration Error Using Commercially Available Deep Brain Stimulation Surgical Planning Software." Stereotactic and functional neurosurgery vol. 99,3 (2021): 196-202. doi: https://doi.org/10.1159/000511114
Burke JF, Tanzillo D, Starr PA, Lim DA, Larson PS. CT and MRI Image Fusion Error: An Analysis of Co-Registration Error Using Commercially Available Deep Brain Stimulation Surgical Planning Software. Stereotact Funct Neurosurg. 2021;99(3):196-202. doi: 10.1159/000511114. Epub 2021 Feb 03. PMID: 33535219.
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