Li DK - Search Results

1

Optimizing the use of radiologist seed points for improved multiple sclerosis lesion segmentation.

McAusland J, Tam RC, Wong E, Riddehough A, Li DK. McAusland J, et al. Among authors: li dk. IEEE Trans Biomed Eng. 2010 Nov;57(11). doi: 10.1109/TBME.2010.2055865. IEEE Trans Biomed Eng. 2010. PMID: 20601307

2

Detection and measurement of coverage loss in interleaved multi-acquisition brain MRIs due to motion-induced inter-slice misalignment.

Tam RC, Riddehough A, Li DK. Tam RC, et al. Among authors: li dk. Med Image Anal. 2009 Jun;13(3):381-91. doi: 10.1016/j.media.2008.12.006. Epub 2009 Jan 8. Med Image Anal. 2009. PMID: 19200772

3

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

Brosch T, Tang LY, Youngjin Yoo, Li DK, Traboulsee A, Tam R. Brosch T, et al. Among authors: li dk. IEEE Trans Med Imaging. 2016 May;35(5):1229-1239. doi: 10.1109/TMI.2016.2528821. Epub 2016 Feb 11. IEEE Trans Med Imaging. 2016. PMID: 26886978

4

Further investigation of safety monitoring guidelines based on magnetic resonance imaging lesion activity in multiple sclerosis clinical trials.

Dong J, Zhao Y, Petkau AJ, Li DK, Riddehough A, Traboulsee A. Dong J, et al. Among authors: li dk. Mult Scler. 2015 Jan;21(1):101-4. doi: 10.1177/1352458514533846. Epub 2014 May 22. Mult Scler. 2015. PMID: 24852923

5

Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning.

Brosch T, Yoo Y, Li DK, Traboulsee A, Tam R. Brosch T, et al. Among authors: li dk. Med Image Comput Comput Assist Interv. 2014;17(Pt 2):462-9. doi: 10.1007/978-3-319-10470-6_58. Med Image Comput Comput Assist Interv. 2014. PMID: 25485412

6

Improving the clinical correlation of multiple sclerosis black hole volume change by paired-scan analysis.

Tam RC, Traboulsee A, Riddehough A, Li DK. Tam RC, et al. Among authors: li dk. Neuroimage Clin. 2012 Sep 5;1(1):29-36. doi: 10.1016/j.nicl.2012.08.004. eCollection 2012. Neuroimage Clin. 2012. PMID: 24179734 Free PMC article.

7

Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.

Yoo Y, Tang LYW, Brosch T, Li DKB, Kolind S, Vavasour I, Rauscher A, MacKay AL, Traboulsee A, Tam RC. Yoo Y, et al. Neuroimage Clin. 2017 Oct 14;17:169-178. doi: 10.1016/j.nicl.2017.10.015. eCollection 2018. Neuroimage Clin. 2017. PMID: 29071211 Free PMC article.

8

Fast computation of myelin maps from MRI T₂ relaxation data using multicore CPU and graphics card parallelization.

Yoo Y, Prasloski T, Vavasour I, MacKay A, Traboulsee AL, Li DK, Tam RC. Yoo Y, et al. Among authors: li dk. J Magn Reson Imaging. 2015 Mar;41(3):700-7. doi: 10.1002/jmri.24604. Epub 2014 Feb 27. J Magn Reson Imaging. 2015. PMID: 24578324

9

The use of MRI as an outcome measure in clinical trials.

Li DK, Li MJ, Traboulsee A, Zhao G, Riddehough A, Paty D. Li DK, et al. Among authors: li mj. Adv Neurol. 2006;98:203-26. Adv Neurol. 2006. PMID: 16400836 Review.

10

Diffusely Abnormal White Matter, T₂ Burden of Disease, and Brain Volume in Relapsing-Remitting Multiple Sclerosis.

Vertinsky AT, Li DKB, Vavasour IM, Miropolsky V, Zhao G, Zhao Y, Riddehough A, Moore GRW, Traboulsee A, Laule C. Vertinsky AT, et al. J Neuroimaging. 2019 Jan;29(1):151-159. doi: 10.1111/jon.12574. Epub 2018 Oct 30. J Neuroimaging. 2019. PMID: 30376195

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