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Shing Chun Benny Lam
Department of Psychiatry and Department of Radiology : Perelman School of Medicine :University of Pennsylvania  
Short CV  < access denied

Contact:
University of Pennsylvania Addiction Treatment Research Center :: 
3900 Chestnut Street, Philadelphia, PA 19104
shinglam@mail.med.upenn.edu  "shinglam@mail.med.upenn.edu" 

Research Interests:
  • Neuroinformatics methods development for brain status characterization
  • Biomedical image analysis
  • Machine learning
  • Signal processing
  • Biomechanics - kinematic analysis

Publications:


Education:
  • PhD Biomedical Engineering University of Southampton, UK 2004-2007
  • MPhil Biomedical Engineering University of Hong Kong, Hong Kong 2001-2003
  • BEng Electrical and Electronic Engineering University of Hong Kong, Hong Kong 1998-2001

People I am working with or I worked with:
  • Cyrus Cooper (University of Southampton and University of Oxford, UK)
  • Yong Hu (University of Hong Kong, Hong Kong)
  • Keith Luk (University of Hong Kong, Hong Kong)

Reviewership:

IEEE Transactions on Medical Imaging, IEEE Signal Processing Letters, Medical Engineering and Physics, The Journal of Signal Processing Systems, International Journal of Data Mining and Bioinformatics, IOP Measurement Science and Technology, Computer Methods and Programs in Biomedicine, BMC Bioinformatics, PLoS One, ISMRM and some others...

Last update: Oct 22, 2012

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  SOURCE:  https://pubmed.ncbi.nlm.nih.gov/21784189/ 

 

 2011 Oct;49(4):895-903. doi: 10.1016/j.bone.2011.07.010. Epub 2011 Jul 19.

Performance of the MRI-based virtual bone biopsy in the distal radius: serial reproducibility and reliability of structural and mechanical parameters in women representative of osteoporosis study populations

Shing Chun Benny Lam 1Michael J WaldChamith S RajapakseYinxiao LiuPunam K SahaFelix W Wehrli

Affiliations 

Affiliation

Abstract

Serial reproducibility and reliability critically determine sensitivity to detect changes in response to intervention and provide a basis for sample size estimates.

Here, we evaluated the performance of the MRI-based virtual bone biopsy in terms of 26 structural and mechanical parameters in the distal radius of 20 women in the age range of 50 to 75 years (mean=62.0 years, S.D.=8.1 years), representative of typical study populations in drug intervention trials and fracture studies.

Subjects were examined three times at average intervals of 20.2 days (S.D.=14.5 days) by MRI at 1.5 T field strength at a voxel size of 137×137×410 μm(3).

Methods involved prospective and retrospective 3D image registration and auto-focus motion correction.

Analyses were performed from a central 5×5×5 mm(3) cuboid subvolume and trabecular volume consisting of a 13 mm axial slab encompassing the entire medullary cavity.

Whole-volume axial stiffness and sub-regional Young's and shear moduli were computed by finite-element analysis. Whole-volume-derived aggregate mean coefficient of variation of all structural parameters was 4.4% (range 1.8% to 7.7%) and 4.0% for axial stiffness; corresponding data in the subvolume were 6.5% (range 1.6% to 13.0%) for structural, and 5.5% (range 4.6% to 6.5%) for mechanical parameters. Aggregate ICC was 0.976 (range 0.947 to 0.986) and 0.992 for whole-volume-derived structural parameters and axial stiffness, and 0.946 (range 0.752 to 0.991) and 0.974 (range 0.965 to 0.978) for subvolume-derived structural and mechanical parameters, respectively.

The strongest predictors of whole-volume axial stiffness were BV/TV, junction density, skeleton density and Tb.N (R(2) 0.79-0.87). The same parameters were also highly predictive of sub-regional axial modulus (R(2) 0.88-0.91).

The data suggest that the method is suited for longitudinal assessment of the response to therapy. The underlying technology is portable and should be compatible with all general-purpose MRI scanners, which is appealing considering the very large installed base of this modality. ... Copyright © 2011 Elsevier Inc. All rights reserved.

Figures

Fig. 1

Fig. 1 

VBB protocol of three imaging…

 
Fig. 2

Fig. 2 

Imaging setup and positioning.

 
Fig. 3

Fig. 3 

Process for extraction of structural…

 
Fig. 4

Fig. 4 

Image data from two study…

 
Fig. 5

Fig. 5 

Test-retest plots of selected parameters…

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