A. Based on published results (Pagani et a., 2010; and others) comparing DTI scalar results between MRI scanners on the same subjects provided significantly different results even though data were acquired at the same magnetic field and identical encoding, spatial and temporal parameters. This may be asily inspected prior to spatial normaliztion. If you have some water phantom measurements from the different scanners along with Temperature, eddy current calibraion maps, field inhomogenieties, B-matrix,.. you may be able to model or ioslate the potential causes, but this may not be readily available always.
B. I would examine carefully some selected regions in the native data space (e.g. splenium CC, genu CC, caudate, putamen,..) as far as FA and mean diffusivity and tabulate the results for spatial hetrogeniety and consitency (e.g. FA(sCC) > FA(gCC)), before any strategy to correct the values is implemented. Look at the white and gray matter histograms, tissue segementation quality to see if at least the mean diffusivity is explained by a global scaling factor that can be corrected, but this scaling should not affect FA.
C. As far as normalization of DTI scalar maps, it has been routinely done (Camara et al., 2007; Snook et al., 2006) using the b0-object and the SPM-EPI-template as Traget and then FA as the other volume to be reslice, just be careful as suggested by Jones et al. (2005) as far as the selection of smoothing params.
D. If the goal is to obtain "automated" volume-based estimates or parcellation. I would segment the T1w data (FSL-see Cherubini et al., 2009 or FreeSurfer- Fjell et al. 2008; Walimuni et al. 2011) or even DTI data (Hasan et al. 2007; Hasan et al., 2011a;2001b;2012) and then show the average in the anatomical labels. This procedure shifts the analysis to the "standardized" labels in each subject native space. I applied this procedure after careful inspection and warping of DTI data unto T1w-labels on large cohorts of healthy controls and MS patients (Hasan et al., 2011; 2012)
1. Càmara E, Bodammer N, Rodríguez-Fornells A, Tempelmann C. Age-related water diffusion changes in human brain: a voxel-based approach.
2. Cherubini A, Péran P, Caltagirone C, Sabatini U, Spalletta G. Aging of subcortical nuclei: microstructural, mineralization and atrophy modifications measured in vivo using MRI. Neuroimage. 2009;48(1):29-36.
3. Fjell AM, Westlye LT, Greve DN, Fischl B, Benner T, van der Kouwe AJ, Salat D, Bjørnerud A, Due-Tønnessen P, Walhovd KB.
The relationship between diffusion tensor imaging and volumetry as measures of white matter properties.
4. Hasan KM, Frye RE. Diffusion tensor-based regional gray matter tissue segmentation using the international consortium for brain mapping atlases. Hum Brain Mapp. 2011a;32(1):107-17.
5. Hasan KM, Walimuni IS, Abid H, Frye RE, Ewing-Cobbs L, Wolinsky JS, Narayana PA.
Multimodal quantitative magnetic resonance imaging of thalamic development and aging across the human lifespan: implications to neurodegeneration in multiple sclerosis.J Neurosci. 2011b;31(46):16826-32.
6. Hasan KM, Walimuni IS, Abid H, Wolinsky JS, Narayana PA. Multi-modal quantitative MRI investigation of brain tissue neurodegeneration in multiple sclerosis.J Magn Reson Imaging. 2012 Jan 12. doi: 10.1002/jmri.23539. [Epub ahead of print]
7. Hasan KM, Walimuni IS, Abid H, Datta S, Wolinsky JS, Narayana PA. Human brain atlas-based multimodal MRI analysis of volumetry, diffusimetry, relaxometry and lesion distribution in multiple sclerosis patients and healthy adult controls: Implications for understanding the pathogenesis of multiple sclerosis and consolidation of quantitative MRI results in MS.
J Neurol Sci. 2012;313(1-2):99-109.
8. Jones DK, Symms MR, Cercignani M, Howard RJ. The effect of filter size on VBM analyses of DT-MRI data.
9. Pagani E, Hirsch JG, Pouwels PJ, Horsfield MA, Perego E, Gass A, Roosendaal SD, Barkhof F, Agosta F, Rovaris M, Caputo D, Giorgio A, Palace J, Marino S, De Stefano N, Ropele S, Fazekas F, Filippi M. Intercenter differences in diffusion tensor MRI acquisition. J Magn Reson Imaging. 2010;31(6):1458-68.
10. Snook L, Plewes C, Beaulieu C. Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment.
11. Walimuni IS, Hasan KM. Atlas-based investigation of human brain tissue microstructural spatial heterogeneity and interplay between transverse relaxation time and radial diffusivity. Neuroimage. 2011 Aug 15;57(4):1402-10.
12. Walimuni IS, Hasan KM. Atlas-based investigation of human brain tissue microstructural spatial heterogeneity and interplay between transverse relaxation time and radial diffusivity. Neuroimage. 2011;57(4):1402-10.
Khader M Hasan, PhD
Associate Professor of Radiology
MSE 168, Tel 713 500 7690 (FAX 713 500 7684)
University of Texas Health Science Center at Houston
Diagnostic and Interventional Imaging
Magnetic Resonance Imaging Research Division
Diffusion Tensor Imaging Lab, Tel 713 500 7683
From: SPM (Statistical Parametric Mapping) [[log in to unmask]] On Behalf Of Lucas Lessa [[log in to unmask]]
Sent: Friday, February 03, 2012 7:00 AM
To: [log in to unmask]
Subject: [SPM] Normalize DTI studies from different scanners
I'm trying to compare the changes in DTI in a case.
I have several studies from one subject, all of them have DTI, but I can't observe changes, probably because of the differences in the studies.
Some studies were made in Philips Achieva 1,5T and other in a GE HD 1,5T.
The good thing is that all studies have 33 directions.
But it's the only similarity.
Can I normalize these studies?