[en] Research Overview Isis Innovation Ltd - FSL Team SIENA is a package for both single-time-point ("cross-sectional") and two-time-point ("longitudinal") analysis of brain change, in particular, the estimation of atrophy (volumetric loss of brain tissue). SIENA has been used in many clinical studies. SIENA is part of the FMRIB Software Library (FSL) - a comprehensive library of analysis tools for FMRI, MRI and DTI brain imaging data. SIENA is a package for both single-time-point ("cross-sectional") and two-time-point ("longitudinal") analysis of brain change, in particular, the estimation of atrophy (volumetric loss of brain tissue). SIENA has been used in many clinical studies. Siena estimates percentage brain volume change (PBVC) between two input images, taken of the same subject, at different points in time. It calls a series of FSL programs to strip the non-brain tissue from the two images, register the two brains (under the constraint that the skulls are used to hold the scaling constant during the registration) and analyze the brain change between the two time points. It is also possible to project the voxelwise atrophy measures into standard space in a way that allows for multi-subject voxelwise statistical testing.An extension for ventricular analysis is provided in FSL5. Sienax estimates total brain tissue volume, from a single image, normalised for skull size. It calls a series of FSL programs: It first strips non-brain tissue, and then uses the brain and skull images to estimate the scaling between the subject's image and standard space. It then runs tissue segmentation to estimate the volume of brain tissue, and multiplies this by the estimated scaling factor, to reduce head-size-related variability between subjects. A paper on SIENA has been published in JCAT; also see a related technical report (PDF). A second paper, on SIENAX and improvements to SIENA has been published in NeuroImage; also see a related technical report (PDF). We have recently extended SIENA ("SIENAr") to allow voxelwise statistical analysis of atrophy across subjects; see ISMRM04 and HBM04. If you use SIENA in your research, please make sure that you reference the following articles. You may alternatively wish to use the brief descriptive methods text and expanded list of references given below. SIENA - Inputs User manual BET options. For example, to increase the size of brain estimation, use: 0.3 Two-class segmentation: don't segment grey an white matter separately - use this if there is poor grey/white contrast Use stantard-space masking as well as BET (e.g. if it is proving hard to reliable brain segmentation from BET, for example if eyes are hard to segment out) - register to standard space in order to use a pre-defined standard-space brain mask Ignore from t(mm) upwards in MNI152/Talairach space - if you need to ignore the top part of the head (e.g. if some subjects have the top missing and you need consistency across subjects) Ignore from b (mm) downwards in MNI152/Talairach space; b should probably be -ve What the script does SIENA carries out the following steps: Run bet on the two input images, producing as output, for each input: extracted brain, binary mask and skull image. If you need to call BET with a different threshold than the default of 0.5, use BET Options Run siena_flirt, a separate script, to register the two brain images. This first calls the FLIRT-based registration script pairreg (which uses the brain and skull images to carry out constrained registration). It then deconstructs the final transform into two half-way transforms which take the two brain images into a space halfway between the two, so that they both suffer the same amount of interpolation-related blurring. Finally the script produces a multi-slice gif picture showing the registration quality, with one transformed image as the background and edges from the other transformed image superimposed in red. The final step is to carry out change analysis on the registered brain images. This is done using the program siena_diff. (In order to improve slightly the accuracy of the siena_diff program, a self-calibration script siena_cal, described later, is run before this.) siena_diff carries out the following steps: Transforms original whole head images and brain masks for each time point into the space halfway between them, using the two halfway transforms previously generated. Combines the two aligned masks using logical OR (if either is 1 then the output is 1). The combined mask is used to mask the two aligned head images, resulting in aligned brain images. The change between the two aligned brain images is now estimated, using the following method (note that options given to the siena script are passed on to siena_diff): Apply tissue segmentation to the first brain image. At all points which are reported as boundaries between brain and non-brain (including internal brain-CSF boundaries), compute the distance that the brain surface has moved between the two time points. This motion of the brain edge (perpendicular to the local edge) is calculated on the basis of sub-voxel correlation (matching) of two 1D vectors; these are taken from the 3D images, a fixed distance either side of the surface point, and perpendicular to it, and are differentiated before correlation, allowing some variation in the two original images. Compute mean perpendicular surface motion and convert to PBVC. To make this conversion between mean perpendicular edge motion and PBVC, it is necessary to assume a certain relationship between real brain surface area, number of estimated edge points and real brain volume. This number can be estimated for general images, but will vary according to slice thickness, image sequence type, etc, causing small scaling errors in the final PBVC. In order to correct for this, self-calibration is applied, in which siena calls siena_cal. This script runs siena_diff on one of the input images relative to a scaled version of itself, with the scaling pre-determined (and therefore known). Thus the final PBVC is known in advance and the estimated value can be compared with this to get a correction factor for the current image. This is done for both input images and the average taken, to give a correction factor to be fed into siena_diff. SIENA - Outputs Report.siena: SIENA log, including the final PBVC estimate SIENA Report: SIENA pdf report including images showing various stages of the analysis, the final result and a description of the SIENA method. Rendered image: A colour-rendered image of edge motion superimposed on the halfway A image. Red-yellow means brain volume increase and Blue means brain volume decrease ("atrophy"). Referencing SIENA/X (minimal version) Two-timepoint percentage brain volume change was estimated with SIENA -Smith 2002-, part of FSL -Smith 2004- or Brain tissue volume, normalised for subject head size, was estimated with SIENAX -Smith 2002-, part of FSL -Smith 2004-. -Smith 2002- S.M. Smith, Y. Zhang, M. Jenkinson, J. Chen, P.M. Matthews, A. Federico, and N. De Stefano. Accurate, robust and automated longitudinal and cross-sectional brain change analysis. NeuroImage, 17(1):479-489, 2002. -Smith 2004- S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004. Referencing SIENA/X (more detailed text and references) SIENA. Two-timepoint percentage brain volume change was estimated with SIENA -Smith 2001, Smith 2002-, part of FSL -Smith 2004-. SIENA starts by extracting brain and skull images from the two-timepoint whole-head input data -Smith 2002b-. The two brain images are then aligned to each other -Jenkinson 2001, Jenkinson 2002- (using the skull images to constrain the registration scaling); both brain images are resampled into the space halfway between the two. Next, tissue-type segmentation is carried out -Zhang 2001- in order to find brain/non-brain edge points, and then perpendicular edge displacement (between the two timepoints) is estimated at these edge points. Finally, the mean edge displacement is converted into a (global) estimate of percentage brain volume change between the two timepoints. SIENAX. Brain tissue volume, normalised for subject head size, was estimated with SIENAX -Smith 2001, Smith 2002-, part of FSL -Smith 2004-. SIENAX starts by extracting brain and skull images from the single whole-head input data -Smith 2002b-. The brain image is then affine-registered to MNI152 space -Jenkinson 2001, Jenkinson 2002- (using the skull image to determine the registration scaling); this is primarily in order to obtain the volumetric scaling factor, to be used as a normalisation for head size. Next, tissue-type segmentation with partial volume estimation is carried out -Zhang 2001- in order to calculate total volume of brain tissue (including separate estimates of volumes of grey matter, white matter, peripheral grey matter and ventricular CSF). Voxelwise multi-subject SIENA statistics. First, SIENA was run separately for each subject. Next, for each subject, the edge displacement image (encoding, at brain/non-brain edge points, the outwards or inwards edge change between the two timepoints) was dilated, transformed into MNI152 space, and masked by a standard MNI152-space brain edge image. In this way the edge displacement values were warped onto the standard brain edge -Bartsch 2004-. Next, the resulting images from all subjects were fed into voxelwise statistical analysis to test for... -Smith 2001- S.M. Smith, N. De Stefano, M. Jenkinson, and P.M. Matthews. Normalised accurate measurement of longitudinal brain change. Journal of Computer Assisted Tomography, 25(3):466-475, May/June 2001. -Smith 2002- S.M. Smith, Y. Zhang, M. Jenkinson, J. Chen, P.M. Matthews, A. Federico, and N. De Stefano. Accurate, robust and automated longitudinal and cross-sectional brain change analysis. NeuroImage, 17(1):479-489, 2002. -Smith 2004- S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004. -Smith 2002b- S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, November 2002. -Jenkinson 2001- M. Jenkinson and S.M. Smith. A global optimisation method for robust affine registration of brain images. Medical Image Analysis, 5(2):143-156, June 2001. -Jenkinson 2002- M. Jenkinson, P.R. Bannister, J.M. Brady, and S.M. Smith. Improved optimisation for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2):825-841, 2002. -Zhang 2001- Y. Zhang, M. Brady, and S. Smith. Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm. IEEE Trans. on Medical Imaging, 20(1):45-57, 2001. -Bartsch 2004- A.J. Bartsch, N. Bendszus, N. De Stefano, G. Homola, and S. Smith. Extending SIENA for a multi-subject statistical analysis of sample-specific cerebral edge shifts: Substantiation of early brain regeneration through abstinence from alcoholism. In Tenth Int. Conf. on Functional Mapping of the Human Brain, 2004.