Spatial Registration Toolbox

Transcription

1 Spatial Registration Toolbox Patrice Weber, Francois Rousseau, Colin Studholme MIDAS Version, Contents Introduction... 1 Basic Functional Components... 2 File Format/ Information Interchange... 3 XML Structure and Process Definitions... 3 Results of a Linear Transformation... 3 Results of a Non-Linear Transformation... 4 The Reference MRI and Atlas... 5 Transforming Data into Common Space... 7 The Atlas Registration Program... 7 Command line options for batch processing... 8 Command Line Operation of Individual Functions... 9 acptrans - Apply Complete Transformation... 9 altrans - Apply linear transformation and resample... 9 festim - First estimate transformation icptrans - Apply Inverse Complete Transformation mmvreg - Multi-Modality Volume REGistration nlr - Non rigid registration warpinf Acknowledgments References Appendix The transformation.dof file format: The transformation.mtp file format: The.mdh file format: Introduction This group of software tools estimates and applies spatial transformations to volumetric MRI and MRSI data. There are three types of spatial registration implemented: 3D Rigid Registration: This accounts for differences in subject positioning between different acquisitions from the same subject, using translations and rotations only (note here that any voxel size sampling issues are dealt with using the DICOM derived geometric information about each image). It is also used as the first estimate registration 1

2 step for spatial normalization between the atlas template brain and each subject (see below). 3D Linear Affine Registration: This is used only during spatial normalization to resolve coarse scale differences in anatomy as a starting estimate for the B-Spline spatial normalization. 3D Non-linear Registration: This estimates a transformation mapping points in the reference (Template) space to a given subjects image (Target) using a non-linear spatial registration parameterized by a B-Spline transformation. For this transformation a Target MRI is defined as the resultant image space, for which the Atlas or BrainWeb MRI is used. All registration steps are intended to be fully automated and the make use of a simple voxel based image similarity criteria (normalized mutual information [1]) to provide robustness to global and some local contrast changes. Applying a linear registration transformation (rigid or affine) is a relatively fast operation and normally this transformation will be determined once, and then applied on-the-fly as needed, which minimizes duplication of large datasets. However, the data can also be saved as a new dataset. (THIS IS NOT THE CASE RIGHT NOW FOR THE AFFINE TRANSFORMATION IS NEEDED AS AN INPUT TO THE NON-LINEAR REGISTRATION (SEE BELOW)). Since both computing and applying non-linear transformations can be time consuming, this will generally be applied only once and a copy of that data saved with a corresponding entry in the subject.xml file. Basic Functional Components For both linear and non-linear, the steps of determining the transformation parameters, and the application of the transformation to images, are carried out as separate processing operations implemented at the lowest level by command line tools. This means that a transformation can be obtained using one dataset, but then applied to a different dataset. The registration steps are carried out by algorithms implemented in C++ and compiled as command line programs using G++/GCC to run under windows or linux. These tools are: mmvreg: Multi-Modality Volume Registration which carries out rigid and affine/linear registration using multi-scale optimization of normalized mutual information [1]. Input parameters are the reference and floating (transform) image and the output can be a transformed image or simply the set of transformation parameters saved in a text file (either dof file, or Midas parameter file). nlr: non-linear (B-Spline) registration tool. Carries out a multi-resolution B-Spline registration driven by normalized mutual information, starting with a coarse B-Spline deformation grid and a low resolution version of the image. Starting and final registration resolutions are user definable. The algorithm is configured for relatively coarse scale deformations for use in spatial normalization of low resolution spectroscopy data in MIDAS. festim: First estimate transformation. Derives a starting estimate for use when co-aligning images acquired within the same visit, but planned using different anatomical locations/fields of 2

3 view, by using the image geometry information stored in the supplied.mdh header files, which is derived from the DICOM information and stored in the MIDAS XML structure. Note: This is only useful for the same visit and when the patient has not moved within the coordinate system of the magnet. altrans: Applies a linear transformation to one image to resample and transform it to a second image. Parameters are supplied in a Midas transformation parameter file. Interpolation is selectable but is usually tri-linear intensity interpolation. Note: Care must be taken not to apply a transformation in a case where there is considerable decrease in sampling resolution (i.e. a reference space that has a significantly greater voxel size than the image being transformed) since significant aliasing due to undersampling, during interpolation, could occur. actrans: Applies a combination of B-Spline (non linear) and affine/rigid transformations to a given file (as above), allowing the transformation of a metabolite image into common space via the mapping between reference and subject MRI scans and the linear transformation between MRI and MRSI within subject. File Format/ Information Interchange These command line tools accept and write data using common image and parameter file formats, allowing communication with the midas XML database batch processing system. These consist of an image file stored as a pair of images.mdh (text header file) and.vol binary volume file. Together with a text file holding affine transformation parameters and a text file holding B-Spline transformation parameters. A.vol /.mhd format was used to allow incorporation of scanner geometry information (axis orientation and origin with the magnet) in a standard way unavailable in other image processing formats such as analyze format. All images are generally stored as signed short integer values. Padding regions (none image measurement regions within the rectangular matrix) are indicated by or 1 (when unsigned images are used). XML Structure and Process Definitions Results of a Linear Transformation The results of a registration process are stored in the MIDAS XML structure under a PROCESS node labeled Registration. A typical registration transforms a floating dataset (also called source) to the coordinate system of a reference dataset (target). Registration PROCESS node are always created under the source dataset. The following results are contained under the Registration Node: FirstEstimate: a simple vector and slice positions alignment between two series. The output of this process is used as a starting estimate for intra subject-study linear registration (linear registrations between studies, subjects, and subject to atlas do not make use of this process). RIGID: rigid registration (6 parameters) between series datasets or image volumes. The output of this process is either the transformation parameters (saved in the XML as data: 3

4 output parameters) or a volume if the transformation is applied (saved as data: new volume filename). AFFINE: affine registration (9 parameters) between series datasets or image volumes. This process is mostly use to generate a starting estimate for the non-linear registration process to an atlas. This output of the process is either a transformation parameters (saved in the XML as data: output parameters) or a volume if the transformation is applied (saved as data: new volume filename). When viewed in the MIDAS browser the result follows this basic structure: MRI_T2 + Original + Volume + Registration - Contains the registration process where the MRI_T2 is the floating dataset + FirstEstimate + RIGID - Contains the rigid registration process info and output + AFFINE - Contains the affine registration process info and output MRI_T1 + Original + Volume + Registration - Contains the registration process where the MRI_T1 is the floating dataset When saving data after applying a linear transformation, the size and format of the output data is always the same as the reference image supplied to the apply trans software (see comments in Basic Functional Components). Results of a Non-Linear Transformation The non-linear registration processes are stored in the XML structure under a MIDAS PROCESS node labeled Normalization. A typical non-linear registration transforms a floating dataset (also called the source) to the coordinate system of a reference MRI, also known as the atlas MRI. The Normalization PROCESS node is ALWAYS created under the source dataset (the series transformed to common space). The following results are contained under the Normalization node for non-linear registration: NLTRANS: non-linear registration parameters. This process takes as input a floating dataset or volume that is typically the result of a previous affine registration to the atlas. The input is a starting estimate that will be warped to the atlas. The input parameters are the knot lattice spacing in mm (default value 10mm). The output of this process is a file contains the non-linear transformation parameters (3 spline parameters per knot). ACTRANS: result of the application of the non-linear transformation to a Series. The result is an image volume in the atlas coordinates system (common space). The data type is the same as the input image data type. 4

5 The Reference MRI and Atlas A copy of the BrainWeb MRI (Montreal Neurological Institute) [3] is used as the target image for spatial normalization. One of the key motivations for using this data as our reference template was that a set of targets with different MRI properties but matching most MRI image contrasts can be created using this simulator. The BrainWeb MRI volume is defined at a 1 mm isotropic voxel grid in Talairach space, with dimensions 181x217x181 (x,y,z) and start coordinates -90,- 126,-72 (x,y,z). A set of T1 weighted images, derived from the standard simulation parameters, and in the same coordinate system as the MNI brainweb simulator data, are included for spatial normalization and regional measurements in MIDAS. For the registration process MIDAS uses the whole brain image from the MNI simulator for initial rigid alignment. A second version of this image that has been edited to have all regions outside the intra-cranial volume excluded and marked with a padding value (-32768) is used as a target for refinement using affine and B- Spline based registration. Finally, this is accompanied by a Brain Atlas in which the major brain regions (on a lobar scale) have been manually marked using the rview manual segmentation tool ( to a standard used in our lab for morphometric analysis. They are encoded in a single volume file with voxel labels for each location and illustrated below. Labels extend to the edge of the image, so that the entire space is divided by lobe, allowing an accurate definition of tissue brain to be derived solely from the segmentation of the subject image data, and not spatial normalization. 5

6 The numeric encoding for lobe regions is shown in Table 1. Further sub regions can be added, but in general they should conform to standards used for spatial normalization. In particular, many of the highly detailed markings that are available on the BrainWeb data from other labs are unsuitable because they make use of landmarks and boundaries that are either not present in other individuals (and thus the boundaries are not applicable across a population) or make use of boundary definitions that are derived from structures external to the structure of interest (for example the temporal pole). In the later case this can lead to significant errors in measuring and defining the marked region in subjects that have anatomical abnormalities in structures external to the region of interest. An example of a parcellation protocol that attempts to define structures with boundaries that are meaningful in different subjects and different diseases is described in [4]. Label # Anatomy 1 R Temporal 2 R Frontal 3 L Temporal 4 R Parietal 5 R Occipital 6 L Frontal 7 L Parietal 13 L Occipital 14 Cerebellum 25 R Caudate 27 R Putamen 29 R Thalamus 31 R Ventricle 32 L Putamen 34 L Thalamus 36 L Ventricle 37 L Caudate Table 1: Numeric Labeling used for lobe regions in the MNI brainweb data NOTE : In MIDAS v1 the atlas was defined in neurological convention, i.e. label Left is image left. As of June 2012 and for MIDAS v2 this has been changed to Radiology convention orientation, where label Left corresponds to image Right and subject Left. The MNI image data is maintained under the MIDAS system using a subject.xml descriptor and associated files. It is possible to substitute other MRI data for the target image, but because of specific assumptions made by the software this would need to be evaluated by the Midas registration project members to ensure the data are in a format suitable for the spatial normalization process. 6

7 Transforming Data into Common Space The transformation of a subject dataset (MRI or MRSI) into a common space (atlas) requires the computation of the rigid, affine, and non-linear transformations. The following flow chart illustrates the multiple steps to be taken for a MRSI dataset, which uses the non-linear transformation parameters determined using the T1 image data Where, T1 is the subject T1 image SI_Ref is the subject water reference image Atlas refers to the atlas image full head. AtlasICV refers to the atlas image skull-stripped. This sequence of processes needed to transform a MR modality (MRI_T1 or MRSI) to common space has been automated with the program Atlas Registration that can be run both in command line (for batch) or GUI mode. The Atlas Registration Program The registration program can be started from the MIDASTools button-bar: or run in batch mode (see documentation on how to execute MIDAS pipelines using the BATCH tool) Important: Before running the program on a project make sure that the project is linked to an atlas. To add an atlas reference to a project launch the importer from the MIDASTools buutonbar, go to the Project Atlas entry, click the browse button and selected the atlas folder (in the current installation the Midas Atlas is located under InstallationFolder/Midas/MNI-Atlas ). When running the program for the first time verify that the program has the paths to the C++ codes set correctly. On the Advanced settings menu, click C programs to display the 7

8 Registration programs window settings. All the fields should show in black font. A red field means the path to the program could not be resolved, use the browse button to locate the missing program, then click save. In the Project section select the data project. If you want to run Atlas Registration on a subset of subjects, click the checkbox Subjects and launch the project browser. You can select multiple subjects by clicking on a subject ID and holding the Shift or the Control key. In the section Modality, use the drop down menu to select which modality (MRI_T1, SI or SI_Ref) you want to transform into common space. In the Atlas section, use the drop down menu to select the resolution of the data into common space. The select the knot spacing used by the non-linear registration process. The spacing is set by default to its optimal value (10 mm isotropic) for the Midas-MNI atlas provided (1x1x1 mm). Coarser knot spacing will result in a poor registration. Finer ones (5x5x5 mm for example) will greatly increase the computation time with minor gains considering the MRSI data resolution. Finally, if you want to recompute existing data use the Force reprocessing option. Command line options for batch processing matlastransform [OPTIONS] -p [--project] : project XML file -s [--subject] : list of subject_id or list of subject XML file. The list must be semi-colon delimited -st [--study] : Study_ID of the selected subject. -a [--atlas] : atlas XML file. Mandatory if '--project' option not used -m [--modality] : modality to transform to atlas space. If omitted default value is 'SI' -r [--resolution] : target resolution in atlas space in mm 8

9 -hs [--headsize] : compensate for head size problems during affine registration. With a small subject's head the registration program can get caught in a local minimum (subject s skull aligning with the edge the atlas brain). The suitable correction factor should be in the range of 0.9 to < 1 -nt [--notransform] : do not apply the complete transformation to the chosen modality -kx [--knotx] : deformation knot spacing along X axis for non-linear transformation to atlas space -ky [--knoty] : deformation knot spacing along Y axis for non-linear transformation to atlas space -kz [--knotz] : deformation knot spacing along Z axis for non-linear transformation to atlas space -rp [--reprocess] : force reprocessing is the process node already exists in the subject XML -h [--help]: to display command line options Command Line Operation of Individual Functions Note: See Appendix for descriptions of file formats used under MIDAS. Additional format information is available from: (updated : 10 th August, 2008) Usage: acptrans - Apply Complete Transformation acptrans [<options>] -ref: reference image -flo: floating image -out: output image -kf: filename for the knot grid estimated with the non rigid registration -tpf1: Affine transformation between atlas and T1 MRI of the subject -tpf2: Rigid transformation between T1 and XXX of the subject -help: Print summary of command-line options and abort altrans - Apply linear transformation and resample Usage: altrans ref reference flo floating tpf trans.txt out registered Applies the transform using trilinear interpolation. 9

10 Options: -ref reference : reference image file -flo floating : floating image file -tpf trans.txt : 6 or 9 parameter transformation file -out output : output image file NOTE: This program adds the extension.mdh to the filenames, even if already part of the filename. festim - First estimate transformation Usage: festim [<options>] Options: -ref : Reference Image (short image only) -flo : Floating Image (short image only) -v : Verbose mode (use 1), Default value: 0 -out : Output Filename (transform.txt for instance) -help : Print summary of command-line options and abort Usage: icptrans - Apply Inverse Complete Transformation icptrans [<options>] -ref: reference image (spectroscopic image) -flo: floating image (atlas) -out: output image -kf: filename for the knot grid estimated with the non rigid registration -tpf1: Affine transformation between atlas and T1 MRI of the subject -tpf2: Rigid transformation between T1 and XXX of the subject -it: Interpolation type (0:nearest neighbour, 1: trilinear), Default value: 1 -help: Print summary of command-line options and abort mmvreg - Multi-Modality Volume REGistration Affine multi-modality registration tool. Calculates rigid or rigid + scaling or rigid + scaling + skew between two image volumes. Example Command line options: mmvreg refim.gipl floatim.gipl -nosave -dofout ref2float.dof Here each of the.gipl files can also be analyze, or nifti format images in stored either 16 or 32 bit integer or 32 bit floating point data format. 10

11 The "-dofout ref2float.dof" saves the affine transformation parameters (default 6) in a dof text file. The "-nosave" option prevents application of the transformation to the image files (so just the parameters are saved) Alternatively you can use: mmvreg refim.gipl floatim.gipl float2refout.gipl -dofout ref2float.dof which transforms the floating image into reference coordinates (using by default linear interpolation) and saves it in the the file "float2refout.gipl". mmvreg [ Outdated : Initial Implementation. (Can be deleted) ] Usage: mmvreg mr.gipl ct.gipl ct2mr.gipl -dofout trans.dof Output of the applied transform image is currently not supported for.mdh format (i.e. that used in the MIDAS implementation), so the following should be used: mmvreg mri_ref.mdh mri_floating.mdh dummyfilename -dofout trans.mtp nosave and warpimf or altrans used to apply the transformation. The nosave stops the transformation being applied. NOTE: Input images must be INTEGER. No errors flagged if passed as float. Options with [default values]: Image Data Selection: -reff num : load reference image from first slice num [first] -refl num : load reference image up to last slice num [last] -tranf num : load transform image from first slice num [first] -tranl num : load transform image up to last slice num [last] -rframe num : Use reference image frame num [first] -tframe num : Use transform image frame num [first] Starting Estimate: -dofin in.dof : use 'in.dof' as starting transformation estimate -midastpin firstestimate.txt : use 6-parameter initial estimate for MIDAS format Resolution/Accuracy: -refsamp mm : maximum data resolution for evaluation [2mm] -transamp mm : maximum data resolution for transform image [2mm] 11

12 Output: -ref2tran : Write 'result' as e.g. MR in PET coordinates rather than PET in MR coordinates -dofout out.dof : write final transformation estimate to file 'out.dof' -midastpout filename.mtp : Save MIDAS transformation parameter files instead of *.dof file. -nosave : Do not apply the transform -p9 : compute rigid 9-parameter registration -quiet : No Progress Messages when running Usage: nlr - Non rigid registration nlr [<options>] -ref: input 3D target image -mask: input 3D target ICV image -flo: input 3D image to register -sm: Similarity Measure Default value: 1 -r: Resolution for resampling "1" -sx: Spacing X for the B-Spline (mm) "10" -sy: Spacing Y for the B-Spline (mm) "10" -sz: Spacing Z for the B-Spline (mm) "10" -l: Lambda parameter for the regularisation " " -fr: Finest Resolution Default value: 0 -cr: Coarsest Resolution Default value: 2 -fk: filename for knot grid file "knotgrid.txt" -ev: filename for nmi evolution -help: Print summary of command-line options and abort SimpleWrap Regional mutual information driven image warping tool. Used for approximate spatial normalisation of brain anatomy and geometric distortion estimation. Usage: estimating warp from Reference Image to Floating Image: SimpleWarp refim.gipl refimseg.gipl floatim.gipl WarpOut.gipl -dofin ref2float.dof -Quiet -NoLog Here each of the.gipl files can also be analyze, or nifti format images in stored either 16 or 32 bit integer or 32 bit floating point data format. The "refimseg.gipl" should have the same dimensions as "refim.gipl" but contain a mask of the region to be warped. (set to 100 inside and -1 outside). 12

13 The optional "-dofin ref2float.dof" option specifies the initial linear transformation from reference to floating coordinates (calculated using mmvreg). This can be as a ".dof" file format or a midas transformation file format. The final warp is saved in a 4D floating point image "WarpOut.gipl" and can be applied to images using the command "warpimf" command (below) The "-Quiet" and "-NoLog" options prevent printing of debugging information and the creation of log files for the program execution. warpinf Program to apply simple linear elastic deformation fields (created by SimpleWarp) to images using specified interpolation. EG: Usage: warpimf inputfield.gipl refim.gipl floatim.gipl float2refout.gipl [-dofin ref2transa.dof] [-dofin2 transa2transb.dof] [-InterpC3 -InterpNN -InterpL3] warpimf warps images using deformation fields (F) (as opposed to parameterized fields). It will also apply a concatenation of a non rigid warp and optionally one or two global linear transformations. It will warp image file floatim.gipl to the coordinate system and resolution of refim.gipl (note: though it will not blurr an image to prevent under-sampling here) using the deformation in file "inputfield.gipl" and save it with name "float2refout.gipl". All of these can be '.mdh' format files. Options: -dofin and -dofin2 - these allow additional transformations to be concatenated onto the warp (either in dof files or midas transformation parameter files). They are applied in the order: refim warpfield ref2transa.dof transa2transb.dof float Image Note: as is standard, all transformations are in the reverse direction to the application of the resampling: refim float image -Interp** - Options on the interpolation to use: -InterpC3 : cubic interpolation -InterpNN : Nearest neighbor interpolation -InterpL3 : Tri-Linear interpolation. 13

14 Use Cubic for best results, but do Not use this when images are discontinuous (i.e. you have masked them or have set some voxels to zero etc creating sharp edge) because the non-bandlimited edges will ring badly. Basically don't transform images with masked voxels. -NullDisp - this sets the displacement field to zero to use warpimf to apply the transformation without a warp. E.g.: warpimf dummywarp.mdh refim.mdh floatim.mdh floatout.mdh -dofin ref2float.dof -NullDisp Acknowledgments These programs were developed by Colin Studholme, Patrice Weber, and Francois Rousseau. This work was supported by NIH grant EB0822 under the MIDAS project. References [1] C. Studholme, D.L.G. Hill, D.J. Hawkes, An Overlap Invariant Entropy Measure of 3D Medical Image Alignment, Pattern Recognition, 1999, 32:1, [2] C. Studholme, E. Novotny, I.G. Zubal, J.S. Duncan, Estimating Tissue Deformation Between Functional Images Induced by Intracranial Electrode Implantation Using Anatomical MRI, NeuroImage, Vol 13(4), pp , [3] DL Collins and AP Zijdenbos and V Kollokian and JG Sled and NJ Kabani and CJ Holmes and AC Evans. Design and Construction of a Realistic Digital Brain Phantom. IEEE Transactions on Medical Imaging, vol.17, , [4] B. Iordanova, D. Rosenbaum, D. Norman, M. Weiner, C. Studholme, MRI study of Neurodegeneration: A Robust Volumetric Parcellation Method of the Frontal Lobe Gyri with Quantitative Validation in Dementia Patients, In Press AJNR, Appendix The transformation.dof file format: The DOF file holds the set of 3 rotations (in degrees) and 3 translations (in mm) from the coordinate system based around the centre of the reference image volume to the co-ordinate system based around the centre of the floating image. These co-ordinate system are all in physical mm co-ordinates and not in the image voxel co-ordinates. A description of this file is available at The MIDAS transformation file format: 14

15 For the MIDAS registration, results are saved in an ASCII file as: paramaters: 6 translation_x: ( in mm, in direction of data indices) translation_y: translation_z: rotation_x: (in degrees) rotation_y: rotation_z: The MIDAS.vol/.mdh file format: For operation under the MIDAS registration program, the binary image data must have a filename with extension.vol, and ASCII parameters file with extension *.mdh. The following is an example: %!MIDASVOL %!version volumizer 1.01 [PATIENT INFO] Patient_ID: [ACQUISITION INFO] Study_Date: Acquisition_Date: Sequence_Name: Dataset_Name: Echo_Time: [VOLUME INFO] Creation_Date: Checksum: Data_Representation: INTEGER Bits_Allocated: 16 Signed: true Spatial_Points_1: 512 Spatial_Points_2: 512 Spatial_Points_3: 96 Number_Of_Frames: 1 Pixel_Spacing_1: Pixel_Spacing_2: Slice_Thickness: Frame_Spacing: 1 Image_Orientation_Xr: Image_Orientation_Yr: Image_Orientation_Zr: e-018 Image_Orientation_Xc: Image_Orientation_Yc: Image_Orientation_Zc: Image_Position_X: Image_Position_Y: Image_Position_Z: (not used) [BINARY DATA] Byte_Offset: 0 Binary_Data_Filename: C:\\Data\MN011\mri\MRI_T1_1800_4.vol 15