Projects:RegistrationDocumentation:UseCaseInventory

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The Slicer Registration Case Library

Welcome to the 3DSlicer Registration Case Library. This page is under continuous development as we add and refine case examples of image registration within 3DSlicer. The goal is for you to find in here a case similar to yours that will address the issues of your particular image registration problem and give you a promising start. Successful registration strategies vary greatly for different image contrast and content. You will find 3 sets of content with each case: 1) a step-by-step tutorial that will show you how to use 3DSlicer to register images of this type, 2) the example dataset used in the tutorial so you can try for yourself, along with the solution transform so you can compare, and 3) a custom Registration Presets file that contains the optimized registration parameters that you can load directly into slicer.

Here's how to get started:

• 1. browse/search the library below for a case similar to yours. The library is organized by organ (brain vs. non-brain), modality (MR, CT, PET), intra- vs. inter-subject). Each case also has a list of keywords, so you may try a direct search for a case matching yours.
• 2. If you find a good match, continue with 3. below. If you cannot find a good match, consider our Call for Datasets & Registration Service.
• 3. from the case description page, follow the links to download the data, preset file and tutorials.
• 4. run the tutorial on your machine with your installation of slicer
• 5. load the preset file and try those settings on your own data
• 5. if you do not get a satisfactory registration results with the presets, have a look at the Registration Challenges and Key Strategies section on the download page of your case. You will find recommendations there on how to venture forth. Try the recommended adjustments.
• 6. if still unsuccessful you may have a case of interest to the library. Consider adding your case to the library for a free registration. Also consider posting a message to the slicer user group.

Source & Format

Data is collected from a variety of sources. Most we hope to originate from user contributions. Because we want to focus on the registration problem and not be distracted by image format or other data management issues, the datasets listed here are reformatted as NiFTI files. Cases under construction may be listed, and when complete a download link is added.

• Call for Datasets
• All image data is anonymized, i.e. there is no identifying or patient specific information in any of the posted data. We do not keep identifiers, so we cannot retrace.
• Options for the download location is a direct link to a download page from the main 3Dslicer user page or XNAT Central. The former seems preferable, since the general XNAT GUI appears too complex for a simple 1-case download. We expect most users to download only 1 or 2 files closest to their particular use case, not entire study sets. Such download should be acessible with 1-2 clicks, as with other slicer resources.
• Disk usage: Likely these example sets will grow no larger than 10GB. Estimating 50-80MB per case would allow 60-100 cases within a 5GB space.
• If desired, links & contact info to the contributor lab are provided.
• Links here also from the NA-MIC resource page.To be updated when moving.

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Case Inventory Brain

• Inter-subject Brain MRI (Tumor growth assessment): sagittal T1 with meningioma
• Intra-subject Brain MRI (Multiple Sclerosis): T2 FLAIR + Labelmap to T1 SPGR
• Intra-subject T1-T2-DTI-fMRI
• Intra-subject T1-T2-DTI
  • DTI baseline to T1 inter-subject with clipped FOV
  • DTI affine & non-rigid alignment
• Intra-subject PD,T2, T1Gd (MS)
  • Scan-rescan intra-subject. No change.
  • UNC Midas Database of healthy volunteers
  • fMRI: inter-subject structural scan to MNI atlas target
• cases with defacing applied with mask avail.
• cases with defacing w/o mask
• example of half-way registration: 2 sets
• BIRN dataset ELUDE: longitudinal study of late-life depression at Duke University. There are 281 depressed subjects and 154 controls included. An MR scan of each subject was obtained every 2 years for up to 8 years (total of 1093 scans). ELUDE link (XNAT)
• BIRN dataset MIRIAD: cross-sectional depression. MIRIAD link (XNAT)
• BIRN dataset OASIS (XNAT)
• AC-PC alignment: can I do this with Slicer? -> fiducial-based alignment as separate module
• Registration comparison w & w.o bias field correction. When it does make a difference.

Case Inventory Non-Brain

• PET-CT whole body, 2 timepoints each -- see here
• breast Cancer MRI: pre- and post-treatment
  • original data, two time points after some registration and processing
• prostate image database (intra- and inter-subject registration use cases) http://prostatemrimagedatabase.com/index.html
• knee registration for Simbios project -- http://slicer.spl.harvard.edu/slicerWiki/index.php/Stanford_Simbios_group
• liver tumor ablation: pre-procedural MRI to intra-procedural CT --> Dr. Stuart Silverman's IGT group: manuscript
• kidney tumor ablation: pre-procedural MRI to intra-procedural CT --> Dr. Stuart Silverman's IGT group
• kidney tumor ablation: intra-procedural CT - CT
• liver CT, intra-subject, contrast enhanced manuscript
• temporal bone (ear) intra-subject CT to MRI of the collab with Soenke in 2005
• tibia CT, Model-based surface registration

Case Inventory Non-human

• Vervet Atlas

Self-validation Sets

These are datasets with artificial misalignment, i.e. the perfect alignment is known.

• testing PET-CT-MRI on the Vanderbuild database: RIRE
• Dual Echo PD/T2 intra subject different contrast self validation
• Isotropic Brain SPGR T1 with single axis rotation
• Parameter Sensitivity Analysis

First 10 cases (not in sequential order)

• Brain intra-subject, same contrast T1, change detection follow-up: tumor growth
• Brain MRI, intra-subject, same contrast PD&T2, change detection follow-up: new MS lesions
• Brain MRI, intra-subject, different contrast, co-register all series of the same exam: T1 SPGR, FLAIR, T2
• Brain MRI, intra-subject, DTI to reference, apply Xform to 25-direction DTI tensor
• Brain MRI, intra-subject, example with clipped FOV, example of masking required
• Brain MRI, inter-subject, co-register T1 SPGR to atlas dataset (ICBM). Resample a label map.
• Liver intra-subject, pre-procedural MRI to intra-procedural CT
• Prostate MRI, intra-subject
• Knee inter-subject registration to initialize segmentation
• PET to CT Whole Body single timepoint
• Breast Cancer: feasible with affine focus?