MIRIT

Welcome to the MIRIT homepage !

What is MIRIT ?
What is MIRIT intended for ?
What can MIRIT do an not do ?
What applications has MIRIT been used for ?
On what platform does MIRIT run ?
How to obtain MIRIT ?
Need more information ?
References

What is MIRIT ?

MIRIT is a software program for 3D image registration by maximization of mutual information.

The program was written at the Katholieke Universiteit Leuven in Belgium by Frederik Maes and colleagues. The algorithm it implements is described in an IEEE Transactions on Medical Imaging paper published in 1997. References of interest can be found here.

Back to top

What is MIRIT intended for ?

MIRIT is intended for geometric alignment of 3D medical image volumes. MIRIT computes fully automatically the spatial transformation that maps points in one 3D image volume into their geometrically corresponding points in another, related 3D image volume. The spatial transformation is an affine transformation with 12 degrees of freedom: 3D translation, 3D rotation, anisotropic scaling along each axis and 3D skew. The transformation can be restricted to fewer than 12 degrees of freedom, e.g. translation only, rigid body transformation or similarity transform. MIRIT can also be used with 2D images, using a 2D affine transformation with 6 or fewer degrees of freedom.

Back to top

What can MIRIT do and not do ?

MIRIT computes the registration transformation (a 4x4 matrix defined by 12 parameters) between 2 3D image volumes. MIRIT can also reformat one volume to match the other using the computed transformation. MIRIT does not provide any user interface to view the original or co-registered data. The interface to MIRIT is file-based, with input files specifying the image volumes and the parameters for MIRIT. MIRIT also does not incorporate any image conversion tools (such as DICOM input), but simply reads the images from file assuming that the data is stored as a single 3D contiguous block on disk. MIRIT can be run from the command line with proper input files provided by you or MIRIT can be called from within some other application you may create that handles image management and provides MIRIT with input. For more information, have a look at the MIRIT manual.

Back to top

What applications has MIRIT been used for ?

MIRIT was originally used for registration of 3D CT, MR and PET brain images of a single object, for instance to correlate functional information from PET with anatomical information from MRI. MIRIT was validated in an indepencent and blind study by comparison with external marker-based registration. But MIRIT has been succesfully applied to many other applications as well, including: CT and PET of the thorax, CT and MR of the abdomen, pre- and post-operative CT, and MR time sequences such as fMRI, follow-up scanning, or dynamic sequences such as cardiac perfusion. MIRIT has also been used for affine registration of brain images of different subjects, for instance matching the study image with an atlas template.

Back to top

On what platform does MIRIT run ?

MIRIT was originally developed on an IBM RS6000 workstation under AIX. It has been ported since to SGI IRIX64, SUN Solaris, Linux and Windows NT.

Back to top

How to obtain MIRIT ?

Originally, MIRIT source code was distributed free of charge for research purposes only to interested institutes and hospitals as mentioned in the IEEE TMI paper. However, the software has in the mean time been licensed to a company and we can no longer distribute it for free. We therefore now distribute the program at a reasonable price, depending on whether you wish to use MIRIT for academical purposes (image processing or biomedical research) or clinical purposes (for patient diagnosis or treatment). Interested users can freely experiment with the software during a 60-day period, after which they must buy or return (destroy) it. If you want to receive a 60-day trial version of MIRIT, fill out this form (UNAVAILABLE FOR THE MOMENT...).

Back to top

Need more information ?

For more information about MIRIT, contact Frederik Maes either by

email: Frederik.Maes@uz.kuleuven.ac.be

or by regular mail at:

Katholieke Universiteit Leuven
Radiologie/ESAT
UZ Gasthuisberg
Herestraat 49
B-3000 Leuven
Belgium

Back to top

References:

The MIRIT algorithm:

F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, P. Suetens, Multimodality image registration by maximization of mutual information , IEEE transactions on Medical Imaging, vol. 16, no. 2, pp. 187-198, April 1997

Back to top

Validation:

J. West, J.M. Fitzpatrick, M.Y. Wang, B.M. Dawant, C.R.,Jr. Maurer, R.M. Kessler, R.J. Maciunas, C. Barillot, D. Lemoine, A. Collignon, F. Maes, P. Suetens, D. Vandermeulen, P.A. van den Elsen, S. Napel, T.S. Sumanaweera, B. Harkness, P.F. Hemler, D.L.G. Hill, D.J. Hawkes, C. Studholme, J.B.A Maintz, M.A. Viergever, G. Malandain, X. Pennec, M.E. Noz, G.Q.,Jr. Maguire, M. Pollack, C.A. Pelizzari, R.A. Robb, D. Hanson, R.P. Woods, Comparison and evaluation of retrospective intermodality brain image registration techniques , Journal of computer assisted tomography, vol. 21, no. 4, pp. 554-566, 1997, Lippincott-Raven Publishers, Philadelphia, PA, USA

Back to top

The use of MIRIT for inter-subject brain image registration:

K. Van Leemput, F. Maes, D. Vandermeulen, P. Suetens, Automated model-based bias field correction of MR images of the brain , IEEE transactions on medical imaging, vol. 18, no. 10, pp. 885-896, October 1999

B. Dawant, S.L. Hartmann, J.-P. Thirion, F. Maes, D. Vandermeulen, P. Demaerel, Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations : part I, methodology and validation on normal subjects , IEEE transactions on medical imaging, vol. 18, no. 10, pp. 909-916, October 1999

Back to top

Clinical papers that used MIRIT for multimodality image registration:

M. Debois, R. Oyen, F. Maes, G. Verswijfel, G. Gatti, H. Bosmans, M. Feron, E. Bellon, J. Kutcher, H. Van Poppel, L. Vanuytsel, The contribution of magnetic resonance imaging to the three-dimensional treatment planning of localized prostate cancer , International journal of radiation oncology, biology, physics, vol. 45, no. 4, pp. 857-865, 1999

J. Vansteenkiste, S. Stroobants, P. Dupont, P. De Leyn, W. De Wever, E. Verbeken, J. Nuyts, F. Maes, J. Bogaert, the Leuven Lung Cancer Group, FDG-PET scan in potentially operable non-small cell lung cancer : do anatometabolic PET-CT fusion images improve the localisation of regional lymph node metastases ? , European Journal of Nuclear Medicine, vol. 25, no. 11, pp. 1495-1501, November 1998, Springer-Verlag

Back to top