MR images

Magnetic resonance imaging (MRI) is a computer-assisted medical imaging technique for creating cross-sectional images by exposing a subject to radio waves in the presence of a powerful magnetic field and measuring signals emitted by certain atoms or molecules (like water) in response to this treatment. Dynamic contrast enhanced MRI (DCE-MRI) uses T1-weighted sequences to record the signal change caused by the contrast agent passing through the measured area, allowing assessment of tissue perfusion.

Using MR images in Turku PET Centre

What are MR images used for?

How to find the reference MRI data?

MRI scans after summer 2006

MR images can be found in PETPACS.

MRI scans before summer 2006

If not found in PETPACS, then ask the IT group.

MRI file format

Images are in DICOM format, each in its own folder. Usually each DICOM image consists of >100 files in a single folder, but can also consist of a single file.

Note that DICOM image contain subject name and identity number. Unless these are removed from the DICOM image e.g. using programs dcmanon or ImageConverter (both can be downloaded from here), it is strictly forbidden to take or send image outside the hospital.

DICOM format can be converted into other image formats using ImageConverter (consult Timo Laitinen), or Vinci.

MRI-PET co-registration

For an Analyze 7.5 MRI image the coregistration to an Analyze 7.5 PET image can be done with coreg.



References

Agarwal N, Port JD (eds.): Neuroimaging: Anatomy Meets Function. Springer, 2018. doi: 10.1007/978-3-319-57427-1.

Carrio I, Ros P (eds.): PET/MRI - Methodology and Clinical Applications. Springer, 2014. doi: 10.1007/978-3-642-40692-8.

Dai Y, Wang Y, Wang L, Wu G, Shi F, Shen D. aBEAT: a toolbox for consistent analysis of longitudinal adult brain MRI. PLoS One. 2013; 8(4): e60344. doi: 10.1371/journal.pone.0060344.

Habas C (ed.): The Neuroimaging of Brain Diseases - Structural and Functional Advances. Springer, 2018. ISBN 978-3-319-78926-2. doi: 10.1007/978-3-319-78926-2.

Ingrisch M, Sourbron S. Tracer-kinetic modeling of dynamic contrast-enhanced MRI and CT: a primer. J Pharmacokinet Pharmacodyn. 2013; 40(3): 281-300. doi: 10.1007/s10928-013-9315-3.

Koh TS, Bisdas S, Koh DM, Thng CH. Fundamentals of tracer kinetics for dynamic contrast-enhanced MRI. J Magn Reson Imaging 2011; 34(6): 1262-1276. doi: 10.1002/jmri.22795.

Li, X: Functional Magnetic Resonance Imaging Processing. Springer, 2014. doi: 10.1007/978-94-007-7302-8.

Malyarenko D, Fedorov A, Bell L, Prah M, Hectors S, Arlinghaus L, Muzi M, Solaiyappan M, Jacobs M, Fung M, Shukla-Dave A, McManus K, Boss M, Taouli B, Yankeelov TE, Quarles CC, Schmainda K, Chenevert TL, Newitt DC. Toward uniform implementation of parametric map Digital Imaging and Communication in Medicine standard in multisite quantitative diffusion imaging studies. J Med Imaging 2018; 5(1):011006. doi: 10.1117/1.JMI.5.1.011006.

Ombao H, Lindquist M, Thompson W, Aston J (eds.): Handbook of Neuroimaging Data Analysis. CRC Press, 2017, ISBN 978-1-4822-2097-1.

Smith DS, Li X, Arlinghaus LR, Yankeelov TE, Welch EB. DCEMRI.jl: a fast, validated, open source toolkit for dynamic contrast enhanced MRI analysis. PeerJ. 2015; 3: e909.

Sourbron SP, Buckley DL. Tracer kinetic modelling in MRI: estimating perfusion and capillary permeability. Phys Med Biol. 2012; 57(2): R1-R33. doi: 10.1088/0031-9155/57/2/R1.

Umutlu L, Herrmann K (eds.): PET/MR Imaging: Current and Emerging Applications. ISBN 978-3-319-69641-6. Springer, 2018. doi: 10.1007/978-3-319-69641-6.

Wu B, Warnock G, Zaiss M, Lin C, Chen M, Zhou Z, Mu L, Nanz D, Tuura R, Delso G. An overview of CEST MRI for non-MR physicists. EJNMMI Phys. 2016; 3:19. doi: 10.1186/s40658-016-0155-2.

Zöllner FG, Daag M, Sourbron SP, Schad LR. Schoenberg SO, Weisser G. An open source software for analysis of dynamic contrast enhanced magnetic resonance images: UMMPerfusion revisited. BMC Med Imaging 2016; 16:7. doi: 10.1186/s12880-016-0109-0.



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Created at: 2013-05-27
Updated at: 2018-11-10
Written by: Vesa Oikonen