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Image Coregistration: Quantitative Processing Framework for the Assessment of Brain Lesions

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Abstract

The quantitative, multiparametric assessment of brain lesions requires coregistering different parameters derived from MRI sequences. This will be followed by analysis of the voxel values of the ROI within the sequences and calculated parametric maps, and deriving multiparametric models to classify imaging data. There is a need for an intuitive, automated quantitative processing framework that is generalized and adaptable to different clinical and research questions. As such flexible frameworks have not been previously described, we proceeded to construct a quantitative post-processing framework with commonly available software components. Matlab was chosen as the programming/integration environment, and SPM was chosen as the coregistration component. Matlab routines were created to extract and concatenate the coregistration transforms, take the coregistered MRI sequences as inputs to the process, allow specification of the ROI, and store the voxel values to the database for statistical analysis. The functionality of the framework was validated using brain tumor MRI cases. The implementation of this quantitative post-processing framework enables intuitive creation of multiple parameters for each voxel, facilitating near real-time in-depth voxel-wise analysis. Our initial empirical evaluation of the framework is an increased usage of analysis requiring post-processing and increased number of simultaneous research activities by clinicians and researchers with non-technical backgrounds. We show that common software components can be utilized to implement an intuitive real-time quantitative post-processing framework, resulting in improved scalability and increased adoption of post-processing needed to answer important diagnostic questions.

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Abbreviations

ADC:

Apparent diffusion coefficient

DCE:

Dynamic contrast-enhanced

DICOM:

Digital Imaging and Communications in Medicine

DSC:

Dynamic susceptibility contrast

DTI:

Diffusion tensor imaging

DWI:

Diffusion-weighted imaging

FA:

Flip angle

fMRI:

Functional magnetic resonance imaging

GUI:

Graphic user interface

iCAD:

Interactive computer-assisted diagnosis

K ep :

Reverse transfer constant

K trans :

Forward transfer constant

MD:

Mean diffusivity

MI:

Mutual information

NIfTI:

Neuroimaging Informatics Technology Initiative

NMI:

Normalized mutual information

QiAUC:

Quick initial area under curve

ROI:

Region of interest

SPGR:

Spoiled gradient recalled

SPM:

Statistical parametric mapping

V e :

Volume of extracellular extravascular space

V p :

Plasma blood volume

References

  1. Klein A, Tourville J: 101 labeled brain images and a consistent human cortical labeling protocol. Front Neurosci 6:171, 2012

    Article  PubMed Central  PubMed  Google Scholar 

  2. Thirion B, et al: Dealing with the shortcomings of spatial normalization: multi-subject parcellation of fMRI datasets. Hum Brain Mapp 27(8):678–693, 2006

    Article  PubMed  Google Scholar 

  3. Van Hecke W, et al: On the construction of an inter-subject diffusion tensor magnetic resonance atlas of the healthy human brain. Neuroimage 43(1):69–80, 2008

    Article  PubMed  Google Scholar 

  4. Thompson PM, et al: Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain. Hum Brain Mapp 9(2):81–92, 2000

    Article  CAS  PubMed  Google Scholar 

  5. Yelnik J, et al: Localization of stimulating electrodes in patients with Parkinson disease by using a three-dimensional atlas-magnetic resonance imaging coregistration method. J Neurosurg 99(1):89–99, 2003

    Article  PubMed  Google Scholar 

  6. Rorden C, et al: Age-specific CT and MRI templates for spatial normalization. Neuroimage 61(4):957–965, 2012

    Article  PubMed Central  PubMed  Google Scholar 

  7. Yoon HJ, et al: Correlated regions of cerebral blood flow with clinical parameters in Parkinson’s disease; comparison using ‘Anatomy’ and ‘Talairach Daemon’ software. Ann Nucl Med 26(2):164–174, 2012

    Article  PubMed  Google Scholar 

  8. Wu M, et al: Quantitative comparison of AIR, SPM, and the fully deformable model for atlas-based segmentation of functional and structural MR images. Hum Brain Mapp 27(9):747–754, 2006

    Article  PubMed Central  PubMed  Google Scholar 

  9. Heiss WD, Raab P, Lanfermann H: Multimodality assessment of brain tumors and tumor recurrence. J Nucl Med 52(10):1585–1600, 2011

    Article  CAS  PubMed  Google Scholar 

  10. Pietrzyk U, Herzog H: Does PET/MR in human brain imaging provide optimal co-registration? A critical reflection. MAGMA 26(1):137–147, 2013

    Article  CAS  PubMed  Google Scholar 

  11. Price JC: Molecular brain imaging in the multimodality era. J Cereb Blood Flow Metab 32(7):1377–1392, 2012

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. Sauter AW, et al: Combined PET/MRI: one step further in multimodality imaging. Trends Mol Med 16(11):508–515, 2010

    Article  PubMed  Google Scholar 

  13. Slomka PJ, Baum RP: Multimodality image registration with software: state-of-the-art. Eur J Nucl Med Mol Imaging 36(Suppl 1):S44–S55, 2009

    Article  PubMed  Google Scholar 

  14. Townsend DW: Multimodality imaging of structure and function. Phys Med Biol 53(4):R1–R39, 2008

    Article  CAS  PubMed  Google Scholar 

  15. Cizek J, et al: Fast and robust registration of PET and MR images of human brain. Neuroimage 22(1):434–442, 2004

    Article  PubMed  Google Scholar 

  16. Andersson JL, Sundin A, Valind S: A method for coregistration of PET and MR brain images. J Nucl Med 36(7):1307–1315, 1995

    CAS  PubMed  Google Scholar 

  17. Kiebel SJ, Friston KJ: Statistical parametric mapping for event-related potentials (II): a hierarchical temporal model. Neuroimage 22(2):503–520, 2004

    Article  PubMed  Google Scholar 

  18. Montgomery AJ, et al: Correction of head movement on PET studies: comparison of methods. J Nucl Med 47(12):1936–1944, 2006

    PubMed  Google Scholar 

  19. Quarantelli M, et al: Integrated software for the analysis of brain PET/SPECT studies with partial-volume-effect correction. J Nucl Med 45(2):192–201, 2004

    PubMed  Google Scholar 

  20. Woods RP, Mazziotta JC, Cherry SR: MRI-PET registration with automated algorithm. J Comput Assist Tomogr 17(4):536–546, 1993

    Article  CAS  PubMed  Google Scholar 

  21. Gutierrez D, et al: Anatomically guided voxel-based partial volume effect correction in brain PET: impact of MRI segmentation. Comput Med Imaging Graph 36(8):610–619, 2012

    Article  PubMed  Google Scholar 

  22. Eklund A, et al: Does parametric fMRI analysis with SPM yield valid results? An empirical study of 1484 rest datasets. Neuroimage 61(3):565–578, 2012

    Article  PubMed  Google Scholar 

  23. Dey D, et al: Automatic three-dimensional multimodality registration using radionuclide transmission CT attenuation maps: a phantom study. J Nucl Med 40(3):448–455, 1999

    CAS  PubMed  Google Scholar 

  24. Grosu AL, et al: Validation of a method for automatic image fusion (BrainLAB System) of CT data and 11C-methionine-PET data for stereotactic radiotherapy using a LINAC: first clinical experience. Int J Radiat Oncol Biol Phys 56(5):1450–1463, 2003

    Article  PubMed  Google Scholar 

  25. Thurfjell L, et al: Improved efficiency for MRI-SPET registration based on mutual information. Eur J Nucl Med 27(7):847–856, 2000

    Article  CAS  PubMed  Google Scholar 

  26. Yokoi T, et al: Accuracy and reproducibility of co-registration techniques based on mutual information and normalized mutual information for MRI and SPECT brain images. Ann Nucl Med 18(8):659–667, 2004

    Article  PubMed  Google Scholar 

  27. Bar-Shalom R, et al: Clinical performance of PET/CT in evaluation of cancer: additional value for diagnostic imaging and patient management. J Nucl Med 44(8):1200–1209, 2003

    PubMed  Google Scholar 

  28. Ellingson BM, et al: Nonlinear registration of diffusion-weighted images improves clinical sensitivity of functional diffusion maps in recurrent glioblastoma treated with bevacizumab. Magn Reson Med 67(1):237–245, 2012

    Article  CAS  PubMed  Google Scholar 

  29. Cohen DS, et al: Effects of coregistration of MR to CT images on MR stereotactic accuracy. J Neurosurg 82(5):772–779, 1995

    Article  CAS  PubMed  Google Scholar 

  30. Daisne JF, et al: Evaluation of a multimodality image (CT, MRI and PET) coregistration procedure on phantom and head and neck cancer patients: accuracy, reproducibility and consistency. Radiother Oncol 69(3):237–245, 2003

    Article  PubMed  Google Scholar 

  31. Grosu AL, et al: An interindividual comparison of O-(2-[18F]fluoroethyl)-L-tyrosine (FET)- and L-[methyl-11C]methionine (MET)-PET in patients with brain gliomas and metastases. Int J Radiat Oncol Biol Phys 81(4):1049–1058, 2011

    Article  CAS  PubMed  Google Scholar 

  32. Thiel A, et al: Enhanced accuracy in differential diagnosis of radiation necrosis by positron emission tomography–magnetic resonance imaging coregistration: technical case report. Neurosurgery 46(1):232–234, 2000

    Article  CAS  PubMed  Google Scholar 

  33. Ashburner J: Computational anatomy with the SPM software. Magn Reson Imaging 27(8):1163–1174, 2009

    Article  PubMed  Google Scholar 

  34. Sui J, et al: A review of multivariate methods for multimodal fusion of brain imaging data. J Neurosci Methods 204(1):68–81, 2012

    Article  PubMed Central  PubMed  Google Scholar 

  35. Friston K: Introduction: experimental design and statistical parametric mapping. Frackowiak RSJ Editor. in Human brain function. Amsterdam ; Boston: Elsevier Academic Press, 2004 p. xvi, 1144 p

  36. Wellcome Trust Centre for Neuroimaging, U.C.L., U.K. SPM8. 4/1/2013; Available from: http://www.fil.ion.ucl.ac.uk/spm/software/spm8/

  37. Grootoonk S, et al: Characterization and correction of interpolation effects in the realignment of fMRI time series. Neuroimage 11(1):49–57, 2000

    Article  CAS  PubMed  Google Scholar 

  38. Studholme C, Hill DLG, Hawkes DJ: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recog 32(1):71–86, 1999

    Article  Google Scholar 

  39. Greve DN, Fischl B: Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48(1):63–72, 2009

    Article  PubMed Central  PubMed  Google Scholar 

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Acknowledgments

Grant support: NIH R21EB013456 and NIH UL1RR031986-01.

Author information

Authors and Affiliations

  1. Department of Radiology, University of Southern California, 1500 San Pablo Street, Second Floor Imaging, Los Angeles, CA, 90033, USA

    Hannu Huhdanpaa, Darryl H. Hwang, Gregory G. Gasparian, Michael T. Booker, Yong Cen, Alexander Lerner, Orest B. Boyko, John L. Go, Paul E. Kim, Anandh Rajamohan, Meng Law & Mark S. Shiroishi

Authors
  1. Hannu Huhdanpaa
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  2. Darryl H. Hwang
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  3. Gregory G. Gasparian
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  4. Michael T. Booker
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  5. Yong Cen
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  6. Alexander Lerner
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  7. Orest B. Boyko
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  8. John L. Go
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  9. Paul E. Kim
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  10. Anandh Rajamohan
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  11. Meng Law
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  12. Mark S. Shiroishi
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Corresponding author

Correspondence to Hannu Huhdanpaa.

Additional information

Presentation: ASNR 2013 May.

Appendices

Learning Objectives

  1. 1)

    Learn about publicly available software components for coregistration and for constructing a quantitative post-processing framework.

  2. 2)

    Understand the fundamentals of coregistration.

  3. 3)

    Understand where coregistration fits in the overall quantitative post-processing framework.

  4. 4)

    Understand the fundamentals of basic image file formats such as DICOM and NIfTI, and when and where these different formats are to be used.

CME Questions

  1. 1)

    Describe what MRIcron is and how it is used for post-processing.

    Choices:

    1. A.

      Tool to coregister images.

    2. B.

      BTool to calculate DCE parametric maps.

    3. C.

      Tool to perform partially automated drawings, such as draw the ROIs

    4. D.

      Tool to perform the statistical analysis to create a logistical regression model

    5. E.

      Tool to convert from DICOM to NIfTI

    [C]. Explanation: *It is a partially automated drawing tool that can be used to mark the region of interest in the coregistered source image. It is a cross-platform NIfTI format image viewer which supports single slice ROIs as well as volumes of interest.

  2. 2)

    Why are NIfTI format files used for image post-processing?

    1. A.

      NIfTI format is used by many PACS systems natively.

    2. B.

      NIfTI format is faster to process than DICOM format.

    3. C.

      NIfTI format is widely accepted and reads header information correctly.

    4. D.

      NIfTI format is more familiar to radiologists than DICOM.

    5. E.

      NIfTI format will replace DICOM format in the future.

    [C]. Explanation: *Using NIfTI files allows for the use of many publicly available neuroimaging toolkits and components used in the post-processing framework (such as SPM, ImageJ). Also, it is generally recommended because of its wide acceptability and uniformity with correctly reading header information.

  3. 3)

    What sequence should be chosen as the reference sequence when coregistering?

    1. A.

      Sequence in the same orientation as the image used to draw the ROIs

    2. B.

      Sequence that best demonstrates the lesion.

    3. C.

      b0 sequence from EPI

    4. D.

      T1 DCE

    5. E.

      Sequence with the highest resolution.

    [E] Explanation: *The highest resolution sequence should be chosen. In our case, coronal SPGR was the chosen sequence.

  4. 4)

    Some sequences are always aligned by default, such as the “dependent” sequence ADC being aligned to the “parent” sequence, b0 EPI, from DTI. Which sequences needs to be coregistered in this case?

    1. A.

      “Parent” sequence to the reference sequence

    2. B.

      “Dependent” sequence to the “parent”, and then “parent” to the reference sequence

    3. C.

      “Parent” and “dependent” sequence to the reference sequence

    4. D.

      “Dependent” sequence to the “parent” sequence

    5. E.

      “Dependent” sequence to the reference sequence

    [A] Answer: *Only the “parent” sequence is coregistered to the reference sequence using SPM coregistration. The coregistration transformation matrix can be automatically copied by SPM to all the “dependent” other sequences sharing the same original alignment as the “parent”.

  5. 5)

    Explain what image coregistration is and why it is important.

    1. A.

      Registration in PACS of each sequence in both the NIfTI and DICOM format to ensure both formats are available for quantitative analysis.

    2. B.

      Conversion of each sequence from the DICOM to NIfTI, to enable use of commonly available neuroimaging toolkits.

    3. C.

      Geometric alignment to ensure corresponding pixels may be fused, to enable quantitative analysis.

    4. D.

      Extraction of the pixels of interest from the pertinent sequences using the ROI as the index, to enable further quantitative analysis.

    5. E.

      Creation of a registry of voxels of interest for quantitative analysis.

    [C] Answer: *It is the process of geometrically aligning two or more images (sequences) so that corresponding pixels representing the same objects may be fused. It is necessary for any quantitative imaging analysis so that different voxel values can be indexed and accessed identically.

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Huhdanpaa, H., Hwang, D.H., Gasparian, G.G. et al. Image Coregistration: Quantitative Processing Framework for the Assessment of Brain Lesions. J Digit Imaging 27, 369–379 (2014). https://doi.org/10.1007/s10278-013-9655-y

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