A semi-automated measuring system of brain diffusion and perfusion magnetic resonance imaging abnormalities in patients with multiple sclerosis based on the integration of coregistration and tissue segmentation procedures

BackgroundDiffusion-weighted imaging (DWI) and perfusion-weighted imaging (PWI) abnormalities in patients with multiple sclerosis (MS) are currently measured by a complex combination of separate procedures. Therefore, the purpose of this study was to provide a reliable method for reducing analysis complexity and obtaining reproducible results.MethodsWe implemented a semi-automated measuring system in which different well-known software components for magnetic resonance imaging (MRI) analysis are integrated to obtain reliable measurements of DWI and PWI disturbances in MS.ResultsWe generated the Diffusion/Perfusion Project (DPP) Suite, in which a series of external software programs are managed and harmonically and hierarchically incorporated by in-house developed Matlab software to perform the following processes: 1) image pre-processing, including imaging data anonymization and conversion from DICOM to Nifti format; 2) co-registration of 2D and 3D non-enhanced and Gd-enhanced T1-weighted images in fluid-attenuated inversion recovery (FLAIR) space; 3) lesion segmentation and classification, in which FLAIR lesions are at first segmented and then categorized according to their presumed evolution; 4) co-registration of segmented FLAIR lesion in T1 space to obtain the FLAIR lesion mask in the T1 space; 5) normal appearing tissue segmentation, in which T1 lesion mask is used to segment basal ganglia/thalami, normal appearing grey matter (NAGM) and normal appearing white matter (NAWM); 6) DWI and PWI map generation; 7) co-registration of basal ganglia/thalami, NAGM, NAWM, DWI and PWI maps in previously segmented FLAIR space; 8) data analysis. All these steps are automatic, except for lesion segmentation and classification.ConclusionWe developed a promising method to limit misclassifications and user errors, providing clinical researchers with a practical and reproducible tool to measure DWI and PWI changes in MS.

[1]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[2]  J Hennig,et al.  RARE imaging: A fast imaging method for clinical MR , 1986, Magnetic resonance in medicine.

[3]  N. Richert,et al.  Characterizing contrast-enhancing and re-enhancing lesions in multiple sclerosis , 2012, Neurology.

[4]  Karl J. Friston,et al.  Image registration using a symmetric prior—in three dimensions , 1999, Human brain mapping.

[5]  Ponnada A Narayana,et al.  Segmentation of gadolinium‐enhanced lesions on MRI in multiple sclerosis , 2007, Journal of magnetic resonance imaging : JMRI.

[6]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[7]  Stephen M. Smith,et al.  A Bayesian model of shape and appearance for subcortical brain segmentation , 2011, NeuroImage.

[8]  Y. Ge.  Multiple sclerosis: the role of MR imaging. , 2006, AJNR. American journal of neuroradiology.

[9]  Stephen M. Smith,et al.  Accurate, Robust, and Automated Longitudinal and Cross-Sectional Brain Change Analysis , 2002, NeuroImage.

[10]  M. Rovaris,et al.  Grey matter damage in progressive multiple sclerosis versus amyotrophic lateral sclerosis: a voxel-based morphometry MRI study , 2015, Neurological Sciences.

[11]  F. Fazekas,et al.  Recommendations to improve imaging and analysis of brain lesion load and atrophy in longitudinal studies of multiple sclerosis , 2012, Journal of Neurology.

[12]  F. Salvi,et al.  Hypoperfusion of brain parenchyma is associated with the severity of chronic cerebrospinal venous insufficiency in patients with multiple sclerosis: a cross-sectional preliminary report , 2011, BMC medicine.

[13]  B. Scheithauer,et al.  Diffusion-weighted imaging characteristics of biopsy-proven demyelinating brain lesions , 2012, Neurology.

[14]  G. Johnson,et al.  Microvascular Abnormality in Relapsing-remitting Multiple Sclerosis: Perfusion Mr Imaging Findings in Normal-appearing White Matter , 2003 .

[15]  Francesca Baglio,et al.  Corticospinal tract integrity is related to primary motor cortex thinning in relapsing–remitting multiple sclerosis , 2015, Multiple sclerosis.

[16]  F. Jacques.  Defining the clinical course of multiple sclerosis: The 2013 revisions , 2015, Neurology.

[17]  D. Centonze,et al.  A diffusion longitudinal MR imaging study in normal-appearing white matter in untreated relapsing-remitting multiple sclerosis. , 2007, AJNR. American journal of neuroradiology.

[18]  Yukio Miki,et al.  [MR imaging of multiple sclerosis]. , 2015, Nihon rinsho. Japanese journal of clinical medicine.

[19]  A J Thompson,et al.  Multiple sclerosis lesion detection in the brain: A comparison of fast fluid-attenuated inversion recovery and conventional T2-weighted dual spin echo , 1997, Neurology.

[20]  G J Barker,et al.  The pathogenesis of lesions and normal-appearing white matter changes in multiple sclerosis: a serial diffusion MRI study. , 2000, Brain : a journal of neurology.

[21]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[22]  David H. Miller,et al.  Cerebral Arterial Bolus Arrival Time is Prolonged in Multiple Sclerosis and Associated with Disability , 2014, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[23]  Glyn Johnson,et al.  Pattern of hemodynamic impairment in multiple sclerosis: Dynamic susceptibility contrast perfusion MR imaging at 3.0 T , 2006, NeuroImage.

[24]  A. Gass,et al.  Reduced Diffusion in a Subset of Acute MS Lesions: A Serial Multiparametric MRI Study , 2012, American Journal of Neuroradiology.

[25]  M Cercignani,et al.  A quantitative study of water diffusion in multiple sclerosis lesions and normal-appearing white matter using echo-planar imaging. , 2000, Archives of neurology.

[26]  Robert Zivadinov,et al.  Quantitative diffusion weighted imaging measures in patients with multiple sclerosis , 2007, NeuroImage.

[27]  Xavier Lladó,et al.  Automated detection of multiple sclerosis lesions in serial brain MRI , 2012, Neuroradiology.

[28]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[29]  J. E. Tanner,et al.  Spin diffusion measurements : spin echoes in the presence of a time-dependent field gradient , 1965 .

[30]  Frauke Zipp,et al.  Changes in cerebral perfusion precede plaque formation in multiple sclerosis: a longitudinal perfusion MRI study. , 2004, Brain : a journal of neurology.

[31]  K. Balashov,et al.  Acute demyelinating lesions with restricted diffusion in multiple sclerosis , 2012, Multiple sclerosis.

[32]  Ponnada A. Narayana,et al.  Segmentation and quantification of black holes in multiple sclerosis , 2006, NeuroImage.

[33]  Glyn Johnson,et al.  Dynamic susceptibility contrast perfusion MR imaging of multiple sclerosis lesions: characterizing hemodynamic impairment and inflammatory activity. , 2005, AJNR. American journal of neuroradiology.

[34]  Jinho Lim,et al.  The Digital Imaging and Communications in Medicine (DICOM): Description, Structure and Applications , 2006 .

[35]  Robert Zivadinov,et al.  The place of conventional MRI and newly emerging MRI techniques in monitoring different aspects of treatment outcome , 2008, Journal of Neurology.

[36]  Rohit Bakshi,et al.  A semiautomated measure of whole-brain atrophy in multiple sclerosis , 2003, Journal of the Neurological Sciences.

[37]  Glyn Johnson,et al.  Deep gray matter perfusion in multiple sclerosis: dynamic susceptibility contrast perfusion magnetic resonance imaging at 3 T. , 2007, Archives of neurology.

[38]  A. Kouzani,et al.  Segmentation of multiple sclerosis lesions in MR images: a review , 2011, Neuroradiology.

[39]  B. Rosen,et al.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis , 1996, Magnetic resonance in medicine.

[40]  Michele Larobina,et al.  Medical Image File Formats , 2014, Journal of Digital Imaging.

[41]  Alessandra Bertoldo,et al.  Heterogeneity of Cortical Lesions in Multiple Sclerosis: An MRI Perfusion Study , 2013, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[42]  N. Abdelrahman,et al.  Use of perfusion- and diffusion-weighted imaging in differential diagnosis of acute and chronic ischemic stroke and multiple sclerosis , 2008, Neurological research.

[43]  K. Hasan,et al.  Hypoperfusion and T1-hypointense lesions in white matter in multiple sclerosis , 2014, Multiple sclerosis.