Statistical normalization techniques for magnetic resonance imaging☆☆☆

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer's disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

Normalization Techniques for Statistical Inference from Magnetic Resonance Imaging

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. We further propose simple and robust biologically motivated normalization techniques for multisequence brain imaging that have the same interpretation across acquisitions and satisfy the proposed criteria. We compare the performance of different normalization methods in thousands of images of patients with Alzheimer\u27s Disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers

OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI☆

Magnetic resonance imaging (MRI) can be used to detect lesions in the brains of multiple sclerosis (MS) patients and is essential for diagnosing the disease and monitoring its progression. In practice, lesion load is often quantified by either manual or semi-automated segmentation of MRI, which is time-consuming, costly, and associated with large inter- and intra-observer variability. We propose OASIS is Automated Statistical Inference for Segmentation (OASIS), an automated statistical method for segmenting MS lesions in MRI studies. We use logistic regression models incorporating multiple MRI modalities to estimate voxel-level probabilities of lesion presence. Intensity-normalized T1-weighted, T2-weighted, fluid-attenuated inversion recovery and proton density volumes from 131 MRI studies (98 MS subjects, 33 healthy subjects) with manual lesion segmentations were used to train and validate our model. Within this set, OASIS detected lesions with a partial area under the receiver operating characteristic curve for clinically relevant false positive rates of 1% and below of 0.59% (95% CI; [0.50%, 0.67%]) at the voxel level. An experienced MS neuroradiologist compared these segmentations to those produced by LesionTOADS, an image segmentation software that provides segmentation of both lesions and normal brain structures. For lesions, OASIS out-performed LesionTOADS in 74% (95% CI: [65%, 82%]) of cases for the 98 MS subjects. To further validate the method, we applied OASIS to 169 MRI studies acquired at a separate center. The neuroradiologist again compared the OASIS segmentations to those from LesionTOADS. For lesions, OASIS ranked higher than LesionTOADS in 77% (95% CI: [71%, 83%]) of cases. For a randomly selected subset of 50 of these studies, one additional radiologist and one neurologist also scored the images. Within this set, the neuroradiologist ranked OASIS higher than LesionTOADS in 76% (95% CI: [64%, 88%]) of cases, the neurologist 66% (95% CI: [52%, 78%]) and the radiologist 52% (95% CI: [38%, 66%]). OASIS obtains the estimated probability for each voxel to be part of a lesion by weighting each imaging modality with coefficient weights. These coefficients are explicit, obtained using standard model fitting techniques, and can be reused in other imaging studies. This fully automated method allows sensitive and specific detection of lesion presence and may be rapidly applied to large collections of images

Revisiting Brain Atrophy and Its Relationship to Disability in Multiple Sclerosis

Brain atrophy is a well-accepted imaging biomarker of multiple sclerosis (MS) that partially correlates with both physical disability and cognitive impairment.Based on MRI scans of 60 MS cases and 37 healthy volunteers, we measured the volumes of white matter (WM) lesions, cortical gray matter (GM), cerebral WM, caudate nucleus, putamen, thalamus, ventricles, and brainstem using a validated and completely automated segmentation method. We correlated these volumes with the Expanded Disability Status Scale (EDSS), MS Severity Scale (MSSS), MS Functional Composite (MSFC), and quantitative measures of ankle strength and toe sensation. Normalized volumes of both cortical and subcortical GM structures were abnormally low in the MS group, whereas no abnormality was found in the volume of the cerebral WM. High physical disability was associated with low cerebral WM, thalamus, and brainstem volumes (partial correlation coefficients ~0.3-0.4) but not with low cortical GM volume. Thalamus volumes were inversely correlated with lesion load (r = -0.36, p<0.005).The GM is atrophic in MS. Although lower WM volume is associated with greater disability, as might be expected, WM volume was on average in the normal range. This paradoxical result might be explained by the presence of coexisting pathological processes, such as tissue damage and repair, that cause both atrophy and hypertrophy and that underlie the observed disability

Partial correlations of normalized brain structure with one another in MS cases.

<p>Results are adjusted for linear effects of age and sex. The <i>p</i>-values are shown in parentheses, and results with <i>p</i><0.01 are shown in boldface.</p

Normalized brain structure volumes.

<p>Results with <i>p</i><0.01 are shown in bold face. <i>Abbreviations</i>. <i>HV</i>, healthy volunteers. <i>MS</i>, multiple sclerosis. <i>SD</i>, standard deviation.</p

Cohort demographic data.

a<p> <i>median (range),</i></p>b<p> <i>mean z-score (standard deviation),</i></p>c<p> <i>median (interquartile range).</i></p>*<p>Vibration units are amplitudes, proportional to the square of the applied voltage.</p><p><i>Abbreviations</i>. <i>RRMS</i>, relapsing remitting multiple sclerosis. <i>SPMS</i>, secondary progressive multiple sclerosis. <i>PPMS</i>, primary progressive multiple sclerosis. <i>CIS</i>, clinically isolated syndrome. <i>EDSS</i>, Expanded Disability Status Scale. <i>MSSS</i>, Multiple Sclerosis Severity Score. <i>MSFC</i>, Multiple Sclerosis Functional Composite. <i>PASAT-3</i>, Paced Auditory Serial-Addition Task, 3 second version.</p

Partial correlations of impairment measures with normalized structure volumes.

<p>Results are adjusted for linear effects of age and sex. The <i>p</i>-values are shown in parentheses, and results with <i>p</i><0.01 are shown in boldface. <i>Abbreviations</i>. <i>EDSS</i>, Expanded Disability Status Scale. <i>MSSS</i>, MS Severity Score. <i>MSFC</i>, Multiple Sclerosis Functional Composite <i>z</i>-score. <i>PASAT-3</i>, Paced Auditory Serial Addition Test, 3-second version.</p

Partial correlations of impairment measures with normalized structure volumes computed by FSL FAST segmentation tool.

<p>Results are adjusted for linear effects of age and sex. The <i>p</i>-values are shown in parentheses, and results with <i>p</i><0.01 are shown in boldface. <i>Abbreviations</i>. <i>EDSS</i>, Expanded Disability Status Scale. <i>MSSS</i>, MS Severity Score. <i>MSFC</i>, Multiple Sclerosis Functional Composite. <i>PASAT-3</i>, Paced Auditory Serial Addition Test, 3-second version.</p