@article {786, title = {Statistical normalization techniques for magnetic resonance imaging.}, journal = {Neuroimage Clin}, volume = {6}, year = {2014}, month = {2014}, pages = {9-19}, abstract = {

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{\textquoteright}s disease, hundreds of patients with multiple sclerosis, and hundreds of healthy subjects obtained in several different studies at dozens of imaging centers.

}, issn = {2213-1582}, doi = {10.1016/j.nicl.2014.08.008}, author = {Shinohara, Russell T and Sweeney, Elizabeth M and Goldsmith, Jeff and Shiee, Navid and Mateen, Farrah J and Calabresi, Peter A and Jarso, Samson and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M} } @article {802, title = {OASIS is Automated Statistical Inference for Segmentation, with applications to multiple sclerosis lesion segmentation in MRI.}, journal = {Neuroimage Clin}, volume = {2}, year = {2013}, month = {2013}, pages = {402-13}, abstract = {

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.

}, issn = {2213-1582}, doi = {10.1016/j.nicl.2013.03.002}, author = {Sweeney, Elizabeth M and Shinohara, Russell T and Shiee, Navid and Mateen, Farrah J and Chudgar, Avni A and Cuzzocreo, Jennifer L and Calabresi, Peter A and Pham, Dzung L and Reich, Daniel S and Crainiceanu, Ciprian M} } @article {811, title = {Relationships between retinal axonal and neuronal measures and global central nervous system pathology in multiple sclerosis.}, journal = {JAMA Neurol}, volume = {70}, year = {2013}, month = {2013 Jan}, pages = {34-43}, abstract = {

OBJECTIVE: To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS).

DESIGN: Cross-sectional study.

SETTING: Johns Hopkins University, Baltimore, Maryland.

PARTICIPANTS: A total of 84 patients with MS and 24 healthy control subjects.

MAIN OUTCOME MEASURES: High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes.

RESULTS: Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer+inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P=.01 and P=.04, respectively) and caudate (P=.04 and P=.03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P=.007) and inversely associated with normal-appearing white matter volume (P=.005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.

}, keywords = {Adult, Axons, Caudate Nucleus, Central Nervous System, Cerebral Cortex, Cross-Sectional Studies, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Multiple Sclerosis, Retina, Retinal Neurons, Tomography, Optical Coherence}, issn = {2168-6157}, doi = {10.1001/jamaneurol.2013.573}, author = {Saidha, Shiv and Sotirchos, Elias S and Oh, Jiwon and Syc, Stephanie B and Seigo, Michaela A and Shiee, Navid and Eckstein, Chistopher and Durbin, Mary K and Oakley, Jonathan D and Meyer, Scott A and Frohman, Teresa C and Newsome, Scott and Ratchford, John N and Balcer, Laura J and Pham, Dzung L and Crainiceanu, Ciprian M and Frohman, Elliot M and Reich, Daniel S and Calabresi, Peter A} } @article {710, title = {Relationships Between Retinal Axonal and Neuronal Measures and Global Central Nervous System Pathology in Multiple Sclerosis.}, journal = {Arch Neurol}, year = {2012}, month = {2012 Oct 1}, pages = {1-10}, abstract = {

OBJECTIVE To determine the relationships between conventional and segmentation-derived optical coherence tomography (OCT) retinal layer thickness measures with intracranial volume (a surrogate of head size) and brain substructure volumes in multiple sclerosis (MS). DESIGN Cross-sectional study. SETTING Johns Hopkins University, Baltimore, Maryland. PARTICIPANTS A total of 84 patients with MS and 24 healthy control subjects. MAIN OUTCOME MEASURES High-definition spectral-domain OCT conventional and automated segmentation-derived discrete retinal layer thicknesses and 3-T magnetic resonance imaging brain substructure volumes. RESULTS Peripapillary retinal nerve fiber layer as well as composite ganglion cell layer\ +\ inner plexiform layer thicknesses in the eyes of patients with MS without a history of optic neuritis were associated with cortical gray matter (P\ =\ .01 and P\ =\ .04, respectively) and caudate (P\ =\ .04 and P\ =\ .03, respectively) volumes. Inner nuclear layer thickness, also in eyes without a history of optic neuritis, was associated with fluid-attenuated inversion recovery lesion volume (P\ =\ .007) and inversely associated with normal-appearing white matter volume (P\ =\ .005) in relapsing-remitting MS. As intracranial volume was found to be related with several of the OCT measures in patients with MS and healthy control subjects and is already known to be associated with brain substructure volumes, all OCT-brain substructure relationships were adjusted for intracranial volume. CONCLUSIONS Retinal measures reflect global central nervous system pathology in multiple sclerosis, with thicknesses of discrete retinal layers each appearing to be associated with distinct central nervous system processes. Moreover, OCT measures appear to correlate with intracranial volume in patients with MS and healthy control subjects, an important unexpected factor unaccounted for in prior studies examining the relationships between peripapillary retinal nerve fiber layer thickness and brain substructure volumes.

}, issn = {1538-3687}, doi = {10.1001/archneurol.2013.573}, author = {Saidha, Shiv and Sotirchos, Elias S and Oh, Jiwon and Syc, Stephanie B and Seigo, Michaela A and Shiee, Navid and Eckstein, Chistopher and Durbin, Mary K and Oakley, Jonathan D and Meyer, Scott A and Frohman, Teresa C and Newsome, Scott and Ratchford, John N and Balcer, Laura J and Pham, Dzung L and Crainiceanu, Ciprian M and Frohman, Elliot M and Reich, Daniel S and Calabresi, Peter A} }