Peter A. Calabresi

YNICL Journal 2025 Journal Article

Association of iron deposition in MS lesion with remyelination capacity using susceptibility source separation MRI

• Hyeong-Geol Shin
• Woojun Kim
• Jung Hwan Lee
• Hyun-soo Lee
• Yoonho Nam
• Jiwoong Kim
• Xu Li
• Peter C.M. van Zijl

OBJECTIVES: signals within MS lesions using χ-separation and evaluate the association between lesional iron and remyelination capability. METHODS: signals. RESULTS: myelin signals (P < 0.001). After adjustment, lesions with early HPS demonstrated an annual loss in myelin signal (-1.94 ppb/year), whereas those without early HPS exhibited annual recovery (+0.66 ppb/year). Participants with confirmed disability improvement (CDI) had fewer HPS-positive lesions at baseline than those without CDI (P < 0.001). CONCLUSION: The presence of HPS is associated with impaired remyelination capacity and a lack of disease improvement in pwMS. Identifying HPS may help demarcate lesions more amenable to myelin repair therapies.

YNIMG Journal 2020 Journal Article

Monitoring diffuse injury during disease progression in experimental autoimmune encephalomyelitis with on resonance variable delay multiple pulse (onVDMP) CEST MRI

• Aline M. Thomas
• Jiadi Xu
• Peter A. Calabresi
• Peter C.M. van Zijl
• Jeff W.M. Bulte

Multiple sclerosis (MS) is an autoimmune disorder that targets myelin proteins and results in extensive damage in the central nervous system in the form of focal lesions as well as diffuse molecular changes. Lesions are currently detected using T1-weighted, T2-weighted, and gadolinium-enhanced magnetic resonance imaging (MRI); however, monitoring such lesions has been shown to be a poor predictor of disease progression. Chemical exchange saturation transfer (CEST) MRI is sensitive to many of the biomolecules in the central nervous system altered in MS that cannot be detected using conventional MRI. We monitored disease progression in an experimental autoimmune encephalomyelitis (EAE) model of MS using on resonance variable delay multiple pulse (onVDMP) CEST MRI. Alterations in onVDMP signal were observed in regions responsible for hindlimb function throughout the central nervous system. Histological analysis revealed glial activation in areas highlighted in onVDMP CEST MRI. onVDMP signal changes in the 3rd ventricle preceded paralysis onset that could not be observed with conventional MRI techniques. Hence, the onVDMP CEST MRI signal has potential as a novel imaging biomarker and predictor of disease progression in MS.

YNICL Journal 2020 Journal Article

TAPAS: A Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis

• Alessandra M. Valcarcel
• John Muschelli
• Dzung L. Pham
• Melissa Lynne Martin
• Paul Yushkevich
• Rachel Brandstadter
• Kristina R. Patterson
• Matthew K. Schindler

Total brain white matter lesion (WML) volume is the most widely established magnetic resonance imaging (MRI) outcome measure in studies of multiple sclerosis (MS). To estimate WML volume, there are a number of automatic segmentation methods available, yet manual delineation remains the gold standard approach. Automatic approaches often yield a probability map to which a threshold is applied to create lesion segmentation masks. Unfortunately, few approaches systematically determine the threshold employed; many methods use a manually selected threshold, thus introducing human error and bias into the automated procedure. In this study, we propose and validate an automatic thresholding algorithm, Thresholding Approach for Probability Map Automatic Segmentation in Multiple Sclerosis (TAPAS), to obtain subject-specific threshold estimates for probability map automatic segmentation of T2-weighted (T2) hyperintense WMLs. Using multimodal MRI, the proposed method applies an automatic segmentation algorithm to obtain probability maps. We obtain the true subject-specific threshold that maximizes the Sørensen-Dice similarity coefficient (DSC). Then the subject-specific thresholds are modeled on a naive estimate of volume using a generalized additive model. Applying this model, we predict a subject-specific threshold in data not used for training. We ran a Monte Carlo-resampled split-sample cross-validation (100 validation sets) using two data sets: the first obtained from the Johns Hopkins Hospital (JHH) on a Philips 3 Tesla (3T) scanner (n = 94) and a second collected at the Brigham and Women's Hospital (BWH) using a Siemens 3T scanner (n = 40). By means of the proposed automated technique, in the JHH data we found an average reduction in subject-level absolute error of 0.1 mL per one mL increase in manual volume. Using Bland-Altman analysis, we found that volumetric bias associated with group-level thresholding was mitigated when applying TAPAS. The BWH data showed similar absolute error estimates using group-level thresholding or TAPAS likely since Bland-Altman analyses indicated no systematic biases associated with group or TAPAS volume estimates. The current study presents the first validated fully automated method for subject-specific threshold prediction to segment brain lesions.

YNIMG Journal 2017 Journal Article

Longitudinal multiple sclerosis lesion segmentation: Resource and challenge

• Aaron Carass
• Snehashis Roy
• Amod Jog
• Jennifer L. Cuzzocreo
• Elizabeth Magrath
• Adrian Gherman
• Julia Button
• James Nguyen

In conjunction with the ISBI 2015 conference, we organized a longitudinal lesion segmentation challenge providing training and test data to registered participants. The training data consisted of five subjects with a mean of 4. 4 time-points, and test data of fourteen subjects with a mean of 4. 4 time-points. All 82 data sets had the white matter lesions associated with multiple sclerosis delineated by two human expert raters. Eleven teams submitted results using state-of-the-art lesion segmentation algorithms to the challenge, with ten teams presenting their results at the conference. We present a quantitative evaluation comparing the consistency of the two raters as well as exploring the performance of the eleven submitted results in addition to three other lesion segmentation algorithms. The challenge presented three unique opportunities: (1) the sharing of a rich data set; (2) collaboration and comparison of the various avenues of research being pursued in the community; and (3) a review and refinement of the evaluation metrics currently in use. We report on the performance of the challenge participants, as well as the construction and evaluation of a consensus delineation. The image data and manual delineations will continue to be available for download, through an evaluation website 2 2 The Challenge Evaluation Website is: http: //smart-stats-tools. org/lesion-challenge-2015 as a resource for future researchers in the area. This data resource provides a platform to compare existing methods in a fair and consistent manner to each other and multiple manual raters.

YNIMG Journal 2017 Journal Article

Thalamus segmentation using multi-modal feature classification: Validation and pilot study of an age-matched cohort

• Jeffrey Glaister
• Aaron Carass
• Tziona NessAiver
• Joshua V. Stough
• shiv saidha
• Peter A. Calabresi
• Jerry L. Prince

Automatic segmentation of the thalamus can be used to measure differences and track changes in thalamic volume that may occur due to disease, injury or normal aging. An automatic thalamus segmentation algorithm incorporating features from diffusion tensor imaging (DTI) and thalamus priors constructed from multiple atlases is proposed. Multiple atlases with corresponding manual thalamus segmentations are registered to the target image and averaged to generate the thalamus prior. At each voxel in a region of interest around the thalamus, a multidimensional feature vector that includes the thalamus prior as well as a set of DTI features, including fractional anisotropy, mean diffusivity, and fiber orientation is formed. A random forest is trained to classify each voxel as belonging to the thalamus or background within the region of interest. Using a leave-one-out cross-validation on nine subjects, the proposed algorithm achieves a mean Dice score of 0. 878 and 0. 890 for the left and right thalami, respectively, which are higher Dice scores than the three state-of-art methods we compared to. We demonstrate the utility of the method with a pilot study exploring the difference in the thalamus fraction between 21 multiple sclerosis (MS) patients and 21 age-matched healthy controls. The left and right thalamic volumes (normalized by intracranial volumes) are larger in healthy controls by 7. 6% and 7. 3% respectively, compared to MS patients (though neither result is statistically significant).

YNICL Journal 2015 Journal Article

Corrigendum to “Statistical normalization techniques for magnetic resonance imaging” [NeuroImage: Clinical 6 (2014) 9–19]

• Russell T. Shinohara
• Elizabeth M. Sweeney
• Jeff Goldsmith
• Navid Shiee
• Farrah J. Mateen
• Peter A. Calabresi
• Samson Jarso
• Dzung L. Pham

YNICL Journal 2014 Journal Article

Statistical normalization techniques for magnetic resonance imaging

• Russell T. Shinohara
• Elizabeth M. Sweeney
• Jeff Goldsmith
• Navid Shiee
• Farrah J. Mateen
• Peter A. Calabresi
• Samson Jarso
• Dzung L. Pham

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.

YNICL Journal 2013 Journal Article

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

• Elizabeth M. Sweeney
• Russell T. Shinohara
• Navid Shiee
• Farrah J. Mateen
• Avni A. Chudgar
• Jennifer L. Cuzzocreo
• Peter A. Calabresi
• Dzung L. Pham

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.

YNIMG Journal 2010 Journal Article

A topology-preserving approach to the segmentation of brain images with multiple sclerosis lesions

• Navid Shiee
• Pierre-Louis Bazin
• Arzu Ozturk
• Daniel S. Reich
• Peter A. Calabresi
• Dzung L. Pham

We describe a new fully automatic method for the segmentation of brain images that contain multiple sclerosis white matter lesions. Multichannel magnetic resonance images are used to delineate multiple sclerosis lesions while segmenting the brain into its major structures. The method is an atlas-based segmentation technique employing a topological atlas as well as a statistical atlas. An advantage of this approach is that all segmented structures are topologically constrained, thereby allowing subsequent processing such as cortical unfolding or diffeomorphic shape analysis techniques. Evaluation with both simulated and real data sets demonstrates that the method has an accuracy competitive with state-of-the-art MS lesion segmentation methods, while simultaneously segmenting the whole brain.

YNIMG Journal 2010 Journal Article

Automated vs. conventional tractography in multiple sclerosis: Variability and correlation with disability

• Daniel S. Reich
• Arzu Ozturk
• Peter A. Calabresi
• Susumu Mori

Diffusion-tensor-imaging fiber tractography enables interrogation of brain white matter tracts that subserve different functions. However, tract reconstruction can be labor and time intensive and can yield variable results that may reduce the power to link imaging abnormalities with disability. Automated segmentation of these tracts would help make tract-specific imaging clinically useful, but implementation of such segmentation is problematic in the presence of diseases that alter brain structure. In this work, we investigated an automated tract-probability-mapping scheme and applied it to multiple sclerosis, comparing the results to those derived from conventional tractography. We found that the automated method has consistently lower scan-rescan variability (typically 0. 7–1. 5% vs. up to 3% for conventional tractography) and avoids problems related to tractography failures within and around lesions. In the corpus callosum, optic radiation, and corticospinal tract, tract-specific MRI indices calculated by the two methods were moderately to strongly correlated, though systematic, tract-specific differences were present. In these tracts, the two methods also yielded similar correlation coefficients relating tract-specific MRI indices to clinical disability scores. In the optic tract, the automated method failed. With judicious application, therefore, the automated method may be useful for studies that investigate the relationship between imaging findings and clinical outcomes in disease.

YNIMG Journal 2008 Journal Article

Tract probability maps in stereotaxic spaces: Analyses of white matter anatomy and tract-specific quantification

• Kegang Hua
• Jiangyang Zhang
• Setsu Wakana
• Hangyi Jiang
• Xin Li
• Daniel S. Reich
• Peter A. Calabresi
• James J. Pekar

Diffusion tensor imaging (DTI) is an exciting new MRI modality that can reveal detailed anatomy of the white matter. DTI also allows us to approximate the 3D trajectories of major white matter bundles. By combining the identified tract coordinates with various types of MR parameter maps, such as T2 and diffusion properties, we can perform tract-specific analysis of these parameters. Unfortunately, 3D tract reconstruction is marred by noise, partial volume effects, and complicated axonal structures. Furthermore, changes in diffusion anisotropy under pathological conditions could alter the results of 3D tract reconstruction. In this study, we created a white matter parcellation atlas based on probabilistic maps of 11 major white matter tracts derived from the DTI data from 28 normal subjects. Using these probabilistic maps, automated tract-specific quantification of fractional anisotropy and mean diffusivity were performed. Excellent correlation was found between the automated and the individual tractography-based results. This tool allows efficient initial screening of the status of multiple white matter tracts.

YNIMG Journal 2007 Journal Article

Multiparametric magnetic resonance imaging analysis of the corticospinal tract in multiple sclerosis☆

• Daniel S. Reich
• Seth A. Smith
• Kathleen M. Zackowski
• Eliza M. Gordon-Lipkin
• Craig K. Jones
• Jonathan A.D. Farrell
• Susumu Mori
• Peter C.M. van Zijl

Background/purpose Muscle weakness is an important feature of multiple sclerosis and is responsible for much of the disability associated with that condition. Here, we describe the quantitative magnetic resonance imaging (MRI) attributes of the major intracerebral motor pathway – the corticospinal tract – in multiple sclerosis. To do so, we develop an intuitive method for creating and displaying spatially normalized tract-specific imaging data. Methods In 75 individuals with multiple sclerosis and 29 healthy controls, the corticospinal tracts were reconstructed from diffusion tensor imaging at 3 T. Multiple MRI indices – T2 relaxation time; fractional anisotropy; mean, longitudinal, and transverse diffusivity; and magnetization transfer ratio – were examined within the reconstructed tracts. Spatially normalized tract profiles were created to compare, across subjects, the variation in MRI index as a function of tract position. Results Each index’s tract profile had a characteristic shape. Individual subjects had markedly abnormal tract profiles, particularly at lesion sites. On average, tract profiles were different between patients and controls, particularly in the subcortical white matter and corona radiata, for all indices examined except for fractional anisotropy. Magnetization transfer ratio was further decreased in subjects with secondary progressive disease. Tract asymmetry was increased in multiple sclerosis compared to controls. Conclusion Multiparametric MRI allows rapid detection, localization, and characterization of tract-specific abnormalities in multiple sclerosis. Tract profiles bridge the gap between whole-brain imaging of neurological disease and the interrogation of individual, functionally relevant subsystems.