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National Alliance for Medical Image Computing http://na-mic.org

The MIND Institute / UNMThe Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis

H Jeremy Bockholt Charles Gasparovic

2007 NA-MIC All Hands MeetingSalt Lake City, Utah


Background and Significance

• Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple tissues, including the brain

• Estimates of SLE prevalence range from 14.6-372 per 105

• Neuropsychiatric SLE (NPSLE), a term that subsumes the neurologic and psychiatric complications of SLE, occurs in up to 95% of SLE patients

• While MRI often reveals distinct white matter abnormalities in active NPSLE, the pathologic processes underlying these lesions, whether purely autoimmune or vascular (e.g., hemostasis), are unknown


Aims of the RO1 Study

• Test hypotheses concerning the possible thrombotic or embolic origin of white matter brain lesions in NPSLE

• Examine whether the incidence of lesions correlates with either levels of thrombosis markers or emboli in the blood or a potential source of emboli in the heart

• Examine whether overall lesion load or the levels of particular classes of lesion correlate with cognitive function


Background and Objective

• Critical to understanding the etiology of brain lesions in NPSLE will be the accurate measurement of their location, size, and time course.

• Lupus brain lesions are known to vary in MRI intensity and temporal evolution and include acute, chronic, and resolving cases.

• Monitoring the time course of image intensity changes in the vicinity of lesions, therefore, may serve to classify them based on their temporal characteristics.

• Major objective of this DBP will be the evaluation of existing tools and the development new tools using the NA-MIC kit for the time series analysis of brain lesions in lupus.


Summary of MRI Protocol

• The MRI data in this project are collected on a 1.5T Siemens Sonata scanner using an 8-channel head coil. – T1-weighted 3D fast low angle shot (FLASH) sequence

(TR/TE = 12/4.76ms, flip angle = 20deg, FOV = 220x220mm, resolution = 192x192, 120 1.5-mm slices, total time = 6m32s)

– T2-weighted fast spin echo sequence (TR/TE = 9040/64ms, turbofactor=5, FOV = 220x220mm, resolution = 192x192, 120 1.5-mm slices, total time = 6m2s)

– Fluid Attenuated Inversion Recovery (FLAIR)/fast spin echo sequence (TR/TE = 1000/105ms, TI = 2500ms, echo train=9, field of view (FOV) = 220x220mm, resolution = 192x192, 88 1.5-mm slices, total time = 9m2s).


Other Protocol Items

• transcranial doppler ultrasound is used to detect microemboli in the brain

• transesophageal echocardiography is performed to evaluate general cardiac status and to detect the presence of heart valve vegetations, as potential sources of emboli.

• markers of hemostasis, – analyses for platelets, coagulation, fibrinolysis, and

anti-phospholipid antibodies.

• battery of neuropsychological tests is administered to evaluate cognitive function.


Summary of Study Design

• Clinical Assessments and MRI will be performed on approximately 60 SLE patients and 30 normal control subjects over a period of 48 months.

• Subset of this group will be approximately 15 SLE subjects with NPSLE and 15 SLE subjects without NSLE

• Subset of initial SLE groups will be re-examined in the each of the subsequent years (2-4) of the study.


Image Processing Needs

• Co-registration of T1, T2, and FLAIR.• A robust and reliable method capable of

segmenting the brain into at least four classes: gray matter, white matter, cerebrospinal fluid, and white matter lesions.

• Longitudinal follow-up registration • Correlations such as appearance of and

changes in particular lesions correlate with the onset and remission of the neuropsychiatric symptoms of NPSLE.


Goals of the NPSLE DBP

• Use and extend the NA-MIC kit to make a fully automated lesion analysis tool. – Input data

• image data from the T1-weighted, T2-weighted, and FLAIR sequences

– Output data • will be probability maps for each tissue class, the number of

lesions, the volume of each lesion, and the total lesion volume at each time point

• Changes in lesion size and changes in pixel intensity within the volume of each lesion will be displayed graphically

• Time course data will also be amenable to time series analysis by statistical tools such as general linear modeling (GLM), independent component analysis (ICA), or potentially Bayesian analysis


Example NPSLE Lesion

Hypointense on T1 Hyperintense T2 Hyperintense on FLAIR


Current Tools

• A number of different MRI data analysis tools are used by researchers at TMI and UNM: – BRAINS2, FSL, SPM, Freesurfer, and Slicer for image

segmentation and labeling

– FSL, SPM, and AFNI are used fMRI data analysis


Year 1 Goals 2007-2008

• Collect Baseline data points• Hire experienced C++ programmer, train and

mentor to become expert at using NA-MIC kit• Evaluate the performance of at least four methods

for lesion segmentation of NPSLE brain images: – EM-Segment Method, developed by Charles

Guttmann– Modification of EM-Segment, developed by

Vincent Magnotta– BRAINS2, developed by Vincent Magnotta– Manual classification by an expert rater.

We will use the STAPLE and/or a method using the Williams Index to cross-validate these methods.



• Complete collection of follow-up data points

• Participate in the overall NA-MIC grant renewal process

• Use and extend the NA-MIC kit to develop a lesion analysis module that provides a workflow for the registration of T1,T2, and FLAIR within and across different scanning sessions, automated segmentation into gray, white csf, and lesion, and summary of lesion location, size, and intensity. A fully-functional prototype will be completed in time for grant resubmission.

• Extend lesion analysis module to provide time series analysis tools as the follow-up data points become available



• Complete a final, stable production time series lesion analysis module to be made available in NA-MIC kit

• Provide final publically available data-sets to support a robust training tool for using the module

• Contingent on renewal funds, try out different statistical methods of time series analysis, drawing from methods that have been successful in the analysis of fMRI data, including GLM and ICA

• Use GLM or multiple regression analysis to examine correlations between changes in lesion intensity or size and measures of thrombo-embolic activity or the onset of clinical symptoms of NPSLE


Direction that this DBP Will Drive NA-MIC

• Methods developed in this DBP will have a broad impact on the study of brain diseases involving MRI-visible lesions

• Characterization of the time evolution of these lesions will undoubtedly help to elucidate not only the origins of the lesions but their relationships to disease symptoms.

• No current image analysis package currently permits this level of automated lesion time series analysis--this will make the NA-MIC kit unique and more desirable to be used by the community


NPSLE DBP Summary

• Using the NA-MIC kit, we will augment, develop, and validate tools for the quantification of brain lesions thought to underlie the cognitive dysfunction of NPSLE.

• We will extend NA-MIC kit to analyze changes in these lesions with time and to relate these changes to the fluctuating symptoms of NPSLE

• We will gain greater insight of NPSLE etiology

• The automated lesion time series analyses should generalize well to other vascular disorders such as vascular

dementia, myotonic dystrophy, and multiple sclerosis.


The MIND Institute / UNMThe Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis

INVESTIGATORS:H. Jeremy BockholtCharles Gasparovic, Ph.D. CONSULTANTS:Vincent Magnotta, Ph.D.Vince Calhoun, Ph.D.Dominic Meier, Ph.D.PROGRAMMER:Sumner Williams, M.S.

BACKGROUND:• NPSLE is an autoimmune disorder that

causes neurological and psychiatric complications

• Afflicted patients have distinct white matter lesions that vary over time

• To understand the etiology of brain lesions in NPSLE, accurate measurement of lesion location, size, and time course must be achieved

AIMS:

• Evaluate existing tools and develop new tools useing the NA-MIC kit for time series analysis of brain lesions found in NPSLE

DATA:

• MRI, DTI, perfusion, transcranial ultrasound, echocardiography, and neuropsychology

Baseline year 1 year 2SLE 30 15 15

NPSLE 30 15 1530 0 0

Lupus

Healthy Normal Volunteers

Subject Type

Data collected under R01-NS35708-04


References

1. Aladjem, H (Editor): LFA study shows between 1,400,000 and 2,000,000 people diagnosed with lupus. Lupus News 14:12, 1994.

2. Sibbitt WL Jr, Brandt JR, Johnson CR, Maldonado ME, Patel SR, Ford CC, Bankhurst, AD, BrooksWM: The incidence and prevalence of neuropsychiatric syndromes in pediatric-onset systemic lupus erythematosus. J Rheum 2002 29:1536-42.

3. Warfield SK, Zou KH, Wells WM. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging. 2004 Jul;23(7):903-21.

4. Martin-Fernandez M, Bouix S, Ungar L, McCarley RW, Shenton ME. Two methods for validating brain tissue classifiers. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv. 2005;8(Pt 1):515-22.


Acknowledgements

• Charles Gasparovic

• Wilber Sibbit

• Carlos Roldan

• Bruce Rosen

• John Rasure


Questions and Discussion

• Preprocessing and filtering• Shape Analysis• Longitudinal Change Shape Analysis• Anatomical Localization of Lesions• Is the timeline reasonable?• Should we extend and generalize the EM

Segment Module?• Other items?

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