Lesion Classification in Lupus Update

NA-MIC National Alliance for Medical Image Computing http://na-mic.org Lesion Classification in Lupus Update H. Jeremy Bockholt DBP2, MIND Background and Significance • Systemic lupus erythematosus (SLE) is an autoimmune disease affecting multiple tissues, including the brain – the facial rash of some people with lupus looked like the bite or scratch of a wolf ("lupus" is Latin for wolf and "erythematosus" is Latin for red). patients may feel weak and fatigued, have muscle aches, loss of appetite, swollen glands, and hair loss, sometimes have abdominal pain, nausea, diarrhea, and vomiting. • Estimates of SLE prevalence range from 14.6-372 per 105 – About 1.5 million americans, 90% diagnosed are female • 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 National Alliance for Medical Image Computing http://na-mic.org 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 National Alliance for Medical Image Computing http://na-mic.org 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 NAMIC kit for the time series analysis of brain lesions in lupus. As a roadmap initiative we are currently engaged in an end-to-end tutorial for lesion analyses in Slicer 3. National Alliance for Medical Image Computing http://na-mic.org The MIND Institute / UNM The Analysis of Brain Lesions in Neuropsychiatric Systemic Lupus Erythematosis INVESTIGATORS: H. Jeremy Bockholt, MIND Charles Gasparovic, UNM Steve Pieper, Isomics Ross Whitaker, Utah Guido Gerig, Utah Marcel Prastawa, Utah Kilian Pohl, BWH Brad Davis, Kitware CONSULTANTS: Vincent Magnotta, UIOWA Vince Calhoun, MIND, UNM PROGRAMMER: Mark Scully, MIND 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: • • Create an end-to-end tutorial in the NA-MIC kit for lesion analyses in NPSLE Using the NA-MIC kit, create a time series analysis tool for brain lesions found in NPSLE DATA: • National Alliance for Medical Image Computing http://na-mic.org MRI sequences T1, T2, and FLAIR Example NPSLE Lesion Hypointense on T1 Hyperintense T2 Hyperintense on FLAIR National Alliance for Medical Image Computing http://na-mic.org Challenges • Lesion trace bronze standard will be manual traces done by expert rater – What happens if automated analyses find broader range of lesions not visible to human • Co-registration of T1, T2, FLAIR – Small lesions can be mostly edges and suffer from partial voluming – Geometric distortion across sequences National Alliance for Medical Image Computing http://na-mic.org Summary of MRI Protocol • The MRI data in the roadmap initiative are collected on both a 1.5T Siemens Sonata scanner using an 8-channel head coil or 3.0T Siemens Trio using a 12-channel head coil. We plan to collect 5 patients and 5 controls with baseline and 6 month follow-up scans. Scanner Contrast Sequence TR TE 1.5T T1 FLASH 12 4.76 T2 FSE 9040 64 FLAIR FSE 1000 105 3.0T T1 T2 FLAIR MEMPR MUGLER MUGLER 2530 1.64 3200 447 6000 105 TI . . 2500 . . 2500 Vox 1.1x1.1x1.5mm 1.1x1.1x1.5mm 1.1x1.1x1.5mm 1.0x1.0x1.0mm 1.0x1.0x1.0mm 1.0x1.0x1.0mm Time 6m32s 6m2s 9m2s 6m3s 7m8s 9m2s National Alliance for Medical Image Computing http://na-mic.org Roadmap • Process baseline 5 lupus + 5 control data-sets – EM Segment within Slicer 3 – Marcel Prastawa custom/ITK – Magnotta BRAINS/ITK – Manual identification of lesions National Alliance for Medical Image Computing http://na-mic.org Progress to Date • Participation in Jan 2008 Programming Week – Training • • • • EM Segmenter Plugins for Slicer3 DBP Engineers Lunch Batchmake • Participation in June 2007 Programming Week – Training • Slicer 3 • KWWidgets • ITK National Alliance for Medical Image Computing http://na-mic.org Progress • Successful build of slicer3 from svn repository on Mac G4/G5 OS 10.4 environment • Becoming comfortable with the NA-MIC and feel confident on • Initial EM Segment results – Working with Brad and Killian, we have run a lupus and control subject successfully • Prastawa/Gerig results – Working with Marcel and Guido, we have an initial result, including lesion segmentation National Alliance for Medical Image Computing http://na-mic.org Jan 08 to June 08 Workplan • Jan: Finish Data collection of 5 patients and 5 controls for roadmap tutorial • Feb: Establish final criteria for lesion definitions and final manual traces for roadmap data-set • Mar/April: Write up methods paper including Brad/Killian/others as co-authors • April/May: apply methods to clinical sample (n=40) • June: Finish Data 6 month follow-up visits National Alliance for Medical Image Computing http://na-mic.org Future • Work with Kilian on tumor growth project – This will give us a head start on the longitudinal lesion analysis phase of this DBP National Alliance for Medical Image Computing http://na-mic.org