Journal Club 11/5/2014: DWI in Head and Neck Tumors

Volumetric Magnetic Resonance Imaging Evaluation: New HorizonseEdE-74-7091, Control #: 1518Gloria J. Guzman, MD, MScChristopher Owen, MADan Marcus, PhDRuss Hornbeck, MCSCMatthew Smyth, MD, FAANS, FACS, FAAPTammie Lee Benzinger, MD, PhD

Financial DisclosuresGloria Guzman, Christopher Owen, Dan Marcus, Russ Hornbeck, Matthew Smyth : NoneTammie Lee Benzinger: Over 10k: She participates in clinical trials sponsored by Eli Lilly, Avid Radiopharmaceuticals, and Roche. and research funding from Avid Radiopharmaceuticals (current) Less 10k: Advisory Board membership for Eli Lilly (2011 She also reports providing expert testimony and receiving compensation from Kujawaski & Associates (2011)

PURPOSETo prepare an educational exhibit on the workflow for volumetric MRI quantification in patients with Alzheimers dementia, multiple sclerosis and in medically intractable epilepsyAPPROACH/ METHODSWhat is volumetric imaging?Technique used to assess volumetry of brain regions or whole brain volume after acquisition of high resolution MR images for the delineation of anatomical boundariesMost frequently used sequences are T1, T2 and proton densityMR scanners are either 1.5 Tesla (T) or 3T systems. 3T systems offer higher resolution of tissue contrast (i.e. increased visualization of the borders between grey matter and white matter and cerebrospinal fluid)Why is volumetry important?Early in the course of dementia, it is difficult to distinguish between dementia due to Alzheimers Disease (AD) versus other, potentially treatable causes of dementia, such as normal pressure hydrocephalus (NPH). Tools that more accurately diagnose early manifestations of this disease, such as hippocampal volumetry, are critical in patient management. It is also useful as a quantitative measure of disease progressionLikewise, evaluation of general whole brain atrophy in Multiple Sclerosis (MS) patients can identify those that present with subtle or no clinical symptoms. These patients could benefit from early aggressive treatment if they demonstrate quantifiable and progressive brain volume loss. It is also useful as a quantitative measure of disease progressionFinally, focal cortical dysplasia (FCD) and other cortical anomalies can be difficult lesions to identify radiographically. These patients have medically intractable epilepsy that significantly affects their quality of life. Surgical excision of these lesions results in marked improvement to complete resolution of the epileptic episodes. A color-coded volumetric map that can easily demarcate abnormal cortical areas would have a significant impact on patient management and outcomes

How is volumetry processed?The FreeSurfer software suite is anMRI-based brainimaging software package used in functional brain mappingthat facilitates the visualization of the different anatomical regions of the brain cortexContains both volume based and surface based analysis which can be used for the reconstruction of topologically correct and geometrically accurate models of both the gray/white matter and pial surfacesSurfaces used in conjunction with segmentation can be used for measuring cortical thickness, surface area and folding, and for computing inter-subject registration based on the pattern of cortical foldsProcessing of volumetric map for MS and ADMR scans are moved to the Radiology supercomputer for parcelation and segmentation via the FreeSurfer software suit Whole brain, gray matter, and white matter volume are calculated and normalized via a comparison of subject intracranial volume to intracranial volume of a supernormal cohort (called ICV or eTIV in FreeSurfer)After normalization, the large ROI's are compared to a loess regression weighted against age generated with the supernormal cohort. Standard deviations and percentiles of specific subjects are calculated from the aforementioned loess regression. Subjects can be displayed longitudinally one a graph to show changes of volume over time with respect to the loess regression

MR scans are moved to theRadiology supercomputer for parcelation and segmentation via the FreeSurfer software suitThickness values for each vertex on the pial layer are extracted from FreeSurfer assessor files, and normalized against the supernormal groupA weighted loess regression is generated for every vertex in the FreeSurfer surface using the supernorm data set, and the z-score (number of standard deviations away from the mean) is calculated for the vertices of a subject's surface Processing of MRI volumetric map for FCDProcessing of MRI volumetric map for FCDThe resulting z-scores are stored in a text file generated by the R-script and then converted to mgh (a file format created byMassachusetts General Hospital), which in this case is an "overlay" for the FreeSurfer suiteAn overlay can then be displayed on the patient's gray matter surface using freeview or tksurfer, and images generated from the z-score mapThe images can then be compared to what can be seen though tkmedit. In tkmedit, we look at the areas of the brain that have abnormal values in our z-score map to ensure that the z-scores are not due to errors in the surface themselves, rather than being a reflection of physiologyExample of adapted FreeSurfer using R coding program for ADFor normalizing the supernormal group and generating a reference graph: #Find linear model between ROI and ICVhippLeftReg = lm(super$Left_Hippocampus_volume~super$IntraCranialVol)#Get head size (ICV) corrected hippocampal volumesuper$corrHippLeftVolume = (super$Left_Hippocampus_volume - hippLeftReg$coefficients[2]*(super$IntraCranialVol-mean(super$IntraCranialVol)))#Smooth population data with loess (weighted regression)smoothHippLeftFit = loess(super$corrHippLeftVolume~super$Age,degree=1,span=0.7)#Generate graph represeting Supernormal DatasmoothHippLeftPred = predict(smoothHippLeftFit,newdata=43:90,se=TRUE)

For normalizing the patient's data:#Calculate corrected hippocampal volume for patient data using the linear model already creatednormHippLeftVolume = all$Left_Hippocampus[all$Session==eval(parse(text=paste("\"",Subject_number,"\"",sep="")))]normCorrHippLeftVolume = normHippLeftVolume - hippLeftReg$coefficients[2]*(all$IntraCranialVol[all$Session==eval(parse(text=paste("\"",Subject_number,"\"",sep="")))]-mean(super$IntraCranialVol))

Normalization of dataFor MS and AD: The Super-Norm cohort is composed of scans from individuals who have tested cognitively normal in the Cognitive Dementia Rating (CDR 0) and have normal biomarker Mean Cortical Binding Potential (MCBP