Brain Fraction and SPM Normalized Penumbra

Slides:



Advertisements
Similar presentations
The Normal Distribution
Advertisements

Journal Club: mcDESPOT with B0 & B1 Inhomogeneity.
Our project dealt with seeing how many quarters a student could toss into a jar within one minute. After one minute, the student was no longer able to.
Chapter 4: Image Enhancement
Correlation Mechanics. Covariance The variance shared by two variables When X and Y move in the same direction (i.e. their deviations from the mean are.
Snow Field over Kansas 2/22 – 2/ It is hard to tell exact dates of snow event. There is little snow seen on the AM pass for 2/20, then cloud cover.
Scales of Measurement n Nominal classificationlabels mutually exclusive exhaustive different in kind, not degree.
ISMRM 2010 Quantitative Imaging and MS. N. D. Gai and J. A. Butman, NIH T1 Error Analysis for Double Angle Technique and Comparison to Inversion Recovery.
MSmcDESPOT: Follow-Ups November 1, Where We Are Baseline cross-section conclusions: – DVF is sensitive to early stages of MS where other measures.
More Raster and Surface Analysis in Spatial Analyst
MSmcDESPOT: Baseline vs. 1- year Diagnosis. N008 Baseline SPGR.
Fundamentals of GIS Lecture Materials by Austin Troy except where noted © 2008 Lecture 14: More Raster and Surface Analysis in Spatial Analyst Using.
Spatial-based Enhancements Lecture 3 prepared by R. Lathrop 10/99 updated 10/03 ERDAS Field Guide 6th Ed. Ch 5: ;
Lesion Segmentation Methodology. Purpose Currently in a dilemma as Hagen did a substantial amount of work editing the z-score FLAIR masks in MNI space,
WHAT IS VRAY? V-ray is a rendering engine that is used as an extension of certain 3D computer graphics software. The core developers of V-Ray are Vladimir.
Normal Distribution Links Standard Deviation The Normal Distribution Finding a Probability Standard Normal Distribution Inverse Normal Distribution.
Agronomic Spatial Variability and Resolution What is it? How do we describe it? What does it imply for precision management?
7T Thalamus and MS Studies Jason Su Sep 16, 2013.
Agronomic Spatial Variability and Resolution What is it? How do we describe it? What does it imply for precision management?
Measures of Variability Objective: Students should know what a variance and standard deviation are and for what type of data they typically used.
Signal and noise. Tiny signals in lots of noise RestPressing hands Absolute difference % signal difference.
Interpreting Performance Data
The Practice of Statistics Third Edition Chapter 13: Comparing Two Population Parameters Copyright © 2008 by W. H. Freeman & Company Daniel S. Yates.
SAS Homework 4 Review Clustering and Segmentation
J OURNAL C LUB : Lankford and Does. On the Inherent Precision of mcDESPOT. Jul 23, 2012 Jason Su.
Statistics- Summer Semester 2011 Group 13 Liz Sherman Rachel Wright Chalyse Mason Lisa Victorine Kristi Miller.
Digital Image Processing Lecture 10: Image Restoration March 28, 2005 Prof. Charlene Tsai.
MSmcDESPOT A Brief Summary April 2, The Technique mcDESPOT (multi-component driven equilibrium single pulse observation of T1/T2) is a quantitative.
Comparison of the New Jersey Landscape Alison Burnett Mark June-Wells December 13, 2006.
Digital Image Processing Lecture 10: Image Restoration
Normal Distribution Links The Normal Distribution Finding a Probability Standard Normal Distribution Inverse Normal Distribution.
J OURNAL C LUB : Deoni et al. One Component? Two Components? Three? The Effect of Including a Nonexchanging ‘‘Free’’ Water Component in mcDESPOT. Jan 14,
Statistics Who Spilled Math All Over My Biology?!.
National Alliance for Medical Image Computing Core What We Need from Cores 1 & 2 NA-MIC National Alliance for Medical Image Computing.
© 2008 McGraw-Hill Higher Education The Statistical Imagination Chapter 5. Measuring Dispersion or Spread in a Distribution of Scores.
Scales of Measurement n Nominal classificationlabels mutually exclusive exhaustive different in kind, not degree.
Quality Control  Statistical Process Control (SPC)
Plan for Today: Chapter 11: Displaying Distributions with Graphs Chapter 12: Describing Distributions with Numbers.
P025 MPRAGE Pre-Contrast. P025 MPRAGE w/ Z-Score < -4.
Case Closed The New SAT Chapter 2 AP Stats at LSHS Mr. Molesky The New SAT Chapter 2 AP Stats at LSHS Mr. Molesky.
Correlation  We can often see the strength of the relationship between two quantitative variables in a scatterplot, but be careful. The two figures here.
Segmentation with Corrected MPRAGE Scans and FSL Jason Su.
R&R Homework Statgraphics “Range Method”. DATA OperatorPartTrialMeasure B B B B B B326.5 B B B C
LESSON 5 - STATISTICS & RESEARCH STATISTICS – USE OF MATH TO ORGANIZE, SUMMARIZE, AND INTERPRET DATA.
ITK. Ch 9 Segmentation Confidence Connected Isolated Connected Confidence Connected in Vector Images Jin-ju Yang.
Warm Up! Write down objective and homework in agenda Lay out homework (Box Plot & Outliers wkst) Homework (comparing data sets) Get a Calculator!!
GS/PPAL Section N Research Methods and Information Systems
the Normal Model chapter 6 (part deux)
Digital Image Processing Lecture 10: Image Restoration
Descriptive Statistics I REVIEW
U4D3 Warmup: Find the mean (rounded to the nearest tenth) and median for the following data: 73, 50, 72, 70, 70, 84, 85, 89, 89, 70, 73, 70, 72, 74 Mean:
Types of T-tests Independent T-tests Paired or correlated t-tests
Inference.
Normal Distribution Links Standard Deviation The Normal Distribution
Sample vs Population comparing mean and standard deviations
ID1050– Quantitative & Qualitative Reasoning
“Forward” vs “Reverse”
Choropleth Map.
Section 2.5 notes Measures of Variation
2010 Market Share Guide.
ISMRM 2012 Prelim. Abstracts Oct 17, 2011 – Jason Su
Basic Practice of Statistics - 3rd Edition Two-Sample Problems
How to Start This PowerPoint® Tutorial
Warm-Up 4 87, 90, 95, 78, 75, 90, 92, 90, 80, 82, 77, 81, 95, Find the 5-Number Summary for the data 2. Address every type of measure of spread.
Common Core Math I Unit 1: One-Variable Statistics Comparing Data Sets
Lindsay Liebert & Julia Calabrese March 26, 2018 Block 2
Normal Distribution.
Chapter 5 Hypothesis Tests With Means of Samples
WLTP CoP Procedure for CO2/FC
Introduction to Behavioral Statistics
Presentation transcript:

Brain Fraction and SPM Normalized Penumbra MSmcDESPOT Brain Fraction and SPM Normalized Penumbra

Updated EDSS

Updated EDSS

Updated EDSS

Updated EDSS This is with DAWM defined by SPM penumbra in WM containing lesion cores. Overall SPM significantly shrunk the DAWM selection by about order of 10 in vol.

Brain Fraction with Prelim. GM Similar to what was seen in Nov.

MWF Std. Dev. In Normals 0–0.11 scale Clearly there is high variation in the GM, most likely due to registration imperfections Similarly at edges of ventricles

FLAIR Std. Dev. In Normals – Raw Scale 0–50, not too relevant here since not quantitative Inhomogeneity variation across normals caused a higher degree of fluctuation in the forebrain

FLAIR Std. Dev. In Normals – Max Normalized Not sure how to window properly so that the std. dev. maps are comparable Wanted to see the image get darker -> lower std. dev. -> tighter distribution Again the forebrain has higher variability

FLAIR Std. Dev. In Normals – SPM Normalized 0–50, same scale as Raw Removes forebrain variability artifact However, the image did not darken vs. Raw so the distribution did not get tighter

FLAIR Std. Dev. In Normals – SPM Normalized This is auto-windowed to the “robust range” as the max normalized one was also Only very slight brightening vs. 0-50 range Interpretation: Max normalized seems to achieve a lower standard deviation across normals, this is confirmed by the larger DAWM selection