Diffusion MRI. Introduction and Modern Methods. John Plass. Department Of Psychology

Transcription

1 Diffusion MRI Introduction and Modern Methods John Plass Department Of Psychology

2 Diffusion MRI Introduction and Modern Methods John Plass Department Of Psychology

3 Overview I. Why use diffusion MRI? II. How does it work? III. Modern methods and best practices IV. Facilitating dmri Research at UMich

4 I. Why use diffusion MRI?

5 Why Use Diffusion MRI?

6 Why Use Diffusion MRI?

7 Why Use Diffusion MRI?

8 Why Use Diffusion MRI?

9 Why Use Diffusion MRI?

10 Why Use Diffusion MRI?

11 Why Use Diffusion MRI?

12 Why Use Diffusion MRI?

13 Why Use Diffusion MRI?

14 Why Use Diffusion MRI?

15 Why Use Diffusion MRI?

16 Why Use Diffusion MRI?

17 Why Use Diffusion MRI?

18 Why Use Diffusion MRI?

19 Why Use Diffusion MRI? To say that the white matter is but a uniform substance like wax in which there is no hidden contrivance, would be too low an opinion of nature s finest masterpiece. Neils Stensen, 1665 Quoted in Schmahmann & Pandya (2009)

20 Why Use Diffusion MRI? 1. Makes white matter structure MR-visible 2. Quantitative analysis of white matter pathways

21 R Bassett et al. (2008) L L R Non-Schiz Schiz De Schotten et al. (2011) Yeatman et al. (2008); Takemura et al. (2015)

22 Why Use Diffusion MRI? 1. Makes white matter structure MR-visible 2. Quantitative analysis of white matter pathways 3. A more holistic approach to neuroscience

23 II. How does it work?

24 ODF Tractography

25 Inferring Fiber Orientations from Diffusion

26 Inferring Fiber Orientations from Diffusion Isotropic Diffusion

27 Inferring Fiber Orientations from Diffusion

28 Inferring Fiber Orientations from Diffusion MR Signal Anisotropic Diffusion indicates oriented structure Apparent Diffusion Coefficient

29 Inferring Fiber Orientations from Diffusion Diffusion-attenuated signal: Inferred diffusion profile:

30 Diffusion Tensor Model FA 0 FA.55 FA.8

31 Diffusion Tensor Model Ennis & G. Kindlmann (2006)

32 ODF Tractography

33 C D How connected are A & B? A B

34 C D 1. Extract primary diffusion directions 2. Tractography A B

35 C D 1. Extract primary diffusion directions 2. Tractography 3. Estimate tract properties Mean FA=.65 A B

36 C D Streamline Tractography 1. Seed from ROI 2. Moved fixed step along local PDD 3. Repeat until termination/rejection A B

37 C D A B

38 Ground Truth C D A B

39 Desired Representation C D Mean FA=.65 A B

40 Actual Representation C D Intermediate PDD Innacurate FA A B

41 The Crossing Fibers Problem b

42

43 ODF Tractography Generation 2

44 Two Schools of Thought 1. Model voxel as N discrete fiber populations with uncertainty around their orientations 2. Model voxel as continuous fiber orientation distribution, incorporating dispersion and uncertainty

45 Ball-and-Stick Model Weighted sum of: 1 isotropic compartment N infinitely anisotropic compartments Estimate uncertainty around stick orientation using numerical methods (e.g., BEDPOSTX)

46 Spherical Deconvolution Recover fiber ODF by deconvolving the single-fiber response function (R) from observed data (A)

47 Two Schools of Thought Ball and Sticks SD Rajkowska and Goldman-Rakic Sotiropoulos, Jbabdi et al, 2013

48 SD Based Tract Metrics True Fiber Density Signal fodf AFD: Raffelt et al HMOA: Dell Acqua et al., 2013

49 Desired Representation C D A B

50 Biases in Tractography Smith et al., 2015

51 ODF Tractography Generation 3

52 III. Modern methods and best practices

53 Streamline Weighting Algorithms C D A B

54 Streamline Weighting Algorithms C D A B

55 Streamline Weighting Algorithms C D A B

56 Streamline Weighting Algorithms C D A B

57 Streamline Weighting Algorithms C D A B

58 Streamline Weighting Algorithms C D A B

59 Streamline Weighting Algorithms C D A B

60 Streamline Weighting Algorithms C D A B

61 Streamline Weighting Algorithms C D A B

62 Streamline Weighting Algorithms C D A B

63 Streamline Weighting Algorithms C D A B

64 Streamline Weighting Algorithms C D A B

65 Streamline Weighting Algorithms C D A B

66 Streamline Weighting Algorithms C D A B

67 Removing False Positive Streamlines C D A B

68 Removing False Positive Streamlines C D A B

69 Removing False Positive Streamlines C D A B

70 Removing False Positive Streamlines C D A B

71 Removing False Positive Streamlines C D A B

72 Removing False Positive Streamlines C D A B

73 Removing False Positive Streamlines C D A B Pestilli et al., 2014

74 Model Comparison and Inference Diffusion Tensor Model Sparse Fascicle Model Pestilli et al., 2014; Rokem et al., 2015

75 Summary: Generation 3 Advantages Estimate meaningful fiber density metrics (regardless of local architecture) Remove spurious streamlines Statistically compare models and tractograms Statistical inference for individual tracts

76 What if We Don t Have A Priori ROIs? Tract Based Spatial Statistics Smith et al., 2006

77 Cluster Inference at the Tract Level Raffelt et al., 2017

78 IV. Facilitating dmri Research at UMich

79 Facilitating dmri Research at UMich 1. Everything shown here available on Flux (MRtrix, FSL) 2. Currently developing general-purpose scripts 3. Hands-on training next semester 4. Dedicated hardware: coming soon?

80 Thank you!