Postprocessing Neuroimaging methods in MRI and PET/MRI

with applications to Multiple Sclerosis and other Neurological diseases

Candidate: D. Poggiali

Supervisor: Prof.ssa E. Pegoraro

Cosupervisor: Prof. P. Gallo

Summary:

  1. Introduction to Neuroimaging
  2. State of the art algorithms and pipelines
  3. Glucose PET/MRI in MS
  4. Amyloid PET/MRI in AD

1. Introduction

A modern neurological study aims to relate several biomarkers from different sources in order to explain the illness evolution improve prognostic accuracy and optimize the treatment.

A modern research group can have at disposal:

  • clinical,
  • imaging,
  • neuropsychological,
  • liquor biomarkers

2. State of the art

1. Registration (rigid, affine, nonlinear)

 

2. Correction (Bias Field, Lesion Filling,

Motion Correction, PVC)

 

3. Segmentation (Manual, Template-based)

 

4. Measurement (mean, std over VOIs)

Registration

Correction

Motion Correction

Correction

Partial Volume Correction

Segmentation

Measurements

Cortical Thickness Estimation

Pipelining

4. Glucose PET/MRI in MS

29 patients, 14 CIS/eRRMS and 15 RRMS underwent a PET/MRI with:

  • MRI sequences: 3D T1, 3D FLAIR, 3D DIR
  • 18F-Fdg PET in list-mode

The aim is to study the relationship between:

  • Cortical Thickness
  • WM/GM lesion number/volume
  • aMRGlu

Results:

CIS/eRRMS differed significantly:

  • 0/66 cortical areas in CTh
  • 33/66 cortical areas in aMRGlu
  • 10/66 cortical areas lesion Number
  • 10/66 cortical areas lesion Volume

No correlation was demonstrated between CTh and WM and GM lesion load

No correlation was found between global and regional aMRglu and CTh.

aMRglu correlated with lesion load

Inverse correlation was found between:

  • the total number of focal lesions (WM+GM lesions) and the global cortical aMRglu (R=-0.36 e p=0.04)
  • WM lesion volume and number and cortical global aMRglu (R=-0.4 and p=0.02 for both)
  • GM lesion number with global aMRglu (R=-0.36, p=0.046)

p<0,01

p=0,18

Histograms of aMRGlu values

5. Amyloid PET/MRI in AD

86 cases (53 rated  negatively and 33 positively by expert amyloid readers) of 18F-Fluorbetaben PET/MRI.

\begin{array}{c} R_1=\frac{PET_1}{PET_1(Cerebcortex)}\\ \\ R_2=\frac{PET_2}{PET_2(Cerebcortex)}\\ \\ SI = R_2- R_1\\ \\ WMR = 100 \frac{PET_2-PET_2(WM)}{PET_1-PET_1(WM)} \end{array}
R1=PET1PET1(Cerebcortex)R2=PET2PET2(Cerebcortex)SI=R2−R1WMR=100PET2−PET2(WM)PET1−PET1(WM) \begin{array}{c} R_1=\frac{PET_1}{PET_1(Cerebcortex)}\\ \\ R_2=\frac{PET_2}{PET_2(Cerebcortex)}\\ \\ SI = R_2- R_1\\ \\ WMR = 100 \frac{PET_2-PET_2(WM)}{PET_1-PET_1(WM)} \end{array}

R2

SI

WMR

ROC (Receiver Operator Charateristic) curves

Normalization

z-score

Thanks to....

MS Centre Padova

Nuclear Medicine Padova

(special thanks to Diego Cecchin)

 

Neurology Padova

Neuroradiology Unit Padova

Nuclear Medicine Leipzig DE

 

all patients involved ...

... the audience for listening

Some References

  • ​C Cobelli, D Foster, G Toffolo,Tracer kinetics in biomedical research, Springer, 2001.

  • SR Das, BB Avants, M Grossman, JC Gee, Registration based cortical thickness measurement, Neuroimage. 2009.

  • RS Desikan, F Sègonne, et. al, An automated labeling system for subdi- viding the human cerebral cortex on MRI scans into gyral based regions of interest, Neuroimage, 2006.

  • M Blinkenberg, HK Mathiesen, T Tscherning, et al., Cerebral metabolism, magnetic resonance spectroscopy and cognitive dysfunction in early multi- ple sclerosis: an exploratory study, Neurol Res, 2012.

  • TG Feeman, The mathematics of medical imaging: A beginners guide, Springer, 2010

  • CR Jack, etal., Amyloid-firstandneurodegeneration-firstprofilescharac- terize incident amyloid PET positivity, Neurology, 2013.

  • CS Patlak, RG Blasberg, Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations, Journal of Cerebral Blood Flow and Metabolism, 1985.

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