In vivo NMR-MRI

8/9/2019 In vivo NMR-MRI

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In Vivo Nuclear MagneticResonance Spectroscopy:

A Metabolomics

PerspectiveRisto Kauppinen

Dartmouth [email protected]


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Contents

How? Hardware and software

What? Biological information obtained

Why? Applications in preclinical andclinical settings: In Vivo Metabolomics

and Imaging Biomarkers Future directions


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abundance relat. sensitivity

1H 99.98% 10031P 100% 6.6

[13C 1.1% 0.016](19F 100% 83)

(17O 0.04% 0.029)

Nuclei for in vivo MRS


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In vivo MRS Hardware

CLINICAL HUMAN RESEARCH EXPERIMENTAL

1.5 3 T 4 7 - 9.4T 9.4 - 11.7 - 16.1T


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Hybrid instrument comprising of multiple imaging modalities One modality provides anatomical specificity, the other

physiological/biochemical/pharmacological data

Pioneered by the Magnetic Resonance Imaging (MRI) andMagnetic Resonance Spectroscopy (MRS) hybrid

CT PET

MRI PET

MRI EEG

MRI MEG

MR Scanner is a Genuine Hybrid Imager


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Excellent B0 homogeneity > focus on RT- shims

RF (or B1)-homogeneity > focus on RF-coils

2 (or more) transmitter channels, one for 1H and

the other for X nuclei (broadband) Special software, pulse sequences or packages

in clinical scanners

Analysis software (some of these are free ofcharge, eg. jMRUI)

What is needed for in vivo MRS?


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Localised MRS

Single Voxel

MRS

Multi Voxel

MRS

= MRSI


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Spatially Selective Excitation

0

B0

Slice select position

Labora

toryframe

frequency()

z0

B0+Gzz0)

=

B0+Gzz

)


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180OO

90 180O

SPIN-

ECHO

TE/4 TE/2 TE/4

G

G

G

x

y

z

rf

Point Resolved Spectroscopy

(PRESS)


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Gz Gx

Gy Signal only from this volume

PRESS Volume Selection


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Chemical shift selective

excitation to suppresswater, but not metabolites

Incorporating a T1 filter Suppression by a factor

1:10000 (50000)

Additional suppression ofwater signal in time domain

Water Suppression for 1H MRS


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Pfeuffer et al. JMR 141:104 (1999)

@9.4T

State-of-the-art in vivo 1H MRS

Brain 1H MRS

Alanine

AspartateCholines

Creatine+P-Creatine

Glucose

Glutamate

Gluthatione

GPC

myo-Inositol

Scyllo-Inositol

N-Acetyl aspartateNAAG

Lactate

Phosphorylcholine

PhosphorylethanolamineTaurine

Water


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State-of-the-art in vivo 1H MRS

art at 1.5, 3 and 7T

PRESS TE = 30 ms STEAM TE = 6 ms

7T


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To reveal hidden peaks

Exploits spin-spin coupling or multiplequantum energy transitions

Metabolites revealed:

Glutamate and glutamine

GABA

Glutathione (GSH)

Ascorbic acid

Spectral Editing for in vivo 1H MRS


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Editing adds:

GABA

AscorbateGlutathione

Threonine

Gruetter et al. MRM 55: 296, 2006

GABA, Glu & Gln Editing


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Spectral analysis

Goal: Quantify metabolite content

Methods for spectral quantification

Peak integration

Peak fitting

Fitting modeled metabolite lineson an experimental spectrum

Referencing:Internal reference (water)

External reference (rf-coil

loading considered)


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LCModel Analysis of in vivo MRS

Model spectra acquired (or simulated) for metabolites

in question

Experimental spectrum analysed as a Linearcombination of Model spectra of metabolite solutions

Provencher S.W. MRM 30:672 (1993)


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MRS Biomarkers andMetabolomics

The concept introduced in 80s

MRS biomarker: a metabolite that is

detected in given cell/tissue type, condition

or disease

Metabolite profile or metabolic phenotypewere predecessors of Metabolomics


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MRS Metabolomics

Hagberg et al. MRM 34:242, 1995


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Tentative Classification of MRS-detectable

Biomarkers

Pre-biomarkers (single centre)

numerous, need qualification for biomarker status

Imaging biomarkers (multi-centre, safety profile) growing number of biochemicals (e.g. choline-

containing compounds, myo-inositol, citrate, 1H MRS

detected lipids, PME, PDE) Imaging surrogate marker(qualified biomarker with

established criteria)

specific to a given biochemical event and/or celltype (e.g. ATP, PCr, NAA)

Imaging licensed biomarker(approved)

yet to-be-established for MRS-detectablebiochemicals


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TUMOURS IN SITU

TISSUE SPECIMENS

CELLS

1H MRS in Context of Brain

Tumour Classification


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CELLS


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1H MRS in Classifying Glioma

Cell Lines

Pan et al. unpublished


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TUMOURSEX VIVO


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1H MRS

in vivo

Astrocytoma Oligodendroglioma

Bruhn et al. Radiology, 1989


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SPECIFICITY 92-100%

Neural Networks for Brain Lesion MRS

LOW GRADE 73%

HIGH GRADE 98%

ABSCESS 83%

TUBERCULOMA 89%Poptani et al. J Clin Oncol 1999


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1H MRS

Classification

Preul et al. Nat Med, 1996

The method classified correctly 104 of 105 spectra

Conventional technique 71 of 91 tumours


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1H

MRS

De Edelnyi et al. Nat Med, 2000


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Cox Regression performed on allmetabolite quantities for 129 brain tumour

patients at the Birmingham ChildrensHospital (UK)

Lip 1.3, Lip 0.9 and MM 0.9 were found to

be significant predictors of survival (model

significance p=0.0388)

1H MRS in the Context of Survival

Analysis

Wilson et al. ISMRM 2009


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Independent Component Analysis

(ICA) of 1H MRSI Data SetDecomposing tissue in each voxel into three tissue types:

Healthy brain, Invasive Tumour and Necrotising Tumour

cb

Pulkkinen J et al Eur J Radiol 56:160, 2004

ICA Healthy

Invasive

Necrotising

Healthy brain: ICA a only

Low grade: ICA b onlyHigh grade: ICA b + c


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ANALYSIS

Input Neural Network

Medical Documents

Patient history

MRI

MRS

Genetics

Proposed

diagnosis

prognosis

Data base Analysis

MRSI of Brain Tumour Patient


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1H MRSI of Prostate

Prostate cancer, Gleeson 3+4, PSA 6.86: MRSI with 1 ml voxels at 3T

Inherent metabolic heterogeneity by 1H MRSI within healthy gland

Healthy periphery Cancer in periphery Cancer in central gland Healthy central gland

CitrateCholine

Scheenen et al. Radiology 245: 507-16 (2007)


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1H MRSI of Prostate

Discrimination of prostate cancer from healthy gland

(Choline + Creatine)/Citrate by 1H MRSI (45 patients)

Tissue Type Threshold Ratio Sensitivity Specificity

Periphery Cancer >0.41 66% 95%

Periphery Normal 0.41 66% 73%

Central Normal


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MRS in Treatment Monitoring

Biochemical information from early cell

events in death process (often apoptosis) MRS may indicate responders earlier than

MRI-detectable changes appear


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Induction

of

apoptosis(p53)

Condensed

chromatin

endonucleases

Acidification

externalisation of

phosphatidylserine

phospholipasescell shrinkage

H+

Apoptotic

bodies

Phagocytosis

Hours Days

H+ H+

H+ H+

Detection by MR

Natural course of apoptosisNatural course of apoptosis

pHi drop Inhibition of glycolysis

Inhibition of

Phosphaditylcholine synthesis

Activation of phospholipases

Reduced cell volume

Cell shrinkage

-> Cell kill

Exogenouscells


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1H MRS: Lipids

Jurkat cells

Doxorubicin

Blankenberg et al. Blood 87: 1951 (1996)

C6 glioma cells

Cell cycle and lipid droplets

Barba et al. Cancer Res 59: 1861 (1999)

1H NMR spectra of a BT4C during


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Normal brain

Day 0

Day 2

Day 4

Day 6

Day 8

1H NMR spectra of a BT4C during

apoptosis in vivo

5.3(vinyl)

2.8(bis-allylic)

Cho

1.3 (-CH2-)

0.9 (-CH3)

Hakumki JM et al. Nat Med 5:1323, 1999


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Metabolomics using MRS Data

PLS model of 1H MRS

to predict apoptosis

Griffin JL et al.

Cancer Res 63:3195, 2003

1H MRS Li id d i G


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Liimatainen et al. MRM 59: 1232, 2008

1H MRS Lipids during Gene

Therapy: Origin


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UNTREATED GCV-TREATED (day 4)

Droplets:0.2-2 m

233 435 pFOV

Transmission EM

Hakumki JM et al. Nat Med 5:1323, 1999


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Diffusion MRS: Lipid Droplets

Evidence for high diffusivity of 1H NMRdetected lipids (Hakumki et al. Cancer Res 1998, Nat

Med 1999)

Mean characteristic compartment size of

lipid bodies in C6 glioma of 4.30.7 m

(Lahrech et al. MRM 2001)


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1H MRSI: Predicting Treatment Response

Grade 4 glioma following radiation therapy

Responding tissue:

-loss of MRS peaks and appearance of lipids

Recurrence

-Choline (and creatine) peaks reappearNelson et al. J Magn Reson Imag 16: 464-76 (2002)


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Tau, mI+gly, Cr, Glx

Cholines Lipids

Lehtimki KK et al. J Biol Chem 278:45915, 2003

Metabolites During Apoptosis (BT4C glioma)


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Baseline 24 hrs AC X 1 AC X 4 Taxol X 2

[tCho] = 4.1LD = 1.7 cm

[tCho] = 4.6LD = 4.0 cm

[tCho] = 3.7LD = 4.0 cm

[tCho] = 0.9LD = 1.7 cm

Meisamy et al, Radiology 233:424 2004

[tCho] units = mmol/kgwaterLD = longest diameter

1H MRS: Cholines in Breast Cancer


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0 24

01

2

3

45

6

7

89

Hours

[tCho]

Objective Response No Response

0 24

0

1

2

3

45

6

7

89

Hours

[tCho

]

Meisamy et al, Radiology 2004

1H MRS Changes in (tCho) at 24 h

Predicted Clinical Response to AC

Ch t i i T i A i l


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Characterising Transgenic Animals:31

P MRS

CK transgenic mouse

[ADP]fdetermined

(Koretsky et al. PNAS 87:3112, 1990)

ODC transgenic mouse

[Mg2+]f determined in the brain

(Kauppinen et al. J Neurochem 58:831, 1992)

Mg2+pH

Ch t i i T i A i l


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Characterising Transgenic Animals

Huntington R6/2 mouse

Decrease in

NAA

Increase in

Cr + PCr

GPCGlu

Gln

Tau

GSH

Tkac et al. J Neurochem 100:1397, 2007


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High throughput MR of rodents

MRI of up to 16 mice at the time

MRS not quite yet there

Nieman et al. NMR Biomed 20: 291, 2007


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Technical Developments in MRMRI: spatial resolution should approach that of histology

Duyn et al. PNAS 104:11796, 2007


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Technical Developments in MRS

Spatial resolution of MRS(I) is an issue

Hardware: Field strength, B0 + B1

-shimming, rf-coils

Some unassigned metabolites yet to bediscovered in vivo

Dynamic Nuclear Polarisation (DNP):improving SNR of 13C, 15N, (1H?)

Ad d MRSI f A B i T


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Advanced MRSI of A Brain Tumour

0.8 x 0.8 x 1 cm3Avdievich et al. Magn Reson Med 2009


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13C MRS and Tumour Drug Treatment

Hyperpolarised substrates (Ardenkjaer-Larsen et al.PNAS 100:10158, 2003)

LDH catalysed exchange assayed in

apoptosing cells using hyperpolarised 13C-

pyruvate (Day et al. Nat Med 13: 1382, 2007)

Conclusions: MRS and in vivo


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Conclusions: MRS and in vivo

Metabolomics Metabolomics in its broad sense descends from

concepts pioneered by in vivo MRS MRS: in vivo metabolite phenotype

Focus currently on data acquisition techniques

to improve metabolite coverage and spatialresolution

Several disease-oriented applications Use of MRS is expanding in clinical centres