Quantitative MRI: the Past, Principles and the Futureqmri.org/qmri-ppf.pdfThe concept of the...
Transcript of Quantitative MRI: the Past, Principles and the Futureqmri.org/qmri-ppf.pdfThe concept of the...
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Quantitative MRI: the Past,
Principles and the Future
Paul Tofts
Emeritus Professor University of Sussex, Brighton, UK
Formerly Chair in Imaging Physics, Brighton and Sussex Medical School , and
Professor at UCL Institute of Neurology, Queen Square, London, UK
www.paul-tofts-phd.org.uk www.qmri.org
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Four questions
1. Why should we Quantify?
2. Why are repeatability and reproducibility
important?
3. What is a Perfect qMRI Machine?
4. What is the proposed MRI medal system?
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Quantitative MRI: three parts
1. the Past
-- discussion –
2. Principles
-- discussion –
3. Future
-- discussion--
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1. Quantitative MRI: the Past
a. 31P MRS in neonates
b. DCE-MRI Gd gives endothelium transfer constant
c. qMT bound protons show myelin
d. MTR histogram predicts clinical score
e. multi-centre – MAGNIMS
f. consensus papers
g. unnoticed qMRI – glioma transformation
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normal
Asphyxia
high Pi
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normal
Asphyxia
high Pi
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31P concentration
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Dynamic Contrast-Enhanced MRI
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vl is the size of the Extravascular Extracellular Space
k the transfer constant (depends on permeability and blood flow)
most applications in cancer
acute MS
chronic MS
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NH3
OH
H
O
H
H
O
H H
O
H
H
O
H
Magnetisation Transfer
Macro molecules (invisible)
(bound protons - short T2) Surface Bulk water (visible)
(free protons- long T2)
Proteins,
Lipids,
etc.
exchange diffusion
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qMT in MS
Davies et al Mult Scler 2004; 10:607
Frontal WM fb (%) p
Control 9.8
NAWM 8.6 <0.01
Lesion 4.6 <0.01
fb = fraction of protons that are bound
≈ myelin concentration
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Alzheimer’s disease
Hippocampal qMT parameter (~ myelin concentration) vs clinical score Ridha, Fox, Tofts. Quantitative magnetization transfer imaging in Alzheimer disease Radiology 2007; 244:832
0.02
0.025
0.03
0.035
0.04
0.045
0.05
0 5 10 15 20 25 30
MMSE
Me
an
Hc
fb/R
A(1
-fb)
(s)
AD patients (n=14)
Normal controls (n=14)
Clinical score (30=normal)
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MTR histograms in Multiple Sclerosis
Whole-brain histogram depends on MS subtype; sensitive to demyelination
Dehmeshki Tofts Magn Reson Med 2001
Current clinical score (EDSS) can be predicted from
histogram (using principle components analysis - PCA)
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Reproducibility across centres
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1. Much work on multi-centre studies (e.g. MAGNIMS 1990’s)
a. EU funded MAGNetic resonance Imaging in Multiple
Sclerosis)
b. e.g. T2w-lesion load: 5 EU experts in one room
2. ‘Protocol Matching’ across different manufacturers using
standard clinical sequences
a. works for simple parameters (T1, D, MT)
b. relatively easy to implement on a wide scale
3. Travelling controls, phantoms + post mortem brain
4. Complex parameters (e.g. DCE Ktrans) are often in a ‘black box’
and may need ‘open source’ software run on each maker’s
machine
May need Research Agreement for each machine
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Between-centre difference can be eliminated
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Consensus papers
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1. Identify leaders
2. Invite them to a meeting
3. Write the paper (on methodology, analysis, terminology)
4. Reject any papers that do not use this consensus
2003 citations (October 2019)
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U3A Oct 7th 2016 16
FLAIR T1w pre_Gd
d c
T1w post_Gd %E
Low Grade
Glioma
‘not visibly
enhancing’
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0
1
2
3
4
5
6
7
8
9
10
-30 -20 -10 0 10 20 30
% enhancement
rela
tiv
e v
olu
me
Normal Appearing
tumours
Measure size of RHS tail = volume of abnormal tissue
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Kaplan-Meier survival plot, using uncorrected volume from baseline scan
p<0.039 at 5 years
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Transformers show
progressive increase in
enhancing volume
different from NT
even at baseline
Non transformers are
stable
small SD;
homogeneous group
Tofts JMRI 2007; 25:208-14 Quantitative analysis of whole-tumor Gd
enhancement histograms predicts malignant
transformation in low-grade gliomas.
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1. Quantitative MRI: the Past
a. 31P MRS in neonates
b. DCE-MRI Gd gives endothelium transfer constant
c. qMT bound protons show myelin
d. MTR histogram predicts clinical score
e. multi-centre – MAGNIMS
f. consensus papers
g. unnoticed qMRI – glioma transformation
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2. Quantitative MRI: Principles
a. why quantify?
b. the Books
c. accuracy & why Random Error is the Enemy
d. phantoms vs healthy controls
e. data acquisition
f. data analysis
g. Upgrades are also an Enemy
h. Statistics are friends
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What is qMRI?
Quantification = measure
Quantity
e.g. body mass
reliable, accurate, reproducible, easy
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Quantification
Quantify – to measure a quantity (size, weight,
blood sugar, cholesterol …)
Medical images have been qualitative
Look; human assessment; experience needed
Imaging is becoming quantitative
Measure e.g. tumour size, water content, tissue
destruction, volume of MS lesions…
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Why is qMR needed?
1. Measurement concepts - sources of variation
2. Specificity - new biological quantities
3. Scientific instrument following long tradition of
measurement in astronomy, physics, chemistry,
electrical engineering…
4. Measure subtle ‘invisible’ changes ; diffuse or small, in
‘Normal-Appearing’ brain tissue
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Psychometric measures desirable properties
Sensitivity
does the quantity alter with disease?
Validity
Is it relevant to the biology?
Reliability
Is it reproducible?
qMRI of the brain, 1st edition p68
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qMRI – a technology whose time has come
‘The pre-eminent role of imaging now requires a new level of metric - quantitative measurements’
Robert I Grossman MD, Chair of Radiology, New York University
Medical Imaging
meets
Measurement Science
British Medical Association Radiology book
prize 2004
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new edition 2018 €120 hardback; €50 eBook (Amazon or CRC press)
see qmri.org (some author pre-prints)
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UK Institute of Physics
and Engineering in
Medicine
Report 112 2017
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Is accuracy important?
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Is accuracy important?
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In a single centre short study – probably not
In longer studies – yes (withstand upgrades)
In multi-centre studies – yes
(unless you can replicate the sources of inaccuracy at each
site – ‘protocol matching’)
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Why does random error matter?
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* ISD = Instrumental Standard Deviation
(repeatability)
* *
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Why is repeatability important?
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Phantoms and healthy Controls for QA
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Normal ranges: T1 4-6%; MD: 3-5%; MTR: 1-2%
With correction for age etc, and control of ISD these would probably be reduced
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Phantoms vs controls
1. Good phantom performance necessary but
not sufficient
2. Phantom – beware RF dielectric resonance
3. Multi-centre: travelling phantoms? Controls?
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Why are physicist so interested in
scanning normals?
Repeatedly!
Understand and minimise all the sources of variation
Serial study
Cross-sectional study
Influence of instrumental variation on sample
size in power calculation
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Some quantities depend on acquisition parameters (e.g. T2, MD depend on TE’s)
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What causes random variation?
Rotterdam Jan 2019 37
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B1 errors
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Quality of parameter estimates depends on quality of
acquisition
B1 and image noise often dominate parameter uncertainty
[poor acquisition cannot be fixed by post-processing!]
1% error in flip angle FA gives 2% error in T1 (in Variable
Flip Angle method)
Slice selection is bad news – use 3D acquisition?
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Optimisation of acquisition procedure Minimising the effect of image noise
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Image data analysis
Region of interest Test a specific location (prior information and hypothesis)
Histogram Whole brain; unbiased; for diffuse disease
Voxel-Based Morphometry VBM Unbiased testing of many locations
Each location can be correlated with external score (clinical, genetic, proteomic, cognitive)
Texture ‘dirty white matter’
tissue often becomes more heterogeneous in disease
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Upgrades are also an Enemy
Any long-term study needs stability
Any serious change will need repeated validation
of qMR method
Changes can be software, hardware, field
strength
Many quantities ought to remain unchanged
with good methodology (e.g. volume)
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Statistics are friends
In a group comparison study, often group differences
are reported as p-values
If no significant difference seen, was this because:
There is no biological difference between the groups
The instrumentation is rubbish (large instrumental SD: ISD)
Better: give confidence limits for group difference,
measured group SD, and estimated ISD
Then studies can be evaluated, compared and pooled
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2. Quantitative MRI: Principles
a. why quantify?
b. the Books
c. accuracy & why Random Error is the Enemy
d. phantoms vs healthy controls
e. data acquisition
f. data analysis
g. Upgrades are also an Enemy
h. Statistics are friends
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3. Quantitative MRI: the Future
a. why are we here??
b. The Perfect Machine
c. Medals
d. Understanding Normality
e. Understanding machine variation
f. Why is qMRI not like a thermometer?
g. Resources at qMRI.org
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why are we here??
No-one ever wished on their death bed that they had
spent more time in the office (from a time-management course)
One of the 10 keys to happiness is to do meaningful work
(from Action for Happiness)
Break out of continual re-implementation of methods
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Perfection is possible
The concept of the ‘Perfect Machine’ originates in the building of the 200 inch
Palomar telescope in 1933-48.
inspiration: In Thomas Mann’s Death in Venice, the writer is on the Venice beach. He
sees the detail, in the foreground: children constructing a sand castle. He turns his
gaze to the horizon, empty and infinite. What would it be to be a measurement hero?
From Quantitative MRI of the Brain p10
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Medals for Perfection
NB A medal could exist for each qMR parameter.
Inspiration: the lifetime work of John Harrison, who constructed stable travelling clocks. The Longitude
prize of £20k was offered by the British parliament in 1714, in response to loss of life at sea and an urgent
need for better navigation. This medal scheme might be attractive to a philanthropist.
from Quantitative MRI of the Brain p10
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Normality: normal range depends on repeatability
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An invisible problem
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Understanding machine variation
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1. More to come
2. Not just image noise
3. Low level ‘noise’ masking subtle Gd enhancement
a. Short Term Long-range Fluctuations probably
originate from pulsatile movement of the
bright Superior Sagittal Sinus (<1%)
b. Movement through the nonuniform B1- receive
field, not corrected by registration software.
c. (ISMRM Paris 2018 poster)
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Why is qMRI not like a thermometer!
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1. Thermometer (or voltmeter): works, reliable .....
2. qMR from vendors: another story
3. Killer App may drive vendor implementation (MD in stroke, Ktrans)
4. drivers: pharma trials ... NHS treatment decisions
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The future
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1. Type A and B errors
a. Papers from NPL and NIST ‘estimating uncertainty in
measurement’
b. Random vs systematic error, depends on time scale
c. for voltage or temperature we just have max uncertainty (95%?)
d. ADC alkane measurements: propagation of errors in G,T etc
e. Consensus paper on how to... ?
2. ISMRM reproducibility challenge
3. National Measurement centres: use their expertise and concepts
NIST – National Institute of Standards and Technology, USA
NPL – National Physical Laboratory, UK
PTB – National Metrology Institute of Germany
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qMR – the future
qMR is becoming a turn-key application
Happy Snappy MRI Camera transforming into
Scientific Instrument
We are witnessing
paradigm shift
technological revolution
Link: qmri.org/hack2019 Nikola Stikoff
- ISMRM special workshop; consensus position paper
- Publish specific medals e.g. T1, MD
(some may already exist)