Clinical applications of MRIProfessor Sir Michael Brady FRS FREng

Department of Engineering ScienceOxford University

Michaelmas 2004

MRI is used for a huge range of clinical applications

• Clinical neurology– Segmentation and classification– Measuring volumes of brain structures– Multiple sclerosis, neurodegeneracy, stroke, …

• Cardiology– Either need to image fast, or deal with heart motion!

• Cancer– Breast, colorectal, liver, prostate, …

• Soft tissue damage – Cartilage, ligaments, …

MRI is also used a great deal in basic science to study brain function and cancer growth

Segmentation and measurement of brain tissueNote the huge overlap between the Gaussian pdf for GM and that for WM

This means that there are many misclassifications of GM pixels as WM and vice versa.

Even small amounts of noise can change the classification.

The segmentation / classification of brain tissue depends upon estimating the likelihood of each class at each voxel x and then iteratively updating and propagating these estimates to the neighbours

This uses Hidden Markov Random Fieldsand will be defined in the Informatics course

MR image of a horizontal slice through the brain.

In this T1-weighted image, grey matter is lightly coloured, while white matter appears darker.

Two spots of plaque, corresponding to multiple sclerosis are visible.

MR angiography – the vasculature of the brain

MR angiography is rapidly gaining in importance; it can be fused with the complementary CT angiography

Diffusion Imaging

• Measure self diffusion of protons in every voxel.• In white matter areas – more diffusion in fibre direction.

We can measure e.g. principle fibre direction and “anisotropy” (Strength of fibre direction).

Algorithms have been developed to trace out fibre tracts – connectivity in the brain

Application of DWI Analysis:Thalamus Segmentation

Seeded in medio-dorsal nucleus in thalamus; in monkey this projects to prefrontal cortex and receives projections from temporal lobe

Work done in the Functional MRI of the Brain Laboratory by Tim Behrens, Dr. Johansson-Berg, Mike Brady, Paul Matthews, Steve Smith, Mark Jenkinson, and Mark Woolrich

Motor pathways

This example shows a tract from VL nucleus going to M1, cerebellum and brainstem

Visual pathways

This example shows a tract from LGN going to optic tract and visual cortex

Segmentation of left and right thalami, based on projections to 4 cortical zones.

Purple: MediodorsalnucleusProjects to PFC, receives from temporal lobeBlue: Ventral posterior nucleus, projects to S1/S2Orange: Ventral lateral and ventral anterior nuclei. Project to M1 and PMC/SMAYellow: Pulvinar, projects to PPC and extrastriate cortex.

Arterial Spin Labelling

This method enables clinicians to estimate cerebral blood flow, in order to manage stroke patients

Source: Daniel Gallichan, FMRIB, May 18 2004

TAGGED MRI DATA (1996)

Normal heartNormal heart

Abnormal heartAbnormal heartinferior infarctinferior infarct

Short Axis 1Short Axis 1 Short Axis 2Short Axis 2 Long AxisLong Axis

TAGGED MRI MOTION RECONSTRUCTION

Normal heartNormal heart

Short Axis 1Short Axis 1tag planestag planes

andandLV boundariesLV boundarieswith SA imageswith SA images

Source: Jérôme Declerck, PhD thesis, 1997

Heartbeat 2.0T cardiac MR, taken by Professor Stefan Neubauer, JR hospital, June 2003

Images courtesy of subject: Mike Brady

MRI and cancer

• Breast cancer– Contrast enhanced MRI and angiogenesis– T1 estimation

• Colorectal cancer– Bias field correction– Anatomical frame of reference– Lymph node detection & classification– Pre- and post-chemotherapy image registration

Limitation of anatomical MR imaging

1. Take one or more images prior to injection of contrast agent

2. Inject contrast agent, typically Gd-DTPA

3. Take images as fast as possible afterwards (note: all of both breasts imaged)

The tumour cannot be differentiated by its T1; conventional MRI gives information about anatomy not physiology

There is a massive adenocarcinoma there –with secondary spread

Contrast agent take-up

Inside the tumour, the enhancement is over 100%

Contrast agent take-up

.. Normal tissue enhances less

Contrast agent take-up

.. Whereas fat barely enhances at all

Unfortunately, some benign tissue can enhance more than malignant, and the amount of uptake is highly variable, making quantitative analysis difficult

Reason: contrast agent take-up is non-linearly related to intensity change

0 1 2 3 4 5 6 7 8 9 10

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Benign & Malignant in CEMRI

No absolute scale for ∆S increase for malignant

Benign processes such as fibroadenoma can enhance as much as tumour

T1 mapping

• Signal enhancement is non-linearly related to Gd concentration, which is directly proportional to local vascularity

• Working from a model of signal enhancement, we have developed a method to estimate the T1 at each time point, hence the change in T1

0 1 2 3 4 5 6 7 8 9 100

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• Malignant to benign distinction is massively improved using concentration based analysis.

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Gradient Echo Signal Model• Use Bloch equation to describe signal for a gradient echo pulse

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• Add effects of contrast agent (T1 & T2 alteration).

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T1 must be measured

Nonlinear variation with T1

Optimum Flip Angle Combinationsn = 2 - 5

16 ms17 °16 °10 °4 °3 °

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Armitage, Behrenbruch & Brady 2001, 2002

Two-Compartment Pharmacokinetic Model

• Models interaction between a blood pool and lesion leakage space (EES).

Blood PlasmaCp(t)

ExtravascularExtracellular Space

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• Solution gives the following:

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Tumour vascularity & angiogenesis

In order to fuel its growth, a tumour grows a network of millions of micro-vessels that tap in (like shunts) into supply arteries

Millions of micro blood vessels are grown – this new blood is angiogenesis

The blood vessels are both small (microns) and leaky, so Gd molecules

stick around the vicinity of a tumour

Parametric T1 mapping for analysing ce-MRI

Conventional analysis based on intensity change

Estimating change in T1 and its visualisation

Armitage, Behrenbruch & Brady, Medical Image Analysis, 2005

Ketsetzis and Brady, IEEE Trans. Med. Im., 2005

Multiple acquisitions prior to injection of Gd is well-known. We have developed a method that minimises the error in the estimation of T1

Measuring effect of chemotherapy

Pre- and post-chemotherapyPercentage increase in intensity at right

Pre- and post-chemotherapy ∆T1 at left

Armitage, Brady and Behrenbruch, Medical Image Analysis (2005)

(non-rigid) registration and pre- and post-chemotherapy, from ∆T1

Results for four patients

Left – the pre-contrast 10 degree image

Right the segmentations:

Blue = fat

Green = normal

Orange = benign lesion

Red brown = malignant lesion

Compare to hand segmentation by a pair of experienced radiologists

Limitation of that “validation” is that they disregard the partial volume effect – where much of the change occurs

Simultaneous segmentation and Registration of ceMRI

Probabilistic labelling of dataset from ceMRI

The need for non-rigid registration.

In this case, relaxation of the pectoral muscles causes severely non-rigid motion...

Registration Results.

The computed non-rigid motion field.

registration and image re-sampling

pre-contrast image post-contrast image subtraction image

motion field corrected post-contrast image corrected subtraction

Axial T2 MR images

Colorectal MRI – as used at Churchill Hospital

The brightness of the data is unbalanced due to RF field inhomogeneities, creating a bias field effect. This makes the images difficult to analyse.

The bias field (B1 inhomogeneity)• a low frequency distortion of the “idealised” intensities in an MRI image

• effect of these bias fields is highly disruptive when using surface coils, affecting intensities with a contribution of up to 60% of the maximum image intensity

• Acquisitions using body coils, such as the ones commonly performed for brain studies, create more uniform magnetic fields, thus reducing intensity variations to the range of 10%-20% of image amplitudes

Estimating the Bias FieldThe brightness of the data is unbalanced due to RF field inhomogeneities, creating a bias field effect. This makes the images difficult to analyse.

( ) ( ) * ( ) ( )s x o x b x n x= +

The bias can be modelled as a multiplicative field:

s(x) is the signal that is received and is seen as the resultant image;

o(x) is the original or ‘ideal’ image that we are trying to extract;

b(x) is the bias field;

n(x) is noise, which is initially assumed negligible.

It can then be converted to logarithmic form:

log( ( )) log( ( )) log( ( ))s x o x b x= +

Legendre polynomials and an ‘Evolutionary Strategy’ optimisation method (Styner et al.) are used to estimate b(x):

Removed Bias Field

Original image Bias field removed by Sarah Bond & Michael Brady

Active Contours

Using an active contour to track through the series of axial images

3D Visualisation of Colorectum

Fusion of CT and MRI requires constrained non-rigid transformation.

Segmenting the Mesorectal Fascia1. Use ‘easy to find’ shapes in order to

create a coordinate frame or reference.

2. Make initial estimate as to position of Mesorectal fascia

3. Refine estimate using active shape models (Staib and Duncan), and training set of images (bias removed). Optimise using gradient descent algorithm.

Shape of Mesorectal fascia is described using spherical harmonics.

Mesorectal fascia (lace) and colorectum(solid)