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A METHOD FOR MOTION DETECTION AND CATEGORIZATION IN PERFUSION

WEIGHTED MRI

Rohit Gautam, Jayanthi SivaswamyCVIT, IIIT Hyderabad, Hyderabad, India

Ravi VarmaKIMS Hospital, Hyderabad, India

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Introduction

• MRI for brain - Application of nuclear magnetic resonance (NMR) to create images of human brain.

From MRI to Perfusion MRI

• Many neurological disorders can be detected using abnormal blood flow.

• Perfusion MRI utilizes this blood flow information in disease diagnosis.

MRI

PerfusionMRI

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What is Perfusion MRI ?

• Perfusion is the delivery of oxygen and nutrients to the cells via capillaries.

• A bolus injected into patient’s blood is tracked over time.

• It provides information regarding rate of blood flow, which helps to determine the affected regions in brain on the onset of disorder.

• Acquired data is a 3D time-series.

1 Nnwin nwout

Time-points

Before Bolus wash-in

After Bolus wash-out

Bolus in transit

Volume

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Problem

Patient motion during MRI scan (> 2 minutes) misaligns (corrupts) the acquired data.

Aim - Motion detection and categorization of volumes corrupted due to patient motion.

Difficulties•Simultaneous local (non-uniform variation in image contrast due to bolus) and global (motion) changes.•Current scenario: Motion correction is a time-limiting

step in PWI analysis [ Straka et al. JMRI 07].

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N

1

Motion

Motion

Motion

Beforebolus

wash-in

After bolus

wash-out

Bolus intransit

No variation in intensity

Non-uniform Variation in intensity

No variation in intensity

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Why motion correction ?

TTP: Time to PeakCBV: Cerebral Blood Volume

Perfusion parameters obtained from motion corrupted data vary with degree of motion.

Error in CBV estimation Error in TTP estimation

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Proposed Divide and Conquer Strategy

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Division of Time-Series

• The signal intensity in perfusion MRI varies proportionally with bolus concentration.

• A gamma-variate function (GVF) can model the change in concentration of bolus with time.

• We fit GVF on the mean-intensity perfusion curve µa(n) to estimate GVF-fit mean intensity curve µg(n).

• Using µg(n), we divide the time-series into 3 sets: Set1: pre-wash-in, Set2: transit and Set3: post-washout setsWash-in

Time pointWash-outTime point

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Divide and Conquer Strategy

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Intensity Correction

• Bolus is present only in Set2. Hence, intensity correction is required for bolus affected regions in these volumes before motion detection.

• A volume (F) is segmented into normal (Fnormal) and bolus affected (Fbolus) regions using clustering technique.

• F is then intensity corrected :

where, µg(n) is the GVF-fit-mean intensity curve.

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Intensity CorrectionSlice 1 Slice 2

Intensity CorrectedSlice 2

AbsoluteDifference

AbsoluteDifference

Ideally, these should be 0

Reduction in absolute intensity

difference

IntensityCorrection

Slice 1

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Divide and Conquer Strategy

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Motion Detection Workflow

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Motion Detection

For each mean shifted blocks of fixed size around each pixel, the flow vector is given by:

where S is the cross power spectrum.

Extract Central Slices

Block wise Phase Correlation

Process is accelerated by down-sampling of central slices.

Block wise Phase Correlation

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Motion Flow Maps

Slice 1 Slice 2

Un Vn

Bolus present andNo motion

Slice 1 Slice 2

Un Vn

Bolus absent andMinimal motion

Slice 1 Slice 2

Un Vn

Bolus absent andMild motion

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Net Entropy Metric

• For a given time series {Fn; n=1…N} , the net entropy (Hn) of flow fields (Un;Vn)is given by:

• The net entropy is 0 for no motion and increases with degree of motion.

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Zero net entropy even in the

presence of bolus.

Net Entropy Profile

1 5 8

33 40Wash-in

Time-pointWash-in

Time-point

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A non-zero net entropy even in the absence of

motion

Does Intensity Correction help ?

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Slice Resolution

Block Size Mean time per slice pair (sec)

Total time(sec)

128x128 32x32 0.00 + 3.48 = 3.48 132.21

128x128 16x16 0.00 + 3.99 = 3.99 151.69

128x128 8x8 0.00 + 4.34 = 4.34 164.84

64x64 16x16 0.01 + 0.77 = 0.78 29.71

64x64 8x8 0.01 + 0.97 = 0.98 37.38

32x32 8x8 0.01 + 0.19 = 0.20 7.68

Time Analysis of motion detection

Large reduction in computation time

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Divide and Conquer Strategy

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Motion Categorization

• Peak entropy of flow fields is used to quantify the degree of motion.

• The peak entropy Hpeak of the flow fields is found by:

where, Hn is the net entropy.

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Such a small motion cannot be

detected.

Peak entropy can distinguish

between different motion categories.

Entropy values for different motion categories for image size – 32x32 and block size 8x8

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Comparison of our approach

• We compare the efficiency of motion detection method by applying it prior to existing motion correction algorithms.

[1] Kosior et al., JMRI 2007.[2] Straka et al., JMRI 2010.[3] Tanner et al., MICCAI 2000.

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Conclusions

• We proposed a motion detection method that is immune to intensity changes due to injected contrast agent.

• We achieved a large reduction in time (~37%) required for motion correction by rejecting the stationary volumes.

• The detection method can be made to be fast but the sensitivity to minimal motion maybe compromised.

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Questions ?

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Thank you