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5D Cardiac PET/CT Imaging: Joint reconstruction

and cardiac and respiratory motion estimation.

Sonal Ambwani, W.C. Karl, Homer Pien

ambwani@bu.edu, wckarl@bu.edu, hpien@partners.org

Abstract

Coronary artery disease (CAD) or atherosclerosis is the leading cause of

death in industrialized nations.. Accurate assessment, characterization and

localization of this disease through non-invasive methods is an important

step towards the treatment of CAD. It has been shown that positron

emission tomography (PET) is capable of detecting large vessel

inflammation via activated macrophage uptake of FDG. However,

respiratory and cardiac motion during image acquisition leads to severe

blurring of the resulting images thereby rendering the spatial resolution

inadequate for detection of inflammation in coronary arteries. The objective

of this paper is to demonstrate the potential of producing high resolution

PET images to enable imaging of coronary artery inflammation.

In this poster, we demonstrate a novel method for joint reconstruction of 5D

coronary PET images and cardiac + respiratory motion correction that we

have termed as JRMC (Joint Reconstruction and Motion Correction). Our

algorithm features the use of all acquired data for SNR preservation and

enhancement of resolution of PET. Breath-hold CT is primarily used for

cardiac motion estimation. This knowledge of cardiac motion is

incorporated in the JRMC framework that iteratively estimates PET activity

images and respiratory motion. We investigated the feasibility of this

technique on simulated cardiac PET/CT data using XCAT and the

preliminary results show a marked qualitative and quantitative improvement

when compared to conventional PET reconstruction.

Motivation

(I) Proposed Algorithm: Image based

Main features

XCAT Simulation Results

(II) Proposed Algorithm: Projection based

Optimization Algorithm

• Cyclic co-ordinate descent method.

• Iteratively alternates between estimates of image

sequence and respiratory motion in the following

manner:

• Perform the R step by using Lange’s modification of

Green’s One Step Late algorithm (ref).

• The M step is performed by solving the non-linear

equation using non-linear Conjugate Gradient.

Simulation results continued ..

Research to Reality

References

(1)Ambwani S., Cho, S., Karl, W. C., Tawakol, A. and Pien, H., A Feasibility study of joint

respiratory and cardiac motion correction for coronary PET/CT imaging. ISBI , july 2009, pp.

935-938.

(2)Bailey, D., Townsend, D., Valk, P., and Maisey, M. Positron Emission Tomography:

Basic Sciences. 2006 Springer.

(3)Segars, W. P., Development and Application of the New Dynamic NURBS-based

Cardiac-Torso (NCAT) Phantom. Ph.D. Dissertation, The University of North Carolina, 2001.

(4)Schwaiger, M., Ziegler, S., and Nekolla, S. (2005). PET/CT: Challenge for nuclear

cardiology. J. Nuc. Med, 46:1664-1678.

• Coronary Artery Disease (CAD) or Atherosclerosis

is the leading cause of mortality in industrialized

nations.

• Positron Emission Tomography (PET) is a non-

invasive imaging modality that provides the

necessary functional information needed to detect

such plaque. Computed Tomography (CT) images

reinforce PET information with high resolution

anatomical information.

R1

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L2

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(a) (b) (c)

Fig 1. Stages of Atherosclerosis(Ross, R. N. Engl. J. Med 1999)

(a) Early: Inflammation at the site.

(b) Moderate: Deposition of lipids,

collagen, calcium etc; Plaque formation.

(c) Advanced : Rupture of plaque.

Fig 2. Inflammation vs.

FDG uptake (Tawakol et al,

JACC 2006)

• Evidence that there is a

direct correlation between the

radiotracer absorption and

atherosclerotic inflammation

and macrophage concentration.

Problem Statement

Fig 3.a Fig 3.b Fig 3.c

PET image. CT image PET/CT fusion.

Black arrow indicates the myocardium. Red arrow indicates stenosis in a coronary vessel.

• PET listmode data is binned into cardiac

phases/time-frames:

• Summing across rows yields:

• Inter-frame cardiac motion; Intra-frame respiratory motion.

Niiy ,,2,1}{

,,,,,,,, 2121 NN CCCCCC

Cycle R1 Cycle R2

• Image based analysis: Post-reconstruction

processing.

• Use of all acquired data, resulting in SNR

preservation.

• Sequential correction of cardiac and respiratory

motion.

• The recovery of the underlying HR image can be

stated as the following inverse problem :

Where S = Subsampling matrix, Bpsf = Blurring matrix due to the imaging system, Wcard

= Cardiac Motion matrix, Bresp = Matrix representing motion blur due to patient’s

respiration. Y = vector of stacked LR images, x = unknown HR image and η =

Gaussian noise vector.

• PML Cost functional :

Fig 4. Notional Diagram

Explicit

correction for

cardiac motion

in a super-

resolution

framework

Implicit

correction for

residual

respiratory

motion blur.

Estimation of cardiac motion via

optical flow using Breath-hold CT-AC images

Blind deconvolution to solve

for LTI respiratory blur.

Niiy ,2,1}{ x̂

Fig 5. High level diagram .

(Estimated HR image)

xMBSBxBWSBY RpsfrespcardPSF 0

2

22

2

201

2

200 ||||||||||||),( resprespcardpsfresp DbDxYxBWSBbxL

Extended Cardiac Torso (XCAT) simulation.

Photon count rate : 1 million/second

Experiment 1: FBP CSTAR

Reference Slice Conventional PETCardiac Motion removed

Both motions removed

Reference Slice Conventional PETCardiac Motion removed

Both motions removed

Experiment 2: ML-EM CSTAR

JRMC: Joint Reconstruction and Motion Correction

• The objective is to jointly estimate PET activity image

sequence {fn}and respiratory motion.

• The relationship between the observation and the

unknowns can be represented as :

• PML Cost functional comprises the following terms :

• Data Fidelity term

• Respiratory Motion Penalty term:

• Cardiac Motion Penalty term:

• Hence, the total PML cost functional:

5D Binning of PET

Listmode data

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})({}){},({}){},{|}({minarg}{ :step R

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XCAT Simulation Results

Experiment 1: Respiratory motion only

• 2D XCAT based experiments.

• Here, we have ignored cardiac motion for the sake

of investigating the success of the joint scheme in a

simple starter case.

Reference Slice Conventional PET Motion completely known JRMC result

Experiment 2: Both Cardiac and Respiratory

Motion.

(a)

Reference Cardiac

Sequence.

(b)

Results of Dual –

Gating methods.

(c)

Motion completely

Known.

(d)

JRMC: Quadratic

Spatial Penalty .

(e)

JRMC: TV based

Spatial Penalty .

• Validation of motion correction is clinical CT images.

• Development of a mechanical motion phantom.

• Clinical PET/CT implementation and develop ways to

outperform conventional PET/CT.