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Datasets for research

Data

Acquisition

Data

Reconstruction

Data

processing


Limitations in medical imaging research

When you do research in medical imaging you face problems:

• Limited clinical data (you also need ethical approval)

• Limited or no access to the reconstruction tools

• Physicians always have limited time to help you with the diagnosis


What is the solution?

Επεξεργασία

δεδομένων

Data simulation Development of

algorithms

Data

Acquisition

Data

Reconstruction


Dynamic MR image acquisition

(high resolution)

• 1.5 T Philips AchievaTM

• T1 weighted turbo field echo (TFE) sequence

• Repetition time = 3.3 ms

• Echo time = 0.9 ms

• Flip angle = 10

• MRI dataset reconstruction = image resolution 1.5×5×1.5 mm3

• (feet-head, right-left, anterior-posterior)

• temporal resolution 0.7s/time frame.


MRI-derived motion model

Diaphragm displacement (mm)

-50 -40 -30 -20 -10 0 10 20

45

40

35

30

25

20

15

10

5

0

- 5Hea

d-fo

ot z

-dis

plac

emen

t (m

m)

InspirationExpiration

Inspiration polynomial fit

Expiration polynomial fit

Motion model


…

MR navigator

& images

Motion

estimation

Dis

pla

ce

ment

Navigator position

Motion model gives the displacement of

each voxel in the image according to the

navigator position for the x, y, z axis.

Use of real time MR data with motion and navigator

signal from the diaphragm in order to create a motion

model that give us motion that has longer duration.


…

MR navigator

& images

Motion

estimation

Dis

pla

ce

ment

Navigator position

Motion model gives the displacement of

each voxel in the image according to the

navigator position for the x, y, z axis.

1. Each of the 105 MRI images was registered to the reference breath

hold image using a hierarchical registration algorithm

2. To measure the respiratory signal, the feet-head position of the

diaphragm was used as an external surrogate

3. For each control point in each image the displacements were

calculated as functions of the surrogate signal


How the MRI-derived motion model was

used for simulations

Motion model Motion signal (0.1 sec) MR motion fields

×

9

For any respiratory signal, the motion model is used to calculate motion fields

necessary to transform the reference distribution (i.e. the 3D tracer uptake distribution)

to the relevant respiratory positions and generate a moving phantom.

No tracer kinetics within the body or radioactivity physical decay was simulated in

this investigation.


10

Motion-model solution

…

MR navigator

& images

Motion

estimation

Motion

model

MR

navigator

Motion

estimates

Model formation (calibration) Model application


How we simulated the 3D PET distribution…


3D PET distribution 3D MR segmentation

L1 L2 L3

L4 L5 L6

L7 L8 L9

L1

L2

L5 L8

L3

L6 L9

Simulate 3D PET distribution

F18-FDG

• 3D MRI reference image was manually segmented into different tissue types

• Uniform SUVs were assigned to each type according to the tracer.

• Emission and attenuation images were simulated


4D PET distribution

3D PET distribution

4D MR motion fields

Analytic

simulations

Attenuated,

scattered, noisy

sinograms

Simulate 3D PET distribution

F18-FDG

STIR software:

• Software for image reconstruction and data manipulation in medical

imaging (http://stir.sourceforge.net)


Motion model Motion signal (0.1 sec)

3D PET phantom Analytic simulations

PET raw data

4D PET datasets Transformation

MR motion fields

×

14


15

× Amplitude: 6-20 mm

×

[5] Schleyer et al Phys Med Biol 2011 [6] Liu et al Phys Med Biol 2009

• Motion signals from PET images [5]

for 3 breathing types [6] : Type A : Long quiescent

motion periods

Type C: Random

baseline shifts

Type B: Regular quiescent

motion periods

Fre

qu

ency

Am

pli

tud

e

Am

pli

tude

Am

pli

tude

Fre

qu

ency

Fre

qu

ency

Time (sec) Time (sec) Time (sec)

Amplitude Amplitude Amplitude

• Lesions: 6-12 mm , contrast: 3:1-7:1

• Simulated PET resolution: 6mm & 3mm

Motion model Motion signal (0.1 sec)

3D PET phantom Analytic simulations

PET raw data

4D PET datasets Transformation

MR motion fields

×


Example of reconstructed images 18F-FDG

0

10 Reference position Breathing type 1

Breathing type 3Breathing type 2

0




What about new radiotracers?

Apply the method for radiotracer : 68Ga

The effect of positron range was included in the 68Ga-PSMA simulations as the

kinetic energy of 68Ga is much higher than 18F.

A) B)

0

18

0

18

Simulated radioactivity distribution for the 68Ga-PSMA A) without

blurring and B) with blurring for 68Ga at 3 T.

Kernels relative to 68Ga in the

A) cortical bone, B) water and

C) lung tissue at 3 T.


What blurring for 68Ga can cause

Table 2: Lesion characteristics before and after applying blurring for GA68-PSMA.

Lesion 1

⌀ 16 mm

Liver

Lesion 2

⌀ 16 mm

Lung

Lesion 3

⌀ 16 mm

Lung

Lesion 4

⌀ 16 mm

Liver

Lesion 5

⌀ 16 mm

Lung

Lesion 6

⌀ 16 mm

Lung

Lesion 7

⌀ 10 mm

Liver

Lesion 8

⌀ 10 mm

Lung

Lesion 9

⌀ 10 mm

Lung

Ideal SUVmax 20 6 6 20 6 6 20 6 6

Blurred

SUVmax 19.99 4.83 4.83 19.99 4.98 4.83 19.77 4.14 4.12

Mean/std SUV 16.34/2.88 3.30/0.94 3.3/0.94 16.23/2.85 3.32/0.93 3.3//0.93 15.07/2.81 2.72/0.80 2.61/0.80

Theoretical

Volume (mm3) 2144 2144 2144 2144 2144 2144 523 523 523

Measured

Volume with

34% SUVmax

threshold

(mm3)

2240 2470 2470 2048 2512 2472 896 1168 1016


Example of simulated data: 68Ga-PSMA

0



0




Example of reconstructed images: 18F-FDG

Static Type A (No-MC) Type B (No-MC) Type C (No-MC)

Reso

luti

on ≈

3m

m

Reso

luti

on ≈

6m

m

8mm , 3:1 lesion/back.




Static Type A (No-MC) Type B (No-MC) Type C (No-MC)

Reso

luti

on ≈

3m

m

Reso

luti

on ≈

6m

m




8mm , 3:1 lesion/back. No-motion correction After motion

correction


Conclusion

1. A scheme to simulate realistic synthetic (i.e. 100 ms) PET data of

two different and widely used radiotracers using a combination of

dynamic and static MRI acquisitions of healthy volunteers.

2. This approach allows incorporation of models of respiratory motion

to generate temporally and spatially correlated MRI and PET

datasets, as expected in simultaneous PET-MRI acquisitions.

3. Simulations with realistic anatomy and motion trajectories as those

observed in real subjects can help investigate the performance of

different reconstruction and motion correction methods.


Acknowledgments

Prof Paul Marsden

Mr Christian Buerger

Dr Andy King

Dr Kris Thielemans (main

STIR developer)

© 2013 laureate international universities ... - ccp pet-mr€¦ · motion signal (0.1 sec) motion...

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