1. Biomarkers from Dynamic Images – Approaches and Challenges
Mike Hayball
Cambridge Computed Imaging Ltd (A Feedback PLC Company)
Big Data, Multimodality & Dynamic Models in Biomedical Imaging Wednesday 9th March 2016, Isaac Newton Institute, Cambridge

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Quantitative Imaging
Quantitative imaging is the extraction of quantifiable features from medical images for the assessment of normal or the severity, degree of change, or status of a disease, injury, or chronic condition relative to normal. (Quantitative Imaging Biomarkers Alliance – QIBA)
Cambridge Computed Imaging Ltd, a Feedback PLC company

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Imaging biomarkers
A quantitative imaging biomarker (QIB) is an objective characteristic derived from an in vivo image MEASURED on a ratio or interval scale as indicators of normal biological processes, pathogenic processes or a response to a therapeutic intervention. (Quantitative Imaging Biomarkers Alliance – QIBA)
Cambridge Computed Imaging Ltd, a Feedback PLC company

4. Quantitative Dynamic imaging
Quantitative imaging biomarkers from time series of images:
Nuclear Medicine
Positron Emission Tomography (PET)
Transmission X-ray - Iodinated contrast
X-ray Computed Tomography - Iodinated contrast
Ultrasound
Magnetic Resonance Imaging
Injected contrast
Arterial Spin Labelling
Diffusion Weighted Imaging
Cambridge Computed Imaging Ltd, a Feedback PLC company

5. “Colour Perfusion” Imaging
Cambridge Computed Imaging Ltd, a Feedback PLC company

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Present
Cambridge Computed Imaging Ltd, a Feedback PLC company

7. Cambridge Computed Imaging Ltd, a Feedback PLC company
History - Axel
Cambridge Computed Imaging Ltd, a Feedback PLC company

8. Anatomy vs Function Conventional CT good resolution, poor function
PET/Nuclear Medicine
Good Function, poor resolution
Cambridge Computed Imaging Ltd, a Feedback PLC company

9. Anatomy and function Contrast Enhanced CT
good resolution, subjective function
Functional CT
Apply quantitative techniques to single location CT

10. Perfusion Imaging Fast injection of iodinated contrast
s images in seconds
Assumptions
CT number proportional to iodine concentration
Retention time in tissue > time to peak
Contrast pharmaceutically inert
By Gccwang - Own work, CC BY-SA 4.0,

11. Dynamic sequence

12. TDC generation
time

13. Fick Principle (1) 𝑄 𝑡 =𝐹 0 𝑡 𝐶 𝑎 𝑡 − 𝐶 𝑣 𝑡 𝑑𝑡 Well mixed volume Q(t)
FCa(t)
FCv(t)
𝑄 𝑡 =𝐹 0 𝑡 𝐶 𝑎 𝑡 − 𝐶 𝑣 𝑡 𝑑𝑡

14. Fick Principle (2)
Well mixed volume
Q(t)
FCa(t)
𝑄 𝑡 =𝐹 0 𝑡 𝐶 𝑎 𝑡 𝑑𝑡

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Fick Principle (3)
Assuming no outflow during the inflow phase
Requires a fast injection of contrast
Contrast accumulation is at peak when the arterial concentration reaches maximum
𝑄 𝑡 =𝐹 0 𝑡 𝐶 𝑎 𝑡 𝑑𝑡
𝑑𝑄(𝑡) 𝑑𝑡 𝑚𝑎𝑥 =𝐹 𝐶 𝑎 (𝑡) 𝑚𝑎𝑥
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16. Perfusion Imaging Initial bolus Recirculation
Arterial enhancement following fast bolus injection

17. Perfusion Imaging
Enhancement in a tissue voxel following bolus injection

18. Perfusion Maximum gradient
Comparison of integrated arterial TDC and tissue TDC

19. Perfusion Integrated artery Tissue TDC
Integrated artery and tissue TDC matched curves

20. Perfusion Perfusion = Maximum gradient in tissue
Maximum enhancement in artery

21. Sample Data

22. Region of interest analysis80

23. Thresholding
Threshold
Applied

24. Smoothing
Binomial
smooth

25. Maximum enhancement Measures the peak enhancement for each pixel
Maximum enhancement (HU)
0.00
23.60
Measures the peak enhancement for each pixel
Can be used as an indicator of blood volume

26. Time to maximum Measures the time of peak enhancement for each pixel
Time to maximum (s)
7.00
17.00
Measures the time of peak enhancement for each pixel
Indicates time of arrival of the blood supply
Can be used to diagnose stenosis

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History – Miles et. al.
Applications:
Stroke imaging
Cardiac imaging
Liver imaging
Oncology
Cambridge Computed Imaging Ltd, a Feedback PLC company

28. Alternative approaches
Mullani-Gould Peak Method
𝐹= 𝐶( 𝑡 𝑚𝑎𝑥 ) 0 𝑡 𝐶 𝑎 𝑡 𝑑𝑡
Similar approach to maximum gradient but requires removal of recirculation
Deconvolution / constrained convolution
More susceptible to noise
Improved by additional time points (-> increased dose)
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29. Example - brain perfusion
Perfusion (ml/min/ml)
1.00
Maximum enhancement (HU)
0.00
23.60
0.00

30. Example - abdominal perfusion

31. Example - myocardial perfusion10 20 30 40
-100
100
200
300
400
Time(s)
Time Density Curve HU RV LV
Aorta

32. Example - myocardial perfusion
220
6.00
Perfusion (ml/min/ml)
1.5 ml/min/ml
2.0 ml/min/ml
1.2 ml/min/ml
-180
0.00

33. Example - MR perfusion Time Density Curve Intensity Time(s) 10 20 3010 20 30 40 50
-20 60
Time(s)
Time Density Curve
Intensity
Right Ventricle
Left Ventricle

34. Example - MR perfusion
2.00
0.00

35. CT DCE – acquisition and performance
1991
2016
Single slice acquisition
512x512 pixels per image
5mm slice
Rotation time – 2s
10 – 20 time points
< 10MB Storage
Typical desktop CPU
33MHz – 0.05 GFlops
< 8MB RAM
Multi-slice detectors
512x512 pixels per image
slice, 4-16cm “slab”
Rotation time – 250s
20 – 40 time points
< 6GB Storage
Typical desktop CPU
I7 - > 100 GFlops
> 8GB RAM
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CT DCE – processing
1991
2016
Transfer the data
Identify inputs
Create time-density curves
For region of interest
For pixels
For each time-density curve
Clean-up date
Calculate derived parameters
Present output
Register the images
Non-rigid deformation
Breathing
Cardiac motion
Movement
Segmentation
Constrained convolution / deconvolution
Cambridge Computed Imaging Ltd, a Feedback PLC company

37. CT DCE – why is it still here?
Simple to implement
“Technologically robust”
Source images unchanged
Improvements extend applicability
Sources of error
Noise
Motion
Calibration (contrast response)
Is it “perfusion”?
Assumptions break down “gracefully”
Consistent in regions with identical input
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Conclusions
Big data – what is it?
>1GB per study
<10 derived value per study?
Ideas often precede the capability
Simplicity and reproducibility are important
Adoption of new ideas takes time and patience
Cambridge Computed Imaging Ltd, a Feedback PLC company