Ivana Isgum

Automatic determination of cardiovascular risk from thoracic CT scans using a

coronary calcium atlas

Image Sciences InstituteUniversity Medical Center Utrecht

NFBI, 10. 09. 2009.

I. Išgum, M. Prokop, P.C. Jacobs, M.J. Gondrie,W.P.Th.M. Mali, M.A. Viergever, B. van Ginneken

Cardiovascular disease

Class of diseases that involve the heart or blood vessels

Usually used to refer to diseases related to atherosclerosis– Condition in which an arterial wall

thickens

Atherosclerosis Arterial calcifications

– High density lesions inside arteries visible in CT scans

Calcium score – Value representing amount of

arterial calcifications – Independent marker of

cardiovascular disease

Calcium scoring in clinical practice

Possible calcifications extracted by thresholding

Calcifications are – Identified manually– Subsequently automatically

quantified

Goal

Automatic detection and

quantification of coronary calcifications in thoracic CT scans from lung cancer screening to determine cardiovascular risk status

Cardiovascular disease in lung cancer screening

More heavy smokers affected by cardiovascular disease than by lung cancer

Dutch-Belgian lung cancer screening trial (NELSON):– ~1% subjects - detected lung cancer

(baseline) – ~3% subjects (0.5% fatal) - cardiovascular

event (10 months follow-up)

Challenges for automatic coronary calcium scoring Thoracic CT scans from screening

– Non-ECG synchronization Cardiac motion

ECG-synchronized

Not ECG-synchronized

Challenges for automatic coronary calcium scoring Thoracic CT scans from screening

– Non-ECG synchronization Cardiac motion

– Non-contrast enhancement Coronaries not visible

Non contrast enhanced Contrast enhanced

Challenges for automatic coronary calcium scoring Thoracic CT scans from screening

– Non-ECG synchronization Cardiac motion

– Non-contrast enhancement Coronaries not visible

– Low-dose Noise with same intensity as calcium

Pattern recognition system Candidate extraction

– Thresholding (130 HU)– 3D connected-component labeling– Discarding too small (noise) and too large

(bony structures) candidates Feature computation

– Position (6 features)– Texture and size (41 features)

Classification– Two stage classification (kNN with feature

selection)

Position features For accurate calcium detection

– Location of the coronaries very important

Coronaries not visible– Segmentation not feasible

Alternative – Segmentation other anatomical

structures to estimate location of the coronaries*

Time consuming, less accurate* Isgum et al; Med Phys 2007; 34:1450-1461

Position Features Calcifications in the coronaries

are visible Design coronary calcium atlas

– Estimate location of the coronaries using calcifications in them

– Estimate probability for spatial appearance of calcifications

Data 121 thoracic CT scans

– 16 x 0.75 mm collimation – 0.7 mm section thickness– 1 mm increment

121 scans

51for creating

coronary calcium atlas

70 for designing

pattern recognition system

35 training 35 testing1 atlas 50 references

elastically registered* (coronary calcium atlas)

elastically registered to compute position features* http://elastix.isi.uu.nl/

Coronary calcium atlas

Probabilistic segmentation Atlas with segmentation

Results on 35 test scans Background Calcium Total

Background 211,673 32 211,705

Calcium 35 90 125

Total 211,708 122

Automatic systemRe

fere

nce

stan

dard

72% sensitivity;0.9 false positives/scan

Risk categories based on the coronary score

5 standard risk categories based on the Agatston score in the coronary arteries*

Risk category

I(very low)

II(low)

III(moderate

)

IV(moderately

high)V

(high)

Agatston score 0 1-10 10-100 100-400 >400

* Rumberger et al; AJR 2003; 181(3):743-74

I II III IV V

I4 0 0 0 0

II3 5 0 0 0

III1 2 7 0 0

IV0 0 0 7 0

V0 0 0 0 6

Reference standardAu

tom

atic

resu

lt

83% - agreement

I II III IV V

I4 0 0 0 0

II3 5 0 0 0

III1 2 7 0 0

IV0 0 0 7 0

V0 0 0 0 6

Reference standardAu

tom

atic

resu

lt

83% - agreement 14% - one category off

I II III IV V

I4 0 0 0 0

II3 5 0 0 0

III1 2 7 0 0

IV0 0 0 7 0

V0 0 0 0 6

Reference standardAu

tom

atic

resu

lt

83% - agreement 3% - two categories off

14% - one category off

I II III IV V

I4 0 0 0 0

II3 5 0 0 0

III1 2 7 0 0

IV0 0 0 7 0

V0 0 0 0 6

Reference standardAu

tom

atic

resu

lt

Conclusion Automatic calcium scoring from

low-dose, non-ECG synchronized thoracic CT scans appears feasible

In lung cancer screening cardiovascular risk can be estimated automatically