Algoritmi per la delineazione del Biological target volume...

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Marco Brambilla - Roberta Matheoud

Medical Physics Department Azienda Ospedaliero Universitaria “Maggiore della Carità” Novara

Algoritmi per la delineazione del Biological target volume in immagini FDG PET/TC

Incontri a Fisica – Torino 2011

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METHODS FOR TARGET VOLUME DELINEATION IN PET

1. Visual contouring of PET scan and definition of contours as judged by the experienced physician

2. Absolute Thresholds: Standardized Uptake Value (SUV) >2.5

3. Thresholding by fixed percentage of the maximum uptake or

mean SUV

4. Algorithms depending on lesion size 5. Algorithms depending on contrast 6. Complex algorithms

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1. Visual Contouring

Author Reference District Patient n°

Faria SL et al Int J Radiat Oncol Biol Phys 2008;79:1035-8 Lung 32

Schwartz D et al. Head Neck 2005;27:478-487 Head Neck 63

Heron DE et al. Int J Radiat Oncol Biol Phys 2004;60:1419-1424 Head Neck 21

Nestle U et al Int J Radiat Oncol Biol Phys 1999;44:593-597 Lung 34

Kiffer JD et al Lung Cancer 1998;19:167-177 Lung 15

This method is the first one applied and still widely used Easily applicable from a technical point of view


But …

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Riegel et al reported significant differences in GTV delineation between multiple observers (A,B,C,D) contouring on PET/CT fusion. Thus, confirming the need for a delineation protocol.

Riegel et al. Int J Radiat Oncol Biol Phys 2006;65:726-732

A B C D

A - 24% 12% 90%

B 15% - 12% 79%

C 45% 56% - 137%

D 5% 8% 1% -


… is affected by a significant interobserver variability

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2. Absolute SUV (GTV= SUV>2.5)

Author Reference District Patient N°

Paulino AC et Int J Radiat Oncol Biol Phys 2004; 59:4-5 -----

Nestle U et al J Nucl Med 2005;46:1342-1348 Lung 25

Nestle U et al. Eur J Nucl Med Mol Imag 2007;34:453-462 Lymphnodes 15

Well applicable to most PET and to some RT planning systems

Due to biological and physical factors there are no “normal” values for SUVs to be similarly used in every case.

It has been shown that this method often fails, e.g. when the physiological background activity lies above the fixed threshold (Nestle et al 2007)


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2. Absolute SUV

18F-FDG PET: SUVmax = 30 Isocontours: narrow = GTV40 wide = GTV2.5

Nestle et al. (2005) concluded that, as for NSCLC: 1. GTV40 does not appear to be suitable for target volume delineation. 2. GTV2.5 provided volumes higher than GTVCT and similar to those

provided by visual interpretation


Nestle et al. (2005): Corresponding planning CT: red = GTV40, green = GTVbg, yellow = GTVCT.

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Many factors affect SUV measurements and therefore tumor contours:

– The metabolic activity of a tumor, the glucose level in the blood,

the time elapsing from injection to acquisition, the reconstruction

algorithm.

– The lesion dimensions (partial volume effect)

– Heterogeneity within a tumor

– Tumor motion

The use of a fixed SUV threshold sorrounding the lesion is not suitable for GTV contouring


2. Absolute SUV

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3. Fixed percentage of mean SUV

Variable threshold for SUVmean: Threshold SUV= 0.307 x SUVmean + 0.588

Author Reference District Patient N° Black QC et al. Bayne M et al

Int J Radiat Oncol Biol Phys 2004; 60:1272-1282 Int J Radiat Oncol Biol Phys 2004; 60:1272-1282

Lung Lung

15 26

SUVmean depends on ROI dimensions At least iterative process

Circular reasoning

Bayne et al reported discrepant GTVs using this algorithm

to evaluate patient data

ROI must be defined to calculate SUVmean


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3. Fixed %Threshold

A few early investigations estimated that a threshold of ~ 40% of maximum uptake approximated tumor volume

Author Reference District Patient N° Erdi YE et al. Radiother Oncol 2002; 62:51-60 Lung 11

Erdi YE et al. Cancer 1997;80:2505-2509 Lung 10

Many clinical studies were undertaken adopting a fixed threshold of maximum uptake comprised between 40% and 50%:

Author Reference District Patient N° Method

Bassi MC et al Int J Radiat Oncol Biol Phys 2007 Rectum 25 FT 42%

Koshy M et al. Head neck 2005;27:494-502 HN 36 FT 50%

Scarfone et al. J Nucl Med. 2004 Apr;45:543-52 HN 6 FT 50%

Bradley et al Int J Radiat Oncol Biol Phys. 2004;59:78-86 Lung 26 FT 40%

Ciernik IF et al Int J Radiat Oncol Biol Phys 2003;57:853-863 HN Lung

Pelvis 39 FT 50%


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This method is not likely to be accurate over the full range of clinically relevant volumes and target contrast, mainly because:

1. Thresholds depend on target size 2. Thresholds depend on target contrast 3. Thresholds are “scanner specific” since they likely depend on the

spatial resolution characteristics of the scanner used

GTV delineation of primary tumors, tresholding by 40% of the maximum accumulation intensity should be avoided due to the risk of insufficient tumor coverage (Nestle et al 2009).


3. Fixed %Threshold

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Erdi YE et al. Cancer 1997;80:2505-2509


Drever L et al. Med Phys 2007;34:1253-1265

4. Algorithms depending on lesion size

Ford EC et al. Med Phys 2006;33:4280-4288

C-PET Plus (Philips)

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Jentzen W et al. J Nucl Med 2007;48:108-114 Daisne JF et al. Radiother Oncol 2003;69:247-250

5. Algorithms depending on contrast

ECAT HR (Siemens)

Th = a + b/(S/B)

Nestle U et al. J Nucl Med 2005;46:1342-1348

Imean= mean intensity of all pixels surrounding the 70% Imax isocontour within the tumor

ECAT Art (Siemens)

ITh = (0.15xImean) + B


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6. Complex algorithms: Background Subtracted Images

Thabs=B + Threl(Smax-B)

Threl= 41% for diameter > 12 mm

ECAT Exact HR+ (Siemens)

Davis JB et al. Radiother Oncol 2006;80:43-50


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6. Complex algorithms: Iterative Image Thresholding

Discovery LS (GE)

Jentzen W et al. J Nucl Med 2007;48:108-114


Th(%) = 7.8%/V(mL) + 61.7%*B/S + 31.6%

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6. Complex algorithms: Gradient based segmentation

Ecat HR Plus (Siemens)

Geets X et al. E J Nucl Med Mol Imaging 2007;34:1427-1438

a. Image denoising b. Image deblurring c. Gradient based segmentation


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Conclusions

• Visual contouring is affected by a significant interobserver variation and should be avoided.

• The use of a fixed SUV threshold surrounding the lesion is not suitable for GTV contouring. The method often fails when the physiological background activity lies above the fixed threshold.

• The use of a fixed threshold of maximum uptake (e.g 40%) should be avoided due to the risk of insufficient tumor coverage.

• Methods for segmentation of non uniform tracer concentration not based on thresholding have been recently proposed. While referring to these promising methods it should be pointed out that until they are further developed and validated, contrast-oriented segmentation methods are and will be used in most clinics and therefore need to be accurately characterized


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Imaging parameters not explored in contrast oriented methods

• Effects of Image Acquisition Parameters – Emission scan duration and background activity concentration are both

related to noise in the reconstructed image. Their effects on TS was not ascertained.

– If different levels of noise play a role in threshold determination, the calibration curves will be specific for the acquisition protocol

• Effects of attenuation and scatter – If attenuation and scatter play a role in threshold determination, site

specific thresholds should be devised. – Threshold values are usually determined using simple phantoms

(cylinder/rectangular box with spherical/rod sources). The results obtained in these simplified conditions may not be reliable in more realistic conditions of attenuation and scattering.

• Effects of Image Reconstruction parameters – More smoothing require higher fractional TS – The role of the convergence of iterative algorithms (i.e iteration number)

was not ascertained.

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Imaging parameters not explored in contrast oriented methods

• Activity distribution – The published thresholding methods provide reliable volume estimates only

if the imaged activity distribution is homogeneous. • Lesion movement

– Errors associated with lesion masses moving during data acquisition, (e.g. imaging of lung lesions), are unpredictable and not yet characterized.

• Spherical vs irregular shape – Most of the published thresholding methods assumed spherical lesions and

the clinical PET volumes calculations are performed using an ellipsoid model. These suppositions are approximation of the irregularly shaped tumors.

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A multivariable approach was adopted to study the dependence of the percentage threshold (TH (%)) used to define the boundaries of 18F-FDG positive tissue on emission scan duration (ESD) and activity at the start of acquisition (Aacq) for different target sizes (Sphere ID) and target-to-background (T/B) ratios.

AIM OF THE STUDY

Med Phys 2008;34:1253-1265

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MATERIALS AND METHODS

• 6 fillable spheres ID: 10, 13, 17, 22, 28 and 37 mm • 4 micro hollow spheres ID: 4.1, 4.7, 6.5, 8.1 mm • 3 cm-thick annular ring of water bags fit over IEC

phantom to match T+S rates estimated for patients

• Polyethylene cylinder (ρ=0.96 g/cm3, Ø = 203 mm, length = 700 mm)

• fillable 80 cm-long plastic tube (ØID = 3.2 mm) parallel to the central axis of the cylinder at a 45 mm radial distance.

SCANNER

TEST PHANTOM SET IEC body phantom + water bags Scatter phantom

The phantom sets used well reproduce clinical count rates.

Scanner: Biograph 16 Hi-Rez PET/CT (Siemens)

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SOURCE PREPARATION

Torso cavity: 12 kBq/mL Spheres (ID=28 and 37 mm): non radioactive water Other spheres: activity concentrations that provided T/B ratios of 23, 10 and 4 Scatter phantom to simulate activity outside FOV (12 kBq/ml in plastic tube) ESD: 1, 2, 3 and 4 min/bed. Repeated acquisitions to have PET images with varying background activity concentrations in the IEC phantom of about 12, 9, 6, 5 and 3 kBq/mL.

IMAGE ACQUISITION

The results are reported for reconstructions using the attenuation weighted-FORE-OSEM iterative reconstruction, with 2 iterations - 8 subsets and 4 mm FWHM Gaussian filter. The data were reconstructed over a 256x256 matrix with 2.625 mm pixel size and 2 mm slice thickness.

IMAGE RECONSTRUCTION

THRESHOLD DETERMINATION The thresholds were determined as a percentage of the maximal activity concentration in the spheres (TH (%)). Agreement was deemed to occur when volumes (measured versus physical) differed by less than 3%.

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STATISTICAL ANALYSIS T/B, Aacq, ID, ESD TH (%)

only on visible spheres

multiple linear regression

Data were fitted separately for Sphere ID ≤ 10 mm and Sphere ID > 10 mm

Model Fitting

EESDBSphereIDBABratioBT

BBTH acq +×+×+×+−×+= 43210 )/

11((%)

The F statistic was used as a criterion for selecting a model, being a higher F value associated with a smaller residual sum of square.

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SphereID.)TB

((%)TH ×−−×−= 6911172309

Neither ESD, nor Aacq resulted as statistically significant predictors of TH (%)

T/B ratio

T/B ratio

Sphere ID (mm)

Sphere ID (mm)

Sphere ID > 10 mm

SphereID.)TB

((%)TH ×−−×−= 98011101151

Sphere ID (β3= - 0.94) T/B ratio (β1= -0.55)

T/B ratio (β1= -0.88) Sphere ID (β3= - 0.32)

Multiple R2 = 0.86

Multiple R2 = 0.86

Sphere ID ≤ 10 mm

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• The model fittings of TH (%) are good, accounting for more than 86% of TH (%) variance.

• Neither ESD, nor Aacq resulted as statistically significant predictors of TH (%)

• The relevant predictors of TH (%) are Sphere ID and T/B ratio • The most relevant contributors to TH (%) variance are Sphere ID for

Sphere ID ≤ 10 mm and T/B ratio for Sphere ID >10 mm • Approaches which focus on determining a single value for the threshold

contour level that returns the actual volume are not likely to depict the actual spectrum of clinically relevant volumes and T/B ratios. Both target size and T/B ratios play a major role in explaining the variance of TH (%), throughout the whole range of target sizes and T/B ratios examined. Thus, algorithms aimed at automatic threshold segmentation should incorporate both variables with a relative weight which critically depends on target size.

DISCUSSION AND CONCLUSIONS

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To evaluate the role of different amount of attenuation and scatter on FDG-PET image segmentation using an adaptive threshold method based on the target-to-background (TB) ratio and target dimensions.

AIMS OF THE STUDY

To first show that also with simplified phantom geometries as those selected in this study, the accuracy of scatter and attenuation correction decreases with increasing amount of attenuation and scatter in the phantom being imaged

To evaluate the possibility of defining a unified contrast-based method for target volume delineation in different anatomical districts

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TEST PHANTOM SET

IEC + 3 cm water bags Hoffman 3D

Scatter

IEC + 9 cm water bags


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STATISTICAL ANALYSIS

TB, sphere A

TS (%) Multiple regression

Covariates

Phantom

Factor

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Starting Model

ETB

BmmAsphereBB +−×+×+= )11()(Y 22

10

EZXBZXBZXBZXBZBZBXBXBBY +++++++++= 228217126115241322110

( ) ( ) EXBBXBBBBY ++++++= 26215130

( ) ( ) EXBBXBBBBY ++++++= 28217140

Suppose now that we wish to compare the separate multiple regressions of TS on A and (1-1/TB) for the three phantoms. For each case in each phantom group, we observe values of the variable Y=TS, X1=A and X2=(1-1/TB); further, we shall suppose that there are ni cases in the ith Phantom group, i=1,2,3. We begin by defining two dummy variables Z1 and Z2 as follows: Z1=1 if Phantom 2 case; 0 otherwise; Z2=1 if Phantom 3 case; 0 otherwise. The complete model to be used is then given as follows:

For each particular Phantom, model (3) specializes as follow: Phantom 1 (Z1=Z2=0) Phantom 2 (Z1=1;Z2=0)

EXBXBBY +++= 22110

Phantom 3 (Z1=0;Z2=1)

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The following hypotheses concerning the parameters in model are of interest: Are all the three regression equations coincident (i.e, test H0: B3=B4=B5=B6=B7=B8=0)? When H0 is true, all three phantom models reduce to the form:

EXBXBBY +++= 22110

The test statistic is the multiple partial F:

[ ])mod(

/)mod()mod(elfullresidualMS

elfullSSresidualelreducedSSresidualF

ν−=

where: residual SS = residual sum of squares; MS = mean square; n = number of independent linear parametric functions specified to be 0 under H0. H0 must be tested using Analysis of Variance tables with n =6 and n1+n2+n3-ñ degrees of freedom, being ñ the number of parameters in the full model.

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RESULTS

Table 2. Physical properties of the phantoms.

Phantom set

Scatter Fraction Peak NECR (k=2) Accuracy of attenuation and

scatter correction (DClung)

Phan 1 27.4 % 136.6* kcps at 70.9 kBq/ml

23.7 %

Phan 2 40.4 % 61.2 kcps at 31.8 kBq/ml

37.6 %

Phan 3 48.7 % 30.8* kcps at 25.2 kBq/ml

40.8 %

* Peak NECR was not reached due to insufficient starting activity

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Hoffman IEC + 3 cm w. IEC + 9 cm w.Phantom

20

22

24

26

28

30

32

34

36

38

40

42

44

Err

or in

sca

tter a

nd a

ttenu

atio

n co

rrec

tion

(%)

Also with simplified phantom geometries as those selected in this study, the accuracy of scatter and attenuation correction decreases with increasing amount of attenuation and scatter in the phantom being imaged.

100,

,, ×=∆

ibkgd

ilungilung c

cC

RESULTS

Errors in scatter and attenuation correction (%)

Overall, this increase is statistically significant (F= 406.8 p <10-6). Individual significant differences (p=<10-4) were found between Phantom 1 (∆Clung=23.7 ± 1.6 %) and Phantom 2 (∆Clung=37.6 ± 3.4 %) and between Phantom 2 and Phantom 3 (∆Clung=40.8 ± 5.1 %) (p=<10-4).

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RESULTS

The TS versus cross sectional area relationship was splitted and fitted into different functional forms as already performed in previous studies. Partial volume effects significantly reduce the contrast recovery for structures less than three-times the reconstructed image resolution which in our scanner is about of 4.5 mm. Thus the choice of sphere A=133 mm2 (or, equivalently, a sphere internal diameter of 13 mm) as a separator of the data was dictated by the resolution characteristics of our scanner.

Cross section A ≤ 133 mm2 )11(1.63)(20.07.127(%) 2TB

mmsphereATS −×−×−=

The adjusted coefficient of determination is R2 =0.80. Both sphere A and (1-1/TB) resulted as statistically significant predictors of TS. TS diminish with increasing sphere A and with increasing TB ratios. The functional dependence of TS on (1-1/TB) is higher (β (1-1/TB) = -0.67) than the influence of A (βA=-0.55) for the small volumes corresponding to sphere A≤133 mm2. The test of the hypothesis of coincident regression lines for the three phantoms provided an F 6, 13 = 0.09 (P= 0.996). This F statistic is extremely small (P is very large); so we do not reject H0 and therefore have no statistical basis for believing that the three lines are not coincident.

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RESULTS

Analysis of Variance table. Test of H0 = coincident regression lines for the three phantoms. A≤133 mm2

Reduced Model

Sum of squares (SS) Degrees of freedom

Mean Square (MS)

F p

Regression 3061.8 2 1530.9 43.7 0,000000

Residuals 665.1 19 35.0

Full Model

Sum of squares (SS) Degrees of freedom

Mean Square (MS)

F p

Regression 3081.9 4 770.5 20.3 0,000003

Residuals 645.0 17 37.9

[ ] 996.009.09.37

6/0.6451.66513,6 ==

−= PF

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RESULTS

Cross section A > 133 mm2 )11(6.622.95(%)TB

TS −×−=

The adjusted coefficient of determination is R2 =0.73. Only (1-1/TB) resulted as a statistically significant predictor of TS, while sphere A is no longer retained into the model. Again TS diminish with increasing TB ratios. The test of the hypothesis of coincident regression lines for the three phantoms provided an F4, 30 = 1.03 (P= 0.408). This F statistic is small (P is large); so we do not reject H0 and therefore have no statistical basis for believing that the three lines are not coincident.

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RESULTS

Analysis of variance table. Test of H0 = coincident regression lines for the three phantoms. A> 133 mm2

Reduced Model

Sum of squares (SS)

Degrees of freedom

Mean Square (MS)

F p

Regression 1178.3 1 1178.3 95.5 0,000000

Residuals 1942.6 34 12.3

Total 1647.8

Full Model

Sum of squares (SS)

Degrees of freedom

Mean Square (MS)

F p

Regression 1226.3 3 408.8 35.2 0,000000

Residuals 371.2 32 11.6

Total 1597.6

[ ] 407.003.16.11

4/2.3713.41930,4 ==

−= PF

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• Different conditions of attenuation and scatter do not play a significant role in explaining the variance of TS the wide range explored of TB ratios, target sizes and reconstruction parameters.

• This, in turn ensures that calibration curves needed to implement contrast-based contouring algorithms of PET volumes can be devised in different conditions of attenuation and scatter.

• This, together with the previously demonstrated independence of TS on acquisition parameters such as emission scan duration or background activity concentration adds to the generalizability of this methods: distinct calibration curves need not to be specifically devised for each anatomical site representing different conditions of attenuation and scatter and, on the other hand, curves generated in one PET center for well defined conditions can be exported to other centers using different working conditions in terms of emission scan duration, administered activities or delay between injection and acquisition, provided that both are equipped with the same scanner and use the same reconstruction parameters.

• This opens the possibility of defining a unified contrast-based method for target delineation in different anatomical districts.

DISCUSSION AND CONCLUSIONS

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To select the reconstruction scheme which maximizes the goodness of fit of the calibration curve and the robustness of the thresholding algorithm .

AIMS OF THE STUDY

To analyze the behaviour of a contouring algorithm for PET images based on adaptive thresholding depending on lesions size and target-to-background (TB) ratio under different conditions of image reconstruction parameters.

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TEST PHANTOM SET IEC + 3 cm water bags Scatter

Image reconstruction parameters

16 iterations 4 mm FWHM









Convergence

Smoothing


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Images of different targets with internal diameters ranging from 10 to 37 mm and acquired with a source-to-background ratio of 8.1.

Changes in image appearance of different targets as number of iterations is increased from 16 (top row) to 64 (bottom row) and as smoothing kernel is increased from FWHM=4 mm (left column) to FWHM= 8 mm (right column). The data show progressively noisier images but with less smoothing and more spatial features as the number of iterations increases.

The amount of Gaussian post-reconstruction smoothing was chosen so to span a resolution range in the reconstructed images of at least twofold the value measured with filtered back projection.

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RESULTS

30

40

50

60

70

80

90

100

0 200 400 600 800 1000 1200

A (mm2)

TS (%

)

TB2

TB3

TB4

TB5-7

TB8-14

TB15-22

TB23-33

TB>33

Example of TS required to produce correct target delineation over the full range of the sphere cross sectional areas examined. The dependence upon TB ratio is also represented in this graph. PET reconstructions were accomplished with iterative algorithm of OSEM obtained with 8 subsets and 2 iterations with a Gaussian smoothing filter with a width of 8 mm. Different reconstruction strategies exhibit similar trends.

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RESULTS Example of TS required to produce correct target delineation over the full range of the sphere cross sectional areas examined. The dependence upon TB ratio is also represented in this graph. PET reconstructions were accomplished with iterative algorithm of OSEM obtained with 8 subsets and 2 iterations with a Gaussian smoothing filter with a width of 8 mm. Different reconstruction strategies exhibit similar trends.

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RESULTS Example of TS required to produce correct target delineation over the full range of the sphere cross sectional areas examined. The dependence upon TB ratio is also represented in this graph. PET reconstructions were accomplished with iterative algorithm of OSEM obtained with 8 subsets and 2 iterations with a Gaussian smoothing filter with a width of 8 mm. Different reconstruction strategies exhibit similar trends.

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RESULTS

Cross section A ≤133 mm2

Both sphere A and (1-1/TB) resulted as statistically significant predictors of TS for each reconstruction algorithm examined. TS diminish with increasing sphere A and with increasing TB ratios. With the exception of 2i8s4mm algorithm, the functional dependence of TS on TB is lower than the influence of cross sectional area for the small volumes corresponding to sphere A≤133 mm2.

)11()((%) 22

10 TBBmmsphereABBTS −×+×+=

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Convergence

Smoothing

16 iterations – 4 mm FWHM R2 = 0.91 16 iterations – 6 mm FWHM R2 = 0.91 16 iterations – 8 mm FWHM R2 = 0.90 32 iterations – 4 mm FWHM R2 = 0.77 32 iterations – 6 mm FWHM R2 = 0.91 32 iterations – 8 mm FWHM R2 = 0.87 64 iterations – 4 mm FWHM R2 = 0.74 64 iterations – 6 mm FWHM R2 = 0.87 64 iterations – 8 mm FWHM R2 = 0.87

Goodness of fit

RESULTS

Only a slight diminishing trend can be observed with increasing EM equivalent iteration number

No clear tendency emerges as for the amount of Gaussian smoothing.

Coefficient of determination

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ANALYSIS OF COVARIANCE

Thresholds are higher when more smoothig is applyed

FWHM = 4 mm FWHM = 6 mm FWHM = 8 mm

Smoothing

56

58

60

62

64

66

68

70

72

74

TS (%

)

Smoothing

sixteen thirtytwo sixtyfour

Iterations

56

58

60

62

64

66

68

70

72

74

TS (%

)

Convergence

Different conditions of convergence during OSEM reconstruction do not influence threshold determination

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Regression equation with parameters of reconstruction

• The regression equation that best summarizes the results obtained pooling all the data in a multiple regression model with TS as predicted variable and TB ratio, sphere A and amount of post-reconstruction Gaussian smoothing as predictor variables may be written as:

• The adjusted coefficient of determination is R2 =0.88. The variables inserted into the model were statistically significant predictors of TS, whose variance can be accounted for, in order of decreasing relevance, by target dimensions (βA= -0.64), contrast (β(1-1/TB)= -0.52), and smoothing (βFWHM= 0.29).

)(82.2)11(96.56)(25.09.112(%) 2 mmFWHMTB

mmsphereATS ×+−×−×−=

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RESULTS

Cross section A > 133 mm2

Only (1-1/TB) resulted as statistically significant predictors of TS, while sphere A is no longer retained into the model as a significant predictor for each reconstruction algorithm examined.

)11((%) 10 TBBBTS −×+=

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Convergence

Smoothing

16 iterations – 4 mm FWHM R2 = 0.91 16 iterations – 6 mm FWHM R2 = 0.95 16 iterations – 8 mm FWHM R2 = 0.94 32 iterations – 4 mm FWHM R2 = 0.74 32 iterations – 6 mm FWHM R2 = 0.88 32 iterations – 8 mm FWHM R2 = 0.92 32 iterations – 8 mm FWHM R2 = 0.90 64 iterations – 6 mm FWHM R2 = 0.82 64 iterations – 8 mm FWHM R2 = 0.90

Goodness of fit

RESULTS

Only a slight diminishing trend can be observed with increasing EM equivalent iteration number

The increase of Gaussian smoothing increases the goodness of fit of the selected reconstruction algorithm.

Coefficient of determination

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ANALYSIS OF COVARIANCE

Thresholds are higher when more smoothing is applyed

Smoothing

Convergence

Different conditions of convergence during OSEM reconstruction do not influence threshold determination

FWHM = 4 mm FWHM = 6 mm FWHM = 8 mm

Smoothing

40,0

40,5

41,0

41,5

42,0

42,5

43,0

43,5

44,0

44,5

45,0

45,5

46,0

TS (%

)

sixteen thirtytwo sixtyfour

Iterations

40,0

40,5

41,0

41,5

42,0

42,5

43,0

43,5

44,0

44,5

45,0

45,5

46,0

TS (%

)

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Regression equation with parameters of reconstruction

• The regression equation that best summarizes the results obtained pooling all the data in a multiple regression model with TS as predicted variable and TB ratio and amount of post-reconstruction Gaussian smoothing as predictor variables may be written as

• The adjusted coefficient of determination is R2 =0.87. The variables inserted into the model were statistically significant predictors of TS, whose variance can be accounted for, in order of decreasing relevance, by contrast (β(1-1/TB)= -0.91), and smoothing (βFWHM= 0.13).

)(60.0)11(27.6040.93(%) mmFWHMTB

TS ×+−×−=

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Discussion

The results of our study show that the degree of convergence of OSEM algorithms does not influence TS determination for target cross sectional areas lower than 133 mm2, while only a slight tendency toward an increase of TS with increasing iterations can be observed for bigger targets. However, this is always a second-order effect in comparison to smoothing. No significant interaction was detected between the number of iterations and the amount of smoothing. This brings together another relevant consequence: the number of iterations and the amount of smoothing can be optimized independently. The inclusion of smoothing in the adaptive thresholding algorithm adds to the generalization of the method allowing its use in centres equipped with the same scanner but using a different set of reconstruction parameters.

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Target definition of moving lung tumors in positron emission tomography: Correlation of optimal activity concentration thresholds with object

size, motion extent, and source-to-background ratio

AIMS OF THE STUDY

To develop a functional relationship between optimal activity concentration threshold, tumor volume, motion extent, and SBR using multiple regression techniques by performing an extensive series of phantom scans simulating tumors of varying sizes, SBR, and motion amplitudes.

Riegel AC et al. et al. Med Phys 2010;37:1742-1752

CONCLUSION

lnw = B1x1/3 + B2y1/3 + B3 ln(z )+ B4(x1/3 ln(z)) + B5(y1/3 ln(z)) + B6(xy)1/3 + B7.

where w is the threshold normalized to background, x is the volume in cm3, y is the motion in mm, z is the SBR which is unitless, and Bn are the regression coefficients.

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Conclusions

Adaptive threshold algorithms do not depend on ESD or background activity concentration.

Image contrast is not the only determinant of threshold since also target dimension play an independent role at least for small targets as can be found when contouring head and neck tumours or lymphnodes.

Iterative methods are necessary since image contrast can be measured but target dimensions can only be guessed.

Different conditions of image noise do not play any role in TS determination, at least in the range explored.

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Conclusions

Adaptive threshold algorithms do not depend on scatter and attenuation conditions

Distinct calibration curves need not to be specifically devised for each anatomical site representing different conditions of attenuation and scatter and, on the other hand, curves generated in one PET center for well defined conditions can be exported to other centers using different working conditions in terms of emission scan duration, administered activities or delay between injection and acquisition, provided that both are equipped with the same scanner and use the same reconstruction parameters.

The degree of convergence of OSEM algorithms does not influence TS determination

The inclusion of smoothing in the adaptive thresholding algorithm adds to the generalization of the method allowing its use in centres equipped with the same scanner but using a different set of reconstruction parameters.

diapositiva 55

Validation of the algorithm

• N= 24 patients with 33 lymphnodes examined • We excluded from the analysis mediastinal lymphnodes.

Breathing excursion do not play a role. • Lymphnodes are spherical and their dimensions are known from

co-registered CT. • Unlike the large and often inhomogeneous primary tumours,

limph nodes are mostly small and FDG uptake appears rather homogeneous.

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Patient CA Lymphnode:

CT Volume: 0.9 mL

PET Volume: 0.8 mL

CASE 1

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Slice Volume TC (mL)

Area TC (mm2)

TB TS % Volume PET (mL)

139 0,23 46 2,6 88 0,21

140 0,36 72 2,9 80 0,36

141 0,26 52 2,1 90 0,23

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Patient DG Lymphnode: Inguinal sx

CT Volume: 3.2 mL

PET Volume: 2.9 mL

CASE 2

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Slice Volume CT (mL)

Area CT (mm2)

TB TS % Volume PET (mL)

36 0,25 50 1,8 94 0,15

37 0,28 56 4,2 78 0,25

38 0,6 120 9,9 56 0,53

39 0,88 176 12,8 42 0,76

40 0,67 134 6,2 48 0,76

41 0,48 96 3,0 73 0,46

3,16 2,91

CASE 2

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CASE 3 Patient DR Lymphnode: Ascilla DX

CT Volume: 7.6 mL

PET Volume: 6.3 mL

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CASE 3

Slice Volume CT (mL)

Area CT (mm2

)

TB Soglia Volume PET (ml)

108 0,75 150 2,8 61 1,14

107 0,77 154 3,1 59 0,59

106 1,32 264 3,8 55 0,74

105 1,33 266 3,9 54 0,92

104 1,44 288 4,1 53 0,95

103 1,27 254 5,1 50 1,07

102 0,74 148 3,7 55 0,9

7,62 6,31

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Plot of differences in Volume for all GTVs vs lesion size

-1,6

-1,2

-0,8

-0,4

0,0

0,4

0,8

1,2

1,6

0 2 4 6 8 10 12 14

volume of lesions derived from CT (mL)

diffe

renc

e fro

m C

T in

vol

ume

(mL)

diapositiva 63

Algoritmi dipendenti dal contrasto

Conclusioni II

Durata acquisizione emissiva Concentrazione attività di background

Attenuazione e scatter

Parametri di ricostruzione: N°iterazioni, smooth

Dimensione della lesione

CNR lesione

soglia = f CNR dimensione smooth

Questo risultato apre la possibilità di definire un unico algoritmo

per la delineazione dei volumi per diversi distretti anatomici, non dipendendo dalle condizioni di attenuazione e scatter

diapositiva 64

Studio multicentrico soglie

Prospettive

AO Niguarda Ca' Granda, MI Istituto Europeo di Oncologia, MI

Fondazione S.Raffaele Monte Tabor, MI A.O. San Gerardo, MB

AOU Maggiore della Carità, NO A.O. Arcispedale S.Maria Nuova, RE

A.O. San Camillo Forlanini, RM AOU Integrata di VR

F.P.O. IRCC Candiolo, TO

‘Multi-soglie’

Questo risultato vale su tutti i tomografi PET ?

Discovery STE, GE

Discovery DST, GE

Discovery 600, GE

Biograph 6 True V, Siemens

Gemini TF TOF 16, Philips

Biograph 16 Hirez-pico3D, Siemens

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+ microsfere + sferona (~100ml)

Saranno realizzati 9 TB, per ogni TB il fantoccio verrà acquisito

a 4 tempi/bed diversi, ogni acquisizione verrà ricostruita in 6

modi diversi (variando iterazioni e smooth):

9 TB x 4acq/TB x 6 ric/TB

216 set di dati per ogni centro

soglia = f CNR dimensione smooth

Algoritmi per la delineazione del Biological target volume...

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