QUANTIFICATION OF DYNAMIC [18F]FDG PET STUDIES

IN ACUTE LUNG INJURY

Journal: Molecular Imaging and Biology

Elisabetta Grecchi1,6, Mattia Veronese2,6, Rosa Maria Moresco3, Giacomo Bellani4,5,

Antonio Pesenti4,5, Cristina Messa3, Alessandra Bertoldo6

1Division of Imaging Science and Biomedical Engineering, King’s College London, UK 2Department of Neuroimaging, Institute of Psychiatry, King’s College London, UK

3Tecnomed Foundation, University of Milan-Bicocca, Milan, Italy 4Department of Health Science, University of Milan-Bicocca, Monza, Italy

5 Department of Emergency and Intensive Care, San Gerardo Hospital, Monza, Italy 6Department of Information Engineering, University of Padova, Padova, Italy

SUPPLEMENTARY MATERIAL - QUANTIFICATION METHODS

A) SAIF

Spectral Analysis (SA) is an input-output model that identifies the kinetic components of

tracer in the tissues without making any specific model assumptions, such as a homogenous

distribution of the tracer [1-2]. In SA, the measure of the radioactivity in the tissue at the time t,

Ctissue(t), is modeled as a convolution of the plasma activity curve Cp(t) with a sum of M + 1 distinct

exponential terms:

!!"##$% = !! ! ⨂!! ∙ !!!!!!

!!!

 

= !! ∙ !!(!)!

!!!!!(!!!)

!

!!!

[1]

where αj and βj (β1 < β2 < ... < βM+1) are assumed positive or zero.

From the total radioactivity measured by the PET scanner in a given volume of observation, one can

write the SA model equation by taking into account the contribution of the blood as well as the

different tissue kinetic components:

!!"#$%&"' =  !!!! ! + 1 − !! !!"##$%   [2]

= !!!! ! + 1 − !! !! !! ! !" +   !! !!(!)!!!!(!!!)!"!

!

!

!!!

!

!

where !! is the tracer concentration in blood and Vb (unitless) accounts for the vascular volume

present in the volume of observation. Within the range of the hypothesized components (M), only few

are estimated with non-zero amplitude, and these represent the “kinetic spectrum” of the tracer.

Notably, each component in the estimated spectrum assumes a different meaning depending on its

position: when the values of βj are very large, they become proportional to Cp(t) via αj and are

considered as “high- frequency” components. Similarly, when the values of βj are close to zero, these

can be viewed as “low-frequency” components. Between these two extremes, “intermediate frequency”

components reflect the uptake of the tracer within the tissue. The parameter Ki (ml/cm3/min),

accounting for tissue uptake, is thus estimated from the αj associated to the βj = 0 term, and the number

of components corresponds to the number of identifiable tissue compartments exchanging with plasma.

This measure is important because, under particular hypotheses, it can reflect tissue heterogeneity [3].

General SA algorithms are highly sensitive to noise, and this is responsible for wrong kinetic

estimations. To overcome this limitation, we used the Spectral Analysis Iterative Filter (SAIF), a new

algorithm developed by Veronese et al. [4]. This algorithm is suitable for the quantification of PET

data at both regional and voxel level [5]. SAIF relies on the same assumptions of traditional Spectral

Analysis, but offers the additional advantage of estimating the net uptake rate of a tracer with great

precision and accuracy. The methodology is described in detail in [4].

B) Patlak

The Patlak graphical method is a widely used linear technique to estimate Ki (ml/cm3/min)

from dynamic PET data. Given an irreversible tracer, it assumes a time t* (for this study t* = 20 min)

after which the reversible exchanges have reached equilibrium. This can be expressed as:

!!"##$%!!

= !!!! ! !"

!!!!(!)

+ ! [3]

where Ki and V (ml/cm3) are the unknown parameters to be estimated. Even if the application of this

method is very straightforward and robust to noise, it assumes that the blood volume is negligible,

which might not be a valid assumption for pulmonary tissues.

C) Ratio

A simple approximation of the metabolic [18F]FDG uptake rate consists in performing the

ratio of the mean tissue activity at the end of the experiment with the plasma activity (!!"#$%#,

kBq/cm3) at the same time

!"#$% =  !"#$ !!"##$%(!!"#!!, !!"#!!, !!"#)!"#$ !!"#$%#(!!"#!!, !!"#!!, !!"#)

[4]

where !!"#!!, !!"#!!, !!"#correspond to the last PET images of the study (in our case the images end

times are 42, 47 and 57 min respectively ).

D) Standardized Uptake Value

The Standardized Uptake Value (SUV, g/ml) is a semi quantitative index that is frequently used in

clinical PET studies for tumor characterization. It is calculated as the ratio of tissue radioactivity

concentration (!!"##$%(!), kBq/ml) at a specific time and the injected dose (e.g. MBq) divided by the

body weight (e.g. kg).

!"# = !!"##$%!"#$%&$'  !"#$ !"#$  !"#$!!

[5]

SUV is very simple to use and does not require blood sampling. It is however vulnerable to several

sources of variability and inevitably oversimplifies complicated metabolic processes [6]. The lean body

weight or the body surface area can be used in the SUV computation instead of body weight. For this

study the SUV was computed taking into account the last acquired frame of the dynamic scan (from 47

to 57 minutes).

References

1. Cunningham VJ, Jones T (1993) Spectral analysis of dynamic PET studies. Journal of Cerebral Blood Flow & Metabolism 13:15-23.

2. Turkheimer F, Moresco R, Lucignani G, Sokoloff L, Fazio F, Schmidt K (1994) The use of spectral analysis to determine regional cerebral glucose utilization with positron emission tomography and [18F] fluorodeoxyglucose: theory, implementation, and optimization procedures. Journal of Cerebral Blood Flow & Metabolism 14:406-422.

3. Schmidt K, Turkheimer F (2002) Kinetic modeling in positron emission tomography. The quarterly journal of nuclear medicine: official publication of the Italian Association of Nuclear Medicine (AIMN)[and] the International Association of Radiopharmacology (IAR) 46:70-85.

4. Veronese M, Bertoldo A, Bishu S, et al. (2010) A spectral analysis approach for determination of regional rates of cerebral protein synthesis with the L-&lsqb; 1-11C&rsqb; leucine PET method. Journal of Cerebral Blood Flow & Metabolism 30:1460-1476.

5. Veronese M, Schmidt KC, Smith CB, Bertoldo A (2012) Use of spectral analysis with iterative filter for voxelwise determination of regional rates of cerebral protein synthesis with L-[lsqb]1-11C[rsqb]leucine PET. J Cereb Blood Flow Metab 32:1073-1085.

6. Hamberg L, Hunter G, Alpert N, Choi N, Babich J, Fischman A (1994) The dose uptake ratio as an index of glucose metabolism: useful parameter or oversimplification? Journal of nuclear medicine: official publication, Society of Nuclear Medicine 35:1308-1312.

Supplementary Figure 1 – Kinetic spectra in healthy subject and ALI patient

Examples of kinetic spectra derived from SAIF quantification of three different functional clusters of

normal (a,c) and hyperdense tissue (b,d) in one representative healthy subject (a,b) and one patient

(c,d). Hyperdense tissue in the healthy control is reported for comparative purposes, since it represents

less than 1% of the entire lung tissue.

 

Supplementary Figure 2 – Correlation analysis of tracer uptake estimation between Non

Linear SA and SAIF

Correlation analysis of tracer uptake estimates at the ROI level between non-linear spectral-analysis (x

axis) and SAIF (y axis). Normal and hyperdense tissues are reported for all the subjects. The solid line

represents the regression line of the estimates. The dashed line is the theoretical perfect correlation.

 

Supplementary Figure 3 – Parametric maps obtained with SAIF in a representative

healthy subject

 Parametric maps obtained with SAIF at the voxel level in a representative healthy subject.

a) Summed PET image; b) blood volume Vb; c) net tracer uptake Ki; d) transport rate from plasma to

tissue K1; e) volume of distribution in the fastest compartment V1; f) volume of distribution in the

slowest compartment V2                              

 

 Supplementary Figure 4 – Boxplot of SUV and RATIO parameter estimates and

 Parameter estimates and statistics obtained with SUV and RATIO in the whole dataset (5 healthy

controls, 4 ALI-A and 7 ALI-B patients). Grey bars: normal tissue. White bars: Hyperdense-collapsed

tissue. ALI patients are divided into two subgroups according to the Ki values. For each bar mean and

standard deviation are reported. The stars (*) indicate statistical significance (p<0.05; t-test) between

hyperdense and normal tissues within a given group of subjects. Panels refer to: a) SUV b) Ratio