Optimization of Tracer Dose for Scintigraphic Imagery

C. Bousnah

1 a

, S. Anebajagane

, O. Monsarrat

, J.-Ph. Conge

1 b

, H. Maaref

1 c

and V. Vigneron

1 d

IBISC EA 4526, Univ. Evry, Université Paris-Saclay, France

Service de Médecine Nucléaire, CHSF, Corbeil, France

Keywords:

Machine Learning, Multi-modal Imaging, Precision Medicine, Myocardial Perfusion Scintigraphy, Dose

Optimization, Patient Radiation Protection.

Abstract:

Myocardial scintigraphy is a non-invasive isotope examination that has played a central role in the management

of these coronary heart diseases for decades.it has proven its performance in nuclear cardiology, mainly for the

diagnosis of ischemia by making it possible to analyze the myocardial perfusion, and precisely, to evaluate the

quality of the irrigation by the arteries and the coronaries, as well as for the diagnosis of coronary heart disease.

It is based on the injection of an intravenous radioactive tracer, which, once injected, is absorbed by the heart

muscle. The radiation emitted by the radioactive tracer is converted into an image by computer tomography.

However, these scintigraphic images suffer from poor spatial resolution in particular, in obese patients, it is

difﬁcult to obtain images of sufﬁcient quality using the recommended standard doses due to the attenuation of

γ−rays by soft tissues (fat, ﬁbrous tissues, etc.). This phenomenon prompts the nuclear physician to overdose

the tracer and the dose of radiation received exceeds the admissible regulatory limits. In this paper we propose

a machine learning model that predict the dose of tracer based on patient’s morphological parameters to obtain

images of sufﬁcient quality to support the cardiovascular diagnosis while exposing him to the lowest possible

doses of radiation. We show the body weight is not the best-predicting parameter for image quality.

1 INTRODUCTION

Myocardial scintigraphy has played a central role in

the management of coronary heart disease for decades

(Piekarski et al., 2020). Cardiovascular diseases are

responsible for 17.7 million deaths worldwide each

year, or 31% of deaths, causing nearly twice as

many deaths as cancer (Bonow et al., 2002). The

myocardiac perfusion scintigrahy (MPS) is a non-

invasive nuclear medicine imaging test that uses a

small amount of a radioactive tracer (usually Tech-

netium

99m

Tc but also Thallium

201

Tl or Rubidium

Rb) to detect any difference in blood ﬂow in the

heart muscle at rest and during exercise. It is widely

used to assess the extent of damage to the heart, detect

myocardial structures in patients with chest pain, to

look for myocardial ischemia or small atypical symp-

toms, etc. (Nance et al., 2014). 80,000 people per

year undergo this examination in France.

https://orcid.org/0000-0002-2459-8284

https://orcid.org/0000-0002-8641-0312

https://orcid.org/0000-0002-2459-8284

https://orcid.org/0000-0001-5917-6041

The radioactive tracer

99m

Tc emits γ radiations

detected by a γ−camera which revolves around the

patient’s body. Once injected, the tracer is absorbed

by the heart muscle. Well-irrigated areas of the heart

will emit more γ−rays than less well-irrigated areas.

The images thus collected are put together to form a

3D image of the organ studied (see Fig. 1).

This assessment of myocardial perfusion can be

performed at rest and / or after a myocardial ischemia

challenge test (pharmacological stress or stress test).

Consequently, any relative ﬁxation deﬁcit will be

interpreted as a perfusion defect called hypoperfu-

sion which may correspond to ischemia or myocar-

dial necrosis (myocardial infarction) (Foraggi et al.,

2008).

However, these scintigraphic images suffer from

poor spatial resolution (a voxel ≈ 1 cm

) (Patton

and Turkington, 2008) and poor signal to noise ra-

tio (SNR). In particular, in obese patients, it is dif-

ﬁcult to obtain images of sufﬁcient quality using the

recommended standard doses due to the attenuation

of γ−rays by soft tissues (fat, ﬁbrous tissue, etc.)

(Tamam et al., 2016). This phenomenon prompts the

nuclear physician to overdose the tracer and the dose

Bousnah, C., Anebajagane, S., Monsarrat, O., Conge, J., Maaref, H. and Vigneron, V.

Optimization of Tracer Dose for Scintigraphic Imagery.

DOI: 10.5220/0010822200003123

In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS, pages 167-175

ISBN: 978-989-758-552-4; ISSN: 2184-4305

167

Figure 1: Myocardial scintigraphy image visualized with

ITKSnap.

of radiation received exceeds the permissible regula-

tory limits. These limits have been speciﬁed by both

the High Authority for Health (HAS) and the Nuclear

Safety Authority (NSA) for the sake of radiation pro-

tection: 1 mSv per year (sum of effective doses) for

the public. There are also regulatory limits for ioniz-

ing radiation for workers and staff: 20 mSv per year

(sum of the effective doses). Exceeding doses may

increase the risk of cancer from radiation. The rec-

ommendations of the European Association of Nu-

clear Medicine (EANM) specify that the dosage of

the common tracer

99m

Tc should be calculated by the

specialist only on the basis of weight. However, the

attenuation of the scintigraphy images is above all in-

ﬂuenced by the thickness of the tissues (muscle ﬁbers,

fatty tissues, bones, etc.) to be passed through, in par-

ticular in obese patients.

The dose of radio-tracer to be administered is

a compromise between image quality and radia-

tion exposure and depends on patient characteristics

(e.g.body weight), choice of radio-pharmaceutical

(

99m

Tc or

201

Tl compounds), acquisition protocol (1

day or 2 days protocols, imaging time, image reso-

lution, gated acquisition), and the type of equipment

(multiple head scintillation camera or a camera based

on cadmium zinc telluride (CZT) detectors). Thus,

the objective of this work consists in proposing a

"ﬁner" dosage, based on the body mass index (BMI),

and other morphological factors to be determined,

easily identiﬁable by the doctors before the examina-

tion and which will allow the injection tracer to be

optimally calibrated, thus limiting patient’s exposure

to radiation while ensuring sufﬁcient image quality.

In the following section, we take stock of the state

of the art on the assay of tracers dosage. Section 3 de-

tails the data collected for this experiment. Section 4

provides a methodology for predicting tracer dose and

image quality and a discussion on the main results.

2 A SHORT REVIEW OF TRACER

DOSAGE CALCULUS

Van Dijk et al. (2014) disclose a method to compen-

sate for the decrease in image quality in obese patients

by administering a patient-speciﬁc dose in myocardial

perfusion imaging using a tomographie par émission

monophotonique (SPECT) camera based on CZT de-

tectors

. The study was carried out on 148 patients.

This article proposes in particular a method for deriv-

ing a dose of tracer to be administered depending on

the body-weight or scan-time protocol by calculating

(Van Dijk et al., 2014):

admin

−b

scan

, (1)

wherein C is the photon count, P is the body weight,

K is the correction factor for the radioactive decay

between the administration of tracer dose and the

SPECT acquisition, T is the scanning time, a and

b are adjustment parameters. The measured photon

numbers were normalized based on the administered

tracer dose and the scan time were correlated with

body weight, body mass index to ﬁnd the best pre-

dictor. From these data, a protocol to provide consis-

tent image quality was validated in 125 new patients.

Van Dijk et al. (2014) claim that weight was the main

morphological parameter of the patient despite it is

not a good indicator of the distribution of fat mass,

especially around the heart.

(Gimelli et al., 2017) develop a software for

automatic calculation of pediatric dosage of radio-

pharmaceuticals, according to European Association

of Nuclear Medicine (EANM) 2007 pediatric dosage

card. This card is based upon the publication by Ja-

cobs et al. (2005) who show that normalization fac-

tors for effective dose can be estimated accurately as

a function of body weight W by holding only one pa-

rameter, called the ’a’ value.

Dogan et al. (2019) assess the validity of auto-

mated quantitative and semi-quantitative visual analy-

sis of total perfusion deﬁcit using SPECT γ−camera

to detect signiﬁcant coronary artery disease. Zhang

et al. (2015) investigate image quality, radiation dose,

and diagnostic performance of prospectively ECG-

triggered high-pitch coronary CT angiography at 70

kVp compared to invasive coronary angiography as

reference standard. Their protocol results in an ef-

fective radiation dose of 0.2 mSv and high diagnos-

tic accuracy for stenosis detection in a selected, non-

obese population. Gould et al. (2008) compare on

a total of 250 consecutive patients resting perfusion

This camera is specially dedicated to the the heart ex-

amination.

BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing

168

images reconstructed using rest CT attenuation data

with the same resting emission data reconstructed

with post-stress CT attenuation data. No clinically

signiﬁcant differences were observed. And eliminat-

ing rest CT scan reduces CT radiation dose by 50%.

Kero et al. (2021) suggest too that stress-ﬁrst per-

fusion imaging (PI) using

99m

Tc tracer can signiﬁ-

cantly reduce the radiation dose compared with stan-

dard dose rest–stress protocols. If the stress PI re-

sults are normal (Duvall et al., 2011). Hamilton-Craig

et al. (2019) examine the impact of dose reduction

techniques using ECG-triggering, kVp/mAs reduc-

tion and high-pitch modes on radiation exposure in a

Australian hospital. 3,000 coronary CT angiography

were analyzed with mean radiation dose decreasing

from 8.4 mSv to 2.8 mSv per year. Implementation of

dose reduction algorithm and staff education program

resulted in a 67% reduction in radiation exposure,

while maintaining diagnostic speciﬁcity. Chinnaiyan

et al. (2013) show that the use of newer scanner tech-

nology resulted in incremental radiation dose reduc-

tion in a statewide coronary CT angiography registry

without image quality degradation.

3 MATERIALS

This experimental study is based on two recent

databases which were retrieved from the nuclear

physicians from Hôpital sud-francilien (CHSF).

3.1 Chest Database

The ﬁrst database contains CT scans and myocardial

scintigraphy images of 130 patients with cardiovascu-

lar pathology, recorded using a Siemens Symbia T2

SPECT CT γ−camera on the upper part of the pa-

tient’s body (chest part). Thus, the physiological pa-

rameters of the patient and the metadata of the images

(resolution, image size, image rotation, date / time,

sex, age, weight, height, BMI, etc.) were recorded.

The scintigraphy images were taken in the nu-

clear medicine department of the CHSF, after the

patient was injected with a radioactive tracer (Tech-

netium

99m

Tc), which binds to the heart. Next, a

NaI γ−camera

captures the photons emitted by the

tracer and rotates around the patient’s body so that

the images collected are grouped together to form a

3D image of the heart (Fig. 1). Most of the examina-

tions were carried out after a stress test that stimulates

the myocardium which better absorbs the radioactive

tracer (in terms of speed and homogeneity). Depend-

Thallium-doped sodium iodide.

ing on the result of this test, another test at rest may

be necessary to improve the image quality.

Figure 2: Boxplot of tracer dose with respect to sex and type

of exam.

An Excel ﬁle containing information about the

patient (sex, age, weight, height, BMI) was also pro-

vided with the doses used during the examination,

as well as the image grade noted by the doctors be-

fore and after correction by the CT scanner. NCIQ

and CIQ stand resp. for non-corrected and corrected

image quality (Tab.3). The nuclear physician has in

fact the possibility of improving manually with a few

manipulations to improve the quality of the image in

order to improve the estimation of the dosage of the

tracer.

The chest database contains 58 men and 38

women aged from 43 to 93 y.o., 104 of them have

done a post stress exam and 26 a rest exam. The ﬁg-

ure 2 represents the distribution of the dose according

to the examination type and to the gender. The mean

dose is signiﬁcantly different in the post-stress and the

rest groups (Kero et al., 2021).

Table 1 summarizes the main statistics of patients.

Table 1: Chest dataset patient statistics.

variable min mean max std error

age 43 69.15 93 1.07

BMI 17 29.11 43 0.44

dose 257 478.45 1337 27.26

Most of the images before correction are of poor

quality. Figure 4 shows the distribution of uncor-

rected image quality (IQ) by examination type (rest

or post-stress). IQ stands for the image quality anno-

tated by doctors ; it is an ordinal qualitative variable

with 5 modalities IQ

, i = {1, 2, 3, 4, 5}, IQ

being

the highest grade to the lowest IQ

Optimization of Tracer Dose for Scintigraphic Imagery

169

(a) non corrected image quality (NCIQ) distribution

(b) corrected image quality (CIQ) distribution. After image

correction, the last IQ class disapear.

Figure 3: IQ distribution. From IQ

(high quality) to IQ

(poor quality).

Figure 4: Distribution of image quality before correction

according to the examination type.

3.2 Full-body Database

The second database contains full body scans of

247 patients acquired from examinations for other

pathologies, using PET CT GE HealthCare Discov-

ery 710 (Fig. 5). This database was used to design

a model capable of predicting the abdominal perime-

ter which is not visible on the CT scans of the ﬁrst

database (see section 3.1). For example, Fig. 5 de-

picts a patient scan with 780 CT slices. The approxi-

Figure 5: Full body CT scans viewed with ITKsnap.

mate intervals of the different upper body parts (tho-

rax, diaphragm and abdomen) are determined manu-

ally using ITKsnap: thorax is located between 450-

600, diaphragm between 430-460, abdomen between

200-430. The data is in DICOM format. The CT sec-

tions are 512×512 pixels, the scintigraphic images

are 128×128×128. The volume of the 1st database

is 12 GiB, the second 114 GiB.

4 METHODS

4.1 Calculus of Abdominal Perimeter

We propose an algorithm based on morphological fac-

tors of the patient, easily identiﬁable by doctors be-

fore the examination, which makes it possible to opti-

mize the quantity of radioactive tracer while ensuring

the good image quality.

The proposed morphological factors are: (a) sex

(b) age (c) body mass index (the weight/height

ratio)

(d) thoracic perimeter (e) abdominal perimeter. The

parameters (a-c) are provided by doctors in the pa-

tient’s medical record. The thoracic and abdominal

perimeters are not measured in the clinical practice;

one of the objectives of this work is to decide if these

parameters are relevant for the choice of the optimal

dose. To extract these two perimeters from the CT

scans, we had to segment the region of interest, so

that we keep the part of the body and we eliminate the

chassis. For this, we used a hysteresis thresholding,

followed by a morphological opening, i.e.an erosion

followed by a dilation (Serra, 1983).

In the hysteresis thresholding technique, the high

threshold represents the most signiﬁcant pixels of the

image which, in our case, correspond to the rib cage

bones (Fig. 6). The lower threshold highlights the

weaker pixels that are connected with the stronger

ones. The threshold values are determined by trial

and error.

After the hysteresis thresholding, some noise re-

mains on some images (see Fig. 7a). To clean it, we

BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing

170

(a) Visualization of a CT

slice.

(b) Hysteresis thresholding.

Figure 6: Thoracic scan segmentation using Pydicom, a

Python package for working with DICOM ﬁles.

used morphological ﬁltering named opening. Next,

we determined the convex hull of the segmented re-

gion, because the thoracic perimeter does not exactly

follow the shape of the rib cage (Fig. 7b). Finally, we

calculated the convex perimeter that we recorded in a

csv ﬁle. This operation is repeated automatically for

all scans for each patient.

(a) Hysteresis threshold (b) Convex hull

Figure 7: Extraction of the perimeter.

For CT scans that only contain the thoracic part of

the body, the maximum of the calculated perimeters is

considered the thoracic perimeter. Since those scans

do not include the patient’s abdominal part, we set up

a training model that predicts the abdominal perime-

ter from the chest perimeter using whole body images

(Fig. 5). First, the entire body perimeters from the

CT scans are extracted for each patient as depicted in

Fig. 8a, producing the graph C in function of the slide

number k.

Here comes a ﬁrst difﬁculty: the number of each

patient’s slices is proportional to the patient’s height

which makes it difﬁcult to localize exactly the thorax

dimension. The total number of slides varies between

705 and 1120 on the whole data set. In the ﬁgure 5

for example, 780 CT slices deﬁne the patient upper

body. ITKsnap is used to determine automatically

the thoracic and abdomen part the interval I

, respec-

tively I

, occupied by the thorax, resp. the abdomen,

as in Figures 8a and 8b. I

and I

are delimited with

bullet points corresponding to the inﬂection points in

(a) Raw graph obtained from the perimeters of the

convex hull of the CT slices.

(b) Approximate form of the graph 8a. The inter-

vals are delimited with bullets that correspond to

curvature change points in the graph.

Figure 8: Graph of the upper-body perimeters.

C. Consider that the function f(x) of graph C is lo-

cally 2 times continuously differentiable. The inﬂec-

tion points are those where the second derivative van-

ishes by changing of sign, i.e.

∂

∂x

= 0. The same pro-

cedure is applied to all the patients. The approximate

intervals of the different parts are I

= [450 : 600]

for the thorax and I

= [200 : 430] for the abdomen.

The second step consists in developing a learn-

ing model that predicts the abdominal perimeter from

the thoracic perimeter, sex, age, size and BMI (see

Fig. 11). So, we separate the database into 3 sub-

bases: learning, validation and test bases representing

resp. 65%, 15% and 20% of the samples. For more

details on neural networks, refer to Bishop (2006).

The table 2 summarizes the characteristics of the pro-

posed neural network.

Table 2: Neural network’s architecture to predict abdominal

perimeter.

Inputs thoracic perimeter P

, sex,

age, height,BMI

hidden neurons 10

activation function ReLU

optimizer Adam

Loss MSE

Batch 7

Epoch 250

Output abdominal perimeter P

Abd

Optimization of Tracer Dose for Scintigraphic Imagery

171

The learning and validation loss decreases to sta-

bility as shown in the ﬁgure 9.

Figure 9: Learning and validation losses.

To evaluate the model accuracy, we used the co-

efﬁcient of determination R-square deﬁned, for uni-

variate series as follows :

i=1

(

−

Y )

i=1

−

Y )

= 1 −

i=1

−

)

i=1

−

Y )

, (2)

where Y

Y are, for this model, resp. the es-

timated, expected and mean abdominal perimeters.

The model performances were evaluated on the test

dataset. The R

is 83%, and Pearson’s product-

moment correlation coefﬁcient (or Pearson correla-

tion coefﬁcient, for short) which measure of the

strength of a linear association between predicted and

expected abdominal perimeters, resp.

Abd

and P

∗

Abd

is 91.11%.

Figure 10: Perimeter values predicted by neural network

regression / real values (50 patients).

4.2 Optimization of the Amount of

Administered Radiotracer Dosage

Unlike conventional guidelines suggesting consider-

ably large dosage levels for obese patients (Karakatsa-

nis et al., 2015), the objective of this study is to mod-

erate dosage for obese patients and dosage reduction

for slimmer patients which are usually based on the

only patient weight. In this section the relevance of

the abdominal perimeter for optimizing tracer dose is

evaluated. The proposed model is designed to help

doctors to choose the best dose / image quality re-

sponse model. We propose utilizing a neural network

model to predict the radioactive tracer dose accord-

ing to the type of examination performed (post stress

or at rest), and according to the IQ, i.e.NCIQ ver-

sus CIQ, plus other morphological parameters (age,

weight, height, BMI, abdominal perimeter). How-

ever, the limited understanding of which physiolog-

ical model is the least biased for the prediction of

radiotracer dosage increases attrition in the decision-

making process. There are several possible causes for

the inconsistencies between predictive and empirical

dosage: patient obesity, differences in metabolic rates

for the same radiotracer, routine practice, equipment

technical evolutions, etc.

Differences in metabolism and anatomical bar-

riers can be challenging to predict and minimize.

Notwithstanding, it is possible to improve predictive

dosage models if the known inaccuracies result from

systematically modeling over- or underestimations,

rather than unpredictable spurious causes.

Figure 11: A shallow network with d inputs, h hidden neu-

rons and ` outputs.

Table 3 summarizes the characteristics of the pro-

posed neural network. The Adam optimizer is used

for training the network with a learning rate equal to

0.001.

We were careful not to include the weight in the

dependent variables (inputs) in Tab. 3 since, in the

database, the doses of tracer were calculated by the

doctors from the weight. Obviously using BMI or

weight in our model would have biased the results,

so they were deliberately omitted.

To measure the relevance of each parameter for

dose prediction, we tested different combinations of

inputs among which age, P

, P

Abd

, sex, type of ex-

amination (post-stress versus rest).

BIOSIGNALS 2022 - 15th International Conference on Bio-inspired Systems and Signal Processing

172

Table 3: Neural network’s architecture to predict tracer

dose.

Inputs thoracic abdominal P

Abd

, sex,

age, height

hidden neurons 12

activation function ReLU

Optimiser Adam

Loss MSELoss

Batch 7

Epoch 12,500

Output Dose

After 12,500 epochs of training, the estimator net-

work converges; the learning and validation losses de-

crease to stability as shown in the ﬁgure 12.

(a) Corrected images.

(b) Non corrected images.

Figure 12: Learning curves of dose prediction model with

corrected images and uncorrected images.

The model was evaluated on never-before-seen

data from the test base which contains (only) 14 pa-

tients due to the short size of the database. The ﬁg-

ure 13 represents the dose values predicted by the

neural network versus to the real values for corrected

images and for non corrected images.

It is possible to stratify the administered dose by

the type of examination status because the latter is

suspected to account for the response variable. The

summary statistics are discernably different than they

would be for unstratiﬁed data. A between groups z-

test is used to compare the results in the rest and post-

stress groups. The null hypothesis with the between

groups z-test is that the R

scores in the two groups

are equal in the population p

= p

where p

and p

are the R

scores for the two groups. z-value is the

ratio:

z =

− p

, (3)

where ’se’ represents the sampling variability, p

the

pooled sample R

score:

+ n

, (4)

with n

= 36 and n

= 94 the sample sizes for each

of the two groups. The standard error of the sam-

pling distribution difference between the two propor-

tions is:

se = p

(1 − p

)

. (5)

The null hypothesis was rejected with a p-value of

0.0028 assessing the prediction dose is better pre-

dicted in the post-stress group than in the rest group.

The R

scores given in Tab. 4 and calculated

from Eq. (4), merge the response variables of the

strata/groups. Thoracic or abdominal perimeters

combined with age or the type of examination give

very good scores. Combining all morphological pa-

rameters as inputs gives R

scores of 91,56% with

corrected image quality, and 89,81% with non cor-

rected image quality.

Table 4: Predicted R

scores for some combinations of in-

puts of corrected images.

Inputs R

score

BMI, type_exam 90,33%

, type_exam 88,35%

Abd

, type_exam 87,39%

age, P

, type_exam 91,56%

age, sex, P

, type_exam 89,14%

age, P

Abd

, type_exam 90,82%

Table 5 gives the scores obtained for some com-

binations of input parameters in the case of corrected

images and non corrected ones.

Table 5: Predicted R

scores for corrected/uncorrected im-

ages.

Inputs R

score

corrected

image

age, P

, type_exam 92,42%

age, type_exam 92,16%

uncorrected

image

age, P

, type_exam 90,73 %

age, type_exam 90,71 %

4.3 Prediction of the Image Grade from

the Tracer Dose

Finally, a last study is presented in this section, i.e.a

new model capable to predict the image quality based

Optimization of Tracer Dose for Scintigraphic Imagery

173

(a) Results for corrected images.

(b) Results for non corrected images.

Figure 13: Predicted radiotracer dose by the neural network

model versus expected values (14 patients).

on age, P

, P

Abd

, sex, type of examination.

It is typically a multi-class problem ith N=130 ob-

servations and K = 5 classes IQ

, i = 1, . . . , 5.

Classiﬁer performance is typically deﬁned according

to the confusion matrix associated with the classiﬁer.

Based on the entries of the matrix, it is possible to

compute sensitivity (recall), speciﬁcity, and precision.

In the following we will use T P

, F P

and F N

resp. indicate true positives, false positives, and false

negatives in the confusion matrix associated with the

i-th class. Precision and recall are indicated resp. by

P and recall by R.

A confusion matrix for every class C

, i ∈

{1, . . . , 5} is computed such that the i-th confusion

matrix considers class C

as the positive class and all

other classes C

with j 6= i as the negative class.

Each confusion matrix pools all observations labeled

with a class other than C

as the negative class. Ta-

ble 6 summarizes the performances across all classes.

These data indicate that, overall, performance is quite

high. However, our hypothetical classiﬁer underper-

forms for individual classes such as class IQ

(preci-

sion) and class IQ

(both precision and recall).

Table 6: Overall performance of the neural classiﬁer.

Class P R

0.945 0.777

0.562 0.900

0.833 0.666

0.793 0.920

0.166 0.200

4.4 Discussion

Decreasing image quality in heavier patients can be

compensated by administration of a patient-speciﬁc

dose in myocardial perfusion imaging using a cad-

mium zinc telluride-based SPECT camera. From col-

lected data, a protocol to provide optimal image qual-

ity was derived, and subsequently validated in 250 pa-

tients. Thoracic and abdominal perimeters are found

to be best predicting parameters for image quality.

These results pave the way to patient-speciﬁc proto-

col resulting in an image quality less depending on

patient’s weight.

5 CONCLUSIONS

The calculation of the effective radiotracer dose for

a myocardial perfusion scintigraphy based on the

known anatomical parameters of the patient is novel.

The prediction results could be further improved by

training on a larger database. The relevance of the

thoracic and abdominal perimeters has been proven,

so they can also be used as dose predictors, especially

in obese patients whose BMI does not really repre-

sent the distribution of fat around the heart. The body

weight may be not the best-predicting parameter for

image quality.

ACKNOWLEDGMENTS

This work was jointly supported by the GENOPOLE

and the Hôpital sud-francilien. We are grateful to Dr.

Michelle Granier, Présidente of the CME in CHSF,

and Mrs Natacha Vitrat for assistance in constructing

the project.

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