Quantification with normal limits: New cameras and low-dose imaging
Quantification with normal limits: New cameras and low-dose imaging
Piotr J. Slomka 0 1 2
Mathieu Rubeaux 0 2
Guido Germano 0
0 Reprint requests: Piotr J. Slomka, PhD, Department of Medicine, Cedars-Sinai Medical Center , Los Angeles
1 David Geffen School of Medicine, UCLA , Los Angeles
2 Department of Medicine, Cedars-Sinai Medical Center , Los Angeles
SPECT myocardial perfusion imaging (MPI) widely
utilizes relative quantification of myocardial perfusion.
This is accomplished by local comparisons of test
patients to other scans of normal patients in most current
quantitative SPECT MPI methods.1–4 These
comparisons allow identification of local areas of
hypoperfusion, typically in polar map coordinates. The
set of normal patients is usually referred to as ‘‘normal
database’’ or ‘‘normal limits’’ (when a collection of
databases is considered, for example stress and rest
databases). Relative quantification of myocardial
perfusion with normal databases is a powerful clinical tool
which has been documented to rival expert physicians’
reading.5 Therefore, the recommendations regarding the
appropriate use of such normal databases should be of
great interest to all nuclear cardiology clinicians.
NEW CAMERAS AND QUANTIFICATION OF
Recently, new SPECT scanners with cadmium
zinc telluride (CZT) crystals have been introduced
together with novel scanner geometries, collimators,
and sometimes upright patient positioning.6 In
addition, most of these technologies utilize novel software
reconstruction techniques incorporating various image
corrections, most notably resolution recovery. The
regional count distribution of a normal SPECT MPI
scan may significantly differ between these new
scanners and conventional Anger cameras, and
therefore new dedicated normal limits may be required.
Indeed, normal distribution differences were observed
in early studies using CZT cameras and separate
specific limits have been recommended for these
systems.7 For quantitative analysis of data obtained
with CZT scanners, dedicated normal databases have
been created, especially for the Discovery NM 530c
(GE Healthcare)8–10 and D-SPECT
NORMAL PERFUSION EVALUATED IN
In this issue of Journal of Nuclear Cardiology,
Zoccarato et al13 examine in detail the normal
distribution of myocardial perfusion in seven different
hardware systems (including conventional Anger
cameras and CZT-based dedicated cardiac scanners) used
to image the same cardiac phantom. Beyond the
impact of the scanners employed, they also investigate
the influence of the reconstruction methods and the
count statistics on the uniformity of the activity in the
myocardium. Whenever possible, the effect of
attenuation correction is taken into account. Authors
observe differences in the relative myocardial count
distribution in the reconstructed images of the
phantom between different systems and reconstruction
methods. They conclude that different perfusion
databases are indeed required for different scanners.
However, they do not detect any significant
differences between the databases when imaging time is
decreased up to 25% of the reference time to simulate
low-count statistics, and therefore they suggest that
databases do not need to be modified for low-dose
SHOULD WE STUDY PATIENTS OR PHANTOMS?
To put in perspective the phantom results presented
by Zoccarato et al, we can contrast it with some recent
work by Nakazato et al evaluating the effect of count
statistics on relative perfusion quantification in
patients.14 In that study, the normal patients’ list-mode
raw data obtained from CZT SPECT scanner are
reframed to simulate low-count images. They
demonstrate that a progressive decrease of the left ventricular
count levels leads to an increase of the segmental
interpatient variation of the normal limits. Indeed, even
though the relative count distribution for the low-count
studies is similar to the high-dose studies, the
interpatient variation is changing due to increased
uncertainty. Consequently, Nakazato et al propose the use of
count-specific normal limits to obtain comparable
quantitative findings for studies with different count levels.
Thus, what could be the reason for this apparent
discrepancy between the findings of phantom-based and
patient-based studies of normal databases? The most
likely answer lies in the definition of the ‘‘variability’’.
In the study by Zoccarato et al, only one phantom
dataset was collected for each camera and the count
variability was thus defined as the coefficient of
variation of counts in a given segment. However, when the
actual normal limits are created in a clinical scenario,
the variability is estimated from inter-patient differences
after image normalization. This local variability measure
is used in most clinical implementations of the relative
quantification.1–4 to derive the severity of the local
perfusion decrease and consequently overall measures of
hypoperfusion such as defect extent or perfusion deficit.
Therefore, although a phantom study is a useful test for
evaluation of the differences in relative perfusion
distribution in the uniform myocardium related to the
scanner hardware and reconstruction methods
(abstracting it from patient or physiological differences), it is less
applicable to testing the variability of the real-world
DO WE NEED LOW-COUNT DATABASES?
Figure 1 shows an example of a low-count SPECT
MPI (1 million counts in the left ventricle) from CZT
camera and quantification of hypoperfusion severity
performed with low-count and high-count normal
databases. As seen in the bottom right of Figure 1, the
use of the high-count database for low-count image
results in an apparent overestimation of the defect size,
since some variation considered within normal threshold
by the low-count database could be considered
belownormal threshold in the high-count database with tighter
normal limits. Thus, the left ventricular count statistics
of test patients should ideally be matched to the count
statistics of the patients used to create the corresponding
normal database. Nevertheless, if the variation increase
is predictable, it would be possible to calibrate the
highcount database for low-count imaging by slightly
relaxing the confidence limits for what is considered a normal
scan. Alternatively, it may be even acceptable to adjust
the global normal threshold of defect extent or perfusion
deficit upwards, when low-count studies are quantified
with high-count databases. Ultimately, the overall
diagnostic accuracy of low-count imaging has to be
validated by comparison of receiver operating
characteristics curves. Further studies are needed to truly
evaluate the potential of normal limits to reducing the
imaging dose and time in SPECT MPI.
HOW MANY DATABASES DO WE NEED?
The evidence in the studies to date indicates that
dedicated normal limits are required for the optimal
relative quantification when scanners and reconstruction
methods differ significantly in design or if they use
features such as resolution recovery or attenuation
correction. To this end, most commercial
implementations allow the creation of dedicated normal limits. On
the other hand, generic databases for typical dual
cameras and standard imaging protocols are likely to
be applicable to any center utilizing such standard
equipment and methods. With respect to low-count
studies, we recommend to consider the photon count
level in the left ventricle. If left ventricular counts are
significantly lower than those in the studies used to
create the database (due to lower injected dose or
shorter imaging time) a new database or some
recalibration of the final quantification results may be
required. It should be noted however that low-dose
studies that are acquired with proportional extension of
the imaging time should present similar count level in
the left ventricle and it is likely that the same database
could be repurposed.
The selection of appropriate normal databases is of
great importance for quantification of myocardial
perfusion. The pattern of normal perfusion in the left ventricle
depends on the scanner geometry, use of attenuation
correction, and resolution recovery techniques.
Therefore, dedicated normal databases may be required for the
most optimal quantification of myocardial perfusion.
Substantial reduction of ventricular counts may also
necessitate updated database or recalibration of the
quantitative results. Further definitive studies utilizing
progressive list-mode reformatting and hard clinical
endpoints are needed to probe the acceptable limits for the
left ventricular count reduction.
This research was supported in part by Grant
R01HL089765 from the National Heart, Lung, and Blood
Institute/National Institute of Health (NHLBI/NIH) (PI: PS).
Its contents are solely the responsibility of the authors and do
not necessarily represent the official views of the NHLBI/NIH.
Cedars-Sinai Medical Center receives royalties for the
quantitative assessment of function, perfusion, and viability,
and a minority portion is distributed to some of the authors of
this manuscript (GG and PS).
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