How reliable are ADC measurements? A phantom and clinical study of cervical lymph nodes
How reliable are ADC measurements? A phantom and clinical study of cervical lymph nodes
Bastien Moreau 0 1
Antoine Iannessi 0 1
Christopher Hoog 0 1
Hubert Beaumont 0 1
0 Research and Development Department , Median Technologies, Les deux arcs - 1800 route des crêtes - Bat. B, 06560 Valbonne , France
1 Department of Radiology, Centre Antoine Lacassagne , 06100 Nice , France
Objective To assess the reliability of ADC measurements in vitro and in cervical lymph nodes of healthy volunteers. Methods We used a GE 1.5 T MRI scanner and a first ice-water phantom according to recommendations released by the Quantitative Imaging Biomarker Alliance (QIBA) for assessing ADC against reference values. We analysed the target size effect by using a second phantom made of six inserted spheres with diameters ranging from 10 to 37 mm. Thirteen healthy volunteers were also scanned to assess the inter- and intra-observer reproducibility of volumetric ADC measurements of cervical lymph nodes. Results On the ice-water phantom, the error in ADC measurements was less than 4.3 %. The spatial bias due to the non-linearity of gradient fields was found to be 24 % at 8 cm from the isocentre. ADC measure reliability decreased when addressing small targets due to partial volume effects (up to 12.8 %). The mean ADC value of cervical lymph nodes was 0.87.10-3 ± 0.12.10-3 mm2/s with a good intra-observer reliability. Inter-observer reproducibility featured a bias of -5.5 % due to segmentation issues. Conclusion ADC is a potentially important imaging biomarker in oncology; however, variability issues preclude its broader adoption. Reliable use of ADC requires technical advances and systematic quality control. Key Points ADC is a promising quantitative imaging biomarker. ADC has a fair inter-reader variability and good intra-reader variability. Partial volume effect, post-processing software and non-linearity of scanners are limiting factors. No threshold values for detecting cervical lymph node malignancy can be drawn.
Magnetic resonance imaging; Diffusion; Biomarkers; Lymph; Quantitative evaluation
ADC Apparent diffusion coefficient
CVR Coefficient of variation
FOV Field of view
GE General electric
IVIM Intra-Voxel Incoherent Motion
LoA Limit of agreements
Recent advances in medical imaging technology and drug
therapeutics have accelerated the emergence of new
quantitative imaging biomarkers (QIB) [
]. The multiplication of
these QIBs is unfortunately not always accompanied by
stringent validations establishing that QIBs are well designed
to characterize a disease and its changes with therapy. This
lack of validation creates a situation where QIBs are routinely
used but with limited knowledge of their performances,
precluding a larger adoption in clinical trials.
Apparent diffusion coefficient (ADC) can quantify the
level of free water diffusion restricted by an increase in tissue
cellularity. Applications of ADC in cancer imaging has
motivated intensive research and ADC is now one of the main
QIBs derived from diffusion MRI.
Several studies have documented the incremental value of
ADC assessment as a complement or substitute to standard
sequences for the detection of malignant tumours [
degree of malignancy [
] or to evaluate response to treatment
Since lymph node involvement is pivotal in oncological
], ADC has been tested for its detection of
malignant adenomegalies [
]. Results are discordant [
Previous literature comprises heterogeneous studies
protocols and results [
]. Several sequential unitary processes are
necessary to output an ADC assessment, the lack of reliability
of any of these unitary processes is likely to degrade the final
ADC assessment. It is therefore particularly relevant to study
if ADC qualifies as a quantitative biomarker.
Over the last decade, a multidisciplinary community has
organized retrospective investigations of QIBs starting by
documenting methodologies [
]. In 2007, the Radiological
Society of North America (RSNA) launched QIBA
(Quantitative Imaging Biomarker Alliance [
]), a specialized
working group aiming at improving the value and usefulness
of QIBs in reducing variability across devices, patients, and
One of QIBA aims consists in releasing ‘Profiles’, which
are documents standardizing imaging protocols to obtain
optimal, reliable and reproducible biomarker measures
according to the current state of the art. The QIBA diffusion imaging
profile is still a work in progress [
QIBA also proposes a standardized protocol for quality
control in diffusion imaging, using a diffusion phantom [
] consisting of a volume of 0 °C stabilized water as the
reference value for ADC assessment [
The main objective of this study was to evaluate the
variability of ADC measurements in vitro on a phantom and
in vivo on cervical lymph nodes. The secondary objective
was to understand and quantify ADC measurement errors, in
view of correcting them in future studies.
We first tested QIBA metrics for quality control (QC) of ADC
image quality, and then performed a reliability analysis of
ADC measurements. Finally we measured ADC values of
cervical lymph nodes in healthy volunteers.
This prospective study was conducted at the Centre
Antoine Lacassagne, cancer centre in Nice, France, between
March and November 2016. We used a GE MRI scanner 1-5T
MR450W and ADW Volume Share 5 4.6 software to process
images (GE Healthcare).
Quality control test
We used a DIN 6858-1 PET-CT phantom (PTW)
consisting of a cylindrical Plexiglas body filled with a
mixture of ice and water. Three smaller cylinders were
inserted into the body, one of which was filled with
water at 0 °C (Fig. 1, left side).
Homogeneity of temperature inside the cylinder was
thermometer-controlled according to the process defined
into the QIBA profile to achieve thermal equilibrium
(>1 h) over the entire MRI exam period. For each b
value, four successive acquisitions spaced in time from
more than 12 min were performed, allowing
The diffusion protocol was 3three directions, DW SS-EPI
with b=0, 100, 600, 800 s/mm2, TR=9,451 ms, TE=80 ms,
Number of average = 2, FOV 320*320 mm, contiguous slice
thickness of 4 mm, encoding frequency axis R/L.
Four successive acquisitions were made for each b value,
the phantom symmetry axis was laser-centred to the magnetic
field positioning the 0 °C water cylinder at the center of the
scanner. Acquisitions of the phantom were performed
horizontally (x-axis) and vertically (y-axis). We measured circular
regions of interest (ROIs) of 2.5 cm diameter and composed of
123 voxels (Fig. 2). Mean ADC and standard deviation (SD)
According to the equations in Table 1, we computed the
measurement repeatability (R), estimated by the coefficient of
variation (CVR) and the repeatability coefficient (RCR), the
accuracy (ADC Bias estimate), ADC noise estimate and
The signal-to-noise ratio (SNR) was computed using
formula F (shown in Table 1) and involved computing the
‘Temporal Noise Image’ from the diffusion mapping at b =
0, with a 2-cm circular ROI.
Results were compared to QIBA ‘s references values
In addition, we analysed the planar spatial correlation
of ADC measures in shifting ROIs along the x and y
axis. The ADC reference value was measured at the
image center using formula C (see Table 1). We used
circular ROIs of 2.2-cm diameter and 2-cm shifts from
the centre either to the right (x-axis) or to the bottom
(y-axis) of the image.
Fig. 1 Phantoms used in the
study. Left ICEWATER phantom
filled with 0° C water (DIN
68581, PTW, Freiburg, Germany).
Right SPHERE phantom at room
temperature featuring spheres of
various sizes between 10- and
37mm diameters (NEMA
NU22012, PTW, Freiburg, Germany)
SPHERE phantom study
A second phantom was used (NEMA NU2-2012 (PTW)),
called SPHERE Phantom (Fig. 1, right side). The SPHERE
phantom embedded six different spheres (diameters 10, 13,
17, 22, 28 and 37 mm), filled with room temperature water.
We simulated clinical conditions in using the cervical level
of the routine whole-body MRI, i.e. axial DW SS EPI with
b=50 and b=1,000 s/mm2, TR=10,384 ms, TE set to minimum
(around 70 ms for all scans). Number of averages=2, parallel
imaging factor=2, FOV=400*400 mm, contiguous 5-mm
slice thickness, encoding frequency axis R/L. The phantom
was laser centred, equidistant from all spheres. Four
acquisitions were made at 1-day intervals. All values were
averaged over 4 days.
ADC measures were obtained from spherical volumes of
interest (VOIs) centred on spheres (Fig. 3).
The relative ADC error was computed for each sphere size,
considering that the reference ADC value was from the
37mm sphere. We analysed the correlation between VOI size
and precision of measurements in computing the CVR.
Additional analysis documented the measurement error, first
in measuring bias, second in computing the CVR through
several concentric VOIs of decreasing size in the largest
sphere, according to Table 1 (Formula A). Then partial
volume effect was quantified by calculating the relative error
within a VOI with a diameter equal to 80 % the diameter of
a sphere compared to a VOI of identical size within the largest
coronal view of ADC mapping. Red circular regions of interest are set
at the centre of the ice water cylinder
Definition of quality control metrics according to QIBA DW-MRI profile
Informed consent was obtained from 13 healthy
volunteers. Exclusion criteria were chronic disease, history or
ongoing symptoms of infection like fever, cough,
rhinorrhoea, dysphagia and odynophagia, history of
cervical surgery, claustrophobia and all usual
contraindications for MRI. Demographic status and smoking habits
were recorded for the 13 volunteers. Volunteers were
scanned using the same machine as the phantom study.
The acquisition was performed with a neck phased-array
coil and the volunteer was instructed to breath normally.
Technical settings of diffusion sequence for volunteers
were identical to those of the SPHERE phantom.
Two readers assessed ADC values of lymph nodes: a senior
radiologist with more than 6 years of experience in cancer
imaging and a junior radiologist.
Lymph node volumes were manually segmented on the
b1000 scan, and the graphic was exported to the ADC map
(Fig. 4). At least four lymph nodes were selected per
volunteer, including the largest. VOIs were segmented in
delineating hyper-intense diffusion areas on b1000 scans while
excluding lymph nodes hilum. Each node was segmented twice
by each observer using the same acquisition with an interval
of 7–60 days (mean 41 days) [
]. Mean and SD ADC values
Inter- and intra-observer agreements were calculated
according to the Bland Altman method using R CRAN
software. Bias and limit of agreement (LoA) were computed.
Inter- and intra-observer differences in segmenting lymph
node volumes and ADC values were analysed using the
sum of Wilcoxon rank for paired values test. A p-value <
0.05 was considered significant.
Firstly, our analysis showed that when VOIs are set within
spheres of decreasing size, relative error and measurements
variability of ADC measurements increased (Table 4).
Secondly, we found no significant mean ADC difference for
VOIs of decreasing sizes set within the largest sphere. In
thoses case, we found less than 2 % error between the largest
and smallest VOIs.
Correlation and variability analysis of ADC
measurements with VOI size seemed to indicate a significant
partial volume effect. Partial volume effect was visually
confirmed on images.
Fig. 3 Measurements on the
SPHERE phantom. Left
Spherical VOIs of decreasing
sizes centered on the largest
sphere. Right Spherical volumes
of interest (VOIs) centered on
sphere of various sizes. VOIs
diameters are set to 80 % of
physical sphere’s diameters
Thirteen volunteers were included in the in vivo study. Age
ranged from 22 to 50 years (mean 32.4), and gender ratio
(M/F) was 38.5 %. Two volunteers were active smokers or
recent ex-smokers (15.4 %). Overall, 54 cervical lymph nodes
were selected for analysis mainly on carotid-jugular sites, with
a mean volume of 1 cm3 (Appendix 1).
The mean value of measured ADC was 0.87 × 10 -3 mm2/s
(0.66–1.28 .10-3 mm2/s, SD was 0.12 .10-3 mm2/s). We found
a significant difference between the average ADC values
measured by readers 1 and 2 (0.84.10-3 and 0.90.10-3 mm2/s,
respectively, p <0.0001).
The inter-reader analysis showed a relative bias of -5.5 %,
LoA was [-18.8 %; 7.7 %]). The absolute bias was 0.045 10-3
mm2 / s, LoA was [-0.146; 0.056]).
We found a significant difference in average segmented
volumes between readers 1 and 2 (respectively 1.18 +/- 0.94
cm3 and 1.92 +/- 1.23 cm3, p <0.0001). There was a low
correlation between measurement differences in terms of
average ADC and volume segmentation (R2 = 0.37) by the two
Intra-observer analysis showed, respectively, for readers 1
and 2, a relative bias = 0.6 %; LoA=[-9.2 %; 10.4 %] and
relative bias = 0.5% ; LoA=[-8.8 %; 7.7 %].
Using the Beaumont et al. method [
] and based on our
intra-observer reproducibility parameters, we can estimate that
on longitudinal studies under strict reproducible conditions
(same patient, same reader), a meaningful relative change of
ADC value should be outside the range [-13 %; + 15% ].
Our QC results showed good compliance with QIBA metrics,
except for ADC bias estimate, which was slightly above the
limit, and with a variability of about 9 %. Results were
independent of the value of b.
We questioned if the main part of error was due to our
phantom design featuring a large off-axis volume of water
and thermally suboptimal materials. Using repeated imaging
of the phantom, we found, however, a good repeatability,
suggesting acceptable thermal equilibrium.
SNR was also lower than QIBA’s recommendation, but
Malyarenko et al. [
] reported that low SNR has no impact
on ADC assessments. Very low SNR without adequate
postprocessing would probably alter measurements as most
software (including the one we used) compute ADC images in
thresholding/removing low intensity voxels.
We highlighted a correlation between ADC measurement
error and the distance of ROIs from the magnetic centre. The
error increased with bottom-shift (up to 24 % when located
8 cm out of isocentre). Conversely, with regard to lateral-shift
we found no correlation with the magnitude of errors. This
result can be explained by non-uniformity of gradient-fields.
As we found a correlation between variability of ADC
assessments and contour segmentations, we concluded that
partial volume effect was a major contributor to the variability.
A visual review of outliers in clinical data confirmed high
variation of signal intensity in the tissue surrounding these
lymph nodes. Consequently, even a small variation in
segmentations led to a significant modification of ADC assessments
We recommend measuring ADC by drawing ROIs smaller
than the anatomical limits of the area of interest. How to
optimize segmentation margins must be further investigated.
We found excellent repeatability and good reproducibility.
This suggest that, if ADC intended to evaluate response to
treatment, changes inferior to [-13 %; +15 %] may not be
clinically relevant. However, longitudinal reproducibility
would require further clinical studies to take into account all
Fig. 4 Measurements of cervical
lymphnodes. Imaging of a healthy
volunteer’s cervical lymph node.
(a) Diffusion mapping at b = 50.
(b) mapping at b = 1000 on which
the volume of interest (VOI) is
contoured before being exported
to other series. (c) and (d)
Mapping of the apparent diffusion
coefficient. In red, the VOI is
determined by operator 1 (a, b
and c) and by operator 2 (d)
According to our dataset, the averaged ADC value for
healthy subject's cervical lymph nodes was 0.87.10-3 ±
0.12 .10-3 mm2/s.
Our results are well supported by the literature.
Regarding the correlation between variability of ADC
assessments and contour segmentation, heterogeneous
segmentation methods are available but several studies
documented the reproducibility issues [
these methods. These different approaches are also
reported as cumbersome and time-consuming [
Specific phantom studies have shown that gradient-field
error would be scanner-dependent [
] and not significant
within 4 cm from the isocentre, explaining the good
reproducibility of our ADC measurements and in other multicentric
Outcome of the quality control after imaging the ICEWATER phantom. The test was done with different b values
as b0-b100, b0-b600 and b0-b800. Tests not meeting QIBA quality claims are displayed in bold
Fig. 5 Spatial correlation of ADC.
Top view apparent diffusion
coefficient (ADC) changes
according to the horizontal
distance from magnetic centre.
Horizontal axis distance in cm.
Vertical axis ADC value in mm2/s.
Bottom view ADC changes
according to the vertical distance
from magnetic center. Horizontal
axis Distance in cm. Vertical axis
ADC value in mm2/s
studies. On multiple scanners, measurements at 12 cm from
the isocentre showed an average error of -20 % according to
vertical shift and +7 % horizontally.
Unlike our observation of an ADC value of 0.87.10-3 ±
0.12 .10-3 mm2/s in healthy subject's cervical lymph nodes,
Kwee et al. [
] reported a range of [1.15 10-3 mm2/s;
1.18 10-3 mm2/s], with similar intra- and inter-observer
variabilities. A review of 12 studies including more than
1,200 benign lymph nodes report ADC values of 0.302 ±
0.062.10-3 mm2/s in inflammatory cervical nodes [
2.38 ± 0.29.10-3 mm2/s for abdominal nodes [
et al. concluded that disparity of results could be due to
the various segmentation methods used.
Our results for non-diseased ADC values overlap with
metastatic or lymphomatous lymph nodes ADC measures [0.410
± 0.105 .10-3 mm2/s; 1.84 ± 0.37 .10-3 mm2/s] as reported by
other groups [
]; however, the ADC values we found
match with other non-diseased ADC studies. A
radiologicalpathological correlation study by Vandecaveye et al.  on
331 cervical lymph nodes proposed an ADC threshold of 0.94
10-3 mm2/s for detecting node malignancy featuring a
specificity of 94 %. According to this threshold, 72 % of our
data would have been misclassified. The use of ADC values to
assess cervical lymph nodes malignancy does not reach a
Other variability factors are described in the literature.
1) Inter-scanner variability. Some authors [
] report less
than 3 % variability while others [
] conclude that 80
% of scanners featured less than 5 % error. Another group
] reports that the CVR was 1.5 % on phantoms, and
less than 4 % for cerebral parenchyma. Impact of
acquisition parameters has also been investigated [
2) Impacts of post-processing software were documented by
Zeilinger et al. [
], reporting up to 8 % variation when
ADC was processed by four different types of software.
This limitation precludes longitudinal assessment of
patients across different centers.
In order to address the limitations we found, variability can
be minimized by standardizing ADC assessments. Efforts
from scanner manufacturers are needed [
] to ease the
Spatial variations of ADC measurements with respect to a reference VOI at center of the magnetic field. Top rows ADC measurements are shifted
horizontally. Bottom rows ADC measurements are shifted to the bottom. In bold are the shift values corresponding to the distance from magnetic centre
in cm.We found that ADC measurement did not change significantly when shifted right (Pearson coefficient=0.25). In opposite, ADC values increased
when measurements were shifted vertically (Pearson coefficient=0.95)
Repeated measurements of SPHERE Phantom images were performed over 4 days. Spheres of different sizes
were measured. Top row (in bold) True size of spheres of interest. Second row Diameter of VOIs centered on
spheres of interest. Third row For each sphere, measurements have been repeated four time over four days. Mean
ADC values have been computed. Fourth row Relative error (%) with respect to the VOI set into the largest
sphere. Bottom row Coefficient of variation (%) of repeated measurements over four days.
To be noted Regarding right side column, unlike for other measurements, true size sphere and VOI have same size
(10mm) because of the sampling limit (8 Voxel into the VOI). As a consequence, a nonlinear effect has been
observed Decrease of the Relative error and the mean ADC value. This point is further developed in Table 5
calibration of diffusion sequences. Also, initiatives from the
scientific community and technological improvements are
expected to avoid sequences artifacts and systematic errors.
Correction of non-linear gradient fields, one of the relevant
issues, is the focus of ongoing research [
Design of standardized validation methodologies and
systematic quality control [
] are key to reach acceptable levels
of compliance. To this end, a commercial version of diffusion
phantom has been developed by QIBA [
] and an automatic
quality control software is under development.
From early quantitative evaluation of diffusion, other
approaches have emerged.
IVIM (Intra-Voxel Incoherent Motion) is aiming to split the
different components of ADC [
]. Some authors indicate that
pure molecular diffusion would be more meaningful than
ADC as being independent from the perfusive component
]. Therefore, IVIM would enable assessing tissue for which
ADC is conceptually limited. Other studies develop
alternative diffusion-derived QIBs in using the non-Gaussian
distribution of diffusion kurtosis, Q-ball imaging and spectrum
analysis in particular.
These approaches also require metrological analysis before
scientific validation can be obtained, although the scientific
literature shows potential added value of ADC.
The physiopathology of ADC changes still remains
unknown. Some malignant tumours may feature increased
ADC with respect to healthy tissue, either by spontaneous
necrosis or cystic transformation, or by the destruction of a
parenchyma with spontaneously low ADC. Some benign
tumours may lead to ADC restriction; this is the case with
Warthin tumours of salivary glands due to their cellular
wealth, which is superior to the normal salivary tissue [
Lastly, microscopic changes triggered by anti-cancer
treatments may interfere with ADC assessment follow-up [
post-chemotherapy cytotoxic oedema leading to increased
restriction of ADC despite a therapeutic response, and delayed
fibrosis that may lead to suspicion of recurrence by dropping
the ADC. Other confounding factors such as the appearance of
extracellular oedema by hyper-hydration or by regional
venous obstacles may also mislead the evaluation of therapeutic
We found several limitations in our study. Phantom designs
are not optimal and may have biased our results, limiting
interpretations. First, regarding our ICEWATER phantom,
even if the thermal equilibrium seemed satisfactory, a 1 °C
error could lead to a 2.4 % ADC error, explaining the slightly
higher value found compared to QIBA recommendation.
Second, with regard to the SPHERE phantom: (1) without
Repeated measurements of SPHERE Phantom images were performed over 4 days. Spheres of different sizes
were measured. Top row (in bold) True size of spheres of interest (first value is the size of reference sphere, second
value the size of sphere of interest). Second row Diameter of measured VOIs centered on spheres. Third row
Number of voxel sampling VOIs. Fourth row For each sphere, measurements have been repeated four time over
four days. Mean ADC values have been computed (first value is the size of reference sphere, second value the size
of sphere of interest). Bottom row Relative error (%) with respect to the VOI set into the largest sphere as a
Fig. 6 Example of inter-observer discordance in terms of volume and
apparent diffusion coefficient (ADC). Example of inter-observer
discordance in terms of volume and ADC on a level II lymph node.
Top row First reader’s measurements. Bottom row Second reader’s
measurements. Left b1000 diffusion maps where volumes of interest
(VOIs) are drawn. Right corresponding ADC maps. Note: The
heterogeneity of the node’s environment featuring areas of high ADC
values (green and red in the right images) without clear correspondence
on the diffusion image
thermal control, measurements were relative, limiting
generalizability, (2) comparison of distant ROIs without gradient
field correction was prone to bias, and (3) analysis of partial
volume effect can be affected by phantom material, gas and
features. Third, we did not evaluate the impact of artifacts
]. QIBA recommends the use of corrective methods for
liver and kidney analysis [
], although other groups [
report no improvement in using such methods. Fourth, the
monocentric design of our study limits the generalizability
of our results.
At this time, variabilities from different sources preclude a
larger adoption of the ADC biomarker even though it is an
important advance in cancer imaging. Generalizing quality
controls and standardization of measurements is crucial to
overcome these ADC variability issues.
Acknowledgements We acknowledge Catherine Klifa for her multiple
reviews and scientific advices. All authors have equally contributed to
Funding The authors state that this work has not received any funding.
Compliance with ethical standards
Guarantor The scientific guarantor of this publication is Dr. Bastien
Conflict of interest Hubert Beaumont, as co-author of this manuscript,
declares relationships with the following companies: Median Technologies.
All other authors of this manuscript declare no relationships with any
companies whose products or services may be related to the subject
matter of the article.
Statistics and biometry
One of the authors has significant statistical
Informed consent Written informed consent was obtained from all
subjects (patients) in this study.
Ethical approval Institutional Review Board approval was obtained.
performed at one institution
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