Download Peripheral Nerve Perfusion by Dynamic Contrast

ORIGINAL ARTICLE
Peripheral Nerve Perfusion by Dynamic Contrast-Enhanced
Magnetic Resonance Imaging
Demonstration of Feasibility
Philipp Bäumer, MD, MSc,* Maximilian Reimann,* Clemens Decker, PhD,Þ
Alexander Radbruch, MD, JD,* Martin Bendszus, MD,* Sabine Heiland, PhD,Þ and Mirko Pham, MD*
Purpose: The aim of this study was to establish dynamic contrast-enhanced
perfusion in peripheral nerves for determination of blood-nerve permeability
(Ktrans) and nerve blood volume (NBV) in peripheral neuropathies as compared with healthy controls.
Methods: The study was approved by the institutional ethics committee, and
written informed consent was obtained from all participants. Forty-three
controls (24 women, 19 men; age, 48.7 T 17.5 years) and 59 patients with
peripheral neuropathy (28 women, 31 men; age, 52.7 T 12.4 years) were examined by a standard protocol including a T1-weighted dynamic contrastenhanced sequence (time of repetition/time of echo, 4.91/1.64; 10 slices;
resolution 0.8 ! 0.6 ! 3.0 mm3). Time - signal intensity analysis was
performed by normalizing to preYbolus arrival and calculating the mean
contrast uptake (MCU) for each patient. Further analyses were performed by
customized software to calculate Ktrans and NBV. Statistical analysis included
2-sided Student’s t tests of controls versus patients, receiver operating characteristic analysis, and subgroup analysis of patients according to etiologies of
neuropathy.
Results: TimeYsignal intensity analysis showed significantly increased contrast
uptake in patients as compared with controls (MCU, 1.29 T 0.15 vs 1.18 T 0.08;
P G 0.001). This was caused mainly by an increase in Ktrans (0.046 T 0.025 vs
0.026 T 0.016 minj1; P G 0.001) and less by an increase in NBV (3.9 T 2.6 vs
3.0 T 1.9 mL/100 mL; P = 0.12). This trend was true for all etiologies except
entrapment neuropathies. Excluding these, receiver operating characteristic
analysis found an area under the curve of 0.78 (95% confidence interval,
0.69Y0.89) for MCU and 0.77 (95% confidence interval, 0.65Y0.90) for Ktrans
to discriminate neuropathy from control.
Conclusions: Dynamic contrast-enhanced perfusion is a feasible technique to
assess Ktrans and NBV in peripheral nerves and may be used in future investigations on peripheral neuropathies.
Key Words: peripheral nerves, MR Neurography, MRI, perfusion, permeability
(Invest Radiol 2014;49: 518Y523)
T
he peripheral nervous system (PNS) is increasingly investigated
by means of magnetic resonance neurography (MRN).1Y4 Although dynamic contrast-enhanced (DCE) perfusion by magnetic
resonance imaging (MRI) has long been established as a standard
Received for publication November 26, 2013; and accepted for publication, after
revision January 22, 2014.
From the *Department of Neuroradiology and †Section of Experimental Radiology,
Department of Neuroradiology, Heidelberg University Hospital, Heidelberg,
Germany.
Conflicts of interest and sources of funding: This study was supported by a
Postdoctoral-Fellowship granted to P.B. from the Medical Faculty of the University of Heidelberg. M.P. is supported by the EFSD/JDRF/Novo Nordisk
European Programme in Type 1 Diabetes Research.
Reprints: Philipp Bäumer, MD, MSc, Department of Neuroradiology, Heidelberg
University Hospital, Im Neuenheimer Feld 400, 69120 Heidelberg, Germany.
E-mail: [email protected].
Copyright * 2014 by Lippincott Williams & Wilkins
ISSN: 0020-9996/14/4908Y0518
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functional imaging technique for the central nervous system
(CNS),5,6 it has, to date, not been implemented in MRN scans. Dynamic contrast-enhanced MRI can be used for estimation of perfusion parameters such as blood volume or permeability, providing
potentially valuable information that is not as readily obtainable
by other methods.7,8 In peripheral nerves, the blood-nerve barrier
(BNB) forms a tight and highly regulated interface in the human
body similar to the blood-brain interface.9 Pathogenic factors in peripheral neuropathies are nerve edema, demyelination, and inflammation, all of which might be associated with perturbations in the
BNB.10 Information on nerve permeability is therefore potentially of
high relevance for diagnostic MRN examinations in neuropathies
of inflammatory, ischemic, or other etiology. Moreover, the pathogenesis of various prevalent and poorly understood metabolic or inflammatory polyneuropathies might be further addressed by this
technique.
The technical challenge of DCE MRI perfusion in peripheral
nerves is their small caliber, ranging from less than 10 mm2 for upper
arm nerves such as the radial nerve to approximately 50 mm2 for the
sciatic nerve.11 This might have hampered previous attempts in the
application and optimization of DCE MRI for MRN. However, current structural MRN sequences are able to resolve peripheral nerves
to the level of individual fascicles so that structural resolution of a
nerve trunk should, in principle, be achievable for DCE sequences.
In this study, we implemented a peripheral nerve perfusion
protocol based on DCE and quantitative evaluation methods and
aimed to assess these techniques for the characterization of healthy
nerve perfusion by MRI and its potential to detect altered perfusion in
neuropathy.
PATIENTS AND METHODS
Clinical and Demographic Patient Data
This study was approved by the institutional ethics board
(S398-2012), and written informed consent was obtained from all
patients. Patients undergoing a clinical MRI study of the extremities
for any indication (eg, musculoskeletal or MRN) and receiving contrast agent were asked to participate in the study. Overall, 43 subjects
who underwent examination of an extremity for indications other
than clinically apparent neuropathy served as healthy controls (24
women, 19 men; age, 48.7 T 17.5 years). A total of 59 subjects were
included as patients with neuropathy (28 women, 31 men; age, 52.7 T
12.4 years). Eight subjects underwent the same protocol but were not
included in the study because MRN imaging did not show any abnormalities and therefore appeared incongruent with clinical information. Diagnosis in patients was based on clinical symptoms and/or
electrophysiological evidence of motor or sensory impairment in the
distribution of nerves examined by MRN.
Patients were further classified according to the etiology of the
disease as entrapment neuropathy, inflammatory neuropathy, traumatic nerve injury, hereditary polyneuropathy, or, if clinical, electrophysiological, and imaging examinations had been inconclusive
Investigative Radiology
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Investigative Radiology
& Volume 49, Number 8, August 2014
Peripheral Nerve Perfusion by DCE MRI
TABLE 1. Sequence Parameters
Sequence/Parameters
T1 DCE VIBE
T2 fs
T1 fs CE
TR, ms
TE, ms
No. Slices
ST, mm
Gap, mm
FoV, mm
Resolution, mm2
n
Time of Acquisition
4.91
7020
850
1.64
52
16
10
45
45
3.0
3.0
3.0
0.6
0.3
0.3
160
130
170
0.8 ! 0.6
0.3 ! 0.3
0.4 ! 0.3
1
3
2
3:34 min
7:17 min
4:41 min
CE indicates contrast enhanced; DCE, dynamic contrast enhanced; FoV, field of view; fs, fat-saturated; n, number of averages; ST, slice thickness; TE, time of
echo; TR, time of repetition; VIBE, volume interpolated breathhold examination.
at the time of examination, as disseminated polyneuropathy of yet
undetermined cause. Entrapment neuropathy was diagnosed if a patient presented with symptoms in the distribution of 1 nerve compatible with carpal tunnel syndrome or ulnar neuropathy at the elbow
and electrophysiological findings supported this diagnosis. Patients
were classified as inflammatory neuropathy if a polyneuropathy was
supported by clinical and electrophysiological examinations or if a
mononeuropathy at a localization atypical for compression neuropathies was detected and the underlying disease was known, such as
multifocal motor neuropathy or chronic inflammatory demyelinating
polyneuropathy. If the underlying etiology was not known at the time
of examination, patients were classified as disseminated neuropathy
of yet undetermined cause. If a hereditary cause for the neuropathy
was known, such as hereditary sensory and motor neuropathy, etiology was classified as hereditary polyneuropathy, and finally, in cases
of known blunt or sharp trauma to a nerve, etiology was classified as
traumatic nerve injury.
MRN Imaging
Examinations were conducted at 3 T magnetic field strength
(Magnetom VERIO; Siemens AG, Erlangen, Germany) between
January and July 2013. First, a T2-weighted turbo-spin-echo sequence with spectral fat saturation and at high spatial resolution for
reliable recognition and segmentation of peripheral nerves was acquired. The slab position of this first sequence was chosen based on the
patient’s known or most likely localization of maximum nerve lesion as
suspected by clinical and electrophysiological findings. At this position, a T1-weighted DCE volume interpolated breathhold examination sequence was acquired for the detection of contrast uptake and
further quantitative analysis. A contrast agent (DOTAREM; Guerbet,
Villepinte, France) was administered at the beginning of the third frame
of the sequence, that is, after 12 to 18 seconds, at a standard concentration of 0.1 mmol/kg and with a flow rate of 3.5 mL/s. A total of
35 frames were recorded at a rate of 6.11 seconds per frame. A T1weighted sequence with fat saturation after administration of contrast
agent was then acquired at the same position in 79 of the 102 subjects.
Sequence parameters are given in Table 1.
A knee 15-channel transmit/receive phased array radiofrequency
coil was used in 88 subjects and a wrist 8-channel receive radiofrequency
coil was used in 14 subjects.
Quantitative Image Analysis
TimeYSignal Intensity Curve Analysis
Using the software mean curve on a Syngo-workstation
(Syngo VE 32 B; Siemens), peripheral nerve signal was analyzed
for all time points, and timeYsignal intensity curves were plotted.
Since no automated method is established for the registration of peripheral nerves, masks for peripheral nerves were obtained by precise
manual segmentation around the epineurial contour (Fig. 1) of all
peripheral nerves at all slice positions (median, ulnar, and radial
nerves for upper arm examinations; median and ulnar nerves for
elbow and wrist; sciatic nerve for thigh; and peroneal and tibial nerve
for knee examinations). The most proximal and distal positions
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(1 and 10) were discarded because of potential 3-dimensional
aliasing artifacts at the extremes of the imaging slab. An additional
mask was placed in the largest artery contained in the imaging slab to
derive the arterial input function (AIF) and to determine the exact time
point of bolus arrival in the examined body region. Absolute signal
intensity values for each mask were read out and then normalized to
the arithmetic mean of the preYbolus arrival frames (6Y8 frames) by
division. These values then allowed plotting of an individual timeY
signal intensity curve for each nerve. Furthermore, all signal intensity
values recorded later than 10 frames (61 seconds) after bolus arrival
were averaged for each nerve to calculate 1 single value, denoted as
mean contrast uptake (MCU).
Quantitative Analysis Using the Patlak Model
We used the Patlak model to estimate the transfer constant
(Ktrans) between intravascular and extravascular space and nerve blood
volume (NBV). Although the Patlak model was developed for irreversible tracer uptake,12 it can also be used in first-order kinetics if the
influence of the diffusion of contrast agent from the extravascular back
to the intravascular space is negligible, which is a valid assumption
when Ktrans is relatively low and the observation time is short. In this
case, Ktrans and NBV can be determined by linear regression of
t
X AIFðTÞ dT
x ¼0
AIFðtÞ
y ¼
cðtÞ
;
AIFðtÞ
and
considering the equation
yðtÞ ¼ NBV þ k trans qxðtÞ;
where AIF(t) is the concentration of contrast agent in the supplying
artery at a given time point t and c(t) is the contrast agent concentration measured in tissue.
We used a customized software package developed in our
department (O2dicom) to determine Ktrans (in minj1) and NBV
(in mL/100 mL) in the peripheral nerve masks. O2dicom is written in
JAVA and is based on the Patlak model12 to calculate Ktrans and NBV
as described above semiautomatically either on a pixel-by-pixel basis
or in regions of interest. A linear relationship between measured
signal intensity and contrast concentration was assumed. All signal
values up to the time point of bolus arrival were used for normalization. Computed analysis in several cases failed to calculate parameters because the subjects’ AIF was insufficient for analysis by
the Patlak model. Furthermore, pulsation artifacts by adjacent vessels
occasionally impaired quantitative analysis. We therefore set respective upper and lower limits for Ktrans as 0.1 and 0.0 minj1 and NBV
as 10 and 0.0 mL/100 mL and excluded all subjects in whom these
limits were passed at any slice position.
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Bäumer et al
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RESULTS
FIGURE 1. Quantitative analysis of peripheral nerve perfusion.
Precise regions of interest were drawn around the epineurial
contour of peripheral nerves in axial T1-vibe source images as
illustrated in the upper left corner. T2- and T1-weighted
images were used as reference if exact delineation of nerves
was unclear in T1-vibe. Quantitative analysis was performed by
timeYsignal intensity course analysis, which yielded a value for
MCU, normalized to baseline before bolus arrival. Quantitative
parameter maps were calculated using the Patlak model and
yielded values for Ktrans and NBV.
A total of 102 participants participated in the study: 43 were
included as control subjects and 59 subjects were included as patients
based on clinical and electrophysiological results. Patients were further
classified by etiology into 12 patients with an entrapment neuropathy,
22 patients with an inflammatory neuropathy, 4 patients with traumatic
nerve injury, 2 patients with a known hereditary polyneuropathy, and 19
patients with a polyneuropathy of yet undetermined cause. Neuropathies caused by tumors of the PNS were excluded from the study. By
body region, examinations covered the upper arm in 12 controls and 21
patients, respectively, the elbow in 10 and 14, the wrist in 5 and 9, the
thigh in 11 and 8, and the knee in 5 and 7.
TimeYsignal intensity curve analysis showed that contrast
agent uptake in the nerves of patients with neuropathy was significantly higher compared with normal nerve tissue (Fig. 2).
Patlak analysis could be applied to 27 controls and 42 patients
(Fig. 3). Quantitative read-out parameters were compared between
patients and controls (Fig. 4). Mean contrast uptake was significantly
increased in patients (1.29 T 0.15 in patients vs 1.18 T 0.08 in controls; P G 0.001). Likewise, Ktrans was significantly higher in patients
(0.046 T 0.025 vs 0.026 T 0.016 minj1; P G 0.001). The increase in
NBV in patients did not reach statistical significance (3.9 T 2.6 vs
3.0 T 1.9 mL/100 mL; P = 0.12).
Subgroup analyses for neuropathies with different etiologies
were performed. All 3 read-out parameters were significantly higher
in traumatic nerve injury than in any other group (Table 2). Average
perfusion parameter values in entrapment neuropathies were found to
fall in the range of control subjects and not in the range of neuropathies of other etiologies.
The ROC analysis was performed to assess the potential of
perfusion read-out parameters as diagnostic markers for the presence
of peripheral neuropathy. Whereas NBV was found to be a weak
marker, MCU and Ktrans provided considerable diagnostic accuracy
for the detection of peripheral neuropathy (ROC values are given
in Fig. 5). Since values in entrapment neuropathies fell in the range
of controls and not in that of neuropathies of other etiologies, their
exclusion yielded improved diagnostic accuracy (Table 3).
Perfusion parameter values were independent of the body region
in which they were assessed in healthy controls; for example, no statistically significant differences were found between proximal versus
distal locations or between upper and lower extremities. Because
Overall, by timeYsignal intensity curve analysis and Patlak
quantitative analysis, 3 quantitative read-out parameters were calculated for each nerve (Fig. 1) and used for further statistical analysis.
Statistical Analysis
Data visualization and statistical analyses were performed using
Origin Pro 9.0 (Northampton, MA). Mean values were calculated for
MCU, Ktrans, and NBV in each subject for each nerve. In controls, if
more than 1 nerve was present in the imaging sections, values were
averaged. In patients, only those nerves affected by neuropathy, that is,
those with objectifiable symptoms and/or electrophysiological evidence of dysfunction, were averaged and used for statistical analysis of
patients. Graphs mapping the timeYsignal intensity curves for group
mean values and box plots for Ktrans and NBV were charted in Origin
Pro 9.0. Mean values were tested against each other for statistical significance using a 2-tailed Student’s t test, with a P value of G0.05
considered significant. The Bonferroni-Holm correction was used to
adjust for the family-wise error rate in multiple comparisons. Pearson
correlation analysis was performed for age versus MCU, Ktrans, and
NBV. Receiver operating characteristic (ROC) analysis was performed
in Origin Pro 9.0.
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FIGURE 2. TimeYsignal intensity curve analysis of healthy
controls and patients with neuropathy. Values are normalized
to baseline before contrast (time points = j7 to j1). Contrast
arrival in the arterial system is at time point = 0. Patients with
neuropathy show distinctly increased uptake already within
the first frames, which persists for more than 2 minutes.
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Peripheral Nerve Perfusion by DCE MRI
FIGURE 3. Parameter maps for Ktrans and NBV in the wrist and thigh. Upper row shows wrist images with median nerve, lower row
shows thigh images with sciatic nerve already divided into tibial (t) and peroneal (p) fascicles. Axial T2-weighted images with fat
saturation on the left show nerves at high spatial resolution for visual recognition and anatomical orientation. Ktrans and NBV were
calculated using the Patlak model and are visualized in quantitative parameter maps. These parameter maps also allow relatively
good differentiation between nerve and surrounding epineurial and fatty tissue, as well as differentiation between tibial and
peroneal branches of the sciatic nerve as displayed in the lower row.
peripheral nerves are known to show age-related degenerative changes
and blood volume is known to decrease with age in the CNS, correlation analyses between perfusion parameters and age were performed. In
healthy controls, this revealed a negative linear correlation between
NBV and age (P = 0.047) (Fig. 6). No significant correlations of age
with Ktrans or MCU were observed.
DISCUSSION
We here report a DCE MRI technique to characterize peripheral
nerve perfusion by parameters of contrast uptake, blood-nerve permeability (Ktrans), and NBV. These measures represent hitherto unused
metabolic markers for human neuropathies and seem particularly
promising to diagnose and understand nerve diseases for which nerve
conduction studies assessing electrophysiological function have previously been the only investigative method. In addition to being the first
investigation to test the feasibility of this method, our study also demonstrates that in a total of 59 patients and 43 controls, perfusion parameters are significantly altered in the presence of neuropathy.
We found that symptomatic nerves demonstrate significantly
increased contrast enhancement and that this is caused by an increase in
Ktrans and not in NBV. This corresponds to a disruption of the BNB,
which, under normal circumstances, forms a tight and highly regulated
interface in the human body similar to the blood-brain barrier.9 Evaluation of time-dependent signal increase after contrast administration
showed a relatively slow and continuous influx of contrast agent to
peripheral nervous tissue during the sequence acquisition. This observation allowed use of the Patlak model for further analysis as a wellknown model for quantifying the unidirectional influx constant for
low-permeating substances.12 The values we report here for Ktrans and
NBV in control nerves are similar to values in normal CNS white matter
reported in the literature. For example, Leenders et al,13 in a study using
positron emission tomography, found a cerebral blood volume (CBV)
of 2.7 mL/100 mL, which is close to the 3.0 mL/100 mL we report for
healthy peripheral nerves. Other studies using DCE T1 MRI arrive at
similar values for CBV.14Y16 The reported increase in permeability in
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patients with neuropathy in this study is plausible given the pathophysiological processes in neuropathy with edema and permeability
increase of the BNB, demyelination, and axon loss.10 The increase in
NBV and permeability in neuropathies is analogous to increases in inflammatory brain lesions detected by DCE MRI16,17 and suggest that
the technique as used here yields accurate estimates for perfusion
parameters.
In addition to these findings, a trend of decreasing NBV with
increasing age was observed in our control group. Little experimental
FIGURE 4. Statistical analysis of perfusion parameters in
controls versus patients. A, Mean-contrast uptake is
significantly increased in patients with peripheral neuropathy.
B, Blood-nerve permeability as assessed by Ktrans is likewise
significantly increased in patients. C, A trend for increased
NBV is observed in patients but does not reach statistical
significance.
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TABLE 2. Perfusion Parameter Values According to Etiology of Neuropathy
MCU
vs controls
vs other etiologies
Ktrans, 1/min
vs controls
vs other etiologies
NBV, mL/100 mL
vs controls
vs other etiologies
Compressive
Neuropathy
(n = 12/3)
Traumatic
Neuropathy
(n = 4/1)
Inflammatory
Neuropathy
(n = 21/9)
Disseminated
Neuropathy
(n = 21/9)
Hereditary
Neuropathy
(n = 2/0)
1.19 T 0.12
P = 0.81
P = 0.049
0.035 T 0.023
P = 0.19
P = 0.16
3.3 T 2.2
P = 0.66
P = 0.46
1.47 T 0.12
P G 0.001
P = 0.043
0.082 T 0.036
P G 0.001
P = 0.005
8.5 T 4.0
P = 0.002
P = 0.006
1.30 T 0.18
P G 0.001
P = 0.51
0.045 T 0.017
P = 0.003
P = 0.87
3.7 T 2.2
P = 0.33
P = 0.79
1.28 T 0.14
P = 0.003
P = 0.97
0.041 T 0.020
P = 0.043
P = 0.29
3.6 T 2.1
P = 0.37
P = 0.51
1.34 T 0.11
P = 0.046
P = 0.59
0.073 T 0.005
P = 0.003
P = 0.12
3.2 T 0.8
P = 0.86
P = 0.71
Controls
(n = 43/16)
1.18 T 0.08
P G 0.001
0.026 T 0.016
P G 0.001
3.0 T 1.9
P = 0.12
Perfusion parameters are given for each subgroup of neuropathy patients according to etiology. Subgroup values were tested against controls and against the
mean of all other neuropathy patients.
P values are adjusted for multiple comparisons by the Bonferroni-Holm correction. Numbers in parentheses indicate total number of subjects and number of
subjects excluded from the analysis because of insufficient AIF or pulsation artifacts for accurate calculation of Ktrans and NBV.
MCU indicates mean contrast uptake; Ktrans, blood-nerve permeability; NBV, nerve blood volume; AIF, arterial input function.
and clinical evidence exists about peripheral nerve perfusion changes
associated with aging. Thickening of basal lamina ensheathment during
aging has been reported18 as well as a decreasing response to vasodilators,19 both of which could factor into decreasing intraneural blood
volume during aging. For the CNS, correlations between perfusion
parameters and age have been established using positron emission tomography.13,20 From these, CBV is known to decrease with increasing
age in white matter as well as the entire brain. The correlation in our
group of healthy controls is analogous and further supports our model.
To our knowledge, this is the first report on investigating human peripheral nerve perfusion in vivo by MRI using quantitative
parameters. One previous study investigated carpal tunnel syndrome
in 9 patients but remained qualitative in the analysis.21 Several
groups have investigated the potential of sonography to assess nerve
perfusion.22Y25 Although technical advances and postprocessing will
enhance the application of sonography for measuring perfusion parameters in nerves,26 the use of sonography has, until now, been
limited to measuring intraneural flow, whereas MRI data allow calculation of both permeability and blood volume. In addition, sonography was applied in these studies only in superficial entrapment
neuropathies, while nerves frequently affected by polyneuropathies
and located deep within tissue, such as the sciatic nerve, are difficult
to examine. Moreover, sonography is highly operator dependent. In
contrast, MRI allows an observer-independent and fully quantitative
analysis.
The diagnostic accuracy calculated by ROC analysis showed
perfusion measures to be diagnostic signs of moderate to good accuracy
in detecting peripheral neuropathy. In this range, they are comparable
with the diagnostic accuracy of diffusion tensor imaging as the hitherto
only available functional imaging method for the PNS.27 Refinement of
the used sequence and postprocessing may yield even better diagnostic
quality. We anticipate that perfusion MRI for peripheral nerves will find
application in the investigation of the time course and localization of
the highly prevalent and poorly understood diseases such as inflammatory and ischemic polyneuropathies. Furthermore, the technique
may also prove useful in monitoring of therapeutic drug effects in the
polyneuropathies.
Our study has several limitations. First, the general assumptions
and limitations of the Patlak model apply to the technique, including
unidirectionality of permeability at least in the first 2 minutes. Second, a
number of patients were excluded from evaluation of Ktrans and NBV,
either because AIF did not allow calculation or because of pulsation
artifacts. Further refinement of sequence parameters to improve AIF
measurement and of software parameters may solve this in the future.
Third, absolute values as reported here are always dependent on the
model chosen; thus, investigators should verify a normal range of
values at their own centers.28,29 Finally, our study consisted of a large
TABLE 3. ROC Analysis of Diagnostic Performance for Perfusion
Read-Out Parameters
All Etiologies
MCU
Ktrans
NBV
FIGURE 5. ROC analysis. Parameters of MCU and Ktrans have
significant diagnostic accuracy in discriminating neuropathies
from healthy controls with AUC of 0.78 and 0.77, respectively.
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Excluding Entrapment
Neuropathies
AUC
95% CI
AUC
95% CI
0.72
0.74
0.60
0.46Y0.74
0.60Y0.87
0.41Y0.83
0.78
0.77
0.62
0.69Y0.89
0.65Y0.90
0.47Y0.76
ROC indicates receiver operating characteristic; AUC, area under the
curve; CI, confidence interval; MCU, mean contrast uptake; Ktrans, bloodnerve permeability; NBV, nerve blood volume.
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Investigative Radiology
& Volume 49, Number 8, August 2014
FIGURE 6. Correlation analysis between NBV and age in
healthy controls. A trend of decreasing NBV with increasing
age is observed (r = j0.41, P = 0.047).
but heterogeneous patient group. Although this was effective to test the
technique at different anatomical positions and in different forms of
neuropathy, future investigations may focus on 1 restricted diagnosis or
etiology.
In conclusion, we have developed a method for quantification of peripheral nerve perfusion parameters. The plausibility of the
technique is supported by findings in healthy controls and in a large
number of patients with neuropathy. Dynamic contrast-enhanced T1
MRI may be used in future investigations on peripheral neuropathy
and in clinical MRN.
REFERENCES
1. Bendszus M, Stoll G. Technology insight: visualizing peripheral nerve injury
using MRI. Nat Clin Pract Neurol. 2006;1:45Y53.
2. Gasparotti R, Lodoli G, Meoded A, et al. Feasibility of diffusion tensor
tractography of brachial plexus injuries at 1.5 T. Invest Radiol. 2013;48:
104Y112.
3. Baumer P, Weiler M, Ruetters M, et al. MR neurography in ulnar neuropathy as
surrogate parameter for the presence of disseminated neuropathy. PLoS One.
2012;7:e49742.
4. Pham M, Baumer P, Meinck HM, et al. Anterior interosseous nerve syndrome:
fascicular motor lesions of median nerve trunk. Neurology. 2014;82:598Y606.
5. Runge VM, Schoerner W, Niendorf HP, et al. Initial clinical evaluation of
gadolinium DTPA for contrast-enhanced magnetic resonance imaging. Magn
Reson Imaging. 1985;3:27Y35.
6. Le Bihan D. Theoretical principles of perfusion imaging: application to magnetic resonance imaging. Invest Radiol. 1992;27(suppl 2):S6YS11.
7. Cuenod CA, Balvay D. Perfusion and vascular permeability: basic concepts
and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging. 2013;94:
1187Y1204.
8. Sourbron S. Technical aspects of MR perfusion. Eur J Radiol. 2010;76:304Y313.
9. Weerasuriya A, Mizisin AP. The blood-nerve barrier: structure and functional
significance. Methods in molecular biology. 2011;686:149Y173.
* 2014 Lippincott Williams & Wilkins
Peripheral Nerve Perfusion by DCE MRI
10. Mizisin AP, Weerasuriya A. Homeostatic regulation of the endoneurial microenvironment during development, aging and in response to trauma, disease
and toxic insult. Acta Neuropathol. 2011;121:291Y312.
11. Cartwright MS, Passmore LV, Yoon JS, et al. Cross-sectional area reference
values for nerve ultrasonography. Muscle Nerve. 2008;37:566Y571.
12. Patlak CS, Blasberg RG. Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data: generalizations. J Cereb Blood Flow
Metab. 1985;5:584Y590.
13. Leenders KL, Perani D, Lammertsma AA, et al. Cerebral blood flow, blood
volume and oxygen utilization: normal values and effect of age. Brain. 1990;
113(pt 1):27Y47.
14. Larsson HB, Courivaud F, Rostrup E, et al. Measurement of brain perfusion,
blood volume, and blood-brain barrier permeability, using dynamic contrastenhanced T(1)-weighted MRI at 3 tesla. Magn Reson Med. 2009;62:1270Y1281.
15. Sourbron S, Ingrisch M, Siefert A, et al. Quantification of cerebral blood flow,
cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn
Reson Med. 2009;62:205Y217.
16. Cramer SP, Simonsen H, Frederiksen JL, et al. Abnormal blood-brain barrier
permeability in normal appearing white matter in multiple sclerosis investigated
by MRI. NeuroImage Clin. 2013;4:182Y189.
17. Ingrisch M, Sourbron S, Morhard D, et al. Quantification of perfusion and
permeability in multiple sclerosis: dynamic contrast-enhanced MRI in 3D at
3 T. Invest Radiol. 2012;47:252Y258.
18. Jacobs JM, Love S. Qualitative and quantitative morphology of human sural
nerve at different ages. Brain. 1985;108(pt 4):897Y924.
19. Cowen T, Ulfhake B, King RHM. Aging in the Peripheral Nervous System.
4th ed. Philadelphia, PA: Elsevier Saunders; 2005.
20. Yamaguchi T, Kanno I, Uemura K, et al. Reduction in regional cerebral metabolic rate of oxygen during human aging. Stroke. 1986;17:1220Y1228.
21. Sugimoto H, Miyaji N, Ohsawa T. Carpal tunnel syndrome: evaluation of median
nerve circulation with dynamic contrast-enhanced MR imaging. Radiology.
1994;190:459Y466.
22. Wang Y, Tang P, Zhang L, et al. Gray-scale contrast-enhanced ultrasonography for
quantitative evaluation of the blood perfusion of the sciatic nerves with crush
injury. Acad Radiol. 2011;18:1285Y1291.
23. Wang Y, Tang P, Zhang L, et al. Quantitative evaluation of the peripheral nerve
blood perfusion with high frequency contrast-enhanced ultrasound. Acad
Radiol. 2010;17:1492Y1497.
24. Ghasemi-Esfe AR, Khalilzadeh O, Vaziri-Bozorg SM, et al. Color and power
Doppler US for diagnosing carpal tunnel syndrome and determining its severity: a quantitative image processing method. Radiology. 2011;261:499Y506.
25. Akcar N, Ozkan S, Mehmetoglu O, et al. Value of power Doppler and gray-scale
US in the diagnosis of carpal tunnel syndrome: contribution of cross-sectional
area just before the tunnel inlet as compared with the cross-sectional area at the
tunnel. Korean J Radiol. 2010;11:632Y639.
26. Sridharan A, Eisenbrey JR, Liu JB, et al. Perfusion estimation using contrastenhanced 3-dimensional subharmonic ultrasound imaging: an in vivo study.
Invest Radiol. 2013;48:654Y660.
27. Guggenberger R, Markovic D, Eppenberger P, et al. Assessment of median nerve
with MR neurography by using diffusion-tensor imaging: normative and pathologic diffusion values. Radiology. 2012;265:194Y203.
28. Buckley DL. Uncertainty in the analysis of tracer kinetics using dynamic
contrast-enhanced T1-weighted MRI. Magn Reson Med. 2002;47:601Y606.
29. Kershaw LE, Buckley DL. Precision in measurements of perfusion and microvascular permeability with T1-weighted dynamic contrast-enhanced MRI.
Magn Reson Med. 2006;56:986Y992.
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