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UMEÅ UNIVERSITY MEDICAL DISSERTATIONS
New series No. 1505
________________________________________________
Cerebral blood flow and
intracranial pulsatility studied
with MRI
Measurement, physiological and pathophysiological aspects
Anders Wåhlin
Departments of Radiation Sciences and Clinical Neuroscience
Umeå University
Department of Biomedical Engineering and Informatics
Umeå University Hospital
Umeå, Sweden, 2012
© Anders Wåhlin, 2012
ISBN: 978-91-7459-428-7
ISSN: 0346-6612
Printed by Print & Media
Umeå University, Sweden 2012
ii
Abstract
During each cardiac cycle pulsatile arterial blood inflates the vascular bed
of the brain, forcing cerebrospinal fluid (CSF) and venous blood out of the
cranium. Excessive arterial pulsatility may be part of a harmful mechanism
causing cognitive decline among elderly. Additionally, restricted venous
flow from the brain is suggested as the cause of multiple sclerosis.
Addressing hypotheses derived from these observations requires accurate
and reliable investigational methods. This work focused on assessing the
pulsatile waveform of cerebral arterial, venous and CSF flows. The overall
aim of this dissertation was to explore cerebral blood flow and intracranial
pulsatility using MRI, with respect to measurement, physiological and
pathophysiological aspects.
Two-dimensional phase contrast magnetic resonance imaging (2D PCMRI)
was used to assess the pulsatile waveforms of cerebral arterial, venous and
CSF flow. The repeatability was assessed in healthy young subjects. The 2D
PCMRI measurements of cerebral arterial, venous and CSF pulsatility were
generally repeatable but the pulsatility decreased systematically during the
investigation.
A method combining 2D PCMRI measurements with invasive CSF infusion
tests to determine the magnitude and distribution of compliance within the
craniospinal system was developed and applied in a group of healthy
elderly. The intracranial space contained approximately two thirds of the
total craniospinal compliance. The magnitude of craniospinal compliance
was less than suggested in previous studies.
iii
The vascular hypothesis for multiple sclerosis was tested. Venous drainage
in the internal jugular veins was compared between healthy controls and
multiple sclerosis patients using 2D PCMRI. For both groups, a great
variability in the internal jugular flow was observed but no pattern specific
to multiple sclerosis could be found.
Relationships between regional brain volumes and potential biomarkers of
intracranial cardiac-related pulsatile stress were assessed in healthy
elderly. The biomarkers were extracted from invasive CSF pressure
measurements as well as 2D PCMRI acquisitions. The volumes of temporal
cortex, frontal cortex and hippocampus were negatively related to the
magnitude of cardiac-related intracranial pulsatility.
Finally, a potentially improved workflow to assess the volume of arterial
pulsatility using time resolved, four-dimensional phase contrast MRI
measurements (4D PCMRI) was evaluated. The measurements showed
good agreement with 2D PCMRI acquisitions.
In conclusion, this work showed that 2D PCMRI is a feasible tool to study
the pulsatile waveforms of cerebral blood and CSF flow. Conventional
views regarding the magnitude and distribution of craniospinal compliance
was challenged, with important implications regarding the understanding
of how intracranial vascular pulsatility is absorbed. A first counterpoint to
previous near-uniform observations of obstructions in the internal jugular
veins in multiple sclerosis was provided. It was demonstrated that large
cardiac-related intracranial pulsatility were related to smaller volumes of
brain regions that are important in neurodegenerative diseases among
elderly. This represents a strong rationale to further investigate the role of
excessive intracranial pulsatility in cognitive impairment and dementia.
For that work, 4D PCMRI will facilitate an effective analysis of cerebral
blood flow and pulsatility.
iv
Table of Contents
1 Original papers
3 1 Introduction
5 1.1 Arterial pulsatility
8 1.2 Restricted venous flow
10 1.3 CSF dynamics
1.4 Structural magnetic resonance imaging
1.5 Flow sensitive magnetic resonance imaging
14 16 21 1.6 Quantification of CSF pulse pressure
25 2 Aims
27 3 Materials and methods
27 3.1 Subjects
28 3.2 Magnetic resonance imaging
30 3.3 Volume of arterial and CSF pulsatility
33 3.4 CSF pulse pressure
34 3.5 Mathematical model
35 3.6 Intracranial volume
35 3.7 Regional brain volumes
37 3.8 Statistics
39 4 Results
39 4.1 Repeatability
41 4.2 Craniospinal compliance
42 4.3 Restricted venous flow
4.4 Intracranial pulsatility and brain volumes
43 45 4.5 Four-dimensional flow measurements
47 5 Discussion
47 5.1 Measurement aspects
49 5.2 Physiological aspects
51 5.3 Pathophysiological aspects
55 6 Conclusion
57 7 Acknowledgements
59 8 References
v
Original papers
This dissertation is based on the following papers, which are referred to by
their Roman numerals in the text.
I.
Wåhlin, A., Ambarki, K., Hauksson, J., Birgander, R., Malm, J., and
Eklund, A. (2012). Phase contrast MRI quantification of pulsatile
volumes
of
brain
arteries,
veins,
and
cerebrospinal
fluids
compartments: Repeatability and physiological interactions. J Magn
Reson Imaging 35, 1055–1062. Reprinted with permission.
II.
Wåhlin, A., Ambarki, K., Birgander, R., Alperin, N., Malm, J., and
Eklund, A. (2010). Assessment of craniospinal pressure-volume
indices. AJNR Am J Neuroradiol 31, 1645–1650. Reprinted with
permission.
III.
Sundström, P., Wåhlin, A., Ambarki, K., Birgander, R., Eklund, A.,
and Malm, J. (2010). Venous and cerebrospinal fluid flow in
multiple sclerosis: a case-control study. Ann. Neurol. 68, 255–259.
Reprinted with permission.
IV.
Wåhlin, A., Ambarki, K., Birgander, R., Malm, J., and Eklund, A. In
healthy elderly the volumes of several brain regions are related to
pulsatility
in
cerebral
arteries
and
cerebrospinal
fluid.
In
Manuscript.
V.
Wåhlin, A., Ambarki, K., Birgander, R., Wieben, O., Johnson, KM.,
Malm, J., and Eklund, A. Measuring pulsatile flow in cerebral
arteries using four-dimensional phase contrast magnetic resonance
imaging. In Manuscript.
1
2
1 Introduction
The adult brain is contained in a closed compartment constituted by the
cranium and dura mater (Mokri, 2001). Inside this space cerebrospinal fluid
(CSF) surrounds the entire central nervous system (Magendie, 1842). During
each cardiac cycle arterial pulsatility causes a systolic increase in cerebral
arterial blood volume (Greitz et al., 1992), something that can be observed
during neurosurgery as a cyclic pulsation of the brain. To compensate for
this volume increase, venous blood is forced out of the cranium and CSF is
pushed down to the spinal canal (Enzmann and Pelc, 1991; Greitz et al.,
1992; Enzmann and Pelc, 1993).
Recently, there has been increased attention on the pulsatility of cerebral
arteries and CSF. Among elderly, an increased pulsatility in the arteries
supplying the brain has been linked to cognitive impairment in several
domains, including memory (Mitchell et al., 2011). It has been hypothesized
that an age-related increase in pulsatility in the cerebral microcirculation is
harmful for the brain (O'Rourke and Hashimoto, 2007). The CSF pulse
pressure (Anile et al., 2010; Eide and Brean, 2010) and cardiac-related flow
pulsatility have also been linked to the outcome following shunt surgery in
subjects with communicating hydrocephalus (Bradley et al., 1996).
Moreover, although multiple sclerosis (MS) is commonly regarded as an
inflammatory disorder caused by a complicated interaction between
environmental and genetic risk factors (Compston and Coles, 2002),
restricted venous flow (Zamboni et al., 2009b) and altered CSF
flow
(Zamboni et al., 2009c) were recently suggested to be parts of the disease
pathophysiology. This controversial theory is appealing as restricted venous
outflow potentially can be treated (Zamboni et al., 2009a).
3
Addressing hypotheses derived from these observations requires reliable
investigational methods. It is also necessary to explore the normal
physiological mechanisms associated with cerebral arterial, venous and CSF
dynamics.
In this dissertation a biomechanical perspective on the interaction between
cerebral arteries, veins and CSF was applied. With a theoretical model as a
starting point, we probed the system from several angles while maintaining a
critical evaluation of measurement reliability. Focus was on assessing the
pulsatile waveforms of arterial and venous blood and CSF with phase
contrast magnetic resonance imaging (PCMRI). These measurements were
also combined with invasive CSF dynamic investigations, quantifying the
magnitude of the CSF pulse pressure occurring in response to the vascular
pulsations.
4
1.1 Arterial pulsatility
The left and right internal carotid artery (ICA) and vertebral artery (VA),
respectively, are four arteries that supply the brain (Figure 1). Closer to the
brain, the VA merge to form the basilar artery (BA) (Purves et al., 2011).
Figure 1. Arterial supply and venous drainage of the brain. ICA=internal
carotid artery, VA=vertebral artery, BA=basilar artery, MCA=middle
cerebral artery, ACA=anterior cerebral artery, SSS=superior sagittal
sinus, IJV=internal jugular vein. The figure is based on a 4D PCMRI
acquisition from Paper V.
5
At the circle of Willis, a hub connecting the major cerebral arteries, the ICA
is divided into the anterior and middle cerebral artery (ACA and MCA,
respectively). Here, the BA is also divided to form the posterior cerebral
arteries. Blood from the ICA supplies a large part of the cerebrum. The VA
and the BA supply the brain stem, cerebellum and posterior parts of the
cerebrum (Hall and Guyton, 2010).
In young subjects with well functioning arterial systems, the arterial
distensibility, or compliance, cushions the arterial pulse. This is sometimes
referred to as the Windkessel effect (Westerhof et al., 2009). This absorption
reduces the systolic pressure peak and smoothens the pulsatility of blood
flow that reaches the capillaries (O'Rourke and Hashimoto, 2007). In
general, for a higher cardiac stroke volume the compensating mechanisms of
the arteries have to absorb a greater volume. The compliance of the arterial
tree together with the cardiac stroke volume ultimately determine the
magnitude of arterial pulse pressure (Hall and Guyton, 2010).
About 30 million heartbeats per year induce arterial fatigue and dilatation in
the later stages of life (O'Rourke and Hashimoto, 2007; Bullitt et al., 2010).
In parallel with atherosclerotic changes the artery is also hardened and less
compliant, and a gradual decline in the Windkessel function is observed
(Avolio et al., 1983). These interrelated changes are most prominent in the
aorta and major proximal arteries and less prominent in the distal regions of
the circulation. Consequently, a stiffer arterial system may result in
increased pulsatility and cyclic distention of distal branches of the arterial
system (Boutouyrie et al., 1992).
For most organs, the small arteries, arterioles and capillaries complete the
transition of pulsatile blood flow into a steady flow. However, the high
perfusion rate of the brain makes arterial pulsatility reach deeply towards
the smallest vessels (O'Rourke and Hashimoto, 2007; Mitchell, 2008).
Research regarding large arteries and their age-related stiffening have
6
promoted questions on what effects this might have on tissues and organs of
the body (Mitchell, 2008). If aortic stiffness impedes appropriate cushioning
of the arterial pulse, the pulsatile stress will extend deeper into the arterial
tree. In this event the sites with the most dilated arterioles, e.g. the brain,
may be at risk to damage caused by the increase in arterial pulsatility.
Research rationale
Recently, a large-scale evaluation focusing on elderly subjects demonstrated
that cognitive performance in several domains, including memory, is
negatively correlated with the amplitude of pulsatility in arteries supplying
the brain (Mitchell et al., 2011). Based on these observations, it is relevant to
evaluate if excessive pulsatility in cerebral arteries catalyze the aging of the
brain, and possibly even pushes a subject into a degenerative dementia. It is
essential to clarify if any specialized brain region is particularly susceptible
to excessive pulsatile stress. Studies evaluating associations between
regional brain volumes and intracranial vascular pulsatility may provide a
first indication of which brain regions that may be harmed by such
processes.
7
1.2 Restricted venous flow
In contrast to the arterial system, the cerebral venous drainage displays large
individual variations (Doepp et al., 2004) and is easily influenced by external
disturbances such as body position (Valdueza et al., 2000). Through
bridging veins and deep cerebral veins the blood is transmitted to the venous
sinuses, formed by the dura mater (Figure 1). The sinuses transport blood to
the neck veins. In supine position, the major venous pathways of the neck
are the left and right internal jugular vein (IJV) (Valdueza et al., 2000;
Alperin et al., 2005). Commonly, the right IJV carries a dominant part of the
total IJV outflow but individual variations of this distribution are large
(Stoquart-ElSankari et al., 2009). In addition to the left and right IJV, extrajugular venous pathways contribute to the venous outflow. These include
vertebral veins, epidural veins and deep neck veins. The large capacity of
these veins indicates that they could take over the entire venous drainage of
the brain if needed (Doepp et al., 2004).
Despite the extensive capacity of collateral pathways, a controversial
hypothesis suggests that the development of MS is a manifestation of
restricted venous flow. MS lesions often develop around a central vein (Tan
et al., 2000), a rationale for suspecting venous involvement in the etiology of
MS. Recently, the pattern of impaired cerebral venous outflow termed
chronic cerebrospinal venous insufficiency (CCSVI) has been linked to MS
(Singh and Zamboni, 2009; Zamboni et al., 2009b). The theory describes
compromised venous outflow as a cause of accumulation of iron in the
central nervous system, which may initiate the development of MS lesions.
The IJV flow is reported as a central component in the hypothesis (Zamboni
et al., 2009b). Stenoses or malformations have been suggested to be the
primary cause of the restricted venous outflow. The restricted venous flow is
also reported to bring about an abnormal CSF flow in the cerebral aqueduct
(Zamboni et al., 2009c). Potentially, venous congestions of the CCSVI type
8
can be treated, improving the symptoms of MS patients (Zamboni et al.,
2009a). Therefore, if proven accurate, this may represent a major
therapeutic option for MS patients.
Research rationale
Because CCSVI potentially can be treated (Zamboni et al., 2009a), centers
around the world are now offering endovascular procedures, i.e. angioplasty
and stenting, to treat CCSVI. This has resulted in “medical tourism” where
MS patients themselves seek help abroad. The scientific process needs to
catch up with this potentially hasty development and the CCSVI theory
needs to be evaluated by accurate, reliable and objective methods. The link
between venous obstructions and MS was observed using ultrasound
methods (Zamboni et al., 2009b). Because venous flow can also be
quantified using PCMRI (Stoquart-ElSankari et al., 2009), it can be expected
that the observations also will appear using this modality. Therefore, studies
clarifying the PCMRI appearance of venous hemodynamics in MS are
urgently needed.
9
1.3 CSF dynamics
The brain and spinal cord are surrounded by CSF (Figure 2). CSF is a clear
fluid (99 % water) that is produced within the choroid plexus of the cerebral
ventricles (Redzic and Segal, 2004) at a rate of approximately 7 µl/s
(Ekstedt, 1978). In healthy elderly, the typical intracranial, ventricular and
spinal CSF volumes have been measured to 195 ml (Tsunoda et al., 2002), 37
ml (Ambarki et al., 2010), and 85 ml (Edsbagge et al., 2011), respectively.
CSF outflow is commonly thought to occur in the arachnoid villi located in
the superior sagittal sinus (Figure 2), although at physiological pressure
levels the significance of cerebral capillary fluid exchange may be more
significant than previously recognized (Bulat and Klarica, 2011). The CSF
production rate, outflow resistance and also venous pressure determine the
CSF resting pressure (Davson et al., 1970; 1973).
The CSF has a role in clearing metabolic waste as well as providing
mechanical support and buoyancy to the central nervous system (Irani,
2009). Furthermore, it provides the craniospinal system with the ability to
compensate for a volume expansion. This function of the system is activated
during every cardiac cycle, when CSF is redistributed upon the systolic
increase in arterial blood volume (Enzmann and Pelc, 1991).
In the short time window of a cardiac cycle, the CSF outflow mechanisms are
ineffective for dampening the CSF pulse pressure generated by the arteries.
Instead the increased CSF pressure compresses the craniospinal venous bed,
forcing venous blood out from the system (Greitz et al., 1992). This
mechanism is a manifestation of craniospinal compliance.
In addition,
elasticity of dura mater, especially in the spinal compartment, is generally
acknowledged to contribute to the overall capacity of craniospinal
compliance (Martins et al., 1972; Tain et al., 2011).
10
Figure 2. Overview of the CSF system surrounding the entire central
nervous system. CSF is produced within the ventricles, circulates and
escapes to blood in the venous sinuses.
11
To describe craniospinal compliance, mathematical models have been
developed. The relationship between pressure and volume of the
craniospinal system can be approximated by a simple exponential function
(Marmarou et al., 1975):
P = Pr e kV
(1)
where P is the CSF pressure, Pr is the baseline pressure, V is the volume
increase and k is the elastance coefficient. This model has been extended
with a constant term to better match experimental observations (Avezaat
and van Eijndhoven, 1986):
P = P1e kV + P0
(2)
where P0 and P1 are pressure constants with a sum that equals Pr. P0 has been
linked to venous pressure (Löfgren et al., 1973; Avezaat and van Eijndhoven,
1986) and hydrostatic pressure gradients within the system (Raabe et al.,
1999). Compliance is defined as the volume change per unit change in
pressure (dV/dP), and for the craniospinal system the pressure dependent
compliance becomes:
C(P) =
1
k(P − P0 )
(3)
Usually, the term pressure volume index (PVI) is used instead of k. The
mathematical relationship between PVI and k is:
PVI =
1
0.4343k
(4)
The PVI is therefore a factor describing the overall compensating capacity of
the craniospinal system.
12
Research rationale
The concept of compliance and PVI also applies to arterial pulsations
(Avezaat et al., 1979). A reduced PVI corresponds to a reduced ability of the
craniospinal system to accommodate arterial pulsatility. A dysfunction of
this type has been suggested to be present in Alzheimer’s disease and
vascular dementia (Bateman et al., 2008). Moreover, the amplitude of the
CSF pulse pressure has been shown to be important in predicting shunt
outcome in idiopathic normal pressure hydrocephalus (Eide and Brean,
2006). Together these observations suggest that increases in arterial
pulsations (often associated with increasing age) and impaired pulsation
dampening may be involved in several degenerative neurological disorders
among the elderly.
For this reason, it is important to develop accurate methods to measure PVI
and remedy the gap in basic physiological knowledge regarding the
magnitude and distribution of craniospinal compliance among healthy, nondemented elderly.
13
1.4 Structural magnetic resonance imaging
Magnetic resonance imaging (MRI) is based upon the interactions between
nuclear spins, a fundamental property of all elementary particles, and
external magnetic fields. Briefly, a powerful external magnetic field aligns all
spins in the body. An external excitation radiofrequency pulse rapidly “tips”
the spins in a prescribed volume (or slice). In their excited state the spins
induce detectable signals in receiver coils, integrated within the system
(Bloch, 1946; Purcell et al., 1946).
Spatial information is encoded to the spins by position-dependent phase and
frequency modulation achieved by adding small magnetic field gradients to
the static field. The detected signals can therefore be reconstructed into an
image (Lauterbur, 1973).
The duration of the induced signal depends on the unique relaxation
properties of the tissue. The difference in relaxation properties between
tissues can be exploited to generate contrast in the images. The relaxation
properties can be described by a longitudinal and transversal relaxation time
(T1 and T2, respectively) and those two parameters differ between white
matter, gray matter and CSF (Figure 3) (Bernstein et al., 2004).
The T1 and T2 contrast can be used to depict brain structures and also to
delineate the intracranial compartment. Although the image intensity alone
under many circumstances is not specific to a certain brain structure, e.g. the
hippocampus and amygdala are inseparable by intensity alone (Fischl,
2012), the spatial information and surrounding pixels can be weighed in to
the decision whether a pixel belongs to the brain structure. This
methodology is being exploited in highly automated structural processing
tools that are capable of providing an accurate segmentation of the entire
brain. In this dissertation Freesurfer was employed. Freesurfer is a set of
tools for automated structural processing that are based on probabilistic
14
models created from manually labeled brains (Fischl et al., 2002; 2004;
Desikan et al., 2006). Freesurfer is capable of utilizing spatial relationships,
e.g. the hippocampus is always behind and below the amygdala and never
above and in front of amygdala (Fischl, 2012), in the automatic labeling of
brain regions.
Figure 3. MRI appearance at different locations and weightings. In the T1
image CSF is black. In the T2 image CSF is white.
15
1.5 Flow sensitive magnetic resonance imaging
There are several techniques for measuring and visualizing motion using
MRI. Typically, dynamic information such as flow or diffusion is acquired
simultaneously as the spatial information. PCMRI is useful for flow rate and
velocity quantification purposes (Bernstein et al., 2004).
PCMRI employs velocity encoding in a way analogous to spatial encoding
(Moran, 1982). Because the angular frequency of a spin is proportional to the
external magnetic field, a spin that travels in a magnetic field gradient (the
velocity encoding gradient) will accumulate a different phase as compared to
a stationary spin. Therefore, a bipolar gradient toggle rewinds stationary
spins but induces a phase shift in moving spins (Figure 4).
Figure 4. Velocity encoding principle of PCMRI. (a) The spin moves in the
gradient direction during a bipolar gradient toggle, G+ and G-. (b) The
phase evolution over time for the spin. Note the resulting phase shift.
16
Phase wrapping occurs at shifts of π and -π. To avoid this, the magnitude of
the velocity encoding gradient is prospectively adjusted according to the
maximum velocity expected in the vessel of interest. The velocity encoding
parameter corresponds to the velocity where phase wrapping occurs
(Bernstein et al., 2004).
The first moment of a gradient field determines the velocity sensitivity of the
acquisition. In two-dimensional (2D) PCMRI, two acquisitions with different
first moments are required to quantify flow in one direction. This way, a
subtraction
or
complex
conjugate
multiplication
between
the
two
acquisitions allows removal of unwanted phase shifts that do not depend on
velocity. The relationship between velocity and phase shift can be described
as:


φ = v • γΔM1
(5)

v is the velocity, γ is the Larmour frequency

constant and ΔM1 the difference in first moments of the velocity encoding
where
φ
is the phase shift,
gradients.
When quantifying flow in all tree spatial directions, a series of three or four
acquisitions together with a reference scan can be utilized to shorten the
scan time (Wigström et al., 1996; Johnson and Markl, 2010). The velocity
can be obtained from solving:


φ = γ Av
(6)
where A is the encoding matrix containing the differences in first moments
in analogy to equation 5. Commonly, time resolved quantification of flow in
all three directions is referred to as four-dimensional (4D) PCMRI.
17
Figure 5. 2D PCMRI images at three time points during the cardiac cycle.
(a1-3) Blood flow at cervical level in the systolic, intermediate and diastolic
cardiac phase, respectively. (b1-3) Spinal CSF flow. Black and white
represents flow from and towards the cranium, respectively. (c1-3)
Aqueduct CSF flow. Black and white represents flow towards and from the
fourth ventricle, respectively.
18
The PCMRI sequence can be used to quantify arterial, venous and CSF flow
waveforms of the cardiac cycle (Figure 5 and 6) (Nayler et al., 1986; Greitz et
al., 1992; Enzmann and Pelc, 1993). To achieve this, an ECG or a peripheral
pulse is recorded by equipment integrated with the MRI system, allowing the
sequence to be prospectively or retrospectively gated (Bernstein et al.,
2004). In prospective gating, the data acquisition is initiated by a trigger
signal. This technique is robust even in mild arrhythmia but a small fraction
of the cardiac cycle, where the sequence waits for the next trigger, is not
acquired. In retrospective gating, continuously acquired data is sorted into
an average cardiac cycle. This technique provides the flow waveform of the
complete cardiac cycle, although some temporal smoothing may occur as a
result of heart rate variability (Lotz et al., 2002).
Figure 6. Blood and CSF flow waveforms. In these acquisitions 32 image
frames represent the cardiac cycle. Positive values represent flow in the
caudal-cranial direction.
19
Research rationale
There are many factors that potentially degrade the quality of the PCMRI
quantification. Partial volume effects (Tang et al., 1995) cause flow rate
overestimations. Misalignment errors influence the observed velocity (Wolf
et al., 1993). Moreover, physiological variability adds to the uncertainties
associated with a measurement.
Net flow rate quantifications have been thoroughly validated, both for 2D
(Spilt et al., 2002) and 4D PCMRI (Gu et al., 2005; Chang et al., 2011).
However, less is known about the repeatability when quantifying the volume
of cerebral arterial, venous and CSF pulsatility. Therefore, to meet with the
increased interest in intracranial pulsatile hemodynamics and CSF flow it is
necessary to evaluate the stability of PCMRI measurements.
20
1.6 Quantification of CSF pulse pressure
The CSF system dynamics can be investigated invasively by inserting two
needles in the spinal lumbar subarachnoid space (Eklund et al., 2007). One
needle is used to infuse artificial CSF and the other is used to determine the
pressure response. Lumbar infusion tests are commonly used to investigate
the CSF outflow resistance in cases of suspected normal pressure
hydrocephalus (Andersson et al., 2005; Sundström et al., 2010; Malm et al.,
2012), but the technique is also useful for investigating compliance related
parameters of the craniospinal system (Juniewicz et al., 2005). Various
infusion patterns are available and they can be divided into two categories,
those regulating the CSF pressure to predetermined levels (Ekstedt, 1977)
and those employing a predetermined rate of infusion (Hussey et al., 1970;
Katzman and Hussey, 1970).
Figure 7. CSF pressure during a constant pressure infusion test. The CSF
pressure is regulated to six levels (each given its own color) above baseline
pressure (yellow).
21
The constant pressure regulated infusion pattern (Figure 7) is designed for
estimations of CSF outflow resistance and does not provide estimations of
PVI (Andersson et al., 2005; 2010). However, in the pressure signals
recorded from a lumbar infusion test it is evident that physiological activity
such as arterial cardiac-related pulsatility influences the CSF pressure.
During an infusion test it can be observed how the CSF pulse pressure,
caused by arterial pulsatility, increases linearly as the average pressure in the
system is raised (Figure 8) (Avezaat et al., 1979; Avezaat and van
Eijndhoven, 1986). This is a manifestation of a gradual decrease in
compliance.
Figure 8. CSF pulse pressure versus average CSF pressure. The colors
correspond to different levels of regulated pressure.
22
Research rationale
Although the relationship between CSF pressure and pulse pressure is
related to the compliance properties of the craniospinal system, i.e. PVI, it is
also related to the volume of cerebral arterial pulsatility, which cannot be
estimated solely from CSF pressure measurements. Interestingly, the volume
of arterial pulsatility can be estimated from PCMRI measurements.
Therefore, bringing together data from CSF infusion measurements and
PCMRI acquisitions can potentially provide estimations of the craniospinal
PVI.
23
24
2 Aims
The overall aim of this dissertation was to explore cerebral blood flow and
intracranial
pulsatility
using
MRI,
with
respect
to
measurement,
physiological and pathophysiological aspects.
The specific aims for Papers I-V were to:
I. Assess 2D PCMRI measurement repeatability for quantifications of the
pulsatile waveforms of cerebral arterial, venous and CSF flow.
II. Combine lumbar CSF infusion tests with 2D PCMRI flow measurements
to assess PVI and its distribution between the intracranial and spinal
compartments.
III. Test the most vital part of the CCSVI hypothesis by comparing IJV flow
as well as CSF dynamics between healthy and MS subjects.
IV. Assess relationships between regional brain volumes and arterial and
CSF pulse pressure and the volume of cerebral arterial and CSF pulsatility.
V. Evaluate 4D PCMRI for assessing pulsatility of large cerebral arteries.
25
26
3 Materials and methods
3.1 Subjects
The regional ethical review board approved all separate studies. Informed
consent was obtained from all participants. The subject groups for all studies
are listed in Table 1. All healthy young (HY) subjects were normal as
determined from anatomical MRI scans. The healthy elderly (HE) passed a
neurological examination, had a mini mental state examination score of at
least 28 and did not suffer from advanced vascular disease (previous stroke,
myocardial infarction, diabetes). The MS group had the relapsing-remitting
disease course with a median disease duration of five years.
Table 1. Overview of subjects enrolled in the five studies. HY=healthy
young, HE=healthy elderly, MS=multiple sclerosis.
Paper
No. of subjects
Male / female
Age (mean ± SD)
I
20 HYa
12/8
33±8
II
37 HEb
15/22
71±6
III
20 HYa, 21 MS
12/8, 8/13
33±8, 34±10
IV
37 HEb
13/24
71±6
V
10 HY
7/3
36±9
aSame
subjects. b34 subjects were shared.
27
3.2 Magnetic resonance imaging
For Paper I-IV MRI measurements were performed on a 3T Achieva scanner
(Philips Healthcare, Best, the Netherlands) using an 8-channel head coil.
Routine anatomical sequences were performed to confirm a healthy status. A
3D T1 weighted turbo field echo sagittal scan with good gray/white matter
contrast was used for automated regional brain volume quantification (echo
time 5.0 ms, repetition time 10 ms, acquisition resolution 0.5 x 0.6 x 1 mm3).
An axial T2 weighted turbo spin-echo (echo time 80 ms, repetition time
3000 ms, acquisition resolution 0.5 x 0.5 x 3.3 mm3) was used for manual
delineation of the intracranial space.
For Paper I-IV three 2D PCMRI protocols were used to assess blood and CSF
flow pulsatility. The tree protocols measured:
1.
ICA and VA flow (level between second and third cervical vertebrae)
2. Spinal CSF flow (level between second and third cervical vertebrae)
3. Aqueduct CSF flow
The scanning parameters for sequences 1 / 2 / 3 were: encoding velocity 70 /
7 / 20 cm s-1, flip angle 15 / 10 / 10 degrees, repetition time 9.6 / 16 / 15 ms,
echo time 5.8 / 11 / 10 ms, voxel size 0.9 x 0.9 x 6 / 1.2 x 1.2 x 5 / 1.2 x 1.2 x 5
mm3 and two signal averages.
In Paper I five repeated measurements of the three sequences were
performed in the 20 HY subjects. An aqueduct measurement with increased
resolution (0.7 x 0.7 x 0.7 mm3) was also added to evaluate potential
influence of partial volume effects. For paper II and IV the first two 2D
PCMRI protocols were utilized (34 HE were shared between the studies).
28
In Paper III, 21 MS subjects underwent the first and last 2D PCMRI
protocols, and the values were subsequently compared with the first
repetition of the same sequences performed on the HY group of Paper I.
In Paper V measurements on 10 HY subjects were performed on a 3T
Discovery MR 750 scanner (General Electric, Waukesha, WI, USA) with a
32-channel head coil. Measurements started with a 3D time of flight
acquisition for vessel localization. Secondly, a 4D PCMRI measurement was
made using a phase contrast vastly undersampled projection imaging
(PCVIPR) sequence (Frydrychowicz et al., 2010), covering the entire
intracranial space. The resolution was 0.7 x 0.7 x 0.7 mm, velocity encoding
110 cm s-1, repetition time/echo time 6.5 / 2.7 ms, flip angle 8 degrees.
Besides velocity images for 20 time positions of the cardiac cycle, a time
averaged complex difference image was reconstructed to provide anatomical
localization of the vascular system.
After the 4D PCMRI acquisition, five 2D PCMRI scans were performed.
These measured flow in:
1.
ICA and BA
2. Right MCA (M1 segment)
3. Left MCA (M1 segment)
4. Right ACA (A1 segment)
5.
Left ACA (A1 segment)
The encoding velocity for protocol 1 / 2 / 3 / 4 / 5 was 80 / 110 / 110 / 90 /
90 cm s-1. The additional 2D PCMRI parameters were: repetition time/echo
time 7.6-8.5 / 4.1-5.0 ms, 15 degrees flip angle, resolution 0.5 × 0.5 x 3.0
mm3, six views per segment and two signal averages. Thirty-two phases were
retrospectively reconstructed.
29
3.3 Volume of arterial and CSF pulsatility
For all 2D PCMRI acquisitions, arterial, venous and CSF lumen
segmentations were performed manually using the free software ImageJ
(National Institute of Health, Bethesda, MD)(Paper I-IV) and Segment
(http://segment.heiberg.se)(Paper V). All measurements were systematically
reviewed and corrected for phase aliasing if needed. The total arterial flow
waveform was calculated from the sum of ICA and VA flow. Total internal
jugular blood flow was calculated from the sum of left and right IJV flow
(Paper III). The venous waveforms were also normalized by the total arterial
inflow (Paper III). An IJV reflux was registered if, during any time of the
cardiac cycle, the flow was in the caudo-cranial direction.
For the 4D PCMRI segmentations in Paper V, in-house software was
developed (Figure 9). The software enabled the user to rotate, zoom and
visualize flow in the 4D PCMRI data. The segmentation was initiated by
selecting a reference pixel in the vessel of interest (a mouse click in a
maximum intensity projection of the complex difference image). The
software included neighboring pixels with values of at least 18 % of the
maximum of the complex difference image. An additional requirement was
used to limit the segmentation to the branch of interest:
 
ri • v0 l
 ≤
v0
2
where
(7)


ri is a vector from the reference pixel to the i:th pixel, v0 is the vessel
direction estimated by the velocity direction (temporally averaged) in the
reference pixel and l is desired segment length. The segment length l was
fixed at five pixel widths (similar to the slice thickness of the 2D PCMRI
scans). The value 18 % was selected from a pre-calibration series indicating
that this threshold provided complete vessel coverage.
30
In this dissertation flow pulsatility was described in terms of volume. This
concept is natural considering that cerebral arterial, venous and CSF
volumes interact as a result of the limited intracranial volume. The volume of
pulsatility (∆V) was calculated using an identical procedure for arteries,
veins and CSF (Figure 10) (Paper I-V). After subtraction of the net flow rate,
cumulative integration was used to estimate the volume variation associated
with the remaining waveform. ∆V was defined as the difference between the
maximum and minimum of the integrated waveform.
Figure 9. A maximum intensity projection of the complex difference
image. Flow rates from the colored arterial segmentations were compared
with the 2D PCMRI data.
31
Figure 10. Arterial waveforms from a 4D PCMRI measurement. (a) The
flow rate during the cardiac cycle for right ICA, MCA and ACA as well as
the basilar artery. (b) Cumulative integral of the ICA waveform after
subtracting the net flow rate. The volume (∆V) of ICA pulsatility was
defined as the maximum minus minimum of this waveform.
32
3.4 CSF pulse pressure
Lumbar CSF pressure and pulse pressure were measured with the subject in
a supine position (Paper II and IV). A special bed, exposing the region for
needle insertion, enabled access to the lumbar space. The highly automated
investigational method has previously been described in detail (Andersson et
al., 2005; Malm et al., 2011). After 10-15 minutes of rest, with the needles in
place, the CSF pressure was sampled for 5 minutes at 100 Hz.
After the resting pressure measurement the CSF pressure was regulated to
six predetermined pressure levels using infusion of artificial CSF. In a few
exceptions, pressure elevation was performed using a constant rate of
artificial CSF infusion.
Figure 11. The ∆P for each regulated pressure level (colored dots) was
defined as the median CSF pulse pressure (gray dots). The colors
correspond to the pressure levels in Figure 8. The line represents the
relationship between ∆P and P determined from a linear regression.
33
In the post-processing step the slow pressure variations, not related to the
cardiac cycle, were eliminated using a high-pass filter. After this operation,
pressure pulses were estimated by analyzing the difference between
maximum and minimum pressure in successive 1.5 second time intervals.
For each regulated pressure level, a ∆P value was calculated as the median
value of all pulse pressures. Resting pressure ∆P was used for Paper IV. In
Paper II a linear regression was used to estimate the relationship between
∆P and P, as a step in calculating PVI (Figure 11). In Paper IV, baseline ∆P
could not be assessed for three subjects because of needle obstruction. In
addition to these measurements a standard automated blood pressure
monitor, Omron M5-I (Omron Health Care, Kyoto, Japan) was used to
assess left upper arm mean arterial pressure and arterial pulse pressure.
3.5 Mathematical model
In Paper II, a way to calculate PVI from PCMRI and CSF infusion data was
presented. Here, the craniospinal model (equation 2) was adapted to suit the
combination of measurements. The response in pressure following an
increase in arterial volume can be written as:
P +∆ P = P1e k (V +∆ V ) + P0
(8)
Where ∆V is the cyclic increase in intracranial arterial blood volume and ∆P
is the CSF pulse pressure. This can be rewritten to:
∆P
= e k∆ V −1
P − P0
(9)
where the left hand is the mathematical expression of the slope between CSF
∆P and P that can be determined from an infusion test (as described in the
previous section and Figure 11), with P0 as the intersection between the
regression line and the pressure axis.
34
Consequently, using both a CSF infusion measurement and a measurement
of ∆V, the elastance coefficient can be calculated as:
k = ln(
∆P
+1) / ∆ V
P − P0
(10)
To comply with common nomenclature, the elastance coefficient was
translated into PVI according to equation 4.
3.6 Intracranial volume
The post-processing software QBrain (version 2.0; Medis Medical Imaging
Systems, Leiden, the Netherlands) was used to segment the intracranial
volume of each subject in Paper IV. This was performed by manual
delineation in T2 weighted images using a threshold based drawing tool
(Ambarki et al., 2010; 2011).
3.7 Regional brain volumes
In Paper IV cortical and subcortical structures were segmented using
FreeSurfer 5.0 (available online at http://surfer.nmr.mgh.harvard.edu). The
software utilizes probabilistic models treating relationships between
characteristic magnetic resonance appearances and spatial orientations
(Figure 12) (Fischl et al., 2002; 2004; Desikan et al., 2006).
An experienced neuroradiologist verified the accuracy of the segmentations
of the 13 brain regions extracted for the analysis (Figure 12). Paper IV
originally included 38 subjects, but one subject had too severe atrophy for
the automated segmentation, which ended with unacceptable errors. This
subject was excluded from all analyses.
35
Figure 12. Result from brain segmentation (Paper IV). (a) Cortical
subdivisions. (b) Subcortical structures. Besides these structures the insula,
cerebral white matter and cerebellum white matter were included in the
analyses.
36
3.8 Statistics
For Paper I the within-subject and between-subject standard deviations were
calculated (SDintra and SDinter, respectively). SDintra describes the variation
over the five repetitions and is thus a measure of repeatability. To put this
value in proportion to the population distribution, an intraclass correlation
coefficient (ICC) was calculated as SD 2inter /(SD 2intra +SD 2inter ) . An ICC above
0.80-0.85 is generally regarded as sufficient repeatability for isolated
measurement. Differences between the results from the two aqueduct
measurements with different resolution were assessed using the Wilcoxon
signed rank test. The Pearson correlation coefficient was used to assess the
relationships between pulsatility measures.
Analysis of variance for repeated measurements was used to assess
systematic differences between the repetitions performed in Paper I. The
association between total arterial ∆V and the sum of cervical CSF and IJV ∆V
was investigated using the Pearson correlation coefficient.
Group differences were assessed by 2-sided t-tests or Mann-Whitney tests
where appropriate (Paper II-III). The chi-square test was used for
comparing prevalence of IJV reflux between HY and MS groups (Paper III).
In Paper IV, multiple linear regression was used to assess relationships
between regional brain volumes and CSF pulse pressure, arterial pulse
pressure, the volume of spinal CSF pulsatility, the total volume of VA
pulsatility (left plus right artery) and the total volume of ICA pulsatility (left
plus right artery). The partial correlation coefficient was used to describe the
strength of identified relationships.
In Paper V, differences between 2D and 4D PCMRI derived measures were
investigated using 2-sided paired samples t-tests. In all studies, a pvalue<0.05 was regarded as significant.
37
38
4 Results
4.1 Repeatability
Results from repeated 2D PCMRI measurements in Paper I are displayed in
Table 2. For all measures, the ICC was above 0.85, except for the volume of
right and left ICA pulsatility (ICC=0.80 and ICC=0.84, respectively) and net
flow in the aqueduct (ICC=0.41). Estimations of the volume of aqueduct CSF
pulsatility were lower for the high-resolution sequence than for the lowresolution sequence (0.03 ml versus 0.05 ml, p<0.001). The difference
between the sequences decreased for larger sizes of the cerebral aqueduct, as
determined from the high-resolution sequence (p=0.031).
There was a significant decrease in the volume of total arterial pulsatility and
net flow rate between repetitions (p=0.017 and p=0.024, respectively).
Intra-subject variations in the volume of total arterial pulsatility correlated
with changes in heart rate (r=-0.51, p<0.001). When removing linear trends
between measurements, i.e. taking away the systematic decrease in
pulsatility that occurred between repetitions on group level, the repeatability
for the volume of both left and right ICA pulsatility increased to ICC>0.85.
A second analysis was aimed towards assessing the relationship between
pulsatility in arteries, veins and CSF. Analyzing the flow into and out of the
cranium showed that the volume of total arterial pulsatility correlated with
the volume of summed spinal CSF and IJV pulsatility (r=0.75, p<0.001).
This relationship is visualized in Figure 13.
39
Table 2. Results from repeated ∆V measurements. Blood and spinal CSF
flows were quantified at cervical C2-C3 level.
∆V (ml)
Location
Net flow (ml/s)
mean±SDinter
SDintra
mean±SDinter
SDintra
ICA (R)
0.56±0.14
0.07
4.7±1.2
0.33
ICA (L)
0.57±0.15
0.07
4.7±1.0
0.32
VA (R)
0.20±0.08
0.03
1.4±0.7
0.17
VA (L)
0.30±0.10
0.03
2.2±0.6
0.22
Total
arterial
1.60±0.35
0.15
12.9±2.5
0.77
IJV (R)
0.64±0.39
0.11
5.6±2.4
0.63
IJV (L)
0.36±0.23
0.08
3.6±2.0
0.43
Total IJV
0.97±0.49
0.16
9.2±2.3
0.70
Aqueduct
CSF
0.045±
0.023
0.005
0.0055±
0.003
0.004
Spinal
CSF
0.77±0.23
0.05
-
-
40
Figure 13. The volume of total arterial pulsatility versus the volume of
summed spinal CSF and total IJV pulsatility. The line represents equality.
4.2 Craniospinal compliance
Paper II provided values on the magnitude and distribution of craniospinal
compliance among healthy elderly. The volume of total cerebral arterial
pulsatility (ICA+VA) measured at cervical level was 1.98±0.43 ml. This was
significantly larger than in the younger group of Paper I (p<0.001). The
volume of spinal CSF pulsatility was 0.68±0.23 ml. The craniospinal PVI
calculated from combined 2D PCMRI and pressure data was 11.8±9.0 ml.
The volume of spinal CSF pulsatility was 35 % of the volume of total arterial
pulsatility. For comparison the corresponding value for the younger group of
Paper I was 49 %. This difference was significant (p<0.001).
41
4.3 Restricted venous flow
The balance between left and right IJV net flow rate for the MS and control
groups of Paper III are visualized in Figure 14. No clear difference between
the groups could be observed. There were no differences in total, left or right
IJV net flow (p=0.38, p=0.51 and p=0.38, respectively). Five MS subjects
and five controls displayed an IJV reflux during the cardiac cycle (p=0.93).
There were no significant differences regarding aqueduct CSF net flow or
volume of pulsatility (p=0.75 and p=0.14, respectively).
Figure 14. Normalized blood flow of the left and right internal jugular
veins. Encircled subjects indicate those with IJV reflux.
42
4.4 Intracranial pulsatility and brain volumes
In healthy elderly, the volumes of several gray matter structures were
negatively related to CSF pulse pressure and the volume of ICA and CSF
pulsatility (Figure 15 and Table 3). The strongest relationships concerned the
temporal cortex and hippocampus. In agreement with these observations,
the ventricular volume increased for larger pulsatility. The volume of VA
pulsatility and arterial pulse pressure were not correlated to the volume of
any brain region.
Figure 15. Partial correlation coefficients between regional brain volumes
and pulsatility variables. Correlation values are stacked on each other for
visibility. All significant relationships are also presented in Table 3.
GM=gray matter, WM=white matter. *Significant at α=0.05, **Significant
at α=0.01
43
Internal relationships between the measures of pulsatility were also
assessed. The volume of ICA pulsatility was related to the volume of spinal
CSF pulsatility as well as CSF pulse pressure, but only after adjusting for net
ICA flow (p=0.01 and p=0.05, respectively). The volume of spinal CSF
pulsatility and CSF pulse pressure were not significantly correlated with each
other.
Table 3. Partial correlations between regional brain volumes and
pulsatility measures. ∆V ICA was calculated from the sum of the left and
right ICA waveforms.
Brain region
∆V ICAa
∆V CSFb
∆P CSFc
Frontal
cortex
p=0.404
p=0.017
r=-0.41
p=0.008
r=-0.48
Temporal
cortex
p=0.003
r=-0.51
p=0.006
r=-0.47
p=0.028
r=-0.40
Cerebral
White Matter
p=0.062
p=0.043
r=-0.35
p=0.698
Hippocampus
p=0.003
r=-0.50
p=0.040
r=-0.36
Ventricular
System
p=0.011
r=0.44
p=0.110
aAdjusted
p=0.679
p=0.008
r=0.47
for net ICA flow, age, sex, intracranial volume and mean arterial
pressure, n=37. bAdjusted for age, sex, intracranial volume and mean
arterial pressure, n=37. cAdjusted for age, sex, intracranial volume and
mean arterial pressure, n=34. r=partial correlation coefficient.
44
4.5 Four-dimensional flow measurements
The comparison in Paper V showed that 2D and 4D PCMRI produced highly
correlated results for the volume of arterial pulsatility as well as for net flow
(r=0.86 and r=0.95, respectively, n=69 vessels). These values improved
further (r=0.93 and r=0.97, respectively) when excluding measurements
with more than a 5 % variation in heart rate between the 4D and 2D
acquisitions (n=31 vessels). The correlation between all vessel segments is
illustrated in Figure 16.
Significant differences between 4D and 2D PCMRI were found for ICA and
MCA net flow (p=0.004 and p<0.001, respectively) as well as for the volume
of MCA pulsatility (p=0.006), n=69 (Table 4 and 5). However, these
differences were attenuated when excluding measurements with large
variations in heart rate (n=31 vessels).
Table 4. Comparison between 2D and 4D PCMRI measurements of
arterial ∆V. Values are given in ml.
∆V 4D
∆V 2D
2D-4D
mean±SD
mean±SD
mean±SD
10
0.20±0.08
0.21±0.08
0.01±0.05
0.504
ICA
20
0.35±0.10
0.36±0.10
0.01±0.06
0.656
MCA
20
0.23±0.05
0.20±0.05
-0.03±0.04
0.006
ACA
19
0.14±0.08
0.12±0.08
-0.02±0.05
0.102
vessel
n
BA
45
p-value
Figure 16. 2D versus 4D PCMRI derived ∆V for major cerebral arteries.
The line represents equality. The correlation coefficient for the entire
ensemble of vessels was r=0.86.
Table 5. Comparison between 2D and 4D PCMRI measurements of
arterial net flow. Values are given in ml/s.
Net flow 4D
Net flow 2D
2D-4D
mean±SD
mean±SD
mean±SD
10
2.15±0.58
2.18±0.49
0.03±0.34
0.787
ICA
20
3.72±0.70
4.02±0.62
0.29±0.40
0.004
MCA
20
2.54±0.36
2.26±0.34
-0.28±0.29
<0.001
ACA
19
1.47±0.52
1.44±0.49
-0.02±0.21
0.627
vessel
n
BA
46
p-value
5 Discussion
This dissertation focused on cardiac-related intracranial pulsatility using a
unique combination of multi-modal MRI and invasive lumbar infusion tests.
The analysis provided insight into the pulsatile forces exerted on the brain,
and how the compensating mechanisms of the CSF system and venous blood
cooperate in attenuating these rapid excitations. The approach taken in this
dissertation has improved the understanding of the stability of PCMRI
measurements, the mechanisms involved in intracranial pulsatility and how
intracranial pulsatility may be related to pathophysiology.
5.1 Measurement aspects
Paper I investigated the repeatability of 2D PCMRI net flow and pulsatility
measurements of cerebral arterial, venous and CSF flows (Table 2). The
volume of arterial pulsatility was sensitive to variations in heart rate and
decreased as the subject remained in the MRI machine. This could be related
to a decrease in anxiety and blood pressure as the subject gradually adapted
to the unusual environment. This favored standardization of the acquisition
order, e.g. always performing the PCMRI measurements last in the MRI
protocol.
The 2D PCMRI net flow measurements were repeatable for the ICA, VA and
IJV. The volume of spinal and aqueduct CSF pulsatility also had sufficient
precision. This indicated that aqueduct stroke volume measurements for
selecting shunt candidates in normal pressure hydrocephalus (Bradley et al.,
1996; Kahlon et al., 2007) are adequate from a measurement repeatability
perspective. However, the small size of the aqueduct makes the
measurements susceptible to partial volume overestimations, whereby as
high resolution as possible should be used. The net aqueduct CSF flow
showed high variability between measurements and interpretations based on
single measurements of this quantity should be avoided (Table 2).
47
Nevertheless, the average aqueduct net CSF flow reported in Paper I is
similar to invasive estimations (Ekstedt, 1978) and a previous 2D PCMRI
study (Huang et al., 2004). Besides limitations associated with the
measurement technique, slow changes in cerebrovascular caliber related to
physiological and neuronal activity likely causes slow oscillations of CSF at
the aqueduct, which will also will be reflected as variability in the
measurements. Therefore, the stability in this measure may be improved by
longer acquisition times.
The 4D PCMRI measurements produced estimations of the volume of
arterial pulsatility (Figure 16) and net flow consistent with results from 2D
PCMRI (Paper V). This supports using a single 4D PCMRI measurement to
assess pulsatility in many intracranial arteries, despite using a velocity
sensitivity adapted for vessels containing high velocities, such as the MCA.
The increase in 2D and 4D PCMRI correlation when excluding
measurements with large differences in heart rates (Paper V) indicated that a
large part of the random differences between measurements is a result of
actual physiological variations. This is in agreement with the results from
Paper I where it was demonstrated that physiological variability influences
the repeatability.
The standard deviation of the difference between 2D and 4D PCMRI for the
volume of ICA pulsatility and net flow was 0.06 ml and 0.40 ml/s,
respectively (Paper V), which agreed with the corresponding values provided
from repeated 2D PCMRI measurements of Paper I (0.07 ml and 0.32-0.33
ml/s, respectively, Table 2). This indicated that 2D and 4D PCMRI were
associated with the same level of measurement variability, supporting that
4D PCMRI can replace 2D PCMRI, at least for the ICA.
Interestingly, the 2D PCMRI measurements of ICA net flow and the volume
of ICA pulsatility in Paper V were smaller than corresponding values from
48
Paper I (approximately one sixth and one third lower for net flow and
pulsatility, respectively). Machine dependence may have contributed to this
discrepancy. However, phantom measurements indicated that for large
vessels, such as the ICA, the accuracy was better than 10 % for both the
Philips (Paper I) and GE system (unpublished data). The large relative
difference
in
ICA
pulsatility
could
originate
from
differences
in
measurement location. In Paper I, ICA measurements were made more
proximally, whereby the arterial pulsatility likely was less attenuated by
arterial compliance. This is in line with previous observations regarding the
dampening of arterial pulsatility along the ICA (Schubert et al., 2011).
Additionally, the true temporal resolution of the 2D PCMRI sequence used
in Paper V is lower than the sequence in Paper I-IV (a consequence of
employing multiple views per segment) (Lotz et al., 2002). This smoothens
the reconstructed arterial waveform, something that potentially decreases
the estimated volume of arterial pulsatility.
The 4D PCMRI workflow required minimal prospective planning. This is
very
different
from
2D
PCMRI
where
skillful
observations
and
interpretations are needed to place the measurement section perpendicular
to the vessel of interest. Moreover, the 4D PCMRI analyses were highly
automated (Figure 9), and only required basic anatomical knowledge from
the user. This simplification in the PCMRI workflow will make the technique
more accessible for future research projects, aiming to determine cerebral
hemodynamics in multiple cerebral vessels.
5.2 Physiological aspects
In Paper I the correlation between arterial pulsatility and the sum of venous
and cervical CSF pulsatility supported the notion that venous blood and CSF
escapes the cranium in response to an increased arterial volume (Figure 13).
This finding was in line with the Monro-Kellie principle and previous
analyses (Enzmann and Pelc, 1993; Greitz, 1993; Balédent et al., 2001; Kim
49
et al., 2007). Essentially, this emphasizes the role of venous blood, as an
important part of craniospinal compliance, in absorbing intracranial
pulsatility.
The PVI is a factor describing the magnitude of craniospinal compliance
(Marmarou et al., 1975; Shapiro et al., 1980). It describes how well arterial
volume pulsations are absorbed at a certain pressure within the system
(Avezaat and van Eijndhoven, 1986). Different infusion methods produce
disparate PVI values (Tans and Poortvliet, 1989; Juniewicz et al., 2005;
Sundström et al., 2010) whereby new methods are desirable. The method
dependence in the PVI estimate may in part be a consequence of slow
vasogenic responses to the infusion (Fridén and Ekstedt, 1983) that
complicate the time series analysis used in the calculation. The presented
method to assess PVI avoids the time series analysis by utilizing the pulsatile
CSF dynamics, averaged over many cardiac cycles, both for infusion and
MRI data. This quantification is likely less prone to slow vasogenic
responses, but can still be obtained regardless of infusion pattern. We note
that the measurement stability for the volume of arterial pulsatility was
poorer (Paper I) than assumed in Paper II. The effect will be a less reliable
estimate of PVI. Given the observations from Paper I, a possible
improvement could be obtained from modeling effects of physiological
variability such as heart rate. Alternatively, several measurements can be
averaged to obtain a more stable value.
The PVI value provided in Paper II was the first estimation in healthy
subjects based on the lumped parameter model in equation 2. The value is
lower than previously reported in adult subjects (Shapiro et al., 1980). We
hypothesize that this difference is related to the inclusion of the constant
term in the mathematical model of the CSF system. Indeed, the studies using
the constant term (Czosnyka et al., 2001; Juniewicz et al., 2005) have
produced lower PVI estimates than studies without the constant term
(Shapiro et al., 1980; Maset et al., 1987).
50
Venous compression was treated as part of the craniospinal compliance. This
is important since otherwise the venous waveform should be subtracted
from the arterial waveform when calculating the volume of the excitation.
This calculation would yield the cyclic change in cerebral blood volume, i.e.
the part of the arterial volume increase that is not directly compensated by
venous outflow. The different approaches have been the subject of
controversy as the method applied in Paper II indicated that the capacity of
the spinal compliance is less than that of the intracranial compartment. This
result has been argued not to conform with anatomical considerations, i.e.
that the spinal dura mater is not as strictly confined in bone as the dura
mater in the cranium (Tain et al., 2011), and with previous observations in
subjects with a cervical block (Magnaes, 1989). However, by considering that
the volume of spinal CSF pulsatility was approximately one third of the
volume of total arterial pulsatility, it is clear that the dominating
compensating mechanism, for processes in the time frame of a cardiac cycle,
was located intracranially.
Interestingly, the proportion of spinal compliance appeared different
between young and elderly subjects (Paper I and II). In elderly subjects a
larger part of the total arterial pulse was absorbed by intracranial
compensating mechanisms. The volume of total arterial pulsatility was also
higher among elderly, something that may be a reflection of reduced aortic
compliance in the elderly, inducing larger pulsatility in distal intracranial
arteries.
5.3 Pathophysiological aspects
With focus on IJV flow in the supine position there were no signs of altered
venous hemodynamics specific to MS (Paper III). The great variability in the
lateralization of the IJV flow appeared similar between cases and controls
(Figure 14). In most cases and controls, the right IJV conducted the major
51
part of the internal jugular drainage, which is in accordance with the
literature (Stoquart-ElSankari et al., 2009).
We were unable to find a difference in the aqueduct CSF flow between MS
and controls. Here, it is important to note that the aqueduct measurements
were associated with partial volume overestimations (Paper I). However,
because only results from sequences with identical settings were compared,
this limitation hardly caused any group specific biases in the CSF waveform.
An IJV reflux was only observed in vessels carrying small flow rates, and
only during a fraction of the cardiac cycle. As CCSVI has been described,
with IJV insufficiency in 94 % (33 of 35) of relapse-remitting MS cases
(Zamboni et al., 2009b), and almost never present in controls, this casecontrol evaluation should have had the ability to detect a difference. The
failure to do so challenges the view of CCSVI as an important factor in MS.
Paper III was one of the first scientific evaluations of CCSVI ever performed.
Today, there is accumulating evidence that the role of IJV congestion does
not have a causative role in MS (Doepp et al., 2010; Centonze et al., 2011;
Doepp et al., 2011; Zivadinov et al., 2011). Nevertheless, although the
association between CCSVI and MS is weaker than initially reported, more
research is required to exclude a role of altered venous hemodynamics in the
MS etiology.
Paper IV investigated relationships between intracranial pulsatile stress and
regional brain volumes. The sizes of several vital brain structures were
related to arterial and CSF pulsatility (Figure 15). The volumes of the frontal
cortex, temporal cortex, hippocampus and ventricles were related to two or
more measures of intracranial cardiac-related pulsatility (Table 3). These
brain regions are reported to have rapid age-related structural alterations
(Tisserand et al., 2004; Fjell et al., 2009a; 2009b), stressing the importance
of adjusting for age in the statistical analysis performed in this study.
52
Although we studied healthy subjects, the array of anatomical features that
were related to large pulsatility resembled features linked to Alzheimer’s
disease, i.e. small hippocampus and large ventricles (Fjell et al., 2009a). The
findings should encourage investigating intracranial cardiac-related pulsatile
stress as a potential risk factor that may cause or contribute to cognitive
impairment and dementia.
There are no previous studies on arterial and CSF pulsatility and regional
brain volumes. However, cortical thickness correlates with risk factors for
cerebrovascular disease (Leritz et al., 2011). Surprisingly, the pattern of agerelated tissue reduction may appear without reduction in local cerebral
perfusion rates (Chen et al., 2011). Furthermore, in Alzheimer’s disease,
where vascular risk factors are increasingly being recognized, the spatial
distribution of gray matter thinning is associated with a matching pattern of
hyperperfusion consistent with the paths of large cerebral vessels and their
early divisions (Alsop et al., 2008). These paradoxical relationships may be
explained if cardiac-related intracranial pulsatile stress harms the brain. In
this scenario, high tissue perfusion rates and proximity to a large artery
likely increases the risk of damage (O'Rourke and Hashimoto, 2007).
The cortical regions with volumes related to intracranial pulsatility were
located near large cerebral vessels and their first branches, where vascular
pulsatility likely is more intense. Still, the unique relationship between
frontal cortex size and CSF pulsatility suggests that other mechanisms than
pure intravascular damage might be active. Additionally the finding of a
relationship between CSF ∆P and ventricular volume further emphasizes the
potential importance of intracranial pulsatility in normal pressure
hydrocephalus.
It is relevant to summarize the factors shaping intracranial pulsatility. The
magnitude of the cardiac stroke volume and compliance of the aorta and precranial arteries determine the magnitude of cerebral arterial pulsatility.
53
Therefore, large vessel stiffness may induce increased intra-vascular
pulsatile stress. The craniospinal compliance determines the corresponding
rise in CSF pressure, which constitutes a force of extra-vascular pulsatile
stress inside the cranium.
The volume of CSF pulsatility and CSF pulse pressure were both related to
the volume of ICA pulsatility, but they were not related to each other. This
was a manifestation of the variability in magnitude and distribution of
craniospinal compliance, in agreement with Paper II. This indicated that
although all measures of pulsatility were related to the same phenomena,
each variable held a considerable amount of unique information, supporting
the choice to run correlation analyses between regional brain volumes and
all available measures related to intracranial cardiac-related pulsatile stress
(Paper IV).
Given the results of Paper IV, it is important to further study the mechanism
behind the relationships between regional brain volumes and intracranial
cardiac-related pulsatility. Potentially, this provides one additional piece of
evidence of the link between arterial and cerebral aging. Furthermore, it may
open an avenue of new treatment options aimed towards cognitive decline.
In perspective of the aging population (Lutz et al., 2008) it will be
increasingly important to evaluate factors contributing to cerebral aging,
something that starts earlier than previously believed (Singh-Manoux et al.,
2012). This dissertation suggests that PCMRI may be an important tool for
that work.
54
6 Conclusion
We found that 2D PCMRI was a feasible tool to study the pulsatile waveform
of cerebral arteries, veins and CSF. However there were differences between
measurements that largely depended on physiological variability in the
parameters of interest. To reduce the impact of this limitation, all flow
quantifications should be performed last in the MRI protocol, so that the
patient can adapt to the potentially stressful environment.
The volume of summed cerebral venous and spinal CSF pulsatility was
related to the volume of intracranial arterial pulsatility. This verified the
exchange in volume that occurs between the main intracranial constituents
during the cardiac cycle, and also supported the view that intracranial veins
contribute to craniospinal compliance.
We showed how PCMRI and CSF infusion tests could be combined to
estimate PVI. The intracranial contribution to the overall craniospinal
compliance was larger than previously thought. The distribution of
craniospinal compliance was also different between age groups.
Using 2D PCMRI we were not able to reproduce the findings associated with
the vascular MS hypothesis, CCSVI. If CCSVI is an entity associated with MS,
the association is likely weaker than previously reported. Importantly, this
did not support endovascular procedures in the veins of MS subjects.
55
Among elderly, CSF pulse pressure and the volume of arterial and CSF
pulsatility were related to the sizes of several vital brain structures. The
anatomical features associated with cardiac-related intracranial pulsatility
resemble features that are strongly linked to Alzheimer’s disease. This
finding should stimulate further research exploring intracranial cardiacrelated pulsatility as a contributing factor to cognitive decline and dementia.
4D PCMRI provided a viable methodology to estimate cerebral arterial
pulsatility. The results were consistent with 2D PCMRI estimations. The
workflow required minimal prospective planning and the output allowed
comprehensive analysis of intracranial hemodynamics. Therefore, 4D
PCMRI is an important tool for future investigations of altered intracranial
hemodynamics and associated disorders.
56
7 Acknowledgements
I wish to express my sincere gratitude to:
Anders Eklund, my main supervisor, for giving me the opportunity to work
in this captivating field. You have been a great source of inspiration and I am
forever grateful for your dedicated mentorship. Jan Malm, my supervisor,
for your enthusiasm and commitment to push our research field further.
Richard Birgander, my supervisor, for sharing and teaching your expertise
regarding complex neuroanatomy. Khalid Ambarki, a great colleague and a
close friend. Working with you has been so much fun. My co-authors, Jon
Hauksson, Peter Sundström, Noam Alperin, Kevin Johnson and Oliver
Wieben. Kristin Nyman, Sonja Edvinsson, Hanna Ackelind and Ann-Catrine
Larsson for skillful measurements and for always being helpful. My
colleagues in the group as well as fellow PhD candidates, Kennet Andersson,
Anders Behrens, Tomas Bäcklund, Gabriel Granåsen, Hanna Isarelsson,
Niklas Lenfeldt, Sara Qvarlander and Nina Sundström. The staff at the
biomedical engineering and radiophysics departments. Anna Wernblom, for
administrative support. Olof Lindahl, Ronnie Lundström and the late Stefan
Karlsson for leading the department and providing a nice environment to
conduct research in. Anders Garpebring and Greger Orädd for fun
experiments, interesting discussions and good collaboration. Göran
Mannberg, for #!/bin/bash and your helpfulness. The staff at AMI centre,
for the angiogram on the cover. All my friends that make the spare time fun.
My relatives (including those I count as relatives) and especially my
grandmother. You are all so supportive and kind. My mother and father. You
are true role models and continue to inspire me. My wife, Mirjam, for your
constant support and encouragement.
57
This project was supported by the Swedish research council; VINNOVA; and
the Swedish Foundation for Strategic Research through their common
initiative: ‘Biomedical engineering for improved health’; Swedish research
council Grant No. 621-2011-5216; EU, Objective 2 Norra Norland, CMTF;
The County Council of Västerbotten; and the Swedish Heart and Lung
Foundation.
58
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