Susceptibility-Weighted Imaging in Neurological Disorders

Robert Zivadinov, M.D., Ph.D.

Buffalo Neuroimaging Analysis Center, The Jacobs Neurological Institute, Department of

Neurology, State University of New York, Buffalo, New York, United States of America

Correspondence to:

Robert Zivadinov, Associate Professor of Neurology, School of Medicine and Biomedical

Sciences, State University of New York, Director, Buffalo Neuroimaging Analysis Center,

The Jacobs Neurological Institute, 100 High St., Buffalo, NY 14203

Tel: 716 859 7031, Fax: 716 859 7874, E-mail: rzivadinov@thejni.org

- 1 -

Abstract

Susceptibility-weighted imaging (SWI) is a new neuroimaging technique that uses tissue

magnetic susceptibility differences to generate a unique contrast, different from that of spin

density, T1, T2, and T2*. SWI involves the use of both magnitude and phase images from a

high-resolution, three-dimensional fully velocity-compensated gradient recalled echo

sequence. Phase masks are created from the MR phase images, and multiplying these with the

magnitude images increases the conspicuousness of the smaller veins and other sources of

susceptibility effects (such as iron deposits), which are depicted using minimal intensity

projection (minIP). The use of SWI in various neurological diseases is in its infancy and more

studies between SWI and clinical and other conventional and non-conventional MRI

outcomes need to be conducted for diagnostic and research purposes. The SWI imaging

correlates with brain iron content, perhaps ferritin specifically. There is no doubt that as SWI

becomes more broadly accepted, we will see many new applications develop because of the

astute observations of clinical researchers and the wide availability of information for the

whole spectrum of neurologic diseases seen on a daily basis in the clinical setting. We focus

here on application of SWI in multiple sclerosis, stroke and hemorrhages, neurodegenerative

disorders, occult vascular diseases, detection of brain tumors and venous vascular

malformations.

- 2 -

Introduction

Over the last two decades, magnetic resonance imaging (MRI) has revolutionized the

diagnosis and monitoring of various neurological diseases. Over this period, MRI technology

has continually evolved with more and more sophisticated techniques becoming available that

may visualize more accurately the disease processes.

For the last century, there has been great physiological interest in brain iron and its role

in brain function and disease. It is well known that iron accumulates in the brain for people

with Huntington’s disease (HD), Alzheimer’s disease (AD), Parkinson’s disease (PD),

multiple sclerosis (MS), chronic hemorrhage, cerebral infarction, thalassemia, anemia,

hemochromatosis, Down syndrome, Hallervorden-Spatz and AIDS. 1 Measurement of

nonheme iron in the body may well lead to not only a better understanding of the disease

progression but an ability to predict outcome. As there are many forms of iron in the brain,

separating them and quantifying each type has been a major challenge. 2, 3 This overview is

not intended to give an in-depth physiological review of brain iron and the interested reader is

referred to the literature for a detailed discussion of iron. 1, 4, 5 In this syllabus, we present our

understanding of clinical attempts to measure brain iron in a variety of neurological disorders

and the potential of doing so with a new MRI technique called susceptibility-weigthed

imaging (SWI).

Susceptibility-weighted imaging: brief overview of technical aspects

SWI is a new neuroimaging technique that uses tissue magnetic susceptibility differences to

generate a unique contrast, different from that of spin density, T1, T2, and T2*. 2, 3 SWI

involves the use of both magnitude and phase images from a high-resolution, three-

dimensional (3D) fully velocity-compensated gradient recalled echo (GRE) sequence (Figure

1). 2, 6 Phase masks are created from the MR phase images, and multiplying these with the

- 3 -

magnitude images increases the conspicuousness of the smaller veins and other sources of

susceptibility effects (such as iron deposits), which are depicted using minimal intensity

projection (minIP). The use of SWI in various neurological diseases is in its infancy 7-11 and

more studies between SWI and clinical and other conventional and non-conventional MRI

outcomes need to be conducted. The phase images seem to correlate with brain iron content,

perhaps ferritin specifically, but still have not been successfully exploited to accurately and

precisely quantify brain iron. 3 With the advent of parallel imaging and the greater availability

of clinical 3T MRI, it is now possible to image the entire brain with SWI in roughly 4-8

minutes depending on the slice thickness and different acquisition parameters used. As

neuroradiologists become more aware of these various applications and as advances in

software technology permit easier acquisition and better interpretation, SWI will likely be

incorporated into the routine diagnostic imaging evaluation of various neurological dsorders.

The more detailed technical concepts of SWI are outlined in the recently published article by

Haacke et al. 2 and would be valuable reading as background to this Syllabus, especially the

discussion about magnitude, SWI filtered phase, SWI processed data, and contrast available

in SWI. The following sections discuss different clinical applications of SWI predominantly

in adults. An excellent clinical review by Tong et al. 12 in the American Journal of

Neuroradiology covers many SWI applications in children. There is no doubt that as SWI

becomes more broadly accepted, we will see many new applications develop because of the

astute observations of clinical researchers and the wide availability of information for the

whole spectrum of neurologic diseases seen on a daily basis in the clinical setting. We will

focus in the following sections about application of SWI in MS, stroke and hemorrhages,

venous vascular malformations, neurodegenerative disorders and detection of brain tumors.

- 4 -

Multiple Sclerosis:

The source of iron deposition in MS has not been completely elucidated. 1 Previous studies in

MS have identified iron accumulation in deep-gray matter (DGM) 13 and plaques, 8 but they

did not establish whether iron deposition was a primary phenomenon in MS pathology or

secondary to chronic inflammation in MS. Iron deposition in MS patients may derive from

myelin/oligodendrocyte debris, destroyed macrophages, or can be the product of hemorrhages

from damaged brain vessels. 1, 4 In addition, iron overload may also lead to oxidative

mitochondrial injury through Fenton reaction and release phospholipid-rich cellular

membrane elements in MS. 14 The mechanism of direct damage to the central nervous system

(CNS) by iron might be related to oxidative stress and the generation of toxic free radicals. 14

Recently, it has been proposed that iron deposits in MS are a consequence of altered

cerebrospinal venous return and chronic insufficient venous drainage. 4, 15 According to this

hypothesis, an excessive amount of iron (result of cerebrospinal venous reflux) may cause the

damage to the blood-brain-barrier and consequent disturbed microcirculation, leading to

erythrocyte extravasation as a primary source of iron stores. Histological and SWI studies

confirm erythrocyte extravasation in brain plaques of MS patients and the presence of iron-

laden macrophages at the perivenular level. 7-11, 15-18 In histological studies, an iron increase

was reported in the DGM in MS patients compared to healthy controls. 16, 18 It has been

observed that the cell involved in iron overload with the greatest effect on immunity is the

macrophage, and there is a close relationship between iron and the major cells of adaptive

immunity, the T lymphocytes, since they are major players in recycling of the iron from

hemoglobin. 19 Therefore, iron may be a powerful chemotactic stimulus that attracts

macrophages and contributes to or causes initial activation of T-cell autoimmunity. The

results from the recent pilot study suggest that chronic cerebrospinal venous insufficiency

(CCSVI) related cerebrospinal blood flow disturbances may lead to iron deposition in DGM

- 5 -

structures and in lesions, as measured by SWI (Figures 2-4). 20 Whether CCSVI is a primary

cause of iron deposition in MS that may precede T cell activation still remains to be

elucidated in future studies.

Transferrin carries iron from the blood into tissues, while ferritin stores excess iron

atoms that are not immediately engaged in metabolic activities. Free iron generally occurs

when iron levels exceed the capacity of transferrin to bind the iron in the CNS. An excess of

iron contributes to a number of CNS neurodegenerative processes because free ferrous iron

reacts with peroxides to produce free radicals, which are highly reactive and can damage

DNA, proteins, lipids, and other cellular components. 4 Both free and bound iron leads to

toxic effects in the cells. As the concentration of transferrin receptors is physiologically

higher in the DGM structures, 4, 15 iron is usually stored in these areas of the brain as a result

of aging and other pathological processes. Iron is a paramagnetic substance that reduces T2

relaxation time on MRI, resulting in hypointensity on T2-weighted images. Bakshi et al.

showed almost a decade ago the presence of DGM T2 hypointensity in patients with MS. 21, 22

These authors recently reported that the DGM T2 hypointensity is present even at the first

symptom onset. 23 Various imaging techniques have been used to evaluate the amount of

DGM iron deposition, including T2 hypointensity, 21, 22 relaxometry, 24, 25 magnetic field

correlation 26 and SWI. 2, 3, 8, 9 SWI is a unique MRI technique that offers a way to visualize

tissues affected by iron deposition in the form of ferritin, deoxyhemoglobin or hemosiderin. 2

Therefore, measurement of iron deposition in the DGM structures of MS patients on SWI

may be an important biomarker of the disease process from the earliest phases of the disease

and can predict iron content more accurately than other available imaging techniques. 2, 3, 8, 9

Recent studies identified the pulvinar nucleus of the thalamus, the thalamus, globus pallidus,

caudate, and hippocampus are the DGM structures with increased iron content in MS patients.

9, 20 The particular involvement of these regions, but not of some other explored DGM

- 6 -

regions, indicates that irrigation of the vascular pathway from the internal jugular vein to

these regions should be investigated in more detail. Hammond et al. 9 quantified local field

shifts in 32 MS patients and investigated their relation to disease duration and disability

status. The local field in the caudate was strongly correlated with disease duration.

Recently published studies detected SWI lesions not seen on conventional MRI. 8, 9 In

one study, phase images showed contrast in 74% of the 403 lesions, increasing the total lesion

count by more than 30% and showing distinct peripheral rings and a close association with

vasculature. 9 In another study, Haacke et al. 8 found a total of 75 lesions seen only with

conventional imaging, 143 only with SWI, and 204 by both. From the iron quantification

measurements, a moderate linear correlation between signal intensity and iron content (phase)

was established. In a pilot study, we used a manual region-of-interest approach that identified

SWI magnitude and phase lesions (Figure 5). 20 Phase-visible lesions were further subdivided

into nodular, ring-shaped, scattered and cortical subcategories (Figure 6). Magnitude lesions

were also assessed. We attempted to evaluate the lesions associated with veins 8 but

determined that the assessment was not reliable in most of the scans. All lesion masks were

co-registered into the subject-specific upsampled FLAIR space and an overlap with respect to

T2, T1 and magnitude intersections of phase lesions was automatically obtained. The mean

number of phase and magnitude lesions was more than 50% lower compared to T2 and T1

lesions. The phase and magnitude lesions largely did not overlap with T2 and T1 lesions. The

most representative phase lesion type was nodular. The highest iron concentration was

detected in the phase lesions and in the overlapping intersections of phase T2, T1 and

magnitude lesions. All in all, these preliminary findings suggest that phase lesions and their

overlaps with T2, T1 and magnitude lesions represent an important new lesion category in

MS patients.

- 7 -

Although the immunology is actively debated, it is clear that variability within MS is

manifested by a recurrent or chronic angiocentric inflammatory process resulting in myelin

loss, coupled with the degeneration of central axons. The thin and linear periventricular

lesions (Dawson's fingers) at the initial stages of MS are often present and oriented around the

long axis of central veins. 27 Much evidence from histopathological studies has confirmed the

close relationship between inflammatory MS plaques and cerebral microvasculature, 17

suggesting that the prime inflammatory process in MS is around blood vessels, with acute

plaques showing lymphocytic perivascular infiltration, hypercellularity, macrophage

infiltration and intra-macrophage myelin debris. These perivenular changes occur in regions

free of myelin and oligodendrocytes, suggesting that venous changes may be the primary

event in the formation of new lesions, and that the vascular changes in MS can be

independent of demyelination. In investigating the pathogenesis of MS, the appreciation and

assessment, in vivo, of early vascular inflammatory changes becomes important in diagnosis,

monitoring progression, and determining therapeutic efficacy. Ultra-high-field 7T MRI has

provided sophisticated imaging capabilities by virtue of increased signal intensity and

enhanced susceptibility effects, fundamental quantities underlying image resolution and

contrast, respectively. As shown in Figure 7, 7 on these high resolution susceptibility sensitive

T2*-weighted images at ultra-high field 7T MRI, most lesions are associated with centrally-

coursing veins. Close examination reveals well-defined central veins surrounded by subtle

abnormalities in signal intensities in a strict perivenous fashion with vascular wall

involvement. These observations reported using T2*-weighted sequence at 7T are consistent

with earlier findings using SWI venography at 1.5T. This intimate relationship between MS

lesions and veins suggests that immune-mediated vasculitis may be a critical element in the

pathogenesis of MS. Precise characterization of microvascular abnormalities in early MS

lesion development may allow for immediate pharmacologic intervention directed at these

- 8 -

initial changes, and may be also crucial in monitoring minute changes in treatment response,

leading to a re-evaluation of algorithms used today. We recently developed an objective

method for quantifying venous vasculature in brain parenchyma on SWI and applied this

technique in 62 MS patients and in age- and sex-matched 22 healthy controls imaged on a 3T

GE scanner using pre-contrast SWI. 28 A subset of MS patients (50) and HC (7) obtained

SWI-post gadolinium contrast sequence (0.1 mMol/Kg Gd-DTPA with 10 min delay). In-

house developed segmentation algorithm, based on a 3D multi-scale line filter, was applied

for vein segmentation. Absolute volumetric measurement for total vein vasculature was

performed in milliliters (ml) and the relative venous intracranial fraction (VIF) was obtained

to correct for head size and amount of brain atrophy. The size of individual veins was

measured in mm and 4 groups were created according to their mean diameter: <0.3mm, 0.3-

0.6mm, 0.6-0.9mm and >0.9 mm. Voxel brain average distance-from-vein maps was also

calculated with higher distance indicating fewer veins. A significantly lower absolute venous

volume was detected in MS patients compared to HC, both in pre-contrast (67.5 vs. 82.7ml, -

18.3%, p<0.001) and post-contrast (70.4 vs. 87.1ml, -19.1%, p<0.011) images. The VIF was

significantly lower in MS patients (p<0.001). The highest mean diameter difference was

found for the smallest veins (<0.3 mm), both on pre- (p<0.001) and post-contrast (p<0.018)

images. The distance-from-veins was also significantly higher in MS patients (p<0.001). We

showed altered visibility of venous vasculature on SWI pre- and post-contrast images in MS

patients. These findings suggest severely compromised brain venous system in MS patients.

Stroke and hemorrhages:

Cerebrovascular ischemia from thromboembolism or arteriosclerotic stenosis leads to acute

infarct with or without hemorrhage. In recent years, diffusion-weighted imaging (DWI),

perfusion-weighted imaging (PWI), and MR angiography (MRA) have been incorporated in

the initial assessment of acute stroke and have helped to direct further treatment. SWI is

- 9 -

exquisitely sensitive in detecting hemorrhage and thus allows facile visualization of the

hemorrhagic region. 29 Thromboembolism can also change susceptibility by decreasing

arterial flow, thus increasing the amount of deoxyhemoglobin, and may additionally cause

increased pooling of deoxygenated blood. Although DWI is a powerful method for detecting

acute cerebral ischemia, SWI may be used as an adjunct to localize the affected vascular

territory further. SWI, as a complementary sequence, can provide additional information by

the following: 1) detecting a hemorrhagic component within the region of infarction, further

helping to distinguish ischemic and hemorrhagic stroke; 2) demonstrating areas of

hypoperfusion and directing the necessity of PWI; 3) detecting acute thromboembolia that

occlude arteries; and 4) predicting the probability of potential hemorrhagic transformation

before thrombolytic treatment by counting the number of microbleeds and early detection of

hemorrhagic complication after intra-arterial thrombolysis. Studies have shown that SWI is

more sensitive in detecting hemorrhage inside acute infarct lesions than computerized

tomography (CT) and 2D-GRE T2*-weighted imaging. 30 SWI also shows prominent

hypointense signals in the draining veins within areas of impaired perfusion (Figure 8).

SWI is so sensitive to such a small amount of hemorrhage that even conventional

long-TE GE images can fail to show the remnant hemorrhage after stroke, as shown in Figure

9. The demonstration of bleed within the infarct may influence the subsequent treatment

decisions. However, it is not clear at present whether detecting microbleeds within infarcts,

which are not picked up by CT, will alter the management. If patients have a small number of

microbleeds, thrombolytic treatment could be used safely, whereas having a large number of

microbleeds may be a greater risk factor for hemorrhagic transformation.

Cerebral microbleeds are observed in various conditions, such as chronic systemic

hypertension, cerebral amyloid angiopathy (CAA), cerebral autosomal dominant arteriopathy

- 10 -

with subcortical infarcts and leukoencephalopathy (CADASIL), and in cases of cerebral

vasculitis. 29, 31, 32

CAA is a small-vessel disease characterized by the deposition of amyloid β protein

within the cerebral arterioles. CAA has a clear association with aging, dementia, Alzheimer

disease, dementia pugilistica, postradiation necrosis, and spongiform encephalopathies. 29, 31,

32 Currently, no in vivo imaging techniques exist to directly visualize or quantify the amyloid

deposits. CAA can only be diagnosed histopathologically following biopsy or at postmortem

examination. Lobar microbleeds are related to CAA (usually involving the cortex and

subcortical white matter within the frontal and parietal lobes) (Figure 10).

Vascular malformations

Cerebral vascular malformations result from localized defects of vascular development. Most

malformations are present at birth and can grow with time. Cerebral cavernous malformations

(CCM), developmental venous angiomas (DVA), and capillary telangiectasias have slow flow

and can be less conspicuous or entirely missed by conventional neuroimaging techniques,

unlike high-flow arterial venous malformations. Although T2*GE imaging can detect small

venous structures, the incorporation of phase information in SWI offers improved sensitivity

and can identify vascular structures that are often not visible on conventional imaging. 33 An

excellent review of venous malformations and use of SWI was recently published. 31

Neurodegenerative diseases

It is well documented that iron content in the brain increases with age, particularly in the basal

ganglia, and that abnormal levels of iron in the CNS are seen in various neurodegenerative

diseases In fact, increased iron deposition occurs in PD, HD and AD. Further work is needed

to determine the usefulness of SWI in differentiating normal and abnormal mineral deposition

in the human brain.

- 11 -

The use of filtered phase images can show not only the presence of veins and

hemorrhage but also may be an indicator of iron content (Figure 11, see arrows highlighting

the motor cortex). The contrast between the gray matter and the cerebrospinal fluid or white

matter is presumably due to the increased iron content in the gray matter. It is thought that

iron around β amyloid plaque or other sources of iron may be an indication of the early

development of AD (Figure 11).

The use of SWI in PD is in its infancy. Recent studies by using T2 methods to estimate

iron content have found the following: 1) there are lateral substantia nigra pars compacta

abnormalities in early PD, 2) there is a significantly increased percentage of low-T2 voxels in

the hippocampus, the ventral pallidum/nucleus basalis of Meynert, and the globus pallidus in

AD. 34

Brain tumors

Various imaging characteristics that have been suggested to be predictors of glioma grade in

humans, including heterogeneity, contrast enhancement, mass effect, cyst formation or

necrosis, metabolic activity and cerebral blood volume. 32 In human glioma cells, the levels of

ferritin and transferrin receptors detected during immunohistochemical analysis have been

shown to correlate with tumor grade. Bagley et al. found T2*-weighted GRE MR images to

be valuable in the preoperative grading of gliomas due to the increased susceptibility artifacts

caused by hemorrhages. 35 SWI was found to be equivalent to T1 contrast-enhanced images in

the majority of cases studied and, in a few cases, to visualize the lesions better than T1

contrast-enhanced images. 29

SWI is better able to define the internal architecture of the lesion in comparison to

conventional MR sequences as well as to contrast-enhanced T1-weighted images. 29, 31, 32

Figure 12 illustrates the hemoglobin breakdown products within the tumor not detected in

conventional images. Hemorrhage can mimic neoplastic venous vasculature in a tumor due to

- 12 -

the similar paramagnetic susceptibility effect produced by both. However, hemorrhage can be

distinguished from veins if SWI is used both before and after the administration of the

contrast agent. Blood vessels change their signal intensity, but regions of inactive hemorrhage

do not.

Conclusion

SWI appears to be a promising MR imaging sequence for delineating cerebral

microvasculature and detecting foci of micro- and macrohemorrhages and low-flow vascular

malformations. It also facilitates the characterization of cerebral tumors and degenerative

cerebral diseases as well as the differentiation between calcification and bleed in the brain.

Although SWI interpretation will require some experience, increasing its clinical use will, no

doubt, reveal new applications. To date, the strongest clinical indications for SWI are in

neurovascular and neurodegenerative disease, with particular emphasis on MS.

- 13 -

References

1. Stankiewicz J, Panter SS, Neema M, Arora A, Batt CE, Bakshi R. Iron in Chronic Brain

Disorders: Imaging and Neurotherapeutic Implications. Neurotherapeutics 2007;4(3):371-

386.

2. Haacke EM, Mittal S, Wu Z, Neelavalli J, Cheng YC. Susceptibility-weighted imaging:

technical aspects and clinical applications, part 1. AJNR Am J Neuroradiol

2009;30(1):19-30.

3. Haacke EM, Xu Y, Cheng YC, Reichenbach JR. Susceptibility weighted imaging (SWI).

Magn Reson Med 2004;52(3):612-618.

4. Singh AV, Zamboni P. Anomalous venous blood flow and iron deposition in multiple

sclerosis. J Cereb Blood Flow Metab 2009;29(12):1867-1878.

5. Haacke EM, Cheng NY, House MJ, Liu Q, Neelavalli J, Ogg RJ, et al. Imaging iron stores

in the brain using magnetic resonance imaging. Magn Reson Imaging 2005;23(1):1-25.

6. Hammond KE, Lupo JM, Xu D, Metcalf M, Kelley DA, Pelletier D, et al. Development of

a robust method for generating 7.0 T multichannel phase images of the brain with

application to normal volunteers and patients with neurological diseases. Neuroimage

2008;39(4):1682-1692.

7. Ge Y, Zohrabian VM, Osa EO, Xu J, Jaggi H, Herbert J, et al. Diminished visibility of

cerebral venous vasculature in multiple sclerosis by susceptibility-weighted imaging at 3.0

Tesla. J Magn Reson Imaging 2009;29(5):1190-1194.

8. Haacke EM, Makki M, Ge Y, Maheshwari M, Sehgal V, Hu J, et al. Characterizing iron

deposition in multiple sclerosis lesions using susceptibility weighted imaging. J Magn

Reson Imaging 2009;29(3):537-544.

- 14 -

9. Hammond KE, Metcalf M, Carvajal L, Okuda DT, Srinivasan R, Vigneron D, et al.

Quantitative in vivo magnetic resonance imaging of multiple sclerosis at 7 Tesla with

sensitivity to iron. Ann Neurol 2008;64(6):707-713.

10. Tallantyre EC, Brookes MJ, Dixon JE, Morgan PS, Evangelou N, Morris PG.

Demonstrating the perivascular distribution of MS lesions in vivo with 7-Tesla MRI.

Neurology 2008;70(22):2076-2078.

11. Tallantyre EC, Morgan PS, Dixon JE, Al-Radaideh A, Brookes MJ, Evangelou N, et al. A

comparison of 3T and 7T in the detection of small parenchymal veins within MS lesions.

Invest Radiol 2009;44(9):491-494.

12. Tong KA, Ashwal S, Obenaus A, Nickerson JP, Kido D, Haacke EM. Susceptibility-

weighted MR imaging: a review of clinical applications in children. AJNR Am J

Neuroradiol 2008;29(1):9-17.

13. LeVine SM. Iron deposits in multiple sclerosis and Alzheimer's disease brains. Brain Res

1997;760(1-2):298-303.

14. Kalman B, Laitinen K, Komoly S. The involvement of mitochondria in the pathogenesis

of multiple sclerosis. J Neuroimmunol 2008;188(1-2):1-12.

15. Zamboni P. The big idea: iron-dependent inflammation in venous disease and proposed

parallels in multiple sclerosis. J R Soc Med 2006;99(11):589-593.

16. Craelius W, Migdal MW, Luessenhop CP, Sugar A, Mihalakis I. Iron deposits

surrounding multiple sclerosis plaques. Arch Pathol Lab Med 1982;106(8):397-399.

17. Adams CW. Perivascular iron deposition and other vascular damage in multiple sclerosis.

J Neurol Neurosurg Psychiatry 1988;51(2):260-265.

18. Levine SM, Chakrabarty A. The role of iron in the pathogenesis of experimental allergic

encephalomyelitis and multiple sclerosis. Ann N Y Acad Sci 2004;1012:252-266.

- 15 -

19. Porto G, De Sousa M. Iron overload and immunity. World J Gastroenterol

2007;13(35):4707-4715.

20. Zivadinov R, Zamboni P, Dwyer M, ME. H, Weinstock-Guttman B, Menegatti E, et al.

Chronic cerebrospinal venous insufficiency and iron deposition on susceptibility-weighted

imaging in patients with multiple sclerosis: A pilot case-control study. Neurology 2010

(abstract in press).

21. Bakshi R, Benedict RH, Bermel RA, Caruthers SD, Puli SR, Tjoa CW, et al. T2

hypointensity in the deep gray matter of patients with multiple sclerosis: a quantitative

magnetic resonance imaging study. Arch Neurol 2002;59(1):62-68.

22. Bakshi R, Dmochowski J, Shaikh ZA, Jacobs L. Gray matter T2 hypointensity is related

to plaques and atrophy in the brains of multiple sclerosis patients. J Neurol Sci

2001;185(1):19-26.

23. Ceccarelli A, Rocca MA, Neema M, Martinelli V, Arora A, Tauhid S, et al. Deep gray

matter T2 hypointensity is present in patients with clinically isolated syndromes

suggestive of multiple sclerosis. Mult Scler;16(1):39-44.

24. Neema M, Goldberg-Zimring D, Guss ZD, Healy BC, Guttmann CR, Houtchens MK, et

al. 3 T MRI relaxometry detects T2 prolongation in the cerebral normal-appearing white

matter in multiple sclerosis. Neuroimage 2009;46(3):633-641.

25. Khalil M, Enzinger C, Langkammer C, Tscherner M, Wallner-Blazek M, Jehna M, et al.

Quantitative assessment of brain iron by R(2)* relaxometry in patients with clinically

isolated syndrome and relapsing-remitting multiple sclerosis. Mult Scler 2009;15(9):1048-

1054.

26. Ge Y, Jensen JH, Lu H, Helpern JA, Miles L, Inglese M, et al. Quantitative Assessment of

Iron Accumulation in the Deep Gray Matter of Multiple Sclerosis by Magnetic Field

Correlation Imaging. AJNR Am J Neuroradiol 2007;28(9):1639-1644.

- 16 -

27. Ge Y, Law M, Herbert J, Grossman RI. Prominent perivenular spaces in multiple sclerosis

as a sign of perivascular inflammation in primary demyelination. AJNR Am J Neuroradiol

2005;26(9):2316-2319.

28. Poloni G, Zamboni P, Haacke E, Bastianello S, Dwyer M, Bergsland N, et al. Quantitative

venous vasculature assessment on susceptibility-weighted imaging reflects presence of

severe chronic venous insufficiency in the brain parenchyma of multiple sclerosis patients.

Acase-control study. Neurology 2010 (abstract in press).

29. Sehgal V, Delproposto Z, Haacke EM, Tong KA, Wycliffe N, Kido DK, et al. Clinical

applications of neuroimaging with susceptibility-weighted imaging. J Magn Reson

Imaging 2005;22(4):439-450.

30. Wycliffe ND, Choe J, Holshouser B, Oyoyo UE, Haacke EM, Kido DK. Reliability in

detection of hemorrhage in acute stroke by a new three-dimensional gradient recalled echo

susceptibility-weighted imaging technique compared to computed tomography: a

retrospective study. J Magn Reson Imaging 2004;20(3):372-377.

31. Mittal S, Wu Z, Neelavalli J, Haacke EM. Susceptibility-weighted imaging: technical

aspects and clinical applications, part 2. AJNR Am J Neuroradiol 2009;30(2):232-252.

32. Thomas B, Somasundaram S, Thamburaj K, Kesavadas C, Gupta AK, Bodhey NK, et al.

Clinical applications of susceptibility weighted MR imaging of the brain - a pictorial

review. Neuroradiology 2008;50(2):105-116.

33. Lee BC, Vo KD, Kido DK, Mukherjee P, Reichenbach J, Lin W, et al. MR high-

resolution blood oxygenation level-dependent venography of occult (low-flow) vascular

lesions. AJNR Am J Neuroradiol 1999;20(7):1239-1242.

34. Schenck JF, Zimmerman EA, Li Z, Adak S, Saha A, Tandon R, et al. High-field magnetic

resonance imaging of brain iron in Alzheimer disease. Top Magn Reson Imaging

2006;17(1):41-50.

- 17 -

35. Bagley LJ, Grossman RI, Judy KD, Curtis M, Loevner LA, Polansky M, et al. Gliomas:

correlation of magnetic susceptibility artifact with histologic grade. Radiology

1997;202(2):511-516.

- 18 -

Figure legends

Figure 1. Reconstruction of the SWI data was done off-line. Following channel

recombination, the magnitude image was processed using the non-parametric non-uniform

intensity normalization program N3 (a). The high-pass filtered phase image (b) was used to

generate a phase mask that was multiplied 4 times onto the magnitude image to generate the

SWI image (c).

- 19 -

Figure 2. Mean phase iron concentration maps in healthy controls (HC) subjects (left) and

multiple sclerosis (MS) patients (right) in the globus pallidus, putamen and caudate. The iron

concentration map is displayed on a scale ranging from dark (low iron concentration) to bright

(high iron concentration) colors. MS patients show more bright areas in the examined

structures, as well as increased intensities. Please note, in MS patients, the increased

intensities of the veins surrounding the examined structures, indicating more iron deposits in

the perivenular areas.

High

Low iron

Healthy Controls Multiple Sclerosis

- 20 -

Figure 3. Mean phase iron concentration maps in healthy control (HC) subjects (left) and

multiple sclerosis (MS) patients (right) in the pulvinar nucleus of the thalamus. The iron

concentration map is displayed on a scale ranging from dark (low iron concentration) to bright

(high iron concentration) colors. MS patients show more bright areas in the the pulvinar

nucleus of the thalamus, as well as increased intensities.

High

Low iron

Healthy Controls Multiple Sclerosis

- 21 -

Figure 4. High-iron voxels identified in deep gray matter structures by thresholding of the

mean phase iron concentration maps at two standard deviations below the healthy control

mean (red color) are significantly more prevalent in multiple sclerosis patients (right) than in

normal controls (left).

Healthy Control Multiple Sclerosis

- 22 -

Figure 5. A manual region-of-interest approach was used to identify SWI magnitude (light

blue) visible lesions and SWI phase (green) visible lesions on corresponding images. Phase-

visible lesions were further subdivided into nodular, ring-shaped, and scattered categories. All

lesion masks were co-registered into the subject-specific upsampled FLAIR space, at which

point spatial overlap T2 lesion (dark blue) and T1 lesion (purple) maps were calculated. These

were used to calculate mean magnitude and phase values for each lesion type and each

intersection of lesion types.

Phase lesionsT1 black holesMagnitude lesionsT2 lesions

- 23 -

Figure 6. A manual region-of-interest approach was used to identify SWI magnitude and SWI

phase lesions. Phase-visible lesions were further subdivided into ring-shaped (left), nodular

(center), and scattered (right) categories.

- 24 -

Figure 7. Conventional T2-weighted (A1,B1,C1) and SWI mIP images (A2,B2,C2) at the

periventricular level (8 mm thick) in a normal control (A1,A2) and two MS patients

(B1,B2,C1,C2) demonstrate a significantly reduced number of veins in patients as compared

to controls. MS patients with more lesions (C2) have fewer venous architectures depicted on

SWI mIP images (arrows) as opposed to patients with less lesions (B2). 7

- 25 -

Figure 8. Cardioembolic stroke in the left MCA, 2 hours after onset. A, MRA shows lack of

time-of-flight signal intensity in the distal M1 segment of the left MCA. B, FLAIR image

displays intra-arterial signal intensity (arrow) of the left MCA branches along the lateral

sulcus, indicating acute occlusion. C, DWI shows a small hyperintense lesion within the

territory of the left MCA. D, SWI reveals localized hyposignal (arrow) in the left M1

segment, representing the acute thromboembolus itself. E and F, SWI demonstrates

prominently hypointense cortical veins within the left MCA territory, suggesting relatively

increased deoxyhemoglobin in the draining veins within the acutely ischemic region. G and

H, the MTT map shows delayed transit times in the left MCA territory, matching SWI well. I,

the rCBF is moderately reduced. 31

- 26 -

Figure 9. A patient who had sudden onset of aphasia and right paresthesias 5 years earlier and

who partially recovered neurologic function after treatment of the acute stroke. A and B,

findings of follow-up MR images are almost normal on the FLAIR (A) and T1-weighted

images (B). C and D, the original TE _ 40 ms magnitude image (C) does not show any

abnormality; however, the SWI phase image (D) shows a small hemorrhagic lesion (white

arrow) in the genu of the internal capsule and internal globus pallidus. 31

- 27 -

Figure 10. Multiple microbleeds in CAA. A and B, T1-weighted (A) and T2-weighted (B)

images do not reveal significant abnormalities except for the lesion in the left temporoparietal

area. C, MRA shows normal brain vascular structure. D, SWI demonstrates, in addition to

hemorrhage in the left temporoparietal region, multiple microbleeds distributed along the

gray/white matter interface in the whole brain, strongly suggesting CAA. 31

- 28 -

Figure 11. Iron deposition on SWI around beta amyloid plaque in Alheimer’s disease patient

(image courtesy of EM. Haacke).

- 29 -

Fig. 8 Glioblastoma multiformae. A, CE fat-suppressed axial T1-weighted images showing

the necrotic heterogeneously enhancing mass in the right frontal lobe. B, axial 2D GRE. C,

minIP SWI. Note that the tumor neovascularity and hemorrhages are better shown in SWI. 32

- 30 -