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European Radiology

, Volume 27, Issue 5, pp 2119–2128 | Cite as

3D-black-blood 3T-MRI for the diagnosis of thoracic large vessel vasculitis: A feasibility study

  • Karla Maria Treitl
  • Stefan Maurus
  • Nora Narvina Sommer
  • Hendrik Kooijman-Kurfuerst
  • Eva Coppenrath
  • Marcus Treitl
  • Michael Czihal
  • Ulrich Hoffmann
  • Claudia Dechant
  • Hendrik Schulze-Koops
  • Tobias Saam
Magnetic Resonance

Abstract

Objectives

To evaluate the feasibility of T1w-3D black-blood turbo spin echo (TSE) sequence with variable flip angles for the diagnosis of thoracic large vessel vasculitis (LVV).

Methods

Thirty-five patients with LVV, diagnosed according to the current standard of reference, and 35 controls were imaged at 3.0T using 1.2 × 1.3 × 2.0 mm3 fat-suppressed, T1w-3D, modified Volumetric Isotropic TSE Acquisition (mVISTA) pre- and post-contrast. Applying a navigator and peripheral pulse unit triggering (PPU), the total scan time was 10–12 min. Thoracic aorta and subclavian and pulmonary arteries were evaluated for image quality (IQ), flow artefact intensity, diagnostic confidence, concentric wall thickening and contrast enhancement (CWT, CCE) using a 4-point scale.

Results

IQ was good in all examinations (3.25 ± 0.72) and good to excellent in 342 of 408 evaluated segments (83.8 %), while 84.1 % showed no or minor flow artefacts. The interobserver reproducibility for the identification of CCE and CWT was 0.969 and 0.971 (p < 0.001) with an average diagnostic confidence of 3.47 ± 0.64. CCE and CWT were strongly correlated (Cohen’s k = 0.87; P < 0.001) and significantly more frequent in the LVV-group (52.8 % vs. 1.0 %; 59.8 % vs. 2.4 %; P < 0.001).

Conclusions

Navigated fat-suppressed T1w-3D black-blood MRI with PPU-triggering allows diagnosis of thoracic LVV.

Key Points

Cross-sectional imaging is frequently applied in the diagnosis of LVV.

Navigated, PPU-triggered, T1w-3D mVISTA pre- and post contrast takes 10–12 min.

In this prospective, single-centre study, T1w-3D mVISTA accurately depicted large thoracic vessels.

T1w-3D mVISTA visualized CWT/CCW as correlates of mural inflammation in LVV.

T1w-3D mVISTA might be an alternative diagnostic tool without ionizing radiation.

Keywords

Systemic vasculitis Magnetic resonance imaging Giant cell arteritis Takayasu arteritis Aortitis 

Abbreviations

2D

Two-dimensional

3D

Three-dimensional

ACR

American College of Rheumatology

BB

Black blood

CDUS

Colour duplex ultrasound

CCE

Concentric contrast enhancement

CWT

Concentric wall thickening

DCL

Diagnostic confidence level

ETL

Echo train length

FAI

Flow artefact intensity

FDG

[18F]fluorodeoxyglucose

LVV

Large vessel vasculitis

MRI

Magnetic resonance imaging

PET/CT

Positron emission tomography/computed tomography

T1w

T1-weighted

TAB

Temporal artery biopsy

TSE

Turbo spin-echo

mVISTA

Modified Volumetric ISotropic TSE Acquisition

Introduction

The diagnosis of thoracic large vessel vasculitis (LVV) is challenging, because the clinical symptoms of this heterogeneous group of inflammatory diseases and the laboratory parameters can be unspecific and evidentiary biomarkers are missing [1, 2, 3]. Additionally, sonography of the aorta or its proximal branches is limited. According to the recommendations of the American College of Rheumatology (ACR), temporal artery biopsy (TAB) is considered the diagnostic gold standard to identify patients with giant cell arteritis [4], although the method is invasive, may be false negative in 15–70 % of cases and cannot detect manifestations besides the temporal artery [5, 6]. Currently, there is no gold standard for the diagnosis of Takayasu arteritis, but imaging of the entire arterial tree is recommended for the detection of inflamed vessel segments or complications [7, 8, 9, 10]. This may also become established for patients with giant cell arteritis, as they are 2.4 times more likely to develop an abdominal aortic aneurysm after a median interval of 2.5–6 years [11, 12, 13].

Despite increasing evidence for [18F]fluorodeoxyglucose positron emission tomography/computed tomography (FDG-PET/CT) as a valuable diagnostic tool in LVV [14, 15, 16], the consecutive radiation exposure is disadvantageous and makes it inappropriate as a screening or monitoring tool to control the therapeutic response. Contrast-enhanced fat suppressed 2D black-blood (BB) magnetic resonance imaging (MRI) is considered a valid and reproducible imaging technique to noninvasively detect wall thickening and contrast media uptake, which are established as morphological correlates for segmental inflammation of thoracic, cervical and intracranial arteries [17, 18, 19]. Conventional 2D-BB sequences, such as double or quadruple inversion recovery T1-weighted (T1w) sequences [20], are time-consuming, provide a limited scan area and cannot be reconstructed in various planes. Thus, vessels which are oriented obliquely, such as the thoracic vessels, cannot be analyzed perpendicularly to their course, but this is particularly important when imaging vasculitis, as concentric wall thickening (CWT) and concentric contrast enhancement (CCE) are the established hallmarks of vasculitis [19, 21].

A high-resolution T1w-3D fat-suppressed turbo spin-echo (TSE) sequence (VISTA Volumetric ISotropic TSE Acquisition) has recently resolved these limitations, as it provides excellent flow suppression and shorter acquisition times [22, 23, 24]. The sequence has been successfully applied for atherosclerotic plaque imaging [25, 26] and imaging of intracranial vessels [27]. The isotropic version of VISTA illustrated excellent image quality and blood flow suppression in deep vein thromboses [28]. In order to examine all the large thoracic arteries in LVV within a reasonable scan time, a modified, non-isotropic, free-breathing, navigated, fat-suppressed version (mVISTA) has been developed.

This study investigated the hypothesis that T1w-3D mVISTA MRI of thoracic large vessels can non-invasively detect the occurrence of LVV.

Materials and methods

Patients

A total of 35 patients with clinical symptoms of LVV were included consecutively between September 2013 and March 2015 according to the following criteria:
  1. 1.

    Diagnosis of LVV diagnosed by a board-certified rheumatologist according to the current recommendations of the management of LVV [7] and the classification criteria of the ACR [1, 2]

     
  2. 2.

    Written informed consent.

     

Additionally, 35 randomly selected patients without clinical evidence of inflammatory vessel disease were included as controls: 15 underwent staging MRI in oncologic imaging and 20 had a cardiac MRI. Predefined exclusion criteria for the cohort were known contraindications against MRI (allergy to MRI contrast; impaired glomerular filtration rate <30 ml/min; ferromagnetic or electrical implants). Case history and cardiovascular risk factors were recorded. The study protocol followed the principles of the Declaration of Helsinki and was approved by the institutional review board (Ethics Committee at the University of Munich; proposal #110-15).

MRI

Both groups underwent MRI examination using a 3.0-T Philips scanner (Ingenia, Philips Healthcare, Koninklijke Philips N.V., Eindhoven, The Netherlands) and applying one standard 16-channel body matrix anterior coil and one integrated posterior spine coil. The study protocol included a multiplanar localizer and a free-breathing, navigated, fat-suppressed T1w-3D mVISTA sequence with an interpolated spatial resolution of 0.6 × 0.6 × 1.0 mm3 and an acquired voxel size of 1.2 × 1.3 × 2.0 mm3. Using peripheral pulse unit triggering and applying a navigator-echo with a pencil-beam excitation that was positioned at the diaphragm, the scan time was 5–6 min. The sequence was conducted before and 3 min after the injection of gadoteric acid (0.2 ml/kg body weight; DOTAREM® 0.5 mmol/ml, Guerbet, Roissy, France). The mVISTA stack covered the lower neck and the chest, extending from the shoulders to the diaphragm, depending on the body size. The typical field-of-view was 365 × 365 × 170 mm3. The T1w-3D mVISTA uses a spectrally selective partial inversion pulse for fat suppression and the SENSitivity Encoding approach for accelerated signal acquisition. Detailed scan parameters are presented in Table 1.
Table 1

Magentic resonance parameters

 

3D-mVISTA

Sequence

3D-TSE

TR (ms)

1000

TE (ms)

35

Fat suppression

SPIR

SENSE factor (RL)/(AP)

3 × 2

TSE /TFE factor

50

Flip Angle (°)

variable

NSA

2

Scan FOV (mm3)

365 × 365 × 170

Recon matrix

576

Number of slices

170

Voxel size (mm3)

1.20 × 1.30 × 2.00

Recon voxel size (mm3)

0.63 × 0.63 × 1.00

Scan time per sequence (min)

3:26

Flow suppression

Yes

3D three-dimensional, mVISTA modified Volumetric ISotropic TSE Acquisition, TSE turbo-spin echo, TR repetition time, TE echo time, SENSE sensitivity encoding, RL right-left, AP anterior-posterior, SPIR Spectral Presaturation with Inversion Recovery, TFE turbo field echo, NSA number of signal averages, FOV field of view

Image analysis

One member of the study group (S.M.), who was aware of the confirmed diagnosis and the clinical and laboratory findings, selected the corresponding pre- and post-contrast image series of each patient and prepared matching sets in coronal, axial and sagittal planes, with a slice thickness of 2.5 mm and an overlap of 1.5 mm.

The pairs selected were anonymized, randomized and independently analyzed by two radiologists with more than 10 years of MRI experience (M.T., E.C.), who were blinded to the diagnosis and all clinical data. The readers could scroll through the axial, coronal and sagittal pre- and post-contrast series to evaluate the entire course of each segment in all planes, since giant cell arteritis typically causes a continuous affection, while Takayasu arteritis causes intermittent, segmental manifestations [6, 29, 30].

The pairs of series selected were evaluated for the presence of CWT and CCE in the following segments: right (1) and left subclavian artery (2), pulmonary arteries (3), ascending aorta (4), aortic arch (5) and descending thoracic aorta (6). The degree of CWT and CCE was rated on a 4-point Likert scale: substantial = 3; moderate = 2; minimal = 1; and none = 0. Each of the two findings was evaluated with a diagnostic confidence level (CWT-DCL, CCE-DCL): 4 = excellent, exact diagnosis possible; 3 = good, definite diagnosis possible; 2 = fair, evaluation of major findings possible; and 1 = poor, definite diagnosis impossible. If the readers disagreed regarding the presence of CWT and CCE, the series were reviewed again and, if necessary, additional orthogonal reformations of the particular segment were constructed with the IMPAX vessel viewing software (IMPAX EE R20; Agfa-Gevaert HealthCare GmbH; Munich, Germany). A consensus decision was then made and the DCLs were adapted.

Overall image quality was rated as follows: 4 = excellent, no relevant artefacts; 3 = good, minimal heterogeneity, only minor flow artefacts; 2 = adequate, delineated lumen, major flow artefacts; and 1 = insufficient for diagnosis. Flow artefact intensity (FAI) was rated as follows: 0 = no artefacts, exact diagnosis possible; 1 = minor artefacts, definite diagnosis possible; 2 = major artefacts, evaluation of major findings possible; and 3 = dominating artefacts, definite diagnosis impossible.

Colour duplex ultrasound (CDUS)

CDUS was performed by an angiologist with more than 9 years of experience in vascular ultrasound (M.C.) using a GE LOGIC E9 ultrasound unit (GE Healthcare, Munich, Germany) with a 5–16 MHz broadband linear transducer. B-Mode and CDUS settings were dynamically adjusted to achieve optimal visualization of the arterial wall. CDUS examination included bilateral cross-sectional and longitudinal evaluation of the carotid, vertebral, subclavian, axillary and temporal arteries with separate analysis of the parietal and frontal branches of the superficial temporal arteries [31, 32]. An echo-poor circumferential wall thickening (halo) in any segment of the temporal or axillary arteries was considered a positive CDUS finding for GCA.

Statistics

Fisher’s exact and Chi-squared tests were applied to evaluate for differences in the distribution of categorical variables, and Wilcoxon’s signed-rank test for unpaired samples was used for metric variables. Cohen’s kappa value (ĸ) was used to calculate the inter-observer reproducibility for the identification of CCE and CWT. Cohen’s ĸ was also used to compare the results of mVISTA-MRI with the clinical diagnosis, with the results of TAB and with CDUS. Analysis was carried out with a statistical package for the social sciences software package (IBM SPSS, version 22.0, IBM North America, New York, USA). P values of 0.05 or less were considered to be statistically significant.

Results

Patients

A total of 35 patients (20.0 % male), mean age 58.5 years (range 24–80) with clinical symptoms of LVV fulfilled the inclusion criteria. One of these patients suffered from unspecific LVV (2.9 %), 11 were diagnosed with Takayasu arteritis (31.4 %) and 23 suffered from giant cell arteritis (65.7 %). Of the 35 LVV-patients, 33 had started corticosteroid treatment 5.2 days (range 2–10) before the MRI examination.

The control group included 35 patients (54.3 % male, 45.7 % female), mean age 53.7 years (range 18–87). The prevalence of cardiovascular disease was comparable for both groups (31.4 % vs. 34.3 %; P = 1.000, Table 2).
Table 2

Patient characteristics of the study population (n = 70); mean/median (range)

 

Patients

Controls

p

n

35

35

n.s.

Male sex (%)

7 / 20.0

19 / 54.3

0.006

Age (years)

58.5 ± 17.1 (24–80)

53.7 ± 16.4 (18–97)

0.230

Weight (kg)

70.8 ± 12.8 (43–92)

71.0 ± 13.9 (44–100)

0.940

Height (m)

1.67 ± 0.07 (1.50–1.80)

1.72 ± 0.08 (1.57–1.85)

0.007

BMI (kg/m2)

25.4 ± 4.0 (19.1–32.8)

24.1 ± 4.5 (16.6–39.6)

0.209

Active smoker (%)

5 / 14.3

11 / 31.4

0.153

Former smoker (%)

14 / 40.0

13 / 37.1

1.000

Hypertension (%)

21 / 60.0

19 / 54.3

0.809

Diabetes (%)

10 / 28.6

1 / 2.9

0.006

Hypercholesterolaemia (%)

9 / 25.7

6 / 17.1

0.561

CVD (%)

11 / 31.4

12 / 34.3

1.000

Family history of CVD (%)

9 / 20.0

7 / 25.7

0.777

Unspecific LVV (%)

1 / 2.9

-

-

Takayasu arteritis (%)

11 / 31.4

-

-

Giant cell arteritis (%)

23 / 65.7

-

-

BMI body mass index, CVD cardiovascular disease, LVV large vessel vasculitis

Image quality and diagnostic reproducibility

All 70 examinations yielded a diagnostic image quality (100 %). The mean image quality (3.25 ± 0.72) and the separate image qualities of every vessel segment were comparable between the vasculitis group and the control group (3.2 ± 0.7 vs. 3.3 ± 0.7; P = n.s.). Image quality in both groups was highest in the descending thoracic aorta and lowest in the pulmonary arteries (Table 3).
Table 3

Comparison of the MRI findings between the vasculitis and the control group: Study population (n = 70); mean ± SD

MRI imaging – vessel segments

Patients

Controls

p

All scanned/ evaluable segments [failure rate (%)]

210/ 199 [5.2]

210/ 209 [0.5]

0.003

 CCE (%)

105/52.8

2/1.0

0.000

 DCL – CCE

3.44 ± 0.58

3.46 ± 0.68

n.s.

 CWT (%)

119/59.8

5/2.4

0.000

 DCL – CWT

3.46 ± 0.63

3.51 ± 0.64

n.s.

 IQ

3.20 ± 0.74

3.30 ± 0.69

n.s.

 FAI

0.87 ± 0.81

0.71 ± 0.74

0.050

Right subclavian artery [failure rate (%)]

35 [0.0]

35 [0.0]

n.s.

 CCE (%)

23/65.7

0/0.0

0.000

 CWT (%)

26/74.3

0/0.0

0.000

 IQ

3.43 ± 0.56

3.54 ± 0.56

n.s.

 FAI

0.77 ± 0.84

0.60 ± 0.70

n.s.

Left subclavian artery [failure rate (%)]

34 [2.9]

35 [0.0]

n.s.

 CCE (%)

26/76.5

0/0.0

0.000

 CWT (%)

32/94.1

2/5.7

0.000

 IQ

3.29 ± 0.63

3.43 ± 0.66

n.s.

 FAI

0.76 ± 0.70

0.49 ± 0.61

n.s.

Pulmonary artery [failure rate (%)]

29 [17.1]

35 [0.0]

0.025

 CCE (%)

2/6.9

0/0.0

n.s.

 CWT (%)

2/6.9

0/0.0

n.s.

 IQ

2.52 ± 0.69

2.71 ± 0.67

n.s.

 FAI

1.38 ± 0.94

1.34 ± 0.77

n.s.

Ascending aorta [failure rate (%)]

33 [5.7]

35 [0.0]

n.s.

 CCE (%)

8/24.2

0/0.0

0.002

 CWT (%)

9/27.3

0/0.0

0.001

 IQ

3.15 ± 0.83

3.26 ± 0.70

n.s.

 FAI

0.85 ± 0.76

0.57 ± 0.66

n.s.

Aortic arch [failure rate (%)]

33 [5.7]

34 [2.9]

n.s.

 CCE (%)

22/66.7

0/0.0

0.000

 CWT (%)

24/72.7

0/0.0

0.000

 IQ

3.24 ± 0.71

3.24 ± 0.70

n.s.

 FAI

0.91 ± 0.84

0.82 ± 0.76

n.s.

Descending aorta [failure rate (%)]

35 [0.0]

35 [0.0]

n.s.

 CCE (%)

24/68.6

2/5.7

0.000

 CWT (%)

26/74.3

3/8.6

0.000

 IQ

3.43 ± 0.66

3.63 ± 0.49

n.s.

 FAI

0.66 ± 0.64

0.46 ± 0.56

n.s.

MRI magnetic resonance imaging, CCE concentric contrast enhancement, DCL diagnostic confidence level, CWT concentric wall thickening, IQ image quality, FAI flow artifact intensity, n.s. not significant

Overall, 343 out of 408 segments showed no or minor flow artefacts (84.1 %). The FAI of the vasculitis group was significantly higher than that of the control group (0.87 ± 0.81 vs. 0.71 ± 0.74; P < 0.05). The pulmonary arteries showed the highest and the descending aorta the lowest FAI (Table 3).

The overall inter-observer reproducibility for the identification of CCE and CWT was 0.81 (P = 0.002) and 0.94 (P = 0.001). Inter-observer reproducibility was 0.76 (P = 0.005) for CCE and 0.84 (P = 0.004) for CWT in the LVV-group. Among the controls it was 1.00 (P < 0.001) for CCE and 0.74 for CWE (P = 0.001).

The average DCL about the presence of CCE and CWT was 3.45 ± 0.63 and 3.49 ± 0.64 and comparable for both groups (Table 3). DCL was highest in the descending thoracic aorta and lowest in the pulmonary arteries (3.74 ± 0.44 and 3.09 ± 0.73).

MRI of the vasculitis group

In the vasculitis group, 199 out of 210 vessel segments scanned (94.8 %) were analyzed. Nine segments (4.3 %) had to be excluded due to severe motion artefacts (#V15: pulmonary arteries, ascending aorta, aortic arch; #V13: pulmonary arteries, ascending aorta; #V5, #V9, #V11, #V16: pulmonary arteries), one segment due to incomplete scan coverage (#V8: aortic arch) and one was occluded and obliterated (left subclavian artery in #V8).

CCE and CWT were frequent in the LVV-group (CCE: 105 of 199 segments/ 52.8 %; CWT: 119 of 199 segments/ 59.8 %) and both parameters were strongly correlated (k = 0.81; P < 0.001).

The subclavian arteries and the descending aorta most frequently and most distinctively exhibited CCE and CWT, whereas the pulmonary arteries were rarely affected (Table 3). Figures 1 and 3 present corresponding examples. A total of 30 out of 35 patients with vasculitis (80.0 %) had at least two arterial segments with CCE, and 33 out of 35 (94.3 %) had at least two arterial segments with CWT.
Fig. 1

T1w-3D mVISTA-MRI of a 77-year-old female with giant cell arteritis. (a) and (e) coronal pre- and post-contrast image pair (slice thickness: 2.5 mm; increment: 1.5 mm); the vessel wall of the aorta (arrowheads) and of both subclavian arteries (arrows) are concentrically thickened and contrast enhanced as they are affected by the giant cell arteritis. (b) – (h) additional orthogonal vessel reconstructions of the thoracic aorta (b, f), of the aorta and the right common and internal carotid artery (c, g) and of the aorta and the right subclavian artery (d, h), that were constructed with the IMPAX vessel viewing software, if necessary, in order to achieve a consensus decision

MRI of the controls

One segment (0.5 %) of the control group was excluded due to susceptibility artefacts (#C35: aortic arch) and 209 out of 210 scanned vessel segments (99.5 %) were evaluated.

As expected, only five controls exhibited signs of vessel wall inflammation (CCE: 2 out of 35, 5.7 %; CWT: 5 out of 35, 14.3 %) in a total of 7 segments (CCE: 2 of 209 segments, 1 %; CWT: 5 of 209 segments, 2.4 %). Two controls (#C13, #C27) had a focal CWT without CCE in the left subclavian artery, another one (#C34) showed focal CWT without CCE in the descending aorta, and two (#C11, C#28) had slight CCE and CWT each in the descending aorta. Figure 2 shows a corresponding image example.
Fig. 2

T1w-3D mVISTA-MRI of a 70-year-old male with recurrent ventricular tachycardia and arteriosclerosis. (a) and (e) coronal pre- and post-contrast image pair (slice thickness: 2.5 mm; increment: 1.5 mm); the arteriosclerotic plaques in the aorta (arrowhead) and in the proximal subclavian artery (arrow) can mimic LVV, but are exocentric. (b) – (h) additional orthogonal vessel reconstructions of the thoracic aorta (b, f) of the aorta and the right common and internal carotid artery (c, g) and of the aorta and the right subclavian artery (d, h)

Comparison to the clinical diagnosis, TAB and CDUS

According to the consensus reading and considering CCE and CWT of either one or two thoracic vessel segments as diagnostic MRI-criterion for vasculitis, mVISTA significantly correlated with the clinical diagnosis (two segments: k = 0.86; one segment: k = 0.89; P < 0.001 for both) (Fig. 3).
Fig. 3

T1w-3D mVISTA-MRI of a 70-year-old female with Takayasu arteritis. Coronal (a, b) and axial (c, d) pre- and post-contrast image pair (slice thickness: 2.5 mm; increment: 1.5 mm); the vessel wall of the aorta (A) and the pulmonal artery (P) appears concentrically thickened and enhanced; more wall-thickening and contrast-enhancement is seen in the subclavian arteries (white arrows) and in the right vertebral artery (black arrow with white margins)

Additionally, 10 out of 23 patients with GCA (43.5 %; 28.6 % of the entire vasculitis group) underwent unilateral TAB. The average time between TAB and MRI was 7.1 days (range 4–9). The histology showed giant cells and mononucleated infiltrates of the three mural layers in all biopsies. Considering either one or two segments with CCE and CWT in mVISTA as criterion for vasculitis, MRI and TAB diagnosis were consistent in all of these patients.

In the vasculitis group, 31 out of 35 patients (88.6 %) underwent CDUS of the carotid, vertebral, temporal, subclavian and axillary arteries (310 segments). The average time between CDUS and MRI was 3.4 days (range 1–7). From a total of 27 out of 31 patients examined (87.1 %), 77.1 % of the entire LVV-group, respectively, had at least one segment with echo-poor CWT (19 temporal, 22 axillary, two vertebral, 15 carotid, 15 subclavian) and were therefore diagnosed with LVV: considering CCE and CWT of either one or two segments diagnostic MRI-criterion for vasculitis, mVISTA significantly correlated with the CDUS diagnosis (k = 0.65). Moreover, echo-poor CWT of the subclavian arteries on CDUS significantly correlated with CCE and CWT on mVISTA (k = 0.71; P < 0.001).

Discussion

The results of this single-centre prospective feasibility study demonstrate that pre- and post-contrast high-resolution 3.0T T1w-3D mVISTA-MRI of the large thoracic arteries accurately detects vessel wall inflammation in patients with LVV, suggesting that mVISTA could be a viable tool in diagnosing LVV.

The mVISTA-MRI pre- and post-contrast detected segmental CWT and CCE in 33 out of 35 patients with suspicion of vasculitis (94.3 %), which was clinically confirmed in all and pathologically confirmed in 10 patients. Only two patients of the control group showed slight CWT and CCE in the descending aorta and one patient had focal CWT without CCE in the descending aorta. In fact, reconstruction of the aorta in orthogonal planes revealed that CWT was more semicircular than concentric, and the CCE did not extend into the surrounding perivascular tissue, suggesting the presence of an atherosclerotic plaque, which was confirmed by corresponding computed tomography (CT) angiography. Two other patients of the control group showed isolated focal CWT without CCE of the left subclavian artery. One of them suffered from arteriosclerotic subclavian steel syndrome, the other had a stenosis of the left subclavian artery due to scarring after lymphadenectomy.

The CCE and CWT were highly correlated with each other, suggesting that both signs appear coincidentally in patients with acute LVV. Only two patients of the vasculitis group showed CWT without CCE: one in both subclavian arteries and one in all evaluated segments except the pulmonary arteries. Both findings might be explained by a more chronic stage of giant cell arteritis, in which acute inflammation has already decayed, but has caused morphological changes of the vessel wall, which are indicative of LVV [1, 6, 33].

The CCE and CWT were most frequently detected in the subclavian arteries, whereas the pulmonary arteries were rarely affected and more difficult to evaluate due to motion artefacts. This is due to the fact that LVV involves the subclavian arteries more frequently than other vessel segments, but rarely affects the pulmonary arteries [5, 6, 33].

Flow artefacts were more common in the vasculitis group, which might be explained by the higher degree of luminal narrowing due to the inflammation of the vessel wall. However, the differences in FAI affected neither the image quality nor the DCL, which were rated as good to excellent in both groups. The best image quality and DCL and the least flow artefacts were achieved in the descending aorta, which has a large diameter and least pulsation or breathing artefacts, as it is situated adjacent to the vertebral column. In contrast, the worst image quality, the most flow artefacts and the lowest DCL were obtained in the pulmonary arteries, which are exposed to the pulsation of the heart and the respiratory movements of the lungs.

As a noninvasive imaging tool of the vessel wall, BB-MRI is established for the characterization and molecular imaging of arteriosclerotic plaques [34, 35, 36], but also for the evaluation of inflammatory changes [37]. While arteriosclerotic plaques can cause eccentric wall thickening and asymmetrical CE, CWT and circumferential CE of the affected arterial wall segments indicate vasculitis. Initially, Schmidt et al. hypothesized that a dark halo of the temporal artery in CDUS in patients with giant cell arteritis is consistent with an oedema of the vessel wall [38]. By constrast, Bley et al. were the first to describe that CCE and CWT enhanced T1w MRI of the temporal artery in patients with giant cell arteritis are signs of acute inflammation of the vessel wall, which resolves following the administration of corticosteroids [39]. They further proved the involvement of the temporalis muscle [40]. Kuecker et al. investigated that CCE and CWT are direct signs of cerebral vasculitis in T1w MRI [41, 42]. Pfefferkorn et al. further confirmed these signs in BB-MRI [18]. PD-weighted versions of the mVISTA sequence were successfully applied for imaging of arteriosclerotic plaques [25] and intracranial vessel wall abnormalities [26, 27], but mVISTA is not yet validated for vasculitis.

In cases of unspecific clinical symptoms and limited sonographic evidence, FDG-PET/CT is increasingly applied for the diagnosis of LVV and the assessment of treatment response [43, 44], because FDG-PET can identify metabolic changes at early stages of vasculitis and CT detects morphological correlates in late stages of disease [45]. In our opinion, using mVISTA-MRI for diagnosing LVV offers several advantages: (1) absence of ionizing radiation, which might be particularly useful in young patients with Takayasu arteritis or in patients who require frequent follow-up examinations to monitor anti-inflammatory therapy; (2) assessment of morphological pathologies of the vessel wall with high-resolution and an increased soft-tissue contrast; and (3) better differentiation between atherosclerotic and vasculitic changes. In addition, mVISTA can be combined with contrast-enhanced MR-angiography, which allows simultaneous assessment of the lumen and the vessel wall.

The main limitation of this in vivo feasibility study is the small number of patients. Future studies with larger patient cohorts are necessary to confirm the results. Secondly, the gender ratio of the vasculitis group significantly differs from the controls, but this difference is in line with the fact that women are more commonly affected by large vessel vasculitis than men [5, 6]. Several studies have established a discrete gender-related difference in the aortic wall thickness [46, 47], but the difference between inflamed and normal vessel segments is more distinctive, and so far no study has shown a significant, gender-related difference in the morphology of vasculitic vessels. Thus we believe that the difference in the presented study cohort cannot substantially affect the results. Thirdly, the study investigates a new MRI-technique on the basis of two quite dichotomous groups, accepting that this may artificially increase the diagnostic accuracy of T1w-3D mVISTA when compared to a prospective study of patients with a clinically indeterminate vasculitis. Moreover, there is no direct comparison with the severity of clinical symptoms or with other cross-sectional imaging modalities. However, we were able to compare mVISTA-MRI in patients with vasculitis to CDUS and a study comparing mVISTA and FDG-PET/CT is under preparation. Additionally, histological evidence of LVV is missing in most cases (71.4 %), but the clinical value of TAB has been debated [48, 49]. According to Schmidt et al., the clinical diagnosis of giant cell arteritis in patients with typical clinical signs and positive CDUS can be derived without TAB [38]. Finally follow-up MRI examinations of the patients with confirmed LVV to monitor the regression of CWT and CCE during immunosuppressive therapy are missing, but will be part of future studies.

This prospective study evaluates the feasibility of a navigated, PPU-triggered, T1w-3D black-blood MRI sequence for the diagnosis of thoracic LVV. T1w-3D mVISTA can accurately depict all thoracic vessels with good to excellent image quality and disposes good blood suppression in 97.2 %. It reliably visualizes CWT and CCE as morphological correlates of mural inflammation in patients with LVV within a scan time of approximately 10–12 min. T1w-3D mVISTA might thus be an alternative, non-invasive, cross-sectional, diagnostic tool, which securely assesses the thoracic disease extend in LVV without ionizing radiation.

Notes

Acknowledgments

The scientific guarantor of this publication is Prof. Dr. Tobias Saam. The authors of this manuscript declare relationships with the following companies: Dr. Hendrik Kooijman-Kurfuerst is a physicist, who works for Philips Healthcare. He modified the original VISTA sequence and so developed the mVISTA sequence.

The other authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. The authors state that this work has not received any funding. One of the authors has significant statistical expertise. Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Some study subjects or cohorts have been previously reported at the RSNA Meeting 2014 and at the ISMRM meeting 2015. Pdf-versions of the corresponding power-point presentations are attached as supplemental material. The RSNA-presentation reports about 14 patients and 14 controls of the entire study cohort. The ISMRM-presentation includes the entire study cohort of 70 subjects.

Methodology: prospective, case-control study, performed at one institution.

Supplementary material

330_2016_4525_MOESM1_ESM.pdf (3.4 mb)
ESM 1 (PDF 3449 kb)
330_2016_4525_MOESM2_ESM.pdf (3.7 mb)
ESM 2 (PDF 3832 kb)

References

  1. 1.
    Arend WP, Michel BA, Bloch DA et al (1990) The American College of Rheumatology 1990 criteria for the classification of Takayasu arteritis. Arthritis Rheum 33:1129–1134CrossRefPubMedGoogle Scholar
  2. 2.
    Hunder GG, Bloch DA, Michel BA et al (1990) The American College of Rheumatology 1990 criteria for the classification of giant cell arteritis. Arthritis Rheum 33:1122–1128CrossRefPubMedGoogle Scholar
  3. 3.
    Monach PA (2014) Biomarkers in vasculitis. Curr Opin Rheumatol 26:24–30CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Fries JF, Hunder GG, Bloch DA et al (1990) The American College of Rheumatology 1990 criteria for the classification of vasculitis. Summary. Arthritis Rheum 33:1135–1136CrossRefPubMedGoogle Scholar
  5. 5.
    Nesher G (2014) The diagnosis and classification of giant cell arteritis. J Autoimmun 48–49:73–75CrossRefPubMedGoogle Scholar
  6. 6.
    de Souza AW, de Carvalho JF (2014) Diagnostic and classification criteria of Takayasu arteritis. J Autoimmun 48–49:79–83CrossRefPubMedGoogle Scholar
  7. 7.
    Mukhtyar C, Guillevin L, Cid MC et al (2009) EULAR recommendations for the management of primary small and medium vessel vasculitis. Ann Rheum Dis 68:310–317CrossRefPubMedGoogle Scholar
  8. 8.
    Grayson PC, Maksimowicz-McKinnon K, Clark TM et al (2012) Distribution of arterial lesions in Takayasu’s arteritis and giant cell arteritis. Ann Rheum Dis 71:1329–1334CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Park MC, Lee SW, Park YB, Chung NS, Lee SK (2005) Clinical characteristics and outcomes of Takayasu’s arteritis: analysis of 108 patients using standardized criteria for diagnosis, activity assessment, and angiographic classification. Scand J Rheumatol 34:284–292CrossRefPubMedGoogle Scholar
  10. 10.
    Ohigashi H, Haraguchi G, Konishi M et al (2012) Improved prognosis of Takayasu arteritis over the past decade--comprehensive analysis of 106 patients. Circ J 76:1004–1011CrossRefPubMedGoogle Scholar
  11. 11.
    Evans JM, O'Fallon WM, Hunder GG (1995) Increased incidence of aortic aneurysm and dissection in giant cell (temporal) arteritis. A population-based study. Ann Intern Med 122:502–507CrossRefPubMedGoogle Scholar
  12. 12.
    Nuenninghoff DM, Hunder GG, Christianson TJ, McClelland RL, Matteson EL (2003) Incidence and predictors of large-artery complication (aortic aneurysm, aortic dissection, and/or large-artery stenosis) in patients with giant cell arteritis: a population-based study over 50 years. Arthritis Rheum 48:3522–3531CrossRefPubMedGoogle Scholar
  13. 13.
    Gonzalez-Gay MA, Garcia-Porrua C, Pineiro A, Pego-Reigosa R, Llorca J, Hunder GG (2004) Aortic aneurysm and dissection in patients with biopsy-proven giant cell arteritis from northwestern Spain: a population-based study. Medicine (Baltimore) 83:335–341CrossRefGoogle Scholar
  14. 14.
    Cyran CC, Sourbron S, Bochmann K et al (2011) Quantification of supra-aortic arterial wall inflammation in patients with arteritis using high resolution dynamic contrast-enhanced magnetic resonance imaging: initial results in correlation to [18F]-FDG PET/CT. Investig Radiol 46:594–599CrossRefGoogle Scholar
  15. 15.
    Muto G, Yamashita H, Takahashi Y et al (2014) Large vessel vasculitis in elderly patients: early diagnosis and steroid-response evaluation with FDG-PET/CT and contrast-enhanced CT. Rheumatol Int. doi: 10.1007/s00296-014-2985-3 PubMedGoogle Scholar
  16. 16.
    Bartels AL, Zeebregts CJ, Bijl M, Tio RA, Slart RH (2009) Fused FDG-PET and MRI imaging of Takayasu arteritis in vertebral arteries. Ann Nucl Med 23:753–756CrossRefPubMedGoogle Scholar
  17. 17.
    Bley TA, Wieben O, Uhl M et al (2005) Integrated head-thoracic vascular MRI at 3 T: assessment of cranial, cervical and thoracic involvement of giant cell arteritis. MAGMA 18:193–200CrossRefPubMedGoogle Scholar
  18. 18.
    Pfefferkorn T, Schuller U, Cyran C et al (2010) Giant cell arteritis of the Basal cerebral arteries: correlation of MRI, dsa, and histopathology. Neurology 74:1651–1653CrossRefPubMedGoogle Scholar
  19. 19.
    Saam T, Habs M, Pollatos O et al (2010) High-resolution black-blood contrast-enhanced T1 weighted images for the diagnosis and follow-up of intracranial arteritis. Br J Radiol 83:e182–e184CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Mani V, Itskovich VV, Szimtenings M et al (2004) Rapid extended coverage simultaneous multisection black-blood vessel wall MR imaging. Radiology 232:281–288CrossRefPubMedGoogle Scholar
  21. 21.
    Bley TA, Wieben O, Uhl M, Thiel J, Schmidt D, Langer M (2005) High-resolution MRI in giant cell arteritis: imaging of the wall of the superficial temporal artery. AJR Am J Roentgenol 184:283–287CrossRefPubMedGoogle Scholar
  22. 22.
    Busse RF, Hariharan H, Vu A, Brittain JH (2006) Fast spin echo sequences with very long echo trains: design of variable refocusing flip angle schedules and generation of clinical T2 contrast. Magn Reson Med 55:1030–1037CrossRefPubMedGoogle Scholar
  23. 23.
    Busse RF, Brau AC, Vu A et al (2008) Effects of refocusing flip angle modulation and view ordering in 3D fast spin echo. Magn Reson Med 60:640–649CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Mugler JP 3rd (2014) Optimized three-dimensional fast-spin-echo MRI. J Magn Reson Imaging 39:745–767CrossRefPubMedGoogle Scholar
  25. 25.
    Fan Z, Zhang Z, Chung YC et al (2010) Carotid arterial wall MRI at 3T using 3D variable-flip-angle turbo spin-echo (TSE) with flow-sensitive dephasing (FSD). J Magn Reson Imaging 31:645–654CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Sakurai K, Miura T, Sagisaka T et al (2013) Evaluation of luminal and vessel wall abnormalities in subacute and other stages of intracranial vertebrobasilar artery dissections using the volume isotropic turbo-spin-echo acquisition (VISTA) sequence: a preliminary study. J Neuroradiol 40:19–28CrossRefPubMedGoogle Scholar
  27. 27.
    Qiao Y, Steinman DA, Qin Q et al (2011) Intracranial arterial wall imaging using three-dimensional high isotropic resolution black blood MRI at 3.0 Tesla. J Magn Reson Imaging 34:22–30CrossRefPubMedGoogle Scholar
  28. 28.
    Treitl KM, Treitl M, Kooijman-Kurfuerst H et al (2015) Three-dimensional black-blood t1-weighted turbo spin-echo techniques for the diagnosis of deep vein thrombosis in comparison with contrast-enhanced magnetic resonance imaging: a pilot study. Investig Radiol 50:401–408CrossRefGoogle Scholar
  29. 29.
    Both M, Nolle B, von Forstner C, Moosig F, Gross WL, Heller M (2009) Imaging techniques in the evaluation of primary large vessel vasculitides: part 1: angiography, interventional therapy, and magnetic resonance imaging. Z Rheumatol 68:471–484CrossRefPubMedGoogle Scholar
  30. 30.
    Brack A, Martinez-Taboada V, Stanson A, Goronzy JJ, Weyand CM (1999) Disease pattern in cranial and large-vessel giant cell arteritis. Arthritis Rheum 42:311–317CrossRefPubMedGoogle Scholar
  31. 31.
    Aschwanden M, Daikeler T, Kesten F et al (2013) Temporal artery compression sign--a novel ultrasound finding for the diagnosis of giant cell arteritis. Ultraschall Med 34:47–50PubMedGoogle Scholar
  32. 32.
    Aschwanden M, Imfeld S, Staub D et al (2015) The ultrasound compression sign to diagnose temporal giant cell arteritis shows an excellent interobserver agreement. Clin Exp Rheumatol 33:S-113–S-115Google Scholar
  33. 33.
    Kermani TA, Warrington KJ (2013) Polymyalgia rheumatica. Lancet 381:63–72CrossRefPubMedGoogle Scholar
  34. 34.
    Wasserman BA, Wityk RJ, Trout HH 3rd, Virmani R (2005) Low-grade carotid stenosis: looking beyond the lumen with MRI. Stroke 36:2504–2513CrossRefPubMedGoogle Scholar
  35. 35.
    Yamada K, Yoshimura S, Kawasaki M et al (2011) Embolic complications after carotid artery stenting or carotid endarterectomy are associated with tissue characteristics of carotid plaques evaluated by magnetic resonance imaging. Atherosclerosis 215:399–404CrossRefPubMedGoogle Scholar
  36. 36.
    Norenberg D, Ebersberger HU, Diederichs G, Hamm B, Botnar RM, Makowski MR (2015) Molecular magnetic resonance imaging of atherosclerotic vessel wall disease. Eur Radiol. doi: 10.1007/s00330-015-3881-2 PubMedGoogle Scholar
  37. 37.
    Swartz RH, Bhuta SS, Farb RI et al (2009) Intracranial arterial wall imaging using high-resolution 3-tesla contrast-enhanced MRI. Neurology 72:627–634CrossRefPubMedGoogle Scholar
  38. 38.
    Schmidt WA, Kraft HE, Vorpahl K, Volker L, Gromnica-Ihle EJ (1997) Color duplex ultrasonography in the diagnosis of temporal arteritis. N Engl J Med 337:1336–1342CrossRefPubMedGoogle Scholar
  39. 39.
    Bley TA, Markl M, Schelp M et al (2008) Mural inflammatory hyperenhancement in MRI of giant cell (temporal) arteritis resolves under corticosteroid treatment. Rheumatology (Oxford) 47:65–67CrossRefGoogle Scholar
  40. 40.
    Veldhoen S, Klink T, Geiger J et al (2014) MRI displays involvement of the temporalis muscle and the deep temporal artery in patients with giant cell arteritis. Eur Radiol 24:2971–2979CrossRefPubMedGoogle Scholar
  41. 41.
    Kuker W (2007) Cerebral vasculitis: imaging signs revisited. Neuroradiology 49:471–479CrossRefPubMedGoogle Scholar
  42. 42.
    Kuker W, Gaertner S, Nagele T et al (2008) Vessel wall contrast enhancement: a diagnostic sign of cerebral vasculitis. Cerebrovasc Dis 26:23–29CrossRefPubMedPubMedCentralGoogle Scholar
  43. 43.
    Besson FL, Parienti JJ, Bienvenu B et al (2011) Diagnostic performance of (1)(8)F-fluorodeoxyglucose positron emission tomography in giant cell arteritis: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 38:1764–1772CrossRefPubMedGoogle Scholar
  44. 44.
    Treglia G, Mattoli MV, Leccisotti L, Ferraccioli G, Giordano A (2011) Usefulness of whole-body fluorine-18-fluorodeoxyglucose positron emission tomography in patients with large-vessel vasculitis: a systematic review. Clin Rheumatol 30:1265–1275CrossRefPubMedGoogle Scholar
  45. 45.
    de Leeuw K, Bijl M, Jager PL (2004) Additional value of positron emission tomography in diagnosis and follow-up of patients with large vessel vasculitides. Clin Exp Rheumatol 22:S21–S26PubMedGoogle Scholar
  46. 46.
    Li AE, Kamel I, Rando F et al (2004) Using MRI to assess aortic wall thickness in the multiethnic study of atherosclerosis: distribution by race, sex, and age. AJR Am J Roentgenol 182:593–597CrossRefPubMedGoogle Scholar
  47. 47.
    Rosero EB, Peshock RM, Khera A, Clagett P, Lo H, Timaran CH (2011) Sex, race, and age distributions of mean aortic wall thickness in a multiethnic population-based sample. J Vasc Surg 53:950–957CrossRefPubMedGoogle Scholar
  48. 48.
    Karassa FB, Matsagas MI, Schmidt WA, Ioannidis JP (2005) Meta-analysis: test performance of ultrasonography for giant-cell arteritis. Ann Intern Med 142:359–369CrossRefPubMedGoogle Scholar
  49. 49.
    Salvarani C, Silingardi M, Ghirarduzzi A et al (2002) Is duplex ultrasonography useful for the diagnosis of giant-cell arteritis? Ann Intern Med 137:232–238CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Karla Maria Treitl
    • 1
    • 2
  • Stefan Maurus
    • 1
  • Nora Narvina Sommer
    • 1
  • Hendrik Kooijman-Kurfuerst
    • 3
  • Eva Coppenrath
    • 1
  • Marcus Treitl
    • 1
  • Michael Czihal
    • 4
  • Ulrich Hoffmann
    • 4
  • Claudia Dechant
    • 5
  • Hendrik Schulze-Koops
    • 5
  • Tobias Saam
    • 1
    • 2
  1. 1.Institute for Clinical Radiology, LMU MunichMunichGermany
  2. 2.German Center for Cardiovascular Disease Research (DZHK e. V.)MunichGermany
  3. 3.Philips HealthcareHamburgGermany
  4. 4.Division of Vascular Medicine, Medical Clinic and Policlinic IVLMU MunichMunichGermany
  5. 5.Division of Rheumatology and Clinical Immunology, Medical Clinic and Policlinic IVLMU MunichMunichGermany

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