Biondetti, E

Jager, R

Karsa, A

Rojas Villabona, A

Shmueli, K

Thomas, DL

UCL Discovery

11/9/2016 submissions.mirasmart.com/ISMRM2017/ViewSubmission.aspx?sbmID=1821http://submissions.mirasmart.com/ISMRM2017/ViewSubmission.aspx?sbmID=1821 1/3Susceptibility Mapping Reveals Inter-Hemispheric Di涕�erences in Venous Density in Patientswith Brain Arteriovenous MalformationsEmma Biondetti , Alvaro Rojas Villabona , Anita Karsa , Rolf Jäger , David L Thomas , and Karin ShmueliMedical Physics and Biomedical Engineering, University College London, London, United Kingdom, Brain Repair and Rehabilitation, Institute ofNeurology, University College London, London, United Kingdom, Leonard Wolfson Experimental Neurology Centre, Institute of Neurology, UniversityCollege London, London, United KingdomSynopsisBrain arteriovenous malformations (AVMs) are vascular abnormalities characterised by arteriovenous shunting with the lack of a capillary bed.Recent studies have shown that it is possible to create diagrams of the cerebral vein network (venograms) from images of magnetic susceptibility (). Here, we used -based venograms to calculate the hemispheric percentage of venous voxels (venous density) in each hemisphere in AVMpatients and healthy subjects. We found larger venous densities in the AVM-containing hemispheres than in the contralateral hemispheres, andmore variable venous density in AVM patients than in healthy subjects.IntroductionBrain arteriovenous malformations (AVMs) are characterised by anomalous connections between arteries and veins leading to arteriovenousshunting through a network of coiled and tortuous vessels, the so called nidus, without a normal intervening capillary bed. This arteriovenous shuntcauses abnormal blood 뿱ow in and around AVMs and the presence of more oxygenated, higher pressure blood in the draining veins .Recent studies  have exploited the bright appearance of paramagnetic deoxygenated venous blood in maps of magnetic susceptibility ( ) tocreate diagrams of the cerebral vein network (venograms).Here, we present the ﴼrst results of a study on AVM pathophysiology in a cohort of patients, each with an untreated brain AVM. We investigated thevascularisation in the two cerebral hemispheres by calculating hemispheric venous density (VD) from -based venograms.MethodsFourteen patients (age: 38±14 years) and four healthy volunteers (HVs) were scanned with a Philips Achieva 3T system (Best, NL) using a 32-channelhead coil. Patients were scanned using an axial time-of-뿱ight (TOF) magnetic resonance angiography (MRA) acquisition (Table 1a) and a whole-brain3D multi-echo (ME) gradient-echo (GRE) sequence for susceptibility mapping (SM) (Table 1b). The volunteers were scanned using a slightly di涕�erent3D ME GRE sequence for SM (Table 1c).For each subject, the  map was calculated as described in Fig. 1. The left (LH) and right hemispheres (RH) were segmented based on the Eveatlas , which was aligned to each patient’s fourth and each volunteer’s ﴼfth GRE magnitude image (TE /TE /TE  = 22.3/24.6/24 ms)using a combination of rigid, a헔ne and non-a헔ne transformations .-based venograms of patients and HVs were calculated using probabilistic Multiscale Vessel Filtering  (MVF) (10 scales). Veins were segmented bythresholding the probability map  at . To better appreciate the vasculature, transverse Maximum Intensity Projections (MIPs) of the maps, vein segmentations and segmented  maps were calculated across slabs of ten slices.In the patients, the TOF images were rigidly coregistered  to the ﴼrst-echo GRE magnitude image and then thresholded at a ﴼxed intensity tohighlight bright vessels. The thresholded TOF images were used to delineate the vessel network in and around the AVM, and to locate the AVM inthe  maps.VD was calculated in each subject’s LH and RH as the ratio (%) between the number of MVF-segmented “venous” voxels and the total number ofvoxels in each hemisphere. The VD in both hemispheres as well as the inter-hemispheric di涕�erence was calculated in each subject, along withgroup-wise means and standard deviations (SDs).Results and DiscussionFigs. 2a and b show that it is possible to locate some AVM features in both TOF images and  maps, despite possible SM confounds due to the lackof 뿱ow compensation. Vessels highlighted in the TOF images and appearing bright in the  maps could be AVM draining veins. However, the AVMs’feeding arteries and the arterial side of the AVMs’ nidi are probably only visible in the TOF images. These arterial features, as well as draining veinscontaining mixed arterial and venous blood, are likely to be less visible in the  map because arterial blood and the surrounding brain parenchymaare both similarly diamagnetic. Notably, the bright veins in Fig. 2c appear well delineated in Fig. 2d and e.Tables 2 and 3 show the VD measurements in the patients and volunteers respectively. In the patients, the hemisphere containing the AVM (Table2a) always had the largest VD (Table 2d) except in patients AVM10, AVM12 and AVM13, in which the AVM was small or located in both hemispheres.The average inter-hemispheric VD di涕�erence was larger in the patients (Table 2g) than in the HVs (Table 3f). Furthermore, the VD average ± SD wasalways larger in the patients (Table 2e-g) than in the HVs (Table 3d-f).Although haemorrhage by-products, such as paramagnetic haemosiderin, might have contributed to increased VD by being erroneously included inthe MVF segmentations, the fact that four patients (starred in Table 2) did not have a previous bleed suggests that the increased VD of the AVM-containing hemisphere is mostly driven by increased vascularisation.Conclusions and Future WorkWe have shown that  maps can be used to calculate hemispheric VD. We report larger VD in the AVM-containing hemisphere than in thecontralateral hemisphere, and an increased variability in VD in AVM patients compared to healthy subjects. This interesting information about AVMphysiology might have implications for the automated detection of brain AVMs.1 2 1 2 3 11 23Y Y1,23-7 YYY154,patients 5,volunteers Eve16,17Y 6, ,	2!%* ß Y Y16YYYYY11/9/2016 submissions.mirasmart.com/ISMRM2017/ViewSubmission.aspx?sbmID=1821http://submissions.mirasmart.com/ISMRM2017/ViewSubmission.aspx?sbmID=1821 2/3Future work will involve investigating the venous oxygenation ( ) in this cohort of patients relative to  in HVs.AcknowledgementsThe authors would like to thank Dr Audrey Fan and Dr Pierre-Louis Bazin for their help with vessel segmentation, and Dr Catarina Veiga for her helpwith image coregistration.References1. Dillon WP. Cryptic vascular malformations: Controversies in terminology, diagnosis, pathophysiology, and treatment. Am J Neuroradiol.1997;18(10):1839–1846.2. Friedlander RM. Arteriovenous Malformations of the Brain. N Engl J Med. 2007;356(26):2704–2712.3. Serres B, Deistung A, Schäfer A et al. Automatic Segmentation of the Venous Vessel Network Based on Quantitative Susceptibility Maps and itsApplication to Investigate Blood Oxygenation. Proc Intl Soc Mag Reson Med. 23. (2015):0169.4. Fan AP, Bilgic B, Gagnon L et al. Quantitative oxygenation venography from MRI phase. Magn Reson Med. 2014;72(1):149–159.5. Xu B, Liu T, Spincemaille P et al. Flow compensated quantitative susceptibility mapping for venous oxygenation imaging. Magn Reson Med.2014;72(2):438–445.6. Bazin PL, Plessis V, Fan AP et al. Vessel segmentation from quantitative susceptibility maps for local oxygenation venography. ISBI 2016:1135–1138.7. Ward PGD, Raniga P, Ferris NJ et al. Venous metrics in a large cohort of healthy elderly individuals from susceptibility-weighted images andquantitative susceptibility maps. Proc Intl Soc Mag Reson Med. 24 (2016):0329.8. Liu T, Wisnie涕� C, Lou M, et al. Nonlinear formulation of the magnetic ﴼeld to source relationship for robust quantitative susceptibility mapping.Magn Reson Med. 2013;69(2):467–476.9. Schweser F, Deistung A, Sommer K et al. Toward online reconstruction of quantitative susceptibility maps: Superfast dipole inversion. MagnReson Med. 2013;69(6):1582–1594.10. Value chosen as in the Matlab MEDI Toolbox v. 02/02/2016, function unwrapLaplacian.m.11. Liu T, Khalidov I, de Rochefort L et al. A novel background ﴼeld removal method for MRI using projection onto dipole ﴼelds (PDF). NMR Biomed.2011;24(9):1129–1136.12. Jenkinson M, Beckmann CF, Behrens TEJ et al. Fsl. NeuroImage. 2012;62(2):782–790.13. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp. 2002;17(3):143–155.14. Wei H, Dibb R, Zhou Y et al. Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range. NMRBiomed. 2015;28(10):1294–1303.15. Lim IAL, Faria A V., Li X et al. Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: Application todetermine iron content in deep gray matter structures. NeuroImage. 2013;82:449–469.16. Ourselin S, Roche A, Subsol G et al. Reconstructing a 3D structure from serial histological sections. Image Vis Comput. 2001;19(1–2):25–31.17. Modat M, Ridgway GR, Taylor ZA et al. Fast free-form deformation using graphics processing units. Comput Methods Programs Biomed.2010;98(3):278–284.2 2FiguresTable 1 The TOF MRA (a) and the 3D ME GRE (b) protocols used to scan the cohort of fourteen patients, and the 3D ME GRE protocol (c) used to scanthe four healthy volunteers. Fig. 1 The  mapping pipeline. (1) The total ﴼeld  was calculated by non-linearly ﴼtting the complex GRE signal over TEs . (2)  was thenLaplacian-unwrapped with SHARP  (threshold  ). (3) The local ﴼeld  was calculated with PDF , using a BET  brain maskcreated from the longest-echo magnitude image. The  images were then eroded to remove three voxels from the border where PDF isY  8 9 10 U  Ã (+ 11 12,13(+11 1411/9/2016 submissions.mirasmart.com/ISMRM2017/ViewSubmission.aspx?sbmID=1821http://submissions.mirasmart.com/ISMRM2017/ViewSubmission.aspx?sbmID=1821 3/3inaccurate . (4)  was calculated with STAR-QSM  to try to reduce streaking from potentially high-  regions in the AVMs, e.g. regions of recenthaemorrhage. Fig. 2 The ﴼgure shows the TOF and  images and the MVF results in four representative subjects. Patients AVM01 and AVM02 had an AVM in theLH, while patients AVM07 and AVM14 had an AVM in the RH. All the AVMs are circled in green. Column a shows the intensity-thresholded TOFimages, column b the  maps, column c the  map MIPs across slabs of ten slices, column d the MIPs of the MVF segmentations across slabs of tenslices and column e the MIPs of the segmented  maps. Table 2 The table shows the hemisphere in which each patient's AVM is located (column a). It then reports each patient's VD in the LH (column b)and in the RH (column c) and the di涕�erence between VD in the LH and VD in the RH (column d). The patients who had a previous bleed are starred(*). The bottom rows in the table show the average ± SD of VD in the LH (column e) and the RH (column f) for the whole group as well as the meanand SD of the hemispheric VD di涕�erence (column g). Table 3 The table shows each HV's VD in the LH (column a) and in the RH (column b) and the di涕�erence between VD in the LH and VD in the RH(column c). The bottom rows in the table show the average ± SD of VD in the LH (column d) and the RH (column e) for the whole group as well asthe mean and SD of the hemispheric VD di涕�erence (column f). 11 Y 14 YYY YY

Susceptibility Mapping Reveals Inter-hemispheric Differences in Venous Density in Patients with Arteriovenous Malformations

http://discovery.ucl.ac.uk/1549784/1/Susceptibility-Mapping-Reveals-Inter-Hemispheric-Differences-in-Venous-Density-in-Patients-with-Brain-Arteriovenous-Malformations.pdf

Susceptibility Mapping Reveals Inter-hemispheric Differences in Venous Density in Patients with Arteriovenous Malformations

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