Quantitative susceptibility mapping (QSM) using the MRI phase to calculate tissue magnetic susceptibility is finding increasing clinical applications. Oblique image slices are often acquired to facilitate radiological viewing and reduce artifacts. Here, we show that artifacts and errors arise in susceptibility maps if oblique acquisition is not properly taken into account in QSM. We performed a comprehensive analysis of the effects of oblique acquisition on brain susceptibility maps and compared tilt correction schemes for three susceptibility calculation methods, using a numerical phantom and human in-vivo images. We demonstrate a robust tilt correction method for accurate QSM with oblique acquisition

Karsa, A

Kiersnowski, OC

Shmueli, K

Thornton, JS

English

UCL Discovery

24/05/2021 https://index.mirasmart.com/ISMRM2021/PDFfiles/0794.htmlhttps://index.mirasmart.com/ISMRM2021/PDFfiles/0794.html 1/40794The E ect of Oblique Image Slices on the Accuracy of Quantitative Susceptibility Mappingand a Robust Tilt Correction MethodOliver C. Kiersnowski , Anita Karsa , John S. Thornton , and Karin Shmueli  Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom, UCL Queens Square Institute of Neurology, London, United KingdomSynopsisQuantitative susceptibility mapping (QSM) using the MRI phase to calculate tissue magnetic susceptibility is  nding increasing clinical applications.Oblique image slices are often acquired to facilitate radiological viewing and reduce artifacts. Here, we show that artifacts and errors arise insusceptibility maps if oblique acquisition is not properly taken into account in QSM. We performed a comprehensive analysis of the e ects ofoblique acquisition on brain susceptibility maps and compared tilt correction schemes for three susceptibility calculation methods, using anumerical phantom and human in-vivo images. We demonstrate a robust tilt correction method for accurate QSM with oblique acquisition.IntroductionQuantitative susceptibility mapping (QSM) is  nding increasing clinical applications. Acquisition of oblique image slices is common clinical practiceto facilitate radiological viewing but pilot studies suggest this gives incorrect susceptibility ( ) estimates when unaccounted for . Using a numericalbrain phantom , we performed a comprehensive analysis of the e ects of oblique acquisition and compared proposed tilt correction methods forthe  nal  calculation step in the QSM pipeline. We con rmed these results in vivo, and demonstrate a robust tilt correction method for accurate calculation.MethodsNumerical Phantom: Local  eld maps were obtained from a non-linear  t  over echo times of complex data, created from magnitude and phase images of a numericalbrain phantom , with no background  elds. These background- eld-free maps were used to independently analyse the  calculation step in theQSM pipeline without confounds from phase unwrapping or background  eld removal. To simulate oblique slice acquisition, the untilted referencelocal  eld map (at 0°) was tilted by -45° to +45° in 5° steps. All rotations were carried out about the x-axis (u-axis, Figure 1) using FSL FLIRT  withtrilinear interpolation. We tested four proposed tilt correction schemes (Figure 1): RotPrior: image rotated into scanner frame prior to  calculation (with a k-space dipole de ned in the scanner frame)DipK: image left unaligned (k-space dipole de ned in the tilted image frame)DipIm: image left unaligned (image-space dipole de ned in the tilted image frame)NoRot: image left unaligned (simulating mistaken de nition of the k-space dipole in the scanner frame misaligned to the tilted image)To facilitate comparisons, all susceptibility maps left in the image-frame after correction (DipK, DipIm and NoRot) were rotated back into alignmentwith the scanner axes. These schemes were compared for three  calculation methods: thresholded k-space division (TKD)  (threshold = 2/3),iterative  tting with Tikhonov regularisation  (α = 0.003), both corrected for  underestimation , and weighted linear total variation (wlTV)regularisation (FANSI toolbox , α =2x10  ). Mean  values were calculated in  ve deep grey matter regions of interest (ROIs) provided with thephantom. Susceptibility maps were compared using the root mean squared error (RMSE) relative to the ground truth susceptibility map and theQSM-tuned structural similarity index (XSIM) .  In Vivo: 3D gradient-echo brain images of a healthy volunteer were acquired on a 3T Siemens Prisma MR system (National Hospital for Neurology andNeurosurgery, London, UK) using a 64-channel head coil. The image volume was tilted about the x-axis from -20° to +20° in 5° increments andacquired in 3 min 23 s (per volume) with TE1/ΔTE/TE5 = 4.92/4.92/24.60ms; TR=30ms; 1.23 mm isotropic voxels; 6/8 partial Fourier; and GRAPPAacceleration = 3.  For all angles/volumes, a total  eld map and noise map were obtained using a non-linear  t of the complex data . A brain mask was created usingBET , eroded by 6 voxels, and multiplied with a mask created by thresholding the inverse noise map at its mean to remove noisy voxels. Residualphase wraps were removed using Laplacian unwrapping  and background  elds were removed using the Laplacian boundary value (LBV)technique as it is independent of tilt angle. The four tilt correction schemes were compared using the same three  calculation methods as for thenumerical phantom.  For each angle, the magnitude image (RMS across echoes) was rigidly registered to the reference (0°) using NiftyReg  and the transformationmatrix was used to transform the  maps into the reference space for comparison. ROIs were obtained by registering the EVE  magnitude imagewith the same reference image and applying the resulting transformation to the EVE ROIs. Mean  values were calculated in these ROIs for allangles. RMSE and XSIM were also used to compare tilt-corrected maps with the 0° reference susceptibility map.ResultsNumerical Phantom:  All QSM methods are most accurate with RotPrior, and least accurate with NoRot when the dipole is misaligned to the main magnetic  eld (Figure 2).wlTV is relatively robust to oblique acquisition, with RotPrior and DipK performing similarly. However, DipK shows variability in  across angles indi erent ROIs.  maps (Figure 3) make clearly apparent the errors resulting from NoRot.  In Vivo:  Figure 4 con rms that NoRot results in large susceptibility errors and that RotPrior is comparable to DipK between 20°, both performing better1 1 2 11 2χ 12χ χ32 χ4χχ 56,7 χ 89,10 -4 χ11PE312,1313,14 13,15χ16χ 17χχχ±24/05/2021 https://index.mirasmart.com/ISMRM2021/PDFfiles/0794.htmlhttps://index.mirasmart.com/ISMRM2021/PDFfiles/0794.html 2/4than DipIm, in agreement with the phantom results. Di erence images also con rm the phantom results (Figure 5). Subtle e ects found in thephantom ROIs (Figure 2) were not apparent in vivo (not shown) due to noise, motion, rotation/registration interpolation e ects and the expectedvariability in QSMs over repeated acquisitions .ConclusionsWe have shown that, for any susceptibility calculation method (TKD, iterative Tikhonov and wlTV) applied to an oblique acquisition, leaving thedipole kernel misaligned with the main magnetic  eld ( ) direction, which is often the default mode of QSM toolboxes, leads to substantial errors. The most accurate susceptibilities can be obtained when local  eld maps are rotated into alignment with the scanner axes prior to calculation (RotPrior). For wlTV, accurate susceptibility calculation can be carried out in the tilted image frame without any rotations provided thecorrect ( ) direction is used in de ning the k-space dipole (DipK).AcknowledgementsOliver Kiersnowski is supported by the EPSRC-funded UCL Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health)(EP/S021930/1). Karin Shmueli is supported by ERC Consolidator Grant DiSCo MRI SFN 770939. We thank Dr Carlos Milovic for his assistance withFANSI.References1. Dixon EC. Applications of MRI Magnetic Susceptibility Mapping in PET-MRI BrainStudies. Dr thesis, UCL (University Coll London). 2018.http://discovery.ucl.ac.uk/10053515/.2. Marques, José P., et al. "QSM Reconstruction Challenge 2.0 Part 1: A Realistic in silicoHead Phantom for MRI data simulation and evaluation of susceptibility mappingprocedures." bioRxiv (2020).3. Liu T, Wisnie  C, Lou M, Chen W, Spincemaille P, Wang Y. Nonlinear formulation of themagnetic  eld to source relationship for robust quantitative susceptibility mapping.Magn Reson Med. 2013 Feb;69(2):467-76. doi: 10.1002/mrm.24272. Epub 2012 Apr 9.PMID: 22488774.4. Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM. FSL. Neuroimage.2012;62:782-790. doi:10.1016/j.neuroimage.2011.09.0155. Shmueli K, De Zwart JA, Van Gelderen P, Li TQ, Dodd SJ, Duyn JH. Magneticsusceptibility mapping of brain tissue in vivo using MRI phase data. Magn Reson Med.2009;62(6):1510-1522. doi:10.1002/mrm.221356. Karsa, A, Punwani, S, Shmueli, K, et al. An optimized and highly repeatable MRIacquisition and processing pipeline for quantitative susceptibility mapping in the head‐and‐neck region. Magn Reson Med. 2020; 84: 3206– 3222.https://doi.org/10.1002/mrm.283777. PMID: 32621302.MRI susceptibility calculation methods.https://xip.uclb.com/i/software/mri_qsm_tkd.html.8. Schweser F, Deistung A, Sommer K, Reichenbach JR. Toward online reconstruction ofquantitative susceptibility maps: Superfast dipole inversion. Magn Reson Med.2013;69(6):1581-1593. doi:10.1002/mrm.244059. Bilgic B., Chatnuntawech I., Langkammer C., Setsompop K.; Sparse Methods forQuantitative Susceptibility Mapping; Wavelets and Sparsity XVI, SPIE 201510. Milovic C, Bilgic B, Zhao B, Acosta-Cabronero J, Tejos C. Fast Nonlinear SusceptibilityInversion with Variational Regularization. Magn Reson Med. Accepted Dec. 12th 2017.DOI: 10.1002/mrm.2707311. Milovic C, Tejos C, and Irarrazaval P. Structural Similarity Index Metric setup for QSMapplications (XSIM). 5th International Workshop on MRI Phase Contrast & QuantitativeSusceptibility Mapping, Seoul, Korea, 2019.12. S.M. Smith. Fast robust automated brain extraction. Human Brain Mapping, 17(3):143-155, November 2002.13. http://pre.weill.cornell.edu/mri/pages/qsm.html18B̂0 χχB̂024/05/2021 https://index.mirasmart.com/ISMRM2021/PDFfiles/0794.htmlhttps://index.mirasmart.com/ISMRM2021/PDFfiles/0794.html 3/414. Scho eld, Marvin A., and Yimei Zhu, ‘Fast Phase Unwrapping Algorithm forInterferometric Applications’, Optics Letters, 2003 https://doi.org/10.1364/ol.28.00119415. Zhou, Dong, Tian Liu, Pascal Spincemaille, and Yi Wang, ‘Background Field Removal bySolving the Laplacian Boundary Value Problem’, NMR in Biomedicine, 27.3 (2014), 312–19 <https://doi.org/10.1002/nbm.3064>16. Modat, et al. (2014). Global image registration using a symmetric block-matchingapproach. Journal of Medical Imaging, 1(2), 024003–024003.doi:10.1117/1.JMI.1.2.02400317. Lim IA, Faria AV, Li X, Hsu JT, Airan RD, Mori S, van Zijl PC. Human brain atlas forautomated region of interest selection in quantitative susceptibility mapping:application to determine iron content in deep gray matter structures. Neuroimage.2013 Nov 15;82:449-69. doi: 10.1016/j.neuroimage.2013.05.127. Epub 2013 Jun 12.PMID: 23769915; PMCID: PMC3966481.18. Xiang Feng, Andreas Deistung, Jürgen R. Reichenbach,Quantitative susceptibilitymapping (QSM) and R2* in the human brain at 3T: Evaluation of intra-scannerrepeatability, Zeitschrift für Medizinische Physik, Volume 28, Issue 1, 2018, Pages 36-48FiguresFigure 1: Schematic illustration of oblique acquisition and proposed tilt correction methods for QSM. The scanner frame (x,y,z) and the image frame(u,v,w) are shown with respect to the main magnetic  eld B =B z (left). Proposed tilt correction methods are shown with the k-space dipole (right).RotPrior involves rotation of the tilted image frame into alignment (u',v',w') with the scanner frame. NoRot represents incorrectly misaligning thedipole kernel with B z simulating a common error. Figure 2: Mean susceptibilities in the Caudate and Thalamus (top rows), and RMSE and XSIM (bottom rows) across all tilt angles for all tilt correctionschemes and all three calculation methods in the numerical phantom. NoRot performs worst across all angles. RotPrior is the most accurate tiltcorrection scheme. For weighted linear TV, DipK and RotPrior have similar XSIM values but the mean thalamus varies more over angles with DipK.Note that DipIm is not shown for wlTV as this method fails. 0 0024/05/2021 https://index.mirasmart.com/ISMRM2021/PDFfiles/0794.htmlhttps://index.mirasmart.com/ISMRM2021/PDFfiles/0794.html 4/4Figure 3:  maps and di erence images illustrating the e ects of all tilt correction schemes in the numerical phantom. An axial and a coronal sliceare shown for a volume tilted at 25° and a reference 0° volume with all  maps calculated using the iterative Tikhonov method. The ROIs analysedare also shown (bottom left). Qualitatively, RotPrior performs the best while NoRot results in substantial  errors across the whole brain. The resultsfrom TKD and weighted linear TV (not shown) are very similar. Figure 4: RMSE and XSIM plots over all angles for all tilt correction schemes and all three  calculation methods in one subject in vivo. These resultsare similar to those in the numerical phantom (Figure 2) with RotPrior and DipK performing best and NoRot performing worst across all methods. Atnon-zero tilt angles, RMSE and XSIM have a respectively high/low baseline level arising from rotation and registration interpolations. DipIm fails forwlTV and is, therefore, omitted from the plots in the last column. Figure 5:  maps and di erence images illustrating the e ects of all tilt correction schemes in vivo. An axial and a coronal slice are shown for avolume tilted at -10° and a reference (0°) volume with all  maps calculated using the iterative Tikhonov method (left) and weighted linear TV (right).NoRot leads to the largest di erences and image artefacts throughout the brain. The EVE ROIs used are shown (bottom left). Results from TKD (notshown) are very similar. χχχχχχProc. Intl. Soc. Mag. Reson. Med. 29 (2021)0794

The Effect of Oblique Image Slices on the Accuracy of Quantitative Susceptibility Mapping and a Robust Tilt Correction Method

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The Effect of Oblique Image Slices on the Accuracy of Quantitative Susceptibility Mapping and a Robust Tilt Correction Method

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