International audienceMR vascular fingerprinting proposes to map vascular properties such as blood volume fraction, average vessel radius or blood oxygenation saturation (SO). The fingerprint pattern used in previous studies provides low sensitivity on SO. We optimised signal patterns built from pre and post USPIO acquisitions. Concatenation of different echoes associated with higher dimensional dictionaries led to better estimates in both healthy and tumoral tissues

Boux, Fabien

Brossard, Clément

Christen, Thomas

Delphin, Aurélien

Lemasson, Benjamin

Luc Barbier, Emmanuel

Warnking, Jan

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HAL Id: hal-02926840https://hal.archives-ouvertes.fr/hal-02926840Submitted on 1 Sep 2020HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.Optimizing signal patterns for MR vascularfingerprintingAurélien Delphin, Fabien Boux, Clément Brossard, Jan Warnking, BenjaminLemasson, Emmanuel Luc Barbier, Thomas ChristenTo cite this version:Aurélien Delphin, Fabien Boux, Clément Brossard, Jan Warnking, Benjamin Lemasson, et al.. Opti-mizing signal patterns for MR vascular fingerprinting. ISMRM & SMRT 2020 - Virtual Conference &Exhibition, Aug 2020, Sidney, Australia. pp.1-3. ￿hal-02926840￿11/7/2019 https://submissions2.mirasmart.com/ISMRM2020/ViewSubmissionPublic.aspx?sei=2qLmgtRVUhttps://submissions2.mirasmart.com/ISMRM2020/ViewSubmissionPublic.aspx?sei=2qLmgtRVU 1/3Optimizing signal patterns for MR vascular fingerprintingAurelien Delphin , Fabien Boux , Clément Brossard , Jan M Warnking , Benjamin Lemasson , Emmanuel Luc Barbier , and Thomas ChristenUniv. Grenoble Alpes, Inserm, U1216, Grenoble Institut Neurosciences, GIN, 38000, Grenoble, France, Univ. Grenoble Alpes, Inria, CNRS, G-INP, 38000, Grenoble, FranceSynopsisMR vascular fingerprinting proposes to map vascular properties such as blood volume fraction, average vessel radius or blood oxygenationsaturation (SO ). The fingerprint pattern used in previous studies provides low sensitivity on SO . We optimised signal patterns built from preand post USPIO acquisitions. Concatenation of different echoes associated with higher dimensional dictionaries led to better estimates in bothhealthy and tumoral tissues.IntroductionMR fingerprinting  is an efficient tool to acquire multiple quantitative maps simultaneously. MR vascular fingerprinting (MRvF) proposes to use thisframework to extract vascular properties with fast acquisition times . The initial implementation of MRvF used the GESFIDSE sequence (Gradient EchoSampling of the FID and Spin Echo) that was known to be sensitive to blood oxygenation saturation (SO ) and microvascular geometry (blood volumefraction (BVf) and vessel averaged radius (R)). More precisely, the ratio of signals acquired pre and post injection of iron-based contrast agent (USPIO)was used to remove B  inhomogeneities and T  effects. However it has been suggested  that this MRvF pattern provides low sensitivity on SO . In thisstudy, we propose to reorganize the signal patterns to design more efficient fingerprints. Dictionaries were built with additional dimensions to take intoaccount effects that were eliminated before. Our fingerprints were tested on 9 healthy and 9 tumor implanted rats. The results were compared against eachother and against conventional steady-state perfusion measurements.Materials and Methods9L tumors were implanted in 9 rats (Fischer) and imaging was performed between 14 and 16 days later. 9 healthy rats (Fischer) were alsoimaged .A 45 minutes MRI protocol including ADC, T  and T * maps was acquired on a 4.7T Bruker system. A GESFIDSE sequence (32 echoes,SE=60ms, see fig. 1) was acquired pre and post injection of USPIO (P904, Guerbet, France, 200µmol Fe/kg).Steady state analysis was performed on the 45 min protocol to obtain vessel size index (VSI), BVf  and SO maps with home-made software .Fingerprint analysis was performed on the two GESFIDSE acquisitions only (8 min protocol).- The dictionary was generated using “MRVox” , a Matlab (The MathWorks Inc., Natick, Ma, USA) based home-made software. Up to 5dimensions were considered: R=[1:10] µm, BVf=[0.34:10.18] %, SO =[0.35:0.9] %, T =[45:130] ms and ADC=[600:1300] µm /s. A total of12,545,280 signals were generated.- Three fingerprints patterns were used, based on different combinations of GESFIDSE signals acquired pre and post USPIO injection. The ratiopost over pre (32 echoes, fig. 2c), the concatenation of the pre and post (64 echoes, fig. 2d) signals and a “hybrid” pattern. The latter is theconcatenation of the 9 pre and post echoes before the 180° pulse and the 23 echoes from the ratio after the 180° pulse, yielding a fingerprint of 41echoes (fig. 2e). The concatenation and hybrid patterns were designed to increase signals variations across the dictionary dimensions, as shownfor simulated SO  on fig. 2. Simulation studies on other dimensions are not shown here.ResultsThe 5D dictionary generated for this study was first compared to a reduced 3D version without T  effects and for ADC=810µm /s (33188 signals). Resultsfor BVf are presented in fig. 3, along with steady-state measurements. Increasing the number of parameters brought little change with the ratio pattern.However, maps obtained with the concatenation pattern using the 5D dictionary had better contrast and lower values than the ones obtained with the 3Ddictionary (BVf=4.2±0.5% vs. 4.8±0.4 N=9). The concatenation pattern provided much sharper contrasts than the ratio pattern in the lower parts of thebrain, where SNR is degraded. It also produced the closest output to the steady-state measurements (BVf=3.5±0.6%, N=9). Results from the three MRvF patterns combined with the 5D dictionary are presented for one tumor animal on fig. 4. All methods showed a BVf increase inthe tumor. The concatenation and hybrid patterns showed an increased VSI in the tumor, similar to that of the steady-state measurement and previoushistological findings . On the opposite, the ratio displayed a decreased VSI in the tumor. SO  values found with the steady-state approach were similar inthe tumor and healthy tissues. However the mathematical model in this approach might not be adapted to this type of vascular environment . Conversely,all three MRvF patterns found reduced SO  estimates in the tumor. Only the hybrid pattern produced SO  values in the contralateral hemisphere that werein line with the steady-state approach and expected values in healthy tissue. The hybrid pattern showed expected contrasts on the three metrics, withvalues in line with what is expected in healthy tissue but produced noisier maps than the concatenation pattern. Fig. 5 presents SO  results obtained afteraveraging pre and post signals (3 NEX) and two slices. The higher SNR led to smoother maps while keeping contralateral values in the expected range ofhealthy tissues and low-oxygenated tumors.ConclusionsAmong the different MRvF signal patterns derived from pre and post USPIO GESFIDSE acquisitions, the hybrid concatenation of different echoesassociated with higher dimensional dictionaries led to the best estimates in both healthy and tumoral tissues. Comparisons with other tumor models andhistological measurements should be performed in order to conclude on the most efficient patterns. Dictionary learning using interpretable regressionmethods  could also give more insight on which parts of the signals should be used for optimal fingerprint design and the required SNR level for accurateestimates. These encouraging results could lead to a reduction of acquisition time from 45 min to a few minutes only.AcknowledgementsThe MRI facility IRMaGe is partly funded by the French program “Investissements d’Avenir” run by the French National Research Agency, grant“Infrastructure d’avenir en Biologie Santé” [ANR-11-INBS-0006]References1. Ma D, Gulani V, Seiberlich N, Liu K, Sunshine JL, Duerk JL, et al. Magnetic resonance fingerprinting. Nature. 2013 Mar;495(7440):187–92.2. Christen T, Pannetier NA, Ni WW, Qiu D, Moseley ME, Schuff N, et al. MR vascular fingerprinting: A new approach to compute cerebral bloodvolume, mean vessel radius, and oxygenation maps in the human brain. NeuroImage. 2014 Apr 1;89:262–70.1 1,2 1 1 1 1 11 22 21220 23242 2526 782 222224222 22911/7/2019 https://submissions2.mirasmart.com/ISMRM2020/ViewSubmissionPublic.aspx?sei=2qLmgtRVUhttps://submissions2.mirasmart.com/ISMRM2020/ViewSubmissionPublic.aspx?sei=2qLmgtRVU 2/33. Christen T, Ni WW, Guo J, Fan A, Moseley M and Zaharchuk G: MR Vascular Fingerprinting during Gas Challenges. (Proceedings of Joint AnnualMeeting ISMRM-ESMRMB 2018)4. Lemasson B, Pannetier N, Coquery N, Boisserand LSB, Collomb N, Schuff N, et al. MR Vascular Fingerprinting in Stroke and Brain TumorsModels. Scientific Reports. 2016 Nov 24;6:37071.5. Troprès I, Grimault S, Vaeth A, Grillon E, Julien C, Payen J-F, et al. Vessel size imaging. Magnetic Resonance in Medicine. 2001;45(3):397–408.6. Christen T, Lemasson B, Pannetier N, Farion R, Segebarth C, Rémy C, et al. Evaluation of a quantitative blood oxygenation level-dependent(qBOLD) approach to map local blood oxygen saturation. NMR in Biomedicine. 2011;24(4):393–403.7. nifm-gin/MP3 [Internet]. nifm-gin; 2019 [cited 2019 Nov 5]. Available from: https://github.com/nifm-gin/MP38. Pannetier NA, Debacker C, Mauconduit F, Christen T, Barbier EL. A Simulation Tool for Dynamic Contrast Enhanced MRI [Internet]. 2013 [cited2019 Oct 10]. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.00576369. Boux F, Forbes F, Arbel J, Barbier EL. Dictionary-Free MR Fingerprinting Parameter Estimation Via Inverse Regression. In: Joint Annual MeetingISMRM-ESMRMB 2018 [Internet]. Paris, France; 2018 [cited 2019 Oct 28]. p. 1–2. (Proceedings of Joint Annual Meeting ISMRM-ESMRMB 2018).Available from: https://hal.archives-ouvertes.fr/hal-01941630FiguresGradient Echo Sampling of the FID and Spin Echo (GESFIDSE) sequence.Simulated signals, each curve corresponds to one value of SO . (a) GESFIDSE Signal pre USPIO injection, (b) GESFIDSE signal post USPIO injection, (c)ratio of Post over Pre pattern, (d) concatenation of Pre and Post pattern, (e) hybrid pattern.3D vs. 5D dictionary results for ratio and concatenation pattern211/7/2019 https://submissions2.mirasmart.com/ISMRM2020/ViewSubmissionPublic.aspx?sei=2qLmgtRVUhttps://submissions2.mirasmart.com/ISMRM2020/ViewSubmissionPublic.aspx?sei=2qLmgtRVU 3/3Comparison of maps obtained with different fingerprint patterns in one ratSO  maps obtained with the ratio and hybrid pattern with 1NEX compared to results from hybrid pattern with 3NEX2

Optimizing signal patterns for MR vascular fingerprinting

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Optimizing signal patterns for MR vascular fingerprinting

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