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Björk, Marcus
Publications (10 of 13) Show all publications
Ramos-Llordén, G., Vegas-Sánchez-Ferrero, G., Björk, M., Vanhevel, F., Parizel, P. M., San José Estépar, R., . . . Sijbers, J. (2018). NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRIT1Mapping. IEEE Transactions on Medical Imaging, 37(11), 2414-2427
Open this publication in new window or tab >>NOVIFAST: A Fast Algorithm for Accurate and Precise VFA MRIT1Mapping
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2018 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 11, p. 2414-2427Article in journal (Refereed) Published
Abstract [en]

In quantitative magnetic resonance T 1 mapping, the variable flip angle (VFA) steady state spoiled gradient recalled echo (SPGR) imaging technique is popular as it provides a series of high resolution T 1 weighted images in a clinically feasible time. Fast, linear methods that estimate T 1 maps from these weighted images have been proposed, such as DESPOT1 and iterative re-weighted linear least squares. More accurate, non-linear least squares (NLLS) estimators are in play, but these are generally much slower and require careful initialization. In this paper, we present NOVIFAST, a novel NLLS-based algorithm specifically tailored to VFA SPGR T 1 mapping. By exploiting the particular structure of the SPGR model, a computationally efficient, yet accurate and precise T 1 map estimator is derived. Simulation and in vivo human brain experiments demonstrate a twenty-fold speed gain of NOVIFAST compared with conventional gradient-based NLLS estimators while maintaining a high precision and accuracy. Moreover, NOVIFAST is eight times faster than the efficient implementations of the variable projection (VARPRO) method. Furthermore, NOVIFAST is shown to be robust against initialization.

Place, publisher, year, edition, pages
IEEE, 2018
Keywords
T-1 mapping, variable flip angle, SPGR, DESPOT1, relaxometry
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-366258 (URN)10.1109/TMI.2018.2833288 (DOI)000449113800004 ()29993537 (PubMedID)
Funder
EU, FP7, Seventh Framework Programme, FP7/2007-2013The European Space Agency (ESA)
Available from: 2018-11-19 Created: 2018-11-19 Last updated: 2019-01-07Bibliographically approved
Björk, M., Zachariah, D., Kullberg, J. & Stoica, P. (2016). A multicomponent T2 relaxometry algorithm for myelin water imaging of the brain. Magnetic Resonance in Medicine, 75(1), 390-402
Open this publication in new window or tab >>A multicomponent T2 relaxometry algorithm for myelin water imaging of the brain
2016 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 75, no 1, p. 390-402Article in journal (Refereed) Published
Keywords
multicomponent T2 relaxometry, estimation algorithm, myelin water fraction, in vivo brain
National Category
Signal Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:uu:diva-237160 (URN)10.1002/mrm.25583 (DOI)000367739200040 ()25604436 (PubMedID)
Projects
SysTEAM (ERC)
Funder
EU, European Research Council, 247035
Available from: 2015-01-21 Created: 2014-11-28 Last updated: 2018-10-01Bibliographically approved
Björk, M. (2015). Contributions to Signal Processing for MRI. (Doctoral dissertation). Uppsala: Acta Universitatis Upsaliensis
Open this publication in new window or tab >>Contributions to Signal Processing for MRI
2015 (English)Doctoral thesis, monograph (Other academic)
Abstract [en]

Magnetic Resonance Imaging (MRI) is an important diagnostic tool for imaging soft tissue without the use of ionizing radiation. Moreover, through advanced signal processing, MRI can provide more than just anatomical information, such as estimates of tissue-specific physical properties.

Signal processing lies at the very core of the MRI process, which involves input design, information encoding, image reconstruction, and advanced filtering. Based on signal modeling and estimation, it is possible to further improve the images, reduce artifacts, mitigate noise, and obtain quantitative tissue information.

In quantitative MRI, different physical quantities are estimated from a set of collected images. The optimization problems solved are typically nonlinear, and require intelligent and application-specific algorithms to avoid suboptimal local minima. This thesis presents several methods for efficiently solving different parameter estimation problems in MRI, such as multi-component T2 relaxometry, temporal phase correction of complex-valued data, and minimizing banding artifacts due to field inhomogeneity. The performance of the proposed algorithms is evaluated using both simulation and in-vivo data. The results show improvements over previous approaches, while maintaining a relatively low computational complexity. Using new and improved estimation methods enables better tissue characterization and diagnosis.

Furthermore, a sequence design problem is treated, where the radio-frequency excitation is optimized to minimize image artifacts when using amplifiers of limited quality. In turn, obtaining higher fidelity images enables improved diagnosis, and can increase the estimation accuracy in quantitative MRI.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. p. xviii+176
Series
Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1104-2516 ; 113
Keywords
Parameter estimation, efficient estimation algorithms, non-convex optimization, multicomponent T2 relaxometry, artifact reduction, T2 mapping, denoising, phase estimation, RF design, MR thermometry, in-vivo brain
National Category
Signal Processing
Research subject
Electrical Engineering with specialization in Signal Processing
Identifiers
urn:nbn:se:uu:diva-246537 (URN)978-91-554-9204-5 (ISBN)
Public defence
2015-05-08, ITC 2446, Polacksbacken, Lägerhyddsvägen 2, Uppsala, 13:15 (English)
Opponent
Supervisors
Funder
EU, European Research Council, 247035
Available from: 2015-04-16 Created: 2015-03-09 Last updated: 2018-10-01Bibliographically approved
Björk, M. & Stoica, P. (2014). Magnitude-constrained sequence design with application in MRI. In: Proc. 39th IEEE International Conference on Acoustics, Speech, and Signal Processing: . Paper presented at IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2014, May 4-9, Florence, Italy (pp. 4943-4947). Piscataway, NJ: IEEE
Open this publication in new window or tab >>Magnitude-constrained sequence design with application in MRI
2014 (English)In: Proc. 39th IEEE International Conference on Acoustics, Speech, and Signal Processing, Piscataway, NJ: IEEE , 2014, p. 4943-4947Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present an algorithm for sequence design with magnitude constraints. We formulate the design problem in a general setting, but also illustrate its relevance to parallel excitation MRI. The formulated non-convex design optimization criterion is minimized locally by means of a cyclic algorithm, consisting of two simple algebraic sub-steps. Since the algorithm truly minimizes the criterion, the obtained sequence designs are guaranteed to improve upon the estimates provided by a previous method, which is based on the heuristic principle of the Iterative Quadratic Maximum Likelihood algorithm. The performance of the proposed algorithm is illustrated in two numerical examples.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2014
Series
International Conference on Acoustics Speech and Signal Processing, ISSN 1520-6149
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-218885 (URN)10.1109/ICASSP.2014.6854542 (DOI)000343655304194 ()978-1-4799-2893-4 (ISBN)
Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2014, May 4-9, Florence, Italy
Projects
SysTEAM
Funder
EU, European Research Council, 247035
Available from: 2014-05-09 Created: 2014-02-19 Last updated: 2018-10-01Bibliographically approved
Björk, M. & Stoica, P. (2014). New approach to phase correction in multi-echo T2 relaxometry. Journal of magnetic resonance, 249, 100-107
Open this publication in new window or tab >>New approach to phase correction in multi-echo T2 relaxometry
2014 (English)In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 249, p. 100-107Article in journal (Refereed) Published
Abstract [en]

Estimation of the transverse relaxation time, T-2, from multi-echo spin-echo images is usually performed using the magnitude of the noisy data, and a least squares (LS) approach. The noise in these magnitude images is Rice distributed, which can lead to a considerable bias in the LS-based T-2 estimates. One way to avoid this bias problem is to estimate a real-valued and Gaussian distributed dataset from the complex data, rather than using the magnitude. In this paper, we propose two algorithms for phase correction which can be used to generate real-valued data suitable for LS-based parameter estimation approaches. The first is a Weighted Linear Phase Estimation algorithm, abbreviated as WELPE. This method provides an improvement over a previously published algorithm, while simplifying the estimation procedure and extending it to support multi-coil input. The algorithm fits a linearly parameterized function to the multi-echo phase-data in each voxel and, based on this estimated phase, projects the data onto the real axis. The second method is a maximum likelihood estimator of the true decaying signal magnitude, which can be efficiently implemented when the phase variation is linear in time. The performance of the algorithms is demonstrated via Monte Carlo simulations, by comparing the accuracy of the estimates. Furthermore, it is shown that using one of the proposed algorithms enables more accurate T-2 estimates; in particular, phase corrected data significantly reduces the estimation bias in multi-component T-2 relaxometry example, compared to when using magnitude data. WELPE is also applied to a 32-echo in vivo brain dataset, to show its practical feasibility.

National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-233700 (URN)10.1016/j.jmr.2014.09.025 (DOI)000346552700014 ()
Projects
SysTEAM
Funder
EU, European Research Council, 247035
Available from: 2014-10-16 Created: 2014-10-08 Last updated: 2018-10-01Bibliographically approved
Björk, M., Ingle, R. R., Gudmundson, E., Stoica, P., Nishimura, D. G. & Barral, J. K. (2014). Parameter estimation approach to banding artifact reduction in balanced steady-state free precession. Magnetic Resonance in Medicine, 72(3), 880-892
Open this publication in new window or tab >>Parameter estimation approach to banding artifact reduction in balanced steady-state free precession
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2014 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 72, no 3, p. 880-892Article in journal (Refereed) Published
Abstract [en]

Purpose: The balanced steady-state free precession (bSSFP) pulse sequence has shown to be of great interest due to its high signal-to-noise ratio efficiency. However, bSSFP images often suffer from banding artifacts due to off-resonance effects, which we aim to minimize in this article. Methods: We present a general and fast two-step algorithm for 1) estimating the unknowns in the bSSFP signal model from multiple phase-cycled acquisitions, and 2) reconstructing band-free images. The first step, linearization for off-resonance estimation (LORE), solves the nonlinear problem approximately by a robust linear approach. The second step applies a Gauss-Newton algorithm, initialized by LORE, to minimize the nonlinear least squares criterion. We name the full algorithm LORE-GN. Results: We derive the Cramer-Rao bound, a theoretical lower bound of the variance for any unbiased estimator, and show that LORE-GN is statistically efficient. Furthermore, we show that simultaneous estimation of T-1 and T-2 from phase-cycled bSSFP is difficult, since the Cramer-Rao bound is high at common signal-to-noise ratio. Using simulated, phantom, and in vivo data, we illustrate the band-reduction capabilities of LORE-GN compared to other techniques, such as sum-of-squares. Conclusion: Using LORE-GN we can successfully minimize banding artifacts in bSSFP.

National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-206701 (URN)10.1002/mrm.24986 (DOI)000340552700033 ()
Funder
EU, European Research Council, 247035
Available from: 2013-10-25 Created: 2013-09-03 Last updated: 2018-10-01Bibliographically approved
Björk, M. & Stoica, P. (2013). Fast denoising techniques for transverse relaxation time estimation in MRI. In: Proc. 21st European Signal Processing Conference: . Paper presented at 21st European Signal Processing Conference, Marrakesh, MOROCCO, Sept 09-13, 2013.
Open this publication in new window or tab >>Fast denoising techniques for transverse relaxation time estimation in MRI
2013 (English)In: Proc. 21st European Signal Processing Conference, 2013Conference paper, Published paper (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-205720 (URN)000341754500171 ()
Conference
21st European Signal Processing Conference, Marrakesh, MOROCCO, Sept 09-13, 2013
Funder
EU, European Research Council, 247035
Available from: 2013-08-22 Created: 2013-08-22 Last updated: 2018-10-01Bibliographically approved
Björk, M., Ingle, R. R., Barral, J. K., Gudmundson, E., Nishimura, D. G. & Stoica, P. (2012). Optimality of Equally-Spaced Phase Increments for Banding Removal in bSSFP. In: Proceedings of the ISMRM 20th annual meeting. Paper presented at ISMRM 20th Annual Meeting.
Open this publication in new window or tab >>Optimality of Equally-Spaced Phase Increments for Banding Removal in bSSFP
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2012 (English)In: Proceedings of the ISMRM 20th annual meeting, 2012Conference paper, Oral presentation with published abstract (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-180922 (URN)
Conference
ISMRM 20th Annual Meeting
Funder
EU, European Research Council, 247035
Available from: 2012-09-13 Created: 2012-09-13 Last updated: 2018-10-01
Ingle, R. R., Barral, J. K., Björk, M., Gudmundson, E., Stoica, P. & Nishimura, D. G. (2012). SNR Requirements for T1 and T2 Estimation using bSSFP. In: Proceedings of the ISMRM 20th annual meeting. Paper presented at ISMRM 20th Annual Meeting.
Open this publication in new window or tab >>SNR Requirements for T1 and T2 Estimation using bSSFP
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2012 (English)In: Proceedings of the ISMRM 20th annual meeting, 2012Conference paper, Oral presentation with published abstract (Refereed)
National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-180923 (URN)
Conference
ISMRM 20th Annual Meeting
Funder
EU, European Research Council, 247035
Available from: 2012-09-13 Created: 2012-09-13 Last updated: 2018-10-01
Björk, M., Berglund, J., Kullberg, J. & Stoica, P. (2011). Signal Modeling and the Cramér-Rao Bound for Absolute Magnetic Resonance Thermometry in Fat Tissue. In: Proc. 45th Asilomar Conference on Signals, Systems, and Computers. Paper presented at 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 6-9, 2011 (pp. 80-84).
Open this publication in new window or tab >>Signal Modeling and the Cramér-Rao Bound for Absolute Magnetic Resonance Thermometry in Fat Tissue
2011 (English)In: Proc. 45th Asilomar Conference on Signals, Systems, and Computers, 2011, p. 80-84Conference paper, Published paper (Refereed)
Abstract [en]

Magnetic Resonance Imaging of tissues with both fat and water resonances allows for absolute temperature mapping through parametric modeling. The fat resonance is used as a reference to determine the absolute water resonance frequency which is linearly related to the temperature. The goal of thispaper is to assess whether or not resonance frequency based absolute temperature mapping is feasible in fat tissue. This is done by examining identifiability conditions and analyzing the obtainable performance in terms of the Cramér-Rao Bound of the temperature estimates. We develop the model by including multiple fat peaks, since even small fat resonances can be significant compared to the small water component in fat tissue. It is showed that a high signal to noise ratio is needed for practical use on a 1.5 T scanner, and that higher field strengths can improve the bound significantly. It is also shown that the choice of sampling interval is important to avoid aliasing. In sum, this type of magnetic resonance thermometry is feasible for fat tissuein applications where high field strength is used or when high signal to noise ratio can be obtained.

National Category
Signal Processing
Identifiers
urn:nbn:se:uu:diva-163132 (URN)10.1109/ACSSC.2011.6189959 (DOI)
Conference
45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 6-9, 2011
Funder
EU, European Research Council, 247035
Available from: 2011-12-08 Created: 2011-12-08 Last updated: 2018-10-01Bibliographically approved
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