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  • 1. Berglund, Johan
    et al.
    Skorpil, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Multi-scale graph-cut algorithm for efficient water-fat separation2017In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, no 3, p. 941-949Article in journal (Refereed)
    Abstract [en]

    PURPOSE: To improve the accuracy and robustness to noise in water-fat separation by unifying the multiscale and graph cut based approaches to B0 -correction.

    METHODS: A previously proposed water-fat separation algorithm that corrects for B0 field inhomogeneity in 3D by a single quadratic pseudo-Boolean optimization (QPBO) graph cut was incorporated into a multi-scale framework, where field map solutions are propagated from coarse to fine scales for voxels that are not resolved by the graph cut. The accuracy of the single-scale and multi-scale QPBO algorithms was evaluated against benchmark reference datasets. The robustness to noise was evaluated by adding noise to the input data prior to water-fat separation.

    RESULTS: Both algorithms achieved the highest accuracy when compared with seven previously published methods, while computation times were acceptable for implementation in clinical routine. The multi-scale algorithm was more robust to noise than the single-scale algorithm, while causing only a small increase (+10%) of the reconstruction time.

    CONCLUSION: The proposed 3D multi-scale QPBO algorithm offers accurate water-fat separation, robustness to noise, and fast reconstruction. The software implementation is freely available to the research community. Magn Reson Med, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

  • 2.
    Brynolfsson, Patrik
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yu, Jun
    Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
    Wirestam, Ronnie
    Lund University.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. CJ Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, Netherlands.
    Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling2015In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 74, no 4, p. 1156-1164Article in journal (Refereed)
    Abstract [en]

    Purpose: The purpose of this study was to investigate, using simulations, a method for improved contrast agent (CA) quantification in DCE-MRI.

    Methods: We developed a maximum likelihood estimator that combines the phase signal in the DCE-MRI image series with an additional CA estimate, e.g. the estimate obtained from magnitude data. A number of simulations were performed to investigate the ability of the estimator to reduce bias and noise in CA estimates. Noise levels ranging from that of a body coil to that of a dedicated head coil were investigated at both 1.5T and 3T.

    Results: Using the proposed method, the root mean squared error in the bolus peak was reduced from 2.24 to 0.11 mM in the vessels and 0.16 to 0.08 mM in the tumor rim for a noise level equivalent of a 12-channel head coil at 3T. No improvements were seen for tissues with small CA uptake, such as white matter.

    Conclusion: Phase information reduces errors in the estimated CA concentrations. A larger phase response from higher field strengths or higher CA concentrations yielded better results. Issues such as background phase drift need to be addressed before this method can be applied in vivo.

  • 3.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Brynolfsson, Patrik
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Yu, Jun
    Sveriges lantbruksuniversitet, Centre of Biostochastiscs.
    Wirestam, Ronnie
    Lunds universitet, Medicinsk strålningsfysik.
    Johansson, Adam
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Asklund, Thomas
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Uncertainty estimation in dynamic contrast-enhanced MRI2013In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 69, no 4, p. 992-1002Article in journal (Refereed)
    Abstract [en]

    Using dynamic contrast-enhanced MRI (DCE-MRI), it is possible to estimate pharmacokinetic (PK) parameters that convey information about physiological properties, e.g., in tumors. In DCE-MRI, errors propagate in a nontrivial way to the PK parameters. We propose a method based on multivariate linear error propagation to calculate uncertainty maps for the PK parameters. Uncertainties in the PK parameters were investigated for the modified Kety model. The method was evaluated with Monte Carlo simulations and exemplified with in vivo brain tumor data. PK parameter uncertainties due to noise in dynamic data were accurately estimated. Noise with standard deviation up to 15% in the baseline signal and the baseline T1 map gave estimated uncertainties in good agreement with the Monte Carlo simulations. Good agreement was also found for up to 15% errors in the arterial input function amplitude. The method was less accurate for errors in the bolus arrival time with disagreements of 23%, 32%, and 29% for Ktrans, ve, and vp, respectively, when the standard deviation of the bolus arrival time error was 5.3 s. In conclusion, the proposed method provides efficient means for calculation of uncertainty maps, and it was applicable to a wide range of sources of uncertainty.

  • 4.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Tommy, Löfstedt
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Parameter estimation using weighted total least squares in the two-compartment exchange model2018In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 79, no 1, p. 561-567Article in journal (Refereed)
    Abstract [en]

    Purpose

    The linear least squares (LLS) estimator provides a fast approach to parameter estimation in the linearized two-compartment exchange model. However, the LLS method may introduce a bias through correlated noise in the system matrix of the model. The purpose of this work is to present a new estimator for the linearized two-compartment exchange model that takes this noise into account.

    Method

    To account for the noise in the system matrix, we developed an estimator based on the weighted total least squares (WTLS) method. Using simulations, the proposed WTLS estimator was compared, in terms of accuracy and precision, to an LLS estimator and a nonlinear least squares (NLLS) estimator.

    Results

    The WTLS method improved the accuracy compared to the LLS method to levels comparable to the NLLS method. This improvement was at the expense of increased computational time; however, the WTLS was still faster than the NLLS method. At high signal-to-noise ratio all methods provided similar precisions while inconclusive results were observed at low signal-to-noise ratio.

    Conclusion

    The proposed method provides improvements in accuracy compared to the LLS method, however, at an increased computational cost. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine.

  • 5.
    Garpebring, Anders
    et al.
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Wirestam, Ronnie
    Östlund, Nils
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Karlsson, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Effects of inflow and radiofrequency spoiling on the arterial input function in dynamic contrast-enhanced MRI: a combined phantom and simulation study2011In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 65, no 6, p. 1670-1679Article in journal (Refereed)
    Abstract [en]

    The arterial input function is crucial in pharmacokinetic analysis of dynamic contrast-enhanced MRI data. Among other artifacts in arterial input function quantification, the blood inflow effect and nonideal radiofrequency spoiling can induce large measurement errors with subsequent reduction of accuracy in the pharmacokinetic parameters. These errors were investigated for a 3D spoiled gradient-echo sequence using a pulsatile flow phantom and a total of 144 typical imaging settings. In the presence of large inflow effects, results showed poor average accuracy and large spread between imaging settings, when the standard spoiled gradient-echo signal equation was used in the analysis. For example, one of the investigated inflow conditions resulted in a mean error of about 40% and a spread, given by the coefficient of variation, of 20% for K(trans) . Minimizing inflow effects by appropriate slice placement, combined with compensation for nonideal radiofrequency spoiling, significantly improved the results, but they remained poorer than without flow (e.g., 3-4 times larger coefficient of variation for K(trans) ). It was concluded that the 3D spoiled gradient-echo sequence is not optimal for accurate arterial input function quantification and that correction for nonideal radiofrequency spoiling in combination with inflow minimizing slice placement should be used to reduce the errors. Magn Reson Med, 2011. © 2011 Wiley-Liss, Inc.

  • 6.
    Wezel, Joep
    et al.
    Leiden University Medical Center, Leiden, The Netherlands.
    Boer, Vincent O.
    University Medical Center Utrecht, Utrecht, The Netherlands.
    van der Velden, Tijl A.
    University Medical Center Utrecht, Utrecht, The Netherlands.
    Webb, Andrew G.
    Leiden University Medical Center, Leiden, The Netherlands.
    Klomp, Dennis W.J.
    University Medical Center Utrecht, Utrecht, The Netherlands.
    Versluis, Maarten J.
    Philips Healthcare Benelux, Eindhoven, The Netherlands.
    van Osch, Matthias J.P.
    Leiden University Medical Center, Leiden, The Netherlands.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Leiden University, Leiden University Medical Center, Leiden, The Netherlands.
    A comparison of navigators, snap-shot field monitoring, and probe-based field model training for correcting B0-induced artifacts in T2*-weighted images at 7 T2017In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, p. 1373-1382Article in journal (Refereed)
    Abstract [en]

    Purpose

    To compare methods for estimating B0 maps used in retrospective correction of high-resolution anatomical images at ultra-high field strength. The B0 maps were obtained using three methods: (1) 1D navigators and coil sensitivities, (2) field probe (FP) data and a low-order spherical harmonics model, and (3) FP data and a training-based model.

    Methods

    Data from nine subjects were acquired while they performed activities inducing B0 field fluctuations. Estimated B0 fields were compared with reference data, and the reductions of artifacts were compared in corrected T2* images.

    Results

    Reduction of sum-of-squares difference relative to a reference image was evaluated, and Method 1 yielded the largest artifact reduction: 27 ± 15%, 20 ± 18% (mean ± 1 standard deviation) for deep breathing and combined deep breathing and hand motion activities. Method 3 performed almost as well (24 ± 18%, 15 ± 17%), provided that adequate training data were used, and Method 2 gave a similar result (21 ± 16%, 19 ± 17%).

    Conclusion

    This study confirms that all of the investigated methods can be used in retrospective image correction. In terms of image quality, Method 1 had a small advantage, whereas the FP-based methods measured the B0 field slightly more accurately. The specific strengths and weaknesses of FPs and navigators should therefore be considered when determining which B0-estimation method to use. 

  • 7.
    Wezel, Joep
    et al.
    Leiden University Medical Center, Leiden, The Netherlands.
    Garpebring, Anders
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics. Leiden University, Leiden University Medical Center, Leiden, The Netherlands.
    Webb, Andrew G.
    Leiden University Medical Center, Leiden, The Netherlands.
    van Osch, Matthias J.P.
    Leiden University Medical Center, Leiden, The Netherlands.
    Beenakker, Jan-Willem M.
    Leiden University Medical Center, Leiden, The Netherlands.
    Automated eye blink detection and correction method for clinical MR eye imaging2017In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 78, no 1, p. 165-171Article in journal (Refereed)
    Abstract [en]

    Purpose: To implement an on-line monitoring system to detect eye blinks during ocular MRI using field probes, and to reacquire corrupted k-space lines by means of an automatic feedback system integrated with the MR scanner.

    Methods: Six healthy subjects were scanned on a 7 Tesla MRI whole-body system using a custom-built receive coil. Subjects were asked to blink multiple times during the MR-scan. The local magnetic field changes were detected with an external fluorine-based field probe which was positioned close to the eye. The eye blink produces a field shift greater than a threshold level, this was communicated in real-time to the MR system which immediately reacquired the motion-corrupted k-space lines.

    Results: The uncorrected images, using the original motion-corrupted data, showed severe artifacts, whereas the corrected images, using the reacquired data, provided an image quality similar to images acquired without blinks.

    Conclusion: Field probes can successfully detect eye blinks during MRI scans. By automatically reacquiring the eye blink-corrupted data, high quality MR-images of the eye can be acquired. 

  • 8. Wiesinger, Florian
    et al.
    Bylund, Mikael
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Yang, Jaewon
    Kaushik, Sandeep
    Shanbhag, Dattesh
    Ahn, Sangtae
    Jonsson, Joakim
    Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Lundman, Josef
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Hope, Thomas
    Nyholm, Tufve
    Umeå University, Faculty of Medicine, Department of Radiation Sciences. Uppsala University, Uppsala, Sweden.
    Larson, Peder
    Cozzini, Cristina
    Zero TE-based pseudo-CT image conversion in the head and its application in PET/MR attenuation correction and MR-guided radiation therapy planning2018In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 80, no 4, p. 1440-1451Article in journal (Refereed)
    Abstract [en]

    Purpose: To describe a method for converting Zero TE (ZTE) MR images into Xray attenuation information in the form of pseudo-CT images and demonstrate its performance for (1) attenuation correction (AC) in PET/MR and (2) dose planning in MR-guided radiation therapy planning (RTP).

    Methods: Proton density-weighted ZTE images were acquired as input for MRbased pseudo-CT conversion, providing (1) efficient capture of short-lived bone signals, (2) flat soft-tissue contrast, and (3) fast and robust 3D MR imaging. After bias correction and normalization, the images were segmented into bone, soft-tissue, and air by means of thresholding and morphological refinements. Fixed Hounsfield replacement values were assigned for air (-1000 HU) and soft-tissue (142 HU), whereas continuous linear mapping was used for bone.

    Results: The obtained ZTE-derived pseudo-CT images accurately resembled the true CT images (i. e., Dice coefficient for bone overlap of 0.73 +/- 0.08 and mean absolute error of 123 +/- 25 HU evaluated over the whole head, including errors from residual registration mismatches in the neck and mouth regions). The linear bone mapping accounted for bone density variations. Averaged across five patients, ZTE-based AC demonstrated a PET error of -0.04 +/- 1.68% relative to CT-based AC. Similarly, for RTP assessed in eight patients, the absolute dose difference over the target volume was found to be 0.23 +/- 0.42%.

    Conclusion: The described method enables MR to pseudo-CT image conversion for the head in an accurate, robust, and fast manner without relying on anatomical prior knowledge. Potential applications include PET/MR-AC, and MR-guided RTP.

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