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Karlsson, Mikael
Publications (10 of 112) Show all publications
Wallstén, E., Axelsson, J., Karlsson, M., Riklund, K. & Larsson, A. (2017). A Study of Dynamic PET Frame-Binning on the Reference Logan Binding Potential. IEEE Transactions on Radiation and Plasma Medical Sciences, 1(2), 128-135
Open this publication in new window or tab >>A Study of Dynamic PET Frame-Binning on the Reference Logan Binding Potential
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2017 (English)In: IEEE Transactions on Radiation and Plasma Medical Sciences, ISSN 2469-7311, Vol. 1, no 2, p. 128-135Article in journal (Refereed) Published
Abstract [en]

Objective: The reference Logan plot is a tool for determining the non-displaceable binding potential for dynamic PET exams using tracers with reversible bindings. Dynamic frame protocols affect noise in PET images and short frames can lead to quantitative uncertainties and noise-induced reconstruction bias. The aim of this study was to analyze the effect of frame binning on 11C-Raclopride striatal binding potential from reference Logan analysis. Methods: 12 healthy volunteers were scanned in list mode using 11C-raclopride, and the image data were reconstructed into 9 different frame binning schemes whereof 3 clinical schemes. Reconstruction was performed with 3 different algorithms, one based on filtered back projection (FBP) and two based on ordered subset expectation maximization (OSEM); one including resolution recovery. Logan plots were used for calculating the non-displaceable binding potential. Variation in binding potential was evaluated using Students t-tests. Results: It was found that frame lengths of up to 60 s gave significantly different results compared to the reference clinical protocol for OSEM, both with and without resolution recovery (maximum deviation: 10.3 % for the 15 s protocol). For FBP, frame lengths of up to 30 s gave significantly different results with a maximum deviation of 2.8 %. The higher sampling dependence of OSEM compared to FBP is likely due to noise-dependent bias in the OSEM algorithm, most apparent at high noise levels. Conclusions: Bias related to OSEM reconstruction of high-noise data is an important factor for dynamic PET protocols. Time frames of 120 s or more generate the most stable values for the striatum binding potential with the reference Logan plot for 11C-Raclopride brain PET.

Keywords
¹¹C-Raclopride, binding potential, dynamic frame protocol, frame binning, Logan analysis, positron emission tomography, time sampling
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-146397 (URN)10.1109/TNS.2016.2639560 (DOI)
Available from: 2018-04-09 Created: 2018-04-09 Last updated: 2018-06-09Bibliographically approved
Häggström, I., Axelsson, J., Schmidtlein, R., Karlsson, M., Garpebring, A., Johansson, L., . . . Larsson, A. (2015). A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET. Journal of Nuclear Medicine Technology, 43(1), 53-60
Open this publication in new window or tab >>A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET
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2015 (English)In: Journal of Nuclear Medicine Technology, ISSN 0091-4916, E-ISSN 1535-5675, Vol. 43, no 1, p. 53-60Article in journal (Refereed) Published
Abstract [en]

Compartmental modeling of dynamic PET data enables quantifi- cation of tracer kinetics in vivo, through the calculated model parameters. In this study, we aimed to investigate the effect of early frame sampling and reconstruction method on pharmacokinetic parameters obtained from a 2-tissue model, in terms of bias and uncertainty (SD). Methods: The GATE Monte Carlo software was used to simulate 2 · 15 dynamic 3′-deoxy-3′-18F-fluorothymidine (18F-FLT) brain PET studies, typical in terms of noise level and kinetic parameters. The data were reconstructed by both 3- dimensional (3D) filtered backprojection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM) into 6 dynamic image sets with different early frame durations of 1, 2, 4, 6, 10, and 15 s. Bias and SD were evaluated for fitted parameter estimates, calculated from regions of interest. Results: The 2-tissue-model parameter estimates K1, k2, and fraction of arterial blood in tissue depended on early frame sampling, and a sampling of 6–15 s generally minimized bias and SD. The shortest sampling of 1 s yielded a 25% and 42% larger bias than the other schemes, for 3DRP and OSEM, respectively, and a parameter uncertainty that was 10%–70% higher. The schemes from 4 to 15 s were generally not significantly different in regards to bias and SD. Typically, the reconstruction method 3DRP yielded less framesampling dependence and less uncertain results, compared with OSEM, but was on average more biased. Conclusion: Of the 6 sampling schemes investigated in this study, an early frame duration of 6–15 s generally kept both bias and uncertainty to a minimum, for both 3DRP and OSEM reconstructions. Veryshort frames of 1 s should be avoided because they typically resulted in the largest parameter bias and uncertainty. Furthermore, 3DRP may be p

Keywords
dynamic PET, Monte Carlo; GATE, compartment modeling, frame sampling
National Category
Other Physics Topics Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-95128 (URN)10.2967/jnmt.114.141754 (DOI)
Funder
Swedish National Infrastructure for Computing (SNIC), HPC2N-2009-001
Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2018-06-07Bibliographically approved
Brynolfsson, P., Nilsson, D., Henriksson, R., Hauksson, J., Karlsson, M., Garpebring, A., . . . Asklund, T. (2014). ADC texture-An imaging biomarker for high-grade glioma?. Medical physics (Lancaster), 41(10), 101903
Open this publication in new window or tab >>ADC texture-An imaging biomarker for high-grade glioma?
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2014 (English)In: Medical physics (Lancaster), ISSN 0094-2405, Vol. 41, no 10, p. 101903-Article in journal (Refereed) Published
Abstract [en]

Purpose:

Survival for high-grade gliomas is poor, at least partly explained by intratumoral heterogeneity contributing to treatment resistance. Radiological evaluation of treatment response is in most cases limited to assessment of tumor size months after the initiation of therapy. Diffusion-weighted magnetic resonance imaging (MRI) and its estimate of the apparent diffusion coefficient (ADC) has been widely investigated, as it reflects tumor cellularity and proliferation. The aim of this study was to investigate texture analysis of ADC images in conjunction with multivariate image analysis as a means for identification of pretreatment imaging biomarkers.

Methods:

Twenty-three consecutive high-grade glioma patients were treated with radiotherapy (2 Gy/60 Gy) with concomitant and adjuvant temozolomide. ADC maps and T1-weighted anatomical images with and without contrast enhancement were collected prior to treatment, and (residual) tumor contrast enhancement was delineated. A gray-level co-occurrence matrix analysis was performed on the ADC maps in a cuboid encapsulating the tumor in coronal, sagittal, and transversal planes, giving a total of 60 textural descriptors for each tumor. In addition, similar examinations and analyses were performed at day 1, week 2, and week 6 into treatment. Principal component analysis (PCA) was applied to reduce dimensionality of the data, and the five largest components (scores) were used in subsequent analyses. MRI assessment three months after completion of radiochemotherapy was used for classifying tumor progression or regression.

Results:

The score scatter plots revealed that the first, third, and fifth components of the pretreatment examinations exhibited a pattern that strongly correlated to survival. Two groups could be identified: one with a median survival after diagnosis of 1099 days and one with 345 days, p = 0.0001.

Conclusions:

By combining PCA and texture analysis, ADC texture characteristics were identified, which seems to hold pretreatment prognostic information, independent of known prognostic factors such as age, stage, and surgical procedure. These findings encourage further studies with a larger patient cohort. (C) 2014 Author(s).

Keywords
texture analysis, glioma, multivariate image analysis, ADC
National Category
Medical and Health Sciences
Identifiers
urn:nbn:se:umu:diva-96625 (URN)10.1118/1.4894812 (DOI)000343032400019 ()
Available from: 2014-11-27 Created: 2014-11-24 Last updated: 2018-06-07Bibliographically approved
Häggström, I., Schmidtlein, C. R., Karlsson, M. & Larsson, A. (2014). Compartment Modeling of Dynamic Brain PET: The Effect of Scatter Corrections on Parameter Errors. In: : . Paper presented at AAPM. AAPM
Open this publication in new window or tab >>Compartment Modeling of Dynamic Brain PET: The Effect of Scatter Corrections on Parameter Errors
2014 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Purpose: To investigate the effects of corrections for random and scattered coincidences on kinetic parameters in brain tumors, by using ten Monte Carlo (MC) simulated dynamic FLT-PET brain scans.

 

Methods: The GATE MC software was used to simulate ten repetitions of a 1 hour dynamic FLT-PET scan of a voxelized head phantom. The phantom comprised six normal head tissues, plus inserted regions for blood and tumor tissue. Different time-activity-curves (TACs) for all eight tissue types were used in the simulation and were generated in Matlab using a 2-tissue model with preset parameter values (K1,k2,k3,k4,Va,Ki). The PET data was reconstructed into 28 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection (3DFBP). Five image sets were reconstructed, all with normalization and different additional corrections C (A=attenuation, R=random, S=scatter): Trues (AC), trues+randoms (ARC), trues+scatters (ASC), total counts (ARSC) and total counts (AC). Corrections for randoms and scatters were based on real random and scatter sinograms that were back-projected, blurred and then forward projected and scaled to match the real counts. Weighted non-linear-least-squares fitting of TACs from the blood and tumor regions was used to obtain parameter estimates.

 

Results: The bias was not significantly different for trues (AC), trues+randoms (ARC), trues+scatters (ASC) and total counts (ARSC) for either 3DFBP or OSEM (p<0.05). Total counts with only AC stood out however, with an up to 160% larger bias. In general, there was no difference in bias found between 3DFBP and OSEM, except in parameter Va and Ki.

 

Conclusion: According to our results, the methodology of correcting the PET data for randoms and scatters performed well for the dynamic images where frames have much lower counts compared to static images. Generally, no bias was introduced by the corrections and their importance was emphasized since omitting them increased bias extensively.

Place, publisher, year, edition, pages
AAPM, 2014
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-98532 (URN)
Conference
AAPM
Funder
Swedish National Infrastructure for Computing (SNIC), 2014/1-260
Available from: 2015-01-23 Created: 2015-01-23 Last updated: 2018-06-07
Häggström, I., Schmidtlein, C. R., Karlsson, M. & Larsson, A. (2014). Compartment modeling of dynamic brain PET: the impact of scatter corrections on parameter errors. Medical physics, 41(11), 111907
Open this publication in new window or tab >>Compartment modeling of dynamic brain PET: the impact of scatter corrections on parameter errors
2014 (English)In: Medical physics, ISSN 0094-2405, Vol. 41, no 11, p. 111907-Article in journal (Refereed) Published
Abstract [en]

Purpose: The aim of this study was to investigate the effect of scatter and its correction on kinetic parameters in dynamic brain positron emission tomography (PET) tumor imaging. The 2-tissue compartment model was used, and two different reconstruction methods and two scatter correction (SC) schemes were investigated.

Methods: The gate Monte Carlo (MC) softwarewas used to perform 2×15 full PET scan simulations of a voxelized head phantom with inserted tumor regions. The two sets of kinetic parameters of all tissues were chosen to represent the 2-tissue compartment model for the tracer 3′-deoxy- 3′-(18F)fluorothymidine (FLT), and were denoted FLT1 and FLT2. PET data were reconstructed with both 3D filtered back-projection with reprojection (3DRP) and 3D ordered-subset expectation maximization (OSEM). Images including true coincidences with attenuation correction (AC) and true+scattered coincidences with AC and with and without one of two applied SC schemes were reconstructed. Kinetic parameters were estimated by weighted nonlinear least squares fitting of image derived time–activity curves. Calculated parameters were compared to the true input to the MC simulations.

Results: The relative parameter biases for scatter-eliminated data were 15%, 16%, 4%, 30%, 9%, and 7% (FLT1) and 13%, 6%, 1%, 46%, 12%, and 8% (FLT2) for K1, k2, k3, k4,Va, and Ki, respectively. As expected, SC was essential for most parameters since omitting it increased biases by 10 percentage points on average. SC was not found necessary for the estimation of Ki and k3, however. There was no significant difference in parameter biases between the two investigated SC schemes or from parameter biases from scatter-eliminated PET data. Furthermore, neither 3DRP nor OSEM yielded the smallest parameter biases consistently although therewas a slight favor for 3DRP which produced less biased k3 and Ki estimates while OSEM resulted in a less biased Va. The uncertainty in OSEM parameterswas about 26% (FLT1) and 12% (FLT2) larger than for 3DRP although identical postfilters were applied.

Conclusions: SC was important for good parameter estimations. Both investigated SC schemes performed equally well on average and properly corrected for the scattered radiation, without introducing further bias. Furthermore, 3DRP was slightly favorable over OSEM in terms of kinetic parameter biases and SDs.

Place, publisher, year, edition, pages
American Association of Physicists in Medicine, 2014
Keywords
compartment modeling; dynamic pet; monte carlo; scatter correction
National Category
Other Physics Topics Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-95115 (URN)10.1118/1.4897610 (DOI)000344999800028 ()
Funder
Swedish National Infrastructure for Computing (SNIC), 2013/1-234
Available from: 2014-10-22 Created: 2014-10-22 Last updated: 2018-06-07Bibliographically approved
Lindström, A., Andersson, C. D., Johansson, A., Karlsson, M. & Nyholm, T. (2013). Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters. Chemometrics and Intelligent Laboratory Systems, 125, 33-39
Open this publication in new window or tab >>Bone contrast optimization in magnetic resonance imaging using experimental design of ultra-short echo-time parameters
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2013 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 125, p. 33-39Article in journal (Refereed) Published
Abstract [en]

For the purpose of improved planning and treatment by radiation of tumours, we present work exploring the effect of controllable ultra-short echo-time (UTE) sequence settings on the bone contrast in magnetic resonance (MR) imaging, using design of experiments (DoE). Images were collected using UTE sequences from MR imaging and from standard computed tomography (CT). CT was used for determining the spatial position of the bony structures in an animal sample and co-registered with the MR images. The effect of the UTE sequence parameter flip angle (Flip), repetition time (T-R), echo time (T-E), image matrix size (Vox) and number of radial sampling spokes (Samp) were studied. The parameters were also investigated in a healthy voluntary and it was determined that the optimal UTE settings for high bone contrast in a clinically relevant set up were: Flip similar to 9 degrees and T-E = 0.07 ms, while T-R was kept at 8 ms, Vox at 192 and Samp at 30,000. The use of response surface maps, describing the modelled relation between bone contrast and UTE settings, founded in the DoE, may provide information and be a tool to more appropriately select suitable UTE sequence settings.

Place, publisher, year, edition, pages
Elsevier Science, 2013
Keywords
Magnetic resonance imaging, Ultra-short echo time, Experimental design, Bone contrast, Multiple linear regression
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-78438 (URN)10.1016/j.chemolab.2013.03.011 (DOI)000320217500004 ()
Available from: 2013-07-23 Created: 2013-07-22 Last updated: 2018-06-08Bibliographically approved
Häggström, I., Schmidtlein, C. R., Karlsson, M. & Larsson, A. (2013). Do scatter and random corrections affect the errors in kinetic parameters in dynamic PET?: a Monte Carlo study. In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC): . Paper presented at 60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging Conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, Seoul, South Korea, Oct 27-Nov 02, 2013. IEEE conference proceedings
Open this publication in new window or tab >>Do scatter and random corrections affect the errors in kinetic parameters in dynamic PET?: a Monte Carlo study
2013 (English)In: 2013 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), IEEE conference proceedings, 2013, , p. 4Conference paper, Published paper (Refereed)
Abstract [en]

Dynamic positron emission tomography (PET) data can be evaluated by compartmental models, yielding model specific kinetic parameters. For the parameters to be of quantitative use however, understanding and estimation of errors and uncertainties associated with them are crucial.

The aim in this study was to investigate the effects of the inclusion of scattered and random counts and their respective corrections on kinetic parameter errors.

The MC software GATE was used to simulate two dynamic PET scans of a phantom containing three regions; blood, tissue and a static background. The two sets of time-activity-curves (TACs) used were generated for a 2-tissue compartment model with preset parameter values (K1, k2, k3, k4 and Va). The PET data was reconstructed into 19 frames by both ordered-subset expectation maximization (OSEM) and 3D filtered back-projection with reprojection (3DFBPRP) with normalization and additional corrections (A=attenuation, R=random, S=scatter, C=correction): True counts (AC), true+random counts (ARC), true+scattered counts (ASC) and total counts (ARSC).

The results show that parameter estimates from true counts (AC), true+random counts (ARC), true+scattered counts (ASC) and total counts (ARSC) were not significantly different, with the exception of Va where the bias increased with added corrections. Thus, the inclusion of and correction for scattered and random counts did not affect the bias in parameter estimates K1, k2, k3, k4 and Ki. Uncorrected total counts (only AC) resulted in biases of hundreds or even thousands of percent, emphasizing the need for proper corrections. Reconstructions with 3DFBPRP resulted in overall 20-40% less biased estimates compared to OSEM.

Place, publisher, year, edition, pages
IEEE conference proceedings, 2013. p. 4
Keywords
PET, dynamic PET, Monte Carlo, GATE, compartment model, scatter correction, random correction, FLT
National Category
Medical Image Processing
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-98415 (URN)10.1109/NSSMIC.2013.6829388 (DOI)000347163501202 ()978-1-4799-0534-8 (ISBN)
Conference
60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging Conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, Seoul, South Korea, Oct 27-Nov 02, 2013
Funder
Swedish National Infrastructure for Computing (SNIC), 2013/1-234Swedish National Infrastructure for Computing (SNIC), HPC2N-2009-001
Available from: 2015-01-22 Created: 2015-01-22 Last updated: 2018-06-07
Johansson, A., Garpebring, A., Karlsson, M., Asklund, T. & Nyholm, T. (2013). Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information: potential application for MR-only radiotherapy and attenuation correction in positron emission tomography. Acta Oncologica, 52(7), 1369-1373
Open this publication in new window or tab >>Improved quality of computed tomography substitute derived from magnetic resonance (MR) data by incorporation of spatial information: potential application for MR-only radiotherapy and attenuation correction in positron emission tomography
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2013 (English)In: Acta Oncologica, ISSN 0284-186X, E-ISSN 1651-226X, Vol. 52, no 7, p. 1369-1373Article in journal (Refereed) Published
Abstract [en]

Background: Estimation of computed tomography (CT) equivalent data, i.e. a substitute CT (s-CT), from magnetic resonance (MR) images is a prerequisite both for attenuation correction of positron emission tomography (PET) data acquired with a PET/MR scanner and for dose calculations in an MR-only radiotherapy workflow. It has previously been shown that it is possible to estimate Hounsfield numbers based on MR image intensities, using ultra short echo-time imaging and Gaussian mixture regression (GMR). In the present pilot study we investigate the possibility to also include spatial information in the GMR, with the aim to improve the quality of the s-CT. Material and methods: MR and CT data for nine patients were used in the present study. For each patient, GMR models were created from the other eight patients, including either both UTE image intensities and spatial information on a voxel by voxel level, or only UTE image intensities. The models were used to create s-CT images for each respective patient. Results: The inclusion of spatial information in the GMR model improved the accuracy of the estimated s-CT. The improvement was most pronounced in smaller, complicated anatomical regions as the inner ear and post-nasal cavities. Conclusions: This pilot study shows that inclusion of spatial information in GMR models to convert MR data to CT equivalent images is feasible. The accuracy of the s-CT is improved and the spatial information could make it possible to create a general model for the conversion applicable to the whole body.

National Category
Radiology, Nuclear Medicine and Medical Imaging Cancer and Oncology
Identifiers
urn:nbn:se:umu:diva-81537 (URN)10.3109/0284186X.2013.819119 (DOI)000324776100016 ()23984810 (PubMedID)
Available from: 2013-10-14 Created: 2013-10-14 Last updated: 2018-06-08Bibliographically approved
Wallstén, E., Axelsson, J., Karlsson, M., Riklund, K., Nyberg, L., Häggström, I. & Larsson, A. (2013). The Influence of Time Sampling on Parameters in the Logan Plot. In: 2013 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC): . Paper presented at 60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging Conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, OCT 27-NOV 02, 2013, Seoul, SOUTH KOREA.
Open this publication in new window or tab >>The Influence of Time Sampling on Parameters in the Logan Plot
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2013 (English)In: 2013 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC), 2013Conference paper, Published paper (Refereed)
Abstract [en]

The Logan plot is a graphical method for reversible tracer bindings. The bias and uncertainties of this method have previously been analyzed with respect to noise, but little is known about the direct effects from varying the time sampling scheme. This study aims to investigate the effect of time sampling on the binding potential from the reference Logan plot. Image data from seven healthy subjects imaged with [11C]raclopride was reconstructed into six dynamic series of equal length time frames with frame times between 15 s and 480 s. Images were reconstructed using both filtered back projection (FBP) and a resolution enhanced ordered subset expectation maximization (OSEM) algorithm, SharpIR. For each sampling scheme, the nondisplaceable binding potential (BPND) parameter was calculated from the reference Logan plot with cerebellum as a reference region. The variation in BPND was analyzed as percentage deviations from the BPND for the 480 s scheme. R-2 of the linear fit was also analyzed. Comparison between all sampling schemes showed that the largest deviation in BPND was 7.4% between the 15 s sampling scheme and the 480 s sampling scheme reconstructed with SharpIR. The corresponding deviation for FBP images was 1.6%. R-2 was highest for long time frames, but all R-2 values were above 0.997 in this study.

National Category
Medical Image Processing Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:umu:diva-129852 (URN)10.1109/NSSMIC.2013.6829389 (DOI)000347163501203 ()978-1-4799-0534-8 (ISBN)
Conference
60th IEEE Nuclear Science Symposium (NSS) / Medical Imaging Conference (MIC) / 20th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, OCT 27-NOV 02, 2013, Seoul, SOUTH KOREA
Available from: 2017-01-11 Created: 2017-01-09 Last updated: 2018-06-09Bibliographically approved
Garpebring, A., Brynolfsson, P., Yu, J., Wirestam, R., Johansson, A., Asklund, T. & Karlsson, M. (2013). Uncertainty estimation in dynamic contrast-enhanced MRI. Magnetic Resonance in Medicine, 69(4), 992-1002
Open this publication in new window or tab >>Uncertainty estimation in dynamic contrast-enhanced MRI
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2013 (English)In: Magnetic Resonance in Medicine, ISSN 0740-3194, E-ISSN 1522-2594, Vol. 69, no 4, p. 992-1002Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2013
Keywords
Uncertainty estimation, dynamic contrast-enhanced-MRI, precision analysis, accuracy
National Category
Medical Image Processing Probability Theory and Statistics
Research subject
radiofysik
Identifiers
urn:nbn:se:umu:diva-49758 (URN)10.1002/mrm.24328 (DOI)000316629300013 ()
Available from: 2011-11-17 Created: 2011-11-17 Last updated: 2018-06-08Bibliographically approved
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