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1.	Björnfot, Cecilia, et al. (författare) Assessing cerebral arterial pulse wave velocity using 4D flow MRI 2021 Ingår i: Journal of Cerebral Blood Flow and Metabolism. - : Sage Publications. - 0271-678X .- 1559-7016. ; 41:10, s. 2769-2777 Tidskriftsartikel (refereegranskat)abstract Intracranial arterial stiffening is a potential early marker of emerging cerebrovascular dysfunction and could be mechanistically involved in disease processes detrimental to brain function via several pathways. A prominent consequence of arterial wall stiffening is the increased velocity at which the systolic pressure pulse wave propagates through the vasculature. Previous non-invasive measurements of the pulse wave propagation have been performed on the aorta or extracranial arteries with results linking increased pulse wave velocity to brain pathology. However, there is a lack of intracranial “target-organ” measurements. Here we present a 4D flow MRI method to estimate pulse wave velocity in the intracranial vascular tree. The method utilizes the full detectable branching structure of the cerebral vascular tree in an optimization framework that exploits small temporal shifts that exists between waveforms sampled at varying depths in the vasculature. The method is shown to be stable in an internal consistency test, and of sufficient sensitivity to robustly detect age-related increases in intracranial pulse wave velocity.
2.	Björnfot, Cecilia, et al. (författare) Cerebral arterial stiffness is linked to white matter hyperintensities and perivascular spaces in older adults : a 4D flow MRI study 2024 Ingår i: Journal of Cerebral Blood Flow and Metabolism. - : Sage Publications. - 0271-678X .- 1559-7016. Tidskriftsartikel (refereegranskat)abstract White matter hyperintensities (WMH), perivascular spaces (PVS) and lacunes are common MRI features of small vessel disease (SVD). However, no shared underlying pathological mechanism has been identified. We investigated whether SVD burden, in terms of WMH, PVS and lacune status, was related to changes in the cerebral arterial wall by applying global cerebral pulse wave velocity (gcPWV) measurements, a newly described marker of cerebral vascular stiffness. In a population-based cohort of 190 individuals, 66–85 years old, SVD features were estimated from T1-weighted and FLAIR images while gcPWV was estimated from 4D flow MRI data. Additionally, the gcPWV’s stability to variations in field-of-view was analyzed. The gcPWV was 10.82 (3.94) m/s and displayed a significant correlation to WMH and white matter PVS volume (r = 0.29, p < 0.001; r = 0.21, p = 0.004 respectively from nonparametric tests) that persisted after adjusting for age, blood pressure variables, body mass index, ApoB/A1 ratio, smoking as well as cerebral pulsatility index, a previously suggested early marker of SVD. The gcPWV displayed satisfactory stability to field-of-view variations. Our results suggest that SVD is accompanied by changes in the cerebral arterial wall that can be captured by considering the velocity of the pulse wave transmission through the cerebral arterial network.
3.	Vikner, Tomas, et al. (författare) Blood-brain barrier permeability, vascular density, and cerebral 4D flow MRI hemodynamics in a population-based elderly cohort Annan publikation (övrigt vetenskapligt/konstnärligt)
4.	Adjeiwaah, Mary, 1980-, et al. (författare) Dosimetric Impact of MRI Distortions : A Study on Head and Neck Cancers 2019 Ingår i: International Journal of Radiation Oncology, Biology, Physics. - : Elsevier. - 0360-3016 .- 1879-355X. ; 103:4, s. 994-1003 Tidskriftsartikel (refereegranskat)abstract Purpose: To evaluate the effect of magnetic resonance (MR) imaging (MRI) geometric distortions on head and neck radiation therapy treatment planning (RTP) for an MRI-only RTP. We also assessed the potential benefits of patient-specific shimming to reduce the magnitude of MR distortions for a 3-T scanner.Methods and Materials: Using an in-house Matlab algorithm, shimming within entire imaging volumes and user-defined regions of interest were simulated. We deformed 21 patient computed tomography (CT) images with MR distortion fields (gradient nonlinearity and patient-induced susceptibility effects) to create distorted CT (dCT) images using bandwidths of 122 and 488 Hz/mm at 3 T. Field parameters from volumetric modulated arc therapy plans initially optimized on dCT data sets were transferred to CT data to compute a new plan. Both plans were compared to determine the impact of distortions on dose distributions.Results: Shimming across entire patient volumes decreased the percentage of voxels with distortions of more than 2 mm from 15.4% to 2.0%. Using the user-defined region of interest (ROI) shimming strategy, (here the Planning target volume (PTV) was the chosen ROI volume) led to increased geometric for volumes outside the PTV, as such voxels within the spinal cord with geometric shifts above 2 mm increased from 11.5% to 32.3%. The worst phantom-measured residual system distortions after 3-dimensional gradient nonlinearity correction within a radial distance of 200 mm from the isocenter was 2.17 mm. For all patients, voxels with distortion shifts of more than 2 mm resulting from patient-induced susceptibility effects were 15.4% and 0.0% using bandwidths of 122 Hz/mm and 488 Hz/mm at 3 T. Dose differences between dCT and CT treatment plans in D-50 at the planning target volume were 0.4% +/- 0.6% and 0.3% +/- 0.5% at 122 and 488 Hz/mm, respectively.Conclusions: The overall effect of MRI geometric distortions on data used for RTP was minimal. Shimming over entire imaging volumes decreased distortions, but user-defined subvolume shimming introduced significant errors in nearby organs and should probably be avoided.
5.	Adjeiwaah, Mary, 1980- (författare) Quality assurance for magnetic resonance imaging (MRI) in radiotherapy 2019 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract The use of Magnetic Resonance Imaging (MRI) in the radiotherapy (RT) treatment planning workflow is increasing. MRI offers superior soft-tissue contrast compared to Computed Tomography (CT) and therefore improves the accuracy in target volume definitions. There are, however concerns with inherent geometric distortions from system- (gradient nonlinearities and main magnetic field inhomogeneities) and patient-related sources (magnetic susceptibility effect and chemical shift). The lack of clearly defined quality assurance (QA) procedures has also raised questions on the ability of current QA protocols to detect common image quality degradations under radiotherapy settings. To fully implement and take advantage of the benefits of MRI in radiotherapy, these concerns need to be addressed.In Papers I and II, the dosimetric impact of MR distortions was investigated. Patient CTs (CT) were deformed with MR distortion vector fields (from the residual system distortions after correcting for gradient nonlinearities and patient-induced susceptibility distortions) to create distorted CT (dCT) images. Field parameters from volumetric modulated arc therapy (VMAT) treatment plans initially optimized on dCT data sets were transferred to CT data to compute new treatment plans. Data from 19 prostate and 21 head and neck patients were used for the treatment planning. The dCT and CT treatment plans were compared to determine the impact of distortions on dose distributions. No clinically relevant dose differences between distorted CT and original CT treatment plans were found. Mean dose differences were < 1.0% and < 0.5% at the planning target volume (PTV) for the head and neck, and prostate treatment plans, respectively. Strategies to reduce geometric distortions were also evaluated in Papers I and II. Using the vendor-supplied gradient non-linearity correction algorithm reduced overall distortions to less than half of the original value. A high acquisition bandwidth of 488 Hz/pixel (Paper I) and 488 Hz/mm (Paper II) kept the mean geometric distortions at the delineated structures below 1 mm. Furthermore, a patient-specific active shimming method implemented in Paper II significantly reduced the number of voxels with distortion shifts > 2 mm from 15.4% to 2.0%.B0 maps from patient-induced magnetic field inhomogeneities obtained through direct measurements and by simulations that used MR-generated synthetic CT (sCT) data were compared in Paper III. The validation showed excellent agreement between the simulated and measured B0 maps.In Paper IV, the ability of current QA methods to detect common MR image quality degradations under radiotherapy settings were investigated. By evaluating key image quality parameters, the QA protocols were found to be sensitive to some of the introduced degradations. However, image quality issues such as those caused by RF coil failures could not be adequately detected.In conclusion, this work has shown the feasibility of using MRI data for radiotherapy treatment planning as distortions resulted in a dose difference of less than 1% between distorted and undistorted images. The simulation software can be used to produce accurate B0 maps, which could then be used as the basis for the effective correction of patient-induced field inhomogeneity distortions and for the QA verification of sCT data. Furthermore, the analysis of the strengths and weaknesses in current QA tools for MRI in RT contribute to finding better methods to efficiently identify image quality errors.
6.	Adjeiwaah, Mary, 1980-, et al. (författare) Quality Assurance for MRI in Radiotherapy : Sensitivity Analysis of Current Methods Annan publikation (övrigt vetenskapligt/konstnärligt)
7.	Adjeiwaah, Mary, 1980-, et al. (författare) Sensitivity analysis of different quality assurance methods for magnetic resonance imaging in radiotherapy 2020 Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 13, s. 21-27 Tidskriftsartikel (refereegranskat)abstract Background and purpose: There are currently no standard quality assurance (QA) methods for magnetic resonance imaging (MRI) in radiotherapy (RT). This work was aimed at evaluating the ability of two QA protocols to detect common events that affect quality of MR images under RT settings.Materials and methods: The American College of Radiology (ACR) MRI QA phantom was repeatedly scanned using a flexible coil and action limits for key image quality parameters were derived. Using an exploratory survey, issues that reduce MR image quality were identified. The most commonly occurring events were introduced as provocations to produce MR images with degraded quality. From these images, detection sensitivities of the ACR MRI QA protocol and a commercial geometric accuracy phantom were determined.Results: Machine-specific action limits for key image quality parameters set at mean±3σ" role="presentation" style="box-sizing: border-box; margin: 0px; padding: 0px; display: inline-block; line-height: normal; font-size: 16.2px; word-spacing: normal; overflow-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; position: relative;">mean±3σ were comparable with the ACR acceptable values. For the geometric accuracy phantom, provocations from uncorrected gradient nonlinearity effects and a piece of metal in the bore of the scanner resulted in worst distortions of 22.2 mm and 3.4 mm, respectively. The ACR phantom was sensitive to uncorrected signal variations, electric interference and a piece of metal in the bore of the scanner but could not adequately detect individual coil element failures.Conclusions: The ACR MRI QA phantom combined with the large field-of-view commercial geometric accuracy phantom were generally sensitive in identifying some common MR image quality issues. The two protocols when combined may provide a tool to monitor the performance of MRI systems in the radiotherapy environment.
8.	Adjeiwaah, Mary, 1980-, et al. (författare) Technical Note : Comparison between simulated and measured B0 field maps of head and neck MRI Annan publikation (övrigt vetenskapligt/konstnärligt)
9.	Asklund, Thomas, et al. (författare) Evaluation of advanced MR techniques for development of early biomarkers for treatment efficacy in malignant brain tumors 2010 Ingår i: Neuro-Oncology. - : Oxford University Press (OUP). - 1523-5866. Konferensbidrag (refereegranskat)
10.	Bayisa, Fekadu L., et al. (författare) Computed Tomography Image Estimation by Statistical Learning Methods Annan publikation (övrigt vetenskapligt/konstnärligt)abstract There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images canbe utilised for attenuation correction, patient positioning, and dose planningin diagnostic and radiotherapy workflows. This study presents a statisticallearning method for CT image estimation. We have used predefined tissuetype information in a Gaussian mixture model to explore the estimation.The performance of our method was evaluated using cross-validation on realdata. In comparison with the existing model-based CT image estimationmethods, the proposed method has improved the estimation, particularly inbone tissues. Evaluation of our method shows that it is a promising methodto generate CT image substitutes for the implementation of fully MR-basedradiotherapy and PET/MRI applications.
11.	Bayisa, Fekadu, et al. (författare) Statistical learning in computed tomography image estimation 2018 Ingår i: Medical physics (Lancaster). - : John Wiley & Sons. - 0094-2405 .- 2473-4209. ; 45:12, s. 5450-5460 Tidskriftsartikel (refereegranskat)abstract Purpose: There is increasing interest in computed tomography (CT) image estimations from magneticresonance (MR) images. The estimated CT images can be utilized for attenuation correction, patientpositioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introducea novel statistical learning approach for improving CT estimation from MR images and to compare theperformance of our method with the existing model-based CT image estimation methods.Methods: The statistical learning approach proposed here consists of two stages. At the trainingstage, prior knowledge about tissue types from CT images was used together with a Gaussian mixturemodel (GMM) to explore CT image estimations from MR images. Since the prior knowledge is notavailable at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimatethe tissue types from MR images. For a new patient, the trained classifier and GMMs were used topredict CT image from MR images. The classifier and GMMs were validated by using voxel-leveltenfold cross-validation and patient-level leave-one-out cross-validation, respectively.Results: The proposed approach has outperformance in CT estimation quality in comparison withthe existing model-based methods, especially on bone tissues. Our method improved CT image estimationby 5% and 23% on the whole brain and bone tissues, respectively.Conclusions: Evaluation of our method shows that it is a promising method to generate CTimage substitutes for the implementation of fully MR-based radiotherapy and PET/MRI applications
12.	Brynolfsson, Patrik, et al. (författare) ADC texture-An imaging biomarker for high-grade glioma? 2014 Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 41:10, s. 101903- Tidskriftsartikel (refereegranskat)abstract 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).
13.	Brynolfsson, Patrik, 1981- (författare) Applications of statistical methods in quantitative magnetic resonance imaging 2017 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Magnetic resonance imaging, MRI, offers a vast range of imaging methods that can be employed in the characterization of tumors. MRI is generally used in a qualitative way, where radiologists interpret the images for e.g. diagnosis, follow ups, or assessment of treatment response. In the past decade, there has been an increasing interest for quantitative imaging, which give repeatable measurements of the anatomy. Quantitative imaging allows for objective analysis of the images, which are grounded in physical properties of the underlying tissues. The aim of this thesis was to improve quantitative measurements of Dynamic contrast enhanced MRI (DCE-MRI), and the texture analysis of diffusion weighted MRI (DW-MRI).DCE-MRI measures perfusion, which is the delivery of blood, oxygen and nutrients to the tissues. The exam involves continuously imaging the region of interest, e.g. a tumor, while injecting a contrast agent (CA) in the blood stream. By analyzing how fast and how much CA leaks out into the tissues, the cell density and the permeability of the capillaries can be estimated. Tumors often have an irregular and broken vasculature, and DCE-MRI can aid in tumor grading or treatment assessment. One step is crucial when performing DCE-MRI analysis, the quantification of CA in the tissue. The CA concentration is difficult to measure accurately due to uncertainties in the imaging, properties of the CA, and physiology of the patient. Paper I, the possibility of using two aspects of the MRI data, phase and magnitude, for improved CA quantification, is explored. We found that the combination of phase and magnitude information improved the CA quantification in regions with high CA concentration, and was more advantageous for high field strength scanners.DW-MRI measures the diffusion of water in and between cells, which reflects the cell density and structure of the tissue. The structure of a tumor can give insights into the prognosis of the disease. Tumors are heterogeneous, both genetically and in the distribution of cells, and tumors with high intratumoral heterogeneity have poorer prognosis. This heterogeneity can be measured using texture analysis. In 1973, Haralick et al. presented a texture analysis method using a gray level co-occurrence matrix, GLCM, to gauge the spatial distribution of gray levels in the image. This method of assessing texture in images has been successfully applied in many areas of research, from satellite images to medical applications. Texture analysis in treatment outcome assessment is studied in Paper II, where we showed that texture can distinguish between groups of patients with different survival times, in images acquired prior to treatment start.However, this type of texture analysis is not inherently quantitative in the way it is calculated today. This was studied in Paper III, where we investigated how texture features were affected by five parameters related to image acquisition and pre-processing. We found that the texture feature values were dependent on the choice of these imaging and preprocessing parameters. In Paper IV, a novel method for calculating Haralick texture features was presented, which makes the texture features asymptotically invariant to the size of the GLCM. This method allows for comparison of textures between images that have been analyzed in different ways.In conclusion, the work in this thesis has been aimed at improving quantitative analysis of tumors using MRI and texture analysis.
14.	Brynolfsson, Patrik, et al. (författare) Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using statistical modeling 2015 Ingår i: Magnetic Resonance in Medicine. - : Wiley-Blackwell. - 0740-3194 .- 1522-2594. ; 74:4, s. 1156-1164 Tidskriftsartikel (refereegranskat)abstract 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.
15.	Brynolfsson, Patrik, et al. (författare) Gray-level invariant Haralick texture features 2018 Ingår i: Radiotherapy and Oncology. - : Elsevier. - 0167-8140 .- 1879-0887. ; 127, s. S279-S280 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
16.	Brynolfsson, Patrik, et al. (författare) Haralick texture features from apparent diffusion coefficient (ADC) MRI images depend on imaging and pre-processing parameters 2017 Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 7 Tidskriftsartikel (refereegranskat)abstract In recent years, texture analysis of medical images has become increasingly popular in studies investigating diagnosis, classification and treatment response assessment of cancerous disease. Despite numerous applications in oncology and medical imaging in general, there is no consensus regarding texture analysis workflow, or reporting of parameter settings crucial for replication of results. The aim of this study was to assess how sensitive Haralick texture features of apparent diffusion coefficient (ADC) MR images are to changes in five parameters related to image acquisition and pre-processing: noise, resolution, how the ADC map is constructed, the choice of quantization method, and the number of gray levels in the quantized image. We found that noise, resolution, choice of quantization method and the number of gray levels in the quantized images had a significant influence on most texture features, and that the effect size varied between different features. Different methods for constructing the ADC maps did not have an impact on any texture feature. Based on our results, we recommend using images with similar resolutions and noise levels, using one quantization method, and the same number of gray levels in all quantized images, to make meaningful comparisons of texture feature results between different subjects.
17.	Bylund, Mikael, et al. (författare) Using deep learning to generate synthetic CTs for radiotherapy treatment planning 2018 Ingår i: Radiotherapy and Oncology. - : Elsevier. - 0167-8140 .- 1879-0887. ; 127, s. S283-S283 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
18.	Frankel, Jennifer, 1981- (författare) Characterization of the MRI patient exposure environment and exposure assessment methods for magnetic fields in MRI scanners 2021 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Magnetic resonance imaging (MRI) has become one of the most common imaging modalities available in modern medicine, and it is an indispensable diagnostic tool thanks to the unparalleled soft-tissue contrast and high image resolution. It is also a unique exposure environment consisting of a complex mix of magnetic fields. During an MRI scan, the patient is simultaneously exposed to a strong static magnetic field, a fast-switching gradient magnetic field, and a pulsed radiofrequency (RF) magnetic field. Transient acute effects, such as nerve excitation and tissue heating, are well known and limited by universal safety guidelines. Long-term health effects related to MRI exposure have, however, not been scientifically established, and no interaction mechanisms have been verified, despite a growing body of research on electromagnetic field exposure. Further epidemiological and experimental research on MRI exposure has been recommended but the lack of a common definition of dose or exposure metric makes evaluation of past research and the design of future experiments difficult.The objectives of this thesis were to characterize the MRI patient exposure environment in terms of the magnetic fields involved, suggest relevant exposure metrics, and introduce exposure assessment methods suitable for epidemiological and experimental research on MRI and long-term health effects.In Paper I, we discussed the MRI exposure environment and its complexity and gave an overview of the current scientific situation. In Paper II, we investigated the exposure variability between different MRI sequences and suggested patient-independent exposure metrics that describe different characteristics of the magnetic field exposure, including mean, peak, and threshold values. In Paper III, we presented three exposure assessment methods, specifically suited to the complex MRI exposure environment: a measurement-based method, a calculation-based method, and a proxy method.Papers I and II showed that MRI exams are not homogenous in terms of exposure, and exposure variability exists between the individual sequences that comprise an exam. Differences in mean exposure between sequences were several-fold, peak exposure differences up to 30-fold, and differences in threshold exposure were in some cases more than 100-fold. Furthermore, within-sequence exposure variability, related to the parameter adjustments that can be made at the scanner console before the start of a scan, gave rise to 5-to-8-fold exposure increases. Paper III showed that magnetic field models could be used to approximate the exposure at arbitrary locations inside the scanner, with slight underestimation of gradient field metrics and large variability in some RF field metrics. With improvements in accuracy and efficiency, the method could become a useful exposure assessment tool for in vitro and in vivo research as well as clinical work on medical implant safety. Our search for suitable exposure metric proxies resulted in a limited selection with low correlation between proxies and their counterpart metrics, but, with further development, the proxy method has the potential to allow for much needed exposure classification relevant to large-scale epidemiological research.The work in this thesis has contributed to increased awareness of the unique MRI exposure environment, the characteristics of the magnetic fields involved, and the inherent exposure variability in MRI exams. The metrics and methods presented are specifically suited to exposure assessment of the unique MRI environment, and may contribute to improved research quality by allowing for meaningful comparisons between study results and for experimental conditions to be easily replicated in future studies.
19.	Frankel, Jennifer, et al. (författare) Exposure assessment methods for magnetic fields in MRI Annan publikation (övrigt vetenskapligt/konstnärligt)
20.	Garpebring, Anders, et al. (författare) A novel estimation method for physiological parameters in dynamic contrast-enhanced MRI : application of a distributed parameter model using Fourier-domain calculations 2009 Ingår i: IEEE Transactions on Medical Imaging. - 0278-0062 .- 1558-254X. ; 28:9, s. 1375-1383 Tidskriftsartikel (refereegranskat)abstract Dynamic contrast-enhanced magnetic resonance imaging (MRI) is a promising tool in the evaluation of tumor physiology. From rapidly acquired images and a model for contrast agent pharmacokinetics, physiological parameters are derived. One pharmacokinetic model, the tissue homogeneity model, enables estimation of both blood flow and vessel permeability together with parameters that describe blood volume and extracellular extravascular volume fraction. However, studies have shown that parameter estimation with this model is unstable. Therefore, several initial guesses are needed for accurate estimates, which makes the estimation slow. In this study a new estimation algorithm for the tissue homogeneity model, based on Fourier domain calculations, was derived and implemented as a Matlab program. The algorithm was tested with Monte-Carlo simulations and the results were compared to an existing method that uses the adiabatic approximation. The algorithm was also tested on data from a metastasis in the brain. The comparison showed that the new algorithm gave more accurate results on the 2.5th and 97.5th percentile levels, for instance the error in blood volume was reduced by 21%. In addition, the time needed for the computations was reduced with a factor 25. It was concluded that the new algorithm can be used to speed up parameter estimation while accuracy can be gained at the same time.
21.	Garpebring, Anders, 1980- (författare) Contributions to quantitative dynamic contrast-enhanced MRI 2011 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Background: Dynamic contrast-enhanced MRI (DCE-MRI) has the potential to produce images of physiological quantities such as blood flow, blood vessel volume fraction, and blood vessel permeability. Such information is highly valuable, e.g., in oncology. The focus of this work was to improve the quantitative aspects of DCE-MRI in terms of better understanding of error sources and their effect on estimated physiological quantities. Methods: Firstly, a novel parameter estimation algorithm was developed to overcome a problem with sensitivity to the initial guess in parameter estimation with a specific pharmacokinetic model. Secondly, the accuracy of the arterial input function (AIF), i.e., the estimated arterial blood contrast agent concentration, was evaluated in a phantom environment for a standard magnitude-based AIF method commonly used in vivo. The accuracy was also evaluated in vivo for a phase-based method that has previously shown very promising results in phantoms and in animal studies. Finally, a method was developed for estimation of uncertainties in the estimated physiological quantities. Results: The new parameter estimation algorithm enabled significantly faster parameter estimation, thus making it more feasible to obtain blood flow and permeability maps from a DCE-MRI study. The evaluation of the AIF measurements revealed that inflow effects and non-ideal radiofrequency spoiling seriously degrade magnitude-based AIFs and that proper slice placement and improved signal models can reduce this effect. It was also shown that phase-based AIFs can be a feasible alternative provided that the observed difficulties in quantifying low concentrations can be resolved. The uncertainty estimation method was able to accurately quantify how a variety of different errors propagate to uncertainty in the estimated physiological quantities. Conclusion: This work contributes to a better understanding of parameter estimation and AIF quantification in DCE-MRI. The proposed uncertainty estimation method can be used to efficiently calculate uncertainties in the parametric maps obtained in DCE-MRI.
22.	Garpebring, Anders, et al. (författare) Density Estimation of Grey-Level Co-Occurrence Matrices for Image Texture Analysis 2018 Ingår i: Physics in Medicine and Biology. - : Institute of Physics and Engineering in Medicine. - 0031-9155 .- 1361-6560. ; 63:19, s. 9-15 Tidskriftsartikel (refereegranskat)abstract The Haralick texture features are common in the image analysis literature, partly because of their simplicity and because their values can be interpreted. It was recently observed that the Haralick texture features are very sensitive to the size of the GLCM that was used to compute them, which led to a new formulation that is invariant to the GLCM size. However, these new features still depend on the sample size used to compute the GLCM, i.e. the size of the input image region-of-interest (ROI).The purpose of this work was to investigate the performance of density estimation methods for approximating the GLCM and subsequently the corresponding invariant features.Three density estimation methods were evaluated, namely a piece-wise constant distribution, the Parzen-windows method, and the Gaussian mixture model. The methods were evaluated on 29 different image textures and 20 invariant Haralick texture features as well as a wide range of different ROI sizes.The results indicate that there are two types of features: those that have a clear minimum error for a particular GLCM size for each ROI size, and those whose error decreases monotonically with increased GLCM size. For the first type of features, the Gaussian mixture model gave the smallest errors, and in particular for small ROI sizes (less than about 20×20).In conclusion, the Gaussian mixture model is the preferred method for the first type of features (in particular for small ROIs). For the second type of features, simply using a large GLCM size is preferred.
23.	Garpebring, Anders, et al. (författare) Effects of Inflow and Radiofrequency Spoiling on the Arterial Input Function in Dynamic Contrast-Enhanced MRI: A Combined Phantom and Simulation Study 2011 Ingår i: Magnetic Resonance in Medicine. - : Wiley. - 1522-2594 .- 0740-3194. ; 65:6, s. 1670-1679 Tidskriftsartikel (refereegranskat)abstract 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 65:1670-1679, 2011. (C)2011 Wiley-Liss, Inc.
24.	Garpebring, Anders (författare) MRI based radiotherapy - what can go wrong and how to QA? 2018 Ingår i: Radiotherapy and Oncology. - : Elsevier. - 0167-8140 .- 1879-0887. ; 127, s. S339-S339 Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)
25.	Garpebring, Anders, et al. (författare) Parameter estimation using weighted total least squares in the two-compartment exchange model 2018 Ingår i: Magnetic Resonance in Medicine. - : John Wiley & Sons. - 0740-3194 .- 1522-2594. ; 79:1, s. 561-567 Tidskriftsartikel (refereegranskat)abstract PurposeThe 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.MethodTo 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.ResultsThe 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.ConclusionThe 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.
26.	Garpebring, Anders, et al. (författare) Phase-based arterial input functions in humans applied to dynamic contrast-enhanced MRI: potential usefulness and limitations 2011 Ingår i: Magma. - : Springer Science and Business Media LLC. - 1352-8661 .- 0968-5243. ; 24:4, s. 233-245 Tidskriftsartikel (refereegranskat)abstract Object Phase-based arterial input functions (AIFs) provide a promising alternative to standard magnitude-based AIFs, for example, because inflow effects are avoided. The usefulness of phase-based AIFs in clinical dynamic contrast-enhanced MRI (DCE-MRI) was investigated, and relevant pitfalls and sources of uncertainty were identified. Materials and methods AIFs were registered from eight human subjects on, in total, 21 occasions. AIF quality was evaluated by comparing AIFs from right and left internal carotid arteries and by assessing the reliability of blood plasma volume estimates. Results Phase-based AIFs yielded an average bolus peak of 3.9 mM and a residual concentration of 0.37 mM after 3 min, (0.033 mmol/kg contrast agent injection). The average blood plasma volume was 2.7% when using the AIF peak in the estimation, but was significantly different (p < 0.0001) and less physiologically reasonable when based on the AIF tail concentration. Motion-induced phase shifts and accumulation of contrast agent in background tissue regions were identified as main sources of uncertainty. Conclusion Phase-based AIFs are a feasible alternative to magnitude AIFs, but sources of errors exist, making quantification difficult, especially of the AIF tail. Improvement of the technique is feasible and also required for the phase-based AIF approach to reach its full potential.
27.	Garpebring, Anders, et al. (författare) Uncertainty estimation in dynamic contrast-enhanced MRI 2013 Ingår i: Magnetic Resonance in Medicine. - : Wiley-Blackwell. - 0740-3194 .- 1522-2594. ; 69:4, s. 992-1002 Tidskriftsartikel (refereegranskat)abstract 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.
28.	Garpebring, Anders, et al. (författare) Uncertainty Maps in Dynamic Contrast-Enhanced MRI 2012 Konferensbidrag (refereegranskat)
29.	Hellström, Max, et al. (författare) Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors 2023 Ingår i: Magnetic Resonance in Medicine. - : John Wiley & Sons. - 0740-3194 .- 1522-2594. ; 90:6, s. 2557-2571 Tidskriftsartikel (refereegranskat)abstract Purpose: To mitigate the problem of noisy parameter maps with high uncertainties by casting parameter mapping as a denoising task based on Deep Image Priors.Methods: We extend the concept of denoising with Deep Image Prior (DIP) into parameter mapping by treating the output of an image-generating network as a parametrization of tissue parameter maps. The method implicitly denoises the parameter mapping process by filtering low-level image features with an untrained convolutional neural network (CNN). Our implementation includes uncertainty estimation from Bernoulli approximate variational inference, implemented with MC dropout, which provides model uncertainty in each voxel of the denoised parameter maps. The method is modular, so the specifics of different applications (e.g., T1 mapping) separate into application-specific signal equation blocks. We evaluate the method on variable flip angle T1 mapping, multi-echo T2 mapping, and apparent diffusion coefficient mapping.Results: We found that deep image prior adapts successfully to several applications in parameter mapping. In all evaluations, the method produces noise-reduced parameter maps with decreased uncertainty compared to conventional methods. The downsides of the proposed method are the long computational time and the introduction of some bias from the denoising prior.Conclusion: DIP successfully denoise the parameter mapping process and applies to several applications with limited hyperparameter tuning. Further, it is easy to implement since DIP methods do not use network training data. Although time-consuming, uncertainty information from MC dropout makes the method more robust and provides useful information when properly calibrated.
30.	Häggström, Ida, 1982-, et al. (författare) A Monte Carlo study of the dependence of early frame sampling on uncertainty and bias in pharmacokinetic parameters from dynamic PET 2015 Ingår i: Journal of Nuclear Medicine Technology. - : Society of Nuclear Medicine. - 0091-4916 .- 1535-5675. ; 43:1, s. 53-60 Tidskriftsartikel (refereegranskat)abstract 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
31.	Häggström, Ida, et al. (författare) The influence of time sampling scheme on kinetic parameters obtained from compartmental modeling of a dynamic PET study : a Monte Carlo study 2012 Ingår i: IEEE Nuclear Science Symposium Conference Record. - Anaheim : IEEE conference proceedings. - 9781467320306 ; , s. 3101-3107 Konferensbidrag (refereegranskat)abstract Compartmental modeling of dynamic PET data enables quantification of tracer kinetics in vivo, through the obtained model parameters. The dynamic data is sorted into frames during or after the acquisition, with a sampling interval usually ranging from 10 s to 300 s. In this study we wanted to investigate the effect of the chosen sampling interval on kinetic parameters obtained from a 2-tissue model, in terms of bias and standard deviation, using a complete Monte Carlo simulated dynamic F-18-FLT PET study. The results show that the bias and standard deviation in parameter K-1 is small regardless of sampling scheme or noise in the time-activity curves (TACs), and that the bias and standard deviation in k(4) is large for all cases. The bias in V-a is clearly dependent on sampling scheme, increasing for increased sampling interval. In general, a too short sampling interval results in very noisy images and a large bias of the parameter estimate, and a too long sampling interval also increases bias. Noise in the TACs is the largest source of bias.
32.	Johansson, Adam, 1984-, et al. (författare) CT substitutes derived from MR images reconstructed with parallel imaging 2014 Ingår i: Medical physics (Lancaster). - : Wiley. - 0094-2405. ; 41:8, s. 474-480 Tidskriftsartikel (refereegranskat)abstract Purpose: Computed tomography (CT) substitute images can be generated from ultrashort echo time (UTE) MRI sequences with radial k-space sampling. These CT substitutes can be used as ordinary CT images for PET attenuation correction and radiotherapy dose calculations. Parallel imaging allows faster acquisition of magnetic resonance (MR) images by exploiting differences in receiver coil element sensitivities. This study investigates whether non-Cartesian parallel imaging reconstruction can be used to improve CT substitutes generated from shorter examination times.Methods: The authors used gridding as well as two non-Cartesian parallel imaging reconstruction methods, SPIRiT and CG-SENSE, to reconstruct radial UTE and gradient echo (GE) data into images of the head for 23 patients. For each patient, images were reconstructed from the full dataset and from a number of subsampled datasets. The subsampled datasets simulated shorter acquisition times by containing fewer radial k-space spokes (1000, 2000, 3000, 5000, and 10 000 spokes) than the full dataset (30 000 spokes). For each combination of patient, reconstruction method, and number of spokes, the reconstructed UTE and GE images were used to generate a CT substitute. Each CT substitute image was compared to a real CT image of the same patient.Results: The mean absolute deviation between the CT number in CT substitute and CT decreased when using SPIRiT as compared to gridding reconstruction. However, the reduction was small and the CT substitute algorithm was insensitive to moderate subsampling (≥5000 spokes) regardless of reconstruction method. For more severe subsampling (≤3000 spokes), corresponding to acquisition times less than aminute long, the CT substitute quality was deteriorated for all reconstructionmethods but SPIRiT gave a reduction in the mean absolute deviation of down to 25 Hounsfield units compared to gridding.Conclusions: SPIRiT marginally improved the CT substitute quality for a given number of radial spokes as compared to gridding. However, the increased reconstruction time of non-Cartesian parallel imaging reconstruction is difficult to motivate from this improvement. Because the CT substitute algorithm was insensitive to moderate subsampling, data for a CT substitute could be collected in as little as minute and reconstructed with gridding without deteriorating the CT substitute quality.
33.	Johansson, Adam, et al. (författare) 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 2013 Ingår i: Acta Oncologica. - 0284-186X .- 1651-226X. ; 52:7, s. 1369-1373 Tidskriftsartikel (refereegranskat)abstract 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.
34.	Jonsson, Joakim H, et al. (författare) Internal fiducial markers and susceptibility effects in MRI : simulation and measurement of spatial accuracy 2012 Ingår i: International Journal of Radiation Oncology, Biology, Physics. - : Elsevier BV. - 0360-3016 .- 1879-355X. ; 82:5, s. 1612-1618 Tidskriftsartikel (refereegranskat)abstract BACKGROUND: It is well-known that magnetic resonance imaging (MRI) is preferable to computed tomography (CT) in radiotherapy target delineation. To benefit from this, there are two options available: transferring the MRI delineated target volume to the planning CT or performing the treatment planning directly on the MRI study. A precondition for excluding the CT study is the possibility to define internal structures visible on both the planning MRI and on the images used to position the patient at treatment. In prostate cancer radiotherapy, internal gold markers are commonly used, and they are visible on CT, MRI, x-ray, and portal images. The depiction of the markers in MRI are, however, dependent on their shape and orientation relative the main magnetic field because of susceptibility effects. In the present work, these effects are investigated and quantified using both simulations and phantom measurements.METHODS AND MATERIALS: Software that simulated the magnetic field distortions around user defined geometries of variable susceptibilities was constructed. These magnetic field perturbation maps were then reconstructed to images that were evaluated. The simulation software was validated through phantom measurements of four commercially available gold markers of different shapes and one in-house gold marker.RESULTS: Both simulations and phantom measurements revealed small position deviations of the imaged marker positions relative the actual marker positions (<1 mm).CONCLUSION: Cylindrical gold markers can be used as internal fiducial markers in MRI.
35.	Jonsson, Joakim H, et al. (författare) Registration accuracy for MR images of the prostate using a subvolume based registration protocol 2011 Ingår i: Radiation Oncology. - 1748-717X. ; 6:1, s. 73- Tidskriftsartikel (refereegranskat)abstract BACKGROUND: In recent years, there has been a considerable research effort concerning the integration of magnetic resonance imaging (MRI) into the external radiotherapy workflow motivated by the superior soft tissue contrast as compared to computed tomography. Image registration is a necessary step in many applications, e.g. in patient positioning and therapy response assessment with repeated imaging. In this study, we investigate the dependence between the registration accuracy and the size of the registration volume for a subvolume based rigid registration protocol for MR images of the prostate.METHODS: Ten patients were imaged four times each over the course of radiotherapy treatment using a T2 weighted sequence. The images were registered to each other using a mean square distance metric and a step gradient optimizer for registration volumes of different sizes. The precision of the registrations was evaluated using the center of mass distance between the manually defined prostates in the registered images. The optimal size of the registration volume was determined by minimizing the standard deviation of these distances.RESULTS: We found that prostate position was most uncertain in the anterior-posterior (AP) direction using traditional full volume registration. The improvement in standard deviation of the mean center of mass distance between the prostate volumes using a registration volume optimized to the prostate was 3.9 mm (p < 0.001) in the AP direction. The optimum registration volume size was 0 mm margin added to the prostate gland as outlined in the first image series.CONCLUSIONS: Repeated MR imaging of the prostate for therapy set-up or therapy assessment will both require high precision tissue registration. With a subvolume based registration the prostate registration uncertainty can be reduced down to the order of 1 mm (1 SD) compared to several millimeters for registration based on the whole pelvis.
36.	Lundman, Josef Axel, et al. (författare) Patient-induced susceptibility effects simulation in magnetic resonance imaging 2017 Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 1, s. 41-45 Tidskriftsartikel (refereegranskat)abstract Background and purpose A fundamental requirement for safe use of magnetic resonance imaging (MRI) in radiotherapy is geometrical accuracy. One factor that can introduce geometrical distortion is patient-induced susceptibility effects. This work aims at developing a method for simulating these distortions. The specific goal being to help objectively identifying a balanced acquisition bandwidth, keeping these distortions within acceptable limits for radiotherapy.Materials and methods A simulation algorithm was implemented in Medical Interactive Creative Environment (MICE). The algorithm was validated by comparison between simulations and analytical solutions for a cylinder and a sphere. Simulations were performed for four body regions; neck, lungs, thorax with the lungs excluded, and the pelvic region. This was done using digital phantoms created from patient CT images, after converting the CT Hounsfield units to magnetic susceptibility values through interpolation between known values.Results The simulations showed good agreement with analytical solutions, with only small discrepancies due to pixelation of the phantoms. The calculated distortions in digital phantoms based on patient CT data showed maximal 95th percentile distortions of 39%, 32%, 28%, and 25% of the fat-water shift for the neck, lungs, thorax with the lungs excluded, and pelvic region, respectively.Conclusions The presented results show the expected pixel distortions for various body parts, and how they scale with bandwidth and field strength. This information can be used to determine which bandwidth is required to keep the patient-induced susceptibility distortions within an acceptable range for a given field strength.
37.	Löfstedt, Tommy, et al. (författare) Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation 2020 Ingår i: Physics in Medicine and Biology. - : Institute of Physics (IOP). - 0031-9155 .- 1361-6560. ; 65:22 Tidskriftsartikel (refereegranskat)abstract Purpose. To develop a method that can reduce and estimate uncertainty in quantitative MR parameter maps without the need for hand-tuning of any hyperparameters.Methods. We present an estimation method where uncertainties are reduced by incorporating information on spatial correlations between neighbouring voxels. The method is based on a Bayesian hierarchical non-linear regression model, where the parameters of interest are sampled, using Markov chain Monte Carlo (MCMC), from a high-dimensional posterior distribution with a spatial prior. The degree to which the prior affects the model is determined by an automatic hyperparameter search using an information criterion and is, therefore, free from manual user-dependent tuning. The samples obtained further provide a convenient means to obtain uncertainties in both voxels and regions. The developed method was evaluated on T1 estimations based on the variable flip angle method.Results. The proposed method delivers noise-reduced T1 parameter maps with associated error estimates by combining MCMC sampling, the widely applicable information criterion, and total variation-based denoising. The proposed method results in an overall decrease in estimation error when compared to conventional voxel-wise maximum likelihood estimation. However, this comes with an increased bias in some regions, predominately at tissue interfaces, as well as an increase in computational time.Conclusions. This study provides a method that generates more precise estimates compared to the conventional method, without incorporating user subjectivity, and with the added benefit of uncertainty estimation.
38.	Löfstedt, Tommy, et al. (författare) Gray-level invariant Haralick texture features 2019 Ingår i: PLOS ONE. - : Public Library of Science. - 1932-6203. ; 14:2 Tidskriftsartikel (refereegranskat)abstract Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.
39.	Simkó, Attila, et al. (författare) Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning 2021 Ingår i: Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR. - : Lübeck University; Hamburg University of Technology. ; , s. 713-727 Konferensbidrag (refereegranskat)abstract The contrast settings to select before acquiring magnetic resonance imaging (MRI) signal depend heavily on the subsequent tasks. As each contrast highlights different tissues, automated segmentation tools for example might be optimized for a certain contrast. While for radiotherapy, multiple scans of the same region with different contrasts can achieve a better accuracy for delineating tumours and organs at risk. Unfortunately, the optimal contrast for the subsequent automated methods might not be known during the time of signal acquisition, and performing multiple scans with different contrasts increases the total examination time and registering the sequences introduces extra work and potential errors. Building on the recent achievements of deep learning in medical applications, the presented work describes a novel approach for transferring any contrast to any other. The novel model architecture incorporates the signal equation for spin echo sequences, and hence the model inherently learns the unknown quantitative maps for proton density, ?1 and ?2 relaxation times (??, ?1 and ?2, respectively). This grants the model the ability to retrospectively reconstruct spin echo sequences by changing the contrast settings Echo and Repetition Time (?? and ??, respectively). The model learns to identify the contrast of pelvic MR images, therefore no paired data of the same anatomy from different contrasts is required for training. This means that the experiments are easily reproducible with other contrasts or other patient anatomies. Despite the contrast of the input image, the model achieves accurate results for reconstructing signal with contrasts available for evaluation. For the same anatomy, the quantitative maps are consistent for a range of contrasts of input images. Realized in practice, the proposed method would greatly simplify the modern radiotherapy pipeline. The trained model is made public together with a tool for testing the model on example images.
40.	Simkó, Attila, 1995- (författare) Contributions to deep learning for imaging in radiotherapy 2023 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Purpose: The increasing importance of medical imaging in cancer treatment, combined with the growing popularity of deep learning gave relevance to the presented contributions to deep learning solutions with applications in medical imaging.Relevance: The projects aim to improve the efficiency of MRI for automated tasks related to radiotherapy, building on recent advancements in the field of deep learning.Approach: Our implementations are built on recently developed deep learning methodologies, while introducing novel approaches in the main aspects of deep learning, with regards to physics-informed augmentations and network architectures, and implicit loss functions. To make future comparisons easier, we often evaluated our methods on public datasets, and made all solutions publicly available.Results: The results of the collected projects include the development of robust models for MRI bias field correction, artefact removal, contrast transfer and sCT generation. Furthermore, the projects stress the importance of reproducibility in deep learning research and offer guidelines for creating transparent and usable code repositories.Conclusions: Our results collectively build the position of deep learning in the field of medical imaging. The projects offer solutions that are both novel and aim to be highly applicable, while emphasizing generalization towards a wide variety of data and the transparency of the results.
41.	Simkó, Attila, et al. (författare) Improving MR image quality with a multi-task model, using convolutional losses 2023 Ingår i: BMC Medical Imaging. - : BioMed Central (BMC). - 1471-2342. ; 23:1 Tidskriftsartikel (refereegranskat)abstract PURPOSE: During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correction, super-resolution, motion-, and noise correction. Machine learning has achieved outstanding results in improving MR image quality for these tasks individually, yet multi-task methods are rarely explored.METHODS: In this study, we developed a model to simultaneously correct for all four aforementioned artefacts using multi-task learning. Two different datasets were collected, one consisting of brain scans while the other pelvic scans, which were used to train separate models, implementing their corresponding artefact augmentations. Additionally, we explored a novel loss function that does not only aim to reconstruct the individual pixel values, but also the image gradients, to produce sharper, more realistic results. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test.RESULTS: Our proposed model generally outperformed other commonly-used correction methods for individual artefacts, consistently achieving equal or superior results in at least one of the evaluation metrics. For images with multiple simultaneous artefacts, we show that the performance of using a combination of models, trained to correct individual artefacts depends heavily on the order that they were applied. This is not an issue for our proposed multi-task model. The model trained using our novel convolutional loss function always outperformed the model trained with a mean squared error loss, when evaluated using Visual Information Fidelity, a quality metric connected to perceptual quality.CONCLUSION: We trained two models for multi-task MRI artefact correction of brain, and pelvic scans. We used a novel loss function that significantly improves the image quality of the outputs over using mean squared error. The approach performs well on real world data, and it provides insight into which artefacts it detects and corrects for. Our proposed model and source code were made publicly available.
42.	Simkó, Attila, et al. (författare) MRI bias field correction with an implicitly trained CNN 2022 Ingår i: Proceedings of the 5th international conference on medical imaging with deep learning. - : ML Research Press. ; , s. 1125-1138 Konferensbidrag (refereegranskat)abstract In magnetic resonance imaging (MRI), bias fields are difficult to correct since they are inherently unknown. They cause intra-volume intensity inhomogeneities which limit the performance of subsequent automatic medical imaging tasks, \eg, tissue-based segmentation. Since the ground truth is unavailable, training a supervised machine learning solution requires approximating the bias fields, which limits the resulting method. We introduce implicit training which sidesteps the inherent lack of data and allows the training of machine learning solutions without ground truth. We describe how training a model implicitly for bias field correction allows using non-medical data for training, achieving a highly generalized model. The implicit approach was compared to a more traditional training based on medical data. Both models were compared to an optimized N4ITK method, with evaluations on six datasets. The implicitly trained model improved the homogeneity of all encountered medical data, and it generalized better for a range of anatomies, than the model trained traditionally. The model achieves a significant speed-up over an optimized N4ITK method—by a factor of 100, and after training, it also requires no parameters to tune. For tasks such as bias field correction - where ground truth is generally not available, but the characteristics of the corruption are known - implicit training promises to be a fruitful alternative for highly generalized solutions.
43.	Simkó, Attila, et al. (författare) Reproducibility of the methods in medical imaging with deep learning 2023 Ingår i: Medical imaging with deep learning 2023. - : ML Research Press. ; , s. 95-106 Konferensbidrag (refereegranskat)abstract Concerns about the reproducibility of deep learning research are more prominent than ever, with no clear solution in sight. The Medical Imaging with Deep Learning (MIDL) conference has made advancements in employing empirical rigor with regards to reproducibility by advocating open access, and recently also recommending authors to make their code public—both aspects being adopted by the majority of the conference submissions. We have evaluated all accepted full paper submissions to MIDL between 2018 and 2022 using established, but adjusted guidelines addressing the reproducibility and quality of the public repositories. The evaluations show that publishing repositories and using public datasets are becoming more popular, which helps traceability, but the quality of the repositories shows room for improvement in every aspect. Merely 22% of all submissions contain a repository that was deemed repeatable using our evaluations. From the commonly encountered issues during the evaluations, we propose a set of guidelines for machine learning-related research for medical imaging applications, adjusted specifically for future submissions to MIDL. We presented our results to future MIDL authors who were eager to continue an open discussion on the topic of code reproducibility.
44.	Simkó, Attila, et al. (författare) Towards MR contrast independent synthetic CT generation 2024 Ingår i: Zeitschrift für Medizinische Physik. - : Elsevier. - 0939-3889 .- 1876-4436. ; 34:2, s. 270-277 Tidskriftsartikel (refereegranskat)abstract The use of synthetic CT (sCT) in the radiotherapy workflow would reduce costs and scan time while removing the uncertainties around working with both MR and CT modalities. The performance of deep learning (DL) solutions for sCT generation is steadily increasing, however most proposed methods were trained and validated on private datasets of a single contrast from a single scanner. Such solutions might not perform equally well on other datasets, limiting their general usability and therefore value. Additionally, functional evaluations of sCTs such as dosimetric comparisons with CT-based dose calculations better show the impact of the methods, but the evaluations are more labor intensive than pixel-wise metrics.To improve the generalization of an sCT model, we propose to incorporate a pre-trained DL model to pre-process the input MR images by generating artificial proton density, T1 and T2 maps (i.e. contrast-independent quantitative maps), which are then used for sCT generation. Using a dataset of only T2w MR images, the robustness towards input MR contrasts of this approach is compared to a model that was trained using the MR images directly. We evaluate the generated sCTs using pixel-wise metrics and calculating mean radiological depths, as an approximation of the mean delivered dose. On T2w images acquired with the same settings as the training dataset, there was no significant difference between the performance of the models. However, when evaluated on T1w images, and a wide range of other contrasts and scanners from both public and private datasets, our approach outperforms the baseline model. Using a dataset of T2w MR images, our proposed model implements synthetic quantitative maps to generate sCT images, improving the generalization towards other contrasts. Our code and trained models are publicly available.
45.	Vu, Minh H., et al. (författare) Compressing the activation maps in deep convolutional neural networks and the regularization effect of compression Annan publikation (övrigt vetenskapligt/konstnärligt)
46.	Vu, Minh Hoang, 1988- (författare) Resource efficient automatic segmentation of medical images 2023 Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract Cancer is one of the leading causes of death worldwide. In 2020, there were around 10 million cancer deaths and nearly 20 million new cancer cases in the world. Radiation therapy is essential in cancer treatments because half of the cancer patients receive radiation therapy at some point. During a radiotherapy treatment planning (RTP), an oncologist must manually outline two types of areas of the patient’s body: target, which will be treated, and organs-at-risks (OARs), which are essential to avoid. This step is called delineation. The purpose of the delineation is to generate a sufficient dose plan that can provide adequate radiation dose to a tumor and limit the radiation exposure to healthy tissue. Therefore, accurate delineations are essential to achieve this goal.Delineation is tedious and demanding for oncologists because it requires hours of concentrating work doing a repeated job. This is a RTP bottleneck which is often time- and resource-intensive. Current software, such as atlasbased techniques, can assist with this procedure by registering the patient’s anatomy to a predetermined anatomical map. However, the atlas-based methods are often slowed down and erroneous for patients with abnormal anatomies.In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNNs), have led to breakthroughs in numerous medical imaging applications. The core benefits of CNNs are weight sharing and that they can automatically detect important visual features. A typical application of CNNs for medical images is to automatically segment tumors, organs, and structures, which is assumed to save radiation oncologists much time when delineating. This thesis contributes to resource efficient automatic segmentation and covers different aspects of resource efficiency.In Paper I, we proposed a novel end-to-end cascaded network for semantic segmentation in brain tumors in the multi-modal magnetic resonance imaging challenge in 2019. The proposed method used the hierarchical structure of the tumor sub-regions and was one of the top-ranking teams in the task of quantification of uncertainty in segmentation. A follow-up work to this paper was ranked second in the same task in the same challenge a year later.We systematically assessed the segmentation performance and computational costs of the technique called pseudo-3D as a function of the number of input slices in Paper II. We compared the results to typical two-dimensional (2D) and three-dimensional (3D) CNNs and a method called triplanar orthogonal 2D. The typical pseudo-3D approach considers adjacent slices to be several image input channels. We discovered that a substantial benefit from employing multiple input slices was apparent for a specific input size.We introduced a novel loss function in Paper III to address diverse issues, including imbalanced datasets, partially labeled data, and incremental learning. The proposed loss function adjusts to the given data to use all accessible data, even if some lack annotations. We show that the suggested loss function also performs well in an incremental learning context, where an existing model can be modified to incorporate the delineations of newly appearing organs semi-automatically.In Paper IV, we proposed a novel method for compressing high-dimensional activation maps, which are the primary source of memory use in modern systems. We examined three distinct compression methods for the activation maps to accomplishing this. We demonstrated that the proposed method induces a regularization effect that acts on the layer weight gradients. By employing the proposed technique, we reduced activation map memory usage by up to 95%.We investigated the use of generative adversarial networks (GANs) to enlarge a small dataset by generating synthetic images in Paper V. We use the real and generated data during training CNNs for the downstream segmentation tasks. Inspired by an existing GAN, we proposed a conditional version to generate high-dimensional and high-quality medical images of different modalities and their corresponding label maps. We evaluated the quality of the generated medical images and the effect of this augmentation on the performance of the segmentation task on six datasets.
47.	Wang, Jianfeng, 1984-, et al. (författare) Bayesian sparsity estimation in compressive sensing with application to MR images 2019 Ingår i: Communications in Statistics: Case Studies, Data Analysis and Applications. - : Taylor & Francis Group. - 2373-7484. ; 5:4, s. 415-431 Tidskriftsartikel (refereegranskat)abstract The theory of compressive sensing (CS) asserts that an unknownsignal x ∈ CN can be accurately recovered from m measurements with m « N provided that x is sparse. Most of the recovery algorithms need the sparsity s = \|\|x\|\|0 as an input. However, generally s is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simulation study and real data study, magnetic resonance image data is used as input signal, which becomes sparse after sparsified transformation. The results from the simulation study confirm the theoretical properties of the estimator. In practice, the estimate from a real MR image can be used for recovering future MR images under the framework of CS if they are believed to have the same sparsity level after sparsification.
48.	Wang, Jianfeng, et al. (författare) Bayesian sparsity estimation in compressivesensing with application to MR images Annan publikation (övrigt vetenskapligt/konstnärligt)
49.	Wang, Jianfeng, et al. (författare) Combining phase and magnitude information for contrast agent quantification in dynamic contrast-enhanced MRI using Bayesian hierarchical model 2016 Ingår i: Proceedings of the 8th International Workshop on Spatio-Temporal Modelling. - 9788460884682 ; , s. 217-217 Konferensbidrag (övrigt vetenskapligt/konstnärligt)
50.	Wang, Jianfeng, et al. (författare) Contrast Agent Quantification by Using Spatial Information in Dynamic Contrast Enhanced MRI Annan publikation (övrigt vetenskapligt/konstnärligt)abstract The purpose of this study is to investigate a method, using simulations, to improve contrast agent quantification in Dynamic Contrast Enhanced MRI. Bayesian hierarchical models (BHMs) are applied to smaller images (10×10×10) such that spatial information can be incorporated. Then exploratory analysis is done for larger images (64×64×64) by using maximum a posteriori (MAP).For smaller images: the estimators of proposed BHMs show improvements in terms of the root mean squared error compared to the estimators in existing method for a noise level equivalent of a 12-channel head coil at 3T. Moreover, Leroux model outperforms Besag models. For larger images: MAP estimators also show improvements by assigning Leroux prior.

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