| 1. |
- Björnfot, Cecilia, et al.
(författare)
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Assessing cerebral arterial pulse wave velocity using 4D flow MRI
- 2021
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Ingår i: Journal of Cerebral Blood Flow and Metabolism. - : Sage Publications. - 0271-678X .- 1559-7016. ; 41:10, s. 2769-2777
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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.
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| 2. |
- Björnfot, Cecilia, et al.
(författare)
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Cerebral arterial stiffness is linked to white matter hyperintensities and perivascular spaces in older adults : a 4D flow MRI study
- 2024
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Ingår i: Journal of Cerebral Blood Flow and Metabolism. - : Sage Publications. - 0271-678X .- 1559-7016.
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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.
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| 3. |
- Vikner, Tomas, et al.
(författare)
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Blood-brain barrier integrity is linked to cognitive function, but not to cerebral arterial pulsatility, among elderly
- 2024
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 14:1
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Tidskriftsartikel (refereegranskat)abstract
- Blood-brain barrier (BBB) disruption may contribute to cognitive decline, but questions remain whether this association is more pronounced for certain brain regions, such as the hippocampus, or represents a whole-brain mechanism. Further, whether human BBB leakage is triggered by excessive vascular pulsatility, as suggested by animal studies, remains unknown. In a prospective cohort (N = 50; 68-84 years), we used contrast-enhanced MRI to estimate the permeability-surface area product (PS) and fractional plasma volume ( formula presented ), and 4D flow MRI to assess cerebral arterial pulsatility. Cognition was assessed by the Montreal Cognitive Assessment (MoCA) score. We hypothesized that high PS would be associated with high arterial pulsatility, and that links to cognition would be specific to hippocampal PS. For 15 brain regions, PS ranged from 0.38 to 0.85 (·10-3 min-1) and formula presented from 0.79 to 1.78%. Cognition was related to PS (·10-3 min-1) in hippocampus (β = - 2.9; p = 0.006), basal ganglia (β = - 2.3; p = 0.04), white matter (β = - 2.6; p = 0.04), whole-brain (β = - 2.7; p = 0.04) and borderline-related for cortex (β = - 2.7; p = 0.076). Pulsatility was unrelated to PS for all regions (p > 0.19). Our findings suggest PS-cognition links mainly reflect a whole-brain phenomenon with only slightly more pronounced links for the hippocampus, and provide no evidence of excessive pulsatility as a trigger of BBB disruption.
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| 4. |
- Adjeiwaah, Mary, 1980-, et al.
(författare)
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Sensitivity analysis of different quality assurance methods for magnetic resonance imaging in radiotherapy
- 2020
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Ingår i: Physics and Imaging in Radiation Oncology. - : Elsevier. - 2405-6316. ; 13, s. 21-27
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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.
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| 5. |
- Frankel, Jennifer, 1981-
(författare)
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Characterization of the MRI patient exposure environment and exposure assessment methods for magnetic fields in MRI scanners
- 2021
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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.
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| 6. |
- Hellström, Max, et al.
(författare)
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Denoising and uncertainty estimation in parameter mapping with approximate Bayesian deep image priors
- 2023
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Ingår i: Magnetic Resonance in Medicine. - : John Wiley & Sons. - 0740-3194 .- 1522-2594. ; 90:6, s. 2557-2571
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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.
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| 7. |
- Löfstedt, Tommy, et al.
(författare)
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Bayesian non-linear regression with spatial priors for noise reduction and error estimation in quantitative MRI with an application in T1 estimation
- 2020
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Ingår i: Physics in Medicine and Biology. - : Institute of Physics (IOP). - 0031-9155 .- 1361-6560. ; 65:22
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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.
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| 8. |
- Simkó, Attila, et al.
(författare)
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Changing the Contrast of Magnetic Resonance Imaging Signals using Deep Learning
- 2021
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Ingår i: Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, PMLR. - : Lübeck University; Hamburg University of Technology. ; , s. 713-727
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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.
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| 9. |
- Simkó, Attila, 1995-
(författare)
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Contributions to deep learning for imaging in radiotherapy
- 2023
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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.
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| 10. |
- Simkó, Attila, et al.
(författare)
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Improving MR image quality with a multi-task model, using convolutional losses
- 2023
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Ingår i: BMC Medical Imaging. - : BioMed Central (BMC). - 1471-2342. ; 23:1
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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.
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