| 1. |
- Fritzell, Peter, et al.
(author)
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Bacteria : back pain, leg pain and Modic sign—a surgical multicentre comparative study
- 2019
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In: European spine journal. - : Springer. - 0940-6719 .- 1432-0932. ; 28:12, s. 2981-2989
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Journal article (peer-reviewed)abstract
- Purpose: To compare bacterial findings in pain-generating degenerated discs in adults operated on for lumbar disc herniation (LDH), and mostly also suffering from low back pain (LBP), with findings in adolescent patients with non-degenerated non-pain-generating discs operated on for scoliosis, and to evaluate associations with Modic signs on magnetic resonance imaging (MRI). Cutibacterium acnes (Propionibacterium acnes) has been found in painful degenerated discs, why it has been suggested treating patients with LDH/LBP with antibiotics. As multidrug-resistant bacteria are a worldwide concern, new indications for using antibiotics should be based on solid scientific evidence.Methods: Between 2015 and 2017, 40 adults with LDH/LBP (median age 43, IQR 33–49) and 20 control patients with scoliosis (median age 17, IQR 15–20) underwent surgery at seven Swedish hospitals. Samples were cultured from skin, surgical wound, discs and vertebrae. Genetic relatedness of C. acnes isolates was investigated using single-nucleotide polymorphism analysis. DNA samples collected from discs/vertebrae were analysed using 16S rRNA-based PCR sequencing. MRI findings were assessed for Modic changes.Results: No bacterial growth was found in 6/40 (15%) LDH patients, compared with 3/20 (15%) scoliosis patients. Most positive samples in both groups were isolated from the skin and then from subcutis or deep within the wound. Of the four disc and vertebral samples from each of the 60 patients, 235/240 (98%) were DNA negative by bacterial PCR. A single species, C. acnes, was found exclusively in the disc/vertebra from one patient in each group. In the LDH group, 29/40 (72%) patients had at least one sample with growth of C. acnes, compared to 14/20 (70%) in the scoliosis group. Bacterial findings and Modic changes were not associated.Conclusions: Cutibacterium acnes found in discs and vertebrae during surgery for disc herniation in adults with degenerated discs may be caused by contamination, as findings in this group were similar to findings in a control group of young patients with scoliosis and non-degenerated discs. Furthermore, such findings were almost always combined with bacterial findings on the skin and/or in the wound. There was no association between preoperative Modic changes and bacterial findings. Antibiotic treatment of lumbar disc herniation with sciatica and/or low back pain, without signs of clinical discitis/spondylitis, should be seriously questioned.
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| 2. |
- Cros, Olivier, et al.
(author)
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Surface and curve skeleton from a structure tensor analysis applied on mastoid air cells in human temporal bones
- 2017
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In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509011728 - 9781509011735 ; , s. 270-274
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Conference paper (peer-reviewed)abstract
- The mastoid of human temporal bone contains numerous air cells connected to each others. In order to gain further knowledge about these air cells, a more compact representation is needed to obtain an estimate of the size distribution of these cells. Already existing skeletonization methods often fail in producing a faithful skeleton mostly due to noise hampering the binary representation of the data. This paper proposes a different approach by extracting geometrical information embedded in the Euclidean distance transform of a volume via a structure tensor analysis based on quadrature filters, from which a secondary structure tensor allows the extraction of surface skeleton along with a curve skeleton from its eigenvalues. Preliminary results obtained on a X-ray micro-CT scans of a human temporal bone show very promising results.
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| 3. |
- Eklund, Anders, 1981-, et al.
(author)
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Cluster failure revisited: Impact of first level design and physiological noise on cluster false positive rates
- 2019
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In: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 40:7, s. 2017-2032
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Journal article (peer-reviewed)abstract
- Methodological research rarely generates a broad interest, yet our work on the validity of cluster inference methods for functional magnetic resonance imaging (fMRI) created intense discussion on both the minutia of our approach and its implications for the discipline. In the present work, we take on various critiques of our work and further explore the limitations of our original work. We address issues about the particular event‐related designs we used, considering multiple event types and randomization of events between subjects. We consider the lack of validity found with one‐sample permutation (sign flipping) tests, investigating a number of approaches to improve the false positive control of this widely used procedure. We found that the combination of a two‐sided test and cleaning the data using ICA FIX resulted in nominal false positive rates for all data sets, meaning that data cleaning is not only important for resting state fMRI, but also for task fMRI. Finally, we discuss the implications of our work on the fMRI literature as a whole, estimating that at least 10% of the fMRI studies have used the most problematic cluster inference method (p = .01 cluster defining threshold), and how individual studies can be interpreted in light of our findings. These additional results underscore our original conclusions, on the importance of data sharing and thorough evaluation of statistical methods on realistic null data.
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| 4. |
- Eklund, Anders, 1981-, et al.
(author)
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Reply to Chen et al.: Parametric methods for cluster inference perform worse for two‐sided t‐tests
- 2019
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In: Human Brain Mapping. - : Wiley. - 1065-9471 .- 1097-0193. ; 40:5, s. 1689-1691
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Journal article (pop. science, debate, etc.)abstract
- One‐sided t‐tests are commonly used in the neuroimaging field, but two‐sided tests should be the default unless a researcher has a strong reason for using a one‐sided test. Here we extend our previous work on cluster false positive rates, which used one‐sided tests, to two‐sided tests. Briefly, we found that parametric methods perform worse for two‐sided t‐tests, and that nonparametric methods perform equally well for one‐sided and two‐sided tests.
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| 5. |
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| 6. |
- Gu, Xuan, et al.
(author)
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Bayesian Diffusion Tensor Estimation with Spatial Priors
- 2017
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In: CAIP 2017. - Cham : Springer International Publishing. - 9783319646893 - 9783319646886 ; , s. 372-383
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Conference paper (peer-reviewed)abstract
- Spatial regularization is a technique that exploits the dependence between nearby regions to locally pool data, with the effect of reducing noise and implicitly smoothing the data. Most of the currently proposed methods are focused on minimizing a cost function, during which the regularization parameter must be tuned in order to find the optimal solution. We propose a fast Markov chain Monte Carlo (MCMC) method for diffusion tensor estimation, for both 2D and 3D priors data. The regularization parameter is jointly with the tensor using MCMC. We compare FA (fractional anisotropy) maps for various b-values using three diffusion tensor estimation methods: least-squares and MCMC with and without spatial priors. Coefficient of variation (CV) is calculated to measure the uncertainty of the FA maps calculated from the MCMC samples, and our results show that the MCMC algorithm with spatial priors provides a denoising effect and reduces the uncertainty of the MCMC samples.
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| 7. |
- Gu, Xuan, et al.
(author)
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Repeated Tractography of a Single Subject: How High Is the Variance?
- 2017
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In: Modeling, Analysis, and Visualization of Anisotropy. - Cham : Springer. - 9783319613574 - 9783319613581 ; , s. 331-354
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Book chapter (other academic/artistic)abstract
- We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.
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| 8. |
- Gu, Xuan, 1988-, et al.
(author)
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Using the wild bootstrap to quantify uncertainty in mean apparent propagator MRI
- 2019
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In: Frontiers in Neuroinformatics. - : Frontiers Media S.A.. - 1662-5196. ; 13
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Journal article (peer-reviewed)abstract
- Purpose: Estimation of uncertainty of MAP-MRI metricsis an important topic, for several reasons. Bootstrap deriveduncertainty, such as the standard deviation, providesvaluable information, and can be incorporated in MAP-MRIstudies to provide more extensive insight.Methods: In this paper, the uncertainty of different MAPMRImetrics was quantified by estimating the empirical distributionsusing the wild bootstrap. We applied the wildbootstrap to both phantom data and human brain data, andobtain empirical distributions for theMAP-MRImetrics returnto-origin probability (RTOP), non-Gaussianity (NG) and propagatoranisotropy (PA).Results: We demonstrated the impact of diffusion acquisitionscheme (number of shells and number of measurementsper shell) on the uncertainty of MAP-MRI metrics.We demonstrated how the uncertainty of these metrics canbe used to improve group analyses, and to compare differentpreprocessing pipelines. We demonstrated that withuncertainty considered, the results for a group analysis canbe different.Conclusion: Bootstrap derived uncertain measures provideadditional information to the MAP-MRI derived metrics, andshould be incorporated in ongoing and future MAP-MRIstudies to provide more extensive insight.
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| 9. |
- Shakya, Snehlata, et al.
(author)
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Multi-fiber estimation and tractography for diffusion mri using mixture of non-central wishart distributions
- 2017
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In: 2017 Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2017. - : Eurographics Association. - 9783038680369 ; , s. 119-123
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Conference paper (peer-reviewed)abstract
- Multi-compartmental models are popular to resolve intra-voxel fiber heterogeneity. One such model is the mixture of central Wishart distributions. In this paper, we use our recently proposed model to estimate the orientations of crossing fibers within a voxel based on mixture of non-central Wishart distributions. We present a thorough comparison of the results from other fiber reconstruction methods with this model. The comparative study includes experiments on a range of separation angles between crossing fibers, with different noise levels, and on real human brain diffusion MRI data. Furthermore, we present multi-fiber visualization results using tractography. Results on synthetic and real data as well as tractography visualization highlight the superior performance of the model specifically for small and middle ranges of separation angles among crossing fibers.
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| 10. |
- Shakya, Snehlata, 1985-, et al.
(author)
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Multi-fiber reconstruction using probabilistic mixture models for diffusion MRI examinations of the brain
- 2017
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In: Modeling, Analysis, and Visualization of Anisotropy. - Cham : Springer Berlin/Heidelberg. - 9783319613574 - 9783319613581 ; , s. 283-308
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Book chapter (peer-reviewed)abstract
- In the field of MRI brain image analysis, Diffusion tensor imaging (DTI) provides a description of the diffusion of water through tissue and makes it possible to trace fiber connectivity in the brain, yielding a map of how the brain is wired. DTI employs a second order diffusion tensor model based on the assumption of Gaussian diffusion. The Gaussian assumption, however, limits the use of DTI in solving intra-voxel fiber heterogeneity as the diffusion can be non-Gaussian in several biological tissues including human brain. Several approaches to modeling the non-Gaussian diffusion and intra-voxel fiber heterogeneity reconstruction have been proposed in the last decades. Among such approaches are the multi-compartmental probabilistic mixture models. These models include the discrete or continuous mixtures of probability distributions such as Gaussian, Wishart or von Mises-Fisher distributions. Given the diffusion weighted MRI data, the problem of resolving multiple fibers within a single voxel boils down to estimating the parameters of such models. In this chapter, we focus on such multi-compartmental probabilistic mixture models. First we present a review including mathematical formulations of the most commonly applied mixture models. Then, we present a novel method based on the mixture of non-central Wishart distributions. A mixture model of central Wishart distributions has already been proposed earlier to resolve intra-voxel heterogeneity. However, we show with detailed experiments that our proposed model outperforms the previously proposed probabilistic models specifically for the challenging scenario when the separation angles between crossing fibers (two or three) are small. We compare our results with the recently proposed probabilistic models of mixture of central Wishart distributions and mixture of hyper-spherical von Mises-Fisher distributions. We validate our approach with several simulations including fiber orientations in two and three directions and with real data. Resistivity to noise is also demonstrated by increasing levels of Rician noise in simulated data. The experiments demonstrate the superior performance of our proposed model over the prior probabilistic mixture models.
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| 11. |
- Sjölund, Jens, 1987-
(author)
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Algorithms for magnetic resonance imaging in radiotherapy
- 2018
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Doctoral thesis (other academic/artistic)abstract
- Radiotherapy plays an increasingly important role in cancer treatment, and medical imaging plays an increasingly important role in radiotherapy. Magnetic resonance imaging (MRI) is poised to be a major component in the development towards more effective radiotherapy treatments with fewer side effects. This thesis attempts to contribute in realizing this potential.Radiotherapy planning requires simulation of radiation transport. The necessary physical properties are typically derived from CT images, but in some cases only MR images are available. In such a case, a crude but common approach is to approximate all tissue properties as equivalent to those of water. In this thesis we propose two methods to improve upon this approximation. The first uses a machine learning approach to automatically identify bone tissue in MR. The second, which we refer to as atlas-based regression, can be used to generate a realistic, patient-specific, pseudo-CT directly from anatomical MR images. Atlas-based regression uses deformable registration to estimate a pseudo-CT of a new patient based on a database of aligned MR and CT pairs.Cancerous tissue has a different structure from normal tissue. This affects molecular diffusion, which can be measured using MRI. The prototypical diffusion encoding sequence has recently been challenged with the introduction of more general gradient waveforms. One such example is diffusional variance decomposition (DIVIDE), which allows non-invasive mapping of parameters that reflect variable cell eccentricity and density in brain tumors. To take full advantage of such more general gradient waveforms it is, however, imperative to respect the constraints imposed by the hardware while at the same time maximizing the diffusion encoding strength. In this thesis we formulate this as a constrained optimization problem that is easily adaptable to various hardware constraints. We demonstrate that, by using the optimized gradient waveforms, it is technically feasible to perform whole-brain diffusional variance decomposition at clinical MRI systems with varying performance.The last part of the thesis is devoted to estimation of diffusion MRI models from measurements. We show that, by using a machine learning framework called Gaussian processes, it is possible to perform diffusion spectrum imaging using far fewer measurements than ordinarily required. This has the potential of making diffusion spectrum imaging feasible even though the acquisition time is limited. A key property of Gaussian processes, which is a probabilistic model, is that it comes with a rigorous way of reasoning about uncertainty. This is pursued further in the last paper, in which we propose a Bayesian reinterpretation of several of the most popular models for diffusion MRI. Thanks to the Bayesian interpretation it possible to quantify the uncertainty in any property derived from these models. We expect this will be broadly useful, in particular in group analyses and in cases when the uncertainty is large.
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| 12. |
- Sjölund, Jens, 1987-, et al.
(author)
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Bayesian uncertainty quantification in linear models for diffusion MRI
- 2018
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In: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 175, s. 272-285
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Journal article (peer-reviewed)abstract
- Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification. © 2018 Elsevier Inc.
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| 13. |
- Sjölund, Jens, et al.
(author)
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Gaussian process regression can turn non-uniform and undersampled diffusion MRI data into diffusion spectrum imaging
- 2017
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In: IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017. - : Institute of Electrical and Electronics Engineers (IEEE). - 9781509011728 - 9781509011735 ; , s. 778-782
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Conference paper (peer-reviewed)abstract
- We propose to use Gaussian process regression to accurately estimate the diffusion MRI signal at arbitrary locations in qspace. By estimating the signal on a grid, we can do synthetic diffusion spectrum imaging: reconstructing the ensemble averaged propagator (EAP) by an inverse Fourier transform. We also propose an alternative reconstruction method guaranteeing a nonnegative EAP that integrates to unity. The reconstruction is validated on data simulated from two Gaussians at various crossing angles. Moreover, we demonstrate on nonuniformly sampled in vivo data that the method is far superior to linear interpolation, and allows a drastic undersampling of the data with only a minor loss of accuracy. We envision the method as a potential replacement for standard diffusion spectrum imaging, in particular when acquistion time is limited.
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| 14. |
- Tampu, Iulian Emil, 1993-, et al.
(author)
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Estimation of the orientationally-averaged magnetic resonance (MR) signal for characterizing neurite morphology
- 2019
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Conference paper (other academic/artistic)abstract
- The orientationally-averaged diffusion magnetic resonance (MR) signal acquired at high diffusion weighting shows great potential for answering fundamental questions about neural tissue microstructure[1]. The noise-induced bias in the magnitude-valued signal and angular resolution limitations in diffusion encoding are among the challenges in obtaining an accurate estimate. Here, we present a data processing framework for computing the orientationally-averaged diffusion signal that corrects the noise induced bias and accounts for the low angular resolution of the acquisition. Noise correction is performed using a statistical transformation framework [2] that converts the noisy MR signal from a noncentralChi distribution to a noisy Gaussian one. Weights for each of the probing directions are computed to improve the rotationally invariant representation of the sample. Synthetic data, generated to mimic diffusion acquisitions with different noise levels and number of acquisition directions, were used to test the data processing framework. The performance of the framework was evaluated by comparing the processed data with the analytical solution of the orientationally-averaged signal. Results show that the computation of the orientationally-averaged signal benefits from both the noise correction and the weighted averaging, especially in the low signal regime. This work provides a tool for processing high diffusion-weighted MR signals whose interpretation could improve our knowledge about neural tissue microstructure.[1] Özarslan E, Yolcu C, Herberthson M, Knutsson H, Westin CF. Influence of the size and curvedness ofneural projections on the orientationally averaged diffusion MR signal. Front Phys, 2018; 6:17.[2] Koay CG, Özarslan E, Basser PJ. A signal transformational framework for breaking the noise floorand its applications in MRI. J Magn Reson 2009; 197(2):108–119.
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| 15. |
- Özarslan, Evren, 1976-, et al.
(author)
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Effective Potential for Magnetic Resonance Measurements of Restricted Diffusion
- 2017
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In: Frontiers in Physics. - : Frontiers Media S.A.. - 2296-424X. ; 5
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Journal article (peer-reviewed)abstract
- The signature of diffusive motion on the NMR signal has been exploited to characterize the mesoscopic structure of specimens in numerous applications. For compartmentalized specimens comprising isolated subdomains, a representation of individual pores is necessary for describing restricted diffusion within them. When gradient waveforms with long pulse durations are employed, a quadratic potential profile is identified as an effective energy landscape for restricted diffusion. The dependence of the stochastic effective force on the center-of-mass position is indeed found to be approximately linear (Hookean) for restricted diffusion even when the walls are sticky. We outline the theoretical basis and practical advantages of our picture involving effective potentials.
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| 16. |
- Özarslan, Evren, 1976-, et al.
(author)
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Influence of the Size and Curvedness of Neural Projections on the Orientationally Averaged Diffusion MR Signal
- 2018
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In: Frontiers in Physics. - : Frontiers Media S.A.. - 2296-424X. ; 6, s. 1-10
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Journal article (peer-reviewed)abstract
- Neuronal and glial projections can be envisioned to be tubes of infinitesimal diameter as far as diffusion magnetic resonance (MR) measurements via clinical scanners are concerned. Recent experimental studies indicate that the decay of the orientationally-averaged signal in white-matter may be characterized by the power-law, Ē(q) ∝ q−1, where q is the wavenumber determined by the parameters of the pulsed field gradient measurements. One particular study by McKinnon et al. [1] reports a distinctively faster decay in gray-matter. Here, we assess the role of the size and curvature of the neurites and glial arborizations in these experimental findings. To this end, we studied the signal decay for diffusion along general curves at all three temporal regimes of the traditional pulsed field gradient measurements. We show that for curvy projections, employment of longer pulse durations leads to a disappearance of the q−1 decay, while such decay is robust when narrow gradient pulses are used. Thus, in clinical acquisitions, the lack of such a decay for a fibrous specimen can be seen as indicative of fibers that are curved. We note that the above discussion is valid for an intermediate range of q-values as the true asymptotic behavior of the signal decay is Ē(q) ∝ q−4 for narrow pulses (through Debye-Porod law) or steeper for longer pulses. This study is expected to provide insights for interpreting the diffusion-weighted images of the central nervous system and aid in the design of acquisition strategies.
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