Elias Kellner

Gibbs‐ringing artifact removal based on local subvoxel‐shifts

To develop a fast and stable method for correcting the gibbs‐ringing artifact.

ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI

&NA; Ischemic stroke is the most common cerebrovascular disease, and its diagnosis, treatment, and study relies on non‐invasive imaging. Algorithms for stroke lesion segmentation from magnetic resonance imaging (MRI) volumes are intensely researched, but the reported results are largely incomparable due to different datasets and evaluation schemes. We approached this urgent problem of comparability with the Ischemic Stroke Lesion Segmentation (ISLES) challenge organized in conjunction with the MICCAI 2015 conference. In this paper we propose a common evaluation framework, describe the publicly available datasets, and present the results of the two sub‐challenges: Sub‐Acute Stroke Lesion Segmentation (SISS) and Stroke Perfusion Estimation (SPES). A total of 16 research groups participated with a wide range of state‐of‐the‐art automatic segmentation algorithms. A thorough analysis of the obtained data enables a critical evaluation of the current state‐of‐the‐art, recommendations for further developments, and the identification of remaining challenges. The segmentation of acute perfusion lesions addressed in SPES was found to be feasible. However, algorithms applied to sub‐acute lesion segmentation in SISS still lack accuracy. Overall, no algorithmic characteristic of any method was found to perform superior to the others. Instead, the characteristics of stroke lesion appearances, their evolution, and the observed challenges should be studied in detail. The annotated ISLES image datasets continue to be publicly available through an online evaluation system to serve as an ongoing benchmarking resource (www.isles‐challenge.org). HighlightsEvaluation framework for automatic stroke lesion segmentation from MRIPublic multi‐center, multi‐vendor, multi‐protocol databases releasedOngoing fair and automated benchmark with expert created ground truth setsComparison of 14+7 groups who responded to an open challenge in MICCAISegmentation feasible in acute and unsolved in sub‐acute cases Graphical abstract Figure. No caption available.

MesoFT: unifying diffusion modelling and fiber tracking.

One overarching challenge of clinical magnetic resonance imaging (MRI) is to quantify tissue structure at the cellular scale of micrometers, based on an MRI acquisition with a millimeter resolution. Diffusion MRI (dMRI) provides the strongest sensitivity to the cellular structure. However, interpreting dMRI measurements has remained a highly ill-posed inverse problem. Here we propose a framework that resolves the above challenge for human white matter fibers, by unifying intra-voxel mesoscopic modeling with global fiber tractography. Our algorithm is based on a Simulated Annealing approach which simultaneously optimizes diffusion parameters and fiber locations. Each fiber carries its by their individual set of diffusion parameters which allows to link them structural relationships.

About the Geometry of Asymmetric Fiber Orientation Distributions

Fiber orientation distributions (FODs) based on diffusion-sensitized magnetic resonance imaging are usually symmetric, primarily due to the nature of the diffusion. In contrast, the underlying fiber configurations are not, as bending or fanning configurations are inherently asymmetric. We propose to dismiss the symmetry of the FOD to additionally encode the asymmetry of the underlying fiber configuration. This is of particular importance for low resolution images that are common in diffusion weighted imaging. We set up the mathematical foundations and geometric interpretations of asymmetric FODs and show how one can benefit from these considerations. We infer a continuity condition that is used as a prior during FOD estimation by constrained spherical deconvolution. This new prior shows superior performance in comparison to other spatial regularization strategies in reliability and accuracy.

Arterial input function measurements for bolus tracking perfusion imaging in the brain

Imaging of cerebral perfusion by tracking the first passage of an exogenous paramagnetic contrast agent (termed dynamic susceptibility contrast, MRI) has been used in the clinical practice for about a decade. However, the primary goal of dynamic susceptibility contrast MRI to directly quantify the local cerebral blood flow remains elusive. The major challenge of dynamic susceptibility contrast MRI is to measure the contrast inflow to the brain, i.e., the arterial input function. The measurement is complicated by the limited dynamic range of MRI pulse sequences that are optimized for a good contrast in brain tissue but are suboptimal for a much higher tracer concentration in arterial blood. In this work, we suggest a novel method for direct arterial input function quantification. The arterial input function is measured in the carotid arteries with a dedicated plug‐in to the conventional pulse sequence to enable resolution of T2 on the order of a millisecond. The new technique is compatible with the clinical measurement protocols. Applied to the pig model (N = 13), the method demonstrates robustness of the arterial input function measurement. The cardiac output and cerebral blood volume, obtained without adjustable parameters, agree well with positron emission tomography measurements and values found in the literature. Magn Reson Med, 2013.

On the design of filters for fourier and oSVD-based deconvolution in bolus tracking perfusion MRI

ObjectBolus tracking perfusion evaluation relies on the deconvolution of a tracers concentration time-courses in an arterial and a tissue voxel following the tracer kinetic model. The object of this work is to propose a method to design a data-driven Tikhonov regularization filter in the Fourier domain and to compare it to the singular value decomposition (SVD)–based approaches using the mathematical equivalence of Fourier and circular SVD (oSVD).Materials and MethodsThe adaptive filter is designed using Tikhonov regularization that depends on only one parameter. Using a simulation, such an optimal parameter that minimizes the sum of statistical and systematic error is determined as a function of the first moment difference between the tissue and the arterial curve and the contrast to noise ratios of the input data (CNRa in arteries and CNRt in tissue). The performance of the method is evaluated and compared to oSVD in simulations and measured data.ResultsThe proposed method yields a smaller flow underestimation especially for high flows when compared to the oSVD approach with constant threshold. However, this improvement comes to the price of an increased uncertainty of the flow values. The translation of the Tikhonov regularization parameter to an adaptive oSVD-threshold is in good agreement with the literature.ConclusionThe proposed method is a comprehensive approach for the design of data-driven filters that can be easily adapted to specific needs.

The Potential of Microvessel Density in Prediction of Infarct Growth: A Two-Month Experimental Study in Vessel Size Imaging

Objectives: Vessel size imaging is a novel technique to evaluate pathological changes of the microvessel density quantity Q and the mean vessel size index (VSI). As a follow-up study, we assessed these parameters for microscopic description of ischemic penumbra and their potentials in predicting lesion growth. Methods: Seventy-five patients with a perfusion-diffusion mismatch were examined within 24 h from symptom onset. We defined three regions of interest: the initial infarct (INF), the ischemic penumbra (IPE), and the healthy region (HEA) symmetric to the IPE. For 23 patients with a 6th-day follow-up, IPE regions were divided into areas of infarct growth and areas of oligemia. Result: The median values of Q and VSI were: for INF 0.29 s-1/3 and 15.8 µm, for IPE 0.33 s-1/3 and 20.6 µm and for HEA 0.36 s-1/3 and 17.4 µm. The Q in the IPE was significantly smaller than in HEA, and VSI was significantly larger. The Q with a threshold of 0.32 s-1/3 predicted the final infarction with a sensitivity of 69% and a specificity of 64%. Conclusions: The reduced Q and increased VSI in the IPE confirmed our previous pilot results. Although Q showed a trend to identify the severity of ischemia in an overall voxel population, its potential in predicting infarct growth needs to be further tested in a larger cohort including a clear status of reperfusion and recanalization.

Mesoscopic imaging of glioblastomas: Are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype?

The purpose of this study was to identify markers from perfusion, diffusion, and chemical shift imaging in glioblastomas (GBMs) and to correlate them with genetically determined and previously published patterns of structural magnetic resonance (MR) imaging. Twenty-six patients (mean age 60 years, 13 female) with GBM were investigated. Imaging consisted of native and contrast-enhanced 3D data, perfusion, diffusion, and spectroscopic imaging. In the presence of minor necrosis, cerebral blood volume (CBV) was higher (median ± SD, 2.23% ± 0.93) than in pronounced necrosis (1.02% ± 0.71), pcorr = 0.0003. CBV adjacent to peritumoral fluid-attenuated inversion recovery (FLAIR) hyperintensity was lower in edema (1.72% ± 0.31) than in infiltration (1.91% ± 0.35), pcorr = 0.039. Axial diffusivity adjacent to peritumoral FLAIR hyperintensity was lower in severe mass effect (1.08*10–3 mm2/s ± 0.08) than in mild mass effect (1.14*10–3 mm2/s ± 0.06), pcorr = 0.048. Myo-inositol was positively correlated with a marker for mitosis (Ki-67) in contrast-enhancing tumor, r = 0.5, pcorr = 0.0002. Changed CBV and axial diffusivity, even outside FLAIR hyperintensity, in adjacent normal-appearing matter can be discussed as to be related to angiogenesis pathways and to activated proliferation genes. The correlation between myo-inositol and Ki-67 might be attributed to its binding to cell surface receptors regulating tumorous proliferation of astrocytic cells.

Molecular differences between cerebral blood volume and vessel size in glioblastoma multiforme

The purpose of this study was to investigate the molecular background of cerebral blood volume (CBV) and vessel size (VS) of capillaries in glioblastoma multiforme (GBM). Both parameters are derived from extended perfusion MR imaging. A prospective case study including 21 patients (median age 66 years, 10 females) was performed. Before operation, CBV and VS of contrast enhancing tumor were assessed. Tissue was sampled from the assessed areas under neuronavigation control. After RNA extraction, transcriptional data was analyzed by Weighted Gene Co-Expression Network Analysis (WGCNA) and split into modules based on its network affiliations. Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the genetic modules. These were applied on 484 GBM samples of the TCGA database. Ten modules were highly correlated to CBV and VS. One module was exclusively associated to VS and highly correlated to hypoxia, another one exclusively to CBV showing strong enrichments in the Epithelial Growth Factor (EGF) pathway and Epithelial-to-Mesenchymal-Transition (EMT). Moreover, patients with increased CBV and VS predominantly showed a mesenchymal gene-expression, a finding that could be corroborated by TCGA data. In conclusion, CBV and VS mirror different genetic pathways and reflect certain molecular subclasses of GBM.

Integrative Diffusion-Weighted Imaging and Radiogenomic Network Analysis of Glioblastoma multiforme

In the past, changes of the Apparent Diffusion Coefficient in glioblastoma multiforme have been shown to be related to specific genes and described as being associated with survival. The purpose of this study was to investigate diffusion imaging parameters in combination with genome-wide expression data in order to obtain a comprehensive characterisation of the transcriptomic changes indicated by diffusion imaging parameters. Diffusion-weighted imaging, molecular and clinical data were collected prospectively in 21 patients. Before surgery, MRI diffusion metrics such as axial (AD), radial (RD), mean diffusivity (MD) and fractional anisotropy (FA) were assessed from the contrast enhancing tumour regions. Intraoperatively, tissue was sampled from the same areas using neuronavigation. Transcriptional data of the tissue samples was analysed by Weighted Gene Co-Expression Network Analysis (WGCNA) thus classifying genes into modules based on their network-based affiliations. Subsequent Gene Set Enrichment Analysis (GSEA) identified biological functions or pathways of the expression modules. Network analysis showed a strong association between FA and epithelial-to-mesenchymal-transition (EMT) pathway activation. Also, patients with high FA had a worse clinical outcome. MD correlated with neural function related genes and patients with high MD values had longer overall survival. In conclusion, FA and MD are associated with distinct molecular patterns and opposed clinical outcomes.