The high patient dose applied during acquisition of C-arm based cone beam computed tomography (CBCT) scans limits the applicability of this imaging technique. Acquisition of only a region-of-interest can decrease the dose significantly. However, reconstruction from such truncated projection data can lead to severe artifacts in the tomographic image. For typical usages of C-arm based CBCT however, there often exists a prior computed tomography or CBCT scan of the patient. The proposed method enables the incorporation of such priors into the reconstruction process by jointly registering and extrapolating the prior to the limited data acquisition scenario by maximizing consistency conditions across thusly extended projections. Results show improved image quality over simple heuristic extrapolation methods typically used in practice. The proposed method can be regarded as a novel hybrid registration and extrapolation algorithm similar to established digitally reconstructed radiographs-based registration methods, but unlike these, working on mutually disjoint regions of the projection data.

Consistency conditions have been successfully utilized for data-driven artifact reductions in cone-beam computed tomography systems equipped with a large-area flat-panel detector. Recently, many formulations and applications of pairwise cone-beam consistency conditions have been published, including the Grangeat consistency condition (GCC), Smith consistency condition (SCC), and fan-beam consistency condition (FBCC). Previous works demonstrated that the polynomial coefficients for beam hardening correction could be directly computed from cone-beam raw data by enforcing consistency conditions on projection pairs. This article compares the effectiveness of pairwise consistency conditions for mono-material beam hardening correction using a second-degree polynomial. The results from our studies show that similar corrections could be achieved for ideal polychromatic projections. We also investigated the effectiveness of corrections after perturbing the projections with an increasing degree of errors other than those caused by beam hardening. The studies indicate the superior robustness of FBCCs toward Poisson noise, axial truncation, detector shift, and scatter, while GCCs were less vulnerable to projection intensity errors. The optimal choice of consistency conditions depends on the CBCT system geometry, physical phenomena other than beam hardening, and the availability and accuracy of preprocessing and artifact corrections algorithms before beam hardening correction.

In many interventional settings it would be beneficial to perform a final CBCT acquisition for outcome control after the intervention is done. However, due to high patient dose this is often omitted. Volume-of-interest acquisitions offer considerable dose reduction, but image reconstruction typically suffers from cupping artifacts and offsets in radiodensity due to the truncated projection data. In a previous work we presented a method which allows to incorporate available prior volume data into the reconstruction of volume-of-interest acquisitions in CBCT. The method works by making use of the fluoroscopic positioning images typically acquired before CBCT acquisitions in a 3D Radon space-based registration method registering the prior volume to the volumeof-interest scenario. Here, we demonstrate the application of this method on real clinical data or the first time.

The efficacy of interventional treatments highly relies on an accurate identification of the target lesions and the interventional tools in the guidance images. Whereas X-ray radiography poses low doses to the patient, its weakness is in the superposition of the different image structures in a 2D image. Cone-beam computed tomography (CBCT) might look ideal providing exact 3D information, however this is at the cost of a higher radiation dose, longer imaging time, and more space requirements in the operating room. Introducing some depth information with relatively low dose, and requiring less space, digital tomosynthesis (DTS) is a potential candidate for guiding interventions. However, due to the few number of projections and to the limited angle acquisition, DTS has poor depth resolution. Since high quality patient-specific prior CT scans are usually performed prior to the intervention for diagnosis or to plan the intervention, and given that such images share a fair amount of information with the intraoperative DTS images, we propose in this work a prior-based iterative reconstruction framework to improve the intraoperative DTS image quality. The framework is based on registering the prior CT image to an intermediate low-quality intraoperative DTS image, then iteratively re-reconstructing the intraoperative DTS image using the co-registered prior CT as the starting image. We acquired prior CT and intraoperative CBCT data of a liver phantom and simulated some intraoperative DTS projection images using a spherical ellipse scan geometry. Our results show a great improvement in the DTS image quality with the proposed method and prove the importance of choosing a good starting point for the iterative DTS reconstruction.

Dynamic dose modulation techniques are applied in CT acquisitions to ensure optimal working conditions of the detector unit and to reduce overall radiation exposure of the patient. In C-arm CT systems, large variations in the desired irradiation may require tube voltage modulation (TVM). Recent studies showed that TVM does not affect the quality of perfusion images obtained by clinical CT. Here, we investigate the impact of TVM in a C-arm cone beam CT perfusion imaging setting. We conduct a simulation study based on a real perfusion acquisition (incl. tube modulation) to directly compare results from acquisitions with and without modulation. Using two different reconstruction techniques, we analyze the influence of TVM on the extracted perfusion parameters and quantify the similarity by their correlation coefficients. Our results demonstrate that high correlation (r < 0.99) between the results with and without TVM are achieved for all perfusion parameters using a straightforward and model-based reconstruction technique. These findings suggest that dose modulation techniques, incl. TVM, can be used in C-arm CT perfusion scans without the need for additional correction methods to retain image quality of constant voltage scans.

Fast and accurate 2D/3D registration plays an important role in many applications, ranging from scientific and engineering domains all the way to medical care. Today's predominant methods are based on computationally expensive approaches, such as virtual forward or back projections, that limit the real-time applicability of the routines. Here, we present a novel concept that makes use of Grangeat's relation to intertwine information from the 3D volume and the 2D projection space in a way that allows pre-computation of all time-intensive steps. The main effort within actual registration tasks is reduced to simple resampling of the pre-calculated values, which can be executed rapidly on modern GPU hardware. We analyze the applicability of the proposed method on simulated data under various conditions and evaluate the findings on real data from a C-arm CT scanner. Our results show high registration quality in both simulated as well as real data scenarios and demonstrate a reduction in computation time for the crucial computation step by a factor of six to eight when compared to state-of-the-art routines. With minor trade-offs in accuracy, this speed-up can even be increased up to a factor of 100 in particular settings. To our knowledge, this is the first application of Grangeat's relation to the topic of 2D/3D registration. Due to its high computational efficiency and broad range of potential applications, we believe it constitutes a highly relevant approach for various problems dealing with cone beam transmission images.

Radon transforms allow to represent n-dimensional objects by all their possible (n-1)-dimensional integrals. They find broad usage in a variety of image processing topics, covering pattern recognition and tomographic imaging. Potentially the most frequently used version is the 2D Radon transform which is commonly computed by means of the ray tracing procedure (Siddon, Joseph etc.).1 The problem comes down to a method of approximating a line integral through a 2D pixelized image. For higher dimensions, however, this problem becomes more and more complex and typically involves a substantial amount of case differentiation, making it particularly ill-suited for use in massively parallelized computation (e.g. on GPUs). Additionally, implementation effort is substantial and quite error-prone. Here, we propose a simple strategy to compute the (n-1)-dimensional integrals in a generalized manner by reducing the sampling problem to a single matrix multiplication. We further present OpenCL implementations for n=2 and n=3, making use of hardware interpolation methods on texture memory of GPU devices to provide a fast computation of the transform.

Simulated data can play an important role in many research topics in the field of X-ray computed tomography (CT). Most existing tools lack the flexibility, the ease of use, or possibilities to incorporate own routines to fulfill all needs of researchers. We propose a novel, modular C++ open-source simulation toolkit that provides full flexibility for system setups, acquisition geometry, forward projection models, as well as physical effects to be considered in the simulation. All mentioned aspects are freely customizable (and extendable) to grant users full control to tailor the toolkit to their specific needs. Here, we present an early version which is under active development. By that, we want to encourage the community to provide feedback and suggestions already at an early stage of development.

Cone-beam computed tomography (CBCT) is a widely used technique for diagnostic or monitoring purposes. Compared to the traditional CT, a CBCT is more affected by scatter artifacts because of the large volume being irradiated by the beam. The research divulged in this paper is about the assessment of the influence that metallic implants may have on degrading image quality of CBCT due to scattered radiation. The evaluation method is based on Monte-Carlo (MC) simulations of the physical processes that X-ray photons undergo in typical CBCT setups, in presence and absence of highly scattering metallic implants (coils used for treatment of aneurysms and pacemakers). The results show that the scattered radiation caused by metallic objects and reaching the detector produces slight degradation of CBCT image quality and, moreover, it is demonstrated that the intrinsic absorption and beam-hardening effect of these implants have bigger impact on the overall image fidelity.

A typical incomplete data problem arising in cone-beam computed tomography (CBCT) occurs when an object is either too large to be projected onto the detector or is deliberately only projected in parts. This problem is called truncation. Tomographic images reconstructed from truncated projection data can be severely impaired by image artifacts depending on the degree of truncation. A typical strategy to counter this is to extend the projection data by some smooth extrapolation. In order to accurately approximate the shape of the scanned object outside of the volume of interest (VOI), we previously presented a method which fits an extrapolation model to the truncated data by minimizing an error function based on the Grangeat consistency condition (GCC). In this work we propose a method of reducing the complexity of the extrapolation by making use of the 0th image moments of the truncated projection data.

Computed tomography (CT) scans are frequently used intraoperatively, for example to control the positioning of implants during intervention. Often, to provide the required information, a full field of view is unnecessary. I nstead, the region-of-interest (ROI) imaging can be performed, allowing for substantial reduction in the applied X-ray dose. However, ROI imaging leads to data inconsistencies, caused by the truncation of the projections. This lack of information severely impairs the quality of the reconstructed images. This study presents a proof-of-concept for a new approach that combines the incomplete CT data with ultrasound data and time of flight measurements in order to restore some of the lacking information. The routine is evaluated in a simulation study using the original Shepp-Logan phantom in ROI cases with different degrees of truncation. Image quality is assessed by means of normalized root mean square error. The proposed method significantly reduces truncation artifacts in the reconstructions and achieves considerable radiation exposure reductions.

Brain-computer-interfaces (BCIs) aim to give mobility to motion-disabled people, therefore they are used to control external devices sending low-level-commands to manipulate single degrees of freedom (DOFs) or high-level commands to directly reach for pre-defined targets. Low-level commands are not suitable for complex tasks where multiple DOFs are manipulated, as information transfer rates are low in general, whereas high-level commands enable complex tasks but lack free navigation (Sakurada et al., 2013, Diez et al., 2011). So far approaches combining both low- and high-level commands have been applied for spelling devices, allowing users to select single letters allong with the opportunity to automatically complete words (Saa et al., 2015). This study investigates the possibility to apply a combined approach to control movable objects. Brain activity is measured with EEG in a steady-state-visual-evoked-potential (SSVEP) experiment. Canonical correlation analysis (CCA) is used for feature generation. Classification is conducted by applying a Naive Bayes approach. The experimental setup contains of 5 stimuli, 4 of which are associated to moving a cursor in a 2D space, one is used to automatically reach a predicted target. Target prediction is based on the extrapolation of the cursors trajectory. Classification achieved high recognition rates. Targets could be infered successfully from the trajectory of the cursor. Once the right target was predicted automatic reaching could be used. As a result, targets were attained substantially faster than with non-automatic reaching. Additionally, users were granted the possibility to cancel automatic cursor movement in case they changed their mind about the target. The investigated approach enables control of different movable objects (e.g. a robotic arm or a wheelchair) in a combined low-level and high-level command fashion, closing the gap between free navigation and the possibility to automatically attain a specific target. This study serves as a working proof-of-concept for a new, more natural BCI control for movable objects. BMBF and FC STIMULATE (13GW0095A).

PURPOSE:The issue of perfusion imaging using a temporal decomposition model is to enable the reconstruction of undersampled measurements acquired with a slowly rotating x-ray-based imaging system, for example, a C-arm-based cone beam computed tomography (CB-CT). The aim of this work is to integrate prior knowledge into the dynamic CT task in order to reduce the required number of views and the computational effort as well as to save dose. The prior knowledge comprises of a mathematical model and clinical perfusion data.METHODS:In case of model-based perfusion imaging via superposition of specified orthogonal temporal basis functions, a priori knowledge is incorporated into the reconstructions. Instead of estimating the dynamic attenuation of each voxel by a weighting sum, the modeling approach is done as a preprocessing step in the projection space. This point of view provides a method that decomposes the temporal and spatial domain of dynamic CT data. The resulting projection set consists of spatial information that can be treated as individual static CT tasks. Consequently, the high-dimensional model-based CT system can be completely transformed, allowing for the use of an arbitrary reconstruction algorithm.RESULTS:For CT, reconstructions of preprocessed dynamic in silico data are illustrated and evaluated by means of conventional clinical parameters for stroke diagnostics. The time separation technique presented here, provides the expected accuracy of model-based CT perfusion imaging. Consequently, the model-based handled 4D task can be solved approximately as fast as the corresponding static 3D task.CONCLUSION:For C-arm-based CB-CT, the algorithm presented here provides a solution for resorting to model-based perfusion reconstruction without its connected high computational cost. Thus, this algorithm is potentially able to have recourse to the benefit from model-based perfusion imaging for practical application. This study is a proof of concept.

Brain-Machine Interfaces (BMIs) can help to regain communication and mobility in severely disabled persons. Especially spelling devices, rehabilitation of stroke patients and prosthesis control are fields of application. However, noninvasive BMIs, commonly using electroencephalography (EEG), suffer from poor signal quality, resulting in erroneous commands. In order to detect such erroneous commands, error potentials (ErrPs) generated in the brain after a user perceived a negative feedback can be decoded. The aim of this study was to investigate how accurate the presence of ErrPs can be detected from simultaneously recorded EEG and magnetoencephalography (MEG). In a BMI experiment involving 19 participants, the selection of a covertly attended object was decoded from EEG/MEG and presented as feedback (Reichert et al., 2017). To facilitate investigation of ErrPs, we artificially presented negative feedback to achieve at least 40% incorrect feedback. Using spatial filtering and SVM classification, we determined the probability of successfully detecting an ErrP. While an accurate error detection permits a reduction of errors made by the covert attention detector (i.e. rejection of potentially erroneous commands), the error rate of the ErrP classification inevitably also introduces accidental rejection of correct commands. In order to evaluate the potential benefit of ErrP detection in a BMI, we define a probability measure that takes into account errors of both the covert attention detector and the error detector. The components extracted by the data-driven spatial filter showed a positive deflection between 200 and 500 ms after feedback presentation, mainly driving the ErrP decoding. The correctness of perceived feedback could be decoded reliably (EEG: 71.9% SE: 1.5%; MEG: 72.7%, SE: 1.2%). However, the actual BMI revealed higher accuracies (EEG: 87.9%, SE: 2.2%; MEG: 95.8%, SE: 1.0%) compared to the ErrP detector. Thus, when applying ErrP detection, the number of erroneous selections was reduced but concurrently an even higher number of correct selections was rejected, which significantly reduced the information transfer rate. Probability theory suggests that ErrP detection only is advantageous if error detection rates exceed the accuracy of the feedback generating BMI itself. Our results indicate that EEG and MEG are comparably suitable to detect the perception of erroneous feedback from brain activity recordings. The achieved prediction rate is in accordance with other approaches reported in the literature using EEG. However, those prediction rates only are advantageous, if the performance of the BMI is lower than that of the ErrP detector. Thus, highly accurate detection of errors would be required to efficiently correct errors made by a BMI.

Objective. A major goal of brain-computer-interface (BCI) technology is to assist disabled people with everyday activities. Although lots of information is available on typical movement procedures, integration of this knowledge is rarely found in motor BCI decoding solutions. Approach. Here, we apply a hidden Markov model (HMM) based approach for continuous decoding of finger movements from electrocorticographic recordings from three human subjects. Information about relative frequencies of consecutive finger movements is included in the decoding routine using so-called bi-gram models. Main results. The presented method achieves accuracies up to 73% for continuous decoding of finger movements. Prior knowledge (PK) incorporation further increases decoding accuracies by up to 12.5% (absolute) in a generic BCI setting and by up to 22% in a more specific, task-related setup. Significance. The results provide evidence for the importance of PK incorporation for motor BCI decoding. We show that this can be done conveniently using HMM decoders. Our results strongly suggest the extension of the use of HMMs from conventional speech-related topics (like spelling devices) towards motor BCI solutions.

Over the past decade electrocorticography (ECoG) recordings have been evaluated as a promising signal platform in basic and clinical neuroscience (Schalk and Leuthardt, xxxx). Their characteristics, e.g. high spatio-temporal resolution, noise resistance and signal fidelity, make them especially suited for single trial analysis of functional paradigms for corticography. Because recording time is limited and electrode positioning is based on clinical indication, paradigms must be chosen carefully in respect of grid electrode positions and the information content of the cortical area covered by the grid. We propose a method using magnetoencephalography (MEG) in single-trial analysis to estimate the information provided by the grids ahead of implantation. In single-trial analysis a main focus in evaluating a study is on classification rates (number of trials a classifier decodes correctly). Higher decoding accuracy means better use of the brain signal. We use these classification rates in order to estimate the information content of the grid in respect of the paradigm. In our concept, MEG data is acquired for a set of paradigms. Source analysis is performed, features extracted and classifiers for each paradigm trained. The channel selection of the classifier is limited to the channel set of the brain areas that will be directly covered by the ECoG grid of the patient. With this information only, classification accuracies for all paradigms are computed. Highest decoding accuracy for a paradigm means it is best suited for this grid location. Therefore, our method suggests choosing the experiment with the best decoding accuracy to be run on this patient. The comparability of ECoG and MEG data and its respective decoding performances has been shown (Heinze et al., xxxx). Focus of this study is to evaluate if restrictions to the channel selection lead to results that are expected in perspective to these restrictions (e.g. drop of decoding accuracy for motor stimuli when motor information is excluded). The confusion matrices and channel maps in Fig. 1 prove this to be true. This shows that MEG data provides a spatio-temporal resolution that is good enough to estimate the information content for any ECoG grid. In principle, our method can be inverted to plan grid implantation for brain-computer-interfaces (BCI): decoding algorithms for the desired BCI application (e.g. prosthesis control) could be run using MEG data. Feature selection routines extract the most important sensors for decoding. Signals of these sensors are mapped to the anatomy using source analysis. The resulting location represents the optimal implantation position. Additionally, alternative placements (e.g. enabling minimally invasive implantation) could be simulated and trade-offs can be made between surgery risk and signal optimization. Funding: Saxony-Anhalt (grant I 60) Forschungscampus STIMULATE

Objective. Adapting classifiers for the purpose of brain signal decoding is a major challenge in brain computer-interface (BCI) research. In a previous study we showed in principle that hidden Markov models (HMM) are a suitable alternative to the well -studied static classifiers. However, since we investigated a rather straightforward task, advantages from modeling of the signal could not be assessed. Approach. Here, we investigate a more complex data set in order to find out to what extent HMMs, as a dynamic classifier, can provide useful additional information. We show for a visual decoding problem that besides category information, HMMs can simultaneously decode picture duration without an additional training required. This decoding is based on a strong correlation that we found between picture duration and the behavior of the Viterbi paths. Main results. Decoding accuracies of up to 80% could be obtained for category and duration decoding with a single classifier trained on category information only. Significance. The extraction of multiple types of information using a single classifier enables the processing of more complex problems, while preserving good training results even on small databases. Therefore, it provides a convenient framework for online real -life BCI utilizations.

Magnetoencephalography (MEG) is a quite over-looked imaging modality within the field of brain-computer-interface (BCI) research, but due to its promising signal quality and non-invasive character it offers a variety of unexplored possibilities for paradigm design in electrocorticography (ECoG). In this study we investigate MEG data from a visual paradigm with motor responses for the influence of brain signals from different brain regions on the achievable decoding accuracies. Across data sets from all four subjects, our results consistently match reasonable expectations. This holds true not only for achievable decoding accuracies, but also for the spatial distrubition of brain regions that contribute most valuable information to the classifier. Therefore, our findings are a step further towards estimations of ECoG outcomes in various grid positions based on a fully non-invasive modality.

Schlüsselworter Brain-Computer-Interfaces - Elektrokortikografie - Magnetoenzephalografie - Klassifikation - Paradigmenplanung

Heterogeneity among cells is a common characteristic of living systems. For mathematical modeling of heterogeneous cell populations, one typically has to reconstruct the underlying heterogeneity from measurements on the population level. Based on recent insights into the mathematical nature of this problem as an inverse problem of tomographic type, we evaluate numerical methods to perform such a reconstruction in basic case studies. We compare a kernel density based optimization approach, filtered back projection, and algebraic reconstruction techniques. The latter two are well established methods in computed tomography.