BIBBOX, a FAIR toolbox and App Store for life science research

Data quality has recently become a critical topic for the research community. European guidelines recommend that scientific data should be made FAIR: findable, accessible, interoperable and reusable. However, as FAIR guidelines do not specify how the stated principles should be implemented, it might not be straightforward for researchers to know how actually to make their data FAIR. This can prevent life-science researchers from sharing their datasets and pipelines, ultimately hindering the progress of research. To address this difficulty, we developed the BIBBOX, which is a platform that supports researchers publishing their datasets and the associated software in a FAIR manner.

Photoacoustic computational ghost imaging

Photoacoustic imaging with optical resolution usually requires a single-pixel raster scan. An alternative approach based on illumination with patterns obtained from a Hadamard matrix, measurement of the generated ultrasound wave with a single detector, followed by a reconstruction known from computational ghost imaging is demonstrated here. Since many pixels on the object are illuminated at the same time, thereby contributing to the recorded signal, this approach gives a better contrast-to-noise ratio compared to the raster scan, as demonstrated in a phantom experiment. Furthermore, exploiting the temporal information for depth-resolved imaging is possible. The proposed method will be beneficial in situations where the radiant exposure of a sample is limited due to either safety precautions or the properties of the available light source.

Long-Term Mutual Training for the CYBATHLON BCI Race With a Tetraplegic Pilot: A Case Study on Inter-Session Transfer and Intra-Session Adaptation

CYBATHLON is an international championship where people with severe physical disabilities compete with the aid of state-of-the-art assistive technology. In one of the disciplines, the BCI Race, tetraplegic pilots compete in a computer game race by controlling an avatar with a brain-computer interface (BCI). This competition offers a perfect opportunity for BCI researchers to study long-term training effects in potential end-users, and to evaluate BCI performance in a realistic environment. In this work, we describe the BCI system designed by the team Mirage91 for participation in the CYBATHLON BCI Series 2019, as well as in the CYBATHLON 2020 Global Edition. Furthermore, we present the BCI’s interface with the game and the main methodological strategies, along with a detailed evaluation of its performance over the course of the training period, which lasted 14 months. The developed system was a 4-class BCI relying on task-specific modulations of brain rhythms. We implemented inter-session transfer learning to reduce calibration time, and to reinforce the stability of the brain patterns. Additionally, in order to compensate for potential intra-session shifts in the features’ distribution, normalization parameters were continuously adapted in an unsupervised fashion. Across the aforementioned 14 months, we recorded 26 game-based training sessions. Between the first eight sessions, and the final eight sessions leading up to the CYBATHLON 2020 Global Edition, the runtimes significantly improved from 255 ± 23 s (mean ± std) to 225 ± 22 s, respectively. Moreover, we observed a significant increase in the classifier’s accuracy from 46 to 53%, driven by more distinguishable brain patterns. Compared to conventional single session, non-adaptive BCIs, the inter-session transfer learning and unsupervised intra-session adaptation techniques significantly improved the performance. This long-term study demonstrates that regular training helped the pilot to significantly increase the distance between task-specific patterns, which resulted in an improvement of performance, both with respect to class separability in the calibration data, and with respect to the game. Furthermore, it shows that our methodological approaches were beneficial in transferring the performance across sessions, and most importantly to the CYBATHLON competitions.

Conical ring array detector for large depth of field photoacoustic macroscopy

Photoacoustic microscopy and macroscopy (PAM) using focused detector scanning are emerging imaging methods for biological tissue, providing high resolution and high sensitivity for structures with optical absorption contrast. However, achieving a constant lateral resolution over a large depth of field for deeply penetrating photoacoustic macroscopy is still a challenge. In this work, a detector design for scanning photoacoustic macroscopy is presented. Based on simulation results, a sensor array geometry is developed and fabricated that consists of concentric ring elements made of polyvinylidene fluoride (PVDF) film in a geometry that combines a centered planar ring with several inclined outer ring elements. The reconstruction algorithm, which uses dynamic focusing and coherence weighting, is explained and its capability to reduce artefacts occurring for single element conical sensors is demonstrated. Several phantoms are manufactured to evaluate the performance of the array in experimental measurements. The sensor array provides a constant axial and lateral resolution of 95 µm and 285 µm, respectively, over a depth of field of 20 mm. The depth of field corresponds approximately to the maximum imaging depth in biological tissue, estimated from the sensitivity of the array. With its ability to achieve the maximum resolution even with a very small scanning range, the array is believed to have applications in the imaging of limited regions of interest buried in biological tissue.

Modeling photoacoustic imaging with a scanning focused detector using Monte Carlo simulation of energy deposition

Photoacoustic imaging using a focused, scanning detector in combination with a pulsed light source is a common technique to visualize light-absorbing structures in biological tissue. In the acoustic resolution mode, where the imaging resolution is given by the properties of the transducer, there are various challenges related to the choice of sensors and the optimization of the illumination. These are addressed by linking a Monte Carlo simulation of energy deposition to a time-domain model of acoustic propagation and detection. In this model, the spatial and electrical impulse responses of the focused transducer are combined with a model of acoustic attenuation in a single response matrix, which is used to calculate detector signals from a volumetric distribution of absorbed energy density. Using the radial symmetry of the detector, the calculation yields a single signal in less than a second on a standard personal computer. Various simulation results are shown, comparing different illumination geometries and demonstrating spectral imaging. Finally, simulation results and experimental images of an optically characterized phantom are compared, validating the accuracy of the model. The proposed method will facilitate the design of photoacoustic imaging devices and will be used as an accurate forward model for iterative reconstruction techniques.

Early detection of coronary artery disease in patients studied with magnetocardiography: an automatic classification system based on signal entropy

We propose an automatic system for the classification of coronary artery disease (CAD) based on entropy measures of MCG recordings. Ten patients with coronary artery narrowing ≥ or ≤ 50% were categorized by a multilayer perceptron (MLP) neural network based on Linear Discriminant Analysis (LDA). Best results were obtained with MCG at rest: 99% sensitivity, 97% specificity, 98% accuracy, 96% and 99% positive and negative predictive values for single heartbeats. At patient level, these results correspond to a correct classification of all patients. The classifier's suitability to detect CAD-induced changes on the MCG at rest was validated with surrogate data.