Three-dimensional cone-beam imaging has become valuable in interventional radiology. Currently, this tool, referred to as C-arm CT, employs a circular short-scan for data acquisition, which limits the axial volume coverage and yields unavoidable cone-beam artifacts. To improve flexibility in axial coverage and image quality, there is a critical need for novel data acquisition geometries and related image reconstruction algorithms. For this purpose, we previously introduced the extended line-ellipse-line trajectory, which allows complete scanning of arbitrary volume lengths in the axial direction together with adjustable axial beam collimation, from narrow to wide depending on the targeted application. A first implementation of this trajectory on a state-of-the-art robotic angiography system is reported here. More specifically, an assessment of the quality of this first implementation is presented. The assessment is in terms of geometric fidelity and repeatability, complemented with a first visual inspection of how well the implementation enables imaging an anthropomorphic head phantom. The geometric fidelity analysis shows that the ideal trajectory is closely emulated, with only minor deviations that have no impact on data completeness and clinical practicality. Also, mean backprojection errors over short-term repetitions are shown to be below the detector pixel size at field-of-view center for most views, which indicates repeatability is satisfactory for clinical utilization. These repeatability observations are further supported by values of the Structural Similarity Index Metric above 94% for reconstructions of the FORBILD head phantom from computer-simulated data based on repeated data acquisition geometries. Last, the real data experiment with the anthropomorphic head phantom shows that the high contrast features of the phantom are well reconstructed without distortions as well as without breaks or other disturbing transition zones, which was not obvious given the complexity of the data acquisition geometry and the major variations in axial coverage that occur over the scan.

In previous work, we proposed a novel data acquisition geometry, called the Extended LEL trajectory, for Carm CT imaging in interventional radiology. This novel geometry aims at enabling larger axial field-of-view coverage without conebeam artifacts for imaging with a full X-ray beam as well as with a collimated X-ray beam used for scatter reduction purposes. In this work, we report on a first implementation of the Extended LEL trajectory on a state-of-the-art C-arm system. Highly satisfactory results are shown in terms of trajectory fidelity and repeatability. Suitability of the data for head imaging is also demonstrated using a Rando head phantom without and with 50% beam collimation.

Smart embedded systems often run sophisticated pattern recognition algorithms and are found in many areas like automotive, sports and medicine. The developer of such a system is often confronted with the accuracy–cost conflict as the resulting system should be as accurate as possible while being able to run on resource constraint hardware. This article introduces a method to support the solution of this design conflict with accuracy–cost reports. These reports compare classification systems regarding their classification rate (accuracy) and the mathematical operations and parameters of the working phase (cost). Our method is used to deduce the specific cost of various popular pattern recognition algorithms and to derive the overall cost of a classification system. We also show how our analysis can be used to estimate the computational cost for specific hardware architectures. A software toolbox to create accuracy–cost reports was implemented to facilitate the automatic classification system comparison with the presented methodology. The software is available for download and as supplementary material. We performed different experiments on synthetic and real-world data to underline the value of this analysis. Accurate and computationally cheap classification systems were easily identified. We were even able to find a better implementation candidate in an existing embedded classification problem. This work is the first step towards a comprehensive support tool for the design of embedded classification systems.

In this paper, we describe a method to enlarge the field-ofview of those scan modes by rotating the detector such that instead of the detector width the diagonal of the detector limits the lateral field-of-view for a Short and two Large Volume Scan trajectories. After implementation of the modifications we obtain a gain of 25.8% in field-of-view diameter accompanied by a simultaneous loss of height of about 50 %. The coverage is increased by 20% for the Short Scan and by 16.7% for the Large Volume Scans. After introducing a detector shift trade-off we still increase the coverage field-of-view width while compensating the axial loss. Also a reduced source-to-detector distance has been investigated, which further increases the coverage. Finally, a Helical Large Volume Scan trajectory was simulated leading to the same width gain and coverage but increasing the height by 20.8% for the maximal shift and 33.3% for the trade-off version in comparison to a standard Large Volume Scan.

In this study, we intended to differentiate patients with essential tremor (ET) from tremor dominant Parkinson disease (PD). Accelerometer and electromyographic signals of hand movement from standardized upper extremity movement tests (resting, holding, carrying weight) were extracted from 13 PD and 11 ET patients. The signals were filtered to remove noise and non-tremor high frequency components. A set of statistical features was then extracted from the discrete wavelet transformation of the signals. Principal component analysis was utilized to reduce dimensionality of the feature space. Classification was performed using support vector machines. We evaluated the proposed method using leave one out cross validation and we report overall accuracy of the classification. With this method, it was possible to discriminate 12/13 PD patients from 8/11 patients with ET with an overall accuracy of 83%. In order to individualize this finding for clinical application we generated a posterior probability for the test result of each patient and compared the misclassified patients, or low probability scores to available clinical follow up information for individual cases. This non-standardized post hoc analysis revealed that not only the technical accuracy but also the clinical accuracy limited the overall classification rate. We show that, in addition to the successful isolation of diagnostic features, longitudinal and larger sized validation is needed in order to prove clinical applicability.

In this study, we intended to differentiate patients with essential tremor (ET) from tremor dominant Parkinson disease (PD). Accelerometer and electromyographic signals of hand movement from standardized upper extremity movement tests (resting, holding, carrying weight) were extracted from 13 PD and 11 ET patients. The signals were filtered to remove noise and non-tremor high frequency components. A set of statistical features was then extracted from the discrete wavelet transformation of the signals. Principal component analysis was utilized to reduce dimensionality of the feature space. Classification was performed using support vector machines. We evaluated the proposed method using leave one out cross validation and we report overall accuracy of the classification. With this method, it was possible to discriminate 12/13 PD patients from 8/11 patients with ET with an overall accuracy of 83%. In order to individualize this finding for clinical application we generated a posterior probability for the test result of each patient and compared the misclassified patients, or low probability scores to available clinical follow up information for individual cases. This non-standardized post hoc analysis revealed that not only the technical accuracy but also the clinical accuracy limited the overall classification rate. We show that, in addition to the successful isolation of diagnostic features, longitudinal and larger sized validation is needed in order to prove clinical applicability.

In this work, we present a method to increase the lateral field-of-view of a C-arm CT system by rotating the detector such that the diagonal of the detector lies on the u-axis of the detector's coordinate system. We investigated three different 3-D scan trajectories for liver imaging of an obese patient (waist circumference 130 cm) — a Short Scan, a Large Volume Scan and a Helical Large Volume Scan. We reconstructed a data set of the Visible Human Project with the SART and the eTV algorithm. Tests revealed that the coverage was increased with the presented method by 25.3 % for the Short Scan and 28.5 % for the Large Volume Scan. Performing helical scans compensated the axial data loss. The two implemented iterative approaches both provide acceptable results, with the eTV algorithm reducing the RMSE compared to SART by about 29 %. Given a liver imaging task, the rotated detector is able to image the entire liver section of the abdomen with a single Large Volume Scan.

There exist historical documents that can not be inspected without damaging them. We present a non-invasive method for the reconstruction of historical documents which have been written with iron gall ink. Iron gall ink has been used since the 5th century and is still in use as an indelible ink for writings and drawings. The fact, that this kind of ink is based on metallic particles leads to the assumption that modern X-Ray scanners could image the writings such that it can be differentiated from the paper mainly consisting of cellulose. Our work shows a method that is capable of imaging writings written with iron gall ink by using a modern X-Ray imaging system that is commonly used for medical applications. Even though the imaging system is not optimized for the given task, the writing can be identified. Furthermore, we present a method that is able to reconstruct the volume even if the phantom is not centered properly or misaligned. Applying this method makes it possible to image books, writings or drawings based on all metal-containing inks without page-turning or unrolling the paper.

Body-worn sensors for movement analysis in swimming have to be unobtrusive and energy-efficient. We present a swimming exercise tracker for the unobtrusive positioning at the back of the head and an energy-efficient analysis using an on-node implementation. To develop the system, we collected head kinematics from 11 subjects in two 200-m medley races comprising breaks, turns, and four swimming styles. Each subject was equipped with a 6-D inertial measurement unit and completed one session in rested and fatigued state. Data were analyzed with a classification system, whereby different classifiers, window sizes, and feature sets were evaluated. Algorithm selection for on-node processing was performed on the basis of classifier accuracy and computational cost. The algorithm with the best tradeoff in accuracy and computational cost was selected and had a classification rate of 85.4%. Energy consumption of both on-node processing and Bluetooth streaming was evaluated on the Shimmer sensor platform. The results revealed energy savings of over 60% when data were processed on the sensor node. The presented analysis approach can be easily applied to other data analysis tasks, and the presented toolchain can support the rapid development of wearable systems in sports and healthcare.

Changes in gait patterns provide important information about individuals’ health. To perform sensor based gait analysis, it is crucial to develop methodologies to automatically segment single strides from continuous movement sequences. In this study we developed an algorithm based on time-invariant template matching to isolate strides from inertial sensor signals. Shoe-mounted gyroscopes and accelerometers were used to record gait data from 40 elderly controls, 15 patients with Parkinson’s disease and 15 geriatric patients. Each stride was manually labeled from a straight 40 m walk test and from a video monitored free walk sequence. A multi-dimensional subsequence Dynamic Time Warping (msDTW) approach was used to search for patterns matching a pre-defined stride template constructed from 25 elderly controls. F-measure of 98% (recall 98%, precision 98%) for 40 m walk tests and of 97% (recall 97%, precision 97%) for free walk tests were obtained for the three groups. Compared to conventional peak detection methods up to 15% F-measure improvement was shown. The msDTW proved to be robust for segmenting strides from both standardized gait tests and free walks. This approach may serve as a platform for individualized stride segmentation during activities of daily living.

The reliability of surface electromyography (EMG) derived parameters is of high importance, but there is distinct lack of studies concerning the reliability during dynamic contractions. Especially Amplitude, Fourier and Wavelet parameter in conjunction have not been tested so far. The interpretation of the EMG variables might be difficult because the movement itself introduces additional factors that affect its characteristics. The aim of this study was to determine the relative and absolute intrasession reliability of electromyographic (EMG) variables of selected arm muscles during concurrent precise elbow extension/flexion movements at different force levels and movement speed. Participants (all-male: n=17, range 20–32years) were asked to adapt to a gross-motor visuomotor tracking task (elbow extension/flexion movement) using a custom-built lever arm apparatus. After sufficient adaptation surface electromyography was used to record the electrical activity of mm. biceps brachii, brachioradialis and triceps brachii, and the signal amplitude (RMS [μV]) and the mean frequency of the power spectrum (MNF [Hz]) were computed. Additionally Wavelet analysis was used. Relative reproducibility (intraclass correlation) for signal amplitude, mean frequency of the power spectrum and Wavelet intensity during dynamic contractions was fair to good, independent of force level and movement speed (ICC=0.71–0.98). The amount of absolute intrasession reliability (coefficient of variation) of EMG variables depends on muscle and force level.

The sequence of source and detector positions in a CT scan determines reconstructable volume and data completeness. Commonly this is regarded already in the design phase of a scanner. Modern flat-panel scanners, however, allow to acquire a broad range of positions. This enables many possibilities for different scan paths. However, every new path or trajectory implies different data completeness. Analytic solutions are either designed for special trajectories like the Tam-window for helical CT scans or do not incorporate the actual detector size such as Tuy's condition. In this paper, we describe a method to determine the voxel-wise data completeness in percent for discretely sampled trajectories. Doing so, we are able to model any sequence of source and detector positions. Using this method, we are able to confirm known theory such as Tuy's condition and data completeness of trajectories using detector offset to increase the field-of-view. As we do not require an analytic formulation of the trajectory, the algorithm will also be applicable to any other source-detector-path or set of source-detector-path segments.

The electrocardiogram (ECG) is a key diagnostic tool in heart disease and may serve to detect ischemia, arrhythmias, and other conditions. Automatic, low cost monitoring of the ECG signal could be used to provide instantaneous analysis in case of symptoms and may trigger the presentation to the emergency department. Currently, since mobile devices (smartphones, tablets) are an integral part of daily life, they could form an ideal basis for automatic and low cost monitoring solution of the ECG signal. In this work, we aim for a realtime classification system for arrhythmia detection that is able to run on Android-based mobile devices. Our analysis is based on 70% of the MIT-BIH Arrhythmia and on 70% of the MIT-BIH Supraventricular Arrhythmia databases. The remaining 30% are reserved for the final evaluation. We detected the R-peaks with a QRS detection algorithm and based on the detected R-peaks, we calculated 16 features (statistical, heartbeat, and template-based). With these features and four different feature subsets we trained 8 classifiers using the Embedded Classification Software Toolbox (ECST) and compared the computational costs for each classification decision and the memory demand for each classifier. We conclude that the C4.5 classifier is best for our two-class classification problem (distinction of normal and abnormal heartbeats) with an accuracy of 91.6%. This classifier still needs a detailed feature selection evaluation. Our next steps are implementing the C4.5 classifier for Android-based mobile devices and evaluating the final system using the remaining 30% of the two used databases.

Wearable body sensors have become an important basis for today's medical and fitness applications. To assist athletes or to take care of elderly people in everyday life situations, sensor data can be collected and processed to give helpful feedback. However, the data collection process of multiple or different sensor systems still had to be done manually by the user or an expert, which usually takes a lot of time and can lead to errors. This paper presents an embedded data collection and communication module based on an AT90USB microcontroller, which can automatically acquire data from various wired and wireless sensors (e.g. via USB or Bluetooth™. The obtained data can be cached, preprocessed and transmitted to a specified central server using LAN, Wi-Fi or GMS/GPRS. Through unified expansion slots, additional communication devices and a BeagleBone™ embedded can be extended to handle many different sensor systems. Moreover, wired sensors can be charged through appropriate circuits. This data collection and communication module operates without any input settings and special knowledge by the user. Five prototypes with different configurations and extension units concerning communication interfaces and computation power have been built up. The evaluation of the transfer reliability with 100%, whereupon 98% of data could be transmitted at once and the remaining 2% with the next attempt, confirms the stability of data transmission.

Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.

Wearable sensors are widely used for data collection in many applications. Ssensor nodes have also been applied for real-time applications, e.g. for ECG analysis or activity and fall detection. Processing of the sensor data is either done on an external device or on the node itself. While on-node processing reduces data rate and increases battery life, development and testing can be time-consuming. To allow faster implementation of such algorithms, we propose a simulation framework for the Shimmer platform using the Cooja simulator, MSPSim and the Contiki operating system. We provide the simulator and example applications compatible with the ShimmerConnect protocol, allowing streaming of raw and pre-processed sensor data to MATLAB, LabView and Android. Additionally, a simple activity and fall detection algorithm was implemented on the sensor node and evaluated using both the simulator and real hardware. In the future this will allow rapid development and testing of on-node pre-processing algorithms.

Motion analysis has become an important tool for athletes to improve their performance. However, most motion analysis systems are expensive and can only be used in a laboratory environment. Ambulatory motion analysis systems using inertial sensors would allow more flexible use, e.g. in a real training environment or even during competitions. This paper presents the calculation of the flexion-extension knee angle from segment acceleration and angular rates measured using body-worn inertial sensors. Using a functional calibration procedure, the sensors are first aligned without the need of an external camera system. An extended Kalman filter is used to estimate the relative orientations of thigh and shank, from which the knee angle is calculated. The algorithm was validated by comparing the computed knee angle to the output of a reference camera motion tracking system. In total seven subjects performed five dynamic motions: walking, jogging, running, jumps and squats. The averaged root mean squared error of the estimated knee angle was 8.2° ± 2.4° over all motions, with an average Pearson-correlation of 0.971 ± 0.020. In the future this will allow the analysis of joint angles during dynamic sports movements.