Evaluation of algorithmic requirements for clinical application of material decomposition using a multi-layer flat panel detector.

Purpose: The combination of multi-layer flat panel detector (FPDT) X-ray imaging and physics-based material decomposition algorithms allows for the removal of anatomical structures. However, the reliability of these algorithms may be compromised by unaccounted materials or scattered radiation.

Approach: We investigated the two-material decomposition performance of a multi-layer FPDT in the context of 2D chest radiography without and with a 13:1 anti-scatter grid employed.

Towards a biomechanical breast model to simulate and investigate breast compression and its effects in mammography and tomosynthesis.

. In mammography, breast compression forms an essential part of the examination and is achieved by lowering a compression paddle on the breast. Compression force is mainly used as parameter to estimate the degree of compression.

Clinical prototype implementation enabling an improved day-to-day mammography compression.

Purpose: In mammography, breast compression is achieved by lowering a compression paddle on the breast. Despite the directive that compression is needed, there is no concrete guideline on its execution. To estimate the degree of compression, current mammography units only provide compression force and breast thickness as parameters.

A novel use of time separation technique to improve flat detector CT perfusion imaging in stroke patients.

Background: CT perfusion imaging (CTP) is used in the diagnostic workup of acute ischemic stroke (AIS). CTP may be performed within the angio suite using flat detector CT (FDCT) to help reduce patient management time.

Purpose: In order to significantly improve FDCT perfusion (FDCTP) imaging, data-processing algorithms need to be able to compensate for the higher levels of noise, slow rotation speed, and a lower frame rate of current FDCT devices.

Design Study of a Novel Positron Emission Tomography System for Plant Imaging.

Positron Emission Tomography is a non-disruptive and high-sensitive digital imaging technique which allows to measure and non invasively the changes of metabolic and transport mechanisms in plants. When it comes to the early assessment of stress-induced alterations of plant functions, plant PET has the potential of a major breakthrough. The development of dedicated plant PET systems faces a series of technological and experimental difficulties, which make conventional clinical and preclinical PET systems not fully suitable to agronomy.

Peripheral neuropathy in Parkinson's disease: prevalence and functional impact on gait and balance.

Peripheral neuropathy is a common problem in patients with Parkinson's disease. Peripheral neuropathy's prevalence in Parkinson's disease varies between 4.8-55%, compared with 9% in the general population.

Rapid safety assessment and mitigation of radiofrequency induced implant heating using small root mean square sensors and the sensor matrix Q.

Purpose: Rapid detection and mitigation of radiofrequency (RF)-induced implant heating during MRI based on small and low-cost embedded sensors.

Theory And Methods: A diode and a thermistor are embedded at the tip of an elongated mock implant. RF-induced voltages or temperature change measured by these root mean square (RMS) sensors are used to construct the sensor Q-Matrix (Q ).

A novel approach to 2D/3D registration of X-ray images using Grangeat's relation.

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.

Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation process of stroke patients. By combining these technologies, therapy effects could be further improved and the rehabilitation process can be supported. In order to combine these technologies, a novel algorithm was developed, which aims to extract gait events based on movement data recorded with inertial measurement units.

Feasibility of a Sensor-Based Gait Event Detection Algorithm for Triggering Functional Electrical Stimulation during Robot-Assisted Gait Training.

Technologies such as robot-assisted gait trainers or functional electrical stimulation can improve the rehabilitation process of people affected with gait disorders due to stroke or other neurological defects. By combining both technologies, the potential disadvantages of each technology could be compensated and simultaneously, therapy effects could be improved. Thus, an algorithm was designed that aims to detect the gait cycle of a robot-assisted gait trainer.