MRI phase contrast imaging methods that assemble slice-wise acquisitions into volumes can contain interslice phase discontinuities (IPDs) over the course of the scan from sources, including unavoidable physiological activity. In magnetic resonance elastography (MRE), this can alter wavelength and tissue stiffness estimates, invalidating the analysis. We first model this behavior as jitter along the z-axis of the phase of 3D complex-valued wave volumes. A two-step image processing pipeline is then proposed that removes IPDs. First, constant slicewise phase shift is removed with a novel, non-convex dejittering algorithm. Then, regional physiological noise artifacts are removed with novel filtering of 3D wavelet coefficients. Calibration of two pipeline coefficients, the dejitter parameter $\alpha $ and the wavelet band high-pass coefficient $\omega _{c}$ , was first performed on a finite-element method brain phantom. A comparative investigation was then performed, on a cohort of 48 brain acquisitions, of four approaches to IPDs: 1) the proposed method; 2) a “control” condition of neglect of IPDs; 3) an anisotropic wavelet-based method; and 4) a method of in-plane (2D) processing. The present method showed medians of $\lvert {G}^{*} \rvert = \textsf {1873}$ Pa for a multifrequency wave inversion centered at 40 Hz which was within 6% of methods 3) and 4), while neglect produced $\lvert {G}^{*} \rvert $ estimates a mean of 17% lower. The proposed method reduced the value range of the cohort against methods 3) and 4) by 29% and 31%, respectively. Such reduction in variance enhances the ability of brain MRE to predict subtler physiological changes. Our theoretical approach further enables more powerful applications of fundamental findings in noise and denoising to MRE.

PurposeTo measure normal renal stiffness in adults, taking into account regional variation, hydration, and urinary status.MethodsThirty‐six healthy volunteers were examined by tomoelastography based on MR elastography at four frequencies, from 40 to 70 Hz and multifrequency shear wave speed recovery. Regional wave speeds were derived for the medulla, cortex (inner cortex and outer cortex), and renal pelvis, and examined for age‐related effects. Subgroups were repeatedly examined for reproducibility, amount of prior water drinking, and urinary status. Variations in renal perfusion were simulated ex vivo using a porcine kidney subjected to venous water inflow at different pressures.ResultsShear wave speed (stiffness) of renal parenchyma was 2.46 ± 0.12 m/s (inner cortex: 2.91 ± 0.17 m/s; outer cortex: 2.52 ± 0.11 m/s; medulla: 2.15 ± 0.08 m/s) without side differences and a tendency toward softening with age (P = 0.028). Corresponding intraclass correlation for reproducibility coefficients were 0.78 (inner cortex: 0.80; outer cortex: 0.81; medulla: 0.80). Water drinking resulted in slightly higher values in inner cortex and lower values in medulla (both P = 0.039), which was consistent with the results in perfused specimens. A full bladder led to higher renal pelvis stiffness (P = 0.004), whereas renal parenchyma remained uninfluenced. Stiffness of the porcine renal cortex increased with venous inflow pressure, whereas medulla stiffness decreased.ConclusionsTomoelastography provides full field of view maps of renal stiffness with highly detailed resolution and sensitivity to physiological effects related to age and fluid–solid tissue interactions. These basic data could be used to compare pathological conditions in the future. Magn Reson Med 79:2126–2134, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Brain function, the brain’s metabolic activity, cerebral blood flow (CBF), and intracranial pressure are intimately linked within the tightly autoregulated regime of intracranial physiology in which the role of tissue viscoelasticity remains elusive. We applied multifrequency magnetic resonance elastography (MRE) paired with CBF measurements in 14 healthy subjects exposed to 5-min carbon dioxide-enriched breathing air to induce cerebral vasodilatation by hypercapnia. Stiffness and viscosity as quantified by the magnitude and phase angle of the complex shear modulus, | G*| and ϕ, as well as CBF of the whole brain and 25 gray matter sub-regions were analyzed prior to, during, and after hypercapnia. In all subjects, whole-brain stiffness and viscosity increased due to hypercapnia by 3.3 ± 1.9% and 2.0 ± 1.1% which was accompanied by a CBF increase of 36 ± 15%. Post-hypercapnia, | G*| and ϕ reduced to normal values while CBF decreased by 13 ± 15% below baseline. Hypercapnia-induced viscosity changes correlated with CBF changes, whereas stiffness changes did not. The MRE-measured viscosity changes correlated with blood viscosity changes predicted by the Fåhræus–Lindqvist model and microvessel diameter changes from the literature. Our results suggest that brain viscoelastic properties are influenced by microvessel blood flow and blood viscosity: vasodilatation and increased blood viscosity due to hypercapnia result in an increase in MRE values related to viscosity.

Viscoelastic properties of the brain reflect tissue architecture at multiple length scales. However, little is known about the relation between vital tissue functions, such as perfusion, and the macroscopic mechanical properties of cerebral tissue. In this study, arterial spin labelling is paired with magnetic resonance elastography to investigate the relationship between tissue stiffness and cerebral blood flow (CBF) in the in vivo human brain. The viscoelastic modulus, | G*|, and CBF were studied in deep gray matter (DGM) of 14 healthy male volunteers in the following sub-regions: putamen, nucleus accumbens, hippocampus, thalamus, globus pallidus, and amygdala. CBF was further normalized by vessel area data to obtain the flux rate q which is proportional to the perfusion pressure gradient. The striatum (represented by putamen and nucleus accumbens) was distinct from the other DGM regions by displaying markedly higher stiffness and perfusion values. q was a predictive marker for DGM stiffness as analyzed by linear regression | G*| = q·(4.2 ± 0.6)kPa·s + (0.80 ± 0.06)kPa ( R2 = 0.92, P = 0.006). These results suggest a high sensitivity of MRE in DGM to perfusion pressure. The distinct mechano-vascular properties of striatum tissue, as compared to the rest of DGM, may reflect elevated perfusion pressure, which could explain the well-known susceptibility of the putamen to hemorrhages.

PurposeTo demonstrate the feasibility of in vivo multifrequency magnetic resonance elastography (MRE) of the prostate using externally placed drivers. MethodsThree pressurized-air drivers were used to excite shear waves within the prostate at vibration frequencies of 60, 70, and 80Hz. Full 3D wave fields were acquired by multislice spin-echo echo-planar imaging in conjunction with tomoelastography wave speed recovery for generating full field-of-view stiffness maps. Twelve healthy volunteers were repeatedly scanned to analyze test-retest reproducibility. Five patients with suspected prostate cancer were investigated to demonstrate the clinical feasibility of the method. ResultsIn healthy volunteers, the shear wave speed of the entire prostate was 2.240.20m/s with a repeatability coefficient of 0.14m/s and 88% intraclass correlation coefficient. No significant difference between the peripheral zone (2.27 +/- 0.20m/s) and the central gland (2.22 +/- 0.23m/s) was observed. In patients, wave-speed maps displayed stiff regions consistent with the localization of suspicious masses detected by other imaging markers. ConclusionsThe proposed method provides reproducible quantitative maps of tissue stiffness throughout the pelvic region and can easily be integrated into clinical imaging protocols. Clinical stiffness maps display many details of potential interest for cancer diagnosis. Magn Reson Med 79:1325-1333, 2018. (c) 2017 International Society for Magnetic Resonance in Medicine.

PURPOSE:To develop a compact magnetic resonance elastography (MRE) protocol for abdomen and to investigate the effect of water uptake on tissue stiffness in the liver, spleen, kidney, and pancreas. METHODS:Nine asymptomatic volunteers were investigated by MRE before and after 1 liter water uptake. Shear-wave excitation at four frequencies was transferred to the abdomen from anterior and posterior directions using pressurized air drivers. Tomographic representations of shear-wave speed were produced by analysis of multifrequency wave numbers in axial and coronal images acquired within four breath-holds or under free breathing, respectively. RESULTS:Pre and post water, stiffness of the spleen (pre/post: 2.20 ± 0.10/2.06 ± 0.18 m/s) and kidney (pre/post: 1.93 ± 0.22/1.97 ± 0.23 m/s) was higher than in the liver (pre/post: 1.36 ± 0.10/1.38 ± 0.13 m/s) and pancreas (pre/post: 1.20 ± 0.12/1.20 ± 0.08 m/s), all P < 0.01. Accounting for four drive frequencies, water drinking only changed the splenic stiffness (-6%, P = 0.03), whereas in the frequency range from 50 to 60 Hz the effect became significant also in the pancreas (-6%, P = 0.04) and liver (+3%, P = 0.03). Elastograms of the kidney in coronal view clearly depicted higher stiffness in cortex than in medulla. CONCLUSION:Tomoelastography reveals sensitivity of tissue mechanical properties to the hydration state of multiple abdominal organs within one scan and in unprecedented resolution of anatomical details. Magn Reson Med 78:976-983, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

In elastography mechanically excited shear waves are captured by medical ultrasound or MRI to reconstruct the elastic parameters of the underlying tissue. Current inversion algorithm use second-order derivatives for elasticity reconstruction which limits the spatial resolution of the elastic parameter maps. Here we propose a noise stable inversion method, which relies on wave number k reconstruction at different harmonic frequencies followed by their amplitude-weighted averaging prior to inversion. The algorithm is tested on abdominal and pelvic data. The resulting shear wave speed maps provide anatomical details in elastic parameter maps due to its inherent sensitivity to noise at pixel-wise resolution producing superior details to current MRE inversion methods.

Palpation is one of the most sensitive, effective diagnostic practices, motivating the quantitative and spatially resolved determination of soft tissue elasticity parameters by medical ultrasound or MRI. However, this so-called elastography often suffers from limited anatomical resolution due to noise and insufficient elastic deformation, currently precluding its use as a tomographic modality on its own. We here introduce an efficient way of processing wave images acquired by multifrequency magnetic resonance elastography (MMRE), which relies on wave number reconstruction at different harmonic frequencies followed by their amplitude-weighted averaging prior to inversion. This results in compound maps of wave speed, which reveal variations in tissue elasticity in a tomographic fashion, i.e. an unmasked, slice-wise display of anatomical details at pixel-wise resolution. The method is demonstrated using MMRE data from the literature including abdominal and pelvic organs such as the liver, spleen, uterus body and uterus cervix. Even in small regions with low wave amplitudes, such as nucleus pulposus and spinal cord, elastic parameters consistent with literature values were obtained. Overall, the proposed method provides a simple and noise-robust strategy of in-plane wave analysis of MMRE data, with a pixel-wise resolution producing superior detail to MRE direct inversion methods.

PURPOSE:To demonstrate the feasibility of in vivo wideband MR elastography (wMRE) using continuous, time-harmonic shear vibrations in the frequency range of 10-50 Hz. THEORY AND METHODS:The method was tested in a gel phantom with marked mechanical loss. The brains and livers of eight volunteers were scanned by wMRE using multislice, single-shot MRE with optimized fractional encoding and synchronization of sequence acquisition to vibration. Multifrequency three-dimensional inversion was used to reconstruct compound maps of magnitude |G*| and phase φ of the complex shear modulus. A new phase estimation, φ*, was developed to avoid systematic bias due to noise. RESULTS:In the phantom, G*-dispersion measured by wMRE agreed well with oscillatory shear rheometry. |G*| and φ* measured at vibrations of 10-25 HZ, 25-35 HZ, and 40-50 HZ were 0.62 ± 0.08, 1.56 ± 0.16, 2.18 ± 0.20 kPa and 0.09 ± 0.17, 0.39 ± 0.16, 0.20 ± 0.13 rad in brain and 0.89 ± 0.11, 1.67 ± 0.20, 2.27 ± 0.35 kPa and 0.15 ± 0.10, 0.24 ± 0.05, 0.26 ± 0.05 rad in liver. Elastograms including all frequencies showed the best resolution of anatomical detail with |G*| = 1.38 ± 0.12 kPa, φ* = 0.24 ± 0.10 rad (brain) and |G*| = 1.79 ± 0.23 kPa, φ* = 0.24 ± 0.05 rad (liver). CONCLUSION:wMRE reveals highly dispersive G* properties of the brain and liver, and our results suggest that the influence of large-scale structures such as fluid-filled vessels and sulci on the MRE-measured parameters increases at low vibration frequencies. Magn Reson Med 76:1116-1126, 2016. © 2015 Wiley Periodicals, Inc.

Gliomas differ from many other tumors as they grow infiltratively into the brain parenchyma rather than forming a solid tumor mass with a well-defined boundary. Tumor cells can be found several centimeters away from the central tumor mass that is visible using current imaging techniques. The infiltrative growth characteristics of gliomas question the concept of a radiotherapy target volume that is irradiated to a homogeneous dose-the standard in current clinical practice. We discuss the use of the Fisher-Kolmogorov glioma growth model in radiotherapy treatment planning. The phenomenological tumor growth model assumes that tumor cells proliferate locally and migrate into neighboring brain tissue, which is mathematically described via a partial differential equation for the spatio-temporal evolution of the tumor cell density. In this model, the tumor cell density drops approximately exponentially with distance from the visible gross tumor volume, which is quantified by the infiltration length, a parameter describing the distance at which the tumor cell density drops by a factor of e. This paper discusses the implications for the prescribed dose distribution in the periphery of the tumor. In the context of the exponential cell kill model, an exponential fall-off of the cell density suggests a linear fall-off of the prescription dose with distance. We introduce the dose fall-off rate, which quantifies the steepness of the prescription dose fall-off in units of Gy mm(-1). It is shown that the dose fall-off rate is given by the inverse of the product of radiosensitivity and infiltration length. For an infiltration length of 3 mm and a surviving fraction of 50% at 2 Gy, this suggests a dose fall-off of approximately 1 Gy mm(-1). The concept is illustrated for two glioblastoma patients by optimizing intensity-modulated radiotherapy plans. The dose fall-off rate concept reflects the idea that infiltrating gliomas lack a defined boundary and are characterized by a continuous fall-off of the density of infiltrating tumor cells. The approach can potentially be used to individualize the prescribed dose distribution if better methods to estimate radiosensitivity and infiltration length on a patient by patient basis become available.

Glioblastoma differ from many other tumors in the sense that they grow infiltratively into the brain tissue instead of forming a solid tumor mass with a defined boundary. Only the part of the tumor with high tumor cell density can be localized through imaging directly. In contrast, brain tissue infiltrated by tumor cells at low density appears normal on current imaging modalities. In current clinical practice, a uniform margin, typically two centimeters, is applied to account for microscopic spread of disease that is not directly assessable through imaging. The current treatment planning procedure can potentially be improved by accounting for the anisotropy of tumor growth, which arises from different factors: anatomical barriers such as the falx cerebri represent boundaries for migrating tumor cells. In addition, tumor cells primarily spread in white matter and infiltrate gray matter at lower rate. We investigate the use of a phenomenological tumor growth model for treatment planning. The model is based on the Fisher–Kolmogorov equation, which formalizes these growth characteristics and estimates the spatial distribution of tumor cells in normal appearing regions of the brain. The target volume for radiotherapy planning can be defined as an isoline of the simulated tumor cell density. This paper analyzes the model with respect to implications for target volume definition and identifies its most critical components. A retrospective study involving ten glioblastoma patients treated at our institution has been performed. To illustrate the main findings of the study, a detailed case study is presented for a glioblastoma located close to the falx. In this situation, the falx represents a boundary for migrating tumor cells, whereas the corpus callosum provides a route for the tumor to spread to the contralateral hemisphere. We further discuss the sensitivity of the model with respect to the input parameters. Correct segmentation of the brain appears to be the most crucial model input. We conclude that the tumor growth model provides a method to account for anisotropic growth patterns of glioma, and may therefore provide a tool to make target delineation more objective and automated.

In radiotherapy of gliomas, a precise definition of the treatment volume is problematic, because current imaging modalities reveal only the central part of the tumor with a high cellular density, but fail to detect all regions of microscopic tumor cell spread in the adjacent brain parenchyma. Mathematical models can be used to integrate known growth characteristics of gliomas into the target delineation process. In this paper, we demonstrate the use of diffusion tensor imaging (DTI) for simulating anisotropic cell migration in a glioma growth model that is based on the Fisher-Kolmogorov equation. For a clinical application of the model, it is crucial to develop a detailed understanding of its behavior, capabilities, and limitations. For that purpose, we perform a retrospective analysis of glioblastoma patients treated at our institution. We analyze the impact of diffusion anisotropy on model-derived target volumes, and interpret the results in the context of the underlying images. It was found that, depending on the location of the tumor relative to major fiber tracts, DTI can have significant influence on the shape of the radiotherapy target volume.

Background: MRE is capable of generating image contrast based on the viscoelastic properties of tissue by inducing and detecting time harmonic shear waves in the body (1). The spatial resolution of so called elastograms depends on the resolution of wave images and the stability of the solution of the time harmonic inverse problem (2). Both can be improved by repetitive scans, e.g. by accounting for signal accumulation in order to increase SNR or by applying different drive frequencies for avoiding wave amplitude nulls. Therefore, MRE measurement time is critical in particular when high resolution elastograms are needed. A limiting factor for fast single shot MRE has been the need for wave synchronization after each image acquisition block by introducing a waiting time in order to avoid transient vibrations (3).

Purpose: Gliomas infiltrate the adjacent brain parenchyma far beyond the tumor mass visible on current imaging modalities. In current clinical practice, an isotropic margin is applied to account for infiltrative disease. However, histopathology suggests that glioma growth is anisotropic: (1) the ventricles and the falx represent barriers for migrating tumor cells; (2) gray matter is infiltrated less than white matter; and (3) glioma cells appear to preferentially spread along white matter fibers. Developing objective and quantitative approaches to account for these growth patterns may improve and automate target delineation for gliomas. Methods: We study the Fisher‐Kolmogorov glioma growth model, which represents a partial differential equation for the tumor cell density and replicates the observed growth characteristics. Via brain segmentation into anatomical barriers, cerebrospinal fluid, and white and gray matter, the model equations are solved on the patient‐specific anatomy. Preferential spread along white matter fibers is incorporated through DTI imaging. The radiotherapy target can be defined as an isoline of the derived tumor cell density. We performed a retrospective study involving 10 glioblastoma patients to (1) identify the anatomical situations for which the model suggests target volumes that differ from the manually drawn targets, and (2) fully understand the capabilities and limitations of the model for target delineation. Results: It was found that tumors located close to the corpus callosum potentially benefited the most from model‐based target delineation because it consistently predicted the contralateral tumor extension across the corpus callosum in addition to easily modeling falx and ventricles as boundaries. Furthermore, for tumors located close to major sulci (Sylvian fissure), modeling reduced gray matter infiltration can potentially reduce the amount of brain tissue targeted for irradiation. Conclusion: The tumor growth model represents a promising tool to objectively create target volumes for radiotherapy of gliomas by consistently account ing for known growth patterns.

Reconfigurable hardware is gaining a steadily growing interest in the domain of space applications. The ability to reconfigure the information processing infrastructure at runtime together with the high computational power of today's FPGA architectures at relatively low power makes these devices interesting candidates for data processing in space applications. Partial dynamic reconfiguration of FPGAs enables maximum flexibility and can be utilized for performance increase, for improving energy efficiency, and for enhanced fault tolerance. To be able to prove the effectiveness of these novel approaches for satellite payload processing, a highly scalable prototyping environment has been developed, combining dynamically reconfigurable FPGAs with the required interfaces such as SpaceWire, MIL-STD-1553B, and SpaceFibre. Up to 30 SpaceWire interfaces, 5 copper-based SpaceFibre interfaces, and 270 GPIOs can be realized and combined with one to five dynamically reconfigurable Xilinx FPGAs and up to 20 GByte of working memory. The implemented approach for dynamic reconfiguration enables partial reconfiguration at 400 MByte/s. Blind and readback scrubbing is supported and the scrub rate can be adapted individually for different parts of the design.

Radiotherapy treatment planning requires a localization of the tumor within the patient. This is challenging to accomplish for micro- scopic in ltrative spread of disease that is not visible on current imaging modalities. Prime examples for in ltrative tumors are gliomas. With the help of mathematical models, common growth characteristics of gliomas, which are known from histopathological studies, can be incorporated in radiotherapy target delineation. This requires an imaging based personal- ization of the model to the individual patient. We demonstrate use cases of the Fisher-Kolmogorov glioma growth model in radiotherapy planning of a clinical case. We further analyze the crucial input parameters to the model, in particular, the need for reliable segmentation of anatomical boundaries such as the falx cerebri and the tentorium cerebelli.

This paper describes the main outcome of the Roadscanner project, in developing a Vehicle Probe prototype equipped with a Global Navigation Satellite System (GNSS) receiver, an Inertial Navigation System (INS) and a Laser Scanner (LS) unit to create digital maps of high accuracy and integrity.GNSS have become an important factor in transport with limitless possibilities. The GNSS concept is based on a constellation of satellites that provides autonomous geo-spatial positioning with global coverage. This enables GNSS receivers to calculate a Position Velocity Time (PVT) solution using the signals transmitted from satellites along the line-of-sight to the receiver. Following the obvious application of navigation and guidance for various types of vehicles, the advances in accuracy and integrity of PVT have enabled other uses, such as Advanced Driver Assistance Systems (ADAS) and digital automotive simulations. However, GNSS also has intrinsic limitations (subject for example to radio-frequency interference, signal availability and vehicle dynamics), which can be a serious hindrance for demanding applications, where accuracy and integrity are critical.In order to remedy GNSS drawbacks, the satellite receiver data can be integrated with additional sensors, such as an INS. The sensor fusion provides a solution of enhanced accuracy, robustness and integrity and also yields attitude information. Furthermore, adding a laser scanner (LS) provides road surface measurements, which are important for simulation, or road surface quality inspection. (C) 2012 Published by Elsevier Ltd. Selection and/or peer view under responsibility of the Programme Committee of the Transport Research Arena 2012

Sensor technology continues to improve at the price of increased data rates, which require being processed. In the space domain, the available bandwidth for effectively transferring the data to the base station is limited, such that there is a need for a high-performance data processing unit on board of the spacecraft. This work targets the development of a scalable high-performance payload data processing system based on dynamically reconfigurable FPGAs. The system, which is called Dynamically Reconfigurable Processing Module (DRPM), enables a multitude of high performance data processing applications to be supported by the same hardware in space. While reconfigurable hardware offers higher performances than traditional DSP-based solutions, it also supports the same flexibility to modify the functionality at run-time. Dynamically Reconfigurable Processing Module (DRPM) The DRPM is a multi-FPGA architecture, which is designed especially for space applications. The FPGAs (Xilinx Virtex-4 family) are used to implement high performance data processing cores for a wide range of applications. In addition, the DRPM supports in-flight reconfiguration during a mission where required, whilst being fault-tolerant to space environment effects typically caused by high energy particles. Figure 1 shows a block diagram of the DRPM. The DRPM can be divided into interface components, control components, and data processing components. The payload data processing is performed on the FPGAs. Each FPGA is connected to a local memory bank, which is used for storing application data. Most of the FPGA resources are used for reconfigurable modules, which perform the payload data processing. Besides the reconfigurable modules each FPGA implements a memory controller for the local memory and a dynamic processing control unit to manage addresses and resources of the reconfigurable modules. The DRPM supports a variety of interfaces (CAN, SpaceWire, MIL-STD1553, etc.) to establish connection to avionics and source data instruments. The system controller is based on a Leon2-FT processor and offers services to the ground station to control all functions executed on the DRPM. The reconfiguration controller performs the reconfiguration processes of the FPGAs. Reconfiguration is used to M. Koester, J. Hagemeyer, F. Margaglia and M. Porrmann are with Heinz Nixdorf Institute, University of Paderborn, Paderborn, Germany. F. Dittmann and M. Ditze are with TWT GmbH Science & Innovation, Neuhausen, Germany. L. Sterpone is with Politecnico di Torino, Torino, Italy. J. Harris is with Swiss Space Technology, Champery, Switzerland. J. Ilstad is with ESTEC, Noordwijk, The Netherlands. Control Components Data Processing Interfaces