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Journal: Zeitschrift fur medizinische Physik

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Diffusion anisotropy in diffusion tensor imaging (DTI) is commonly quantified with normalized diffusion anisotropy indices (DAIs). Most often, the fractional anisotropy (FA) is used, but several alternative DAIs have been introduced in attempts to maximize the contrast-to-noise ratio (CNR) in diffusion anisotropy maps. Examples include the scaled relative anisotropy (sRA), the gamma variate anisotropy index (GV), the surface anisotropy (UAsurf), and the lattice index (LI). With the advent of multidimensional diffusion encoding it became possible to determine the presence of microscopic diffusion anisotropy in a voxel, which is theoretically independent of orientation coherence. In accordance with DTI, the microscopic anisotropy is typically quantified by the microscopic fractional anisotropy (μFA). In this work, in addition to the μFA, the four microscopic diffusion anisotropy indices (μDAIs) μsRA, μGV, μUAsurf, and μLI are defined in analogy to the respective DAIs by means of the average diffusion tensor and the covariance tensor. Simulations with three representative distributions of microscopic diffusion tensors revealed distinct CNR differences when differentiating between isotropic and microscopically anisotropic diffusion. q-Space trajectory imaging (QTI) was employed to acquire brain in-vivo maps of all indices. For this purpose, a 15min protocol featuring linear, planar, and spherical tensor encoding was used. The resulting maps were of good quality and exhibited different contrasts, e.g. between gray and white matter. This indicates that it may be beneficial to use more than one μDAI in future investigational studies.

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Quantitative susceptibility mapping (QSM) reveals pathological changes in widespread diseases such as Parkinson’s disease, Multiple Sclerosis, or hepatic iron overload. QSM requires multiple processing steps after the acquisition of magnetic resonance imaging (MRI) phase measurements such as unwrapping, background field removal and the solution of an ill-posed field-to-source-inversion. Current techniques utilize iterative optimization procedures to solve the inversion and background field correction, which are computationally expensive and lead to suboptimal or over-regularized solutions requiring a careful choice of parameters that make a clinical application of QSM challenging. We have previously demonstrated that a deep convolutional neural network can invert the magnetic dipole kernel with a very efficient feed forward multiplication not requiring iterative optimization or the choice of regularization parameters. In this work, we extended this approach to remove background fields in QSM. The prototype method, called SHARQnet, was trained on simulated background fields and tested on 3T and 7T brain datasets. We show that SHARQnet outperforms current background field removal procedures and generalizes to a wide range of input data without requiring any parameter adjustments. In summary, we demonstrate that the solution of ill-posed problems in QSM can be achieved by learning the underlying physics causing the artifacts and removing them in an efficient and reliable manner and thereby will help to bring QSM towards clinical applications.

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Non-conventional scan trajectories for interventional three-dimensional imaging promise low-dose interventions and a better radiation protection to the personnel. Circular tomosynthesis (cTS) scan trajectories yield an anisotropical image quality distribution. In contrast to conventional Computed Tomographies (CT), the reconstructions have a preferred focus plane. In the other two perpendicular planes, limited angle artifacts are introduced. A reduction of these artifacts leads to enhanced image quality while maintaining the low dose. We apply Deep Artifact Correction (DAC) to this task. cTS simulations of a digital phantom are used to generate training data. Three U-Net-based networks and a 3D-ResNet are trained to estimate the correction map between the cTS and the phantom. We show that limited angle artifacts can be mitigated using simulation-based DAC. The U-Net-corrected cTS achieved a Root Mean Squared Error (RMSE) of 124.24 Hounsfield Units (HU) on 60 simulated test scans in comparison to the digital phantoms. This equals an error reduction of 59.35% from the cTS. The achieved image quality is similar to a simulated cone beam CT (CBCT). Our network was also able to mitigate artifacts in scans of objects which strongly differ from the training data. Application to real cTS test scans showed an error reduction of 45.18% and 26.4% with the 3D-ResNet in reference to a high-dose CBCT.

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Sodium magnetic resonance imaging (MRI) of the human abdomen is of increasing clinical interest for e.g. kidney, intervertebral disks, prostate and tumor monitoring examinations in the abdomen. To overcome the low MR sensitivity of sodium, optimal radio frequency (RF) structures should be used. A common approach is to combine a volumetric transmit coil for homogeneous excitation with an array of sensitive receive coils adapted to the human shape. Additionally, proton imaging is required to match the physiological sodium images to the morphological proton images. In this work, we demonstrated the feasibility of a double resonant proton/sodium RF setup for abdominal MRI at 3T, providing a high sodium sensitivity. After extensive simulations, a 16-channel sodium receive array was built and used in combination with a volumetric sodium transmit coil. Additionally, a local proton coil was included in the setup for anatomical localizations. The setup was investigated using electromagnetic field simulations, phantom measurements and final in-vivo measurements of a healthy volunteer. A 3 to 6-fold sensitivity improvement of the sodium receive array compared to the volumetric sodium coil was achieved using the phantom simulations and measurements. Safety assessments of the local proton transmit/receive coil were performed using specific absorption rate simulations. Finally, the feasibility of such a setup was proven by in-vivo measurements.

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The characteristic depth-dose profile of protons traveling through material is the main advantage of proton therapy over conventional radiotherapy with photons or electrons. However, uncertainties regarding the range of the protons in human tissue prevent to exploit the full potential of proton therapy. Therefore, a non-invasive in-vivo dose monitoring is desired. At the ion beam center MedAustron in Wiener Neustadt/Austria, patient treatment with proton beams started in December 2016. A PET/CT is available in close vicinity of the treatment rooms, exclusively dedicated to offline PET monitoring directly after the therapeutic irradiation. Preparations for a patient study include workflow tests under realistic clinical conditions using two different phantoms, irradiated with protons prior to the scan in the PET/CT. GATE simulations of the C-11 production are used as basis for the prediction of the PET measurement. We present results from the workflow tests in comparison with simulation results, and by this, we demonstrate the applicability of the PET monitoring at the MedAustron facility.

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This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modeling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep ()learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

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The increasing frequency and complexity of medical radiation exposures to humans inevitably result in higher risks of harmful unintended or accidental radiation exposures. To ensure a high level of protection and its continuous improvement, the Directive 2013/59/Euratom thus requires to systematically record and analyze both events and near-miss events as well as, in the case of their significance, to disseminate information regarding lessons learned from these events promptly and nationwide to improve radiation protection in medicine. These requirements have been transposed into German legislation by the new radiation protection law and radiation protection ordinance that entered into force simultaneously on December 31th, 2018. The reporting and information system as provided by these regulations as well as the tasks, duties and powers of the parties involved are presented in the first part of this review article. In the second part, the established application-specified criteria for the significance - and thus the notification requirement - of (near-miss) events are itemized and explicated.

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This addendum provides correction factors for the recombination and the polarity effect for the new ionization chamber PTW PinPoint (type 31023). The measurements were made in filtered (WFF) and unfiltered (FFF) high-energy photon beams. It could be confirmed that both the initial and the general recombination effect of the chamber mainly depends on dose per pulse at the point of measurement and is independent of the filtration of the photon beam.

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Glioblastoma multiforme is the most frequent innate brain tumor and still yields an unfavorable prognosis of 15 months of survival after diagnosis. Animal models play an important role in the investigation of therapeutic strategies of brain tumors. Thereby, MRI is substantial to individual follow-up examination for therapeutic response. Contrast agent dosage at 1.5 and 3T MRI has been thoroughly tested, while there is hardly any data for 9.4T. Therefore, the aim of this study was to find the optimal contrast agent dosage at 9.4T for examination of T1 relaxation time and apparent tumor volume in an animal model.

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Inter-fractional variations of breathing pattern and patient anatomy introduce dose uncertainties in proton therapy. One approach to monitor these variations is to utilize the cone-beam computed tomography (CT, CBCT) scans routinely taken for patient positioning, reconstruct them as 4DCBCTs, and generate ‘virtual CTs’ (vCTs), combining the accurate CT numbers of the diagnostic 4DCT and the geometry of the daily 4DCBCT by using deformable image registration (DIR). In this study different algorithms for 4DCBCT reconstruction and DIR were evaluated. For this purpose, CBCT scans of a moving ex vivo porcine lung phantom with 663 and 2350 projections respectively were acquired, accompanied by an additional 4DCT as reference. The CBCT projections were sorted in 10 phase bins with the Amsterdam-shroud method and reconstructed phase-by-phase using first a FDK reconstruction from the Reconstruction Toolkit (RTK) and again an iterative reconstruction algorithm implemented in the Gadgetron Toolkit. The resulting 4DCBCTs were corrected by DIR of the corresponding 4DCT phases, using both a morphons algorithm from REGGUI and a b-spline deformation from Plastimatch. The resulting 4DvCTs were compared to the 4DCT by visual inspection and by calculating water equivalent thickness (WET) maps from the phantom’s surface to the distal edge of a target from various angles. The optimized procedure was successfully repeated with mismatched input phases and on a clinical patient dataset. Proton treatment plans were simulated on the 4DvCTs and the dose distributions compared to the reference based on the 4DCT via gamma pass rate analysis. A combination of iterative reconstruction and morphons DIR yielded the most accurate 4DvCTs, with median WET differences under 2mm and 3%/3mm gamma pass rates per phase between 89% and 99%. These results suggest that image correction of iteratively reconstructed 4DCBCTs with a morphons DIR of the planning CT may yield sufficiently accurate 4DvCTs for daily time resolved proton dose calculations.