2D Respiration Navigation Framework for 3D Continuous Cardiac Magnetic Resonance Imaging - Hoppe, Elisabeth; Wetzl, Jens; Roser, Philipp; Felsner, Lina; Preuhs, Alexander; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Abstract: Simultaneous Estimation of X-ray Back-scatter and Forward-scatter using Multi-task Learning - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2021)
CRIS-Team Scopus Importer:2021-04-19

Appearance Learning for Image-based Motion Estimation in Tomography - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Hoppe, Elisabeth; Huang, Yixing; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (IEEE Transactions on Medical Imaging, 2020)

Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior - Huang, Yixing; Preuhs, Alexander; Lauritsch, Guenter; Manhart, Michael; Huang, Xiaolin; Maier, Andreas (Machine Learning for Medical Image Reconstruction, 2019)
Robustness of deep learning methods for limited angle tomography is challenged by two major factors: a) due to insufficient training data the network may not generalize well to unseen data; b) deep learning methods are sensitive to noise.Thus, generating reconstructed images directly from a neural network appears inadequate. We propose to constrain the reconstructed images to be consistent with the measured projection data, while the unmeasured information is complemented by learning based methods. For this purpose, a data consistent artifact reduction (DCAR) method is introduced: First, a prior image is generated from an initial limited angle reconstruction via deep learning as a substitute for missing information. Afterwards, a conventional iterative reconstruction algorithm is applied, integrating the data consistency in the measured angular range and the prior information in the missing angular range. This ensures data integrity in the measured area, while inaccuracies incorporated by the deep learning prior lie only in areas where no information is acquired. The proposed DCAR method achieves significant image quality improvement: for 120-degree cone-beam limited angle tomography more than 10% RMSE reduction in noise-free case and more than 24% RMSE reduction in noisy case compared with a state-of-the-art U-Net based method.
Keywords: Deep learning; Limited angle tomography; Data consistency; Poisson noise; Robustness; Generalization ability

Data Extrapolation from Learned Prior Images for Truncation Correction in Computed Tomography - Huang, Yixing; Preuhs, Alexander; Manhart, Michael; Lauritsch, Günter; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
Data truncation is a common problem in computed tomography (CT). Truncation causes cupping artifacts inside the field-of-view (FOV) and anatomical structures missing outside the FOV. Deep learning has achieved impressive results in CT reconstruction from limited data. However, its robustness is still a concern for clinical applications. Although the image quality of learning-based compensation schemes may be inadequate for clinical diagnosis, they can provide prior information for more accurate extrapolation than conventional heuristic extrapolation methods. With extrapolated projection, a conventional image reconstruction algorithm can be applied to obtain a final reconstruction. In this work, a general plug-and-play (PnP) method for truncation correction is proposed based on this idea, where various deep learning methods and conventional reconstruction algorithms can be plugged in. Such a PnP method integrates data consistency for measured data and learned prior image information for truncated data. This shows to have better robustness and interpretability than deep learning only. To demonstrate the efficacy of the proposed PnP method, two state-of-the-art deep learning methods, FBPConvNet and Pix2pixGAN, are investigated for truncation correction in cone-beam CT in noise-free and noisy cases. Their robustness is evaluated by showing false negative and false positive lesion cases. With our proposed PnP method, false lesion structures are corrected for both deep learning methods. For FBPConvNet, the root-mean-square error (RMSE) inside the FOV can be improved from 92HU to around 30HU by PnP in the noisy case. Pix2pixGAN solely achieves better image quality than FBPConvNet solely for truncation correction in general. PnP further improves the RMSE inside the FOV from 42HU to around 27HU for Pix2pixGAN. The efficacy of PnP is also demonstrated on real clinical head data.<br
Keywords: Deep learning; robustness; interpretability; truncation correction; computed tomography

Deep autofocus with cone-beam CT consistency constraint - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Stimpel, Bernhard; Syben-Leisner, Christopher; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (Bildverarbeitung für die Medizin, 2020)

DeepECG: Towards 3-D Continuous Cardiac MRI without ECG-Gating - Deep Learning based R-Wave Classification for Automated Cardiac Phase Binning - Hoppe, Elisabeth; Wetzl, Jens; Yoon, Seung Su; Schneider, Manuel; Stimpel, Bernhard; Preuhs, Alexander; Maier, Andreas (International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition, 2020)

Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging - Hoppe, Elisabeth; Wetzl, Jens; Yoon, Seung Su; Bacher, Mario; Roser, Philipp; Stimpel, Bernhard; Preuhs, Alexander; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
For the clinical assessment of cardiac vitality, time-continuous tomographic imaging of the heart is used. To further detect e.g., pathological tissue, multiple imaging contrasts enable a thorough diagnosis using magnetic resonance imaging (MRI). For this purpose, timecontinous and multi-contrast imaging protocols were proposed. The acquired signals are binned using navigation approaches for a motion-resolved reconstruction. Mostly, external sensors such as electrocardiograms (ECG) are used for navigation, leading to additional workflow efforts. Recent sensor-free approaches are based on pipelines requiring prior knowledge, e.g., typical heart rates. We present a sensor-free, deep learning-based navigation that diminishes the need for manual feature engineering or the necessity of prior knowledge compared to previous works. A classifier is trained to estimate the R-wave timepoints in the scan directly from the imaging data. Our approach is evaluated on 3-D protocols for continuous cardiac MRI, acquired in-vivo and free-breathing with single or multiple imaging contrasts. We achieve an accuracy of >98% on previously unseen subjects, and a well comparable image quality with the state-of-the-art ECG-based reconstruction. Our method enables an ECG-free workflow for continuous cardiac scans with simultaneous anatomic and functional imaging with multiple contrasts. It can be potentially integrated without adapting the sampling scheme to other continuous sequences by using the imaging data for navigation and reconstructio

Deep Scatter Splines: Learning-Based Medical X-ray Scatter Estimation Using B-splines - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (The 6th International Conference on Image Formation in X-Ray Computed Tomography (CT-Meeting), 2020)

Double Your Views: Exploiting Symmetry in Transmission Imaging - Preuhs, Alexander; Maier, Andreas; Manhart, Michael; Fotouhi, Javad; Navab, Nassir; Unberath, Mathias (Medical Image Computing and Computer Assisted Intervention, 2018)
CRIS-Team Scopus Importer:2019-05-14

Fast Epipolar Consistency without the Need for Pseudo Matrix Inverses - Preuhs, Alexander; Manhart, Michael; Maier, Andreas (Proceedings of the Fifth International Conference on Image Formation in X-Ray Computed Tomography, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.fastep

Field of View Extension in Computed Tomography Using Deep Learning Prior - Huang, Yixing; Gao, Lei; Preuhs, Alexander; Maier, Andreas (Bildverarbeitung für die Medizin: Algorithmen – Systeme – Anwendungen, 2020)
In computed tomography (CT), data truncation is a common problem. Images reconstructed by the standard filtered back-projection algorithm from truncated data suffer from cupping artifacts inside the field-of-view (FOV), while anatomical structures are severely distorted or missing outside the FOV. Deep learning, particularly the U-Net, has been applied to extend the FOV as a post-processing method. Since image-to-image prediction neglects the data fidelity to measured projection data, incorrect structures, even inside the FOV, might be reconstructed by such an approach. Therefore, generating reconstructed images directly from a post-processing neural network is inadequate. In this work, we propose a data consistent reconstruction method, which utilizes deep learning reconstruction as prior for extrapolating truncated projections and a conventional iterative reconstruction to constrain the reconstruction consistent to measured raw data. Its efficacy is demonstrated in our study, achieving small average root-mean-square error of 24 HU inside the FOV and a high structure similarity index of 0.993 for the whole body area on a test patient's CT data.
Keywords: Deep learning; data truncation; FOV extension; data consistency

Fully-automatic CT data preparation for interventional X-ray skin dose simulation - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Stimpel, Bernhard; Syben, Christopher; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik Aktuell Bildverarbeitung für die Medizin 2020 Algorithmen - Systeme - Anwendungen, 2020)
Recently, deep learning (DL) found its way to interventional X-ray skin dose estimation. While its performance was found to be acceptable, even more accurate results could be achieved if more data sets were available for training. One possibility is to turn to computed tomography (CT) data sets. Typically, computed tomography (CT) scans can be mapped to tissue labels and mass densities to obtain training data. However, care has to be taken to make sure that the different clinical settings are properly accounted for. First, the interventional environment is characterized by wide variety of table setups that are significantly different from the typical patient tables used in conventional CT. This cannot be ignored, since tables play a crucial role in sound skin dose estimation in an interventional setup, e.,g., when the X-ray source is directly underneath a patient (posterior-anterior view). Second, due to interpolation errors, most CT scans do not facilitate a clean segmentation of the skin border. As a solution to these problems, we applied connected component labeling (CCL) and Canny edge detection to (a) robustly separate the patient from the table and (b) to identify the outermost skin layer. Our results show that these extensions enable fully-automatic, generalized pre-processing of CT scans for further simulation of both skin dose and corresponding X-ray projection
Keywords: X-ray simulation; Computed tomography; Data preparation

Image Quality Assessment for Rigid Motion Compensation - Preuhs, Alexander; Manhart, Michael; Roser, Philipp; Stimpel, Bernhard; Syben-Leisner, Christopher; Psychogios, Marios; Kowarschik, Markus; Maier, Andreas (MedNeurips, 2019)

Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency - Preuhs, Alexander; Ravikumar, Nishant; Manhart, Michael; Stimpel, Bernhard; Hoppe, Elisabeth; Syben-Leisner, Christopher; Kowarschik, Markus; Maier, Andreas (Informatik aktuell, 2019)
Open gantry C-arm systems that are placed within the interventional room enable 3-D imaging and guidance for stroke therapy without patient transfer. This can profit in drastically reduced time-totherapy, however, due to the interventional setting, the data acquisition is comparatively slow. Thus, involuntary patient motion needs to be estimated and compensated to achieve high image quality. Patient motion results in a misalignment of the geometry and the acquired image data. Consistency measures can be used to restore the correct mapping to compensate the motion. They describe constraints on an idealized imaging process which makes them also sensitive to beam hardening, scatter, truncation or overexposure. We propose a probabilistic approach based on the Student’s t-distribution to model image artifacts that affect the consistency measure without sourcing from motion.
CRIS-Team Scopus Importer:2019-05-14

Model-Based and Data-Driven Geometry Alignment for Angiography Systems - Preuhs, Alexander (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
omography is the reconstruction of internal structures based on a sensory signal. X-ray cone-beam computed tomography (CBCT) measures the X-ray absorption with a source-detector gantry mounted on a C-shaped arm that rotates around the object. During the rotation, projection images are acquired. To obtain a tomographic reconstruction, the projection data is filtered and back-projected using the geometry information from the scan trajectory. This technique is of high relevance for stroke applications, where a hemorrhagic stroke is to be distinguished from an ischemic stroke. By performing the stroke diagnostic on the interventional CBCT system, no patient transfer is required which minimizes the door-to-groin time. However, due to the prolonged acquisition time of CBCT compared to diagnostic computed tomography (CT), the likelihood of patient motion increases. This provides the demand for rigid motion compensation and geometry alignment algorithms. In this thesis, we investigate model-based and data-driven approaches for the compensation of geometry misalignment. Grangeat’s theorem provides a measure to compare the geometric consistency of two projections. In an ideal scenario, the derivative of corresponding epipolar line integrals is equal. Geometric inaccuracies and motion lead to unmatched epipolar line pairs and impair a corresponding consistency measure. We present algorithmic extensions for the computation of Grangeat’s consistency based on a pair of projection images. Using these extensions, we derive analytic motion gradients describing the change of consistency with respect to a rigid motion. Grangeat’s theorem is known to be eÿcient for out-plane motion compensation within circular trajectories, however, in-plane motion is barely detectable. We devise the X-trajectory which is sensitive to both, in-plane and out-plane motion. The X-trajectory can be computed from a regular short-scan, if the object is symmetric, by mirroring the trajectory at the symmetry plane. This requires the estimation of the object’s symmetry plane. We show that Grangeat’s theorem is suitable for the estimation of symmetry planes using an anthropomorphic head phantom. With experiments using digital phantoms and real acquisitions, we show that the X-trajectory improves in-plane motion com-pensation. Hence, symmetry is a powerful concept with encouraging initial results when applied to head imaging, which is worth further investigations to validate its clinical applicability. We also investigate data-driven approaches, where we learn a cost function di-rectly from the clinical data. In this context, we use an appearance learning ap-proach, where a network is applied to regress the reprojection error (RPE) from the reconstructed slice images. The RPE is available during training of the network, be-cause the ground-truth geometry as well as the motion-affected geometry are known. The RPE measures the reconstruction-relevant deviations and is convex. We devise a siamese triplet network to regress the mean reprojection error (mRPE) as well as a projectionwise RPE. The latter provides a rough estimate which parts of the trajec-tory are affected by motion, whereas the mRPE is used as optimization target. The motion compensated geometry is found by a constraint optimization of the mRPE, where only projections are optimized that are classified by the network as motion-corrupted. Using a motion compensation benchmark we show that our approach is superior compared to state-of-the-art image quality metrics (IQMs)<br

Motion Gradients for Epipolar Consistency - Preuhs, Alexander; Manhart, Michael; Hoppe, Elisabeth; Kowarschik, Markus; Maier, Andreas (15th International Meeting on Fully Three-Dimensional Image Reconstruction, 2019)

Over-Exposure Correction in CT Using Optimization-Based Multiple Cylinder Fitting - Preuhs, Alexander; Berger, Martin; Xia, Yan; Maier, Andreas; Hornegger, Joachim; Fahrig, Rebecca (Bildverarbeitung für die Medizin 2015, 2015)
UnivIS-Import:2015-04-17:Pub.2015.tech.IMMD.IMMD5.overex_3
Keywords: GRK-1773

Reconstruction of Voxels with Position- and Angle-Dependent Weightings - Felsner, Lina; Würfl, Tobias; Syben-Leisner, Christopher; Roser, Philipp; Preuhs, Alexander; Maier, Andreas; Riess, Christian (The 6th International Conference on Image Formation in X-Ray Computed Tomography, 2020)

Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning - Roser, Philipp; Zhong, Xia; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020)
Scattered radiation is a major concern impacting X-ray image-guided procedures in two ways. First, back-scatter significantly contributes to patient (skin) dose during complicated interventions. Second, forward-scattered radiation reduces contrast in projection images and introduces artifacts in 3-D reconstructions. While conventionally employed anti-scatter grids improve image quality by blocking X-rays, the additional attenuation due to the anti-scatter grid at the detector needs to be compensated for by a higher patient entrance dose. This also increases the room dose affecting the staff caring for the patient. For skin dose quantification, back-scatter is usually accounted for by applying pre-determined scalar back-scatter factors or linear point spread functions to a primary kerma forward projection onto a patient surface point. However, as patients come in different shapes, the generalization of conventional methods is limited. Here, we propose a novel approach combining conventional techniques with learning-based methods to simultaneously estimate the forward-scatter reaching the detector as well as the back-scatter affecting the patient skin dose. Knowing the forward-scatter, we can correct X-ray projections, while a good estimate of the back-scatter component facilitates an improved skin dose assessment. To simultaneously estimate forward-scatter as well as back-scatter, we propose a multi-task approach for joint back- and forward-scatter estimation by combining X-ray physics with neural networks. We show that, in theory, highly accurate scatter estimation in both cases is possible. In addition, we identify research directions for our multi-task framework and learning-based scatter estimation in general.<br
Keywords: X-ray scatter Skin dose Multi-task learning

Symmetry prior for epipolar consistency - Preuhs, Alexander; Maier, Andreas; Manhart, Michael; Kowarschik, Markus; Hoppe, Elisabeth; Fotouhi, Javad; Navab, Nassir; Unberath, Mathias (International Journal of Computer Assisted Radiology and Surgery, 2019)
Purpose: For a perfectly plane symmetric object, we can find two views—mirrored at the plane of symmetry—that will yield the exact same image of that object. In consequence, having one image of a plane symmetric object and a calibrated camera, we automatically have a second, virtual image of that object if the 3-D location of the symmetry plane is known. Methods: We propose a method for estimating the symmetry plane from a set of projection images as the solution of a consistency maximization based on epipolar consistency. With the known symmetry plane, we can exploit symmetry to estimate in-plane motion by introducing the X-trajectory that can be acquired with a conventional short-scan trajectory by simply tilting the acquisition plane relative to the plane of symmetry. Results: We inspect the symmetry plane estimation on a real scan of an anthropomorphic human head phantom and show the robustness using a synthetic dataset. Further, we demonstrate the advantage of the proposed method for estimating in-plane motion using the acquired projection data. Conclusion: Symmetry breakers in the human body are widely used for the detection of tumors or strokes. We provide a fast estimation of the symmetry plane, robust to outliers, by computing it directly from a set of projections. Further, by coupling the symmetry prior with epipolar consistency, we overcome inherent limitations in the estimation of in-plane motion.
CRIS-Team Scopus Importer:2019-08-06
Keywords: Cone-beam CT; Consistency conditions; Data completeness; Motion compensation; Symmetry; Tomographic reconstruction

Tenfold your photons: A physically-sound approach to filtering-based variance reduction of monte-carlo-simulated dose distributions - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (Informatik aktuell, 2020)
X-ray dose constantly gains interest in the interventional suite. With dose being generally diffcult to monitor reliably, fast computational methods are desirable. A major drawback of the gold standard based on Monte Carlo (MC) methods is its computational complexity. Besides common variance reduction techniques, filter approaches are often applied to achieve conclusive results within a fraction of time. Inspired by these methods, we propose a novel approach. We down-sample the target volume based on the fraction of mass, simulate the imaging situation, and then revert the down-sampling. To this end, the dose is weighted by the mass energy absorption, up-sampled, and distributed using a guided filter. Eventually, the weighting is inverted resulting in accurate high resolution dose distributions. The approach has the potential to considerably speed-up MC simulations since less photons and boundary checks are necessary. First experiments substantiate these assumptions. We achieve a median accuracy of 96.7% to 97.4% of the dose estimation with the proposed method and a down-sampling factor of 8 and 4, respectively. While maintaining a high accuracy, the proposed method provides for a tenfold speed-up. The overall findings suggest the conclusion that the proposed method has the potential to allow for further effciency.
CRIS-Team Scopus Importer:2020-04-21

Viewpoint Planning for Quantitative Coronary Angiography - Preuhs, Alexander; Berger, Martin; Bauer, Sebastian; Redel, Thomas; Unberath, Mathias; Achenbach, Stephan; Maier, Andreas (Computer Assisted Radiology and Surgery Proceedings of the 32nd International Congress and Exhibition, 2018)
Purpose: In coronary angiography, the condition of myocardial blood supply is assessed by analyzing 2-D X-ray projections of contrasted coronary arteries. This is done using a flexible C-arm system. Due to the X-ray immanent dimensionality reduction projecting the 3-D scene onto a 2-D image, the viewpoint is critical to guarantee an appropriate view onto the affected artery and, thus, enable reliable diagnosis. In this work, we introduce an algorithm computing optimal viewpoints for the assessment of coronary arteries without the need for 3-D models. Methods: We introduce the concept of optimal viewpoint planning solely based on a single angiographic X-ray image. The subsequent viewpoint is computed such that it is rotated precisely around a vessel, while minimizing foreshortening. Results: Our algorithm reduces foreshortening substantially compared to the input view and completely eliminates it for 90 ∘ rotations. Rotations around isocentered foreshortening-free vessels passing the isocenter are exact. The precision, however, decreases when the vessel is off-centered or foreshortened. We evaluate worst-case boundaries, providing insight in the maximal inaccuracies to be expected. This can be utilized to design viewpoints guaranteeing desired requirements, e.g., a true rotation around the vessel of at minimum 30 ∘. In addition, a phantom study is performed investigating the impact of input views to 3-D quantitative coronary angiography (QCA). Conclusion: We introduce an algorithm for optimal viewpoint planning from a single angiographic X-ray image. The quality of the second viewpoint—i.e., vessel foreshortening and true rotation around vessel—depends on the first viewpoint selected by the physician; however, our computed viewpoint is guaranteed to reduce the initial foreshortening. Our novel approach uses fluoroscopy images only and, thus, seamlessly integrates with the current clinical workflow for coronary assessment. In addition, it can be implemented in the QCA workflow without increasing user interaction, making vessel-shape reconstruction more stable by standardizing viewpoints.
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.viewpo_9
Keywords: Active vision; C-arm; Coronary angiography; Foreshortening; Interventional imaging; Patient-specific imaging; QCA

Viewpoint Planning for Quantitative Coronary Angiography (IJCARS) - Preuhs, Alexander; Berger, Martin; Bauer, Sebastian; Redel, Thomas; Unberath, Mathias; Achenbach, Stephan; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2018)
UnivIS-Import:2018-09-11:Pub.2018.tech.IMMD.IMMD5.viewpo_5
Keywords: coronary angiography; C-arm; interventional imaging; QCA; active vision; patient specific imaging; foreshortening

XDose: toward online cross-validation of experimental and computational X-ray dose estimation - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Ochs, Philipp; Stepina, Elizaveta; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (International Journal of Computer Assisted Radiology and Surgery, 2020)
Purpose: As the spectrum of X-ray procedures has increased both for diagnostic and for interventional cases, more attention is paid to X-ray dose management. While the medical benefit to the patient outweighs the risk of radiation injuries in almost all cases, reproducible studies on organ dose values help to plan preventive measures helping both patient as well as staff. Dose studies are either carried out retrospectively, experimentally using anthropomorphic phantoms, or computationally. When performed experimentally, it is helpful to combine them with simulations validating the measurements. In this paper, we show how such a dose simulation method, carried out together with actual X-ray experiments, can be realized to obtain reliable organ dose values efficiently. Methods: A Monte Carlo simulation technique was developed combining down-sampling and super-resolution techniques for accelerated processing accompanying X-ray dose measurements. The target volume is down-sampled using the statistical mode first. The estimated dose distribution is then up-sampled using guided filtering and the high-resolution target volume as guidance image. Second, we present a comparison of dose estimates calculated with our Monte Carlo code experimentally obtained values for an anthropomorphic phantom using metal oxide semiconductor field effect transistor dosimeters. Results: We reconstructed high-resolution dose distributions from coarse ones (down-sampling factor 2 to 16) with error rates ranging from 1.62 % to 4.91 %. Using down-sampled target volumes further reduced the computation time by 30 % to 60 %. Comparison of measured results to simulated dose values demonstrated high agreement with an average percentage error of under 10 % for all measurement points. Conclusions: Our results indicate that Monte Carlo methods can be accelerated hardware-independently and still yield reliable results. This facilitates empirical dose studies that make use of online Monte Carlo simulations to easily cross-validate dose estimates on-site.
CRIS-Team Scopus Importer:2020-12-11
Keywords: Anthropomorphic phantom; Dosimetry; Monte Carlo simulation; MOSFET

X-ray Scatter Estimation Using Deep Splines - Roser, Philipp; Birkhold, Annette; Preuhs, Alexander; Syben-Leisner, Christopher; Felsner, Lina; Hoppe, Elisabeth; Strobel, Norbert; Kowarschik, Markus; Fahrig, Rebecca; Maier, Andreas (IEEE Transactions on Medical Imaging, 2021)
X-ray scatter compensation is a very desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based scatter removal approaches yielded promising results. However, as there are no physics’ constraints applied to the output of the U-Net, it cannot be ruled out that it yields spurious results. Unfortunately, in the context of medical imaging, those may be misleading and could lead to wrong conclusions. To overcome this problem, we propose to embed B-splines as a known operator into neural networks. This inherently constrains their predictions to well-behaved and smooth functions. In a study using synthetic head and thorax data as well as real thorax phantom data, we found that our approach performed on par with U-net when comparing both algorithms based on quantitative performance metrics. However, our approach not only reduces runtime and parameter complexity, but we also found it much more robust to unseen noise levels. While the U-net responded with visible artifacts, the proposed approach preserved the X-ray signal’s frequency characteristics.
CRIS-Team Scopus Importer:2021-05-07
Keywords: Approximation; B-spline; Detectors; Estimation; Neural network; Neural networks; Photonics; Splines (mathematics); Thorax; X-ray imaging; X-ray scatter