14 citations · 38 across the 13 of their papers we have counts for
15 papers
Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
Jennifer Maier, Marlies Nitschke, Jang-Hwan Choi +4
Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compe…
X-ray Scatter Estimation Using Deep Splines
Philipp Roser, Annette Birkhold, Alexander Preuhs +7
Algorithmic X-ray scatter compensation is a desirable technique in flat-panel X-ray imaging and cone-beam computed tomography. State-of-the-art U-net based image translation approa…
Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
Jennifer Maier, Marlies Nitschke, Jang-Hwan Choi +4
Involuntary motion during weight-bearing cone-beam computed tomography (CT) scans of the knee causes artifacts in the reconstructed volumes making them unusable for clinical diagno…
Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning
Philipp Roser, Xia Zhong, Annette Birkhold +7
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…
Pitfalls in interventional X-ray organ dose assessment -- combined experimental and computational phantom study: application to prostatic artery embolization
Philipp Roser, Annette Birkhold, Xia Zhong +5
With X-ray radiation protection and dose management constantly gaining interest in interventional radiology, novel procedures often undergo prospective dose studies using anthropom…
Effects of Tissue Material Properties on X-Ray Image, Scatter and Patient Dose Determined using Monte Carlo Simulations
Philipp Roser, Annette Birkhold, Xia Zhong +4
With increasing patient and staff X-ray radiation awareness, many efforts have been made to develop accurate patient dose estimation methods. To date, Monte Carlo (MC) simulations…
Fully-automatic CT data preparation for interventional X-ray skin dose simulation
Philipp Roser, Annette Birkhold, Alexander Preuhs +6
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 achie…
Tenfold your photons -- a physically-sound approach to filtering-based variance reduction of Monte-Carlo-simulated dose distributions
Philipp Roser, Annette Birkhold, Alexander Preuhs +3
X-ray dose constantly gains interest in the interventional suite. With dose being generally difficult to monitor reliably, fast computational methods are desirable. A major drawbac…
Deep Scatter Splines: Learning-Based Medical X-ray Scatter Estimation Using B-splines
Philipp Roser, Annette Birkhold, Alexander Preuhs +5
The idea of replacing hardware by software to compensate for scattered radiation in flat-panel X-ray imaging is well established in the literature. Recently, deep-learningbased ima…
An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process
Siming Bayer, Ute Spiske, Jie Luo +8
For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely emp…
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
Jennifer Maier, Luis Carlos Rivera Monroy, Christopher Syben +7
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis. A precise segmentation of the cartila…
Action Learning for 3D Point Cloud Based Organ Segmentation
Xia Zhong, Mario Amrehn, Nishant Ravikumar +6
We propose a novel point cloud based 3D organ segmentation pipeline utilizing deep Q-learning. In order to preserve shape properties, the learning process is guided using a statist…
Precision Learning: Towards Use of Known Operators in Neural Networks
Andreas Maier, Frank Schebesch, Christopher Syben +4
In this paper, we consider the use of prior knowledge within neural networks. In particular, we investigate the effect of a known transform within the mapping from input data space…
Precision Learning: Reconstruction Filter Kernel Discretization
Christopher Syben, Bernhard Stimpel, Katharina Breininger +4
In this paper, we present substantial evidence that a deep neural network will intrinsically learn the appropriate way to discretize the ideal continuous reconstruction filter. Cur…
Path-based Iterative Reconstruction (PBIR) for X-ray Computed Tomography
Meng Wu, Andreas Maier, Qiao Yang +1
Model-based iterative reconstruction (MBIR) techniques have demonstrated many advantages in X-ray CT image reconstruction. The MBIR approach is often modeled as a convex optimizati…