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Found 9 papers, 0 papers with code
Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
We perform a simulation study using real motion recorded with an optical tracking system.
X-ray Scatter Estimation Using Deep Splines
Algorithmic X-ray scatter compensation is a desirable technique in flat-panel X-ray imaging and cone-beam computed tomography.
Medical Physics Image and Video Processing
Inertial Measurements for Motion Compensation in Weight-bearing Cone-beam CT of the Knee
In our proposed multi-stage algorithm, these signals are transformed to the global coordinate system of the CT scan and applied for motion compensation during reconstruction.
Simultaneous Estimation of X-ray Back-Scatter and Forward-Scatter using Multi-Task Learning
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.
An Investigation of Feature-based Nonrigid Image Registration using Gaussian Process
For a wide range of clinical applications, such as adaptive treatment planning or intraoperative image update, feature-based deformable registration (FDR) approaches are widely employed because of their simplicity and low computational complexity.
Multi-Channel Volumetric Neural Network for Knee Cartilage Segmentation in Cone-beam CT
Analyzing knee cartilage thickness and strain under load can help to further the understanding of the effects of diseases like Osteoarthritis.
Action Learning for 3D Point Cloud Based Organ Segmentation
From this we conclude that our method is robust, and we believe that our method can be successfully applied to many more applications, in particular, in the interventional imaging space.
Precision Learning: Towards Use of Known Operators in Neural Networks
We demonstrate that use of known transforms is able to change maximal error bounds.
Precision Learning: Reconstruction Filter Kernel Discretization
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.
