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Found 25 papers, 2 papers with code
The Artificial Neural Twin -- Process Optimization and Continual Learning in Distributed Process Chains
Industrial process optimization and control is crucial to increase economic and ecologic efficiency.
Task-based Generation of Optimized Projection Sets using Differentiable Ranking
We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis.
Optimizing CT Scan Geometries With and Without Gradients
In computed tomography (CT), the projection geometry used for data acquisition needs to be known precisely to obtain a clear reconstructed image.
Geometric Constraints Enable Self-Supervised Sinogram Inpainting in Sparse-View Tomography
By reconstructing independent stacks of projection data, a self-supervised loss is calculated in the CT image domain and used to directly optimize projection image intensities to match the missing tomographic views constrained by the projection geometry.
Deep Learning-based Patient Re-identification Is able to Exploit the Biometric Nature of Medical Chest X-ray Data
Our verification system is able to identify whether two frontal chest X-ray images are from the same person with an AUC of 0. 9940 and a classification accuracy of 95. 55%.
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
Reconstruction of Voxels with Position- and Angle-Dependent Weightings
In this work we first formulate this reconstruction problem in terms of a system matrix and weighting part.
Cephalogram Synthesis and Landmark Detection in Dental Cone-Beam CT Systems
The method trained on conventional cephalograms can be directly applied to landmark detection in the synthetic cephalograms, achieving 93. 0% and 80. 7% successful detection rate in 4 mm precision range for synthetic cephalograms from 3D volumes and 2D projections respectively.
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.
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.
Deep autofocus with cone-beam CT consistency constraint
To adapt the backprojection operation accordingly, a motion estimation strategy is necessary.
Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging
Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging.
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing
In medical imaging, this lack of comprehensibility of the results is a sensitive issue.
Image Quality Assessment for Rigid Motion Compensation
Diagnostic stroke imaging with C-arm cone-beam computed tomography (CBCT) enables reduction of time-to-therapy for endovascular procedures.
Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks
Magnetic Resonance Fingerprinting (MRF) is an imaging technique acquiring unique time signals for different tissues.
RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting
Although the acquisition is highly accelerated, the state-of-the-art reconstruction suffers from long computation times: Template matching methods are used to find the most similar signal to the measured one by comparing it to pre-simulated signals of possible parameter combinations in a discretized dictionary.
Learning with Known Operators reduces Maximum Training Error Bounds
We assume that our analysis will support further investigation of known operators in other fields of physics, imaging, and signal processing.
PYRO-NN: Python Reconstruction Operators in Neural Networks
The high level Python API allows a simple use of the layers as known from Tensorflow.
A Gentle Introduction to Deep Learning in Medical Image Processing
This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications.
User Loss -- A Forced-Choice-Inspired Approach to Train Neural Networks directly by User Interaction
This approach opens the way towards implementation of direct user feedback in deep learning and is applicable for a wide range of application.
Deriving Neural Network Architectures using Precision Learning: Parallel-to-fan beam Conversion
The results demonstrate that the proposed method is superior to ray-by-ray interpolation and is able to deliver sharper images using the same amount of parallel-beam input projections which is crucial for interventional applications.
Projection image-to-image translation in hybrid X-ray/MR imaging
Our approach is capable of creating X-ray projection images with natural appearance.
Precision Learning: Towards Use of Known Operators in Neural Networks
We demonstrate that use of known transforms is able to change maximal error bounds.
MR to X-Ray Projection Image Synthesis
The perceptual-loss showed to be able to preserve most of the high-frequency details in the projection images and, thus, is recommended for the underlying task and similar problems.
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.
