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Found 9 papers, 3 papers with code
EAGLE: An Edge-Aware Gradient Localization Enhanced Loss for CT Image Reconstruction
However, the choice of loss function profoundly affects the reconstructed images.
Deep Learning Computed Tomography based on the Defrise and Clack Algorithm
The filter is designed for a specific orbit geometry and is obtained using a data-driven approach based on deep learning.
A 2D Sinogram-Based Approach to Defect Localization in Computed Tomography
We use a U-Net-based architecture for defect segmentation.
Data-Driven Filter Design in FBP: Transforming CT Reconstruction with Trainable Fourier Series
In this study, we introduce a Fourier series-based trainable filter for computed tomography (CT) reconstruction within the filtered backprojection (FBP) framework.
Integer Optimization of CT Trajectories using a Discrete Data Completeness Formulation
X-ray computed tomography (CT) plays a key role in digitizing three-dimensional structures for a wide range of medical and industrial applications.
A gradient-based approach to fast and accurate head motion compensation in cone-beam CT
The analytic Jacobian for the backprojection operation, which is at the core of the proposed method, is made publicly available.
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.
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
The cost function is parameterized by a trained neural network which regresses an image quality metric from the motion affected reconstruction alone.
