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Found 12 papers, 1 papers with code
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.
Routine Usage of AI-based Chest X-ray Reading Support in a Multi-site Medical Supply Center
Research question: How can we establish an AI support for reading of chest X-rays in clinical routine and which benefits emerge for the clinicians and radiologists.
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
2-D Respiration Navigation Framework for 3-D Continuous Cardiac Magnetic Resonance Imaging
Continuous protocols for cardiac magnetic resonance imaging enable sampling of the cardiac anatomy simultaneously resolved into cardiac phases.
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.
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.
Appearance Learning for Image-based Motion Estimation in Tomography
To this end, we train a siamese triplet network to predict the reprojection error (RPE) for the complete acquisition as well as an approximate distribution of the RPE along the single views from the reconstructed volume in a multi-task learning approach.
Data Consistent CT Reconstruction from Insufficient Data with Learned Prior Images
For example, for truncated data, DCR achieves a mean root-mean-square error of 24 HU and a mean structure similarity index of 0. 999 inside the field-of-view for different patients in the noisy case, while the state-of-the-art U-Net method achieves 55 HU and 0. 995 respectively for these two metrics.
Deep autofocus with cone-beam CT consistency constraint
To adapt the backprojection operation accordingly, a motion estimation strategy is necessary.
Field of View Extension in Computed Tomography Using Deep Learning Prior
In computed tomography (CT), data truncation is a common problem.
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.
Data Consistent Artifact Reduction for Limited Angle Tomography with Deep Learning Prior
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.
