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Found 11 papers, 9 papers with code
Liver Segmentation in Time-resolved C-arm CT Volumes Reconstructed from Dynamic Perfusion Scans using Time Separation Technique
Perfusion imaging is a valuable tool for diagnosing and treatment planning for liver tumours.
Liver Segmentation using Turbolift Learning for CT and Cone-beam C-arm Perfusion Imaging
This paper shows the potential of segmenting the liver from CT, CBCT, and CBCT TST, learning from the available limited training data, which can possibly be used in the future for the visualisation and evaluation of the perfusion maps for the treatment evaluation of liver diseases.
Primal-Dual UNet for Sparse View Cone Beam Computed Tomography Volume Reconstruction
In this paper, the Primal-Dual UNet for sparse view CT reconstruction is modified to be applicable to cone beam projections and perform reconstructions of entire volumes instead of slices.
Dual Branch Prior-SegNet: CNN for Interventional CBCT using Planning Scan and Auxiliary Segmentation Loss
This paper proposes an extension to the Dual Branch Prior-Net for sparse view interventional CBCT reconstruction incorporating a high quality planning scan.
DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI
However, such MRI acquisition with high temporal resolution suffers from limited spatial resolution - also known as the spatio-temporal trade-off of dynamic MRI.
Sinogram upsampling using Primal-Dual UNet for undersampled CT and radial MRI reconstruction
However, the undersampled reconstruction problem for these two modalities was always considered as two different problems and tackled separately by different research works.
Cutting Voxel Projector a New Approach to Construct 3D Cone Beam CT Operator
We have implemented this cutting voxel projector and a less accurate, speed-optimized version of it together with two established projectors, a ray tracing projector based on Siddon's algorithm and a TT footprint projector.
ReconResNet: Regularised Residual Learning for MR Image Reconstruction of Undersampled Cartesian and Radial Data
It has been shown that the proposed framework can successfully reconstruct even for an acceleration factor of 20 for Cartesian (0. 968$\pm$0. 005) and 17 for radially (0. 962$\pm$0. 012) sampled data.
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge
Nonetheless during dynamic MRI, while achieving a high temporal resolution, the spatial resolution is compromised.
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors.
Breathing deformation model -- application to multi-resolution abdominal MRI
This deformation model was then applied to the high resolution images to obtain high resolution images of different breathing phases.
