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Works16 from public data
- MO-DE-207A-05: Dictionary Learning Based Reconstruction with Low-Rank Constraint for Low-Dose Spectral CT6
A novel dictionary learning method for spectral CT that combines dictionary-based sparse representation method and the patch based low-rank constraint to simultaneously improve the reconstruction in each channel and to address the inter-channel correlations to further improved the reconstruction.
- Deep fusion network based on two-stream CNN for radar target recognition2
An end-to-end HRRP/SAR fusion network is proposed, which can make full use of the different characteristics obtained from HRRP data and SAR images and significantly improves the recognition accuracy.
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- WE-G-18A-04: 3D Dictionary Learning Based Statistical Iterative Reconstruction for Low-Dose Cone Beam CT Imaging2
3D dictionary learning based statistical reconstruction algorithm is able to sense the structural information while suppressing noise, and hence to achieve high quality reconstruction, making this algorithm more feasible for potential clinical application.
- A Spatial Analysis Algorithm Based on 3D Topographic Map2
Using positive dispel and counter dispel means, projection transformation connection is established between the two dimension graphics target dot and the three dimension DEM grid dot to obtain target dot's space coordinate in three dimension relief map and achieve several basic interspace analyzing arithmetic.
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- SU-E-I-41: Dictionary Learning Based Quantitative Reconstruction for Low-Dose Dual-Energy CT (DECT)1
The proposed dictionary learning based reconstruction framework for DECT for improved image quality while reducing the imaging dose is established and leads to improved results with much lower imaging dose.
- Image Retrieval Method Based on Contour Feature Point1
A novel method for image retrieval based on feature points of contour of an interesting object based on Mean Shift algorithm and described with its eigenvector comprised of these feature points.
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- SU‐C‐206‐04: Region‐Specific Total‐Variation Regularization for X‐Ray CT Reconstruction–
The proposed region specific regularization can improve the current TV-based reconstruction and make a good balance between the noise and structural details in all regions simultaneously.
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- SU-E-I-39: Combining Conventional Tomographic Imaging Strategy and Interior Tomography for Low Dose Dual-Energy CT (DECT)–
A novel hybrid DECT image acquisition and reconstruction strategy, in which one of the energies is dealt by interior tomography while the other one is obtained using conventional tomography approach, which provides an effective way to extract tissue density information in the ROI and in the context of anatomical images of CT imaging, with much reduced imaging dose.
- On-Board CBCT Imaging Using 2.5 MV Beam on a Radiation Therapy Linac for Metal Artifact Reduction–
Using the new 2.5 MV beam for on-board CBCT imaging, significantly reduced metal artifacts compared with kV imaging, although with a decrease in soft-tissue contrast.
- SU‐E‐I‐72: First Experimental Study of On‐Board CBCT Imaging Using 2.5MV Beam On a Radiotherapy Linac–
An iterative volumetric image reconstruction technique specific to the beam is developed and it is suggested that on-board CBCT imaging with the new 2.5MV beam can effectively reduce metal artifacts, although with a reduced softtissue contrast.
- The HRV Signal Analysis of Two Medicine Based on the Kolmogorov Complexity Measure–
The Kolmogorov complexity can be used to analysize the the clinical HRV signal and the acting states of the vagus nerve and the sympathetic nerve can be characterized.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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