1 citations · 1 across the 6 of their papers we have counts for
6 papers
B-FIRE: Binning-Free Diffusion Implicit Neural Representation for Hyper-Accelerated Motion-Resolved MRI
Di Xu, Hengjie Liu, Yang Yang +11
Accelerated dynamic volumetric magnetic resonance imaging (4DMRI) is essential for applications relying on motion resolution. Existing 4DMRI produces acceptable artifacts of averag…
Accelerated Patient-specific Non-Cartesian MRI Reconstruction using Implicit Neural Representations
Di Xu, Hengjie Liu, Xin Miao +9
The scanning time for a fully sampled MRI can be undesirably lengthy. Compressed sensing has been developed to minimize image artifacts in accelerated scans, but the required itera…
Rapid Reconstruction of Extremely Accelerated Liver 4D MRI via Chained Iterative Refinement
Di Xu, Xin Miao, Hengjie Liu +8
Abstract Purpose: High-quality 4D MRI requires an impractically long scanning time for dense k-space signal acquisition covering all respiratory phases. Accelerated sparse sampling…
Exploring Automated Contouring Across Institutional Boundaries: A Deep Learning Approach with Mouse Micro-CT Datasets
Lu Jiang, Di Xu, Qifan Xu +4
Image-guided mouse irradiation is essential to understand interventions involving radiation prior to human studies. Our objective is to employ Swin UNEt Transformers (Swin UNETR) t…
Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI
Di Xu, Xin Miao, Hengjie Liu +8
Purpose: 4D MRI with high spatiotemporal resolution is desired for image-guided liver radiotherapy. Acquiring densely sampling k-space data is time-consuming. Accelerated acquisiti…
Nodule detection and generation on chest X-rays: NODE21 Challenge
Ecem Sogancioglu, Bram van Ginneken, Finn Behrendt +16
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep lea…