Zobrazeno 1 - 10
of 922
pro vyhledávání: '"Fessler, Jeffrey A"'
Shorter SPECT Scans Using Self-supervised Coordinate Learning to Synthesize Skipped Projection Views
Purpose: This study addresses the challenge of extended SPECT imaging duration under low-count conditions, as encountered in Lu-177 SPECT imaging, by developing a self-supervised learning approach to synthesize skipped SPECT projection views, thus sh
Externí odkaz:
http://arxiv.org/abs/2406.18840
Diffusion models face significant challenges when employed for large-scale medical image reconstruction in real practice such as 3D Computed Tomography (CT). Due to the demanding memory, time, and data requirements, it is difficult to train a diffusi
Externí odkaz:
http://arxiv.org/abs/2406.10211
Diffusion models can learn strong image priors from underlying data distribution and use them to solve inverse problems, but the training process is computationally expensive and requires lots of data. Such bottlenecks prevent most existing works fro
Externí odkaz:
http://arxiv.org/abs/2406.02462
Model-based methods play a key role in the reconstruction of compressed sensing (CS) MRI. Finding an effective prior to describe the statistical distribution of the image family of interest is crucial for model-based methods. Plug-and-play (PnP) is a
Externí odkaz:
http://arxiv.org/abs/2405.03854
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces of very high dimensional and high arrival-rate data with missing entries and corrupting noise. However,
Externí odkaz:
http://arxiv.org/abs/2310.06277
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve heterogeneous data that varies in quality due to noise characteristics
Externí odkaz:
http://arxiv.org/abs/2307.02745
Phase retrieval (PR) is a crucial problem in many imaging applications. This study focuses on resolving the holographic phase retrieval problem in situations where the measurements are affected by a combination of Poisson and Gaussian noise, which co
Externí odkaz:
http://arxiv.org/abs/2305.07712
Autor:
Schwartz, Jonathan, Di, Zichao Wendy, Jiang, Yi, Manassa, Jason, Pietryga, Jacob, Qian, Yiwen, Cho, Min Gee, Rowell, Jonathan L., Zheng, Huihuo, Robinson, Richard D., Gu, Junsi, Kirilin, Alexey, Rozeveld, Steve, Ercius, Peter, Fessler, Jeffrey A., Xu, Ting, Scott, Mary, Hovden, Robert
Publikováno v:
Nat Commun 15, 3555 (2024)
Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that
Externí odkaz:
http://arxiv.org/abs/2304.12259
Dynamic subspace estimation, or subspace tracking, is a fundamental problem in statistical signal processing and machine learning. This paper considers a geodesic model for time-varying subspaces. The natural objective function for this model is non-
Externí odkaz:
http://arxiv.org/abs/2303.14851