We propose a workflow for modeling generalized mid-spatial frequency (MSF) errors in optical imaging systems. This workflow enables the classification of MSF distributions, filtering of bandlimited signatures, propagation of MSF errors to the exit pupil, and performance predictions that differentiate performance impacts due to the MSF distributions. We demonstrate the workflow by modeling the performance impacts of MSF errors for both transmissive and reflective imaging systems with near-diffraction-limited performance.
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http://dx.doi.org/10.1364/OE.511349 | DOI Listing |
Magn Reson Med
December 2024
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts, USA.
Purpose: Proton magnetic resonance spectroscopic imaging ( -MRSI) provides noninvasive spectral-spatial mapping of metabolism. However, long-standing problems in whole-brain -MRSI are spectral overlap of metabolite peaks with large lipid signal from scalp, and overwhelming water signal that distorts spectra. Fast and effective methods are needed for high-resolution -MRSI to accurately remove lipid and water signals while preserving the metabolite signal.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Chaoyang District, Beijing 100020, China.
Drug resistance in Mycobacterium tuberculosis (Mtb) is a significant challenge in the control and treatment of tuberculosis, making efforts to combat the spread of this global health burden more difficult. To accelerate anti-tuberculosis drug discovery, repurposing clinically approved or investigational drugs for the treatment of tuberculosis by computational methods has become an attractive strategy. In this study, we developed a virtual screening workflow that combines multiple machine learning and deep learning models, and 11 576 compounds extracted from the DrugBank database were screened against Mtb.
View Article and Find Full Text PDFStruct Dyn
November 2024
Second Target Station, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA.
We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
Computer Science Dept., University of Turin, Italy.
In this paper, we present the significant results from the Covid Radiographic imaging System based on AI (Co.R.S.
View Article and Find Full Text PDFPract Radiat Oncol
December 2024
Department of Radiation Oncology, Willis Knighton Cancer Center, 2600 Kings Highway, Shreveport, Louisiana, USA 71103 &, Department of Clinical Research, University of Jamestown, Fargo, ND, USA. Electronic address:
Purpose: Motion management presents a significant challenge in thoracic stereotactic ablative radiotherapy (SABR). Currently, a 5.0 mm standard planning target volume (PTV) margin is widely used to ensure adequate dose to the tumor.
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