Momentum-Net for Low-Dose CT Image Reconstruction
Siqi Ye; Yong Long; Il Yong Chun · 2020 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
This paper applies the recent fast iterative neural network framework, Momentum-Net, using appropriate models to low-dose X-ray computed tomography (LDCT) image reconstruction. At each layer of the proposed Momentum-Net, the model-based image reconstruction module solves the majorized penalized weighted least-square problem, and the image refining module uses a four-layer convolutional neural network (CNN). Experimental results with the NIH AAPM-Mayo Clinic Low Dose CT Grand Challenge dataset show that the proposed Momentum-Net architecture significantly improves image reconstruction accuracy,
Excerpt shown for reference under fair use — read the full paper at the publisher.
About to run something similar?
Run an AI Precheck on your own design to catch failure modes like this one before you spend the time. Your first desk check is free.
Related failures
Aggregation Kinetics of Graphene Oxides in Aqueous Solutions: Experiments, Mechanisms, and Modeling
Negative / Null Result ReportThe reaction between metakaolin and limestone and its effect in porosity refinement and mechanical properties
Negative / Null Result ReportTuning Alginate Bioink Stiffness and Composition for Controlled Growth Factor Delivery and to Spatially Direct MSC Fate within Bioprinted Tissues
Negative / Null Result ReportEx-situ characterisation of gas diffusion layers for proton exchange membrane fuel cells
Negative / Null Result ReportCobb Angle Measurement of Spine from X-Ray Images Using Convolutional Neural Network
Negative / Null Result ReportDeterminants of residential water consumption: Evidence and analysis from a 10‐country household survey
WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: arXiv