Patient-specific hyperparameter learning for optimization-based CT image reconstruction.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

3 citing papers in PubMed.

1. Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction.Sensors (Basel, Switzerland) · 2023
   Article
2. Linearized Analysis of Noise and Resolution for DL-Based Image Generation.IEEE transactions on medical imaging · 2023
   Article
3. Convex optimization algorithms in medical image reconstruction-in the age of AI.Physics in medicine and biology · 2022
   Review

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

2 authors.

Funding

Abstract

Indexed as