Evidence map›Paper›PMID 41636571›Full record

ReviewAJR. American journal of roentgenology2026

CT Radiation Dose Reduction With Preserved Diagnostic Performance: How Far Have We Come Over 25 Years?

Cynthia H McCollough, Lifeng Yu

Abstract readReview
In one paragraph

Review in AJR. American journal of roentgenology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors.

Cynthia H McColloughDepartment of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
Lifeng YuDepartment of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.

Funding

Critical resources to evaluate CT scan techniques and dose reduction approachesU01EB017185 · NIBIB · MAYO CLINIC ROCHESTER · PI MCCOLLOUGH, CYNTHIA H · 2013 to 2016
$3.6M
NIBIB NIH HHS U01 EB017185
6 · The paper itself

Abstract

Concerns about the risk of radiation from CT have driven a spectrum of major advances in radiation dose reduction technology since the 2000s, including added beam filtration, dynamic z-axis collimation, automatic tube current modulation and tube potential selection, advanced iterative reconstruction, and deep learning-based reconstructions. The introduction of photon-counting detector CT further improved dose efficiency through electronic noise rejection, optimal photon-energy weighting, and high-resolution data acquisition. This Review summarizes key milestones and technologic innovations that have led to substantial reductions in CT radiation doses and provides examples of the cumulative impact of these advances on CT doses and image quality for routine examinations in various anatomic sites. This article also highlights the importance of an objective task-based image quality assessment to ensure that diagnostic performance is not compromised as dose is reduced, particularly for emerging deep learning-based reconstruction and postprocessing noise reduction methods.

Indexed as

Radiation DosageRadiation ProtectionTomography, X-Ray ComputedDeep LearningHumansRadiographic Image Interpretation, Computer-AssistedCTdeep learning reconstructionimage quality assessmentiterative reconstructionphoton-counting detector CTradiation doseradiation dose reduction

Identifiers

PMID41636571
PMCPMC12969835

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.