ArticleSkeletal radiology2021
Clinical evaluation of a new adaptive iterative metal artifact reduction method in whole-body low-dose CT skeletal survey examinations.
Article in Skeletal radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed, 3 citations in OpenAlex.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo evaluate a new adaptive iterative metal artifact reduction algorithm (AiMAR) in whole-body low-dose CT (WBLDCT) skeletal survey examinations.
methodsProjection data were retrospectively obtained from 25 clinical WBLDCT skeletal survey patients, each with two types of metal implants. Images were reconstructed with bone and soft tissue kernels using four settings-original and AiMAR with strengths of 2, 4, and 5. All images were anonymized and randomized for a reader study, where three musculoskeletal radiologists independently determined the overall ranking of all series based on diagnostic quality, and local scoring of metal artifact and anatomy visualization for each implant. Quantitative image noise analysis was performed in areas close to the implants. Intraclass correlation coefficients (ICC) and Krippendorff's alpha were computed for inter-rater reliability.
resultsAiMAR 4 was ranked the highest for 64.3% of the series across eight types of implants. For local scoring task, AiMAR 4 showed better metal artifact and anatomy visualization than the original and AiMAR 2. AiMAR 4 was comparable in anatomy visualization but inferior to AiMAR 5 in metal artifact scores. AiMAR 4 led to 56.3% noise reduction around the implant areas compared with the original images, and AiMAR 5 68.1% but also resulted in anatomy blurring in 40% of the implants. ICC and Krippendorff's alpha revealed at least substantial reliability in the local scores among the readers.
conclusionsAiMAR was evaluated in WBLDCT skeletal surveys. AiMAR 4 demonstrated the highest overall quality ranking and improved local scores with noise reduction around implant areas.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.