ArticleRadiological physics and technology2021
Method for determining slice sensitivity profile of iterative reconstruction CT images using low-contrast sphere phantom.
Article in Radiological physics and technology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A novel method for measuring the slice sensitivity profile (SSP) of computed tomography (CT) images reconstructed using an iterative reconstruction (IR) algorithm is proposed herein. A phantom that included a low-contrast spherical object was scanned and consecutive cross-sectional images were reconstructed. The mean CT values in a region including the sphere were measured for all images and plotted as a function of slice position along the longitudinal [Formula: see text] direction to yield a mean CT value profile [Formula: see text]. Next, we numerically generated an object function corresponding to the sphere and obtained the mean CT value profile [Formula: see text]. Subsequently, the SSP was modeled as a product of the Gaussian and cosine functions. We convolved [Formula: see text] with the modeled SSP to obtain [Formula: see text]. The difference between [Formula: see text] and [Formula: see text] was evaluated using the root mean square error (RMSE), which was minimized via optimization of the SSP model parameters. To validate the methodology, we first used filtered back projection (FBP) images to compare the SSPs determined using the proposed and standard coin methods. Subsequently, the proposed method was applied to measure the SSPs of four types of IR algorithms in two scanners. The SSPs of the FBP images determined using the proposed and coin methods showed good agreement. Additionally, in the SSP measurements using the proposed method, [Formula: see text] agreed well with [Formula: see text] for every IR algorithm. The RMSEs for all measurements were less than 0.7 HU, indicating the accuracy of the SSPs. Thus, the proposed method is effective for obtaining valid SSPs.
Indexed as
Identifiers
34523081What 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.