Evidence map›Paper›PMID 38962692›Full record

ArticleF1000Research2024

Comparison of image quality between Deep learning image reconstruction and Iterative reconstruction technique for CT Brain- a pilot study.

Obhuli Chandran M, Saikiran Pendem, Priya P S, Cijo Chacko, Priyanka, Rajagopal Kadavigere

Abstract readComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Article in F1000Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

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

5 · Who and what money

Authors and funding

6 authors.

Obhuli Chandran MDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.ORCID https://orcid.org/0000-0001-5515-6377
Saikiran PendemDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.ORCID https://orcid.org/0000-0001-7933-1192
Priya P SDepartment of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.ORCID https://orcid.org/0000-0002-7201-5733
Cijo ChackoClinical Scientist, Philips Research and Development, Philips innovation campus, Yelahanka, Karnataka, 560064, India.
PriyankaDepartment of Medical Imaging Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.ORCID https://orcid.org/0000-0002-9792-6242
Rajagopal KadavigereDepartment of Radio Diagnosis and Imaging, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.ORCID https://orcid.org/0000-0003-3486-8740

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Non-contrast Computed Tomography (NCCT) plays a pivotal role in assessing central nervous system disorders and is a crucial diagnostic method. Iterative reconstruction (IR) methods have enhanced image quality (IQ) but may result in a blotchy appearance and decreased resolution for subtle contrasts. The deep-learning image reconstruction (DLIR) algorithm, which integrates a convolutional neural network (CNN) into the reconstruction process, generates high-quality images with minimal noise. Hence, the objective of this study was to assess the IQ of the Precise Image (DLIR) and the IR technique (iDose Methods: This is a prospective study. Thirty patients who underwent NCCT brain were included. The images were reconstructed using DLIR-standard and iDose Results: Quantitative IQ analysis showed significant differences (p<0.05) in IN, SNR, and CNR between the iDose Conclusion: DLIR standard yielded superior quantitative and qualitative IQ compared to the IR technique (iDose4). The DLIR-standard significantly reduced the IN and artifacts compared to iDose

Indexed as

BrainDeep LearningImage Processing, Computer-AssistedTomography, X-Ray ComputedAdultAgedAlgorithmsFemaleHumansMaleMiddle AgedPilot ProjectsProspective StudiesSignal-To-Noise RatioCT BrainDeep learning image reconstructionFiltered back projectioniDose4Image quality

Identifiers

PMID38962692
PMCPMC11221345

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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.