Evidence map›Paper›PMID 40095024›Full record

ArticleAbdominal radiology (New York)2025

Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.

Hakki Serdar Sagdic, Mohammadreza Hosseini-Siyanaki, Abheek Raviprasad, Sefat Munjerin, Daniella Fabri, Joseph Grajo, Victor Martins Tonso, Laura Magnelli, Daniela Hochhegger, Evelyn Anthony and 2 more

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Abdominal radiology (New York), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

12 authors.

Hakki Serdar SagdicRadiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA. drsagdic@gmail.com.
Mohammadreza Hosseini-SiyanakiRadiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA.
Abheek RaviprasadRadiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA.
Sefat MunjerinRadiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA.
Daniella FabriDepartment of Neurosurgery, University of Florida College of Medicine, Gainesville, USA.
Joseph GrajoDepartment of Radiology, University of Florida College of Medicine, Gainesville, FL, USA.
Victor Martins TonsoDepartment of Radiology, University of Florida College of Medicine, Gainesville, FL, USA.
Laura MagnelliDepartment of Radiology, University of Florida College of Medicine, Gainesville, FL, USA.
Daniela HochheggerDepartment of Radiology, University of Florida College of Medicine, Gainesville, FL, USA.
Evelyn AnthonyRadiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA.
Bruno HochheggerRadiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA.
Reza ForghaniRadiomics and Augmented Intelligence Laboratory (RAIL), Department of Radiology and the Norman Fixel Institute for Neurological Diseases, University of Florida College of Medicine, Gainesville, FL, USA. reza.forghani.md@adventhealth.com.

Funding

Canon Medical Systems USA MRA_CT_UF_V6.00
6 · The paper itself

Abstract

purposeDeep Learning Spectral Reconstruction (DLSR) potentially improves dual-energy CT (DECT) image quality, but there is a paucity of research involving human abdominal DECT scans. The purpose of this study was to comprehensively evaluate image quality by quantitatively and qualitatively comparing strong and standard levels of a DLSR algorithm. Optimal virtual monochromatic image (VMI) energy levels were also evaluated.

methodsDECT scans of the abdomen/pelvis from 51 patients were retrospectively evaluated. VMIs were reconstructed at energy levels ranging from 35 to 200 keV using both standard and strong DLSR levels. For quantitative analysis, various abdominal structures were assessed using regions of interest, and mean signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) values were calculated. This was supplemented with a qualitative evaluation of VMIs reconstructed at 35, 45, 55, and 65 keV.

resultsThe strong-level DLSR demonstrated significantly better SNR and CNR values (p < 0.0001) compared to standard-level DLSR across all structures. The optimal SNR was observed at 70 keV (p < 0.0001), while the optimal CNR was found at 65 keV (p < 0.0001). The average qualitative scores between standard and strong DLSR were significantly different at 45, 55, and 65 keV (p < 0.0001). There was a moderate level of agreement between observers (ICC = 0.427, p < 0.0001).

conclusionA DLSR set to a strong level significantly improves image quality compared to standard-level DLSR, potentially enhancing the diagnostic evaluation of abdominal DECT scans. In addition to achieving a very high SNR, 65 keV VMIs had the highest CNR, which differs from what is typically observed with traditional DECT using non-deep learning reconstruction approaches.

Indexed as

Deep LearningRadiographic Image Interpretation, Computer-AssistedRadiography, AbdominalRadiography, Dual-Energy Scanned ProjectionTomography, X-Ray ComputedAdultAgedAged, 80 and overAlgorithmsFemaleHumansMaleMiddle AgedRetrospective StudiesSignal-To-Noise RatioAbdomenComputer-assistedDeep learningDual-Energy scanned projectionImage processingImage reconstructionRadiographyTomographyX-Ray computed

Identifiers

PMID40095024

What Socratic holds

Textmetadata
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.