ArticleJournal of digital imaging2023
A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results.
Article in Journal of digital imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 12 citations in OpenAlex.
- Deep learning image reconstruction improves visualization of arterial phase hyperenhancement and washout appearance on dual-energy CT for hepatocellular carcinoma: a non-inferiority study.Abdominal radiology (New York) · 2026Article
- Impact of a deep learning image reconstruction algorithm on the robustness of abdominal computed tomography radiomics features using standard and low radiation doses.Quantitative imaging in medicine and surgery · 2025Article
- Characterization of hepatocellular carcinoma with CT with deep learning reconstruction compared with iterative reconstruction and 3-Tesla MRI.European radiology · 2025Article
- Deep learning reconstruction enhances tophus detection in a dual-energy CT phantom study.Scientific reports · 2025Article
- Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study.Medical physics · 2025Article
- The Usefulness of Low-Kiloelectron Volt Virtual Monochromatic Contrast-Enhanced Computed Tomography with Deep Learning Image Reconstruction Technique in Improving the Delineation of Pancreatic Ductal Adenocarcinoma.Journal of imaging informatics in medicine · 2025Article
- Comparison of image quality in carotid dual-energy computed tomography angiography at 55 keV virtual monoenergetic imaging using deep learning and adaptive iterative reconstruction algorithm.Journal of clinical imaging science · 2025Article
- Research Progress of Dual-energy CT in Diagnosis and Evaluation of Curative Effect of Liver Cancer: A ReviewCurrent medical imaging · 2025Review
- State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.Radiographics : a review publication of the Radiological Society of North America, Inc · 2024Review
- What to Expect (and What Not) from Dual-Energy CT Imaging Now and in the Future?Journal of imaging · 2024Review
- Functional Connectivity Networks with Latent Distributions for Mild Cognitive Impairment Identification.Journal of digital imaging · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to compare the performance of deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in improving image quality and diagnostic performance using virtual monochromatic spectral images in abdominal dual-energy computed tomography (DECT). Sixty-two patients [mean age ± standard deviation (SD): 56 years ± 13; 30 men] who underwent abdominal DECT were prospectively included in this study. The 70-keV DECT images in the portal phase were reconstructed at 5-mm and 1.25-mm slice thicknesses with 40% ASIR-V (ASIR-V40%) and at 1.25-mm slice with deep learning image reconstruction at medium (DLIR-M) and high (DLIR-H) levels and then compared. Computed tomography (CT) attenuation, SD values, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured in the liver, spleen, erector spinae, and intramuscular fat. The lesions in each reconstruction group at 1.25-mm slice thickness were counted. The image quality and diagnostic confidence were subjectively evaluated by two radiologists using a 5-point scale. For the 1.25-mm images, DLIR-M and DLIR-H had lower SD, higher SNR and CNR, and better subjective image quality compared with ASIR-V40%; DLIR-H performed the best (all P values < 0.001). Furthermore, the 1.25-mm DLIR-H images had similar SD, SNR, and CNR values as the 5-mm ASIR-V40% images (all P > 0.05). Three image groups had similar lesion detection rates, but DLIR groups exhibited higher confidence in diagnosing lesions. Compared with ASIR-V40% at 70 keV, 70-keV DECT with DLIR-H further reduced image noise and improved image quality. Additionally, it improved diagnostic confidence while ensuring a consistent lesion detection rate of liver lesions.
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.