ArticleAbdominal radiology (New York)2026
Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.
Article in Abdominal radiology (New York), 2026. 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
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVirtual monoenergetic imaging (VMI) at 40 keV improves iodine attenuation in colon cancer CT but is constrained by severe image noise. Deep learning image reconstruction (DLIR) may address this limitation, but its effect on anatomical edge preservation across multiple targets requires investigation. PURPOSE: To evaluate the impact of DLIR on objective and subjective image quality of 40-keV VMIs in colon adenocarcinoma, with emphasis on the trade-off between noise reduction and edge definition.
methodsIn this completed retrospective study (patient enrollment window: May 2024 to February 2025), 60 patients (mean age, 62.8 years ± 15.1; 34 men) with confirmed colon adenocarcinoma underwent dual-energy CT using a low-iodine protocol (1.0 mL/kg). Portal venous phase data were reconstructed at 40 keV using adaptive statistical iterative reconstruction-V (ASIR-V) 50%, medium-strength DLIR (DLIR-M), and high-strength DLIR (DLIR-H). Objective metrics, including contrast-to-noise ratio (CNR), edge rise distance (ERD), and edge rise slope (ERS), were measured. Two radiologists independently scored five qualitative categories, including regional lymph node visualization, using a 5-point Likert scale.
resultsDLIR-H yielded the lowest image noise and highest CNR across all anatomical targets compared with DLIR-M and ASIR-V 50% (all P < 0.001). For colon tumors, the CNR of DLIR-H (5.4 ± 2.2) was 80% higher than that of ASIR-V 50% (3.0 ± 1.1, P < 0.001). Although ASIR-V 50% maintained a higher ERS than DLIR-H (median, 108.3 vs. 101.5 HU/mm; P < 0.001), the ERD remained highly stable across all algorithms (median, 2.5 mm; pairwise P > 0.05). Subjectively, DLIR-H received the highest scores for overall image quality and regional lymph node visualization (median, 5.0 and 4.5, respectively, vs. 3.0 for both in ASIR-V 50%; all P < 0.001).
conclusionIn 40-keV virtual monoenergetic CT of colon cancer, DLIR-H significantly improves objective and subjective image quality for tumors, vessels, and lymph nodes. While a minor objective edge-smoothing effect exists, DLIR-H provides an optimal balance between robust noise suppression and anatomical clarity. Although these findings suggest the potential to facilitate low-iodine spectral protocols, future diagnostic accuracy trials are required to confirm their true clinical impact.
Indexed as
Identifiers
41940980What 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.