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. 2025 May 12.
doi: 10.1007/s10278-025-01529-z. Online ahead of print.

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography

Yasutaka Ichikawa  1 , Daisuke Hasegawa  2 , Kensuke Domae  2 , Motonori Nagata  2 , Hajime Sakuma  2
Affiliations

Effect of Deep Learning-Based Image Reconstruction on Lesion Conspicuity of Liver Metastases in Pre- and Post-contrast Enhanced Computed Tomography

Yasutaka Ichikawa et al. J Imaging Inform Med. .

Abstract

The purpose of this study was to investigate the utility of deep learning image reconstruction at medium and high intensity levels (DLIR-M and DLIR-H, respectively) for better delineation of liver metastases in pre-contrast and post-contrast CT, compared to conventional hybrid iterative reconstruction (IR) methods. Forty-one patients with liver metastases who underwent abdominal CT were studied. The raw data were reconstructed with three different algorithms: hybrid IR (ASiR-V 50%), DLIR-M (TrueFildelity-M), and DLIR-H (TrueFildelity-H). Three experienced radiologists independently rated the lesion conspicuity of liver metastases on a qualitative 5-point scale (score 1 = very poor; score 5 = excellent). The observers also selected each image series for pre- and post-contrast CT per patient that was considered most preferable for liver metastases assessment. For pre-contrast CT, lesion conspicuity scores for DLIR-H and DLIR-M were significantly higher than those for hybrid IR for two of the three observers, while there was no significant difference for one observer. For post-contrast CT, the lesion conspicuity scores for DLIR-H images were significantly higher than those for DLIR-M images for two of the three observers on post-contrast CT (Observer 1: DLIR-H, 4.3 ± 0.8 vs. DLIR-M, 3.9 ± 0.9, p = 0.0006; Observer 3: DLIR-H, 4.6 ± 0.6 vs. DLIR-M, 4.3 ± 0.6, p = 0.0013). For post-contrast CT, all observers most often selected DLIR-H as the best reconstruction method for the diagnosis of liver metastases. However, in the pre-contrast CT, there was variation among the three observers in determining the most preferred image reconstruction method, and DLIR was not necessarily preferred over hybrid IR for the diagnosis of liver metastases.

Keywords: Computed tomography; Deep-learning image reconstruction; Hybrid iterative reconstruction; Image quality; Lesion conspicuity; Liver metastasis.

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Conflict of interest statement

Declarations. Ethics Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Mie University Hospital (approval number: H2019-207). Consent to Participate: Written informed consent was waived because this study used existing clinical CT imaging data. The opportunity to withdraw from this study was provided via a notice on the hospital website. No patient expressed an intention to withdraw from this study. Competing Interests: The authors declare no competing interests.

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