Detectability of Small Low-Attenuation Lesions with Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study

Giuseppe V. Toia, David A. Zamora, Michael Singleton, Arthur Liu, Edward Tan, Shuai Leng, William P. Shuman, Kalpana M. Kanal, Achille Mileto

Research output: Contribution to journalArticlepeer-review


BACKGROUND. Iterative reconstruction (IR) techniques are susceptible to contrast-dependent spatial resolution, limiting overall radiation dose reduction potential. Deep learning image reconstruction (DLIR) may mitigate this limitation. OBJECTIVE. The purpose of our study was to evaluate low-contrast detectability performance and radiation-saving potential of a DLIR algorithm in comparison with filtered back projection (FBP) and IR using a human multireader noninferiority study design and task-based observer modeling. METHODS. A dual-phantom construct, consisting of a low-contrast detectability module (21 low-contrast hypoattenuating objects in seven sizes [2.4–10.0 mm] and three contrast levels [–15, –10, –5 HU] embedded within liver-equivalent background) and a phantom, was imaged at five radiation exposures (CTDIvol range, 1.4–14.0 mGy; size-specific dose estimate, 2.5–25.0 mGy; 90%-, 70%-, 50%-, and 30%-reduced radiation levels and full radiation level) using an MDCT scanner. Images were reconstructed using FBP, hybrid IR (ASiR-V), and DLIR (TrueFidelity). Twenty-four readers of varying experience levels evaluated images using a two-alternative forced choice. A task-based observer model (detectability index [d’]) was calculated. Reader performance was estimated by calculating the AUC using a noninferiority method. RESULTS. Compared with FBP and IR methods at routine radiation levels, DLIR medium and DLIR high settings showed noninferior performance through a 90% radiation reduction (except DLIR medium setting at 70% reduced level). The IR method was noninferior to routine radiation FBP only for 30% and 50% radiation reductions. No significant difference in d’ was observed between routine radiation FBP and DLIR high setting through a 70% radiation reduction. Reader experience was not correlated with diagnostic accuracy (R2 = 0.005). CONCLUSION. Compared with FBP or IR methods at routine radiation levels, certain DLIR algorithm weightings yielded noninferior low-contrast detectability with radiation reductions of up to 90% as measured by 24 human readers and up to 70% as assessed by a task-based observer model. CLINICAL IMPACT. DLIR has substantial potential to preserve contrast-dependent spatial resolution for the detection of hypoattenuating lesions at decreased radiation levels in a phantom model, addressing a major shortcoming of current IR techniques.

Original languageEnglish (US)
Pages (from-to)283-295
Number of pages13
JournalAmerican Journal of Roentgenology
Issue number2
StatePublished - Feb 2023


  • CT
  • image processing
  • radiation dosage

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Detectability of Small Low-Attenuation Lesions with Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study'. Together they form a unique fingerprint.

Cite this