Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study

Takashi Shirasaka, Tsukasa Kojima, Yoshinori Funama, Yuki Sakai, Masatoshi Kondo, Ryoji Mikayama, Hiroshi Hamasaki, Toyoyuki Kato, Yasuhiro Ushijima, Yoshiki Asayama, Akihiro Nishie

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Purpose: In an ultrahigh-resolution CT (U-HRCT), deep learning-based reconstruction (DLR) is expected to drastically reduce image noise without degrading spatial resolution. We assessed a new algorithm's effect on image quality at different radiation doses assuming an abdominal CT protocol. Methods: For the normal-sized abdominal models, a Catphan 600 was scanned by U-HRCT with 100%, 50%, and 25% radiation doses. In all acquisitions, DLR was compared to model-based iterative reconstruction (MBIR), filtered back projection (FBP), and hybrid iterative reconstruction (HIR). For the quantitative assessment, we compared image noise, which was defined as the standard deviation of the CT number, and spatial resolution among all reconstruction algorithms. Results: Deep learning-based reconstruction yielded lower image noise than FBP and HIR at each radiation dose. DLR yielded higher image noise than MBIR at the 100% and 50% radiation doses (100%, 50%, DLR: 15.4, 16.9 vs MBIR: 10.2, 15.6 Hounsfield units: HU). However, at the 25% radiation dose, the image noise in DLR was lower than that in MBIR (16.7 vs. 26.6 HU). The spatial frequency at 10% of the modulation transfer function (MTF) in DLR was 1.0 cycles/mm, slightly lower than that in MBIR (1.05 cycles/mm) at the 100% radiation dose. Even when the radiation dose decreased, the spatial frequency at 10% of the MTF of DLR did not change significantly (50% and 25% doses, 0.98 and 0.99 cycles/mm, respectively). Conclusion: Deep learning-based reconstruction performs more consistently at decreasing dose in abdominal ultrahigh-resolution CT compared to all other commercially available reconstruction algorithms evaluated.

Original languageEnglish
Pages (from-to)286-296
Number of pages11
JournalJournal of Applied Clinical Medical Physics
Issue number7
Publication statusPublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Radiation
  • Instrumentation
  • Radiology Nuclear Medicine and imaging


Dive into the research topics of 'Image quality improvement with deep learning-based reconstruction on abdominal ultrahigh-resolution CT: A phantom study'. Together they form a unique fingerprint.

Cite this