Noise2Quality: Non-Reference, Pixel-Wise Assessment of Low Dose CT Image Quality

Ayaan Haque, Adam Wang, Abdullah Al Zubaer Imran

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations


CT image quality is reliant on radiation dose, as low dose CT (LDCT) scans contain increased noise in images. This compromises the diagnostic performance on such scans. Therefore, it is desirable to perform Image Quality Assessment (IQA) prior to diagnostic use of CT scans. Often, image quality is assessed with full-reference methods, where a LDCT is algorithmically compared against its full dose counterpart. However due to health concerns, acquiring full dose CT scans is challenging and not desirable. As an alternative, non-reference IQA (NR-IQA) can be performed. Moreover, IQA at the pixel level is important, as most IQA methods only provide a global assessment, which means localized regions of interest cannot be specifically assessed. A solution for localized-IQA is to produce visually-interpretable quality maps. Deep learning methods could be employed by leveraging computer vision techniques, such as Self-Supervised learning (SSL). In this work, we propose Noise2Quality (N2Q) a novel self-supervised, non-reference, pixel-wise image quality assessment model to predict IQA maps from LDCTs. Self-supervised dose level prediction as an auxiliary task further improves the model performance. Our experimental evaluation both qualitatively and quantitatively demonstrates the effectiveness of the model in accurately predicting IQA maps over various baselines. 2022 SPIE.

Original languageEnglish
Title of host publicationMedical Imaging 2022
Subtitle of host publicationImage Perception, Observer Performance, and Technology Assessment
EditorsClaudia R. Mello-Thoms, Claudia R. Mello-Thoms, Sian Taylor-Phillips
ISBN (Electronic)9781510649453
StatePublished - 2022
EventMedical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment - Virtual, Online
Duration: Mar 21 2022Mar 27 2022

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2022: Image Perception, Observer Performance, and Technology Assessment
CityVirtual, Online

Bibliographical note

Publisher Copyright:
© 2022 SPIE. All rights reserved.


  • Computed Tomography
  • Image Quality Assessment
  • Low-Dose CT
  • Self-Supervised Learning

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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