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Advancements in biomedical rendering: A survey on AI-based denoising techniques.

Elena Denisova1, Piergiorgio Francia2, Cosimo Nardi2

  • 1University of Florence, via di Santa Marta 3, Florence, 50139, Italy; Imaginalis S.R.L., via Rodolfo Morandi 13, Sesto Fiorentino, 50019, Italy.

Computers in Biology and Medicine
|August 29, 2025
PubMed
Summary

Healthcare professionals prefer AI-enhanced computed tomography (CT) images for better visualization. More experienced users show higher confidence and satisfaction with AI-assisted denoising, indicating its clinical potential.

Keywords:
Artificial intelligenceDiagnostic imagingRealistic volumetric renderingSurveys and questionnaires

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Science

Background:

  • Deep learning-based denoising for Monte Carlo (MC) Path Tracing in computed tomography (CT) volume visualization shows promise but has inconsistent qualitative results.
  • Evaluating the qualitative impact of AI on CT image denoising requires understanding user perception beyond standard metrics.

Purpose of the Study:

  • To investigate the reasons for incongruent quantitative and qualitative assessments of deep learning-based denoising in CT volume visualization.
  • To assess healthcare professionals' perceptions of AI-enhanced CT image and video quality, confidence, and clinical applicability.

Main Methods:

  • A web-based SurveyMonkey questionnaire was distributed to radiologists, residents, orthopedic surgeons, and veterinarians.
  • Participants evaluated randomized sections on AI-enhanced image/video quality, confidence in reference images, and clinical applicability.
  • Seventy-four participants with varying experience levels (<1 to >5 years) completed the survey.

Main Results:

  • A majority (77%) preferred AI-enhanced images over traditional MC estimates, with preference influenced by experience (adjusted OR 0.81).
  • Participant experience correlated positively with confidence in AI-generated images and satisfaction with video previews (adjusted OR 0.96-0.98).
  • Significant monotonic relationships were found between experience, confidence, and satisfaction.

Conclusions:

  • AI post-processing holds potential for improving biomedical volume rendering in CT, particularly for experienced users.
  • User preferences for AI-enhanced CT images may not always align with objective quality metrics like PSNR and SSIM.
  • The study highlights the importance of qualitative assessments and user experience in evaluating AI's impact on medical image denoising.