[Opportunities for artificial intelligence in radiation protection : Improving safety of diagnostic imaging]

CLINICAL/METHODOLOGICAL ISSUE

Imaging of structures of internal organs often requires ionizing radiation, which is a health risk. Reducing the radiation dose can increase the image noise, which means that images provide less information.

STANDARD RADIOLOGICAL METHODS

This problem is observed in commonly used medical imaging modalities such as computed tomography (CT), positron emission tomography (PET), single photon emission computed tomography (SPECT), angiography, fluoroscopy, and any modality that uses ionizing radiation for imaging.

METHODOLOGICAL INNOVATIONS

Artificial intelligence (AI) can improve the quality of low-dose images and help minimize radiation exposure. Potential applications are explored, and frameworks and procedures are critically evaluated.

PERFORMANCE

The performance of AI models varies. High-performance models could be used in clinical settings in the near future. Several challenges (e.g., quantitative accuracy, insufficient training data) must be addressed for optimal performance and widespread adoption of this technology in the field of medical imaging.

PRACTICAL RECOMMENDATIONS

To fully realize the potential of AI and deep learning (DL) in medical imaging, research and development must be intensified. In particular, quality control of AI models must be ensured, and training and testing data must be uncorrelated and quality assured. With sufficient scientific validation and rigorous quality management, AI could contribute to the safe use of low-dose techniques in medical imaging.

Reduced-dose abdominopelvic CT using hybrid iterative reconstruction in suspected left-sided colonic diverticulitis

OBJECTIVES

To assess the effect of hybrid iterative reconstruction (HIR) and filtered back projection (FBP) on abdominopelvic CT with reduced-dose (RD-APCT) in the evaluation of acute left-sided colonic diverticulitis (ALCD).

METHODS

Twenty-five consecutive patients with suspected ALCD who underwent RD-APCT (mean CTDIvol 11.2 ± 4.2 mGy) were enrolled in this study. Raw data were reconstructed using FBP and two increasing HIR levels, L4 & L6. Two radiologists assessed image quality, image noise and reviewer confidence in interpreting findings of ALCD, including wall thickening, pericolic fat inflammation, pericolic abscess, and contained or free extraluminal air. Objective image noise (OIN) was measured.

RESULTS

OIN was reduced up to 54 % with HIR compared to FBP. Subjective image quality of HIR images was superior to FBP; subjective image noise was reduced. The detection rate of extraluminal air was higher with HIR L6. Reviewer confidence in interpreting CT findings of ALCD significantly improved with application of HIR.

CONCLUSIONS

RD-APCT with HIR offers superior image quality and lower image noise compared to FBP, allowing a high level of reviewer confidence in interpreting CT findings in ALCD. HIR facilitates detection of ALCD findings that may be missed with the FBP algorithm.

KEY POINTS

HIR significantly reduces objective image noise in comparison to conventional FBP. HIR offers superior subjective image quality in comparison to conventional FBP. HIR allows reduced-dose abdominopelvic CT with acceptable image quality. Reviewer confidence in interpreting CT findings in ALCD significantly improves with HIR.