Improving image quality and diagnostic performance using deep learning image reconstruction in 100-kVp CT enterography for patients with wide-range body mass index.

OBJECTIVE

To assess the clinical value of the deep learning image reconstruction (DLIR) algorithm compared with conventional adaptive statistical iterative reconstruction-Veo (ASiR-V) in image quality, diagnostic confidence, and intestinal lesion detection in 100-kVp CT enterography (CTE) for patients with wide-range body mass index (BMI).

METHODS

A total of 84 patients underwent 100-kVp dual-phase CTE were included. Images were reconstructed using filtered back projection (FBP), ASiR-V 30 %, ASiR-V 60 %, and DLIR with low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H). The CT value, standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of small and large intestines were compared using repeated measures analysis of variance with the Bonferroni correction or Friedman test. The correlation between relative CNR increment and BMI was analyzed using Pearson's correlation coefficient. The overall image quality and diagnostic confidence scores were evaluated. Additionally, lesion detection of intestinal disease was conducted by three readers with different experience and compared between DLIR-M and ASiR-V 60 % images using McNemar's test.

RESULTS

SD decreased sequentially from FBP, ASiR-V 30 %, DLIR-L, ASiR-V 60 %, DLIR-M, to DLIR-H, which corresponded with improvements in CNR and SNR (all p < 0.001). The relative CNR increment of DLIR exhibited a significantly positive linear correlation with BMI (r:0.307-0.506, all p ≤ 0.005). For overall image quality scores, the ranking was: FBP < ASiR-V 30 % < ASiR-V 60 % ≈DLIR-L < DLIR-M ≈ DLIR-H. DLIR-M outperformed ASiR-V 60 % in diagnostic confidence (p ≤ 0.018 for all three readers). In lesion detection, for the two junior readers, DLIR-M exhibited higher sensitivity for inflammatory lesions compared to ASiR-V 60 % (0.700 (95 % confidence interval [95 % CI]: 0.354-0.919) vs. 0.300 (95 % CI: 0.081-0.646) for reader 1 and 0.700 (95 %CI: 0.354-0.919) vs. 0.500 (95 % CI: 0.201-0.799) for reader 2), though no statistical significance was reached.

CONCLUSION

DLIR effectively reduces noise and improves image quality in 100-kVp dual-phase CTE for wide-range BMIs. DLIR-M exhibits superior performance in image quality and diagnostic confidence, also provide potential value in improving intestinal inflammatory lesion detection in junior readers and sheds lights on benefiting clinical decision making, which needs further investigation.