Performance of deep-learning reconstruction combined with metal artifact reduction algorithm for dual-energy computed
Lina Tao1, Yuhan Zhou1, Limin Lei1
1Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan Province 450052, China.
Purposes:
To evaluate the diagnostic confidence in cerebral aneurysm embolization coil follow-up using the deep learning image reconstruction (DLIR) based virtual monoenergetic images (VMI) combined with metal artifact reduction (MAR) algorithm, with a focus on selecting the most optimal scheme.
Methods:
A CTA database of 54 patients was prospectively assembled and reconstructed utilizing adaptive statistical iterative reconstruction-Veo(ASIR-V50 %), DLIR at medium and high levels (DLIR-M and H). VMIs were generated within the 40-140 keV range at 10 keV intervals, both with or without MAR. Objective parameters such as artifact index (AI), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured. Subjective evaluation was assessed according to the Likert scale scoring method. The post-embolization therapeutic efficacy was assessed by the aneurysm neck, parent artery, and postprocedural complications.
Results:
Firstly, 80 keV to 90 keV provided the best objective and subjective scores for a balance between artifact reduction and vascular display. Secondly, the DLIR-H+MAR combination exhibited the highest CNR at 80 keV to 90 keV, while also receiving the best subjective scores. Moreover, the MAR group showed significantly smaller discrepancies in aneurysm neck length and bilateral parent artery diameters compared to the non-MAR group when compared to DSA (p < 0.001). Importantly, the MAR group demonstrated two cases of aneurysm recurrence, four cases of residual filling, ten cases of parent artery stenosis, and four cases of aneurysmal rupture that were undetected by the non-MAR group.
Conclusion:
DLIR-H+MAR at 80 keV to 90 keV proved to be the optimal method for visualizing cerebral arteries and mitigating metal artifacts. Simultaneously, it significantly enhanced the efficacy assessment and complication detection of post-embolization aneurysm.
