Key result
Deep learning reconstruction improves vascular measurement accuracy and precision on CTA models over standard methods.
Why the study?
The performance of deep learning image reconstruction compared with filtered back projection and iterative reconstruction for vascular diameter measurement on CT angiography was unclear.
DLIR may refine vascular measurements in models; leaves open clinical translation without patient studies.
OBJECTIVE: This study aimed to compare the performance of deep learning image reconstruction (DLIR) with that of standard filtered back projection (FBP) and adaptive statistical iterative reconstruction V (ASiR-V) for measurement of the vascular diameter on computed tomography (CT) angiography model. METHODS: We used 6 vascular models of 3 wall thicknesses. We used DLIR, FBP, and ASiR-V for reconstruction, and compared the accuracy and precision of vascular diameter measurement, as well as the image noise, among the 3 reconstruction methods. RESULTS: Image noise was in the order of FBP > ASiR-V > DLIR. The vascular diameters measured using DLIR and ASiR-V were comparable with, or significantly closer to, the actual diameter than those measured using FBP. The precision of the diameter measurement using DLIR was comparable with or significantly superior to that using FBP/ASiR-V. CONCLUSIONS: Use of DLIR, as compared with FBP or ASiR-V, for image reconstruction can improve the precision and accuracy of vascular diameter measurement.
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A 2021 study studied this question. Deep learning image reconstruction (DLIR) vs. Filtered back projection (FBP) and adaptive statistical iterative reconstruction V (ASiR-V) was evaluated on Accuracy and precision of vascular diameter measurement and image noise. Deep learning image reconstruction improved the precision and accuracy of vascular diameter measurement on CT angiography models compared with filtered back projection or ASiR-V.
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