Impact of deep learning reconstructions on image quality and liver lesion detectability in dual-energy CT: An anthropomorphic phantom study

Background

Deep learning image reconstruction (DLIR) algorithms allow strong noise reduction while preserving noise texture, which may potentially improve hypervascular focal liver lesions.

Purpose

To assess the impact of DLIR on image quality (IQ) and detectability of simulated hypervascular hepatocellular carcinoma (HCC) in fast kV-switching dual-energy CT (DECT).

Methods

An anthropomorphic phantom of a standard patient morphology (body mass index of 23 kg m⁻²) with customized liver, including mimickers of hypervascular lesions in both late arterial phase (AP) and portal venous phase (PVP) enhancement, was scanned on a DECT. Virtual monoenergetic images were reconstructed from raw data at four energy levels (40/50/60/70 keV) using filtered back-projection (FBP), adaptive statistical iterative reconstruction-V 50% and 100% (ASIRV-50 and ASIRV-100), DLIR low (DLIR-L), medium (DLIR-M), and high (DLIR-H). The contrast between the lesion and the liver parenchyma, the noise magnitude, the average and peak frequencies (f_avg and f_peak) of the noise power spectrum (NPS) reflecting noise texture, and the task-based measure of the modulation transfer function (MTF_task) were measured to evaluate spatial resolution. A detectability index (d′) was computed to model the detection of hypervascular lesions in both AP and PVP. Metrics were compared between reconstructions and between energy levels using a Friedman test with follow-up post-hoc multiple comparison.

Results

Lesion-to-liver contrast significantly increased with decreasing energy level in both AP and PVP (p ≤ 0.042) but was not affected by reconstruction algorithm (p ≥ 0.57). Overall, noise magnitude increased with decreasing energy levels and was the lowest with ASIRV-100 at all energy levels in both AP and PVP (p ≤ 0.01) and significantly lower with DLIR-M and DLIR-H reconstructions compared to ASIRV-50 and DLIR-L (p < 0.001). For all reconstructions, noise texture within the liver tended to get smoother with decreasing energy; f_avg significantly shifted towards lower frequencies from 70 to 40 keV (p ≤ 0.01). Noise texture was the smoothest with ASIRV-100 (p < 0.001) while DLIR-L had the noise texture closer to the one of FBP. The spatial resolution was not significantly affected by the energy level, but it was degraded when increasing the level of ASIRV and DLIR. For all reconstructions, the detectability indices increased with decreasing energy levels and peaked at 40 and 50 keV in AP and PVP, respectively. In both AP and PVP, the highest d′ values were observed with ASIRV-100 and DLIR-H, whatever the energy level studied (p ≤ 0.01) without statistical significance between those two reconstructions.

Conclusions

Compared to the routinely used level of iterative reconstruction, DLIR reduces noise without consequential noise texture modification, and may improve the detectability of hypervascular liver lesions while enabling the use of lower energy virtual monoenergetic images. The optimal energy level and DLIR level may depend on the lesion enhancement.