Low kV versus dual-energy virtual monoenergetic CT imaging for proven liver lesions: what are the advantages and trade-offs in conspicuity and image quality? A pilot study.

PURPOSE: Single-energy low tube potential (SE-LTP) and dual-energy virtual monoenergetic (DE-VM) CT images both increase the conspicuity of hepatic lesions by increasing iodine signal. Our purpose was to compare the conspicuity of proven liver lesions, artifacts, and radiologist preferences in dose-matched SE-LTP and DE-VM images. METHODS: Thirty-one patients with 72 proven liver lesions (21 benign, 51 malignant) underwent full-dose contrast-enhanced dual-energy CT (DECT). Half-dose images were obtained using single tube reconstruction of the dual-source SE-LTP projection data (80 or 100 kV), and by inserting noise into dual-energy projection data, with DE-VM images reconstructed from 40 to 70 keV. Three blinded gastrointestinal radiologists evaluated half-dose SE-LTP and DE-VM images, ranking and grading liver lesion conspicuity and diagnostic confidence (4-point scale) on a per-lesion basis. Image quality (noise, artifacts, sharpness) was evaluated, and overall image preference was ranked on per-patient basis. Lesion-to-liver contrast-to-noise ratio (CNR) was compared between techniques. RESULTS: Mean lesion size was 1.5 ± 1.2 cm. Across the readers, the mean conspicuity ratings for 40, 45, and 50 keV half-dose DE-VM images were superior compared to other half-dose image sets (p < 0.0001). Per-lesion diagnostic confidence was similar between half-dose SE-LTP compared to half-dose DE-VM images (p ≥ 0.05; 1.19 vs. 1.24-1.32). However, SE-LTP images had less noise and artifacts and were sharper compared to DE-VM images less than 70 keV (p < 0.05). On a per-patient basis, radiologists preferred SE-LTP images the most and preferred 40-50 keV the least (p < 0.0001). Lesion CNR was also higher in SE-LTP images than DE-VM images (p < 0.01). CONCLUSION: For the same applied dose level, liver lesions were more conspicuous using DE-VM compared to SE-LTP; however, SE-LTP images were preferred more than any single DE-VM energy level, likely due to lower noise and artifacts.

Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT.

PURPOSE: The aim of this study was to assess the effect of denoising on objective heterogeneity scores and its diagnostic capability for the diagnosis of angiomyolipoma (AML) and renal cell carcinoma (RCC). MATERIALS AND METHODS: A total of 158 resected renal masses ≤4 cm [98 clear cell (cc) RCCs, 36 papillary (pap)-RCCs, and 24 AMLs] from 139 patients were evaluated. A representative contrast-enhanced computed tomography (CT) image for each mass was selected by a genitourinary radiologist. A largest possible region of interest was drawn on each mass by the radiologist, from which three objective heterogeneity indices were calculated: standard deviation (SD), entropy (Ent), and uniformity (Uni). Objective heterogeneity indices were also calculated after images were processed with a denoising algorithm (non-local means) at three strengths: weak, medium, and strong. Two genitourinary radiologists also subjectively scored each mass independently using a three-point scale (1-3; with 1 the least and 3 the most heterogeneous), which were added to represent the final subjective heterogeneity score of each mass. Heterogeneity scores were compared among mass types, and area under the ROC curve (AUC) was calculated. RESULTS: For all heterogeneity indices, cc-RCC was significantly more heterogeneous than pap-RCC and AML (p < 0.001), but no significant difference was found between pap-RCC and AML (p > 0.01). For cc-RCC and pap-RCC differentiation, AUCs were 0.91, 0.81, 0.78, and 0.78 for the subjective score, SD, Ent, and Uni, respectively, using original images. The corresponding AUC values were 0.84, 0.74, 0.79, and 0.80 for differentiation of AML and cc-RCC. Noise reduction at weak setting improves AUC values by 0.03, 0.05, and 0.05 for SD, entropy, and uniformity for differentiation of cc-RCC from pap-RCC. Further increase of filtering strength did not improve AUC values. For differentiation of AML vs. cc-RCC, the AUC values stayed relatively flat using the noise reduction technique at different strengths for all three indices. CONCLUSIONS: Both subjective and objective heterogeneity indices can differentiate cc-RCC from pap-RCC and AML. Noise reduction improved differentiation of cc-RCC from pap-RCC, but not differentiation of AML from cc-RCC.