OBJECTIVE: To improve the detection of liver lesions in patients with hepatocellular carcinoma (HCC) via an iodine contrast enhancement tool. METHODS: Thirty-two patients with clinically proven HCCs underwent imaging with a three-phase protocol on a 256-slice MDCT. The contrast enhancement in the reconstructed slices was improved via a post-processing tool. Mean image noise was measured in four different regions: liver lesion, healthy liver, subcutaneous fat and bone. For each image set the image noise and contrast-to-noise ratio (CNR) were assessed. For subjective image assessment, four experienced radiologists evaluated the diagnostic quality. RESULTS: While employing the post-processing algorithm, CNR between the liver lesion and healthy liver tissue improves significantly by a factor of 1.78 (CNRwithout vC = 2.30 ± 1.92/CNRwith vC = 4.11 ± 3.05) (P* = 0.01). All results could be achieved without a strengthening of artefacts; mean HU values of subcutaneous fat and bone did not significantly change. Subjective image analysis illustrated a significant improvement when employing post-processing for clinically relevant criteria such as diagnostic confidence. CONCLUSION: With post-processing we see a significantly improved detection of arterial uptake in hepatic lesions compared with non-processed data. The improvement in CNR was confirmed by subjective image assessment for small lesions and for lesions with limited uptake. KEY POINTS: • Enhancement with iodine-based contrast agents is an essential part of CT. • A new post-processing tool significantly improves the diagnostics of hepatocellular carcinoma. • It also improves detection of small lesions with limited iodine uptake.
OBJECTIVE: To improve the detection of liver lesions in patients with hepatocellular carcinoma (HCC) via an iodine contrast enhancement tool. METHODS: Thirty-two patients with clinically proven HCCs underwent imaging with a three-phase protocol on a 256-slice MDCT. The contrast enhancement in the reconstructed slices was improved via a post-processing tool. Mean image noise was measured in four different regions: liver lesion, healthy liver, subcutaneous fat and bone. For each image set the image noise and contrast-to-noise ratio (CNR) were assessed. For subjective image assessment, four experienced radiologists evaluated the diagnostic quality. RESULTS: While employing the post-processing algorithm, CNR between the liver lesion and healthy liver tissue improves significantly by a factor of 1.78 (CNRwithout vC = 2.30 ± 1.92/CNRwith vC = 4.11 ± 3.05) (P* = 0.01). All results could be achieved without a strengthening of artefacts; mean HU values of subcutaneous fat and bone did not significantly change. Subjective image analysis illustrated a significant improvement when employing post-processing for clinically relevant criteria such as diagnostic confidence. CONCLUSION: With post-processing we see a significantly improved detection of arterial uptake in hepatic lesions compared with non-processed data. The improvement in CNR was confirmed by subjective image assessment for small lesions and for lesions with limited uptake. KEY POINTS: • Enhancement with iodine-based contrast agents is an essential part of CT. • A new post-processing tool significantly improves the diagnostics of hepatocellular carcinoma. • It also improves detection of small lesions with limited iodine uptake.
Authors: Peter B Noël; Edgar Bendik; Daniela Münzel; Armin Schneider; Liran Goshen; Asher Gringauz; Yechiel Lamash; Alain Vlassenbroek; Alexander A Fingerle; Ernst J Rummeny; Martin Dobritz Journal: Eur Radiol Date: 2012-10-19 Impact factor: 5.315
Authors: T Murakami; T Kim; M Takamura; M Hori; S Takahashi; M P Federle; K Tsuda; K Osuga; S Kawata; H Nakamura; M Kudo Journal: Radiology Date: 2001-03 Impact factor: 11.105
Authors: Seong Hyun Kim; Dongil Choi; Seung Hoon Kim; Jae Hoon Lim; Won Jae Lee; Min Ju Kim; Hyo K Lim; Soon Jin Lee Journal: AJR Am J Roentgenol Date: 2005-04 Impact factor: 3.959