Evaluation of a method for improving the detection of hepatocellular carcinoma.

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.

A method for improving iodine contrast enhancement in abdominal computed tomography: experimental study in a pig model.

OBJECTIVES: To investigate the improvement in diagnostic image quality of an iodine contrast enhancement tool in an animal model for computed tomography (CT). METHODS: One pig was examined over several consecutive days with a CT system. The quantity of iodine as contrast medium (0.6-1.2 ml/kg) varied among different acquisitions. The contrast enhancement in the reconstructed slices was improved via a post-processing tool. The post-processing tool is an algorithm designed for enhancement of iodine contrast in CT data. Contrast-to-noise ratio (CNR), the detectability between soft-tissue and vascular structures, and quantitative image analysis were assessed. RESULTS: When reducing the quantity of contrast medium, our subjective image quality assessment revealed that it is visually possible to generate similar enhancement with less iodine. This observation was confirmed quantitatively in our CNR results. While employing the algorithm, the CNR between vascular structures and subcutaneous fat significantly improved. For unenhanced regions, we identified no change in HU values and no significant strengthening of artefacts. CONCLUSIONS: With post-processing there was a significantly improved diagnostic image quality compared with non-processed data. In particular, similar contrast enhancement could be achieved with a reduced quantity of contrast medium injected during the CT acquisition.

Validation of a low dose simulation technique for computed tomography images.

PURPOSE: Evaluation of a new software tool for generation of simulated low-dose computed tomography (CT) images from an original higher dose scan. MATERIALS AND METHODS: Original CT scan data (100 mAs, 80 mAs, 60 mAs, 40 mAs, 20 mAs, 10 mAs; 100 kV) of a swine were acquired (approved by the regional governmental commission for animal protection). Simulations of CT acquisition with a lower dose (simulated 10-80 mAs) were calculated using a low-dose simulation algorithm. The simulations were compared to the originals of the same dose level with regard to density values and image noise. Four radiologists assessed the realistic visual appearance of the simulated images. RESULTS: Image characteristics of simulated low dose scans were similar to the originals. Mean overall discrepancy of image noise and CT values was -1.2% (range -9% to 3.2%) and -0.2% (range -8.2% to 3.2%), respectively, p>0.05. Confidence intervals of discrepancies ranged between 0.9-10.2 HU (noise) and 1.9-13.4 HU (CT values), without significant differences (p>0.05). Subjective observer evaluation of image appearance showed no visually detectable difference. CONCLUSION: Simulated low dose images showed excellent agreement with the originals concerning image noise, CT density values, and subjective assessment of the visual appearance of the simulated images. An authentic low-dose simulation opens up opportunity with regard to staff education, protocol optimization and introduction of new techniques.