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RESEARCH PRODUCT

Effect of Kernels Used for the Reconstruction of MDCT Datasets on the Semi-Automated Segmentation and Volumetry of Liver Lesions

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Purpose: To evaluate the effect of different reconstruction kernels on the semi-automated segmentation of liver lesions in MDCT. Materials and Methods: A total 62 liver lesions were measured by three independent radiologists with the semi-automated segmentation software Oncology-Prototype (Fraunhofer MEVIS, Siemens Healthcare, Germany) using MDCT datasets (3-mm slice thickness, 2-mm increment) reconstructed with standard, soft and detailed kernels (Philips B, A and D). To ensure objective measurements, only lesions with satisfactory initial segmentation were included, and manual correction was not used. The effective diameter and volume were recorded for each lesion. Segmentation in the soft and detailed kernel datasets was performed by copying the initial seed's position from the standard kernel dataset. Results: The mean effective lesion diameter was 19.9 ± 9.7 mm using the standard kernel. Comparing the three kernels, no significant differences were found. The mean difference was 1 % ± 6 % for the standard kernel compared to the soft kernel, 3 % ± 13 % for the standard kernel vs. the detailed kernel and 2 % ± 9 % for the soft kernel compared to the detailed kernel. The intra-class correlation coefficients were > 0.96 in all cases. Conclusion: The semi-automated segmentation and volumetry of liver lesions shows reliable measurements regardless of the kernel used for reconstruction of the MDCT dataset. Key Points: • Semi-automated segmentation and volumetry of liver lesions is reliable regardless of the kernel used for reconstruction of the MDCT dataset. • Until today the gold standard for the evaluation of tumor response has been unidimensional manual measurement. • Volumetric measurements could improve the assessment of tumor growth. Citation Format: • Pinto dos Santos D, Klockner R, Wunder K et al. Effect of Kernels Used for the Reconstruction of MDCT Datasets on the Semi-Automated Segmentation and Volumetry of Liver Lesions. Fortschr Rontgenstr 2014; 186: 780 – 784