Volumetry of low‐contrast liver lesions with CT: Investigation of estimation uncertainties in a phantom study. Issue 12 (23rd November 2016)

• Record Type: Journal Article
• Authors: Li, Qin; Liang, Yongguang; Huang, Qiao; Zong, Min; Berman, Benjamin; Gavrielides, Marios A.; Schwartz, Lawrence H.; Zhao, Binsheng; Petrick, Nicholas
• Is Part Of: Medical physics. Volume 43:Issue 12(2016)
• Page Start: 6608
• Page End: 6620
• Publication Date: 2016-11-23
• Subjects: computerised tomography -- liver -- medical image processing -- phantoms -- statistical analysis; Computed tomography -- Image analysis; Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general -- Biological material, e.g. blood, urine; Haemocytometers -- Computerised tomographs; quantitative imaging biomarker -- liver lesion volumetry -- computed tomography -- phantom study; Liver -- Image scanners -- Germanium -- Computed tomography -- Dosimetry -- Medical image reconstruction -- Cancer -- Statistical analysis -- Medical image contrast; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4967776
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: To evaluate the performance of lesion volumetry in hepatic CT as a function of various imaging acquisition parameters. Methods: An anthropomorphic abdominal phantom with removable liver inserts was designed for this study. Two liver inserts, each containing 19 synthetic lesions with varying diameter (6–40 mm), shape, contrast (10–65 HU), and both homogenous and mixed‐density were designed to have background and lesion CT values corresponding to arterial and portal‐venous phase imaging, respectively. The two phantoms were scanned using two commercial CT scanners (GE 750 HD and Siemens Biograph mCT) across a set of imaging protocols (four slice thicknesses, three effective mAs, two convolution kernels, two pitches). Two repeated scans were collected for each imaging protocol. All scans were analyzed using a matched‐filter estimator for volume estimation, resulting in 6080 volume measurements across all of the synthetic lesions in the two liver phantoms. A subset of portal venous phase scans was also analyzed using a semi‐automatic segmentation algorithm, resulting in about 900 additional volume measurements. Lesions associated with large measurement error (quantified by root mean square error) for most imaging protocols were considered not measurable by the volume estimation tools and excluded for the statistical analyses. Imaging protocols were grouped into distinct imaging conditions based on ANOVA analysis of factors for repeatability testing. Statistical analyses, including overall linearity analysis, grouped bias analysis with standard deviation evaluation, and repeatability analysis, were performed to assess the accuracy and precision of the liver lesion volume biomarker. Results: Lesions with lower contrast and size ≤10 mm were associated with higher measurement error and were excluded from further analysis. Lesion size, contrast, imaging slice thickness, dose, and scanner were found to be factors substantially influencing volume estimation. Twenty‐four distinct repeatable imaging conditions were determined as protocols for each scanner with a fixed slice thickness and dose. For the matched‐filter estimation approach, strong linearity was observed for all imaging data for lesions ≥20 mm. For the Siemens scanner with 50 mAs effective dose at 0.6 mm slice thickness, grouped bias was about −10%. For all other repeatable imaging conditions with both scanners, grouped biases were low (−3%–3%). There was a trend of increasing standard deviation with decreasing dose. For each fixed dose, the standard deviations were similar among the three larger slice thicknesses (1.25, 2.5, 5 mm for GE, 1.5, 3, 5 mm for Siemens). Repeatability coefficients ranged from about 8% to 75% and showed similar trend to grouped standard deviation. For the segmentation approach, the results led to similar conclusions for both lesion characteristic factors and imaging factors but with increasing magnitude in all the error metrics assessed. Conclusions: Results showed that liver lesion volumetry was strongly dependent on lesion size, contrast, acquisition dose, and their interactions. The overall performances were similar for images reconstructed with larger slice thicknesses, clinically used pitches, kernels, and doses. Conditions that yielded repeatable measurements were identified and they agreed with the Quantitative Imaging Biomarker Alliance's (QIBA) profile requirements in general. The authors' findings also suggest potential refinements to these guidelines for the tumor volume biomarker, especially for soft‐tissue lesions.