Perfusion quantification using dynamic contrast-enhanced ultrasound: The impact of dynamic range and gain on time–intensity curves

• Published in: Ultrasonics 2011, Vol.51 (1), p.102-106
• Main Authors: Gauthier, Thomas P., Averkiou, Michalakis A., Leen, Edward L.S.
• Format: Article
• Language: English
• Subjects: Acoustic signal processing; Acoustics; Area Under Curve; Biological and medical sciences; Cardiovascular system; Contrast agents; Contrast Media; Deviation; Dynamic range; Exact sciences and technology; Fundamental areas of phenomenology (including applications); Gain; Humans; Image Enhancement - methods; Image Processing, Computer-Assisted; In Vitro Techniques; In vitro testing; Investigative techniques, diagnostic techniques (general aspects); Liver malignancy; Liver Neoplasms - diagnostic imaging; Medical sciences; Miscellaneous. Technology; Perfusion quantification; Phantoms, Imaging; Phospholipids; Physics; Sulfur Hexafluoride; Therapy monitoring; Transducers; Ultrasonic investigative techniques; Ultrasonography - instrumentation; Ultrasonography - methods; Ultrasound; Ultrasound contrast agents
• ISSN: 0041-624X; 1874-9968
• DOI: 10.1016/j.ultras.2010.06.004

The objective of this study was to assess the impact of dynamic range and gain on perfusion quantification using linearized log-compressed data. An indicator-dilution experiment was developed with an in vitro flow phantom setup used with SonoVue contrast agent (Bracco SpA, Milan, Italy). Imaging was performed with a Philips iU22 scanner and a C5-1 curvilinear transducer using a contrast-specific nonlinear pulse sequence (power modulation) at 1.7 MHz. Clinical dynamic contrast-enhanced ultrasound image loops of liver tumors were also collected for preliminary validation of the in vitro findings. Time–intensity curves were extracted from image loops with two different approaches: from linearized log-compressed data and from linear (uncompressed) data. The error of time–intensity curve parameters derived from linearized log-compressed data (deviation from linear data) was found to be less than 2.1% and 5.4% for all studied parameters in the in vitro experiment and in the clinical study, respectively, when a high dynamic range setting (at least 50 dB on the iU22) is used. The gain must be carefully adjusted to ensure a high signal-to-noise ratio and to avoid signal saturation. From the time–intensity curve analysis it was also found that rise time of the bolus time–intensity curve is the least variable of all the studied time–intensity curve parameters.