Objective: To translate quantitative ultrasound (QUS) from the laboratory into the clinic, it is necessary to demonstrate that the measurements are platform independent. Because the backscatter coefficient (BSC) is the fundamental estimate from which additional QUS estimates are calculated, agreement between BSC results using different systems must be demonstrated. This study was an intercomparison of BSCs from in vivo spontaneous rat mammary tumors acquired by different groups using 3 clinical array systems and a single-element laboratory scanner system. Methods: Radio frequency data spanning the 1- to 14-MHz frequency range were acquired in 3 dimensions from all animals using each system. Each group processed their radio frequency data independently, and the resulting BSCs were compared. The rat tumors were diagnosed as either carcinoma or fibroadenoma. Results: Carcinoma BSC results exhibited small variations between the multiple slices acquired with each transducer, with similar slopes of BSC versus frequency for all systems. Somewhat larger variations were observed in fibroadenomas, although BSC variations between slices of the same tumor were of comparable magnitude to variations between transducers and systems. The root mean squared (RMS) errors between different transducers and imaging platforms were highly variable. The lowest RMS errors were observed for the fibroadenomas between 4 and 5 MHz, with an average RMS error of 4 × 10-5 cm-1Sr-1 and an average BSC value of 7.1 × 10-4 cm-1Sr-1, or approximately 5% error. The highest errors were observed for the carcinoma between 7 and 8 MHz, with an RMS error of 1.1 × 10-1 cm-1Sr-1 and an average BSC value of 3.5 ×10-2 cm-1Sr-1, or approximately 300% error. Conclusions: This technical advance shows the potential for QUS technology to function with different imaging platforms.
- Backscatter coefficient
- Quantitative ultrasound
- Spontaneous mammary tumors
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Radiological and Ultrasound Technology
- General Medicine