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Biomedical Paper

Intra-operative feedback and dynamic compensation for image-guided robotic focal ultrasound surgery

, , , , &
Pages 353-368 | Received 08 Jan 2008, Accepted 15 May 2008, Published online: 06 Jan 2010

Figures & data

Figure 1. Schematic of multi-probe approach (left) and multi-probes arranged on a robotic end-effector (right). (Three probes are shown as an example: the actual number may vary.)

Figure 1. Schematic of multi-probe approach (left) and multi-probes arranged on a robotic end-effector (right). (Three probes are shown as an example: the actual number may vary.)

Figure 2. Left: Multiple HIFU transducers mounted on a robotic end-effector submerged in a water tank. Right: The GUI for planning and calibration. [Color version available online.]

Figure 2. Left: Multiple HIFU transducers mounted on a robotic end-effector submerged in a water tank. Right: The GUI for planning and calibration. [Color version available online.]

Figure 3. Schematic diagram of the lesion tracking system.

Figure 3. Schematic diagram of the lesion tracking system.

Figure 4. Schematic of sensor array interface with DAC.

Figure 4. Schematic of sensor array interface with DAC.

Figure 5. Dynamic compensation using lesion position and temperature feedback. [Color version available online.]

Figure 5. Dynamic compensation using lesion position and temperature feedback. [Color version available online.]

Figure 6. Image processing algorithm for calculating the lesion position.

Figure 6. Image processing algorithm for calculating the lesion position.

Figure 7. Phase-shift processing algorithm for calculating the lesion temperature.

Figure 7. Phase-shift processing algorithm for calculating the lesion temperature.

Figure 8. Image data fusion correlated with temperature map. [Color version available online.]

Figure 8. Image data fusion correlated with temperature map. [Color version available online.]

Figure 9. Color map of the tissue region with respect to the selected noise tolerance. [Color version available online.]

Figure 9. Color map of the tissue region with respect to the selected noise tolerance. [Color version available online.]

Figure 10. Variation of acoustic pulse amplitude with temperature rise (base temperature of the bath = 25°C). [Color version available online.]

Figure 10. Variation of acoustic pulse amplitude with temperature rise (base temperature of the bath = 25°C). [Color version available online.]

Figure 11. Phase shift versus temperature. [Color version available online.]

Figure 11. Phase shift versus temperature. [Color version available online.]

Figure 12. (a) The FUSBOT system as seen under the operating table, with target tissue descended through the breast operating window. (b) Lesions induced in porcine kidney with 22 W/8 seconds exposures at 2 mm (top) and 1 mm (bottom) separation. [Color version available online.]

Figure 12. (a) The FUSBOT system as seen under the operating table, with target tissue descended through the breast operating window. (b) Lesions induced in porcine kidney with 22 W/8 seconds exposures at 2 mm (top) and 1 mm (bottom) separation. [Color version available online.]

Figure 13. Image display on GUI window shown as a result of fusion of sensory data for lesion tracking and thermal mapping. [Color version available online.]

Figure 13. Image display on GUI window shown as a result of fusion of sensory data for lesion tracking and thermal mapping. [Color version available online.]

Table I.  Representative results averaged from 80 experiments in in vitro and ex vivo tissues (porcine adipose and kidneys) for lesion positioning algorithm performance.

Figure 14. Gray level versus power for various exposure durations in excised porcine (a) and lamb (b) tissues. [Color version available online.]

Figure 14. Gray level versus power for various exposure durations in excised porcine (a) and lamb (b) tissues. [Color version available online.]

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