A focused ultrasound treatment system for moving targets (part I):generic system design and in-silico first-stage evaluation

Background Focused ultrasound (FUS) is entering clinical routine as a treatment option. Currently, no clinically available FUS treatment system features automated respiratory motion compensation. The required quality standards make developing such a system challenging. Methods A novel FUS treatment system with motion compensation is described, developed with the goal of clinical use. The system comprises a clinically available MR device and FUS transducer system. The controller is very generic and could use any suitable MR or FUS device. MR image sequences (echo planar imaging) are acquired for both motion observation and thermometry. Based on anatomical feature tracking, motion predictions are estimated to compensate for processing delays. FUS control parameters are computed repeatedly and sent to the hardware to steer the focus to the (estimated) target position. All involved calculations produce individually known errors, yet their impact on therapy outcome is unclear. This is solved by defining an intuitive quality measure that compares the achieved temperature to the static scenario, resulting in an overall efficiency with respect to temperature rise. To allow for extensive testing of the system over wide ranges of parameters and algorithmic choices, we replace the actual MR and FUS devices by a virtual system. It emulates the hardware and, using numerical simulations of FUS during motion, predicts the local temperature rise in the tissue resulting from the controls it receives. Results With a clinically available monitoring image rate of 6.67 Hz and 20 FUS control updates per second, normal respiratory motion is estimated to be compensable with an estimated efficiency of 80%. This reduces to about 70% for motion scaled by 1.5. Extensive testing (6347 simulated sonications) over wide ranges of parameters shows that the main source of error is the temporal motion prediction. A history-based motion prediction method performs better than a simple linear extrapolator. Conclusions The estimated efficiency of the new treatment system is already suited for clinical applications. The simulation-based in-silico testing as a first-stage validation reduces the efforts of real-world testing. Due to the extensible modular design, the described approach might lead to faster translations from research to clinical practice

Growth temperature and genotype both play important roles in sorghum grain phenolic composition.

Polyphenols in sorghum grains are a source of dietary antioxidants. Polyphenols in six diverse sorghum genotypes grown under two day/night temperature regimes of optimal temperature (OT, 32/21 °C and high temperature (HT, 38/21 °C) were investigated. A total of 23 phenolic compounds were positively or tentatively identified by HPLC-DAD-ESIMS. Compared with other pigmented types, the phenolic profile of white sorghum PI563516 was simpler, since fewer polyphenols were detected. Brown sorghum IS 8525 had the highest levels of caffeic and ferulic acid, but apigenin and luteolin were not detected. Free luteolinidin and apigeninidin levels were lower under HT than OT across all genotypes (p ≤ 0.05), suggesting HT could have inhibited 3-deoxyanthocyanidins formation. These results provide new information on the effects of HT on specific polyphenols in various Australian sorghum genotypes, which might be used as a guide to grow high antioxidant sorghum grains under projected high temperature in the future