Optical backscatter sensors (OBSs) are commonly used to measure the turbidity, or light obscuration, of water in fresh and marine environments and various industrial applications. These turbidity data are commonly calibrated to yield total suspended solids (TSS) or suspended sediment concentration (SSC) measurements for water quality, sediment transport, and diverse other research and environmental management applications. Commercial sensors generally cost > $1000-3000. Here we leveraged simple, low-cost microprocessors, electronics, and housing components to design and construct open-source OBSs for < $150 per unit. The circuit relies on a photodiode to sense backscattered light, two stages of signal amplification, and a high-resolution analog-to-digital converter to read the detected signal. The instrument and logger utilize inexpensive, custom-printed circuit boards with through-hole soldering mounts; micro-SD card reader and real-time clock modules; and PVC housings with commercial end caps and epoxy-potted diode emitter and receiver. All parts are readily and publicly available, and minimal experience in soldering and coding is required to build and deploy the sensor. In lab and field tests, standard deviations were comparable to those measured by commercial sensors ($ 2-3% of the mean for suspended muds and 20-30% for suspended sands). These open-source sensors represent a useful advance in inexpensive sensing technology with broad applications across scientific and environmental management disciplines.
Purpose Develop and evaluate the effectiveness of a T1‐based correction method for errors in proton resonant frequency shift thermometry due to non‐local field effects caused by heating in fatty breast tissues. Methods Computational models of human breast tissue were created by segmenting MRI data from a healthy human volunteer. MR‐guided focused ultrasound (MRgFUS) heating and MR thermometry measurements were simulated in several locations in the heterogeneous segmented breast models. A T1‐based correction method for PRF thermometry errors was applied and the maximum positive and negative errors and the root mean squared error (RMSE) in a region around each heating location was evaluated with and without correction. The method uses T1 measurements to estimate the temperature change in fatty tissues and correct for their influence. Experimental data from a heating study in cadaver breast tissue were analyzed, and the expected PRFS error computed. Results The simulated MR thermometry had maximum single voxel errors ranging between 10% and 18% when no correction was applied. Applying the correction led to a considerable improvement, lowering the maximum error range to 2%–5%. The 5th to 95th percentile interval of the temperature error distribution was also lowered with correction, from approximately 3.5 to 1°C. This correction worked even when T1 times were uniformly raised or lowered by 5%–10%. The experimental data showed predicted errors of 15%. Conclusions This simulation study demonstrates that the T1‐based correction method reduces MR thermometry errors due to non‐local effects from heating in fatty tissues, potentially improving the accuracy of thermometry measurements during MRgFUS treatments. The presented correction method is reliant on having a patient‐specific 3D model of the breast, and may be limited by the accuracy of the fat temperatures which in turn may be limited by noise or bias present in the T1 measurements.
Optical backscatter sensors (OBSs) are commonly used to measure the turbidity, or light obscuration, of water in fresh and marine environments and various industrial applications. These turbidity measurements are commonly calibrated to yield total suspended solids (TSS) or suspended sediment concentration (SSC) measurements for water quality, sediment transport, and diverse other research and environmental management applications. Commercial sensors generally cost >$1000-3000. Here we leveraged simple, low-cost microprocessors, electronics, and housing components to design and construct open-source OBSs for <$150 per unit. The circuit relies on a photodiode to sense the backscattered light, two stages of signal amplification, and a high resolution analogue-to-digital convert to read the detected value. The instrument and logger utilize inexpensive, custom-printed circuit boards with through-hole soldering mounts; micro-SD card reader and real-time clock modules; and PVC housings with commercial end caps and epoxy-potted diode emitter and receiver. All parts are readily and publicly available, and minimal experience in soldering and coding is required to build and deploy the sensor. In lab and field tests, standard deviations were comparable to those measured by commercial sensors (~2-3% of the mean for suspended muds and 20-30% for suspended sands). These open-source sensors represent a useful advance in inexpensive sensing technology with broad applications across scientific and environmental management disciplines.
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