Inline 3D Volumetric Measurement of Moisture Content in Rice Using Regression-Based ML of RF Tomographic Imaging

The moisture content of stored rice is dependent on the surrounding and environmental factors which in turn affect the quality and economic value of the grains. Therefore, the moisture content of grains needs to be measured frequently to ensure that optimum conditions that preserve their quality are maintained. The current state of the art for moisture measurement of rice in a silo is based on grab sampling or relies on single rod sensors placed randomly into the grain. The sensors that are currently used are very localized and are, therefore, unable to provide continuous measurement of the moisture distribution in the silo. To the authors’ knowledge, there is no commercially available 3D volumetric measurement system for rice moisture content in a silo. Hence, this paper presents results of work carried out using low-cost wireless devices that can be placed around the silo to measure changes in the moisture content of rice. This paper proposes a novel technique based on radio frequency tomographic imaging using low-cost wireless devices and regression-based machine learning to provide contactless non-destructive 3D volumetric moisture content distribution in stored rice grain. This proposed technique can detect multiple levels of localized moisture distributions in the silo with accuracies greater than or equal to 83.7%, depending on the size and shape of the sample under test. Unlike other approaches proposed in open literature or employed in the sector, the proposed system can be deployed to provide continuous monitoring of the moisture distribution in silos.

Deep-learning reconstruction for ultra-low-dose lung CT: Volumetric measurement accuracy and reproducibility of artificial ground-glass nodules in a phantom study

Objectives The lung nodule volume determined by CT is used for nodule diagnoses and monitoring tumor responses to therapy. Increased image noise on low-dose CT degrades the measurement accuracy of the lung nodule volume. We compared the volumetric accuracy among deep-learning reconstruction (DLR), model-based iterative reconstruction (MBIR), and hybrid iterative reconstruction (HIR) at an ultra-low-dose setting. Methods Artificial ground-glass nodules (6 mm and 10 mm diameters, −660 HU) placed at the lung-apex and the middle-lung field in chest phantom were scanned by 320-row CT with the ultra-low-dose setting of 6.3 mAs. Each scan data set was reconstructed by DLR, MBIR, and HIR. The volumes of nodules were measured semi-automatically, and the absolute percent volumetric error (APEvol) was calculated. The APEvol provided by each reconstruction were compared by the Tukey-Kramer method. Inter- and intraobserver variabilities were evaluated by a Bland-Altman analysis with limits of agreements. Results DLR provided a lower APEvol compared to MBIR and HIR. The APEvol of DLR (1.36%) was significantly lower than those of the HIR (8.01%, p = 0.0022) and MBIR (7.30%, p = 0.0053) on a 10-mm-diameter middle-lung nodule. DLR showed narrower limits of agreement compared to MBIR and HIR in the inter- and intraobserver agreement of the volumetric measurement. Conclusions DLR showed higher accuracy compared to MBIR and HIR for the volumetric measurement of artificial ground-glass nodules by ultra-low-dose CT. Advances in knowledge DLR with ultra-low-dose setting allows a reduction of dose exposure, maintaining accuracy for the volumetry of lung nodule, especially in patients which deserve a long-term follow-up.

Review of metering and gas measurements in high-volume shale gas wells

Coriolis, turbine, V-cone, and orifice meters have been used in measurement of gas production in shale wells. Flange-tapped concentric orifice meters are commonly used in measurement of shale gas production volumes due to their low cost, accuracy, and ease of maintenance compared to other types of meters. However, shale gas wells are producing at high flow rates, high pressure, and possibly gas compositions change, which might affect volumetric measurement accuracy that was developed for conventional gas wells. Thus, it is critical to investigate the metering and measurements technologies that are being applied in shale gas wells to further understand and improve the accuracy of gas volumetric measurements. This paper provides a comprehensive review and analysis of background information, design, measurement, and uncertainties associated with Coriolis meters, turbine meters, V-cone meters, and orifice meters. We also discussed the lessons learned through our field experiences in computing gas volumes using SCADA information in shale gas and conventional gas production.

Increased Brain Volumetric Measurement Precision from Multi-Site 3D T1-weighted 3T Magnetic Resonance Imaging by Correcting Geometric Distortions

Magnetic resonance imaging (MRI) scanner-specific geometric distortions may contribute to scanner induced variability and decrease volumetric measurement precision for multi-site studies. The purpose of this study was to determine whether geometric distortion correction increases the precision of brain volumetric measurements in a multi-site multi-scanner study. Geometric distortion variation was quantified over a one-year period at 10 sites using the distortion fields estimated from monthly 3D T1-weighted MRI geometrical phantom scans. The variability of volume and distance measurements were quantified using synthetic volumes and a standard quantitative MRI (qMRI) phantom. The effects of geometric distortion corrections on MRI derived volumetric measurements of the human brain were assessed in two subjects scanned on each of the 10 MRI scanners and in 150 subjects with cerebrovascaular disease (CVD) acquired across imaging sites. Geometric distortions were found to vary substantially between different MRI scanners but were relatively stable on each scanner over a one-year interval. Geometric distortions varied spatially, increasing in severity with distance from the magnet isocenter. In measurements made with the qMRI phantom, the geometric distortion correction decreased the standard deviation of volumetric assessments by 35% and distance measurements by 42%. The average coefficient of variance decreased by 16% in gray matter and white matter volume estimates in the two subjects scanned on the 10 MRI scanners. Geometric distortion correction using an up-to-date correction field is recommended to increase precision in volumetric measurements made from MRI images.

Three-Dimensional Volumetric Measurement of Endolymphatic Hydrops in Meniere's Disease

Objective: We used volumetric three-dimensional (3D) analysis to quantitatively evaluate the extent of endolymphatic hydrops (EH) in the entire inner ear. We tested for correlations between the planimetric and volumetric measurements, to identify their advantages and disadvantages.Methods: HYDROPS2-Mi2 EH images were acquired for 32 ears (16 patients): 16 ipsilateral ears of MD patients (MD-ears) and 16 contralateral ears. Three-T MR unit with a 32-channel phased-array coil/the contrast agent to fill the perilymphatic space and the HYDROPS2-Mi2 sequence. We calculated the EH% [(endolymph)/(endolymph+perilymph)] ratio and analyzed the entire inner ear in terms of the volumetric EH% value, but only single cochlear and vestibular slices were subjected to planimetric EH% evaluation. The EH% values were compared between MD ears and non-MD ears, to evaluate the diagnostic accuracy of the two methods.Results: The volumetric EH% was significantly higher for MD vestibules (50.76 ± 13.78%) than non-MD vestibules (39.50 ± 8.99%). The planimetric EH% was also significantly higher for MD vestibules (61.98 ± 20.65%) than non-MD vestibules (37.22 ± 12.95%). The vestibular and cochlear volumetric EH% values correlated significantly with the planimetric EH% values of the MD ear.Conclusion: Volumetric and planimetric EH measurements facilitate diagnosis of MD ears compared to non-MD ears. Both methods seem to be reliable and consistent; the measurements were significantly correlated in this study. However, the planimetric EH% overestimates the extent of vestibular hydrops by 26.26%. Also, planimetric data may not correlate with volumetric data for non-MD cochleae with normal EH% values.

Uncertainty Quantification for PTV/LPT data and Adaptive Track Filtering

Particle Tracking Velocimetry (PTV) or Lagrangian Particle Tracking (LPT) picked up a lot of interest over the last years due to their ability to acquire global flow fields at high spatial and temporal resolution. The most recent research focused mainly on algorithmic advancements in order to increase the obtainable data density and on its application to new flow cases. Only a small amount of studies tried to quantify the measurement uncertainties linked to these volumetric measurement approaches. Within this contribution we want to present how to acquire measurement uncertainties for the position, velocity and acceleration for each data point along a trajectory by means of linear regression analysis tools. Based on these uncertainties, an adaptive filtering approach is introduced, which eliminates the user’s choice of the filter kernel length and which automatically determines its optimal value.

Accuracy of the Chula's formula for calculation of standard liver volume in Thai population

Background: Standard liver volume (SLV) is an important concept in living donor liver transplantation for treatment of end-stage liver disease. Accurate estimation of the SLV of living donor and recipient is crucial to ensure optimal graft function and avoid complications. Objective: 1) to assess the proposed formula for calculation of SLV in Thai population, using computed tomography (CT) volumetric measurement as a gold standard. 2) to evaluate the factors (e.g. age, sex, body weight and body surface area) related to differences between SLV calculated from the proposed formula and CT volumetric measurement. Materials and methods: We evaluated 497 patients underwent contrast-enhanced abdominal multi-detector CT for conditions unrelated to hepatobiliary system with normal liver radiology between October 1, 2014 and August 31, 2015 were included. Calculated SLV by the proposed formula (SLV = 20.76 x body weight) were compared to the total liver volume (TLV) measured from multi-detector CT by using computerized tool automatically. Factors related to the difference between SLV and TLV were evaluated. Result: The aforementioned formula showed a high accuracy in estimating the liver volume with some limitations in overweight or underwent patients. The mean difference between SLV and TLV is 3.36 cm3 with SD of 224.65 cm3. Conclusion: We proposed a new formula ("Chula's standard liver volume") that demonstrates a high accuracy for calculation of SLV in Thai population. Keywords: Liver volume; MDCT; standard liver volume; Thai population