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Effects of X-Ray Dose On Rhizosphere Studies Using X-Ray Computed Tomography.

ABSTRACT: X-ray Computed Tomography (CT) is a non-destructive imaging technique originally designed for diagnostic medicine, which was adopted for rhizosphere and soil science applications in the early 1980s. X-ray CT enables researchers to simultaneously visualise and quantify the heterogeneous soil matrix of mineral grains, organic matter, air-filled pores and water-filled pores. Additionally, X-ray CT allows visualisation of plant roots in situ without the need for traditional invasive methods such as root washing. However, one routinely unreported aspect of X-ray CT is the potential effect of X-ray dose on the soil-borne microorganisms and plants in rhizosphere investigations. Here we aimed to i) highlight the need for more consistent reporting of X-ray CT parameters for dose to sample, ii) to provide an overview of previously reported impacts of X-rays on soil microorganisms and plant roots and iii) present new data investigating the response of plant roots and microbial communities to X-ray exposure. Fewer than 5% of the 126 publications included in the literature review contained sufficient information to calculate dose and only 2.4% of the publications explicitly state an estimate of dose received by each sample. We conducted a study involving rice roots growing in soil, observing no significant difference between the numbers of root tips, root volume and total root length in scanned versus unscanned samples. In parallel, a soil microbe experiment scanning samples over a total of 24 weeks observed no significant difference between the scanned and unscanned microbial biomass values. We conclude from the literature review and our own experiments that X-ray CT does not impact plant growth or soil microbial populations when employing a low level of dose (<30 Gy). However, the call for higher throughput X-ray CT means that doses that biological samples receive are likely to increase and thus should be closely monitored.

SUBMITTER: Zappala S

PROVIDER: S-EPMC3693932 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:In current computed tomography (CT) examinations, the associated x-ray radiation dose is of a significant concern to patients and operators. A simple and cost-effective means to perform the examinations is to lower the milliampere-seconds (mAs) or kVp parameter (or delivering less x-ray energy to the body) as low as reasonably achievable in data acquisition. However, lowering the mAs parameter will unavoidably increase data noise and the noise would propagate into the CT image if no adequate noise control is applied during image reconstruction. Since a normal-dose high diagnostic CT image scanned previously may be available in some clinical applications, such as CT perfusion imaging and CT angiography (CTA), this paper presents an innovative way to utilize the normal-dose scan as a priori information to induce signal restoration of the current low-dose CT image series.Unlike conventional local operations on neighboring image voxels, nonlocal means (NLM) algorithm utilizes the redundancy of information across the whole image. This paper adapts the NLM to utilize the redundancy of information in the previous normal-dose scan and further exploits ways to optimize the nonlocal weights for low-dose image restoration in the NLM framework. The resulting algorithm is called the previous normal-dose scan induced nonlocal means (ndiNLM). Because of the optimized nature of nonlocal weights calculation, the ndiNLM algorithm does not depend heavily on image registration between the current low-dose and the previous normal-dose CT scans. Furthermore, the smoothing parameter involved in the ndiNLM algorithm can be adaptively estimated based on the image noise relationship between the current low-dose and the previous normal-dose scanning protocols.Qualitative and quantitative evaluations were carried out on a physical phantom as well as clinical abdominal and brain perfusion CT scans in terms of accuracy and resolution properties. The gain by the use of the previous normal-dose scan via the presented ndiNLM algorithm is noticeable as compared to a similar approach without using the previous normal-dose scan.For low-dose CT image restoration, the presented ndiNLM method is robust in preserving the spatial resolution and identifying the low-contrast structure. The authors can draw the conclusion that the presented ndiNLM algorithm may be useful for some clinical applications such as in perfusion imaging, radiotherapy, tumor surveillance, etc.

Project description:BackgroundWalnuts are grown worldwide in temperate areas and producers are facing an increasing demand. In a climate change context, the industry also needs cultivars that provide fruits of quality. This quality includes satisfactory filling ratio, thicker shell, ease of cracking, smooth shell and round-shaped walnut, and larger nut size. These desirable traits have been analysed so far using calipers or micrometers, but it takes a lot of time and requires the destruction of the sample. A challenge to take up is to develop an accurate, fast and non-destructive method for quality-related and morphometric trait measurements of walnuts, that are used to characterize new cultivars or collections in any germplasm management process.ResultsIn this study, we develop a method to measure different morphological traits on several walnuts simultaneously such as morphometric traits (nut length, nut face and profile diameters), traits that previously required opening the nut (shell thickness, kernel volume and filling kernel/nut ratio) and traits that previously were difficult to quantify (shell rugosity, nut sphericity, nut surface area and nut shape). These measurements were obtained from reconstructed 3D images acquired by X-ray computed tomography (CT). A workflow was created including several steps: noise elimination, walnut individualization, properties extraction and quantification of the different parts of the fruit. This method was applied to characterize 50 walnuts of a part of the INRAE walnut germplasm collection made of 161 unique accessions, obtained from the 2018 harvest. Our results indicate that 50 walnuts are sufficient to phenotype the fruit quality of one accession using X-ray CT and to find correlations between the morphometric traits. Our imaging workflow is suitable for any walnut size or shape and provides new and more accurate measurements.ConclusionsThe fast and accurate measurement of quantitative traits is of utmost importance to conduct quantitative genetic analyses or cultivar characterization. Our imaging workflow is well adapted for accurate phenotypic characterization of a various range of traits and could be easily applied to other important nut crops.

Project description:This work concerns computed tomography (CT)-based cardiac functional analysis (CFA) with a reduced radiation dose. As CT-CFA requires images over the entire heartbeat, the scans are often performed at 10-20% of the tube current settings that are typically used for coronary CT angiography. A large image noise then degrades the accuracy of motion estimation. Moreover, even if the scan was performed during the sinus rhythm, the cardiac motion observed in CT images may not be cyclic with patients with atrial fibrillation. In this study, we propose to use two CT scan data, one for CT angiography at a quiescent phase at a standard dose and the other for CFA over the entire heart beat at a lower dose.We have made the following four modifications to an image-based cardiac motion estimation method we have previously developed for a full-dose retrospectively gated coronary CT angiography: (a) a full-dose prospectively gated coronary CT angiography image acquired at the least motion phase was used as the reference image; (b) a three-dimensional median filter was applied to lower-dose retrospectively gated cardiac images acquired at 20 phases over one heartbeat in order to reduce image noise; (c) the strength of the temporal regularization term was made adaptive; and (d) a one-dimensional temporal filter was applied to the estimated motion vector field in order to decrease jaggy motion patterns. We describe the conventional method iME1 and the proposed method iME2 in this article. Five observers assessed the accuracy of the estimated motion vector field of iME2 and iME1 using a 4-point scale. The observers repeated the assessment with data presented in a new random order 1 week after the first assessment session.The study confirmed that the proposed iME2 was robust against the mismatch of noise levels, contrast enhancement levels, and shapes of the chambers. There was a statistically significant difference between iME2 and iME1 (accuracy score, 2.08 ± 0.81 versus 2.77 ± 0.98, P < 0.01) and the improvement by the score of + 0.69 seemed clinically relevant. Inter-observer concordance was good: The inter-class correlation coefficient was 0.63 and Kendall's rank correlation coefficients were in the range of 0.41-0.67 (P < 0.01), respectively. Intra-observer reproducibility between sessions was good with the inter-class correlation coefficient of 0.76.We have proposed iME2 method for CT-CFA with two CT scans. The observer study verified the robustness and accuracy of iME2 method and its improved performance over iME1 method.

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Effects of X-Ray Dose On Rhizosphere Studies Using X-Ray Computed Tomography.

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