Characterizing the Fate and Transport of Solutes in Soil

Increasing concerns about contamination of soil and aquatic environments have emphasized the importance of information about the fate and transport of agricultural chemicals in soil. The objective of this research was to provide an improved understanding of the behavior of reactive chemicals including nitrate, phosphate, and antibiotics in soil through leaching and surface runoff in order to develop appropriate technologies that can prevent or minimize contamination of soil and water by agricultural activity. In a first experiment, a time domain reflectometry (TDR) method was tested for its ability to measure preferential flow of nitrate and phosphate in soil. Saturated miscible displacement experiments were conducted using three undisturbed soil cores and tracer solution containing chloride, phosphate, and nitrate. Predicted breakthrough curves (BTCs) obtained from the mobile-immobile model parameters fitted to the TDR data were comparable to the measured effluent nitrate BTCs. Phosphate BTCs distinctly differed from chloride and nitrate BTCs, thus the TDR method did not work for phosphate. The vertical TDR probe technique proved to be a practical method for a first approximation of nitrate preferential flow in soil. The second experiment used a localized compaction and doming (LCD) applicator that was developed to reduce nitrate leaching and increase nitrogen use efficiency. During a two-year period, sediment and nutrient losses from plots prepared using the LCD were compared to those prepared using conventional no-till broadcast (NTB) and no-till coulter injection (NTC). Concentrations of nitrogen and bromide in the soil profile were also determined to quantify anion movement. Total sediment loss for LCD was significantly greater than sediment loss for NTC and NTB. Masses of bromide, nitrate, phosphate, total nitrogen, and total phosphorus in runoff for LCD were significantly less than the corresponding masses for NTB and NTC in 2004. Residual concentration profile values implied that nitrate applied by the LCD applicator was transported more slowly through soil compared with the other methods. Therefore, the LCD method can reduce phosphorus loss in runoff, although on sloping fields it appears to result in more soil erosion. In the third and final experiment, the effects of soil properties on the fate and transport of chlortetracycline (CTC), tylosin (TYL), and sulfamethazine (SMT) were examined by conducting batch and column experiments. Sorption of CTC and TYL to montmorillonite and kaolinite generally decreased with increasing pH and ionic strength. Decreased retention of CTC and TYL to clays and soils was observed in the presence of Ca2+ compared with Na+. Greater SMT sorption was observed for surface soils having higher soil organic matter compared with subsurface soils, indicating that SMT mainly binds to soil organic matter in soils. Addition of dissolved organic carbon (DOC) derived from dairy manure resulted in decreased sorption and increased mobility of CTC and TYL, while increasing sorption of SMT. Changes in pH, ionic strength, DOC level, and background electrolyte cation type in soil solution caused by concomitant application of animal manure can influence fate and transport of agricultural antibiotics in soils. Therefore, failure to take the animal manure application effects into account can lead to conclusions that have little relevance to real situations

Patient-Specific Method of Generating Parametric Maps of Patlak K(i) without Blood Sampling or Metabolite Correction: A Feasibility Study.

Currently, kinetic analyses using dynamic positron emission tomography (PET) experience very limited use despite their potential for improving quantitative accuracy in several clinical and research applications. For targeted volume applications, such as radiation treatment planning, treatment monitoring, and cerebral metabolic studies, the key to implementation of these methods is the determination of an arterial input function, which can include time-consuming analysis of blood samples for metabolite correction. Targeted kinetic applications would become practical for the clinic if blood sampling and metabolite correction could be avoided. To this end, we developed a novel method (Patlak-P) of generating parametric maps that is identical to Patlak K(i) (within a global scalar multiple) but does not require the determination of the arterial input function or metabolite correction. In this initial study, we show that Patlak-P (a) mimics Patlak K(i) images in terms of visual assessment and target-to-background (TB) ratios of regions of elevated uptake, (b) has higher visual contrast and (generally) better image quality than SUV, and (c) may have an important role in improving radiotherapy planning, therapy monitoring, and neurometabolism studies

Attenuation correction for brain PET imaging using deep neural network based on dixon and ZTE MR images

Positron Emission Tomography (PET) is a functional imaging modality widely used in neuroscience studies. To obtain meaningful quantitative results from PET images, attenuation correction is necessary during image reconstruction. For PET/MR hybrid systems, PET attenuation is challenging as Magnetic Resonance (MR) images do not reflect attenuation coefficients directly. To address this issue, we present deep neural network methods to derive the continuous attenuation coefficients for brain PET imaging from MR images. With only Dixon MR images as the network input, the existing U-net structure was adopted and analysis using forty patient data sets shows it is superior than other Dixon based methods. When both Dixon and zero echo time (ZTE) images are available, we have proposed a modified U-net structure, named GroupU-net, to efficiently make use of both Dixon and ZTE information through group convolution modules when the network goes deeper. Quantitative analysis based on fourteen real patient data sets demonstrates that both network approaches can perform better than the standard methods, and the proposed network structure can further reduce the PET quantification error compared to the U-net structure.Comment: 15 pages, 12 figure

Effect of Time-of-Flight and Regularized Reconstructions on Quantitative Measurements and Qualitative Assessments in Newly Diagnosed Prostate Cancer With 18F-Fluorocholine Dual Time Point PET/MRI.

Recent technical advances in positron emission tomography/magnetic resonance imaging (PET/MRI) technology allow much improved time-of-flight (TOF) and regularized iterative PET reconstruction regularized iterative reconstruction (RIR) algorithms. We evaluated the effect of TOF and RIR on standardized uptake values (maximum and peak SUV [SUVmax and SUVpeak]) and their metabolic tumor volume dependencies and visual image quality for 18F-fluorocholine PET/MRI in patients with newly diagnosed prostate cancer. Fourteen patients were administered with 3 MBq/kg of 18F-fluorocholine and scanned dynamically for 30 minutes. Positron emission tomography images were divided to early and late time points (1-6 minutes summed and 7-30 minutes summed). The values of the different SUVs were documented for dominant PET-avid lesions, and metabolic tumor volume was estimated using a 50% isocontour and SUV threshold of 2.5. Image quality was assessed via visual acuity scoring (VAS). We found that incorporation of TOF or RIR increased lesion SUVs. The lesion to background ratio was not improved by TOF reconstruction, while RIR improved the lesion to background ratio significantly ( P < .05). The values of the different VAS were all significantly higher ( P < .05) for RIR images over TOF, RIR over non-TOF, and TOF over non-TOF. In conclusion, our data indicate that TOF or RIR should be incorporated into current protocols when available

Multiresolution spatiotemporal mechanical model of the heart as a prior to constrain the solution for 4D models of the heart.

In several nuclear cardiac imaging applications (SPECT and PET), images are formed by reconstructing tomographic data using an iterative reconstruction algorithm with corrections for physical factors involved in the imaging detection process and with corrections for cardiac and respiratory motion. The physical factors are modeled as coefficients in the matrix of a system of linear equations and include attenuation, scatter, and spatially varying geometric response. The solution to the tomographic problem involves solving the inverse of this system matrix. This requires the design of an iterative reconstruction algorithm with a statistical model that best fits the data acquisition. The most appropriate model is based on a Poisson distribution. Using Bayes Theorem, an iterative reconstruction algorithm is designed to determine the maximum a posteriori estimate of the reconstructed image with constraints that maximizes the Bayesian likelihood function for the Poisson statistical model. The a priori distribution is formulated as the joint entropy (JE) to measure the similarity between the gated cardiac PET image and the cardiac MRI cine image modeled as a FE mechanical model. The developed algorithm shows the potential of using a FE mechanical model of the heart derived from a cardiac MRI cine scan to constrain solutions of gated cardiac PET images

Automatic Labeling of Special Diagnostic Mammography Views from Images and DICOM Headers.

Applying state-of-the-art machine learning techniques to medical images requires a thorough selection and normalization of input data. One of such steps in digital mammography screening for breast cancer is the labeling and removal of special diagnostic views, in which diagnostic tools or magnification are applied to assist in assessment of suspicious initial findings. As a common task in medical informatics is prediction of disease and its stage, these special diagnostic views, which are only enriched among the cohort of diseased cases, will bias machine learning disease predictions. In order to automate this process, here, we develop a machine learning pipeline that utilizes both DICOM headers and images to predict such views in an automatic manner, allowing for their removal and the generation of unbiased datasets. We achieve AUC of 99.72% in predicting special mammogram views when combining both types of models. Finally, we apply these models to clean up a dataset of about 772,000 images with expected sensitivity of 99.0%. The pipeline presented in this paper can be applied to other datasets to obtain high-quality image sets suitable to train algorithms for disease detection

Imaging Hepatocellular Carcinoma With 68Ga-Citrate PET: First Clinical Experience.

While cross-sectional imaging with computed tomography (CT) and magnetic resonance imaging is the primary method for diagnosing hepatocellular carcinoma (HCC), they provide little biological insight into this molecularly heterogeneous disease. Nuclear imaging tools that can detect molecular subsets of tumors could greatly improve diagnosis and management of HCC. To this end, we conducted a patient study to determine whether HCC can be resolved using 68Ga-citrate positron emission tomography (PET). One patient with recurrent HCC was injected with 300 MBq of 68Ga-citrate and imaged with PET/CT 249 minutes post injection. Four (28%) of 14 hepatic lesions were avid for 68Ga-citrate. One extrahepatic lesion was not PET avid. The average maximum standardized uptake value (SUVmax) for the lesions was 7.2 (range: 6.2-8.4), while the SUVmax of the normal liver parenchyma was 4.7 and blood pool was 5.7. The avid lesions were not significantly larger than the quiescent lesions, and a prior contrast CT showed uniform enhancement among the lesions, suggesting that tumor signals are due to specific binding of the radiotracer to the transferrin receptor, rather than enhanced vascularity in the tumor microenvironment. Further studies are required in a larger patient cohort to verify the molecular basis of radiotracer uptake and the clinical utility of this tool