Thin Sectioning of Carbonaceous Adsorbent Spheres for Visualization by Light Microscopy and Scanning Electron Microscopy

Three different types of Rohm and Haas carbonaceous adsorbent spheres (XE-340, XE-347 and XE-348) were prepared for light and scanning electron microscopy by embedding in resin and by thin sectioning. Spurr\u27s low viscosity resin, because of its penetrating and wetting ability, contributed to the production of the most uniform and artifact free thin sections. In addition to thin sectioning, gas adsorption surface area measurements were made on batches of each type of sphere. There was an apparent relationship between the surface area measurements of 417.8 m2/g for XE-340, 583.4 m2/g for XE-347 and 752.9 m2/g for XE-348 and the microstructural appearances of the internal morphologies of each type of sphere

Generative Invertible Networks (GIN): Pathophysiology-Interpretable Feature Mapping and Virtual Patient Generation

Machine learning methods play increasingly important roles in pre-procedural planning for complex surgeries and interventions. Very often, however, researchers find the historical records of emerging surgical techniques, such as the transcatheter aortic valve replacement (TAVR), are highly scarce in quantity. In this paper, we address this challenge by proposing novel generative invertible networks (GIN) to select features and generate high-quality virtual patients that may potentially serve as an additional data source for machine learning. Combining a convolutional neural network (CNN) and generative adversarial networks (GAN), GIN discovers the pathophysiologic meaning of the feature space. Moreover, a test of predicting the surgical outcome directly using the selected features results in a high accuracy of 81.55%, which suggests little pathophysiologic information has been lost while conducting the feature selection. This demonstrates GIN can generate virtual patients not only visually authentic but also pathophysiologically interpretable

Virtual clinical trials in medical imaging: a review

The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities

LROC Investigation of Three Strategies for Reducing the Impact of Respiratory Motion on the Detection of Solitary Pulmonary Nodules in SPECT

The objective of this investigation was to determine the effectiveness of three motion reducing strategies in diminishing the degrading impact of respiratory motion on the detection of small solitary pulmonary nodules (SPNs) in single-photon emission computed tomographic (SPECT) imaging in comparison to a standard clinical acquisition and the ideal case of imaging in the absence of respiratory motion. To do this nonuniform rational B-spline cardiac-torso (NCAT) phantoms based on human-volunteer CT studies were generated spanning the respiratory cycle for a normal background distribution of Tc-99 m NeoTect. Similarly, spherical phantoms of 1.0-cm diameter were generated to model small SPN for each of the 150 uniquely located sites within the lungs whose respiratory motion was based on the motion of normal structures in the volunteer CT studies. The SIMIND Monte Carlo program was used to produce SPECT projection data from these. Normal and single-lesion containing SPECT projection sets with a clinically realistic Poisson noise level were created for the cases of 1) the end-expiration (EE) frame with all counts, 2) respiration-averaged motion with all counts, 3) one fourth of the 32 frames centered around EE (Quarter Binning), 4) one half of the 32 frames centered around EE (Half Binning), and 5) eight temporally binned frames spanning the respiratory cycle. Each of the sets of combined projection data were reconstructed with RBI-EM with system spatial-resolution compensation (RC). Based on the known motion for each of the 150 different lesions, the reconstructed volumes of respiratory bins were shifted so as to superimpose the locations of the SPN onto that in the first bin (Reconstruct and Shift). Five human observers performed localization receiver operating characteristics (LROC) studies of SPN detection. The observer results were analyzed for statistical significance differences in SPN detection accuracy among the three correction strategies, the standard acquisition, and the ideal case of the absence of respiratory motion. Our human-observer LROC determined that Quarter Binning and Half Binning strategies resulted in SPN detection accuracy statistically significantly below (P \u3c 0.05) that of standard clinical acquisition, whereas the Reconstruct and Shift strategy resulted in a detection accuracy not statistically significantly different from that of the ideal case. This investigation demonstrates that tumor detection based on acquisitions associated with less than all the counts which could potentially be employed may result in poorer detection despite limiting the motion of the lesion. The Reconstruct and Shift method results in tumor detection that is equivalent to ideal motion correction

Native Texas Ornamental Bunchgrass Performance Under Water Restrictions

Growing human populations and increasing drought conditions compete with ornamental grassland landscapes for freshwater resources. With outdoor use as the largest consumer of municipal water, irrigation restrictions will likely be increasingly implemented, restricting ornamental municipal grasslands. Substituting irrigation-dependent exotic grasses with drought-adapted native bunchgrasses could help mitigate this problem. Greenhouse (GH) trials revealed exotic ornamental bunchgrasses declined faster than natives under progressive water stress, with natives performing best under moderate water with maximum water treatments decreasing aesthetic quality. There was wide variability among accessions, indicating promising genetic diversity from which to select drought resistance for ornamentals. Native grasses performed best in field trials with supplemental irrigation during warm-season growth and restricted irrigation during the cool season. In northcentral Texas, native little bluestem (LBS; Schizachyrium scoparium L.) accessions outperformed exotics in health and aesthetics across environments. Most response variables were species as well as accession dependent. Select LBS accessions are recommended for commercialization for municipal grasslands due to superior field performance under water restrictions. Replacing favored water-intensive exotic grasses with adapted native grasses could help reduce irrigation water use

A 5D computational phantom for pharmacokinetic simulation studies in dynamic emission tomography

Introduction: Dynamic image acquisition protocols are increasingly used in emission tomography for drug development and clinical research. As such, there is a need for computational phantoms to accurately describe both the spatial and temporal distribution of radiotracers, also accounting for periodic and non-periodic physiological processes occurring during data acquisition. Methods: A new 5D anthropomorphic digital phantom was developed based on a generic simulation platform, for accurate parametric imaging simulation studies in emission tomography. The phantom is based on high spatial and temporal information derived from real 4D MR data and a detailed multi-compartmental pharmacokinetic modelling simulator. Results: The proposed phantom is comprised of three spatial and two temporal dimensions, including periodic physiological processes due to respiratory motion and non-periodic functional processes due to tracer kinetics. Example applications are shown in parametric [18F]FDG and [15O]H2O PET imaging, successfully generating realistic macro- and micro-parametric maps. Conclusions: The envisaged applications of this digital phantom include the development and evaluation of motion correction and 4D image reconstruction algorithms in PET and SPECT, development of protocols and methods for tracer and drug development as well as new pharmacokinetic parameter estimation algorithms, amongst others. Although the simulation platform is primarily developed for generating dynamic phantoms for emission tomography studies, it can easily be extended to accommodate dynamic MR and CT imaging simulation protocols

Creation of 3D Digital Anthropomorphic Phantoms which Model Actual Patient Non-rigid Body Motion as Determined from MRI and Position Tracking Studies of Volunteers

Background: Patient motion during emission imaging can create artifacts in the reconstructed emission distributions, which may mislead the diagnosis. For example, in myocardial-perfusion imaging, these artifacts can be mistaken for defects. Various software and hardware approaches have been developed to detect and compensate for motion. There are various ways of testing the effectiveness of motion correction methods applied in emission tomography, including the use of realistic digital anthropomorphic phantoms. Purpose: The purpose of this study was to create 3D digital anthropomorphic phantoms based on MRI data of volunteers undergoing a series of clinically relevant motions. These phantoms with combined position tracking were used to investigate both imaging-data-driven and motion tracking strategies to estimate and correct for patient motion. Methods: MRI scans were obtained of volunteers undergoing a series of clinically relevant movements. During the MRI, the motions were recorded by near-infra-red cameras tracking using external markers on the chest and abdomen. Individual-specific extended cardiac-torso (XCAT) phantoms were created fit to our volunteer MRI imaging data representing pre- and post-motion states. These XCAT phantoms were then used to generate activity and attenuation distributions. Monte Carlo methods will then be performed to simulate SPECT acquisitions, which will be used to evaluate various motion estimation and correction strategies. Results: Three volunteers were scanned in the MRI with concurrent external motion tracking. Each volunteer performed five separate motions including an axial slide, roll, shoulder twist, spine bend, and arm motion. These MRI scans were then manually digitalized into 3D anthropomorphic XCAT phantoms. Activity and attenuation distributions were created for each XCAT phantom, representing fifteen individual-specific motions. Conclusions: Our results will be combined with the external motion tracking data to determine if external motion tracking accurately reflects heart position in patients undergoing cardiac SPECT imaging. This data will also be used to evaluate other motion correction methods in the future

XCAT-GAN for Synthesizing 3D Consistent Labeled Cardiac MR Images on Anatomically Variable XCAT Phantoms

Generative adversarial networks (GANs) have provided promising data enrichment solutions by synthesizing high-fidelity images. However, generating large sets of labeled images with new anatomical variations remains unexplored. We propose a novel method for synthesizing cardiac magnetic resonance (CMR) images on a population of virtual subjects with a large anatomical variation, introduced using the 4D eXtended Cardiac and Torso (XCAT) computerized human phantom. We investigate two conditional image synthesis approaches grounded on a semantically-consistent mask-guided image generation technique: 4-class and 8-class XCAT-GANs. The 4-class technique relies on only the annotations of the heart; while the 8-class technique employs a predicted multi-tissue label map of the heart-surrounding organs and provides better guidance for our conditional image synthesis. For both techniques, we train our conditional XCAT-GAN with real images paired with corresponding labels and subsequently at the inference time, we substitute the labels with the XCAT derived ones. Therefore, the trained network accurately transfers the tissue-specific textures to the new label maps. By creating 33 virtual subjects of synthetic CMR images at the end-diastolic and end-systolic phases, we evaluate the usefulness of such data in the downstream cardiac cavity segmentation task under different augmentation strategies. Results demonstrate that even with only 20% of real images (40 volumes) seen during training, segmentation performance is retained with the addition of synthetic CMR images. Moreover, the improvement in utilizing synthetic images for augmenting the real data is evident through the reduction of Hausdorff distance up to 28% and an increase in the Dice score up to 5%, indicating a higher similarity to the ground truth in all dimensions.Comment: Accepted for MICCAI 202

Radiation dosimetry in digital breast tomosynthesis: Report of AAPM Tomosynthesis Subcommittee Task Group 223

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134828/1/mp2600.pd