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

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

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

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

Development and evaluation of a new fully automatic motion detection and correction technique in cardiac SPECT imaging

In cardiac SPECT perfusion imaging, motion correction of the data is critical to the minimization of motion introduced artifacts in the reconstructed images. Software-based (data-driven) motion correction techniques are the most convenient and economical approaches to fulfill this purpose. However, the accuracy is significantly affected by how the data complexities, such as activity overlap, non-uniform tissue attenuation, and noise are handled. We developed STASYS, a new, fully automatic technique, for motion detection and correction in cardiac SPECT. We evaluated the performance of STASYS by comparing its effectiveness of motion correcting patient studies with the current industry standard software (Cedars-Sinai MoCo) through blind readings by two readers independently. For 204 patient studies from multiple clinical sites, the first reader identified (1) 69 studies with medium to large axial motion, of which STASYS perfectly or significantly corrected 86.9% and MoCo 72.5%; and (2) 20 studies with medium to large lateral motion, of which STASYS perfectly or significantly corrected 80.0% and MoCo 60.0%. The second reader identified (1) 84 studies with medium to large axial motion, of which STASYS perfectly or significantly corrected 82.2% and MoCo 76.2%; and (2) 34 studies with medium to large lateral motion, of which STASYS perfectly or significantly corrected 58.9% and MoCo 50.0%. We developed a fully automatic software-based motion correction technique, STASYS, for cardiac SPECT. Clinical studies showed that STASYS was effective and corrected a larger percent of cardiac SPECT studies than the current industrial standard software

Articulated Whole-Body Atlases for Small Animal Image Analysis: Construction and Applications

Bone and mineral researc

GPU-based Low Dose CT Reconstruction via Edge-preserving Total Variation Regularization

High radiation dose in CT scans increases a lifetime risk of cancer and has become a major clinical concern. Recently, iterative reconstruction algorithms with Total Variation (TV) regularization have been developed to reconstruct CT images from highly undersampled data acquired at low mAs levels in order to reduce the imaging dose. Nonetheless, TV regularization may lead to over-smoothed images and lost edge information. To solve this problem, in this work we develop an iterative CT reconstruction algorithm with edge-preserving TV regularization to reconstruct CT images from highly undersampled data obtained at low mAs levels. The CT image is reconstructed by minimizing an energy consisting of an edge-preserving TV norm and a data fidelity term posed by the x-ray projections. The edge-preserving TV term is proposed to preferentially perform smoothing only on non-edge part of the image in order to avoid over-smoothing, which is realized by introducing a penalty weight to the original total variation norm. Our iterative algorithm is implemented on GPU to improve its speed. We test our reconstruction algorithm on a digital NCAT phantom, a physical chest phantom, and a Catphan phantom. Reconstruction results from a conventional FBP algorithm and a TV regularization method without edge preserving penalty are also presented for comparison purpose. The experimental results illustrate that both TV-based algorithm and our edge-preserving TV algorithm outperform the conventional FBP algorithm in suppressing the streaking artifacts and image noise under the low dose context. Our edge-preserving algorithm is superior to the TV-based algorithm in that it can preserve more information of fine structures and therefore maintain acceptable spatial resolution.Comment: 21 pages, 6 figures, 2 table

Atlas construction and image analysis using statistical cardiac models

International audienceThis paper presents a brief overview of current trends in the construction of population and multi-modal heart atlases in our group and their application to atlas-based cardiac image analysis. The technical challenges around the construction of these atlases are organized around two main axes: groupwise image registration of anatomical, motion and fiber images and construction of statistical shape models. Application-wise, this paper focuses on the extraction of atlas-based biomarkers for the detection of local shape or motion abnormalities, addressing several cardiac applications where the extracted information is used to study and grade different pathologies. The paper is concluded with a discussion about the role of statistical atlases in the integration of multiple information sources and the potential this can bring to in-silico simulations

Research Methodologies and Business Discourse Teaching

This chapter will:; ; ; Define English for specific purposes and indicate the specific ways in which it has been influential on business discourse teaching;; ; ; Discuss the most relevant approaches to genre analysis that have been used in business discourse teaching;; ; ; Explore the most relevant approaches to critical discourse analysis and organizational rhetoric for business discourse teaching;; ; ; Identify the most relevant aspects of multimodal discourse analysis for business discourse teaching;; ; ; Provide a case study that illustrates the use of one approach to business discourse teaching, showing how practitioners can incorporate it into their classroom- or consultancy-based ideas