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

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

Patient-specific radiation dose and cancer risk estimation in CT: Part II. Application to patients: Patient-specific CT dose and risk: Application to patients

Purpose: Current methods for estimating and reporting radiation dose from CT examinations are largely patient-generic; the body size and hence dose variation from patient to patient is not reflected. Furthermore, the current protocol designs rely on dose as a surrogate for the risk of cancer incidence, neglecting the strong dependence of risk on age and gender. The purpose of this study was to develop a method for estimating patient-specific radiation dose and cancer risk from CT examinations

Patient-specific radiation dose and cancer risk estimation in CT: Part I. Development and validation of a Monte Carlo program: Patient-specific CT dose and risk: Monte Carlo program

Purpose: Radiation-dose awareness and optimization in CT can greatly benefit from a dose-reporting system that provides dose and risk estimates specific to each patient and each CT examination. As the first step toward patient-specific dose and risk estimation, this article aimed to develop a method for accurately assessing radiation dose from CT examinations

‘The only way is Essex’: Gender, union and mobilisation among fire service control room staff

This contribution to On the Front Line records a dialogue between two female Fire Brigades Union (FBU) representatives in the Essex Emergency Control Room who led industrial action over the imposition of a shift system that stretched their work–life balance to breaking point and constrained their ability to work full-time. Their testimony reveals how male members were mobilised in the interests of predominantly female control staff. Kate and Lynne’s discussion illuminates the interaction of gender and class interests and identities in the union and in the lives of its women members. It provides insight into the efficacy of trade unions for women’s collective action

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