An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging

The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed

Region based 4D tomographic image reconstruction: Application to cardiac x-ray CT

X-ray computed tomography (CT) is a powerful tool for noninvasive cardiac imaging. However, radiation dose is a major issue. In this paper, we propose an iterative reconstruction method that reduces the radiation dose without compromising image quality. This is achieved by exploiting prior knowledge in two ways: the reconstructed object is assumed to consist of both stationary and dynamic regions over time and the dynamic region is assumed to have sparse structures after a proper sparsifying space-time transform. Experiments on simulation data and a real micro-CT cardiac mouse dataset show that, with comparable image quality, the radiation dose can be substantially reduced compared to conventional acquisition/reconstruction protocols

Pore REconstruction and Segmentation (PORES) method for improved porosity quantification of nanoporous materials

Electron tomography is currently a versatile tool to investigate the connection between the structure and properties of nanomaterials. However, a quantitative interpretation of electron tomography results is still far from straightforward. Especially accurate quantification of pore-space is hampered by artifacts introduced in all steps of the processing chain, i.e., acquisition, reconstruction, segmentation and quantification. Furthermore, most common approaches require subjective manual user input. In this paper, the PORES algorithm “POre REconstruction and Segmentation” is introduced; it is a tailor-made, integral approach, for the reconstruction, segmentation, and quantification of porous nanomaterials. The PORES processing chain starts by calculating a reconstruction with a nanoporous-specific reconstruction algorithm: the Simultaneous Update of Pore Pixels by iterative REconstruction and Simple Segmentation algorithm (SUPPRESS). It classifies the interior region to the pores during reconstruction, while reconstructing the remaining region by reducing the error with respect to the acquired electron microscopy data. The SUPPRESS reconstruction can be directly plugged into the remaining processing chain of the PORES algorithm, resulting in accurate individual pore quantification and full sample pore statistics. The proposed approach was extensively validated on both simulated and experimental data, indicating its ability to generate accurate statistics of nanoporous materials

Elevated enhancer-oncogene contacts and higher oncogene expression levels by recurrent CTCF inactivating mutations in acute T cell leukemia

Monoallelic inactivation of CCCTC-binding factor (CTCF) in human cancer drives altered methylated genomic states, altered CTCF occupancy at promoter and enhancer regions, and deregulated global gene expression. In patients with T cell acute lymphoblastic leukemia (T-ALL), we find that acquired monoallelic CTCF-inactivating events drive subtle and local genomic effects in nearly half of t(5; 14) (q35; q32.2) rearranged patients, especially when CTCF-binding sites are preserved in between the BCL11B enhancer and the TLX3 oncogene. These solitary intervening sites insulate TLX3 from the enhancer by inducing competitive looping to multiple binding sites near the TLX3 promoter. Reduced CTCF levels or deletion of the intervening CTCF site abrogates enhancer insulation by weakening competitive looping while favoring TLX3 promoter to BCL11B enhancer looping, which elevates oncogene expression levels and leukemia burden

Next-generation sequencing-based genome diagnostics across clinical genetics centers: Implementation choices and their effects

Implementation of next-generation DNA sequencing (NGS) technology into routine diagnostic genome care requires strategic choices. Instead of theoretical discussions on the consequences of such choices, we compared NGS-based diagnostic practices in eight clinical genetic centers in the Netherlands, based on genetic testing of nine pre-selected patients with cardiomyopathy. We highlight critical implementation choices, including the specific contributions of laboratory and medical specialists, bioinformaticians and researchers to diagnostic genome care, and how these affect interpretation and reporting of variants. Reported pathogenic mutations were consistent for all but one patient. Of the two centers that were inconsistent in their diagnosis, one reported to have found 'no causal variant', thereby underdiagnosing this patient. The other provided an alternative diagnosis, identifying another variant as causal than the other centers. Ethical and legal analysis showed that informed consent procedures in all centers were generally adequate for diagnostic NGS applications that target a limited set of genes, but not for exome- and genome-based diagnosis. We propose changes to further improve and align these procedures, taking into account the blurring boundary between diagnostics and research, and specific counseling options for exome- and genome-based diagnostics. We conclude that alternative diagnoses may infer a certain level of 'greediness' to come to a positive diagnosis in interpreting sequencing results. Moreover, there is an increasing interdependence of clinic, diagnostics and research departments for comprehensive diagnostic genome care. Therefore, we invite clinical geneticists, physicians, researchers, bioinformatics experts and patients to reconsider their role and position in future diagnostic genome care

An Iterative CT Reconstruction Algorithm for Fast Fluid Flow Imaging

The study of fluid flow through solid matter by computed tomography (CT) imaging has many applications, ranging from petroleum and aquifer engineering to biomedical, manufacturing, and environmental research. To avoid motion artifacts, current experiments are often limited to slow fluid flow dynamics. This severely limits the applicability of the technique. In this paper, a new iterative CT reconstruction algorithm for improved a temporal/spatial resolution in the imaging of fluid flow through solid matter is introduced. The proposed algorithm exploits prior knowledge in two ways. First, the time-varying object is assumed to consist of stationary (the solid matter) and dynamic regions (the fluid flow). Second, the attenuation curve of a particular voxel in the dynamic region is modeled by a piecewise constant function over time, which is in accordance with the actual advancing fluid/air boundary. Quantitative and qualitative results on different simulation experiments and a real neutron tomography data set show that, in comparison with the state-of-the-art algorithms, the proposed algorithm allows reconstruction from substantially fewer projections per rotation without image quality loss. Therefore, the temporal resolution can be substantially increased, and thus fluid flow experiments with faster dynamics can be performed

Unsupervised respiratory signal extraction from ungated cardiac magnetic resonance imaging at rest and during exercise

We propose and evaluate a method to estimate a respiratory signal from ungated cardiac magnetic resonance (CMR) images. Ungated CMR images were acquired in five subjects who performed exercise at different intensity levels under different physiological conditions while breathing freely. The respiratory motion was estimated by applying principal components analysis (PCA). A sign correction procedure was developed to correctly define inspiration and expiration, based on either tracking of the diaphragmatic motion or estimation of the lung volume or a combination of both. Evaluation was done using a plethysmograph signal as reference. There was a good correspondence between the plethysmograph and the estimated respiratory signals. Respiratory motion was effectively captured by one of the PCA components in 88% of the cases. Moreover, the proposed method successfully estimated the respiratory phase in 91% of the evaluated slices. The pipeline is robust, admitting a slight decline in performance with increased exercise intensity. Respiratory motion was accurately estimated by means of PCA and the application of a sign correction procedure. Our method showed promising results even for acquisitions during exercise where excessive body motion occurs. The proposed method provides a way to extract the respiratory signal from ungated CMR images, at rest as well as during exercise, in a fully unsupervised fashion, which may reduce the clinician's workload drastically.status: Published onlin