CTCF looping is established during gastrulation in medaka embryos.

Chromatin looping plays an important role in genome regulation. However, because ChIP-seq and loop-resolution Hi-C (DNA-DNA proximity ligation) are extremely challenging in mammalian early embryos, the developmental stage at which cohesin-mediated loops form remains unknown. Here, we study early development in medaka (the Japanese killifish, Oryzias latipes) at 12 time points before, during, and after gastrulation (the onset of cell differentiation) and characterize transcription, protein binding, and genome architecture. We find that gastrulation is associated with drastic changes in genome architecture, including the formation of the first loops between sites bound by the insulator protein CTCF and a large increase in the size of contact domains. In contrast, the binding of the CTCF is fixed throughout embryogenesis. Loops form long after genome-wide transcriptional activation, and long after domain formation seen in mouse embryos. These results suggest that, although loops may play a role in differentiation, they are not required for zygotic transcription. When we repeated our experiments in zebrafish, loops did not emerge until gastrulation, that is, well after zygotic genome activation. We observe that loop positions are highly conserved in synteny blocks of medaka and zebrafish, indicating that the 3D genome architecture has been maintained for >110-200 million years of evolution.

Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI.

OBJECTIVE: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. METHOD & MATERIALS: Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer. An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI. Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers. The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN). RESULTS: ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN. However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per [Formula: see text].

Elastographic Tomosynthesis From X-Ray Strain Imaging of Breast Cancer.

Noncancerous breast tissue and cancerous breast tissue have different elastic properties. In particular, cancerous breast tumors are stiff when compared to the noncancerous surrounding tissue. This difference in elasticity can be used as a means for detection through the method of elastographic tomosynthesis by means of physical modulation. This paper deals with a method to visualize elasticity of soft tissues, particularly breast tissues, via x-ray tomosynthesis. X-ray tomosynthesis is now used to visualize breast tissues with better resolution than the conventional single-shot mammography. The advantage of X-ray tomosynthesis over X-ray CT is that fewer projections are needed than CT to perform the reconstruction, thus radiation exposure and cost are both reduced. Two phantoms were used for the testing of this method, a physical phantom and an in silico phantom. The standard root mean square error in the tomosynthesis for the physical phantom was 2.093 and the error in the in silico phantom was negligible. The elastographs were created through the use of displacement and strain graphing. A Gaussian Mixture Model with an expectation-maximization clustering algorithm was applied in three dimensions with an error of 16.667%. The results of this paper have been substantial when using phantom data. There are no equivalent comparisons yet in 3D x-ray elastographic tomosynthesis. Tomosynthesis with and without physical modulation in the 3D elastograph can identify feature groupings used for biopsy. The studies have potential to be applied to human test data used as a guide for biopsy to improve accuracy of diagnosis results. Further research on this topic could prove to yield new techniques for human patient diagnosis purposes.

Multicolumn RBF Network.

This paper proposes the multicolumn RBF network (MCRN) as a method to improve the accuracy and speed of a traditional radial basis function network (RBFN). The RBFN, as a fully connected artificial neural network (ANN), suffers from costly kernel inner-product calculations due to the use of many instances as the centers of hidden units. This issue is not critical for small datasets, as adding more hidden units will not burden the computation time. However, for larger datasets, the RBFN requires many hidden units with several kernel computations to generalize the problem. The MCRN mechanism is constructed based on dividing a dataset into smaller subsets using the k-d tree algorithm. resultant subsets are considered as separate training datasets to train individual RBFNs. Those small RBFNs are stacked in parallel and bulged into the MCRN structure during testing. The MCRN is considered as a well-developed and easy-to-use parallel structure, because each individual ANN has been trained on its own subsets and is completely separate from the other ANNs. This parallelized structure reduces the testing time compared with that of a single but larger RBFN, which cannot be easily parallelized due to its fully connected structure. Small informative subsets provide the MCRN with a regional experience to specify the problem instead of generalizing it. The MCRN has been tested on many benchmark datasets and has shown better accuracy and great improvements in training and testing times compared with a single RBFN. The MCRN also shows good results compared with those of some machine learning techniques, such as the support vector machine and k-nearest neighbors.

A Novel Method of Cone Beam CT Projection Binning Based on Image Registration.

Accurate sorting of beam projections is important in 4D cone beam computed tomography (4D CBCT) to improve the quality of the reconstructed 4D CBCT image by removing motion-induced artifacts. We propose image registration-based projection binning (IRPB), a novel marker-less binning method for 4D CBCT projections, which combines intensity-based feature point detection and trajectory tracking using random sample consensus. IRPB extracts breathing motion and phases by analyzing tissue feature point trajectories. We conducted experiments with two phantom and six patient datasets, including both regular and irregular respirations. In experiments, we compared the performance of the proposed IRPB, Amsterdam Shroud method (AS), Fourier transform-based method (FT), and local intensity feature tracking method (LIFT). The results showed that the average absolute phase shift of IRPB was 3.74 projections and 0.48 projections less than that of FT and LIFT, respectively. AS lost the most breathing cycles in the respiration extraction for the five patient datasets, so we could not compare the average absolute phase shift between IRPB and AS. Based on the peak signal-to-noise ratio (PSNR) of the reconstructed 4D CBCT images, IRPB had 5.08, 1.05, and 2.90 dB larger PSNR than AS, FT, and LIFT, respectively. The average Structure SIMilarity Index (SSIM) of the 4D CBCT image reconstructed by IRPB, AS, and LIFT were 0.87, 0.74, 0.84, and 0.70, respectively. These results demonstrated that IRPB has superior performance to the other standard methods.

Respiratory motion prediction and prospective correction for free-breathing arterial spin-labeled perfusion MRI of the kidneys.

PURPOSE: Respiratory motion prediction using an artificial neural network (ANN) was integrated with pseudocontinuous arterial spin labeling (pCASL) MRI to allow free-breathing perfusion measurements in the kidney. In this study, we evaluated the performance of the ANN to accurately predict the location of the kidneys during image acquisition. METHODS: A pencil-beam navigator was integrated with a pCASL sequence to measure lung/diaphragm motion during ANN training and the pCASL transit delay. The ANN algorithm ran concurrently in the background to predict organ location during the 0.7-s 15-slice acquisition based on the navigator data. The predictions were supplied to the pulse sequence to prospectively adjust the axial slice acquisition to match the predicted organ location. Additional navigators were acquired immediately after the multislice acquisition to assess the performance and accuracy of the ANN. The technique was tested in eight healthy volunteers. RESULTS: The root-mean-square error (RMSE) and mean absolute error (MAE) for the eight volunteers were 1.91 ± 0.17 mm and 1.43 ± 0.17 mm, respectively, for the ANN. The RMSE increased with transit delay. The MAE typically increased from the first to last prediction in the image acquisition. The overshoot was 23.58% ± 3.05% using the target prediction accuracy of ± 1 mm. CONCLUSION: Respiratory motion prediction with prospective motion correction was successfully demonstrated for free-breathing perfusion MRI of the kidney. The method serves as an alternative to multiple breathholds and requires minimal effort from the patient.

Intra- and Inter-Fractional Variation Prediction of Lung Tumors Using Fuzzy Deep Learning.

Tumor movements should be accurately predicted to improve delivery accuracy and reduce unnecessary radiation exposure to healthy tissue during radiotherapy. The tumor movements pertaining to respiration are divided into intra-fractional variation occurring in a single treatment session and inter-fractional variation arising between different sessions. Most studies of patients' respiration movements deal with intra-fractional variation. Previous studies on inter-fractional variation are hardly mathematized and cannot predict movements well due to inconstant variation. Moreover, the computation time of the prediction should be reduced. To overcome these limitations, we propose a new predictor for intra- and inter-fractional data variation, called intra- and inter-fraction fuzzy deep learning (IIFDL), where FDL, equipped with breathing clustering, predicts the movement accurately and decreases the computation time. Through the experimental results, we validated that the IIFDL improved root-mean-square error (RMSE) by 29.98% and prediction overshoot by 70.93%, compared with existing methods. The results also showed that the IIFDL enhanced the average RMSE and overshoot by 59.73% and 83.27%, respectively. In addition, the average computation time of IIFDL was 1.54 ms for both intra- and inter-fractional variation, which was much smaller than the existing methods. Therefore, the proposed IIFDL might achieve real-time estimation as well as better tracking techniques in radiotherapy.

Kernel association for classification and prediction: a survey.

Kernel association (KA) in statistical pattern recognition used for classification and prediction have recently emerged in a machine learning and signal processing context. This survey outlines the latest trends and innovations of a kernel framework for big data analysis. KA topics include offline learning, distributed database, online learning, and its prediction. The structural presentation and the comprehensive list of references are geared to provide a useful overview of this evolving field for both specialists and relevant scholars.

An interactive framework for acquiring vision models of 3-D objects from 2-D images.

This paper presents a human-computer interaction (HCI) framework for building vision models of three-dimensional (3-D) objects from their two-dimensional (2-D) images. Our framework is based on two guiding principles of HCI: 1) provide the human with as much visual assistance as possible to help the human make a correct input; and 2) verify each input provided by the human for its consistency with the inputs previously provided. For example, when stereo correspondence information is elicited from a human, his/her job is facilitated by superimposing epipolar lines on the images. Although that reduces the possibility of error in the human marked correspondences, such errors are not entirely eliminated because there can be multiple candidate points close together for complex objects. For another example, when pose-to-pose correspondence is sought from a human, his/her job is made easier by allowing the human to rotate the partial model constructed in the previous pose in relation to the partial model for the current pose. While this facility reduces the incidence of human-supplied pose-to-pose correspondence errors, such errors cannot be eliminated entirely because of confusion created when multiple candidate features exist close together. Each input provided by the human is therefore checked against the previous inputs by invoking situation-specific constraints. Different types of constraints (and different human-computer interaction protocols) are needed for the extraction of polygonal features and for the extraction of curved features. We will show results on both polygonal objects and object containing curved features.

A Multiple Model Approach to Track Head Orientation With Delta Quaternions.

Virtual reality and augmented reality environments using helmet-mounted displays create a sense of immersion by closely coupling user head motion to display content. Delays in the presentation of visual information can destroy the sense of presence in the simulation environment when it causes a lag in the display response to user head motion. The effect of display lag can be minimized by predicting head orientation, allowing the system to have sufficient time to counteract the delay. In this paper, anew head orientation prediction technique is proposed that uses a multiple delta quaternion (DQ) extended Kalman filter to track angular head velocity and angular head acceleration. This method is independent of the device used for orientation measurement, relying on quaternion orientation as the only measurement data. A new orientation prediction algorithm is proposed that estimates future head orientation as a function of the current orientation measurement and a predicted change in orientation, using the velocity and acceleration estimates. Extensive experimentation shows that the new method improves head orientation prediction when compared to single filter DQ prediction.

Local intensity feature tracking and motion modeling for respiratory signal extraction in cone beam CT projections.

Accounting for respiration motion during imaging can help improve targeting precision in radiation therapy. We propose local intensity feature tracking (LIFT), a novel markerless breath phase sorting method in cone beam computed tomography (CBCT) scan images. The contributions of this study are twofold. First, LIFT extracts the respiratory signal from the CBCT projections of the thorax depending only on tissue feature points that exhibit respiration. Second, the extracted respiratory signal is shown to correlate with standard respiration signals. LIFT extracts feature points in the first CBCT projection of a sequence and tracks those points in consecutive projections forming trajectories. Clustering is applied to select trajectories showing an oscillating behavior similar to the breath motion. Those "breathing" trajectories are used in a 3-D reconstruction approach to recover the 3-D motion of the lung which represents the respiratory signal. Experiments were conducted on datasets exhibiting regular and irregular breathing patterns. Results showed that LIFT-based respiratory signal correlates with the diaphragm position-based signal with an average phase shift of 1.68 projections as well as with the internal marker-based signal with an average phase shift of 1.78 projections. LIFT was able to detect the respiratory signal in all projections of all datasets.

Irregular breathing classification from multiple patient datasets using neural networks.

Complicated breathing behaviors including uncertain and irregular patterns can affect the accuracy of predicting respiratory motion for precise radiation dose delivery [3-6, 25, 36]. So far investigations on irregular breathing patterns have been limited to respiratory monitoring of only extreme inspiration and expiration [37]. Using breathing traces acquired on a Cyberknife treatment facility, we retrospectively categorized breathing data into several classes based on the extracted feature metrics derived from breathing data of multiple patients. The novelty of this paper is that the classifier using neural networks can provide clinical merit for the statistical quantitative modeling of irregular breathing motion based on a regular ratio representing how many regular/irregular patterns exist within an observation period. We propose a new approach to detect irregular breathing patterns using neural networks, where the reconstruction error can be used to build the distribution model for each breathing class. The proposed irregular breathing classification used a regular ratio to decide whether or not the current breathing patterns were regular. The sensitivity, specificity, and receiver operating characteristic (ROC) curve of the proposed irregular breathing pattern detector was analyzed. The experimental results of 448 patients breathing patterns validated the proposed irregular breathing classifier.

Head orientation prediction: delta quaternions versus quaternions.

Display lag in simulation environments with helmet-mounted displays causes a loss of immersion that degrades the value of virtual/augmented reality training simulators. Simulators use predictive tracking to compensate for display lag, preparing display updates based on the anticipated head motion. This paper proposes a new method for predicting head orientation using a delta quaternion (DQ)-based extended Kalman filter (EKF) and compares the performance to a quaternion EKF. The proposed framework operates on the change in quaternion between consecutive data frames (the DQ), which avoids the heavy computational burden of the quaternion motion equation. Head velocity is estimated from the DQ by an EKF and then used to predict future head orientation. We have tested the new framework with captured head motion data and compared it with the computationally expensive quaternion filter. Experimental results indicate that the proposed DQ method provides the accuracy of the quaternion method without the heavy computational burden.