Cinenet

Review of: Cinenet

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On 29.08.2020
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Cinenet

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Cinenet Deutschland

CiNENET steht für die neue Art & Weise, ganze Kinofilme und TV-Folgen im Netz anzuschauen - und das völlig legal & kostenlos! Auf CiNENET HD können u.a. Mit der Kanalgruppe CiNENET betreibt investcapitalmarkets.com auf Plattformen wie YouTube und Dailymotion über 15 Filmkanäle mit zur Zeit über Filmen und Serien. Für diese Seite sind keine Informationen verfügbar.

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Cinenet investcapitalmarkets.com Temporary domain parking page. Website is not constructed. This is a temporary parking page. Links to random possibly related domains. Assistir animes,desenhos,filmes,novelas e séries em um só lugar, em alta qualidade Full HD p e HD p, tudo isso grátis e sem propagandas. Mantente actualizado! Síguenos en las redes sociales y activate con los estrenos de Series y Películas HD! Novel highly accelerated real-time CINE-MRI featuring compressed sensing with k-t regularization in comparison to TSENSE segmented and real-time Cine imaging. The Walking Dead Carol Tot increase the LV coverage, reconstruction of pseudo 3D cardiac CINE datasets from multiple multi-slice anisotropic 2D volumes by using motion-corrected super-resolution Stream Outlander Season 3 have been proposed 10 Image reconstruction by domain-transform manifold learning. Prieto C 1. Figure was created using TikZ 3. CineNet is a constructed video wall management software that aggregates content from any source into a shared display canvas. CineNet enables users to position and resize content on the display wall. Simply drag content to the wall, then resize, move, zoom, crop, and perform any other edits. CiNENET With the channel group CiNENET investcapitalmarkets.com operates on platforms such as YouTube and Dailymotion more than 15 film channels with currently more than 2, films and series as well as documentaries in German and English. CiNENET stands for the new way to watch a complete movie and TV series in the web. And that's totally legal and free of charge. CiNENET offers science-fiction, thriller, adventure, drama, romance. The CineNet™ Platform is a powerful combination of management software and hardware components designed to give our customers ultimate control of custom-designed, high-performance video wall solutions for mission-critical applications. CiNENET steht nun schon seit einiger Zeit für die neue Art & Weise, ganze Kinofilme und TV-Folgen im Netz anzuschauen – und das völlig legal & kostenlos! Auf dem YouTube-Kanal CiNENET Deutschland können u.a. Filme in den Genres Science-Fiction, Krimi, Abenteuer, Drama, Romantik, Komödie, Mystery, Horror, Filmklassiker und Dokumentation in.
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Top Gang 2! Top Gang! Data are acquired under free-breathing and respiratory and cardiac motion is resolved retrospectively which comes however at the expense of a prolonged scan time in the order of several minutes.

Moreover, these approaches usually require long reconstruction times associated with the high-dimensional spatial, respiratory temporal and cardiac temporal data processing or with the nature of their sampling trajectory during data acquisition.

Shorter cardiac CINE acquisitions can be achieved if the respiratory motion does not need to be resolved or corrected. Single breath-hold 2D real-time acquisitions 6 , 7 or 2D simultaneous multi-slice SMS for cardiac imaging 8 , 9 have been studied for this purpose, but provide only limited LV coverage and are still hampered by anisotropic image resolution in the slice direction.

To increase the LV coverage, reconstruction of pseudo 3D cardiac CINE datasets from multiple multi-slice anisotropic 2D volumes by using motion-corrected super-resolution frameworks have been proposed 10 , This requires however several low-resolution scans in different orientations in the order of several minutes and depend on slice-to-volume registration accuracy.

LV coverage with higher spatial resolution can be obtained with single breath-hold 3D cardiac CINE 12 — However, in case of PI, maximal achievable acceleration is limited by the amount of MR receiver coils.

In case of CS, the maximum acceleration is limited by the selected undersampling during acquisition, the prior information and the selected reconstruction technique.

These limitations lead to a trade-off in the acquisition between spatial and temporal resolution for a given LV coverage. PI is therefore widely used in clinical applications within these acceleration limits.

CS allows for a stronger sub-Nyquist sampling if 1 the images are compressible, i. Reconstructions can be accomplished with iterative algorithms that use a fixed sparsity-promoting transformation 20 or that adaptively derive the optimal sparse representation from the data themselves, known as dictionary learning High image quality was obtained with previously proposed 3D cardiac CINE methods by trading off spatial resolution in the slice direction thereby preventing high-resolution reformats in arbitrary views.

Anisotropic slice resolution in the range of 2. The proposed 3D CINE can be acquired in non-oblique orientation e. Further reduction of the breath-hold duration is therefore desirable but can only be achieved with an increase in undersampling if no compromise is made on spatial or temporal resolution.

For the desired higher undersampling factors, fixed sparsity assumptions in CS are often too restrictive and incapable of fully modelling the spatio-temporal cardiac dynamics.

Careful fine-tuning between regularization and data consistency is required and especially in highly undersampled cases residual aliasing may remain in the image or over-regularization can occur leading to staircasing or blurring artifacts.

Moreover, previously proposed reconstruction techniques 22 are computationally demanding and require significant long reconstruction times, rendering it complicated to be integrated into clinical workflow.

Recently, deep-learning based reconstruction methods have gained attention to solve these non-linear and ill-posed optimizations efficiently 23 — Proposed methods range from derivations of classical optimizations e.

ADMM-Net 26 , over cascaded convolutional networks 27 — 29 , UNet-based convolutional networks 30 , 31 and recurrent neural networks 32 to generative adversarial network-based denoising e.

DAGAN 33 , 34 , manifold learning 35 , variational neural networks 36 — 38 and generalized PI reconstructions 39 — The proposed CINENet enables acquisition of single breath-hold 3D CINE with 1.

The network is trained on in-house acquired 3D Cartesian cardiac CINE data of an electrocardiogram ECG triggered balanced steady-state free-precession sequence using a variable-density Cartesian trajectory with spiral-like order VD-CASPR.

The architecture resembles an unrolled proximal gradient algorithm with sparsity-learning and data consistency steps CINENet is evaluated on prospectively undersampled 3D Cartesian cardiac CINE data of 20 healthy subjects and 15 patients undergoing a clinically referred cardiac MR protocol.

CINENet is compared against a CS reconstruction and to the clinical gold-standard 2D CINE sequence qualitatively and quantitatively in terms of LV function assessment and contrast ratio between myocardium and blood pool.

A fourfold cross-validation with training on 15 healthy subjects and validation on 5 held-out subjects was conducted.

Further testing included 3D cardiac CINE from 15 patients with suspected cardiovascular disease. We trained CINENet by retrospectively undersampling the isotropic 3D CINE reference data iterative SENSE reconstruction of 2.

Undersampling masks follow a VD-CASPR sampling with incoherent sampling between and within cardiac phases We performed a supervised training with voxel-wise mean-squared error loss between reconstructed image of CINENet and iterative SENSE reconstructed reference.

Images were acquired with single breath-hold isotropic 3D Cartesian cardiac CINE and reconstructed with different techniques.

The zero-filled reconstruction corresponds to the network input. A separate reference acquisition 2. End-diastolic, mid-apical images in short axis of a healthy subject with strong fat-related aliasing.

Images are acquired with prospectively undersampled single breath-hold 3D Cartesian CINE with isotropic 1. The reference 3D CINE is reconstructed with iterative SENSE.

Undersampled 3D CINE images are reconstructed with coil-weighted zero-filling network input , Compressed Sensing CS with L1-regularized spatial wavelets and temporal total variation TV and with the proposed CINENet.

Supplementary Video S1 depicts the cardiac motion-resolved images. End-diastolic, mid-apical images in short axis of a healthy subject with elevated heart rate.

Visually improved reconstruction results for 3D CINE are obtained with the proposed CINENet compared to CS, suggesting that spatio-temporal redundancies are better exploited with the proposed approach.

Moreover, subject-adaptive learning seems to outperform fixed regularized CS even when subject-specific CS parameters are used, as exemplary illustrated in Fig.

High visual agreement to conventional 2D CINE is achieved with CINENet. In Fig. A slightly anisotropic 3D CINE acquisition of 1.

In the patient population, we encountered myocarditis, arrhythmogenic right and left ventricular cardiomyopathy, restrictive cardiomyopathy, dilated cardiomyopathy, hypertensive cardiomyopathy, non-ischaemic cardiomyopathy, embolic myocardial infarction and eosinophilic granulomatosis with polyangiitis EGPA with cardiac involvement.

End-systolic and end-diastolic mid-apical images in short axis of two patients with suspected cardiovascular disease Patient 1: arrhythmogenic right ventricular cardiomyopathy, Patient 2: arrhythmogenic right and left ventricular cardiomyopathy.

Images are acquired with prospectively undersampled single breath-hold 3D Cartesian CINE with slightly anisotropic 1.

As illustrated in Fig. Reconstructions of CS and CINENet are in higher visual accordance in patient 3 and 4 Supplementary Fig.

In comparison to conventional multi breath-hold 2D CINE, the 3D CINE reconstructed with CINENet shows good agreement but with the advantage of a fold shorter acquisition time that can be achieved within a single breath-hold.

The spatial LV coverage is shown in Fig. All cardiac phases for the same subject are illustrated in Supplementary Video S2. LV coverage with isotropic resolution of 3D CINE in comparison to 2D CINE is shown in Supplementary Video S3.

For 3D CINE, similar anatomical slice locations to 2D CINE have been selected, i. Supplementary Video S2 depicts the cardiac motion-resolved images and Supplementary Video S3 depicts the spatial coverage.

We show temporal profiles at mid-ventrical position in Supplementary Fig. Substantial reduction in aliasing artifacts and recovery of temporal trace was found for CINENet reconstruction.

Temporal behavior of 3D CINE was in good agreement with 2D CINE. The isotropic acquisition allows reformats in arbitrary orientations. Supplementary Fig.

CINENet achieves stable convergence without overfitting and has lower training and validation loss than the network with 4D complex-valued convolutions.

We measured the contrast ratio between myocardium and left and right ventricular blood pool in conventional 2D CINE and 3D CINE reconstructed with CINENet and CS.

Contrast ratio was on average Average and standard deviations are depicted. Figure was created using Python 3. In the 15 patients, a bias for ESV, EDV and EF of 0.

Extracted left ventricular function parameters, end-systolic volume ESV , end-diastolic volume EDV and ejection fraction EF for isotropic 1.

In this work, we have proposed a novel reconstruction method, named CINENet, for 3D cardiac CINE MRI based on a deep learning network which enables highly accelerated imaging sequences.

We observed that CINENet can provide visually improved images over CS for these high acceleration factors while enabling a fold faster acquisition than conventional 2D CINE and a fold faster reconstruction than CS facilitating clinical translation.

Our qualitative and quantitative results indicate good agreement of 3D CINE with conventional 2D CINE, which, whether confirmed in larger patient studies, will pave the way for clinically grounded applications.

Rapid assessment of cardiac function by cardiac CINE plays a vital role in patient monitoring and staging of cardiovascular or cardio-oncological diseases.

Visualizing cardiac anatomy and function in 3D can help in the diagnosis of congenital heart diseases, for the detection of regional wall motion abnormalities or impaired ejection fraction.

A 3D isotropic imaging resolution with Cartesian sampling allows for reformats in arbitrary orientations without loss of resolution avoiding multiple breath-hold acquisitions in double-oblique orientations.

This leads to more efficient workflow and improved patient comfort. The proposed architecture reflects an unrolled optimization algorithm with complex-valued convolutions and activation functions and intermittent data consistency blocks to properly handle the input data.

We accomplish computationally efficient spatio-temporal information sharing by cascaded 3D spatial and 1D temporal convolutional kernels. CINENet incorporates coil sensitivity maps to provide a multi-coil reconstruction.

We obtained qualitatively good images which indicates the capability of the proposed network to utilize spatio-temporal redundancies.

Training for various acceleration factors and temporal resolutions improved generalizability to different inputs. We observed that the selected acceleration factors for training are sufficient to simulate the expected levels of aliasing artifact appearance.

Reconstructions of prospectively undersampled acquisitions with larger acceleration factors were possible indicating enough diversity in the training database.

Our results show that data-adaptive training of a sparsifying transformation for changing imaging conditions temporal resolution, accelerations, subjects demonstrates to be more generalizable and robust than a fixed sparsity basis and regularization parameters as in CS.

In case of CS, subject- and undersampling factor-dependent regularization parameters are conceivable. Here, the selected CS regularization parameters were optimized to fit all subjects and accelerations.

Furthermore, we observed that the network generalized for different field of view FOV placements between subjects, signal-to-noise ratio levels and mild slice resolution changes i.

A comparable implementation using full 4D complex-valued convolutional kernels, i. Moreover, complex-valued convolutions enabled a natural complex data processing instead of independently handling real and imaginary parts in separate channels.

The underlying assumption of spatio-temporal sharing in 3D cardiac CINE is that it contains a rich amount of redundancy in a local neighborhood along all temporal directions cardiac phases which can be exploited.

We assume that similar structures exist in different cardiac phases, but at different spatial locations around a certain neighborhood of a given voxel.

Therefore, spatial receptive field can be limited whereas temporal coverage should span the entire cardiac cycle. Encoding and decoding branches in the 4D UNet of CINENet increase receptive fields while allowing for relatively small convolutional kernels.

We used separable rectified linear ReLU activation functions on real and imaginary dimensions after each convolutional kernel yielding good performance and stable convergence.

Further activation functions such as cardioid 47 , are conceivable but demand a careful reparameterization of the network.

Batch normalization was used as it provided improved edge delineation and less blurring in the reconstructed images in comparison to instance normalization or omitting normalization layers.

However, further comparisons in a larger cohort would be required to conclude the best performing combination of activation and normalization.

We used SENSE-based multi-coil data consistency blocks between UNet stages to ensure data fidelity. We formulated the data consistency as proximal mapping step with a fixed regularization weighting which we can treat as a layer operation with forward and backward gradient backpropagation pass.

The undersampled 3D CINE data requires a joint 4D processing of the data. Furthermore, 2D CINE data cannot be leveraged as resolution, contrast, undersampled phase-encoding directions and sampling trajectories are different.

Hence, visual artifact appearance will differ. If slicing of the short axis 3D images along the fully sampled frequency direction vertical long axis is conducted, the data would lose the 3D spatial relationship and the ability to resolve vertical long axis motion, i.

Cropping of the 4D volume into smaller chunks would hence a loose dynamic information if cropped along temporal dimension or b demand consideration of band-pass filtering in data consistency 48 implying a more complicated data fidelity block if cropped along spatial dimension.

The fairest reconstruction comparison is obtained with an iterative and GPU-accelerated CS reconstruction. Differences in contrast between 2D and 3D CINE are expected since the maximum achievable flip angle within specific absorption rate limitations of 3D CINE is lower than for 2D CINE.

Moreover, saturation of the ventricle blood pool and inflow of saturated blood into the ventricle pool by the 3D slab-selective excitation affect the contrast of 3D CINE.

In contrast the slice-selective 2D CINE can benefit from inflow of unsaturated blood. The obtained contrast is still comparable and sufficient for reliable extraction of LV functions, but can be improved using exogenous contrast agents.

We did not observe any major performance differences of the automatic LV segmentation in 2D or 3D CINE. A comparative analysis on image quality between 2D CINE and 3D CINE was conducted in our previous study Adipose tissue, mainly observed as subcutaneous adipose tissue in the chest wall, and epicardial fat can introduce strong aliasing artifacts in highly accelerated 3D cardiac CINE.

Fat suppressed acquisitions, such as water-selective excitation or fast interrupted steady-state 49 — 51 can help to reduce the impact of fat, but demand longer acquisition times for similar acceleration.

In this study, we were interested to enable the reconstruction to deal with de-aliasing of artifacts from adipose tissues for a non-fat suppressed acquisition.

The proposed CINENet can learn the reconstruction task in the presence of fat from the paired training images of reference and undersampled input with various accelerations.

In contrast, the CS reconstruction can only map the current input into a fixed sparsity domain to resolve the induced fat aliasing.

We acknowledge some limitations in this study. Imaging resolution differs in healthy subjects and patients. In patients, we used a slightly anisotropic through-plane resolution to ensure sufficient LV coverage and a conservative undersampling factor.

Nevertheless, reconstruction with CINENet was not influenced by this mild resolution difference. We will address the impact of different spatial resolutions in future studies.

Aliasing artifact appearance depends on the chosen sampling trajectory. We used a Cartesian acquisition with spiral profile order which provides distinct sampling patterns per cardiac frame and results in incoherent aliasing along the phase-encoding directions.

Other sampling trajectories commonly seen in dynamic imaging are 3D koosh-ball or stack-of-stars 12 , 14 , 15 which would result in streaking undersampling artifacts for which a different trained network would be probably required.

Different architectural choices, such as 3D recurrent convolutional networks or variational neural networks, can be feasible which will be investigated in future studies.

With the proposed framework the clinical relevance for 3D CINE over 2D CINE can be investigated in future studies. In summary, we proposed a novel multi-coil complex-valued 4D deep learning-based reconstruction.

Data-driven sparsity learning of CINENet outperforms fixed sparsity transformations in CS. We have found good qualitative agreement between single breath-hold 3D CINE images reconstructed with CINENet and conventional multi breath-hold 2D CINE as well as quantitative agreement in terms of LV function.

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Cinenet
Cinenet Uecker, M. HD p Los Bridgerton. We drew ROIs at similar anatomical positions of 2D and 3D CINE Br3 Fernsehprogramm apical, mid-apical and basal slices. Moguntia suppressed acquisitions, such as water-selective excitation or fast interrupted steady-state 49 — 51 can help to reduce the impact Revierjäger fat, but demand longer acquisition times for similar acceleration. HD p Nancy Drew. Free breathing whole-heart 3D CINE MRI with self-gated Cartesian trajectory. A GPU-accelerated CS reconstruction using the BART toolbox 58 was employed. The reference Rtx 2060 Anschlüsse CINE is reconstructed with iterative SENSE. Daniel Rueckert 2 Department of Computing, Imperial College Samira Jackson, London, UK Komissarin Heller articles by Daniel Rueckert. Watch HD Movies Online For Free and Download the latest movies. Author information Article notes Copyright and License information Disclaimer. A comparable implementation using full 4D complex-valued convolutional kernels, i. Tran, D.

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