401 research outputs found

    Shearlet-based compressed sensing for fast 3D cardiac MR imaging using iterative reweighting

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    High-resolution three-dimensional (3D) cardiovascular magnetic resonance (CMR) is a valuable medical imaging technique, but its widespread application in clinical practice is hampered by long acquisition times. Here we present a novel compressed sensing (CS) reconstruction approach using shearlets as a sparsifying transform allowing for fast 3D CMR (3DShearCS). Shearlets are mathematically optimal for a simplified model of natural images and have been proven to be more efficient than classical systems such as wavelets. Data is acquired with a 3D Radial Phase Encoding (RPE) trajectory and an iterative reweighting scheme is used during image reconstruction to ensure fast convergence and high image quality. In our in-vivo cardiac MRI experiments we show that the proposed method 3DShearCS has lower relative errors and higher structural similarity compared to the other reconstruction techniques especially for high undersampling factors, i.e. short scan times. In this paper, we further show that 3DShearCS provides improved depiction of cardiac anatomy (measured by assessing the sharpness of coronary arteries) and two clinical experts qualitatively analyzed the image quality

    Performance of a NiO-based oxygen carrier for chemical looping combustion and reforming in a 120 kW unit

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    AbstractIn this study the performance of two different Ni-based oxygen carriers in a 120kW chemical looping pilot rig at Vienna University of Technology is presented. A dual circulating fluidized bed (DCFB) system has been designed with the important characteristics of high solid circulation, very low residence times and a high power to solid inventory ratio. For all presented results the pilot rig is fueled with methane at 140kW fuel power. For both oxygen carriers high CH4 conversion and CO2 yield is achieved. Air to fuel ratio and temperature are varied. CH4 conversion at higher air to fuel ratio as well as at higher temperature seems to decrease. This phenomenon is linked to the Ni/NiO ratio of the particle which determines the catalytic activity and thus influences the CH4 conversion and the CO2 yield

    Biomechanical comparison of fatigue and load-bearing -performance of elastic stable intramedullary nailing

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    Purpose: Elastic stable intramedullary nailing (ESIN) is a very common method for the treatment of pediatric long bone fractures. Because of the fact that ESIN nails offer the chance of micromotion during the healing process, this method is beneficial in comparison to rigid bone fixation and stimulates the formation of a callus [1]. The time between the incident of the fracture and complete generation of the stabilizing callus seems to be a critical phase for the implants’ load-bearing. Torsional and axial stability has to be ensured by the ESIN implant during this phase. Methods: Because of the studies aim of monitoring the period until the formation of a callus, ovine cadaver -tibiae (3–4 months old) were implanted regarding clinical standards after osteotomy at the mid diaphyseal region. Four different combinations of locking systems and ESIN implants were observed during this study. Synthes TEN -Titanium with endcaps (n = 7), Hofer Medical HSNesin Titanium unlocked (n = 8), Hofer Medical STEN Steel with eye and 3-mm screw (n = 8), and Hofer Medical HSNesin Titanium with plug and 3mm screw (n = 8) were used. All nails were 3 mm in diameter. Cyclic mechanical loading was applied using a commercial uniaxial testing device (1710DLL-5KN, Dynamess, Germany), and a pneumatic torsion testing module which was constructed by one of the authors. This device is able to apply axial load and torque to the specimen simultaneously. Results: Juvenile ovine bones were used in this study to generate similar conditions as in pediatric long bones. All samples failed by a closure of the initial osteotomy gap of 10 mm. The results of biomechanical tests showed significantly higher load bearing capability with each interlocking system than with the unlocked ESIN. (1000 N max. compared with 200 N). The unlocked system and the endcap ESIN failed very abrupt, whereas the 3-mm plug and the steel system failed slowly. Above all, the 3-mm plug with steel ESIN experienced gap closure without any damage to plugs or screws, which led to a distal penetration of the diaphysis by the nails. Conclusions: Interlocking systems seem to be beneficial for stability of ESIN nailing under cyclic and simultaneous axial and torsional loading. The strongest combinations in this study were Hofer steel nails and Hofer plugs with 3-mm locking screws. Significance: Different combinations of ESIN nails and interlocking systems show diverse load bearing behaviors. Desirable characteristics of nonabrupt failure during the nails loading and maximal strength of interlocking systems could be established. REFERENCE [1] Bishop, N.E., van Rhijn, M., Tami, I., Corveleijn, R., Schneider, E., Ito, K. Shear does not necessarily inhibit bone healing. Clinical Orthopaedics and Related Research. 443

    In vivo myocardial tissue characterization of all four chambers using high-resolution quantitative MRI

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    Quantitative native T(1) Mapping of the myocardium without the application of contrast agents can be used to detect fibrosis in the left ventricle. Spatial resolution of standard native T(1) mapping is limited by cardiac motion and hence is not sufficient to resolve small myocardial structures, such as the right ventricle and the atria. Here, we present a novel MR approach which provides cardiac motion information and native T(1) maps from the same data. Motion information is utilized to optimize data selection for T(1) mapping and a model-based iterative reconstruction scheme ensures high-resolution T(1) maps for the entire heart. Feasibility of the approach was demonstrated in three healthy volunteers. In the T(1) maps, the myocardium of all four chambers can be visualized and T(1) values of the left atrium and right chambers were comparable to left ventricular T(1) values

    Autoadaptive motion modelling for MR-based respiratory motion estimation

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    © 2016 The Authors.Respiratory motion poses significant challenges in image-guided interventions. In emerging treatments such as MR-guided HIFU or MR-guided radiotherapy, it may cause significant misalignments between interventional road maps obtained pre-procedure and the anatomy during the treatment, and may affect intra-procedural imaging such as MR-thermometry. Patient specific respiratory motion models provide a solution to this problem. They establish a correspondence between the patient motion and simpler surrogate data which can be acquired easily during the treatment. Patient motion can then be estimated during the treatment by acquiring only the simpler surrogate data.In the majority of classical motion modelling approaches once the correspondence between the surrogate data and the patient motion is established it cannot be changed unless the model is recalibrated. However, breathing patterns are known to significantly change in the time frame of MR-guided interventions. Thus, the classical motion modelling approach may yield inaccurate motion estimations when the relation between the motion and the surrogate data changes over the duration of the treatment and frequent recalibration may not be feasible.We propose a novel methodology for motion modelling which has the ability to automatically adapt to new breathing patterns. This is achieved by choosing the surrogate data in such a way that it can be used to estimate the current motion in 3D as well as to update the motion model. In particular, in this work, we use 2D MR slices from different slice positions to build as well as to apply the motion model. We implemented such an autoadaptive motion model by extending our previous work on manifold alignment.We demonstrate a proof-of-principle of the proposed technique on cardiac gated data of the thorax and evaluate its adaptive behaviour on realistic synthetic data containing two breathing types generated from 6 volunteers, and real data from 4 volunteers. On synthetic data the autoadaptive motion model yielded 21.45% more accurate motion estimations compared to a non-adaptive motion model 10 min after a change in breathing pattern. On real data we demonstrated the methods ability to maintain motion estimation accuracy despite a drift in the respiratory baseline. Due to the cardiac gating of the imaging data, the method is currently limited to one update per heart beat and the calibration requires approximately 12 min of scanning. Furthermore, the method has a prediction latency of 800 ms. These limitations may be overcome in future work by altering the acquisition protocol

    Multilevel comparison of deep learning models for function quantification in cardiovascular magnetic resonance: On the redundancy of architectural variations

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    Background: Cardiac function quantification in cardiovascular magnetic resonance requires precise contouring of the heart chambers. This time-consuming task is increasingly being addressed by a plethora of ever more complex deep learning methods. However, only a small fraction of these have made their way from academia into clinical practice. In the quality assessment and control of medical artificial intelligence, the opaque reasoning and associated distinctive errors of neural networks meet an extraordinarily low tolerance for failure. Aim: The aim of this study is a multilevel analysis and comparison of the performance of three popular convolutional neural network (CNN) models for cardiac function quantification. Methods: U-Net, FCN, and MultiResUNet were trained for the segmentation of the left and right ventricles on short-axis cine images of 119 patients from clinical routine. The training pipeline and hyperparameters were kept constant to isolate the influence of network architecture. CNN performance was evaluated against expert segmentations for 29 test cases on contour level and in terms of quantitative clinical parameters. Multilevel analysis included breakdown of results by slice position, as well as visualization of segmentation deviations and linkage of volume differences to segmentation metrics via correlation plots for qualitative analysis. Results: All models showed strong correlation to the expert with respect to quantitative clinical parameters (r(z)(') = 0.978, 0.977, 0.978 for U-Net, FCN, MultiResUNet respectively). The MultiResUNet significantly underestimated ventricular volumes and left ventricular myocardial mass. Segmentation difficulties and failures clustered in basal and apical slices for all CNNs, with the largest volume differences in the basal slices (mean absolute error per slice: 4.2 +/- 4.5 ml for basal, 0.9 +/- 1.3 ml for midventricular, 0.9 +/- 0.9 ml for apical slices). Results for the right ventricle had higher variance and more outliers compared to the left ventricle. Intraclass correlation for clinical parameters was excellent (>= 0.91) among the CNNs. Conclusion: Modifications to CNN architecture were not critical to the quality of error for our dataset. Despite good overall agreement with the expert, errors accumulated in basal and apical slices for all models

    Neural networks-based regularization for large-scale medical image reconstruction

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    In this paper we present a generalized Deep Learning-based approach for solving ill-posed large-scale inverse problems occuring in medical image reconstruction. Recently, Deep Learning methods using iterative neural networks (NNs) and cascaded NNs have been reported to achieve state-of-the-art results with respect to various quantitative quality measures as PSNR, NRMSE and SSIM across different imaging modalities. However, the fact that these approaches employ the application of the forward and adjoint operators repeatedly in the network architecture requires the network to process the whole images or volumes at once, which for some applications is computationally infeasible. In this work, we follow a different reconstruction strategy by strictly separating the application of the NN, the regularization of the solution and the consistency with the measured data. The regularization is given in the form of an image prior obtained by the output of a previously trained NN which is used in a Tikhonov regularization framework. By doing so, more complex and sophisticated network architectures can be used for the removal of the artefacts or noise than it is usually the case in iterative NNs. Due to the large scale of the considered problems and the resulting computational complexity of the employed networks, the priors are obtained by processing the images or volumes as patches or slices. We evaluated the method for the cases of 3D cone-beam low dose CT and undersampled 2D radial cine MRI and compared it to a total variation-minimization-based reconstruction algorithm as well as to a method with regularization based on learned overcomplete dictionaries. The proposed method outperformed all the reported methods with respect to all chosen quantitative measures and further accelerates the regularization step in the reconstruction by several orders of magnitude

    Fast myocardial T(1) mapping using cardiac motion correction

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    PURPOSE: To improve the efficiency of native and postcontrast high-resolution cardiac T(1) mapping by utilizing cardiac motion correction. METHODS: Common cardiac T(1) mapping techniques only acquire data in a small part of the cardiac cycle, leading to inefficient data sampling. Here, we present an approach in which 80% of each cardiac cycle is used for T(1) mapping by integration of cardiac motion correction. Golden angle radial data was acquired continuously for 8 s with in-plane resolution of 1.3 × 1.3 mm(2). Cine images were reconstructed for nonrigid cardiac motion estimation. Images at different TIs were reconstructed from the same data, and motion correction was performed prior to T(1) mapping. Native T(1) mapping was evaluated in healthy subjects. Furthermore, the technique was applied for postcontrast T(1) mapping in 5 patients with suspected fibrosis. RESULTS: Cine images with high contrast were obtained, leading to robust cardiac motion estimation. Motion-corrected T(1) maps showed myocardial T(1) times similar to cardiac-triggered T(1) maps obtained from the same data (1288 ± 49 ms and 1259 ± 55 ms, respectively) but with a 34% improved precision (spatial variation: 57.0 ± 12.5 ms and 94.8 ± 15.4 ms, respectively, P < 0.0001) due to the increased amount of data. In postcontrast T(1) maps, focal fibrosis could be confirmed with late contrast-enhancement images. CONCLUSION: The proposed approach provides high-resolution T(1) maps within 8 s. Data acquisition efficiency for T(1) mapping was improved by a factor of 5 by integration of cardiac motion correction, resulting in precise T(1) maps
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