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In this work we reduce undersampling artefacts in two-dimensional ($2D$) golden-angle radial cine cardiac MRI by applying a modified version of the U-net. We train the network on $2D$ spatio-temporal slices which are previously extracted from the image sequences. We compare our approach to two $2D$ and a $3D$ Deep Learning-based post processing methods and to three iterative reconstruction methods for dynamic cardiac MRI. Our method outperforms the $2D$ spatially trained U-net and the $2D$ spatio-temporal U-net. Compared to the $3D$ spatio-temporal U-net, our method delivers comparable results, but with shorter training times and less training data. Compared to the Compressed Sensing-based methods $kt$-FOCUSS and a total variation regularised reconstruction approach, our method improves image quality with respect to all reported metrics. Further, it achieves competitive results when compared to an iterative reconstruction method based on adaptive regularization with Dictionary Learning and total variation, while only requiring a small fraction of the computational time. A persistent homology analysis demonstrates that the data manifold of the spatio-temporal domain has a lower complexity than the spatial domain and therefore, the learning of a projection-like mapping is facilitated. Even when trained on only one single subject without data-augmentation, our approach yields results which are similar to the ones obtained on a large training dataset. This makes the method particularly suitable for training a network on limited training data. Finally, in contrast to the spatial $2D$ U-net, our proposed method is shown to be naturally robust with respect to image rotation in image space and almost achieves rotation-equivariance where neither data-augmentation nor a particular network design are required., Comment: To be published in IEEE Transactions on Medical Imaging

Models and simulation tools for gravitational mass flows (GMFs) such as snow avalanches, rockfall, landslides, and debris flows are important for research, education, and practice. In addition to basic simulations and classic applications (e.g., hazard zone mapping), the importance and adaptability of GMF simulation tools for new and advanced applications (e.g., automatic classification of terrain susceptible for GMF initiation or identification of forests with a protective function) are currently driving model developments. In principle, two types of modeling approaches exist: process-based physically motivated and data-based empirically motivated models. The choice for one or the other modeling approach depends on the addressed question, the availability of input data, the required accuracy of the simulation output, and the applied spatial scale. Here we present the computationally inexpensive open-source GMF simulation tool Flow-Py. Flow-Py's model equations are implemented via the Python computer language and based on geometrical relations motivated by the classical data-based runout angle concepts and path routing in three-dimensional terrain. That is, Flow-Py employs a data-based modeling approach to identify process areas and corresponding intensities of GMFs by combining models for routing and stopping, which depend on local terrain and prior movement. The only required input data are a digital elevation model, the positions of starting zones, and a minimum of four model parameters. In addition to the major advantage that the open-source code is freely available for further model development, we illustrate and discuss Flow-Py's key advancements and simulation performance by means of three computational experiments. Implementation and validation. We provide a well-organized and easily adaptable solver and present its application to GMFs on generic topographies. Performance. Flow-Py's performance and low computation time are demonstrated by applying the simulation tool to a case study of snow avalanche modeling on a regional scale. Modularity and expandability. The modular and adaptive Flow-Py development environment allows access to spatial information easily and consistently, which enables, e.g., back-tracking of GMF paths that interact with obstacles to their starting zones. The aim of this contribution is to enable the reader to reproduce and understand the basic concepts of GMF modeling at the level of (1) derivation of model equations and (2) their implementation in the Flow-Py code. Therefore, Flow-Py is an educational, innovative GMF simulation tool that can be applied for basic simulations but also for more sophisticated and custom applications such as identifying forests with a protective function or quantifying effects of forests on snow avalanches, rockfall, landslides, and debris flows.

PURPOSE Iterative convolutional neural networks (CNNs) which resemble unrolled learned iterative schemes have shown to consistently deliver state-of-the-art results for image reconstruction problems across different imaging modalities. However, because these methods include the forward model in the architecture, their applicability is often restricted to either relatively small reconstruction problems or to problems with operators which are computationally cheap to compute. As a consequence, they have not been applied to dynamic non-Cartesian multi-coil reconstruction problems so far. METHODS In this work, we propose a CNN architecture for image reconstruction of accelerated 2D radial cine MRI with multiple receiver coils. The network is based on a computationally light CNN component and a subsequent conjugate gradient (CG) method which can be jointly trained end-to-end using an efficient training strategy. We investigate the proposed training strategy and compare our method with other well-known reconstruction techniques with learned and non-learned regularization methods. RESULTS Our proposed method outperforms all other methods based on non-learned regularization. Further, it performs similar or better than a CNN-based method employing a 3D U-Net and a method using adaptive dictionary learning. In addition, we empirically demonstrate that even by training the network with only iteration, it is possible to increase the length of the network at test time and further improve the results. CONCLUSIONS End-to-end training allows to highly reduce the number of trainable parameters of and stabilize the reconstruction network. Further, because it is possible to change the length of the network at the test time, the need to find a compromise between the complexity of the CNN-block and the number of iterations in each CG-block becomes irrelevant.

Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. SPECT is most commonly used for clinical myocardial perfusion imaging, whereas PET is the clinical reference standard for the quantification of myocardial perfusion. MRI does not involve exposure to ionizing radiation, similar to echocardiography, which can be performed at the bedside. CT perfusion imaging is not frequently used but CT offers coronary angiography data, and invasive catheter-based methods can measure coronary flow and pressure. Technical improvements to the quantification of pathophysiological parameters of myocardial ischaemia can be achieved. Clinical consensus recommendations on the appropriateness of each technique were derived following a European quantitative cardiac imaging meeting and using a real-time Delphi process. SPECT using new detectors allows the quantification of myocardial blood flow and is now also suited to patients with a high BMI. PET is well suited to patients with multivessel disease to confirm or exclude balanced ischaemia. MRI allows the evaluation of patients with complex disease who would benefit from imaging of function and fibrosis in addition to perfusion. Echocardiography remains the preferred technique for assessing ischaemia in bedside situations, whereas CT has the greatest value for combined quantification of stenosis and characterization of atherosclerosis in relation to myocardial ischaemia. In patients with a high probability of needing invasive treatment, invasive coronary flow and pressure measurement is well suited to guide treatment decisions. In this Consensus Statement, we summarize the strengths and weaknesses as well as the future technological potential of each imaging modality., Cardiac imaging has a pivotal role in the prevention, diagnosis and treatment of ischaemic heart disease. In this Consensus Statement, the authors summarize the use of SPECT, PET, MRI, echocardiography, CT and invasive coronary flow and pressure measurement, and describe the relative strengths and weaknesses of each modality.

In this work, we consider the task of image reconstruction in 2D radial cardiac cine MRI using deep learning (DL)-based regularization. As the regularization is achieved by employing an image-prior predicted by a pre-trained convolutional neural network (CNN), the quality of the image-prior is of essential importance. The achievable performance of any DL-based method is limited by the amount and the quality of the available training data. For fast dynamic processes, obtaining good-quality MR data is challenging because of technical and physiological reasons. In this work, we try to overcome these problems by a transfer-learning approach which is motivated by a previously presented DL-method (XT,YT U-Net). There, instead of training the network on the whole 2D dynamic images, it is trained on 2D spatio-temporal profiles (xt,yt-slices) which show the temporal changes of the imaged object. Therefore, for the training and test data, it is more important that their spatio-temporal profiles share similar local features rather than being images of the same anatomy. This allows us to equip arbitrary data with simulated motion that resembles the cardiac motion and use it as training data. By doing so, it is possible to train a CNN which is applicable to cardiac cine MR data without using ground-truth cine MR images for training. We demonstrate that combining XT,YT U-Net with the proposed transfer-learning strategy delivers comparable performance to CNNs trained on cardiac cine MR images and in some cases even qualitatively surpasses these. Additionally, the transfer-learning strategy was investigated for a 2D and 3D U-Net. The images processed by the the CNNs were used as image-priors in the CNN-regularized iterative reconstruction. The XT,YT U-Net yielded visibly better results than the 2D U-Net and slightly better results than the 3D U-Net when used in combination with the presented transfer learning-strategy.

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.

We propose a new convolutional neural network architecture for image reconstruction in sparse view computed tomography. The proposed network consists of a cascade of U-nets and data consistency layers. While the U-nets address the undersampling artifacts, the data consistency layers model the specific scanner geometry and make direct use of measured data. We train the network cascade end-to-end on sparse view cardiac CT images. The proposed network’s performance is evaluated according to different quantitative measures and compared to the one of a cascade with fully convolutional neural networks with residual connections and to the one of a single U-net with approximately the same number of trainable parameters. While in both experiments the methods show similar performance in terms of quantitative measures, our proposed U-nets cascade yields superior visual results and better preserves the overall image structure as well as fine diagnostic details, e.g. the coronary arteries. The latter is also confirmed by a statistically significant increase of the Haar-wavelet-based perceptual similarity index measure in all the experiments.

BACKGROUND: It is difficult to predict the biologic behavior of pancreatic endocrine tumors in absence of metastases or invasion into adjacent organs. The World Health Organization (WHO) has proposed in 2004 size, angioinvasion, mitotic activity, and MIB1 proliferation index as prognostic criteria. Our aim was to test retrospectively the predictive value of these 2004 WHO criteria and of CK19, CD99, COX2, and p27 immunohistochemistry in a large series of patients with long-term follow-up. DESIGN: The histology of 216 pancreatic endocrine tumor specimens was reviewed and the tumors were reclassified according to the 2004 WHO classification. The prognostic value of the WHO classification and the histopathologic criteria necrosis and nodular fibrosis was tested in 113 patients. A tissue microarray was constructed for immunohistochemical staining. The staining results were scored quantitatively for MIB1 and semiquantitatively for CK19, COX2, p27, and CD99. The prognostic value of these markers was tested in 93 patients. RESULTS: The stratification of the patients into 4 risk groups according to the 2004 WHO classification was reliable with regard to both time span to relapse and tumor-specific death. In a multivariate analysis, the CK19 status was shown to be independent of the WHO criteria. By contrast, the prognostic significance of COX2, p27, and CD99 could not be confirmed. CONCLUSIONS: The 2004 WHO classification with 4 risk groups is very reliable for predicting both disease-free survival and the time span until tumor-specific death. CK19 staining is a potential additional prognostic marker independent from the WHO criteria for pancreatic endocrine tumors.

Objective: To evaluate the efficacy and safety of simplified maintenance therapy (SMT) compared with continued protease inhibitor (PI) therapy. Design: Meta-analysis of nine randomized controlled trials in which 833 patients were switched to SMT (abacavir, efavirenz or nevirapine) and 616 continued PI, assessing virologic failure (primary outcome), discontinuation of therapy for reasons other than virologic failure, CD4 cell count, total plasma cholesterol and triglycerides. Results: The risk ratio for virologic failure for SMT compared to continued PI was 1.06 [95% confidence interval (Cl) 0.58-1.92; test for homogeneity P = 0.01] for SMT, 2.56, (95% Cl, 1.17-5.64) for abacavir, 0.83 (95% Cl, 0.36-1.91) for efavirenz and 0.54 (95% Cl, 0.29-1.02) for nevirapine. The risk ratio for premature discontinuation of therapy with SMT was 0.61 (95% Cl, 0.48-0.77; test for homogeneity P < 0.10). The difference in absolute mean cholesterol for SMT compared to continued PI was -0.15 mmol/l, (95% Cl, -0.40 to 0.09; test for homogeneity P < 0.01) for SMT, -0.51 mmol/l (95% Cl, -0.70 to -0.33) for abacavir, 0.22 mmol/l (95% Cl, 0 to 0.43) for efavirenz and -0.19 mmol/l (95% Cl, -0.48 to 0.09) for nevirapine. Conclusions: Current evidence suggests that SMT with abacavir rather than continued PI increases the risk of virologic failure, this increased risk may be confined to patients with prior mono or dual therapy with reverse transcriptase inhibitors. There is not enough evidence on whether SMT with efavirenz and nevirapine influences the risk of virologic failure. SMT with any of the three drugs reduces the risk of discontinuation of therapy, and SMTwith abacavir reduces plasma cholesterol.

Nitrous oxide (N2O) use during anesthesia for intracranial procedures has been a subject of controversy in the past. To date, the isolated influence of N2O on mean cerebral blood flow velocity in the middle cerebral artery (VMCA) has not been investigated during hypocapnia in patients with brain tumors. We compared VMCA during normocapnic (ETCO2: 40 mm Hg) and hypnocapnic (ETCO2: 25 mm Hg) inhalation of air and 50% nitrous oxide in oxygen N2O/O2 in eight patients with unilateral brain tumors on both the tumor side and the healthy side. Six patients completed the study. Mean VMCA increased during normocapnic inhalation of N2O/O2 (tumor side: 86 +/- 16 cm sec-1; healthy side: 74 +/- 17 cm sec-1) when compared with air (tumor side: 72 +/- 18 cm sec-1; healthy side: 62 +/- 14 cm sec-1, p < 0.01), whereas during hyperventilation VMCA decreased on both sides (p < 0.001). Mean VMCA values were quite similar during hypocapnic inhalation of 50% N2O/O2 (tumor side: 50 +/- 12 cm sec-1; healthy side: 45 +/- 13 cm sec-1) and air (tumor side: 51 +/- 14 cm sec-1; healthy side: 45 +/- 12 cm sec-1). The data of our study suggest that in patients with cerebral tumors the N2O-induced increase in mean VMCA can be completely reversed by hyperventilation.

Background: Persons infected with human immunodefi ciency virus (HIV) have an increased risk for several cancers, but the infl uences of behavioral risk factors, such as smoking and intravenous drug use, and highly active antiretroviral therapy (HAART) on cancer risk are not clear. Methods: Patient records were linked between the Swiss HIV Cohort Study and Swiss cantonal cancer registries. Observed and expected numbers of incident cancers were assessed in 7304 persons infected with HIV followed for 28 836 person-years. Relative risks for cancer compared with those for the general population were determined by estimating cancer registry – , sex-, age-, and period-standardized incidence ratios (SIRs). Results: Highly elevated SIRs were confi rmed in persons infected with HIV for Kaposi sarcoma (KS) (SIR = 192, 95% confi dence interval [CI] = 170 to 217) and non-Hodgkin lymphoma (SIR = 76.4, 95% CI = 66.5 to 87.4). Statistically signifi cantly elevated SIRs were also observed for anal cancer (SIR = 33.4, 95% CI = 10.5 to 78.6); Hodgkin lymphoma (SIR = 17.3, 95% CI = 10.2 to 27.4); cancers of the cervix (SIR = 8.0, 95% CI = 2.9 to 17.4); liver (SIR = 7.0, 95% CI = 2.2 to 16.5); lip, mouth, and pharynx (SIR = 4.1, 95% CI = 2.1 to 7.4); trachea, lung, and bronchus (SIR = 3.2, 95% CI = 1.7 to 5.4); and skin, nonmelanomatous (SIR = 3.2, 95% CI = 2.2 to 4.5). In HAART users, SIRs for KS (SIR = 25.3, 95% CI = 10.8 to 50.1) and non-Hodgkin lymphoma (SIR = 24.2, 95% CI = 15.0 to 37.1) were lower than those for nonusers (KS SIR = 239, 95% CI = 211 to 270; non-Hodgkin lymphoma SIR = 99.3, 95% CI = 85.8 to 114). Among HAART users, however, the SIR (although not absolute numbers) for Hodgkin lymphoma (SIR = 36.2, 95% CI = 16.4 to 68.9) was comparable to that for KS and non-Hodgkin lymphoma. No clear impact of HAART on SIRs emerged for cervical cancer or non – acquired immunodefi ciency syndrome-defi ning cancers. Cancers of the lung, lip, mouth, or pharynx were not observed among nonsmokers. Conclusion: In persons infected with HIV, HAART use may prevent most excess risk of KS and non-Hodgkin lymphoma, but not that of Hodgkin lymphoma and other non – acquired immunodefi ciency syndrome-defi ning cancers. No cancers of the lip, mouth, pharynx, or lung were observed in nonsmokers. [J Natl Cancer Inst 2005;97:425 – 32]