Deep Boosted Regression for MR to CT Synthesis

Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification. However, attenuation correction is particularly challenging for PET-MRI as neither PET nor magnetic resonance imaging (MRI) can directly image tissue attenuation properties. MRI-based computed tomography (CT) synthesis has been proposed as an alternative to physics based and segmentation-based approaches that assign a population-based tissue density value in order to generate an attenuation map. We propose a novel deep fully convolutional neural network that generates synthetic CTs in a recursive manner by gradually reducing the residuals of the previous network, increasing the overall accuracy and generalisability, while keeping the number of trainable parameters within reasonable limits. The model is trained on a database of 20 pre-acquired MRI/CT pairs and a four-fold random bootstrapped validation with a 80:20 split is performed. Quantitative results show that the proposed framework outperforms a state-of-the-art atlas-based approach decreasing the Mean Absolute Error (MAE) from 131HU to 68HU for the synthetic CTs and reducing the PET reconstruction error from 14.3% to 7.2%.Comment: Accepted at SASHIMI201

Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning

The ability to synthesise Computed Tomography images - commonly known as pseudo CT, or pCT - from MRI input data is commonly assessed using an intensity-wise similarity, such as an L2-norm between the ground truth CT and the pCT. However, given that the ultimate purpose is often to use the pCT as an attenuation map ($\mu$-map) in Positron Emission Tomography Magnetic Resonance Imaging (PET/MRI), minimising the error between pCT and CT is not necessarily optimal. The main objective should be to predict a pCT that, when used as $\mu$-map, reconstructs a pseudo PET (pPET) which is as close as possible to the gold standard PET. To this end, we propose a novel multi-hypothesis deep learning framework that generates pCTs by minimising a combination of the pixel-wise error between pCT and CT and a proposed metric-loss that itself is represented by a convolutional neural network (CNN) and aims to minimise subsequent PET residuals. The model is trained on a database of 400 paired MR/CT/PET image slices. Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69.68HU) compared to a baseline CNN (66.25HU), but lead to significant improvement in the PET reconstruction - 115a.u. compared to baseline 140a.u.Comment: Aceppted at SASHIMI201

In-plane polarized collective modes in detwinned YBa2_{2}Cu3_{3}O6.95_{6.95} observed by spectral ellipsometry

The in-plane dielectric response of detwinned YBa$_{2}$Cu$_{3}$O$_{6.95}$ has been studied by far-infared ellipsometry. A surprisingly lare number of in-plane polarized modes are observed. Some of them correspond to pure phonon modes. Others posses a large electronic contribution which strongly increases in the superconducting state. The free carrier response and the collective modes exhibit a pronounced a-b anisotropy. We discuss our results in terms of a CDW state in the 1-d CuO chains and induced charge density fluctuations within the 2-d CuO$_{2}$ planes

Experimental evidence for fast cluster formation of chain oxygen vacancies in YBa2Cu3O7-d being at the origin of the fishtail anomaly

We report on three different and complementary measurements, namely magnetisation measurements, positron annihilation spectroscopy and NMR measurements, which give evidence that the formation of oxygen vacancy clusters is on the origin of the fishtail anomaly in YBa2Cu3O7-d. While in the case of YBa2Cu3O7.0 the anomaly is intrinsically absent, it can be suppressed in the optimally doped state where vacancies are present. We therefore conclude that the single vacancies or point defects can not be responsible for this anomaly but that clusters of oxygen vacancies are on its origin.Comment: 10 pages, 4 figures, submitted to PR

Separation of Quasiparticle and Phononic Heat Currents in YBCO

Measurements of the transverse (k_{xy}) and longitudinal (k_{xx}) thermal conductivity in high magnetic fields are used to separate the quasiparticle thermal conductivity (k_{xx}^{el}) of the CuO_2-planes from the phononic thermal conductivity in YBa_2Cu_3O_{7-\delta}. k_{xx}^{el} is found to display a pronounced maximum below T_c. Our data analysis reveals distinct transport (\tau) and Hall (\tau_H) relaxation times below T_c: Whereas \tau is strongly enhanced, \tau_H follows the same temperature dependence as above T_c