48 research outputs found
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 (-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
-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 YBaCuO observed by spectral ellipsometry
The in-plane dielectric response of detwinned YBaCuO 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 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