Sparse Signal Recovery Using Structured Total Maximum Likelihood

In this paper, we consider the sparse signal recovery problem when the dictionary is a Fourier frame. Based on the annihilation relation, the sparse signal recovery from noisy observations is posed as a structured total maximum likelihood (STML) problem. The recent structured total least squares (STLS) approach for finite rate of innovation signal recovery can be viewed as a particular version of our method. We transform the STML problem which has an additional logdet term into a form similar to the STLS problem. It can be effectively tackled using an iterative quadratic maximum likelihood like algorithm. From simulation results, our proposed STML approach outperforms the STLS based algorithm and the state-of-the-art sparse recovery algorithms

Reconstruction of FRI signals using deep neural network approaches

Finite Rate of Innovation (FRI) theory considers sampling and reconstruction of classes of non-bandlimited continuous signals that have a small number of free parameters, such as a stream of Diracs. The task of reconstructing FRI signals from discrete samples is often transformed into a spectral estimation problem and solved using Prony's method and matrix pencil method which involve estimating signal subspaces. They achieve an optimal performance given by the Cramér-Rao bound yet break down at a certain peak signal-to-noise ratio (PSNR). This is probably due to the so-called subspace swap event. In this paper, we aim to alleviate the subspace swap problem and investigate alternative approaches including directly estimating FRI parameters using deep neural networks and utilising deep neural networks as denoisers to reduce the noise in the samples. Simulations show significant improvements on the breakdown PSNR over existing FRI methods, which still outperform learning-based approaches in medium to high PSNR regimes

Learning-based reconstruction of FRI signals

Finite Rate of Innovation (FRI) sampling theory enables reconstruction of classes of continuous non-bandlimited signals that have a small number of free parameters from their low-rate discrete samples. This task is often translated into a spectral estimation problem that is solved using methods involving estimating signal subspaces, which tend to break down at a certain peak signal-to-noise ratio (PSNR). To avoid this breakdown, we consider alternative approaches that make use of information from labelled data. We propose two model-based learning methods, including deep unfolding the denoising process in spectral estimation, and constructing an encoder-decoder deep neural network that models the acquisition process. Simulation results of both learning algorithms indicate significant improvements of the breakdown PSNR over classical subspace-based methods. While the deep unfolded network achieves similar performance as the classical FRI techniques and outperforms the encoder-decoder network in the low noise regimes, the latter allows to reconstruct the FRI signal even when the sampling kernel is unknown. We also achieve competitive results in detecting pulses from in vivo calcium imaging data in terms of true positive and false positive rate while providing more precise estimations

Prognostic Value of 18 F-FDG PET-CT in Nasopharyngeal Carcinoma: Is Dynamic Scanning Helpful?

published_or_final_versio

Non-intubated uniportal anatomical lung resection: a propensity score matched analysis shows faster recovery is possible even in the early experience

OBJECTIVES: Non-intubated uniportal video-assisted thoracoscopic surgery (VATS) has gained considerable interest for major lung resections in recent years. However, characteristics of the learning curve and whether benefits can be shown in the early experience of adapting this technique have hitherto not been investigated ...postprin

Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Regional Nodal Metastasis in Nasopharyngeal Carcinoma: Correlation with Nodal Staging

published_or_final_versio

Intravoxel incoherent motion MR imaging in nasopharyngeal carcinoma: Comparison and correlation with dynamic contrast enhanced MR imaging

published_or_final_versio

Radiation dose and cancer risk in retrospectively and prospectively ECG-gated coronary angiography using 64-slice multidetector CT

This study aimed to estimate the radiation dose and cancer risk to adults in England, the USA and Hong Kong associated with retrospectively and prospectively electrocardiogram (ECG)-gated coronary computed tomography angiography (CTA) using currently practised protocols in Hong Kong. The doses were simulated using the ImPACT spreadsheet. For retrospectively ECG-gated CTAwith pitches of 0.2, 0.22 and 0.24, the effective doseswere 27.7, 23.6 and 20.7 mSv, respectively, formales and 23.6, 20.0 and 18.8 mSv, respectively, for females. For prospectively ECG-gated CTA, the effective dose was 3.7 mSv for both males and females. A table of lifetime attributable risks (LAR) of cancer incidence was set up for the English population for the purpose of estimating cancer risk induced by low-dose radiation exposure, as previously reported for US and Hong Kong populations. From the tables, the LAR of cancer incidence for a representative 50-year-old subject was calculated for retrospectively ECG-gated CTA to be 0.112% and 0.227% for English males and females, respectively, 0.103%and 0.228%for USmales and females, respectively, and was comparatively higher at 0.137% and 0.370% for Hong Kong males and females, respectively; for prospectively ECG-gated CTA, the corresponding values were calculated to be 0.014% and 0.035% for English males and females, respectively, and 0.013%and 0.036%for US males and females, respectively, and againwere higher at 0.017%and 0.060% for Hong Kongmales and females, respectively. Our study shows that prospectively ECG-gated CTA reduces radiation dose and cancer risks by up to 87% compared with retrospectively ECG-gated CTA. © 2010 The British Institute of Radiology.link_to_OA_fulltex

Radiation dose and cancer risk from pediatric CT examinations on 64-slice CT: A phantom study

Objective: To measure the radiation dose from CT scans in an anthropomorphic phantom using a 64-slice MDCT, and to estimate the associated cancer risk. Materials and methods: Organ doses were measured with a 5-year-old phantom and thermoluminescent dosimeters. Four protocols; head CT, thorax CT, abdomen CT and pelvis CT were studied. Cancer risks, in the form of lifetime attributable risk (LAR) of cancer incidence, were estimated by linear extrapolation using the organ radiation doses and the LAR data. Results: The effective doses for head, thorax, abdomen and pelvis CT, were 0.7 mSv, 3.5 mSv, 3.0 mSv, 1.3 mSv respectively. The organs with the highest dose were; for head CT, salivary gland (22.33 mGy); for thorax CT, breast (7.89 mGy); for abdomen CT, colon (6.62 mGy); for pelvis CT, bladder (4.28 mGy). The corresponding LARs for boys and girls were 0.015-0.053% and 0.034-0.155% respectively. The organs with highest LARs were; for head CT, thyroid gland (0.003% for boys, 0.015% for girls); for thorax CT, lung for boys (0.014%) and breast for girls (0.069%); for abdomen CT, colon for boys (0.017%) and lung for girls (0.016%); for pelvis CT, bladder for both boys and girls (0.008%). Conclusion: The effective doses from these common pediatric CT examinations ranged from 0.7 mSv to 3.5 mSv and the associated lifetime cancer risks were found to be up to 0.16%, with some organs of higher radiosensitivity including breast, thyroid gland, colon and lungs. © 2010 Elsevier Ireland Ltd. All rights reserved.postprin