Conditional Variational Autoencoder for Learned Image Reconstruction

Learned image reconstruction techniques using deep neural networks have recently gained popularity and have delivered promising empirical results. However, most approaches focus on one single recovery for each observation, and thus neglect information uncertainty. In this work, we develop a novel computational framework that approximates the posterior distribution of the unknown image at each query observation. The proposed framework is very flexible: it handles implicit noise models and priors, it incorporates the data formation process (i.e., the forward operator), and the learned reconstructive properties are transferable between different datasets. Once the network is trained using the conditional variational autoencoder loss, it provides a computationally efficient sampler for the approximate posterior distribution via feed-forward propagation, and the summarizing statistics of the generated samples are used for both point-estimation and uncertainty quantification. We illustrate the proposed framework with extensive numerical experiments on positron emission tomography (with both moderate and low-count levels) showing that the framework generates high-quality samples when compared with state-of-the-art methods

Feasibility Test of the MedaCube

Poor adherence is a significant barrier to achieve better patient outcomes. Rates of non-adherence approach 40% resulting in 10% of all emergency department visits and 23% of admissions into skilled nursing facilities. Many factors contribute to medication non-adherence including psychological and memory disorders, aging and pill burden. The MedaCube is a medication management system intended to help solve unintentional medication non-adherence. The device is designed to dispense scheduled and as-needed oral medications. The MedaCube provides audio and visual prompts alerting subjects to administer their medications. Caregivers receive notification of missed doses, late doses and refill requests. The null hypothesis is that use of the MedaCube results in no difference in medication adherence when compared with six month prior adherence in individual subjects

Uncertainty quantification in medical image synthesis

Machine learning approaches to medical image synthesis have shown outstanding performance, but often do not convey uncertainty information. In this chapter, we survey uncertainty quantification methods in medical image synthesis and advocate the use of uncertainty for improving clinicians’ trust in machine learning solutions. First, we describe basic concepts in uncertainty quantification and discuss its potential benefits in downstream applications. We then review computational strategies that facilitate inference, and identify the main technical and clinical challenges. We provide a first comprehensive review to inform how to quantify, communicate and use uncertainty in medical synthesis applications

Magnetothermodynamics: Measuring equations of state in a relaxed magnetohydrodynamic plasma

We report the first measurements of equations of state of a fully relaxed magnetohydrodynamic (MHD) laboratory plasma. Parcels of magnetized plasma, called Taylor states, are formed in a coaxial magnetized plasma gun, and are allowed to relax and drift into a closed flux conserving volume. Density, ion temperature, and magnetic field are measured as a function of time as the Taylor states compress and heat. The theoretically predicted MHD and double adiabatic equations of state are compared to experimental measurements. We find that the MHD equation of state is inconsistent with our data.Comment: 4 pages, 4 figure

Measuring The Equations Of State In A Relaxed Magnetohydrodynamic Plasma

We report measurements of the equations of state of a fully relaxed magnetohydrodynamic (MHD) laboratory plasma. Parcels of magnetized plasma, called Taylor states, are formed in a coaxial magnetized plasma gun, and are allowed to relax and drift into a closed flux conserving volume. Density, ion temperature, and magnetic field are measured as a function of time as the Taylor states compress and heat. The theoretically predicted MHD and double adiabatic equations of state are compared to experimental measurements. We find that the MHD equation of state is inconsistent with our data

Unsupervised Knowledge-Transfer for Learned Image Reconstruction

Deep learning-based image reconstruction approaches have demonstrated impressive empirical performance in many imaging modalities. These approaches generally require a large amount of high-quality training data, which is often not available. To circumvent this issue, we develop a novel unsupervised knowledge-transfer paradigm for learned iterative reconstruction within a Bayesian framework. The proposed approach learns an iterative reconstruction network in two phases. The first phase trains a reconstruction network with a set of ordered pairs comprising of ground truth images and measurement data. The second phase fine-tunes the pretrained network to the measurement data without supervision. Furthermore, the framework delivers uncertainty information over the reconstructed image. We present extensive experimental results on low-dose and sparse-view computed tomography, showing that the proposed framework significantly improves reconstruction quality not only visually, but also quantitatively in terms of PSNR and SSIM, and is competitive with several state-of-the-art supervised and unsupervised reconstruction techniques

Magnetothermodynamics: Measurements Of The Thermodynamic Properties In A Relaxed Magnetohydrodynamic Plasma

We have explored the thermodynamics of compressed magnetized plasmas in laboratory experiments and we call these studies ‘magnetothermodynamics’. The experiments are carried out in the Swarthmore Spheromak eXperiment device. In this device, a magnetized plasma source is located at one end and at the other end, a closed conducting can is installed. We generate parcels of magnetized plasma and observe their compression against the end wall of the conducting cylinder. The plasma parameters such as plasma density, temperature and magnetic field are measured during compression using HeNe laser interferometry, ion Doppler spectroscopy and a linear dot{B} probe array, respectively. To identify the instances of ion heating during compression, a PV diagram is constructed using measured density, temperature and a proxy for the volume of the magnetized plasma. Different equations of state are analysed to evaluate the adiabatic nature of the compressed plasma. A three-dimensional resistive magnetohydrodynamic code (NIMROD) is employed to simulate the twisted Taylor states and shows stagnation against the end wall of the closed conducting can. The simulation results are consistent to what we observe in our experiments

Inspiratory muscle workload due to dynamic intrinsic PEEP in stable COPD patients: effects of two different settings of non-invasive pressure-support ventilation.

BACKGROUND: In severe stable hypercapnic COPD patients the amount of pressure time product (PTP) spent to counterbalance their dynamic intrinsic positive end expiratory pressure (PEEPi,dyn) is high: no data are available on the best setting of non invasive pressure support ventilation (NPSV) to reduce the inspiratory muscle workload due to PEEPi,dyn. METHODS: The objectives of this randomised controlled physiological study were: 1. To measure the inspiratory muscle workload due to PEEPi,dyn 2. To measure the effects on this parameter of two settings of NPSV in stable COPD patients with chronic hypercapnia admitted in a Pulmonary Division of two Rehabilitation Centers. Twenty-three stable COPD patients with chronic hypercapnia on domiciliary nocturnal NPSV for 30 +/- 20 months were submitted to an evaluation of breathing pattern, PEEPi,dyn, inspiratory muscle workload and its partitioning during both assisted and unassisted ventilation. Two settings of NPSV were randomly applied for 30 minutes each: i- "at patient's comfort" (C): Inspiratory pressure support (IPS) was the maximal tolerated pressure able to reduce awake PaCO2 with the addition of a pre-set level of external PEEP (PEEPe); ii- "physiological setting" (PH): the level of IPS able to achieve a > 40% and < 90% decrease in transdiaphragmatic pressure in comparison to spontaneous breathing (SB). A PEEPe level able to reduce PEEPi,dyn by at least 50% was added. RESULTS: During SB the tidal diaphragmatic pressure-time product (PTPdi/b) was 17.62 +/- 7.22 cmH2O*sec, the component due to PEEPi,dyn (PTPdiPEEPi,dyn) being 38 +/- 17% (range: 16-65%). Compared to SB,PTPdiPEEPi,dyn was reduced significantly with both settings, the reduction being greater with PH compared to C. CONCLUSIONS: In conclusion in severe COPD patients with chronic hypercapnia the inspiratory muscle workload due to PEEPidyn is high and is reduced by NPSV at a greater extent when ventilator setting is tailored to patient's mechanics