There is an interest to replace computed tomography (CT) images with magnetic resonance (MR) images for a number of diagnostic and therapeutic workflows. In this article, predicting CT images from a number of magnetic resonance imaging (MRI) sequences using regression approach is explored. Two principal areas of application for estimated CT images are dose calculations in MRI based radiotherapy treatment planning and attenuation correction for positron emission tomography (PET)/MRI. The main purpose of this work is to investigate the performance of hidden Markov (chain) models (HMMs) in comparison to hidden Markov random field (HMRF) models when predicting CT images of head. Our study shows that HMMs have clear advantages over HMRF models in this particular application. Obtained results suggest that HMMs deserve a further study for investigating their potential in modeling applications where the most natural theoretical choice would be the class of HMRF models.
Purpose There is increasing interest in computed tomography (CT) image estimations from magnetic resonance (MR) images. The estimated CT images can be utilized for attenuation correction, patient positioning, and dose planning in diagnostic and radiotherapy workflows. This study aims to introduce a novel statistical learning approach for improving CT estimation from MR images and to compare the performance of our method with the existing model‐based CT image estimation methods. Methods The statistical learning approach proposed here consists of two stages. At the training stage, prior knowledge about tissue types from CT images was used together with a Gaussian mixture model (GMM) to explore CT image estimations from MR images. Since the prior knowledge is not available at the prediction stage, a classifier based on RUSBoost algorithm was trained to estimate the tissue types from MR images. For a new patient, the trained classifier and GMMs were used to predict CT image from MR images. The classifier and GMMs were validated by using voxel‐level tenfold cross‐validation and patient‐level leave‐one‐out cross‐validation, respectively. Results The proposed approach has outperformance in CT estimation quality in comparison with the existing model‐based methods, especially on bone tissues. Our method improved CT image estimation by 5% and 23% on the whole brain and bone tissues, respectively. Conclusions Evaluation of our method shows that it is a promising method to generate CT image substitutes for the implementation of fully MR‐based radiotherapy and PET/MRI applications.
The development of compressive sensing in recent years has given much attention to sparse signal recovery. In sparse signal recovery, spike and slab priors are playing a key role in inducing sparsity. The use of such priors, however, results in non-convex and mixed integer programming problems. Most of the existing algorithms to solve non-convex and mixed integer programming problems involve either simplifying assumptions, relaxations or high computational expenses. In this paper, we propose a new adaptive alternating direction method of multipliers (AADMM) algorithm to directly solve the suggested non-convex and mixed integer programming problem. The algorithm is based on the one-to-one mapping property of the support and non-zero element of the signal. At each step of the algorithm, we update the support by either adding an index to it or removing an index from it and use the alternating direction method of multipliers to recover the signal corresponding to the updated support.Moreover, as opposed to the competing "adaptive sparsity matching pursuit" and "alternating direction method of multipliers" methods our algorithm can solve non-convex problems directly. Experiments on synthetic data and real-world images demonstrated that the proposed AADMM algorithm provides superior performance and is computationally cheaper than the recently developed iterative convex refinement (ICR) and adaptive matching pursuit (AMP) algorithms.
This study investigates the spatial distribution of ambulance/emergency alarm call events in order to identify spatial covariates associated with the events and discern hotspot regions of the events. The study, which focuses on the Swedish municipality of Skellefteå, is motivated by the problem of developing optimal dispatching strategies for prehospital resources such as ambulances. The dataset at hand is a large-scale multivariate spatial point pattern of call events stretching between the years 2014-2018. For each event, we have recordings of the spatial location of the call as well as marks containing the associated priority level, given by 1 (highest priority) or 2, and sex labels, given by female or male. To achieve our goals, we begin by modeling the spatially varying call occurrence risk as an intensity function of a (multivariate) inhomogeneous spatial Poisson process that we assume is a log-linear function of some underlying spatial covariates. The spatial covariates used in this study are related to road network coverage, population density, and the socio-economic status of the population in Skellefteå. Since mobility is clearly a factor that has a large impact on where people are in need of an ambulance, and since none of our spatial covariates quantify human mobility patterns, we here take a pragmatic approach where, in addition to other spatial covariates, we include a non-parametric intensity estimate of the events as a covariate in the intensity function. A new heuristic algorithm has been developed to select an optimal estimate of the kernel bandwidth in order to obtain the non-parametric intensity estimate of the events and to generate other covariates. Since we consider a large number of spatial covariates as well as their products (the first-order interaction terms), and since some of them may be strongly correlated, lasso-like elastic-net regularisation has been used in the log-likelihood intensity modeling to perform variable selection and reduce variance inflation from overfitting and bias from underfitting. As a result of the variable selection, the fitted model structure contains individual covariates of both road network and demographic types. We discovered that hotspot regions of calls have been observed along dense parts of the road network in Skellefteå. Furthermore, a mean absolute error evaluation of the proposed model to generate the intensity of emergency alarm/ambulance call events indicates that the estimated model is stable and can be used to generate a reliable intensity estimate over the region, which can be used as an input in the problem of designing prehospital resource dispatching strategies.
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