Accelerated gradient methods for total-variation-based CT image reconstruction

Total-variation (TV)-based Computed Tomography (CT) image reconstruction has shown experimentally to be capable of producing accurate reconstructions from sparse-view data. In particular TV-based reconstruction is very well suited for images with piecewise nearly constant regions. Computationally, however, TV-based reconstruction is much more demanding, especially for 3D imaging, and the reconstruction from clinical data sets is far from being close to real-time. This is undesirable from a clinical perspective, and thus there is an incentive to accelerate the solution of the underlying optimization problem. The TV reconstruction can in principle be found by any optimization method, but in practice the large-scale systems arising in CT image reconstruction preclude the use of memory-demanding methods such as Newton's method. The simple gradient method has much lower memory requirements, but exhibits slow convergence. In the present work we consider the use of two accelerated gradient-based methods, GPBB and UPN, for reducing the number of gradient method iterations needed to achieve a high-accuracy TV solution in CT image reconstruction. The former incorporates several heuristics from the optimization literature such as Barzilai-Borwein (BB) step size selection and nonmonotone line search. The latter uses a cleverly chosen sequence of auxiliary points to achieve a better convergence rate. The methods are memory efficient and equipped with a stopping criterion to ensure that the TV reconstruction has indeed been found. An implementation of the methods (in C with interface to Matlab) is available for download from http://www2.imm.dtu.dk/~pch/TVReg/. We compare the proposed methods with the standard gradient method, applied to a 3D test problem with synthetic few-view data. We find experimentally that for realistic parameters the proposed methods significantly outperform the gradient method.Comment: 4 pages, 2 figure

Additional file 1: of Evidence for the low recording of weight status and lifestyle risk factors in the Danish National Registry of Patients, 1999–2012

International Classification of Diseases (ICD) and treatment codes used in the study. (DOC 35 kb

Kidney function before and after acute kidney injury: a nationwide population-based cohort study

Abstract Background Acute kidney injury (AKI) is a common and serious condition defined by a rapid decline in kidney function. Data on changes in long-term kidney function following AKI are sparse and conflicting. Therefore, we examined the changes in estimated glomerular filtration rate (eGFR) from before to after AKI in a nationwide population-based setting. Methods Using Danish laboratory databases, we identified individuals with first-time AKI defined by an acute increase in plasma creatinine (pCr) during 2010 to 2017. Individuals with three or more outpatient pCr measurements before and after AKI were included and cohorts were stratified by baseline eGFR (≥/&amp;lt;60 ml/min/1.73m2). Linear regression models were used to estimate and compare individual eGFR slopes and eGFR levels before and after AKI. Results Among individuals with a baseline eGFR ≥60 ml/min/1.73m2 (n = 64 805), first-time AKI was associated with a median difference in eGFR level of −5.6 ml/min/1.73m2 (interquartile range [IQR], −16.1 to 1.8) and a median difference in eGFR slope of −0.4 ml/min/1.73m2/year (IQR, −5.5 to 4.4). Correspondingly, among individuals with a baseline eGFR &amp;lt;60 ml/min/1.73m2 (n = 33 267), first-time AKI was associated with a median difference in eGFR level of −2.2 ml/min/1.73m2 (IQR, −9.2 to 4.3) and a median difference in eGFR slope of 1.5 ml/min/1.73m2/year (IQR, −2.9 to 6.5). Conclusion Among individuals with first-time AKI surviving to have repeated outpatient pCr measurements, AKI was associated with changes in eGFR level and eGFR slope for which the magnitude and direction depend on baseline eGFR. </jats:sec

Defining measures of kidney function in observational studies using routine healthcare data:methodological and reporting considerations

The availability of electronic health records and access to a large number of routine measurements of serum creatinine and urinary albumin enhance the possibilities for epidemiologic research in kidney disease. However, the frequency of health care use and laboratory testing is determined by health status and indication, imposing certain challenges when identifying patients with kidney injury or disease, when using markers of kidney function as covariates, or when evaluating kidney outcomes. Depending on the specific research question, this may influence the interpretation, generalizability, and/or validity of study results. This review illustrates the heterogeneity of working definitions of kidney disease in the scientific literature and discusses advantages and limitations of the most commonly used approaches using 3 examples. We summarize ways to identify and overcome possible biases and conclude by proposing a framework for reporting definitions of exposures and outcomes in studies of kidney disease using routinely collected health care data