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Arwa Aljared, Abdullah Awad Alharbi, Martin W Huellner
Block sequential regularized expectation maximization (BSREM) is a Bayesian penalized-likelihood reconstruction algorithm for PET, which reaches full convergence without the detriment of deteriorating the image quality by noise. Therefore, BSREM might have implications particularly for the detection of small lesions, which may be beneficial in melanoma patients. Our case of a 70-year-old man with metastasized malignant melanoma illustrates the impact of such a novel iterative PET reconstruction algorithm. Whereas the lymph node metastases are seen with the latest generation ordered subset expectation maximization reconstruction, the in-transit metastases are identified straightforward only with BSREM reconstruction...
May 2018: Clinical Nuclear Medicine
Elin Lindström, Anders Sundin, Carlos Trampal, Lars Lindsjö, Ezgi Ilan, Torsten Danfors, Gunnar Antoni, Jens Sörensen, Mark Lubberink
Resolution and quantitative accuracy of positron emission tomography (PET) are highly influenced by the reconstruction method. Penalized likelihood estimation algorithms allow for fully convergent iterative reconstruction, generating a higher image contrast while limiting noise compared to ordered subsets expectation maximization (OSEM). In this study, block-sequential regularized expectation maximization (BSREM) was compared to time-of-flight OSEM (TOF-OSEM). Various strengths of noise penalization factor β were tested along with scan durations and transaxial field of views (FOVs) with the aim to evaluate the performance and clinical use of BSREM for 18 F-FDG-PET-computed tomography (CT), both in quantitative terms and in a qualitative visual evaluation...
February 15, 2018: Journal of Nuclear Medicine: Official Publication, Society of Nuclear Medicine
Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A Fessler
The sparsity of signals in a transform domain or dictionary has been exploited in applications such as compression, denoising and inverse problems. More recently, data-driven adaptation of synthesis dictionaries has shown promise compared to analytical dictionary models. However, dictionary learning problems are typically non-convex and NP-hard, and the usual alternating minimization approaches for these problems are often computationally expensive, with the computations dominated by the NP-hard synthesis sparse coding step...
December 2017: IEEE Transactions on Computational Imaging
Ellen L Goode, Matthew S Block, Kimberly R Kalli, Robert A Vierkant, Wenqian Chen, Zachary C Fogarty, Aleksandra Gentry-Maharaj, Aleksandra Tołoczko, Alexander Hein, Aliecia L Bouligny, Allan Jensen, Ana Osorio, Andreas Hartkopf, Andy Ryan, Anita Chudecka-Głaz, Anthony M Magliocco, Arndt Hartmann, Audrey Y Jung, Bo Gao, Brenda Y Hernandez, Brooke L Fridley, Bryan M McCauley, Catherine J Kennedy, Chen Wang, Chloe Karpinskyj, Christiani B de Sousa, Daniel G Tiezzi, David L Wachter, Esther Herpel, Florin Andrei Taran, Francesmary Modugno, Gregg Nelson, Jan Lubiński, Janusz Menkiszak, Jennifer Alsop, Jenny Lester, Jesús García-Donas, Jill Nation, Jillian Hung, José Palacios, Joseph H Rothstein, Joseph L Kelley, Jurandyr M de Andrade, Luis Robles-Díaz, Maria P Intermaggio, Martin Widschwendter, Matthias W Beckmann, Matthias Ruebner, Mercedes Jimenez-Linan, Naveena Singh, Oleg Oszurek, Paul R Harnett, Peter F Rambau, Peter Sinn, Philipp Wagner, Prafull Ghatage, Raghwa Sharma, Robert P Edwards, Roberta B Ness, Sandra Orsulic, Sara Y Brucker, Sharon E Johnatty, Teri A Longacre, Eilber Ursula, Valerie McGuire, Weiva Sieh, Yanina Natanzon, Zheng Li, Alice S Whittemore, deFazio Anna, Annette Staebler, Beth Y Karlan, Blake Gilks, David D Bowtell, Estrid Høgdall, Francisco J Candido dos Reis, Helen Steed, Ian G Campbell, Jacek Gronwald, Javier Benítez, Jennifer M Koziak, Jenny Chang-Claude, Kirsten B Moysich, Linda E Kelemen, Linda S Cook, Marc T Goodman, María José García, Peter A Fasching, Stefan Kommoss, Suha Deen, Susanne K Kjaer, Usha Menon, James D Brenton, Paul DP Pharoah, Georgia Chenevix-Trench, David G Huntsman, Stacey J Winham, Martin Köbel, Susan J Ramus
Importance: Cytotoxic CD8+ tumor-infiltrating lymphocytes (TILs) participate in immune control of epithelial ovarian cancer; however, little is known about prognostic patterns of CD8+ TILs by histotype and in relation to other clinical factors. Objective: To define the prognostic role of CD8+ TILs in epithelial ovarian cancer. Design, Setting, and Participants: This was a multicenter observational, prospective survival cohort study of the Ovarian Tumor Tissue Analysis Consortium...
December 1, 2017: JAMA Oncology
Ricardo Rendall, Ana Cristina Pereira, Marco S Reis
In this paper we test and compare advanced predictive approaches for estimating wine age in the context of the production of a high quality fortified wine - Madeira Wine. We consider four different data sets, namely, volatile, polyphenols, organic acids and the UV-vis spectra. Each one of these data sets contain chemical information of a different nature and present diverse data structures, namely a different dimensionality, level of collinearity and degree of sparsity. These different aspects may imply the use of different modelling approaches in order to better explore the data set's information content, namely their predictive potential for wine age...
August 15, 2017: Talanta
Scott A Bruce, Martica H Hall, Daniel J Buysse, Robert T Krafty
Many studies of biomedical time series signals aim to measure the association between frequency-domain properties of time series and clinical and behavioral covariates. However, the time-varying dynamics of these associations are largely ignored due to a lack of methods that can assess the changing nature of the relationship through time. This article introduces a method for the simultaneous and automatic analysis of the association between the time-varying power spectrum and covariates, which we refer to as conditional adaptive Bayesian spectrum analysis (CABS)...
May 8, 2017: Biometrics
Andrew M Kuriyama, Austin S Nakatsuka, Loren G Yamamoto
There is greater attention to head-related injuries and concussions in American football. The helmet's structural safety and the way that football players use their helmets are important in preventing head injuries. Current strategies include penalizing players for high-risk behavior such as leading with their helmet or hitting an opposing player above the shoulder. Passive strategies include helmet modification to better protect the head of the players or to change the playing style of the players. Hawai'i high school varsity football players were surveyed to determine how they use their helmets and how a new helmet design would affect their style of play...
March 2017: Hawai'i Journal of Medicine & Public Health: a Journal of Asia Pacific Medicine & Public Health
Kuangnan Fang, Shuangge Ma
Data with a large p (number of covariates) and/or a large n (sample size) are now commonly encountered. For many problems, regularization especially penalization is adopted for estimation and variable selection. The straightforward application of penalization to large datasets demands a "big computer" with high computational power. To improve computational feasibility, we develop bootstrap penalization, which dissects a big penalized estimation into a set of small ones, which can be executed in a highly parallel manner and each only demands a "small computer"...
March 2017: Biometrical Journal. Biometrische Zeitschrift
Bert-Ram Sah, Paul Stolzmann, Gaspar Delso, Scott D Wollenweber, Martin Hüllner, Yahya A Hakami, Marcelo A Queiroz, Felipe de Galiza Barbosa, Gustav K von Schulthess, Carsten Pietsch, Patrick Veit-Haibach
PURPOSE: To investigate the clinical performance of a block sequential regularized expectation maximization (BSREM) penalized likelihood reconstruction algorithm in oncologic PET/computed tomography (CT) studies. METHODS: A total of 410 reconstructions of 41 fluorine-18 fluorodeoxyglucose-PET/CT studies of 41 patients with a total of 2010 lesions were analyzed by two experienced nuclear medicine physicians. Images were reconstructed with BSREM (with four different β values) or ordered subset expectation maximization (OSEM) algorithm with/without time-of-flight (TOF/non-TOF) corrections...
January 2017: Nuclear Medicine Communications
Dan Nguyen, Qihui Lyu, Dan Ruan, Daniel O'Connor, Daniel A Low, Ke Sheng
PURPOSE: Volumetric modulated arc therapy (VMAT) is a widely employed radiation therapy technique, showing comparable dosimetry to static beam intensity modulated radiation therapy (IMRT) with reduced monitor units and treatment time. However, the current VMAT optimization has various greedy heuristics employed for an empirical solution, which jeopardizes plan consistency and quality. The authors introduce a novel direct aperture optimization method for VMAT to overcome these limitations...
July 2016: Medical Physics
Andriani Asproudi, Maurizio Petrozziello, Silvia Cavalletto, Silvia Guidoni
The influence exerted by bunch microclimate on some C13-norisoprenoid precursors content was investigated for the first time in Nebbiolo grapes during ripening. Samples were collected, during two consecutive seasons, from two vineyards, which are characterized by different microclimatic conditions caused by vine vigour heterogeneity and different vineyard aspects. Enzymatic hydrolysis of the glycosides extracted from the grapes, and subsequent GC-MS determination of the aglycones, highlighted that the majority of norisoprenoid glycosides accumulated in Nebbiolo berries from pre-veraison until 3-4weeks post-veraison...
November 15, 2016: Food Chemistry
Sara Viotti
BACKGROUND: Correctional officers (COs) are exposed to various factors likely to jeopardize their health and safety. Even if numerous studies have been focused on work-related stress among COs, few studies have been carried out in Italy. OBJECTIVE: Indentify the work-related factors and comprehend how they negatively affect the COs' psychological health in the Italian penal system. METHODS: A qualitative approach was employed. Twenty-eight COs employed in a detention block of an Italian jail were interviewed face-to-face...
January 25, 2016: Work: a Journal of Prevention, Assessment, and Rehabilitation
Koen Michielsen, Johan Nuyts
PURPOSE: The authors wish to evaluate the possible advantages of using a multigrid approach to maximum-a-posteriori reconstruction in digital breast tomosynthesis together with block-iterative updates in the form of either plane-by-plane updates or ordered subsets. METHODS: The authors previously developed a penalized maximum likelihood reconstruction algorithm with resolution model dedicated to breast tomosynthesis [K. Michielsen et al., "Patchwork reconstruction with resolution modeling for digital breast tomosynthesis," Med...
November 2015: Medical Physics
Amala Rahmah, James Blogg, Nurlan Silitonga, Muqowimul Aman, Robert Michael Power
PURPOSE: Indonesian law provides prisoners with basic rights, including access to education, health care and nutrition. Yet, structural and institutional limitations, notably overcrowding and under-resourcing, prohibits penal institutions from fulfilling these commitments for female prisoners. The purpose of this paper is to explore their health concerns. DESIGN/METHODOLOGY/APPROACH: Six prisons and one detention centre were researched, comprising: female prisoners (n=69); clinical officers (six); clinic heads (seven); wardens (seven); heads of prisons (seven); and a Directorate representative...
2014: International Journal of Prisoner Health
Yunzhang Zhu, Xiaotong Shen, Wei Pan
Gaussian graphical models are useful to analyze and visualize conditional dependence relationships between interacting units. Motivated from network analysis under di erent experimental conditions, such as gene networks for disparate cancer subtypes, we model structural changes over multiple networks with possible heterogeneities. In particular, we estimate multiple precision matrices describing dependencies among interacting units through maximum penalized likelihood. Of particular interest are homogeneous groups of similar entries across and zero-entries of these matrices, referred to as clustering and sparseness structures, respectively...
October 2014: Journal of the American Statistical Association
Yu Han, Huiqian Du, Wenbo Mei, Liping Fang
This work aims to develop a novel magnetic resonance (MR) image reconstruction approach motivated by the recently proposed sampling framework with union-of-subspaces model (SUoS). Based on SUoS, we propose a mathematical formalism that effectively integrates a block sparsity constraint and support information which is estimated in an iterative fashion. The resulting optimization problem consists of a data fidelity term and a support detection based block sparsity (SDBS) promoting term penalizing entries within the complement of the estimated support...
June 2015: Magnetic Resonance Imaging
SeyyedMajid Valiollahzadeh, John W Clark, Osama Mawlawi
PURPOSE: Most positron emission tomography/computed tomography (PET/CT) scanners consist of tightly packed discrete detector rings to improve scanner efficiency. The authors' aim was to use compressive sensing (CS) techniques in PET imaging to investigate the possibility of decreasing the number of detector elements per ring (introducing gaps) while maintaining image quality. METHODS: A CS model based on a combination of gradient magnitude and wavelet domains (wavelet-TV) was developed to recover missing observations in PET data acquisition...
January 2015: Medical Physics
Rahul Mazumder, Trevor Hastie
The graphical lasso [5] is an algorithm for learning the structure in an undirected Gaussian graphical model, using ℓ1 regularization to control the number of zeros in the precision matrix Θ = Σ (-1) [2, 11]. The R package GLASSO [5] is popular, fast, and allows one to efficiently build a path of models for different values of the tuning parameter. Convergence of GLASSO can be tricky; the converged precision matrix might not be the inverse of the estimated covariance, and occasionally it fails to converge with warm starts...
November 9, 2012: Electronic Journal of Statistics
B Clarke, J-H Chu
Consider a regression problem in which there are many more explanatory variables than data points, i.e., p ≫ n. Essentially, without reducing the number of variables inference is impossible. So, we group the p explanatory variables into blocks by clustering, evaluate statistics on the blocks and then regress the response on these statistics under a penalized error criterion to obtain estimates of the regression coefficients. We examine the performance of this approach for a variety of choices of n, p, classes of statistics, clustering algorithms, penalty terms, and data types...
2014: Journal of the Indian Society of Agricultural Statistics
Jenna Dixon, Isaac N Luginaah, Paul Mkandawire
This article addresses the implications of the mandatory delay in coverage for individuals residing in the Upper West Region (UWR) of Ghana who have dropped out of the National Health Insurance Scheme (NHIS) but later attempt to reenroll. Using data collected in 2011 in Ghana's UWR, we use a negative log-log model (n=1,584) to compare those who remain enrolled in the scheme with those who have dropped out. Women with unreliable incomes, who reported being food-insecure and those living with young children were more likely to drop out (OR range: 1...
August 2014: Journal of Health Care for the Poor and Underserved
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