Myelin water imaging from multi-echo T-2 MR relaxometry data using a joint sparsity constraint

Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce a more efficient and noise robust method to determine the MWF using a joint sparsity constraint and a pre-computed B-1(+)-T-2 dictionary.A single component analysis with this dictionary is used in an initial step to obtain a B-1(+) map. The T-2 distribution is then determined from a reduced dictionary corresponding to the estimated B-1(+) map using a combination of a non-negativity and a joint sparsity constraint.The non-negativity constraint ensures that a feasible solution with non-negative contribution of each T-2 component is obtained. The joint sparsity constraint restricts the T-2 distribution to a small set of T-2 relaxation times shared between all voxels and reduces the noise sensitivity.The applied Sparsity Promoting Iterative Joint NNLS (SPIJN) algorithm can be implemented efficiently, reducing the computation time by a factor of 50 compared to the commonly used regularized non-negative least squares algorithm. The proposed method was validated in simulations and in 8 healthy subjects with a 3D multiecho gradient- and spin echo scan at 3 T. In simulations, the absolute error in the MWF decreased from 0.031 to 0.013 compared to the regularized NNLS algorithm for SNR = 250. The in vivo results were consistent with values reported in literature and improved MWF-quantification was obtained especially in the frontal white matter. The maximum standard deviation in mean MWF in different regions of interest between subjects was smaller for the proposed method (0.0193) compared to the regularized NNLS algorithm (0.0266). In conclusion, the proposed method for MWF estimation is less computationally expensive and less susceptible to noise compared to state of the art methods. These improvements might be an important step towards clinical translation of MWF measurements.Neuro Imaging Researc

Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols

Online and offline measurements of ambient particulate matter (PM) near the urban and industrial Houston Ship Channel in Houston, Texas, USA, during May 2015 were utilized to characterize its chemical composition and to evaluate the relative contributions of primary, secondary, biogenic, and anthropogenic sources. Aerosol mass spectrometry (AMS) on nonrefractory PM1 (PM  ≤  1&thinsp;µm) indicated major contributions from sulfate (averaging 50&thinsp;% by mass), organic aerosol (OA, 40&thinsp;%), and ammonium (14&thinsp;%). Positive matrix factorization (PMF) of AMS data categorized OA on average as 22&thinsp;% hydrocarbon-like organic aerosol (HOA), 29&thinsp;% cooking-influenced less-oxidized oxygenated organic aerosol (CI-LO-OOA), and 48&thinsp;% more-oxidized oxygenated organic aerosol (MO-OOA), with the latter two sources indicative of secondary organic aerosol (SOA). Chemical analysis of PM2.5 (PM  ≤  2.5&thinsp;µm) filter samples agreed that organic matter (35&thinsp;%) and sulfate (21&thinsp;%) were the most abundant components. Organic speciation of PM2.5 organic carbon (OC) focused on molecular markers of primary sources and SOA tracers derived from biogenic and anthropogenic volatile organic compounds (VOCs). The sources of PM2.5 OC were estimated using molecular marker-based positive matric factorization (MM-PMF) and chemical mass balance (CMB) models. MM-PMF resolved nine factors that were identified as diesel engines (11.5&thinsp;%), gasoline engines (24.3&thinsp;%), nontailpipe vehicle emissions (11.1&thinsp;%), ship emissions (2.2&thinsp;%), cooking (1.0&thinsp;%), biomass burning (BB, 10.6&thinsp;%), isoprene SOA (11.0&thinsp;%), high-NOx anthropogenic SOA (6.6&thinsp;%), and low-NOx anthropogenic SOA (21.7&thinsp;%). Using available source profiles, CMB apportioned 41&thinsp;% of OC to primary fossil sources (gasoline engines, diesel engines, and ship emissions), 5&thinsp;% to BB, 15&thinsp;% to SOA (including 7.4&thinsp;% biogenic and 7.6&thinsp;% anthropogenic), and 39&thinsp;% to other sources that were not included in the model and are expected to be secondary.This study presents the first application of in situ AMS-PMF, MM-PMF, and CMB for OC source apportionment and the integration of these methods to evaluate the relative roles of biogenic, anthropogenic, and BB-SOA. The three source apportionment models agreed that  ∼ &thinsp;50&thinsp;% of OC is associated with primary emissions from fossil fuel use, particularly motor vehicles. Differences among the models reflect their ability to resolve sources based upon the input chemical measurements, with molecular marker-based methods providing greater source specificity and resolution for minor sources. By combining results from MM-PMF and CMB, BB was estimated to contribute 11&thinsp;% of OC, with 5&thinsp;% primary emissions and 6&thinsp;% BB-SOA. SOA was dominantly anthropogenic (28&thinsp;%) rather than biogenic (11&thinsp;%) or BB-derived. The three-model approach demonstrates significant contributions of anthropogenic SOA to fine PM. More broadly, the findings and methodologies presented herein can be used to advance local and regional understanding of anthropogenic contributions to SOA.</p

Longitudinal peak strain detects a smaller risk area than visual assessment of wall motion in acute myocardial infarction

<p>Abstract</p> <p>Background</p> <p>Opening of an occluded infarct related artery reduces infarct size and improves survival in acute ST-elevation myocardial infarction (STEMI). In this study we performed tissue Doppler analysis (peak strain, displacement, mitral annular movement (MAM)) and compared with visual assessment for the study of the correlation of measurements of global, regional and segmental function with final infarct size and transmurality. In addition, myocardial risk area was determined and a prediction sought for the development of infarct transmurality ≥50%.</p> <p>Methods</p> <p>Twenty six patients with STEMI submitted for primary percutaneous coronary intervention (PCI) were examined with echocardiography on the catheterization table. Four to eight weeks later repeat echocardiography was performed for reassessment of function and magnetic resonance imaging for the determination of final infarct size and transmurality.</p> <p>Results</p> <p>On a global level, wall motion score index (WMSI), ejection fraction (EF), strain, and displacement all showed significant differences (p ≤ 0.001, p ≤ 0.001, p ≤ 0.001 and p = 0.03) between the two study visits, but MAM did not (p = 0.17). On all levels (global, regional and segmental) and both pre- and post PCI, WMSI showed a higher correlation with scar transmurality compared to strain. We found that both strain and WMSI predicted the development of scar transmurality ≥50%, but strain added no significant information to that obtained with WMSI in a logistic regression analysis.</p> <p>Conclusions</p> <p>In patients with acute STEMI, WMSI, EF, strain, and displacement showed significant changes between the pre- and post PCI exam. In a ROC-analysis, strain had 64% sensitivity at 80% specificity and WMSI around 90% sensitivity at 80% specificity for the detection of scar with transmurality ≥50% at follow-up.</p

A palaeoenvironmental reconstruction of the Middle Jurassic of Sardinia (Italy) based on integrated palaeobotanical, palynological and lithofacies data assessment

During the Jurassic, Sardinia was close to continental Europe. Emerged lands started from a single island forming in time a progressively sinking archipelago. This complex palaeogeographic situation gave origin to a diverse landscape with a variety of habitats. Collection- and literature-based palaeobotanical, palynological and lithofacies studies were carried out on the Genna Selole Formation for palaeoenvironmental interpretations. They evidence a generally warm and humid climate, affected occasionally by drier periods. Several distinct ecosystems can be discerned in this climate, including alluvial fans with braided streams (Laconi-Gadoni lithofacies), paralic swamps and coasts (Nurri-Escalaplano lithofacies), and lagoons and shallow marine environments (Ussassai-Perdasdefogu lithofacies). The non-marine environments were covered by extensive lowland and a reduced coastal and tidally influenced environment. Both the river and the upland/hinterland environments are of limited impact for the reconstruction. The difference between the composition of the palynological and palaeobotanical associations evidence the discrepancies obtained using only one of those proxies. The macroremains reflect the local palaeoenvironments better, although subjected to a transport bias (e.g. missing upland elements and delicate organs), whereas the palynomorphs permit to reconstruct the regional palaeoclimate. Considering that the flora of Sardinia is the southernmost of all Middle Jurassic European floras, this multidisciplinary study increases our understanding of the terrestrial environments during that period of time