Albert Guart

Francisco Bono-Blay

Antonio Borrell

Silvia Lacorte

Migration of

Keywords: migration, plasticizer, phthalate, alkylphenol, bisphenol A, nonylphenol, 30 SPE

published or not. The documents may come

In the last years there has been an increase in the use of plastic materials in the food 35 sector and consumer products, both for primary and secondary packaging. In the 36 bottling industry, bottles are manufactured from specific polymers depending on the 37 capacity of the container, each of them with unique characteristics as regards bottle 38 strength, storage time, type of dispenser and disposal. Primary packaging is made with 39 high density polyethylene (HDPE), polyethylene terephthalate (PET) and polycarbonate 40 (PC) while caps are made of high density polyethylene (HDPE), low density 41 polyethylene (LDPE) and polystyrene (PS) is used as septa in many caps (World 42 Packaging Organization (WPO) 2008). These polymers contain additives such as 43 antifogging, reinforcing and antistatic agents, blowing agents, colorants, fillers, 44 lubricants, nucleating agents, optical brighteners, heat and light stabilizers, antiacids, 45 antimicrobials, antioxidants, chain-breaking, photo-and hydroperoxide deactivating 46 antioxidants, dehydrating agents, light screening pigments and UV absorbers (Bolgar 47 2008;[START_REF] Piringer | Plastic Packaging. Interactions with Food and 538 Pharmaceuticals[END_REF]. Mixtures of plastic components and additives can be made to 48 obtain improved plastic characteristics. In addition, several water bottle formats, shapes 49 and colours are used in the different bottling industries. 50

The safety of some of polymeric materials is nowadays a subject of concern in 51 the bottling sector due to the potential migration of plasticizers and additives to water 52 by a diffusion process [START_REF] Biscardi | Evaluation of the migration of mutagens/carcinogens from PET bottles into 488 mineral water by Tradescantia/micronuclei test, Comet assay on leukocytes and 489 GC/MS[END_REF], as described for bisphenol A (BPA) (Biles 53 et al. 1997;[START_REF] Casajuana | Presence and release of phthalic esters and other 500 endocrine disrupting compounds in drinking water[END_REF][START_REF] Le | Bisphenol A is released from 523 polycarbonate drinking bottles and mimics the neurotoxic actions of estrogen in 524 developing cerebellar neurons[END_REF][START_REF] Loyo-Rosales | 526 Migration of Nonylphenol from Plastic Containers to Water and a Milk Surrogate[END_REF]), phthalates 54 [START_REF] Casajuana | Presence and release of phthalic esters and other 500 endocrine disrupting compounds in drinking water[END_REF][START_REF] Peñalver | Determination of phthalate esters 540 in water samples by solid-phase microextraction and gas chromatography with mass 541 spectrometric detection[END_REF] or nonylphenol (NP) (Casajuana et al. 55 2003). These substances are either plastic components, such as BPA used as monomer 56 in the production of polycarbonate bottles (the so called "coolers"), or additives used to 57 improve the plastic properties (such as NP), polymerization accelerators or agents to 58 increase flexibility (phthalates) [START_REF] Casajuana | Presence and release of phthalic esters and other 500 endocrine disrupting compounds in drinking water[END_REF][START_REF] Loyo-Rosales | 526 Migration of Nonylphenol from Plastic Containers to Water and a Milk Surrogate[END_REF]59 Peñalver et al. 2000;[START_REF] Shen | Simultaneous 552 determination of seven phthalates and four parabens in cosmetic products using 553 HPLC-DAD and GC-MS methods[END_REF]. 60

The presence of plastic components or additives in water can modify the 61 organoleptic properties and if present at high concentrations, may trigger health 62 problems due to the potential toxic properties of some chemicals. Epidemiological 63 studies in test animals indicate an increase of some kinds of cancer, behaviour changes 64 and anomalies in the reproductive and immunologic functions of some species 65 [START_REF] Casajuana | Presence and release of phthalic esters and other 500 endocrine disrupting compounds in drinking water[END_REF][START_REF] Rivas | Human exposure to endocrine-disrupting 543 chemicals: Assessing the total estrogenic xenobiotic burden[END_REF], reason that some of the target compounds are 66 considered as endocrine disruptor compounds (EDC). Possible human health endpoints 67 and prostate cancers in men, abnormal sexual development, reduced male fertility, 69 alteration in pituitary and thyroid gland functions, immune suppression, and 70 neurobehavioral effects (Environmental Protection Agency (EPA) 1997). For safety 71 reasons, polymers used for packaging which are in contact with food must be analyzed 72 before use to prevent migration of any of its components to the food (Council of Europe 73 2002). 74

Several methods are used to evaluate the potential migration of plastic 75 components or additives. The migration capacity varies depending on conditions used in 76 migration tests and depending on the type of food simulant. [START_REF] Nerín | Behavior of Some Solid Food Simulants in Contact with 531 Several Plastics Used in Microwave Ovens[END_REF] used 77 different migration tests, one consisted in two sorbents (Tenax and Porapak) in contact 78 with plastic, and another consisted in a total dissolution procedure with 79 dichloromethane and methanol [START_REF] Nerín | Determination of potential 534 migrants in polycarbonate containers used for microwave ovens by high-535 performance liquid chromatography with ultraviolet and fluorescence detection[END_REF]. These migration tests were set up for 80 several plastic containers used in microwave ovens. Schmidt et al. performed a 81 quantitative determination of the plasticizers bis(2-ethylhexyl) adipate (DEHA) and 82 bis(2-ethylhexyl) phthalate (DEHP) in PET bottles and revealed maximum 83 concentrations of 0.046 and 0.71 µg L -1 , respectively [START_REF] Schmid | Does the reuse of PET 549 bottles during solar water disinfection pose a health risk due to the migration of 550 plasticisers and other chemicals into the water?[END_REF]. Casajuana et 84 al. (2003) detected BPA in HDPE and PET bottles at levels between 0.003 and 0.011 µg 85 L -1 after exposing water bottles at sunlight for 10 weeks. BPA was also detected in 86 waters of public distribution system at levels of 0.006 and 0.025 µg L -1 . The same 87 authors analyzed BPA in milk packed in Tetra Pack or HDPE bottles and concentrations 88 were between 0.28 and 2.64 µg kg -1 of milk, depending on the brand (Casajuana et al. 89 2004). This indicates that fatty foods have a better ability to extract BPA from plastics 90 than water. 91

This study has investigated the potential migration of plasticizers and additives 92 from several plastic containers. Three migration tests were conducted: i) the standard 93 method UNE-EN ISO 177 to test overall migration from plastics (not necessarily food 94 contact materials) where plastics are in contact with an adsorbent and incubated at 40ºC 95 during 10 days (European Communities 1988); ii) ultrasonic forced extraction of 96 plastics incubated in water at different times and iii) the standard method UNE-EN 97 13130 (2005, Part 1) in which plastics were incubated at 40ºC in water for 10 days. This 98 last method is described and legislated in the Spanish Royal Decree 866/2008 99 Although article filling is a conventional test to evaluate the migration of plastic 140 components, total immersion was used because in our laboratory conditions, it was 141 impossible to perform migration test of containers as big as 18 L (e.g. coolers). 142

According to the specific migration test performed, values are given in µg dm -2 because 143 the samples were extracted from bottles as sheets and not as filled samples (Table 3). 144 Also, the migration tests used in this study can be considered as total immersion tests, 145

where the two plastic layers, inside (in contact with food) and outside (in contact with 146 the environment) are in contact with the food stimulant. In European standards, it is 147 considered the most severe test. 148

All sample manipulation was done using polytetrafluoroethylene (PTFE) tubing 149 and glass material to avoid external contamination of the samples. 150 UNE-EN ISO 177 method was used as a screening tool for the determination of 151 the overall migration of plasticizers, expressed as loss of weight in the incubated plastic 152 and gain in the adsorbent. It applies to evaluate the tendency of plasticizers and 153 additives to migrate from plastic materials to other materials placed in close contact 154 with them. It cannot be compared with food legislation, although it shows the possible 155 migration of plasticizers and represented the starting point for the next tests. First, C 18 156 disks of 47 mm diameter and the plastic samples cut at the same diameter were 157 weighed. The plastic sample was placed in a "sandwich" made of the plastic chip placed 158 in between two C 18 adsorbent disks, as it is shown in Figure 1. The "sandwich" was 159 placed under a weight of 5 kg, and was introduced in an oven at 40ºC during 10 days, as 160 described in Directive 82/711/EC and their modifications (Directive 93/8/EC and 161

Directive 97/48/EC). After that, the "sandwich" was removed and the adsorbent disks 162 and samples were re-weighed to determine the weight difference. All polymers were 163 tested except LDPE and PS sample which were adhered to the sorbent and could not be 164 weighed after incubation. 165 Ultrasonic extraction method was developed as an accelerated method to force 166 the migration of plastic components and additives to water. Although the results 167 obtained in this method are not comparables with legislated values, it permits to identify 168 the compounds that tend to migrate. 1 g of 0.5-1 cm 2 plastic chips was introduced in 169 Directive 82/711/EC), and 5 ng of surrogates standards were added. Then samples were 171 incubated in an ultrasonic bath during 5, 10 and 15 min (Figure 1). Afterwards, 100 mL 172 of the incubated water was solid phase extracted [START_REF] Céspedes | 505 Integrated protocol for the determination of endocrine disrupting activity in the 506 surface waters and sediments using biological techniques by recombinant yeast 507 assay and chemical analysis by LC-ESI-MS[END_REF]) and analyzed by 173 GC-MS to identify target compounds. All assays were performed in triplicate. 174 UNE-EN 13130 method was used for the identification of plastic components 175 and to compare the concentrations obtained with legislated values (Table 1). 1 g of 176 plastic chips was introduced in 100 mL of unbuffered HPLC water (used as food 177 simulant at pH>4.5, as described in Directive 82/711/EC) and 5 ng of surrogates 178 standards were added. Samples were incubated in a water bath at a temperature of 40ºC 179 during 10 days (Figure 1). After incubation, water was extracted by SPE and analyzed 180 by GC-MS. GC-MS was used because it permitted to simultaneously analyze 181 phthalates, alkylphenols and BPA, although UNE-EN 1313-13 describes the 182 determination of BPA in food stimulants by high performance liquid chromatography 183 (HPLC) with ultraviolet (UV) detection. All assays were performed in triplicate. 184 185

Solid Phase Extraction procedure 186

Resulting water from the migration tests was solid phase extracted using 200 mg Oasis 187 HLB SPE cartridges using a Baker vacuum system with 12 cartridges capacity. All 188 Baker vacuum system connections and tubing were of PTFE to avoid contamination of 189 target compounds. Cartridges were conditioned prior to sample loading with 10 mL of 190 hexane, followed by 10 mL of dichloromethane, 10 mL of methanol and 15 mL of 191 HPLC water, all by gravity. This extensive cartridge cleaning was performed to 192 eliminate any traces of target compounds. Then, 100 mL of water were preconcentrated 193 at a flow of 8-13 mL min -1 and afterwards the cartridge was dried under vacuum during 194 1 hour. Elution was performed with 10 mL of dichloromethane:hexane (1:1) and 10 mL 195 dichloromethane:acetone (1:1). The extract was preconcentrated in a Turbovap nitrogen 196 evaporator and extracts were reconstituted with 240 µL of ethyl acetate and 10 µL of 197 anthracene d10 (10 ng µL -1 ) was added as internal standard. USA) coated with 5% phenyl-95% dimethylpolysiloxane (film thickness 0.25 µm). The 205 oven temperature was programmed from 70ºC (holding time 2 min) to 135ºC at 10ºC 206 min -1 , to 160ºC at 3ºC min -1 , to 175ºC at 1ºC min -1 , to 195 ºC at 3ºC min -1 and finally to 207 310ºC at 10ºC min -1 , keeping the final temperature for 5 min. 2 µL were injected in the 208 splitless mode, keeping the split valve closed for 1 min. Helium was the carrier gas (1.2 209 mL min -1 ). Injector, GC interface temperature and ion source temperatures were 280ºC, 210 280ºC and 200ºC, respectively. 211 Peak detection and integration were carried out using Xcalibur software. Full 212 scan data (from m/z 60 to 400) was used for the identification of target compounds in 213 the migration tests. 214 215

Quantification and quality parameters 216

Internal standard quantification was performed using the deuterated surrogate standards 217 corresponding to each chemical family (alkylphenols, phthalates and BPA) to correct 218 any lose during sample manipulation. Calibration curves were constructed for all target 219 compounds over a concentration of 0.01-1 mg L -1 . Limits of detection (LOD) were 220 calculated using 3 times the standard deviation of n=5 blank samples, which is an 221 adequate procedure when there is a contribution of target compounds in the blank 222 samples due to the extraction and MS analysis. This procedure provides higher LOD 223 than when calculated from a spiked sample at low concentration and using 3 times the 224 noise signal. However, it is more realistic and provides better accuracy by reducing the 225 risk of overestimation due to sample contamination by the ubiquitous presence of 226 phthalates in laboratory air and even in the MS injection port. Quality controls were 227 performed using HPLC water spiked at 1 µg L -1 and were incubated in the absence of 228 To identify those compounds that migrate from plastic and quantify their 238 levels, ultrasonic and incubation of plastics at 40ºC were used in combination of GC-239 MS whose capabilities permitted the determination of target compounds from test 240 materials with good performance as regards to identification and precise quantification. 241

The identification of plastic components was done in scan acquisition mode, which 242 provided high selectivity and good identification capabilities and no response 243 enhancement was observed due to the migration of other polymerized plastic to the 244 extracts. The method allowed the determination of target analytes in water samples at 245 levels of sub µg L -1 (Table 1). The LOD of phthalates were high compared to other 246 studies [START_REF] Peñalver | Determination of phthalate esters 540 in water samples by solid-phase microextraction and gas chromatography with mass 541 spectrometric detection[END_REF] because they were calculated from the 3 times the 247 standard deviation of the blank samples which always contained traces of phthalates that 248 originate both from the extraction and from the GC injection port. The use of surrogate 249 standards to quantify each compound was necessary to achieve accurate results, taking 250 into consideration the loss of analytes during incubation or extraction. The recoveries of 251 the analytes were between 93 and 125% for the SPE-GC/MS method (Table 1). 252 253

Migration of plastic components and additives from bottled water and caps 254

UNE-EN ISO 177 provides an unspecific method capable to determine the total mass of 255 compounds migrating from plastic containers. Table 2 proves that tested plastics 256 incubated at 40ºC lost weight, which was gained by the adsorbent, although there was 257 some mismatch due to the fact that some volatile compounds were lost during 258 incubation at 40ºC, as considered in UNE-EN ISO 177. For example, for the PC plastic, 259 there was a 0.1 mg increase in the plastic due to humidity increase in the weighing 260 process but the gain of 2.3 mg in the sorbent after incubation should be taken into 261 consideration. Overall, the lose in plastic weight provides first evidence on the overall 262 migration of plasticizers from incubated plastics although this method failed to identify 263 and quantify specific compounds prone to migrate. 264

Migration tests UNE-EN 13130 and ultrasonic incubation were able to identify 265 target compounds. Ultrasonic method is a fast extraction method and was developed in 266 an attempt to validate it by comparing the results with the incubation at 40ºC. We found 267 that qualitatively, both methods provided similar information, but quantitatively, 268 compounds were extracted with different efficiency. Phthalates were not detected in any 269 of the plastics tested and in any of the treatments used, and indicates that either these 270 compounds were not added in the tested plastics or either that they do not migrate at the 271 were of 45% and 76% for respectively, and using the UNE-EN 13130, recoveries were 274 92% and 97% for NP and BPA, respectively. The different recoveries using both 275 migration tests reflect the losses that occur during the incubation period, which are 276 higher when using ultrasonic extraction, especially for NP. 277 Ultrasonic incubation forces the migration of compounds by applying an 278 ultrasonic wave which enhances the detachment of plastic components or additives 279 which are released to water. Results showed that there was not any difference in the 280 migration of plastic compounds using different extraction times (5, 10 or 15 min) and 281

thus, an extraction time of 5 min was chosen. Using this technique, NP was identified in 282 the incubated plastics at levels of 0.212-0.242 µg dm 2 while BPA was only detected at 283 1.870 µg dm 2 in the PC plastic (Table 3). 284 UNE-EN 13130 describes the methods for determining the specific migration of 285 plastic materials and UNE-EN 1186 describes the methods for determining the overall 286 migration of materials, plastics and articles in contact with foodstuffs. This UNE-EN 287 13130 migration test was based on Part 1, guide to test methods for the specific 288 migration of substances from plastics (see also UNE-EN 1186, Part 1 guide for the 289 selection of conditions and test methods to calculate the overall migration). Using this 290 method, NP and BPA were quantified at levels of 0.332-1.282 µg dm 2 and 0.128-3.423 291 µg dm 2 , respectively (Table 3). This method was more efficient in extracting 292 plasticizers and additives from plastics than ultrasonic extraction, and was able to 293 determine NP in all types of plastic and BPA in PC, HDPE, LDPE and PS. Both NP and 294 BPA can be used as plastic additives to improve the quality of plastic. 295

These two last methods are complementary and provide similar information on 296 the migration potential, qualitatively. Ultrasonic bath provides a lower migration of 297 BPA and NP compared to the UNE-EN 13130 (Table 3). Among both methods, 298 ultrasonic extraction can be considered a fast extraction method but with lower 299 efficiency than UNE-EN 13130 and so far, is not validated yet. Most important is that 300 Figure 2. PET was the plastic type for which NP migration was lower, followed by 311 HDPE bottles and PC. For caps material, NP levels increased from LDPE<PS<HDPE 312 with NP migration between 0.413 µg dm -2 and 1.282 µg dm -2 . Contrarily, BPA was not 313 detected in PET or HDPE bottles while the highest migration was found in PC bottles at 314 3.423 µg dm -2 . BPA was also detected in caps and septa with levels ranging from 0.128 315 µg dm -2 to 0.145 µg dm -2 . 316 PET is one of the most common polymers used in bottled water container for its 317 lightness, its gas barrier and its possibility to be recycled, and it is generally used in 318 volumes from 0.33 up to 8 L bottles. Although in general it is for a single use, some 319 companies recycle up to 25% of this plastic in the manufacture of new bottles. NP was 320 detected at a mean concentration of 0.332 µg dm -2 (n=3) and BPA was not detected in 321 any replicate in this kind of plastic (Table 3). Casajuana et al. (Casajuana et al. 2003) 322 found the presence of DMP, DEP, 4-NP, DBP, BPA, BBP and DEHP in PET bottled 323 water samples at levels of 0.002 to 0.214 µg L -1 after 10 weeks storage at high 324 temperature and using SPE-GC-MS. Contrarily, Loyo-Rosales et al. (Loyo-Rosales et 325 al. 2004) describe that neither NP nor OP were found in extracts from water stored in 326 PET containers after 240 h. This discrepancy is attributed to the migration test and 327 conditions used. NP is not legislated by the European Community and thus, a 328 comparison with maximum permissible levels cannot be withdrawn. 329 HDPE is characterized by its strength and resistance to many solvents and is 330 used in bottles of 5-8 L. This material is also used in the manufacture of caps, since it is 331 resistant and it has a good sealing capacity. NP was released at concentrations 0.579 µg 332 dm -2 and 1.282 µg dm -2 for HDPE bottles and caps, respectively (Table 3). For HDPE 333 caps, BPA was detected at 0.145 µg dm -2 and no traces were found in HDPE bottles 334 (Table 3). Other studies describe the presence of NP in water bottled in HDPE and PVC 335 containers at a concentration of 0.230 µg L -1 after 120 h at 40ºC (Loyo-Rosales et al. 336 2004). 337 PC, a more resistant plastic, is used in containers over 10 L capacity and after 338 use, it is cleaned and reused. PC plastic is made of BPA monomers, which can migrate 339 [START_REF] Le | Bisphenol A is released from 523 polycarbonate drinking bottles and mimics the neurotoxic actions of estrogen in 524 developing cerebellar neurons[END_REF]. The BPA amount detected in PC plastic was of 3.423 340 µg dm -2 and NP was detected at 0.694 µg dm -2 . Another study using bottled water in PC 341 describes the migration of this compound from used and new bottles at 0.7 µg L -1 and 342 1.0 µg L -1 , respectively, after 7 days at room temperature [START_REF] Le | Bisphenol A is released from 523 polycarbonate drinking bottles and mimics the neurotoxic actions of estrogen in 524 developing cerebellar neurons[END_REF]. Nerín et al. 343 (Nerín et al. 2003) detected that BPA migrated from PC plastic stored at room 344 temperature. [START_REF] Biles | Determination of Bisphenol-A 484 in Reusable Polycarbonate Food-Contact Plastics and Migration to Food-Simulating 485 Liquids[END_REF] detected BPA in PC baby bottles and cups at 345 levels ranging from non detected to 5 µg L -1 . 346 LDPE is the polymer used in the cooler caps, its structure is the same as HDPE 347 but the density is lower so LDPE is used in caps and not in bottles. NP and BPA 348 migrated as shown in Table 3 at levels of 0.413 and 0.128 µg dm -2 , respectively. 349 Finally, PS was tested because it is used in the septum of several caps, such as 350 the ones used in coolers. The amounts detected were 0.801 and 0.136 µg dm -2 for NP 351 and BPA, respectively. PS is one of the most produced plastics in food contact materials 352 and can be copolymerized with many monomers. Products formed from PS are hard and 353 transparent [START_REF] Piringer | Plastic Packaging. Interactions with Food and 538 Pharmaceuticals[END_REF]). However, cap septa are whitish due to additives that 354 improve the brittleness and the sensitivity to stress cracking [START_REF] Piringer | Plastic Packaging. Interactions with Food and 538 Pharmaceuticals[END_REF]). These 355 additives may migrate as it happens in other type of plastics. 356

The presence of phthalates and BPA in food in contact with plastic is legislated 357 in the European Union. Directive 2002/72/EC, Directive 2004/19/EC and Directive 358 2007/19/EC (transposed to the Spanish Royal Decree 866/2008) establishes migration 359 specific limits in food and food simulants of 600 µg kg -1 or 100 µg dm -2 for BPA, 1500 360 µg kg -1 or 250 µg dm -2 for DEHP, 300 µg kg -1 or 50 µg dm -2 for DBP and 30000 µg kg - 361 1 or 5000 µg dm -2 for BBP. The levels for phthalates are explicit in Directive 362 2007/19/EC and are different according to i) whether the packing is a single or multiple 363 use article, ii) whether it is in contact with aqueous or fatty foods and 3) whether it is for 364 infants. So there are a variety of limits for different cases. From the different polymers 365 tested, none of the samples exceeded the legislated value of 100 µg dm -2 for BPA. 366

These legislated levels are lower or equal with the former Directive 2002/72/EC, who 367 established migration specific limits in food and food simulants of 3000 µg kg -1 for 368 BPA and 1500 µg kg -1 for DEHP. Specifically, the levels of BPA decreased 5 times 369 from 2002 to 2007 Directives because the increasing information and studies on BPA 370 toxicity and the potential effects on humans if ingested daily [START_REF] Bredhult | Gene expression analysis of human 494 endometrial endothelial cells exposed to Bisphenol A[END_REF]371 Ghisari et al. 2009;[START_REF] Huang | Bisphenol A downregulates CYP19 transcription in JEG-3 521 cells[END_REF][START_REF] Le | Bisphenol A is released from 523 polycarbonate drinking bottles and mimics the neurotoxic actions of estrogen in 524 developing cerebellar neurons[END_REF][START_REF] Salian | Impairment in protein expression profile of 546 testicular steroid receptor coregulators in male rat offspring perinatally exposed to 547 Bisphenol A[END_REF]

Analysis of bottled water 374

The presence of plasticizers in bottled water (PET, HDPE and PC) was determined 375 using the same SPE-GC/MS method extracting 1 L of water. In 1.5 L PET bottles, 2 out 376 of 10 samples contained one target compound; OP was detected in one sample at 0.003 377 µg L -1 and NP in another at 0.019 µg L -1 . In HDPE bottles, OP was detected in two 378 samples at 0.003 µg L -1 and 0.004 µg L -1 , (N=7, volume = 5-10 L). The trace presence 379 of these compounds is attributed to the use of OP and NP in the production of the 380 specific polymers which vary among brands. 381

Finally, all PC coolers (N=10, volume = 18.9-20 L) contained BPA at levels 382 ranging from 1.60 µg L -1 to 4.44 µg L -1 , with an average concentration of 2.64 µg L -1 . 383

These levels are in agreement with other studies which indicate that BPA can migrate 384 from both PC and epoxy resins containers in contact with water at levels of few µg L -1 . 385

In a former study regarding the migration of BPA, the U.S. FDA (Food & Drug 386 Administration of the United States) analyzed BPA in water coolers stored for 39 weeks 387 and found BPA at levels between 0.1 and 4.7 µg L -1 (Environmental Protection Agency 388 (EPA) 1993). In a 1997 study, it was found that BPA migrated when exposing 389 polymeric material to water, ethanol/water and Miglyol (an oil) to temperatures of 65ºC 390 for 10 days. In the case of water in contact with polycarbonate bottles where the BPA is 391 the main monomer, the levels of BPA were from not detected to 5 µg L -1 (Biles et al. 392 1997). In another study, the migration of BPA in PC bottles filled with water and 393 exposed to 100ºC for 1 h showed BPA migration levels of 0.23 ± 0.12 µg L -1 , while 394 levels increased to 8.4 ± 4 µg L -1 and 6.7 ± 4 µg L -1 after using a domestic dishwasher 395 between 51 and 169 times (Unit on food contact materials, enzymes, flavourings and 396 processing aids (CEF) et al. 2008). 397 398

Daily Intake and toxicological values 399

The Tolerable Daily Intake (TDI) is a set of reference values for the acceptable intake of 400 a variety of nutrients, as well as energy. A way to express the toxicity of a chemical 401 specie is to calculate the TDI, which represents a lifetime exposure level that is 402 considered to be safe. In this study, BPA is the only compound considered for 403 evaluating the TDI since it was the only one detected in all samples analyzed. Based on 404 the migration data from bottled water, the total exposure to BPA from PC plastic was 405 estimated to be of 4.00 10 -5 mg BPA kg -1 bw day -1 (1.5 L water day -1 , 60 kg person and 406 1.60 µg BPA L -1 ) and an upper range exposure of 1.48 10 -4 mg BPA kg -1 bw day -1 (2 L 407 (NOAEL) for BPA was 5 mg kg -1 bw day -1 and set a temporary TDI of 0.01 mg kg -1 410 bw day -1 (European Food Safety Authority (EFSA), 2006), which was changed in 2008 411 by a TDI of 0.05 mg kg -1 bw day -1 (Unit on food contact materials, enzymes, 412 flavourings and processing aids (CEF) et al. 2008). In a former study realized in 1993, 413 the U.S. EPA had already suggested a reference value of 0.05 mg kg -1 of body weight 414 (Environmental Protection Agency (EPA) 1993). These organizations state that PC 415 products are safe for being used in products in contact with food and beverage and pose 416 no known risk to human health. The levels of BPA from PC coolers were below the 417 NOAEL and TDI, although it should be considered that BPA consumption from PC 418 bottled water is only a part of the total oral Daily Intake. 419

Many studies have been carried out on the toxicity and potential estrogenic 420 effects of BPA using various animal models, cellular lines and at molecular and 421 biochemical level. These studies set the initial basis to carry out risk assessment studies 422 of BPA on humans. However, discrepancies have been observed in these on going 423 studies regarding both the effects of BPA and the levels that cause these effects. From a 424 toxicological point of view, if the aquatic toxicity is considered using Daphnia magna, 425 EC 50 values range between 1 and 20 mg L -1 (Table 1), and although these 426 concentrations cannot be extrapolated to a human model toxicity, they are an indicator 427 that effects caused to a very sensitive specie are initiated at the mg level. Cespedes et al. 428 developed a more specific method to assess the estrogenicity of BPA using recombinant 429 yeast using the human estrogen receptor [START_REF] Céspedes | 505 Integrated protocol for the determination of endocrine disrupting activity in the 506 surface waters and sediments using biological techniques by recombinant yeast 507 assay and chemical analysis by LC-ESI-MS[END_REF]. It was demonstrated 430 that the estrogenicity of BPA is 1644 ± 380 µg L -1 , much lower than the natural 431 hormone estradiol (0.04 µg L -1 ), used as positive control (Table 1). That means that at 432 concentrations below 1644 µg L -1 , BPA does not activate the human estrogen receptor 433 and therefore does not trigger any estrogenic effect. Concluding, the concentration of 434 BPA from PC containers in the samples analyzed were below the aquatic toxicity, the 435 EC 50 obtained with the recombinant yeast assay and the legislated levels (Table 1). 436 However, epidemiological studies carried out by Vandenberg et al. indicate that BPA is 437 present in human fluids at concentration levels higher than those that induce effects in 438 vitro and one order of magnitude higher than the levels that induce effects in animal 439 models [START_REF] Vandenberg | Human exposure 568 to bisphenol A (BPA)[END_REF]). Another study detected that 92.6% of the examined 440 From 5 polymers tested, phthalates were not present in the plastics while NP and BPA 455 were identified at concentrations from 0.128 up to 3.423 µg dm -2 . PET and HDPE 456 bottles were the bottles with the lowest amount of EDCs. The biggest problem 457 compound was BPA which was detected in all polycarbonate bottled waters analyzed 458 and although the total daily intake was below the legislative values, it may contribute to 459 the total daily intake of this compound considering all types of food.

F o r P e e r R e v i e w O n l y

Table 1. Plasticizers and additives studied, their molecular formula, water octanol coefficient (log Kow), toxicity using Daphnia magna and Recombinant Yeast Assay (RYA), limits of detection calculated from 3 times the standard deviation of the blank samples (n=5), recoveries calculated at 1 µg L -1 and legislated values.

  agents include breast cancer and endometriosis in women, testicular 68

  unbuffered HPLC water (used as food simulant at pH>4.5, as described in 170

  were analysed by gas chromatography coupled to a quadrupole mass 201 spectrometer (Trace GC-2000 series from Thermo Electron, San José, CA, USA). The 202 system was operated in electron ionization mode (EI 70 eV). The separation was 203 30 m x 0.25 mm I.D. DB-5MS column(J&W Scientific, Folsom, CA, 204

  any plastic using the ultrasonic extraction and the UNE-EN 13130 method, in triplicate 229 and using closed glass containers. tests permit to assess the potential leaching or migration of plastic 235 components or additives. The UNE-EN ISO 177 method was used as a screening tool to 236 determine the migration of plasticizers but was not adequate to identify specific 237

  The same holds for octylphenol. NP and BPA were the only 272 compounds identified. The recoveries of NP and BPA using the ultrasonic incubation 273

  UNE-EN 13130 is based on a technical interpretation of Directive 2007/19/EC and 301 provides maximum legislated migration levels for a large number of compounds. Thus, 302 this technique was further used to determine the migration levels of target compounds in 303 the 5 polymers studied and to compare the specific migration levels with legislated migration levels according to UNE-EN 13130 307 NP and BPA were the only compounds identified in PC, HDPE, LDPE and PS plastics 308 and for PET and HDPE bottles NP was the only compound identified. The MS 309 chromatogram of BPA in the PC sample with UNE-EN 13130 method is shown in 310

  60 kg person and 4.44 µg BPA L -1 ). The Scientific Committee on Food 408 (SCF) in 2002 considered that the overall oral No-Observed-Adverse-Effect Level 409

  BPA in urine due to the routine ingestion of this compound from 441 plastic food containers[START_REF] Calafat | Exposure of the U.S. 497 population to bisphenol A and 4-tiertary-Octylphenol: 2003-2004[END_REF]use of plastic in the bottling packaging sector, migration tests 447 permit the identification of plasticizers and additives with endocrine disrupting 448 properties that can cause human health effect when ingested continuously. Out of the 3 449 migration tests used, the UNE-EN 13130 method permitted identification of 450 compounds leaching from plastics and the ability to compare the migration levels with 451 legislative values. The ultrasonic extraction was an alternative to evaluate compounds 452 migrating from different polymers and qualitatively had a comparative performance 453 although with less efficiency and therefore, is not yet a validated and accepted method. 454

Figure 2 .

 2 Figure 1. Illustration of the three tests.

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		F o r F o r F o r
		P P P
		e e e
		e r e r e r
		R R R
		e e e
	460 461 462	Numerous studies indicate that BPA causes adverse effects in experimental animals and the constant v i e v i e v i e exposure, metabolism and long term risk in humans is a matter of concern. The w w w
	463 464 465 466	European Food Safety Authority (EFSA), the US Environmental Protection Agency and other Agencies are currently undertaking studies to integrate data from migration tests, exposure levels and toxicity endpoints to assess the possible effects of BPA on humans and initiate actions to protect human health. At this point, BPA has recently been O n l O n l O n l
	467	banned in Europe (by summer 2011).	y y y
	468
	474	"Ministerio de Educación y Ciencia" in the context of the project "Desarrollo y
			17

  Table 2. UNE-EN ISO 177 weights for 4 test plastics where it is observed that plastics lose weight while the adsorbent gains weight.

	Page 19 of 22	Food Additives and Contaminants
	Aquatic toxicity a µg L -1 45900 2990 960 nf 160 1000-20000 90-1140 90-470 ∆m disks mg Log K ow 1.60 4.50 4.73 6.11 7.60 3.32 5.28 5.92 8.1 5.0 2.4 5.9 F o r P e e r R e v i e w RYA b Acronym Molecular formula µg L -1 DMP C 10 H 10 O 4 4080 ± 1200 DBP C 16 H 22 O 4 3960 ± 1500 BBP C 19 H 20 O 4 5160 ± 2160 DEHA C 22 H 42 O 4 nf DEHP C 24 H 38 O 4 2310 ± 1460 BPA C 16 H 15 O 2 1640 ± 380 OP C 14 H 22 O 180 ± 80 NP C 15 H 24 O 78 ± 20 ∆m plastic mg -0.3 HDPE bottle Compound Dimethyl phthalate Di-n-butyl phthalate Benzylbutyl phthalate Di(2-ethylhexyl) adipate Di(2-ethylhexyl) phthalate Bisphenol A Octylphenol Nonylphenol PET bottle -2.7 PC bottle 0.1 HDPE cap -5.1 Blank _ 0.3	LOD µg L -1 0.010 0.230 0.190 0.080 0.460 0.009 0.001 0.017 O n l y Recoveries % 112 104 125 97 107 97 93 98	Legislative level µg dm -2 nl 50 c 5000 c nl 250 c 100 c nl nl	Legislative level c µg kg -1 nl 300 cd 30000 cd nl 1500 cd 600 ce nl nl

a EC50 -48 hr, Daphnia magna. b EC50 -Recombinant Yeast Assay (EPA/630/R-96/012). c Spanish Royal Decree 866/2008 d According Directive 2007/19/EC e According Directive 2004/19/EC nf: not found, nl: not legislated Page 18 of 22 http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.uk Food Additives and Contaminants F o r P e e r R e v i e w O n l y

Table 3 .

 3 Compounds identified using ultrasonic extraction and the conditions specified in UNE-EN 13130 expressed in mg dm -2 of plastic ± standard deviation and % recoveries of target compounds in the 1 µg L -1 spiked HPLC water quality control.

										Page 20 of 22
	Method	Compound	PET µg dm -2	Bottles HDPE µg dm -2	PC µg dm -2	HDPE µg dm -2	Caps	LDPE µg dm -2	Septum PS µg dm -2	Recov. (%)
	Ultrasonic	NP	<LOD	0.242 ± 0.005	<LOD	<LOD		<LOD	0.212 ± 0.034	45
	extraction	BPA	<LOD	<LOD	1.870 ± 0.088	<LOD		<LOD	<LOD	76
	UNE-EN	NP	0.332 ± 0.022	0.579 ± 0.008	0.694 ± 0.091	1.282 ± 0.178	0.413 ± 0.004	0.801 ± 0.176	92
	13130	BPA	<LOD	<LOD	3.423 ± 0.217	0.145 ± 0.026	0.128 ± 0.019	0.136 ± 0.028	97

http://mc.manuscriptcentral.com/tfac Email: fac@tandf.co.

uk Food Additives and Contaminants

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"Talent-Empresa (TEM 2009)" for the schoolarship. This study was financed by

Acknowledgments 469

This work was supported by "el Departament d'Universitats, Recerca i Societat de la 470 Informació de la Generalitat de Catalunya" and A. Guart acknowledges the "Agència de 471 Gestió d'Ajuts Universitaris i de Recerca (AGAUR)" in the context of the program


Yang Chen

Zhou Yang

Yining Hu

Guanyu Yang

Yongcheng Zhu

Yinsheng Li

Limin Luo

Wufan Chen

Christine Toumoulin

Thoracic Low-dose CT Image Processing Using an Artifact Suppressed Large-scale Nonlocal Means

Keywords:

Thoracic low-dose CT image processing using an artifact suppressed large-scale nonlocal means.

I. Introduction

The lung and bronchus cancer is a leading cause of cancer death in the United States with a mortality rate that amounts to 29% [1]. Computed tomography (CT) is widely used for lung cancer screening because it is more sensitive than chest radiography in the detection of small nodules and lung carcinoma at an early stage [START_REF] Macredmond | Screening for lung cancer using low dose CT scanning[END_REF][START_REF] Macredmond | Screening for lung cancer using low dose CT scanning: results of 2 year follow up[END_REF]. Nevertheless, a trouble spot is the x-ray dose delivered by the scanner. It is indeed given to be relatively high , which may be dangerous at more or less long term for patients having to pass several CT examinations over a certain period of time. Thus to limit the radiation dose, low-dose computed tomography (LDCT) could be applied for screening in patients at high risk for developing lung cancer [START_REF] Brenner | Computed tomography-an increasing source of radiation exposure[END_REF]. Low dose CT can be achieved by decreasing the milliamperage and the voltage [START_REF] Mannudeep | Strategies for CT Radiation Dose Optimization[END_REF][START_REF] Angel | Monte Carlo simulations to assess the effects of tube current modulation on breast dose for multidetector CT[END_REF], which, however, leads to a degraded signal to noise ratio [START_REF] Nelson | Low-Dose CT Screening for Lung Cancer Produces High Rate of False Positives[END_REF][START_REF] Hsieh | Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise[END_REF][START_REF] Wang | Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography[END_REF][START_REF] Kubo | Use of 3D adaptive raw-data filter in CT of the lung: effect on radiation dose reduction[END_REF][START_REF] Chen | Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior[END_REF]. This is due to a severe increase of the quantum and electronic noise. As a consequence, reconstructed images appear degraded by the presence of mottled noise and pronounced streak artifacts [START_REF] Brenner | Computed tomography-an increasing source of radiation exposure[END_REF][START_REF] Mannudeep | Strategies for CT Radiation Dose Optimization[END_REF][START_REF] Angel | Monte Carlo simulations to assess the effects of tube current modulation on breast dose for multidetector CT[END_REF][START_REF] Nelson | Low-Dose CT Screening for Lung Cancer Produces High Rate of False Positives[END_REF][START_REF] Hsieh | Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise[END_REF]. It might be difficult, thus for radiologists to distinguish between benign and malignant nodules on LDCT. A recent report of the American Society of Clinical Oncology emphasizes that the number of false-positive in the detection of lung nodules, was high for individuals who underwent lung cancer screening with low-dose computed tomography (LDCT): 21% after a first LDCT scan, and 33% after a second one [START_REF] Nelson | Low-Dose CT Screening for Lung Cancer Produces High Rate of False Positives[END_REF].

II. Methods

A. The Original LNLM Method

The Non Local Means (NLM) Filter, originally introduced by Buades et al. [START_REF] Buades | A non-local algorithm for image denoising[END_REF] for 2D image denoising, relies on the information redundancy within a neighbourhood. The basic idea is to replace the value of a pixel by the weighted average of pixels located in a search neighborhood window of size N. Each weight expresses the similarity in intensity between the central pixel in the window and each neighboring pixel and is given by the pair-wise difference between patches surrounding each pair of considered pixels. Some adaptations have been proposed for optimizing the estimation of this pixel considering the selection of the most relevant pixels in the search neighborhood [START_REF] Mahmoudi | Fast image and video denoising via non-local means of similar neighborhoods[END_REF], the block-wise computation [START_REF] Coupé | An Optimized Blockwise NonLocal Means Denoising Filter for 3DMagnetic Resonance Images[END_REF] or a large scale neighborhood [START_REF] Chen | Improving Low-dose x-ray CT Images by Weighted Intensity Averaging over Large-scale Neighborhood[END_REF].

We consider here our adaptation of the NLM filter that is Large scale Non-Linear Means Filter (LNLM) [START_REF] Chen | Improving Low-dose x-ray CT Images by Weighted Intensity Averaging over Large-scale Neighborhood[END_REF]. Let i f and ˆi f denoting the intensities of pixel i before and after processing, the LNLM method can be outlined as follows:

ˆi i i i j j i j j N j N f w f w      (1)   2 2, ( ) ( ) exp , ij i j v n v n w h n             ; : : i l j l i j v n v n f l n f l n     (2)
where, i N is the search neighborhood centered at pixel i, j the pixels located in the neighborhood of i. ij w characterizes the pair-wise similarity measure between the two patches i n and j n surrounding pixel i and j, respectively. We define ( ) i v n and ( ) j v n as the two pixel intensity vectors which include all the pixels in the two patches i n and j n , respectively. The ij w in ( 2) is calculated as the attenuating function of the Gaussian-weighted distance 2 2, ( ) ( )

i j v n v n  
between the two patches i n and j n . n denotes the pixel number in n , and is used as a normalization parameter to make the processing independent of the different size settings of n . denotes the standard deviation of the Gaussian kernel.

Parameter h is used to control the smoothing effect in the attenuating exponent function in [START_REF] Macredmond | Screening for lung cancer using low dose CT scanning[END_REF]. The application of the LNLM method is based on the assumption that in clinical CT images the pixels representing different tissue attenuation often distribute over a large scale, ad pixels of similar surrounding distribution have a higher probability to belong to the same tissues. The main contribution of [START_REF] Chen | Improving Low-dose x-ray CT Images by Weighted Intensity Averaging over Large-scale Neighborhood[END_REF] has been to demonstrate that the weighted intensity averaging within large searching neighborhood (such as 81 81



or 161 161  N) can lead to effective suppression of the mottled noise in low-dose abdominal CT images. It has been shown in [START_REF] Chen | Improving Low-dose x-ray CT Images by Weighted Intensity Averaging over Large-scale Neighborhood[END_REF] that, with up to one fifth of the routine tube current setting, clinically acceptable abdominal LDCT images can be obtained by using this LNLM method. , ,

B. The Proposed AS-LNLM Method

chd cvd cdd f f f and , , chd cvd cdd f f f    respectively denote the SWT cvd f ISWT in row s t  in colum ns t  LNLM f  restored f chd f  cdd f  c a f cdd f original f c a f chd f cvd f  in row s& colum ns t 
original and nonlinear diffusion processed high frequency 2D subbands (horizontal, vertical and diagonal). t

 is the 1-D nonlinear diffusions applied on each extracted 1D column or row data, which are in the orthotropic directions to the high frequency components in the high frequency subbands. f  is the reconstructed image from the subbands

( ca f , , , chd cvd cdd f f f    ).
f is the final processed LDCT image from the LNLM operation of f  .

It was pointed out in [START_REF] Chen | Improving Low-dose x-ray CT Images by Weighted Intensity Averaging over Large-scale Neighborhood[END_REF] that the LNLM method was not effective in suppressing the streak artifacts in thoracic LDCT images because the directional patterns of streak artifacts often prevent an effective suppression. To overcome this, we devised a two-step processing AS-LNLM that applies different artifact/noise reduction in different scales. In this method (outlined in Fig. 1), the streak artifacts in LDCT images are pre-suppressed in wavelet domain by directional 1-D nonlinear diffusions before the LNLM operation. To preserve the position invariance for each decomposed 2D subband, here the translation-invariant Stationary Wavelet Transform (SWT) is used [START_REF] Gyaourova | Undecimated wavelet transforms for image de-noising[END_REF]. Compared to the familiar discrete wavelet transform, at each scale in SWT the translation-invariance does not perform downsampling, but otherwise upsamples the filter coefficients by a factor of two [START_REF] Gyaourova | Undecimated wavelet transforms for image de-noising[END_REF][START_REF] Mark | The Discrete Wavelet Transform: Wedding the A Trous and Mallat Algorithms[END_REF]. The Haar wavelet is used in SWT for it is fast and found to suffer less from the so-called "ringing" or pseudo-Gibbs artifacts when compared to other wavelets with wider filter bases [START_REF] Cohen | Nonlinear approximation and the space by(r 2 )[END_REF][START_REF] Lohmann | Image restoration and spatial resolution in 7 Tesla magnetic resonance imaging[END_REF].

To achieve effective artifact suppression, the 1-D nonlinear diffusions are applied along the directions orthotropic to the high frequency orientations in the decomposed 2D wavelet subbands. For the high frequency subbands of chd f and cvd f (horizontal and vertical), we perform 1D column-wise and row-wise nonlinear diffusions ((4)-( 5)). To suppress artifacts in the diagonal high frequency subbands cdd f , we sequentially perform the 1D column-wise and row-wise nonlinear diffusions through (( 6)-( 7)). The whole processing includes the following (3)- [START_REF] Chen | Bayesian statistical reconstruction for low-dose x-ray computed tomography using an adaptive-weighting nonlocal prior[END_REF]:

    , , , SWT , ca chd cvd cdd f f f f f S  (3)
        t I div c I t I     ,           2 exp c I t I t K     (8)   ISWT , , , , ca chd cvd cdd f f f f f S          (9)
ˆi

i i i j j i j j N j N f w f w       (10)   2 2, ( ) ( ) exp ij i j v n v n w h n     (11)
In [START_REF] Macredmond | Screening for lung cancer using low dose CT scanning: results of 2 year follow up[END_REF]

      2 exp I t K  
with K controlling the tradeoff between artifact-suppression and edge-preservation [START_REF] Perona | Scale-space and edge detection using nonlinear diffusion[END_REF]. In [START_REF] Wang | Multiscale penalized weighted least-squares sinogram restoration for low-dose x-ray computed tomography[END_REF], with the original low frequency subband ca f and the three processed high frequency subbands and

chd cvd cdd f f f    ,
, the Inverse Stationary Wavelet Transform (ISWT) is carried out to provide the artifact-suppressed image f  . The final restored image f can be then obtained through the application of LNLM (equations ( 10)-( 11)) of f  .

(

(a2) (b2) (e2) (f2) (g2) (h2) (a1) (b1) (c1) (d1) (e1) (f1) (g1) (h1) (c2) (d2)
the second scale, the low frequency subband Fig. 2. Illustration of the 1D directional nonlinear diffusions in wavelet domain (Haar wavelet, two scales (S=2)) in the proposed method. Note that this is only for illustration purposes. One typical LDCT and HDCT image are used. In the central table above Fig. 2 (3), the blue and grey arrows represent the directions of the applied nonlinear diffusions together with the high frequency components in the decomposed subbands, respectively.  Fig. 2 illustrates the decomposition of the high frequency components for a LDCT image and depicts in the table (between Fig. 2 (2) and (3) the directions for the applied nonlinear diffusions and also the high frequency components in decomposed subbands. The nonlinear diffusions were applied based on (4)-( 8) with K=200, S=2, 10 iterations and an increment equal to 0.05. Fig. 2 (1) and (2) display the decomposed wavelet subbands for a thoracic HDCT image and LDCT image, respectively. In Fig. 2 (2) , we can see that, with respect to the illustration of HDCT image in Fig. 2 (1), the streak artifacts in LDCT images are much more prominent (with higher amplitudes) in the decomposed high frequency subbands (Fig. 2 (2) (b2)-( d2) and (f2)-(h2)) than in the decomposed low frequency subbands (Fig. 2 (2) (a2) and (e2)). It can be observed in Fig. 2 (3) that the high frequency artifact components were notably suppressed through the developed wavelet domain directional 1D nonlinear diffusions

(a3) (b3) (c3) (d3) (e3) (f3) (g3) (h3) (2)
(3)

III. Experiment settings

Approval of this study was granted by our institutional review board. One anthropomorphic phantom and 25 patients have been involved in the experiments. The study, including the data collection and processing, was conducted according to the authorized protocol. All the 25 patients have given their written consent to the participation and received remuneration for it. A non-conflict of interest for this work was declared. CT images were acquired on a multi-detector row Siemens Somatom Sensation 16 CT scanner.

The thoracic phantom is an anatomical model of a human chest torso (Fig. 3). Its size is 43 40 48 cm   and its measurements are the following: chest size 94 cm, height 45 cm and weight 18 kg. The inner components consists of mediastinum (heart, trachea), pulmonary vasculature and an abdomen block. The thickness of the chest wall is based on measurements of clinical data, and the soft tissue substitute material and synthetic bones have x-ray absorption rates close to those of human tissues.

The patient cohort includes 9 women and 16 men with an average age of 64 years (age range: 52-71 years). 8 of all the patients have verified lung cancer with lesions in the range 5mm-12mm. For both phantom and patient experiments, the LDCT images were collected by using the reduced 30 mAs (routine clinical mAs setting is 120mAs) under chest scanning mode. For each phantom and patient scan, 40 slices were collected. Other scanning parameters were: kVp, 120; Slice thickness, 2 mm; Gantry rotation time, 0.5 s; detector configuration (number of detector rows section thickness),16 mm 1.5 mm  ; table feed per gantry rotation, 24 mm; pitch, 1:1; reconstruction method: Filtered Back Projection (FBP) algorithm with convolution kernel "B70f" and "B31f" ("B31f" is the smoothing reconstruction kernel for mediastinal window illustration in Siemens CT). The CT dose index volume (CTDI vol ) is a linear function of the tube currents [START_REF] Macredmond | Screening for lung cancer using low dose CT scanning[END_REF]. We recorded the accumulated doses from the workstation for each scan with 40 slices. The recorded doses are 9.36 mGy for the routine (or standard) 120mAs protocol, and 2.34 mGy for the low dose 30mAs protocol. For the phantom experiment, to obtain a high-quality reference volume for quantitative evaluation, HDCT images were acquired with a higher current of 240mAs because there is no radiation concern for phantom. The experiments aim at illustrating the behavior of the algorithms on chest structures and pulmonary vessels. The windows and level setting are thus chosen to optimize the visualization of these data. We will so consider the mediastinal (window center, 50HU; window width, 350HU) and lung (window center, -600HU; window width, 1600HU) windows respectively [START_REF] Bruzzi | Multi-detector row CT of hemoptysis[END_REF].

For evaluation purposes, we compared the HDCS filter in [START_REF] Mendrik | Noise reduction in computed tomography scans using 3D anisotropic hybrid diffusion with continuous switch[END_REF] and the LNLM method described in Section II. We applied the hybrid diffusion filter with a continuous switch (HDCS) method for LDCT images by using the code provided by the author of [START_REF] Mendrik | Noise reduction in computed tomography scans using 3D anisotropic hybrid diffusion with continuous switch[END_REF] (http://www.insight-journal.org/browse/publication/748). This HDCS method uses local eigenvalue information to determine whether the local structures are tubular or planar, and then apply either the coherence-enhancing diffusion (CED) or edge-enhancing diffusion (EED) based on the calculated eigenvalue information. All the CT images were exported as DICOM files and then processed offline under a PC workstation (Intel Core™ 2 Quad CPU and 4096 Mb RAM, GPU (NVIDIA GTX465)) with Visual C++ as the developing language (Visual Studio 2008 software; Microsoft).

In the latter LNLM step of the AS-LNLM method, intensive computation is required in the distance calculation between each two translated patches n in each neighborhood N . In our experiments, we applied a size for the search neighborhood N equal to 81 81  and for the patches n 7 7  , which is found practically robust to provide good results. A GPU parallel implementation with CUDA framework has been designed to accelerate the processing. Threads, blocks and grids make up of the physical structure of a CUDA multithreading frame [START_REF]NVIDIA CUDA TM Programming Guide (Version 3.0[END_REF][START_REF]Accelerating MATLAB with CUDA Using MEX Files[END_REF]. We set the total number of blocks in grid to the row size of the image, and the total number of threads in each block to the column size of the image. In the processing of LNLM, all threads in the block-grid structure execute simultaneously to perform all the pixel-wise operations, which include the pixel-wise 1D nonlinear diffusion in ( 4)-( 8), the pixel-wise weight calculations for each patch pairs in [START_REF] Borsdorf | Wavelet based noise reduction in CT-images using correlation analysis[END_REF], and the pixel-wise ˆi f calculations in [START_REF] Kubo | Use of 3D adaptive raw-data filter in CT of the lung: effect on radiation dose reduction[END_REF]. We also further reduced the computation reduction by applying the parallelization optimization in [START_REF] Goossens | A GPU-accelerated real-time NLMeans algorithm for denoising color video sequences[END_REF][START_REF] Chen | Joint-MAP Tomographic Reconstruction with Patch Similarity Based Mixture Prior Model[END_REF]. Runtime comparison in the practical experiments indicates that the parallelized operation is more than 100 times faster than the previous serial version. 4 (a) and (b) that, even with high tube current setting, noise and artifacts still remain in the reconstructed images (c.f. the zoomed regions in Fig. 4 (a) and (b)). We applied the AS-LNLM method to processthe HDCT images using the parameters given in TABLE I. This parameter setting was set to find the optimal parameter combination that provided the best qualitative results. This qualitative evaluation was carried out in collaboration with three experienced radiologists (refer to Section. IV. B. 2)). Fig. 4 (c) and (d) illustrate the corresponding processed HDCT images of Fig. 4 (a) and (b) from the AS-LNLM method. We can see in Fig. 4 (c)-(d) that the AS-LNLM method leads to more preferable CT images with a better noise/artifact suppression. Thus instead of using the original HDCT images as the ground-truth reference images, we will consider thereafter the processed HDCT images for quantitative calculations in this phantom experiment.

IV. Results

A. Phantom Data Experiment

In the implementation of the LNLM and the AS-LNLM method, different parameter settings were used for the visualizations in mediastinum and lung windows. For reminder, the AS-LNLM method involved 7 parameters to set, namely the total decomposition scale S of SWT, the K, iter and inc for the nonlinear diffusion, and the decaying parameter h and the sizes of n and N for the subsequent LNLM processing. Practically, in the implementation of the AS-LNLM processing, we set S to 2 because in SWT operation most high frequency features are found concentrated within the first two scales, and the iteration number iter and step size inc in the nonlinear diffusions, and the sizes of n and N are fixed, and K and h are modulated to obtain the visually good results in mediastinal and lung windows. In the following, only h related to the LNLM filter and the [K, h ] used in the AS-LNLM method, will be specified and discussed. As for the AS-LNLM method, the parameter setting for the HDCS method was set based on [START_REF] Mendrik | Noise reduction in computed tomography scans using 3D anisotropic hybrid diffusion with continuous switch[END_REF] to provide the best qualitative results. We present first a visual assessment of the performance, then a quantitative evaluation based on the signal to noise ratio (SNR) and the standard deviation computation(STD).

1) Visual Assessment:

Fig. 5 (a) and Fig. 6 (a) display the original and processed LDCT 16 th slice of the volume respectively using the mediastinal and lung windows. We observe that the LDCT image quality is significantly degraded by mottled noise and streak artifacts. Results are provided after the application of the HDCS filter (Fig. 5 (b) and Fig. 6 (b)), LNLM filter with a decaying parameter h respectively equal to 10 (Fig. 5 (c) and 6 (c)) and 200 (Fig. 5 (d) and 6 (d)), AS-LNLM method (Fig. 5 (e) and 6 (e)). The HDCS filter does not completely remove the streak artifacts and introduce some false structures which are not presents in the original LDCT images (see the red dash circles superposed on the images). These aliasing structures were introduced by the 3D processing that takes into account the neighboring slices. A small value of h in the LNLM method does not allow a good suppression of the streak artifacts in LDCT image (see the zoomed ROI in Fig. 5 (c) and Fig. 6 (c)), and increasing h tends to enforce the smoothing of the structures and make disappear the thin features or on the contrary remove the slight discontinuity between two structures (see the zoomed ROI in Fig. 5 (d) and Fig. 6 (d)). The AS-LNLM method provides a good compromise between removing the streak artifacts and smoothing the features while preserving the edges. Fig. 5 (f) provides an example of the LDCT image (mediastinal window) that is obtained when performing the FBP reconstruction using the smoothing kernel B31f: edges were blurred and artifacts were not effectively suppressed. If now we compare these results with the ground truth images (Fig. 4 (c) and (d)), we can conclude that the AS-LNLM method leads to a better structure preservation and noise/artifacts reduction than the HDCS and the LNLM methods. 2) Quantitative Assessment:

We computed the SNR with respect to the ground truth data (processed HDCT images), and then the STD for selected homogeneous regions. This STD is computed for all the original LDCT images and the processed images (from HDCS, LNLM and AS-LNLM methods). We perform analysis in both mediastinum and lung windows. Attenuation intensities in Hounsfield units were used in calculations. The SNR is calculated as follows:

    2 2 10 SNR 10 log 1 z z z z i i i z i i F F F f Z              ( 13
)
where, with Z representing the total slice number, z f  denotes the slice z for test image f  . processed LDCT), the averaged STD is computed:

  Ω2 Ω2 Ω3 STD STD STD STD 1 + + 3 = (14)   1 2 2 Ω 1 1 STD 1 z z z i i Z f f                   (15)
where, z f   corresponds to the mean intensities of region  ( 1  , 2  and 3  ) in slice z. 1  , 2  and 3  represent the three selected homogeneous regions depicted in Fig. 8, and  denotes the pixel numbers of the selected region  . TABLE II provides the calculated comparative SNR and STD in the both two cases of mediastinum and lung windows. It allows the concluding on the supremacy of the AS-LNLM method that brings the highest SNR, and the STD closest to that of the reference images. TABLE II also points out that, for the LNLM method, a large value of h lowers the STD in the homogenous regions but it also leads to a lower SNR.

Computation costs are given in Table III for the HDCS method, the CUDA accelerated LNLM method and the AS-LNLM methods. The total time (in CPU seconds) is given for the processing of a 512 512 40   CT dataset. The HDCS filter computation time is low comparing with the other methods. The optimized parallelization for the AS-LNLM method allows speeding up this computation time by more than 100 times, when comparing with its serial version.

B. Patient Data Experiment 1) Visual Assessment:

The anthropomorphic phantom and human chest data suffer at quite similar degrees from noise/artifacts when the acquisition is performed in the same low dose scan protocol. So here the parameter settings selected by experts in phantom experiments (refer to TABLE I) were re-used for patient data. This strategy of parameter setting has been validated by the experts.

Fig. 8 and Fig. 9 show the original and processed LDCT images (30mAs) of a 59 years old man in different windows. Fig. 8 (e)-(h) and Fig. 9 (d)-(f) are the selected zoomed regions. Mottle noise and streak artifacts can be easily observed in the original LDCT images depicted in Fig. 8 (a) and Fig. 9 (a). Results provided by the HDCS are displayed in Fig. 8 (b) and Fig. 9 (b). As previously observed on the thoracic phantom, the same false structures appears that relate to the introduction of some aliasing features (See in Fig. 8 (f) and Fig. 9 (e) the structures in red dash circles). The AS-LNLM method has been applied with two different parameter settings [K=200, h =20] and [K=100, h =5] for mediastinum and lung windows, and the results were given in Fig. 8 (c) and Fig. 9 (c). The LDCT image reconstructed using the built-in smoothing kernel B31f (Fig. 8 (d)) is currently clinically used for mediastinum window in Siemens CT system, and we can find the overall image contrast was degraded when using this smoothing kernel. Compared to other images, the AS-LNLM method can lead to images with a better structure preservation (see the ascending aorta walls pointed by red arrows in Fig. 8 (e)-(h)) and noise /artifact reduction in both mediastinum and lung windows.

In the supplementary file affiliated to this paper, two other patient examples are shown in Fig. 10 and Fig. 11 (two men of 62 and 68 years old respectively) The supplementary file also includes a visual comparison (Fig. 12 and Fig. 13) between the proposed method and the Adaptive Statistical Iterative Reconstruction (ASIR) algorithm developed in the latest GE CT750 HD system [START_REF] Yanagawa | Pulmonary nodules: Effect of adaptive statistical iterative reconstruction (ASIR) technique on performance of a computer-aided detection (CAD) system-Comparison of performance between different-dose CT scans[END_REF]. The results in Fig. 10-Fig. 13 also validated the above merits of the proposed processing.

Different methods CPU time cost (seconds)

HDCS method 75

LNLM method (CUDA accelerated) 407 2) Qualitative Assessment:

25 original and 25 AS-LNLM processed LDCT images from the 25 patients were assessed separately for noise suppression, artifact suppression, contrast preservation and overall image quality using a five-point subjective scale (1=unacceptable, 2=substandard, 3=acceptable, 4=above average, 5=excellent). Here, we define artifacts as the shapes with a passive effect on subjective diagnosis. Three radiological readers (R1 (X.D.Y. with 15 years of experience.), R2 (X.H.Y. with 8 years of experience.), R3 (Y.M.D. with 5 years of experience.) independently evaluated the randomized LDCT images, HDCT images and the AS-LNLM processed LDCT images in a digital DICOM archiving/assessing workstation (ViewDEX 2.0 [START_REF] Hakansson | a Java-based DICOM-compatible software for observer performance studies[END_REF]). To compare the processed LDCT images with routine HDCT images, we selected 25 thoracic HDCT images (120mAs) of other patients from previous scans in the database. All the involved patients for these HDCT images gave their written consent to use their CT images. In this way, the 4 image features were assessed in all the 75 images, which include 50 original/processed LDCT images, and 25 HDCT images. This results in a total of 900 parameter ratings for the 4 image quality parameters and the 3 readers ( 75 4 3 900    ). For each subset of images, the 4 image scores were reported as SDs means  (averaged scores of the 3 radiologists ± standard deviations ). The subjective quality parameters of the original LDCT images and the processed LDCT images were compared with those of the original HDCT images. Individual subjective image quality scores for each parameter were compared using the Wilcoxon signed rank test (SAS/STAT software; SAS Institute, Cary, NC). The differences between each two groups were determined by the Student t test (Excel; Microsoft) with P<0.05 considered a statistically significant difference. As illustrated in TABLE IV, all the 3 readers rated that, for all the 4 image quality scores, the qualities of the original LDCT images were inferior to those of the processed LDCT images and the original HDCT images. In TABLE IV, statistically significant differences between the original HDCT and LDCT images (P<0.05) are noted in all the subjective image quality scores. And with reference to the original HDCT images, there are no statistically significant differences of the 4 subjective quality scores for the processed LDCT images (P>0.05). TABLE IV also shows that the AS-LNLM processing leads to CT images with qualities even higher than the original HDCT images. Inter-observer agreements on the above 4 quality scores were accessed by the Bland-Altman statistic method [START_REF] Bland | Statistical methods for assessing agreement between two methods of clinical measurement[END_REF] . For the cases without known truth, the Bland-Altman method assumes 95% of variations lie between the mean difference plus or minus 2 SD  (Standard Deviation) of the variations. These are termed the 95% limits of the agreements that represent the limits within which the true values will lie. Fig. 14 (1)-( 3) display the Bland-Altman plots for the 4 quality scores for the scoring agreements between R1 and R2, R1 and R3, R2 and R3, respectively. We can see in Fig. 14 that, for the 4 image quality scores, the 95% limits between the 3 readers lie within the range from -0.4 to 0.4, which implies a substantial overall concordance among the scores of the 3 readers.

V. Discussion

Visual results in Fig. 5-Fig. 6 and Fig. 8-Fig. 11 show that the proposed AS-LNLM processing behaves better than the HDCS and LNLM methods in the suppression of noise/artifacts. Notably, in Fig. 5-Fig. 6, Fig. 8-Fig. 9 andS.1-S.2 (in the supplementary file) we can see the HDCS processing tends to introduce some false structures into the processed images. Fig. 5 and Fig. 6 illustrate that suppressing the streak artifacts by using large decaying parameter will lead to blurred anatomical structures in the LNLM processing. Especially in the supplementary file, S.3 and S.4 show that the proposed processing can potentially achieve results with comparable performance as the iterative reconstruction used in the latest GE CT system. To evaluate the proposed method quantitatively, TABLE II lists the SNR with respect to the reference phantom images (in Fig. 4 (c)-(d)) and the STD for selected homogenous regions. TABLE II validates that the proposed processing can lead to processed thoracic LDCT images with the highest SNR and the closest STD with respect to the reference images. In qualitative test, as to the 4 subjective image scores (noise suppression, artifact suppression, contrast preservation and overall image quality), notable improvements over the original LDCT images were obtained (P<0.05) for the AS-LNLM processed LDCT images. The Bland and Altman analysis in Fig. 10 also shows there is substantial overall concordance among the scores of the 3 readers.

Although this algorithm demonstrated a good potential in improving thoracic LDCT images, the following limits should also be noted: In S.1 (f) and S.2 (f), we can still discern some artifacts remaining

Noise Suppression

Artifact Suppression

Contrast Preservation

Overall Image Quality in the images processed by the AS-LNLM method, and some tiny structures tend to be obscured when some aggressive parameters were used to suppress the artifacts and noise. The reason is that, for the LDCT images scanned from human breast parts with much high-density tissues, some streak artifacts are too severe to be well suppressed. In this work the parameters calibrated in the phantom test were reused in routine clinical experiments. This strategy of parameter setting is based on the assumption that the anthropomorphic thoracic phantom and human chests suffer from quite similar noise/artifacts disturbance when using the same scan protocols in the same CT scanners. Though proved effective by above experiments, the parameter settings trained in phantom experiments might not produce satisfying results for some patients with abnormal body shapes, so empirical modulation may be needed in those cases. Also, TABLE III shows that nearly 10 seconds are needed for processing one 512 512  DICOM image even after GPU acceleration. This might form a computational burden for those radiological units that require fast and real-time clinical diagnosis.

VI. Conclusion

This paper presented a two-step method named AS-LNLM to improve the quality of thoracic LDCT images. In this AS-LNLM method, before the operation of LNLM, the streak artifacts in LDCT images were suppressed by directional 1-D nonlinear diffusions in stationary wavelet domain. Compared to ordinary processing methods in single image scale, artifact-suppressing operations on high frequency wavelet subbands can produce less obscuring on the original low frequency information, which suffers less from artifacts and noise. The parameter settings in processing the patient data can be tractably made according to the previous phantom experiment results validated by radiological expert. In addition, to enhance clinical applicability, a parallel acceleration using GPU-based CUDA has been made.

A CT workstation is currently under development to facilitate the on-going clinical evaluations and applications. LDCT images with smaller nodules will be used to further test the proposed AS-LNLM method. We are also trying extending the method from 2D to 3D with the objective to further suppress the streak artifacts by taking into account the 3D local geometries. An improved nonlinear diffusion, with parameters controlled by the artifact directional prominence, is also being devised and tested. Other work in the future will include further lowering the computation cost by testing the processing under more efficient hardware architectures, exploring the applications with thinner slice thicknesses (<2mm), and taking a thorough comparison between the proposed processing with iterative reconstruction algorithms.

Fig. 1 .

 1 Fig. 1. The outline of the proposed AS-LNLM method. f denotes the original degraded LDCT image. ca f denote all the wavelet decomposed low frequency 2D subbands.

  ), Decomposed subbands of a typical HDCT image (120mAs). (a1)-(d1): for the first scale, the low frequency subband )-(h1): for the second scale, the low frequency subband Decomposed subbands of a typical LDCT image (30mAs) with the similar body position as above HDCT images. (a2)-(d2): for the first scale, the low frequency subband   :,:,1 ca f , the horizontal high frequency subband

  Decomposed subbands of the LDCT image in (2). (a3)-(d3): for the first scale, the original low frequency subband )-(h3): for the second scale, the original low frequency subband

Fig. 3 .

 3 Fig. 3. The anthropopathic thoracic phantom used in experiment.

Fig. 4 .

 4 Fig. 4. 16 th slice of the volume acquired in HDCT condition for the thoracic phantom. (a) and (b): visualization in mediastinal and lung windows of the original HDCT image; (c) Processed HDCT image from the AS-LNLM method with [K=50, h =10] in mediastinal window; (d), Processed HDCT image from the AS-LNLM method with [K=20, h =5] in lung window

Fig. 5 .Fig. 6 .

 56 Fig. 5. Visualization of the 16 th slice of the volume of both original and processed LDCT thoracic phantom, in a mediastinal window. (a), original LDCT image (30 mAs); (b), LDCT image after HDCS filtering[START_REF] Mendrik | Noise reduction in computed tomography scans using 3D anisotropic hybrid diffusion with continuous switch[END_REF]; (c), LDCT image after LNLM filtering with h =20; (d), LDCT image after LNLM filtering with h =200; (e), LDCT image after application of the AS-LNLM method with [K=200, h =20]; (f), original LDCT image reconstructed with a smoothing kernel B31f.

z F and z F

 z define the reference image and the mean intensity of this image respectively. Three 40 40  homogeneous regions have been selected and for each slice of each volume (original LDCT and (f) (c) Processed LDCT image (LNLM with small h) (d) Processed LDCT image (LNLM with large h) (e) Processed LDCT image (proposed AS-LNLM)

Fig. 7 .

 7 Fig. 7. Selected homogeneous regions ( 1  , 2  and 3  ) in mediastinum window (left) and lung window (right) for STD calculation.

Fig. 8 .

 8 Fig. 8. Visualization of the original and processed LDCT images (30mAs) of a 59 years old man using a mediastinal window. (a), original LDCT image reconstructed with the kernel B70f; (b), LDCT images after HDCS filtering; (c), LDCT image after application of the AS-LNLM method; (d), original LDCT image reconstructed with the kernel B31f; (e)-(h) shows the zoomed ROI in (a)-(d).

Fig. 9 .

 9 Fig. 9. Visualization of the original and processed LDCT images (30mAs; reconstruction kernel B70f) of a 59 years old man using a lung window. (a), original LDCT image; (b), LDCT images after HDCS filtering; (c), LDCT image after application of the AS-LNLM method; (d)-(f) shows the zoomed ROI in (a)-(c).

  (a) LDCT image (b) Processed LDCT image (HDCS) (c) Processed LDCT image (proposed AS-LNLM)

( 3 )Fig. 10 .

 310 Fig. 10. Bland and Altman analysis of inter-observer agreements.

TABLE I .

 I THE PARAMETER SETTINGS OF DIFFERENT METHODS IN THE PHANTOM EXPERIMENT ON LDCT IMAGES

		Mediastinum window	Lung window
	Reference Images	S=2, K=50, iter=10, inc=0.05,	S=2, K=20, iter=10, inc=0.05,
	(Processed HDCT images From AS-LNLM)	h =10, 7 7 n 	, 81 81 N 	h =5, 7 7 n 	, 81 81 N 
	Processed LDCT images from HDCS	1   ,	20

e   , 15 c   ,

TABLE II .

 II THE CALCULATED SNR AND STD IN THE PHANTOM EXPERIMENT

	Images	Mediastinum window SNR STD	Lung window SNR STD
	Original LDCT images	19.96	75.20	19.96	29.97
	Processed LDCT from HDCS	22.92	39.29	22.92	7.01
	Processed LDCT images from LNLM (small h)	22.81	35.44	20.95	21.95
	Processed LDCT images from LNLM (large h)	20.95	9.55	20.37	3.56
	Processed LDCT images From AS-LNLM	24.63	23.61	24.42	2.62
	Original LDCT images with smoothing kernel B31f	20.95	17.58
	Reference Images (Processed HDCT images From AS-LNLM)		21.64		2.25

TABLE III .

 III COMPARISON OF CPU TIME COST (SECOND) BETWEEN THE DIFFERENT METHODS

TABLE IV .

 IV IMAGE QUALITY SCORES ( mean SDs ± ) FOR THE ORIGINAL AND AS-LNLM PROCESSED LDCT IMAGES, AND THE ORIGINAL HDCT IMAGES

	Image Denoising	Tube Current settings (mAs)	Noise Suppression	Artifact Suppression	Contrast Preservation	Overall Image Quality
	None	30mAs	1.52 0.34 	* 1.77 0.41 	* 1.87 0.38 	* 1.90 0.43 	*
	None	120mAs	3.21 0.32 	3.36 0.25 	3.46 0.32 	3.30 0.29 
	Processed	30mAs	3.53 0.31 	3.53 0.32 	3.52 0.29 	3.47 0.36 
	* Significantly (P<0.05) different from the mean scores for HDCT images.

Bland and Altman plot of inter-observer agreement between R1 and R3 Contrast Preservation Overall Image Quality Noise Suppression Artifact Suppression Contrast Preservation Overall Image Quality

		0.6						0.6
	Score Differences of R2 and R3	-0.4 -0.2 0 0.2 0.4			+2SD -2SD Mean		Score Differences of R2 and R3	0.4 -0.4 -0.2 0 0.2							+2SD -2SD Mean
		-0.6						-0.6
	Score Differences of R2 and R3	1.5 -0.6 0.4 0.6 -0.4 -0.2 0 0.2	2 Score Averages of R2 and R3 2.5 3 2.5 3 Score Averages of R2 and R3 (2) 1.5 2	3.5 3.5	+2SD -2SD Mean	Score Differences of R1 and R3 Score Differences of R2 and R3	6 1.6 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 -0.6 0.4 0.6 -0.4 -0.2 0 0.2	1.8 1.5 1.8	2 2	2.2 Score Averages of R1 and R3 2.4 2.6 2.8 3 2 2.5 3 Score Averages of R2 and R3 2.2 2.4 2.6 2.8 3 Score Averages of R2 and R3	3.2 3.2	3.4 3.5 3.4	3.6 3.6	+2SD -2SD Mean

(c) Processed HDCT image (mediastinal window) (d) Processed HDCT image (lung window)

AS-LNLM method (CPU serial version)

Acknowledgment

This research was supported by National Basic Research Program of China under grant (2010CB732503), National Natural Science Foundation under grants (81000636, 31100713, 81101104, 60801009), and the Project supported by Natural Science Foundations of Jiangsu Province (BK2009012 and BK2011593). The authors also thank the Kyoto Kagaku Company for providing the phantom "n1 Lungman". We also show great appreciation for the constructive comments of the reviewers.