Assessment of the Density Loss in Anobiid Infested Pine Using X-ray Micro-Computed Tomography

buildings A icle Assessmen o he Densi y Loss in Anobiid In es ed Pine Using X- ay Mic o-Compu ed Tomog aphy João Pa acha 1,2 , Manuel Pe ei a 3, An ónio Mau ício 3, Paulina Fa ia 2,4 , Daniel F. Lima 5, Ma ina Tenó io 5and Lina Nunes 1,6,*   Ci a ion: Pa acha, J.; Pe ei a, M.; Mau ício, A.; Fa ia, P.; Lima, D.F.; Tenó io, M.; Nunes, L. Assessmen o he Densi y Loss in Anobiid In es ed Pine Using X- ay Mic o-Compu ed Tomog aphy. Buildings 2021,11, 173. h ps://doi.o g/10.3390/ buildings11040173 Academic Edi o s: Jo ge Manuel B anco, Hélde Sousa and Elisa Pole i Recei ed: 7 Ma ch 2021 Accep ed: 14 Ap il 2021 Published: 17 Ap il 2021 Publishe ’s No e: MDPI s ays neu al wi h ega d o ju isdic ional claims in published maps and ins i u ional a il- ia ions. Copy igh : © 2021 by he au ho s. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps:// c ea i ecommons.o g/licenses/by/ 4.0/). 1Depa men o S uc u es, Na ional Labo a o y o Ci il Enginee ing, 1700-066 Lisbon, Po ugal; [email p o ec ed] 2CERIS, Ins i u o Supe io Técnico, Uni e si y o Lisbon, 1049-001 Lisbon, Po ugal; [email p o ec ed] 3 CERENA, Ins i u o Supe io Técnico, Uni e si y o Lisbon, 1049-001 Lisbon, Po ugal; [email p o ec ed] (M.P.); [email p o ec ed] (A.M.) 4School o Science and Technology, NOVA Uni e si y o Lisbon, 2829-516 Capa ica, Po ugal 5ISISE, Campus de Azu ém, Uni e si y o Minho, 4800-058 Guima ães, Po ugal; [email p o ec ed] (D.F.L.); [email p o ec ed] (M.T.) 6cE3c, Azo ean Biodi e si y G oup, Uni e si y o Azo es, 9700-042 Azo es, Po ugal *Co espondence: [email p o ec ed] Abs ac : The p esen s udy aims a e alua ing he impac o anobiid damage on pine imbe elemen s. Anobiid a ack p oduces a di use damage o he elemen s wi h a se o unnels in andom di ec ions and sizes, hus con using quan i ica ion. The e o e, a me hod was de eloped based on X- ay mic o-compu ed omog aphy ( µ -XCT) o ob ain, o na u ally in es ed imbe samples, an empi ical co ela ion be ween los ma e ial pe cen age (consumed by bee les) and imbe appa en densi y (o iginal, be o e deg ada ion—OTD and esidual, a e deg ada ion—RTD). The quan i ied densi y loss can hen be used in u he assessmen o he s uc u e. The esul s o he es s pe o med showed high co ela ion be ween o iginal appa en densi y and los ma e ial pe cen age ( 2 = 0.60) and be ween esidual appa en densi y and los ma e ial pe cen age ( 2 = 0.83), which con i ms µ -XCT as a aluable ool o he equi ed quan i ica ion. The loss o densi y esul s can be u he applied on he de ini ion o an assessmen me hod o he e alua ion o he esidual s eng h o anobiids in es ed imbe , hus con ibu ing o educing unnecessa y eplacemen . The op imized p ocedu e o he µ -XCT s udy o in es ed Ma i ime pine (Pinus pinas e ) is p esen ed and discussed in his a icle. Keywo ds: wood; anobiid in es a ion; h ee-dimensional econs uc ion; esidual appa en densi y; damage assessmen 1. In oduc ion Building ma e ials like s one, ea h o wood ha e been used o millennia. Wood, in pa icula , has played a e y impo an ole in he his o y o cons uc ion wi h i s dis inc physical and mechanical cha ac e is ics. Howe e , due o i s biological o igin, i is suscep ible o biode e io a ion. Wood-decaying ungi and sub e anean e mi es a e widely ega ded as he mos des uc i e agen s o imbe in usu [ 1 , 2 ], hough wood bo ing bee les like se e al species o he anobiid amily (Coleop e a: Anobiidae) ha e also o be aken in o conside a ion. The de e io a ion p oblems a e mainly limi ed o he sapwood [ 3 ] and a e a esul om he ex ensi e ne wo k o galle ies, consequen o he anobiids’ wood consump ion a he la ae s age. On he su ace o imbe , he damage ends o be only de ec ed ei he by he occasional p esence o ass o exi holes le by he adul bee les when eme ging [4]. The olde he s uc u e, he highe he p obabili y o imbe ha ing su e ed some anobiid in es a ion du ing i s se ice li e, and hus he e is a need o egula inspec ion Buildings 2021,11, 173. h ps://doi.o g/10.3390/buildings11040173 h ps://www.mdpi.com/jou nal/buildings Buildings 2021,11, 173 2 o 13 and main enance. A co ec s uc u al sa e y assessmen o a imbe s uc u e mus include he iden i ica ion o he de e io a ion agen s and he le el o deg ada ion. When anobiid damage is ound, he assessmen o he emaining imbe s uc u al soundness is a om easy [ 5 ]. The diagnosis ends o be done by isual assessmen o exi holes on he su ace o he imbe elemen s (e.g., [ 6 – 8 ]) and eplacemen maybe o en chosen as e o i ing solu ion wi h ob ious cos s bo h o he owne and o he en i onmen [ 6 , 9 ]. Howe e , non-des uc i e es s (NDTs) o semi-des uc i e es s (SDTs) can also be used o he ulne abili y assessmen o imbe s uc u al elemen s. Some examples a e he d illing esis ance, ha dness, and X- ay adioscopy es s, and bo h NDTs and SDTs a e used o he e alua ion o imbe densi y, biological de ec s, and oids [ 10 – 12 ]. Mo eo e , NIR spec oscopy can be applied o he e alua ion o imbe decay [ 13 ], whe eas s ess wa es, ul asound me hods, and ac i e he mog aphy es s can be used o he de ec ion o oids [14–16]. I a decision o keep he in es ed s uc u al elemen is eached o accoun o he insec s’ damage, a e y conse a i e app oach is ypically ollowed whe e a signi ican ly educed c oss sec ion o educed mechanical p ope ies o he o iginal c oss sec ions a e assumed [7,9] e en hough he exac le el o de e io a ion is, in ac , no known. The isual assessmen o deg aded imbe elemen s is no comple ely ep esen a i e since he es ima ed in e nal holes olume g ade (%) ends o be much g ea e han he isually es ima ed oids’ su ace g ades [ 6 , 8 ]. This highligh s he impo ance o knowing he pe cen age o eal loss o ma e ial (wood consumed by anobiids) as well as he loss o densi y. This will allow o conclude on he s uc u al sa e y, as he knowledge o imbe ’s o iginal (be o e anobiids’ deg ada ion) and esidual (a e anobiids’ deg ada ion) appa en densi ies is undamen al o assess imbe soundness as hese pa ame e s a e highly co ela ed wi h he ele an mechanical cha ac e is ics. In his sense, X- ay mic o-compu ed omog aphy ( µ -XCT) may be used o isualize and quan i y a h ee-dimensional s uc u e. I uses he same p inciples o medical com- pu ed omog aphy (CT) wi h he di e ence ha allows’ seeing mic ome ic 3D de ails in e y small samples [ 17 ]. Howe e , i is no possible ye o use µ -XCT o in si u obse a ions and small-size samples need o be collec ed om he s uc u al elemen s o be submi ed o µ -XCT in he labo a o y [ 18 ]. In addi ion, µ -XCT has mic ome ic scale ange esolu ion, which enables highe accu acy in 3D olume isualiza ion and measu emen s [19]. The use o CT o µ -XCT has been inc easing in ecen yea s, namely o he s udy o de e io a ion pa e ns o di e en ma e ials. Examples a e he de ec ion o insec damage on wood (i.e., [ 20 – 22 ]) o bamboo [ 23 ] and also he quan i ica ion o galle ies buil by shipwo ms on wood applied in ma ine en i onmen [24]. Fuchs e al. [ 20 ] used CT o obse e he nes galle y o he d ywood e mi e C yp- o e mes secundus in o de o con ibu e o e mi e managemen by de e mining wood consump ion a es o e mi e colonies. Himmi e al. [ 21 ] analyzed he nes ing biology o he d ywood e mi e Incisi e mes mino using CT images, whe eas Wa anabe e al. [ 23 ] used X- ay-CT o he e alua ion o he la al g ow h p ocess and bamboo consump ion by he bee le Dinode us minu us. These la e au ho s obse ed ha each la a p oduced a unnel leng h o 0.98 mm and a bo ed olume o 1.06 mm3pe day. This pape aims a e alua ing he impac o anobiid damage on pine imbe elemen s. A me hod was de eloped based on X- ay mic o-compu ed omog aphy ( µ -XCT) o ob ain, o na u ally in es ed imbe samples, an empi ical co ela ion be ween los ma e ial pe - cen age (consumed by bee les) and imbe appa en densi y (o iginal, be o e deg ada ion and esidual, a e deg ada ion). The loss o densi y alues we e hen applied on he de ini ion o an ini ial app oach o an on-si e assessmen o in es ed imbe s uc u al elemen s [ 5 ]. This pape p esen s and discusses he di e se s eps needed du ing he µ -XCT s udy, in ending o imp o e he e iciency and scope o he echnique o he s udy o wood. Buildings 2021,11, 173 3 o 13 2. Ma e ials and Me hods 2.1. X-Ray Mic o-Compu ed Tomog aphy S udy In his s udy, µ -XCT was used as a 3D mic oscopy echnique o assess he anobiids galle ies ( isualizing i s spa ial dis ibu ion and mo phology) and calcula e he olume o he oids, and he e o e he amoun o los ma e ial (consumed by anobiids). The usual p ocedu e applied o all µ -XCT scanning p ocesses was also applied in he p esen s udy and consis s o he ollowing majo s eps (Figu e 1) ha a e u he desc ibed below in Sec ions 2.2–2.5. Buildings 2021, 11, x 3 o 13 XCT s udy, in ending o imp o e he e iciency and scope o he echnique o he s udy o wood. 2. Ma e ials and Me hods 2.1. X-Ray Mic o-Compu ed Tomog aphy S udy In his s udy, μ-XCT was used as a 3D mic oscopy echnique o assess he anobiids galle ies ( isualizing i s spa ial dis ibu ion and mo phology) and calcula e he olume o he oids, and he e o e he amoun o los ma e ial (consumed by anobiids). The usual p ocedu e applied o all μ-XCT scanning p ocesses was also applied in he p esen s udy and consis s o he ollowing majo s eps (Figu e 1) ha a e u he de- sc ibed below in Sec ions 2.2–2.5. Figu e 1. Scheme o he usual p ocedu e applied o all X- ay mic o-compu ed omog aphy (μ- XCT) scanning p ocesses. 2.2. Rep esen a i e Sample P epa a ion/Selec ion A ma i ime pine (Pinus pinas e Ai .) oo beam was collec ed om a esiden ial build- ing in Lisbon, Po ugal a e abou 60 yea s o se ice li e. The p esence o di e en le els o anobiid in es a ion was easily ecognized on he sapwood. The main insec esponsible o he de e io a ion was iden i ied as Nicobium cas aneum Oli ie by he size o he galle - ies, he cha ac e is ics o he ass, and o he se e al cocoons ha we e e ie ed om he samples. A e he insec iden i ica ion, imbe was di ided in 4 segmen s (3 con aining deg- ada ion and he sound hea wood) and cu o p oduce app oxima ely 40 × 20 × 40 mm 3 samples. These samples we e hen c osscu o ob ain 17 “new” pai ed samples, wi h ap- p oxima ely 40 × 20 × 10 mm 3 , ha we e adequa e o μ-XCT [5]. These samples (Figu e 2) ep esen ing he a ying deg ees o deg ada ion along he beam we e submi ed o μ- XCT. The hea wood was no used. The samples we e assigned in o 3 le els o deg ada ion (le el 1V, 2V, and 3V) h ough a isual analysis o he de e io a ion and conside ing he a ea o he eme gence holes a he exposed su aces o he deg aded samples (Figu e 2). Le el 1V co esponds o he lowes deg ada ion le el and le el 3V o he highes . I should be no ed ha his isual g ading app oach had al eady been p o ed as no ully ep esen a i e o he in e nal deg- ada ion [7,8]. A e de e mining he omog aphic pa ame e s o in e es (wood and oids’ olume), he designa ion le els o he samples we e eadjus ed acco ding o he esul s o he los ma e ial pe cen age. Figu e 1. Scheme o he usual p ocedu e applied o all X- ay mic o-compu ed omog aphy (µ-XCT) scanning p ocesses. 2.2. Rep esen a i e Sample P epa a ion/Selec ion A ma i ime pine (Pinus pinas e Ai .) oo beam was collec ed om a esiden ial building in Lisbon, Po ugal a e abou 60 yea s o se ice li e. The p esence o di e en le els o anobiid in es a ion was easily ecognized on he sapwood. The main insec esponsible o he de e io a ion was iden i ied as Nicobium cas aneum Oli ie by he size o he galle ies, he cha ac e is ics o he ass, and o he se e al cocoons ha we e e ie ed om he samples. A e he insec iden i ica ion, imbe was di ided in 4 segmen s (3 con aining deg a- da ion and he sound hea wood) and cu o p oduce app oxima ely 40 × 20 × 40 mm 3 samples. These samples we e hen c osscu o ob ain 17 “new” pai ed samples, wi h ap- p oxima ely 40 × 20 × 10 mm 3 , ha we e adequa e o µ -XCT [ 5 ]. These samples (Figu e 2) ep esen ing he a ying deg ees o deg ada ion along he beam we e submi ed o µ -XCT. The hea wood was no used. Buildings 2021, 11, x 4 o 13 Figu e 2. Sample p epa a ion: 17 a ying deg ees o in es ed samples o be analyzed in μ-XCT we e isually di ided in o 3 le els o deg ada ion (all samples ha we e edis ibu ed o a di e en le el a e μ-XCT esul s a e ma ked in ed). A e age size o samples = 40 × 20 × 10 mm3. Be o e he scanning p ocedu e, all samples we e condi ioned o 2 weeks in a clima ic chambe ( empe a u e (T) o 20 ± 2 °C and a ela i e humidi y (RH) o 65 ± 5%) o s abilize and each ma i ime pine sapwood’ e e ence mois u e con en (12%) [25]. No p e ious ea men was equi ed o he samples o be submi ed o μ-XCT as his echnique e- qui es e y li le sample p epa a ion and, egula ly, a sample can be scanned exac ly as p o ided; howe e , he bes sample size equi ed o he scanning p ocedu e has o be aken in o conside a ion [26]. 2.3. Scanning P ocedu e (Acquisi ion) All ma i ime pine samples we e scanned wi h a compac desk op wi h mic ome ic ange esolu ion, μ-XCT Skyscan 1172 mic o omog aph (B uke Ins umen s, Inc., Bille - ica, MA, USA), using compu e -con olled omog aphy acquisi ion, p ocessing, econ- s uc ion, and analysis so wa e packages (CTAn and ImageJ so wa e p o ided by B uke (Bille ica, MA, USA) and NIH (Be hesda, MD, USA), espec i ely). Howe e , p io o each sample scanning p ocedu e, a decision mus be made con- ce ning he bes sample size, which is de e mined based on a leas he ollowing c i e ia: he aim o he p oblem o be s udied and he physical, he geome ical, and ope a ing cons ain s o he scanne . The nex s ep in he p epa a ion o scanning p ocedu e o he μ-XCT me hodology is choosing op imal scanne se ings. The e is a wide ange o scan se ings ha subs an ially a ec image quali y such as esolu ion, scanne ol age, scan ime, numbe o images acqui ed, angle o o a ion, e c. Resolu ion selec ion may be he i s majo ac o ha a ec s a μ-XCT scan. A esolu- ion abou 1000 imes smalle han he wid h o he sample is ecommended; howe e , e e ence s anda ds exis and may be used [26,27]. A maximum esolu ion o 2 μm was used in he p esen s udy (Table 1), al hough, a gene ally accep ed s anda d o indus ial CT sys ems does no exis ye . X- ay scanne ol age is highly dependen on he ype o ma e ial s udied and hei composi ion. Fo biological samples, a ol age in a ange o 30 o 100 kV is ecommended [26]. The scanning ime used o each sample was 1.5 h. Scanning ime a ies acco ding o he sys em used, o example, due o he de ec o sensi i i y o o he dis ance om sou ce o de ec o [28], while he numbe o images equi ed a ies wi h he sample size. Fu - he mo e, in ou case, he need o ob ain he mos in o ma ion om each scan sugges s conside ing a leas an o e size and mul i-acquisi ion se ing as a cons ain . The e o e, a la ge sample could equi e a longe ime o acquisi ion i mul iple scans we e pe o med. Mo eo e , la ge samples ha e magni ica ion limi a ions due o conic beam con igu a ion. A educ ion in he angle o o a ion as well as an inc ease in he numbe o images ac- Figu e 2. Sample p epa a ion: 17 a ying deg ees o in es ed samples o be analyzed in µ -XCT we e isually di ided in o 3 le els o deg ada ion (all samples ha we e edis ibu ed o a di e en le el a e µ-XCT esul s a e ma ked in ed). A e age size o samples = 40 ×20 ×10 mm3. The samples we e assigned in o 3 le els o deg ada ion (le el 1V, 2V, and 3V) h ough a isual analysis o he de e io a ion and conside ing he a ea o he eme gence holes a he exposed su aces o he deg aded samples (Figu e 2). Le el 1V co esponds o he lowes deg ada ion le el and le el 3V o he highes . I should be no ed ha his isual g ading app oach had al eady been p o ed as no ully ep esen a i e o he in e nal deg ada ion [ 7 , 8 ]. A e de e mining he omog aphic pa ame e s o in e es (wood and Buildings 2021,11, 173 4 o 13 oids’ olume), he designa ion le els o he samples we e eadjus ed acco ding o he esul s o he los ma e ial pe cen age. Be o e he scanning p ocedu e, all samples we e condi ioned o 2 weeks in a clima ic chambe ( empe a u e (T) o 20 ± 2 ◦ C and a ela i e humidi y (RH) o 65 ± 5%) o s abilize and each ma i ime pine sapwood’ e e ence mois u e con en (12%) [ 25 ]. No p e ious ea men was equi ed o he samples o be submi ed o µ -XCT as his echnique equi es e y li le sample p epa a ion and, egula ly, a sample can be scanned exac ly as p o ided; howe e , he bes sample size equi ed o he scanning p ocedu e has o be aken in o conside a ion [26]. 2.3. Scanning P ocedu e (Acquisi ion) All ma i ime pine samples we e scanned wi h a compac desk op wi h mic ome ic ange esolu ion, µ -XCT Skyscan 1172 mic o omog aph (B uke Ins umen s, Inc., Bille ica, MA, USA), using compu e -con olled omog aphy acquisi ion, p ocessing, econs uc ion, and analysis so wa e packages (CTAn and ImageJ so wa e p o ided by B uke (Bille ica, MA, USA) and NIH (Be hesda, MD, USA), espec i ely). Howe e , p io o each sample scanning p ocedu e, a decision mus be made con- ce ning he bes sample size, which is de e mined based on a leas he ollowing c i e ia: he aim o he p oblem o be s udied and he physical, he geome ical, and ope a ing cons ain s o he scanne . The nex s ep in he p epa a ion o scanning p ocedu e o he µ -XCT me hodology is choosing op imal scanne se ings. The e is a wide ange o scan se ings ha subs an ially a ec image quali y such as esolu ion, scanne ol age, scan ime, numbe o images acqui ed, angle o o a ion, e c. Resolu ion selec ion may be he i s majo ac o ha a ec s a µ -XCT scan. A eso- lu ion abou 1000 imes smalle han he wid h o he sample is ecommended; howe e , e e ence s anda ds exis and may be used [ 26 , 27 ]. A maximum esolu ion o 2 µ m was used in he p esen s udy (Table 1), al hough, a gene ally accep ed s anda d o indus ial CT sys ems does no exis ye . X- ay scanne ol age is highly dependen on he ype o ma e ial s udied and hei composi ion. Fo biological samples, a ol age in a ange o 30 o 100 kV is ecommended [26]. Table 1. Summa y o he pa ame e s used in he scanning p ocedu e. Pa ame e s Maximum esolu ion 2 µm Vol age 60 kV Filamen cu en 165 µA Numbe o images 288 Angle o o a ion 0.7◦Fil e s Al 0.5 mm Voxel Size 18.09 µm File ype 16-bi The scanning ime used o each sample was 1.5 h. Scanning ime a ies acco ding o he sys em used, o example, due o he de ec o sensi i i y o o he dis ance om sou ce o de ec o [ 28 ], while he numbe o images equi ed a ies wi h he sample size. Fu he mo e, in ou case, he need o ob ain he mos in o ma ion om each scan sugges s conside ing a leas an o e size and mul i-acquisi ion se ing as a cons ain . The e o e, a la ge sample could equi e a longe ime o acquisi ion i mul iple scans we e pe o med. Mo eo e , la ge samples ha e magni ica ion limi a ions due o conic beam con igu a ion. A educ ion in he angle o o a ion as well as an inc ease in he numbe o images acqui ed will, na u ally, lead o a p oduc ion o images o be e quali y. Howe e , he bene i le el may no be jus i ica o y because such op ion will con ibu e o inc easing scan ime, econs uc ion ime, and da a size [ 29 ]. A he end, he bes se ings solu ion o each scanning s ep should be ound conside ing he ou pu pa ame e s ha we e p e iously de ined. Du ing he scanning p ocess, he s udied sample mus be ixed on a suppo while o a ing in s eps a ound a ixed e ical axis [ 30 ]. A shadow p ojec ion image a each angula posi ion is aken and 2D digi al adiog aphy images se s a e ob ained. La e , Buildings 2021,11, 173 5 o 13 h ough econs uc ion and ende ed p ocesses, using Skyscan-B uke so wa e, a se o 2D econs uc ed objec slice images can be combined/ ende ed o p oduce a i ual 3D econs uc ed objec model. A summa y o he pa ame e s used in he scanning p ocedu e a e p esen ed in Table 1. Besides hese pa ame e s, he dis ance be ween he X- ay sou ce and he sample, as well as he dis ance be ween he X- ay sou ce and he de ec o was 257 and 350 mm, espec i ely. The scanning ime was 1.5 h and a 0.5 mm aluminium il e was used o abso bing he low-ene gy X- ays, he eby educing noise. The pa ame e s p esen ed in Table 1we e applied in he scanning p ocedu e o all s udied samples. A e he selec ion o he shape and he size o samples, he X- ay mic o omog aphy ope a ion p ocedu e was op imized o p oduce he bes possible images [30–34]. 2.4. Recons uc ion Recons uc ion p ocess ollows he scanning p ocedu e, and he e he 3D olume is econs uc ed om he 2D digi al adiog aphy image s acks ha we e p e iously acqui ed. Fo he econs uc ion p ocess, he NRecon so wa e, p o ided by B uke , was used. This so wa e allows he adjus men o h ee econs uc ion pa ame e s: smoo hing, beam- ha dening ac o , and ing a e ac educ ion. These pa ame e s will ob iously a ec he quali y o he acqui ed 3D objec . Howe e , he e a e no gene alized uni e sal op imal pa ame e s, and he bes solu ion o each case s udy depends on he ype o scanne used as well as he ype o ma e ial s udied. I is also possible o choose he ype o ou pu ile: 16-bi o 32-bi . Though bo h op ions a e alid, he se o images expo ed in a 32-bi ile has he bes quali y. The a ea o be econs uc ed also needs o be selec ed and should co espond o he a ea o in e es . To sa e ime and o ha e a manageable size o he da a se s, some less ele an a eas should no be conside ed [ 29 ], and a sligh ly la ge a ea han ha equi ed should be econs uc ed, as his ensu es ha c i ical da a a e e ained. Table 2shows he econs uc ion pa ame e s used in his s udy. These pa ame e s we e used o he econs uc ion o all s udied eplica es. The da a was expo ed o 32-bi iles. Table 2. Recons uc ion pa ame e s adop ed using NRecon so wa e. Pa ame e Desc ip ion Sugges ed Se ing Smoo hing Smoo h images and emo es noise Wid h; 3 pixels Beam-ha dening ac o co ec ion Co ec o he abso p ion o lowe -ene gy X- ay on he ou side o he specimen 30–55% Ring a e ac educ ion Co ec o he nonlinea beha io o pixels causing ing a e ac s ≈20 Nex , Da aViewe so wa e was used o adjus he g ayscale o images o enable a be e isualiza ion (Figu e 3). I is also necessa y o choose he images iewing plane ha a e going o be examined. The e a e 3 possible op ions (3D objec s): co onal images ( adial plane)—axis xy; sagi al images ( angen ial plane)—axis xz; ans axial images ( ans e se plane)—axis yz. In his s udy, he co onal plan was chosen because i was he plan whe e he images had less isible a e ac s and, because o ha , i was easie o dis inguish be ween wood and oids ( unnels o med by bee les). Wi h he acquisi ion o he 3D objec , i is now possible o isualize i using a isual- iza ion p og am (e.g., CTVox so wa e, p o ided by B uke ). The econs uc ed 3D objec was isualized using CTVox so wa e (Figu e 4). Buildings 2021,11, 173 6 o 13 Buildings 2021, 11, x 6 o 13 Ring a e ac educ ion Co ec o he nonlinea beha io o pixels causing ing a e- ac s ≈20 Nex , Da aViewe so wa e was used o adjus he g ayscale o images o enable a be e isualiza ion (Figu e 3). (a) (b) (c) Figu e 3. Two-dimensional images o he econs uc ed objec (samples app oxima ely 40 × 20 × 10 mm 3 ): (a) xy: co onal plane; (b) xz: sagi al plane; (c) yz: ans axial plane. I is also necessa y o choose he images iewing plane ha a e going o be examined. The e a e 3 possible op ions (3D objec s): co onal images ( adial plane)—axis xy; sagi al images ( angen ial plane)—axis xz; ans axial images ( ans e se plane)—axis yz. In his s udy, he co onal plan was chosen because i was he plan whe e he images had less isible a e ac s and, because o ha , i was easie o dis inguish be ween wood and oids ( unnels o med by bee les). Wi h he acquisi ion o he 3D objec , i is now possible o isualize i using a isual- iza ion p og am (e.g., CTVox so wa e, p o ided by B uke ). The econs uc ed 3D objec was isualized using CTVox so wa e (Figu e 4). Figu e 4. Recons uc ed 3D objec . Visualiza ion using CTVox so wa e. Figu e 3. Two-dimensional images o he econs uc ed objec (samples app oxima ely 40 × 20 × 10 mm 3 ): (a) xy: co onal plane; (b) xz: sagi al plane; (c) yz: ans axial plane. Buildings 2021, 11, x 6 o 13 Ring a e ac educ ion Co ec o he nonlinea beha io o pixels causing ing a e- ac s ≈20 Nex , Da aViewe so wa e was used o adjus he g ayscale o images o enable a be e isualiza ion (Figu e 3). (a) (b) (c) Figu e 3. Two-dimensional images o he econs uc ed objec (samples app oxima ely 40 × 20 × 10 mm 3 ): (a) xy: co onal plane; (b) xz: sagi al plane; (c) yz: ans axial plane. I is also necessa y o choose he images iewing plane ha a e going o be examined. The e a e 3 possible op ions (3D objec s): co onal images ( adial plane)—axis xy; sagi al images ( angen ial plane)—axis xz; ans axial images ( ans e se plane)—axis yz. In his s udy, he co onal plan was chosen because i was he plan whe e he images had less isible a e ac s and, because o ha , i was easie o dis inguish be ween wood and oids ( unnels o med by bee les). Wi h he acquisi ion o he 3D objec , i is now possible o isualize i using a isual- iza ion p og am (e.g., CTVox so wa e, p o ided by B uke ). The econs uc ed 3D objec was isualized using CTVox so wa e (Figu e 4). Figu e 4. Recons uc ed 3D objec . Visualiza ion using CTVox so wa e. Figu e 4. Recons uc ed 3D objec . Visualiza ion using CTVox so wa e. 2.5. Analysis A e he econs uc ion p ocedu e, he images can be analyzed and pa ame ized o ob ain quan i a i e in o ma ion ( he alues o he pa ame e s o in e es ). This p ocess is highly dependen on he so wa e used. Fo his s udy, CTAn so wa e, p o ided by B uke , was used oge he wi h ImageJ so wa e (h ps://imagej.nih.go /ij/, accessed on 13 Ap il 2021), p o ided by NIH, o image p ocessing and calcula ion. In his case s udy, ImageJ so wa e allows complemen a i y wi h CTAn, enabling easie and mo e lexible de ini ion o egions o in e es (ROI) in image s acks and an au o in e pola ion o a subse o images, which can b ing some ad an ages linked o he elimina ion o local noise in s acks and sub s acks o omog aphic slices, o ins ance. In his case s udy, he segmen a ion s ep is one o he mos impo an imaging ea men s ha is made in his phase and consis s in alidly ans o ming he o iginal images, in a scale o 256 g ay le els, in o simple bina y images (Figu e 5). Wi h hese images, i is now possible o sepa a e he ea u es o in e es (wood and oids). These ea u es mus be sepa a ed as good as possible o he calcula ion p ocess. No e ha hose images con inue o ha e noise, and i is impossible o emo e i comple ely du ing he en i e µ -XCT p ocess. Fo his eason, Buildings 2021,11, 173 7 o 13 his s ep mus be ca ied ou wi h special a en ion and by a single use , so ha he s udy is no mis ep esen ed, mis aking, o example, wood (e.g., ea ly g ow h) wi h oids o oids wi h noise. I is e y impo an o emo e he noise while keeping he s uc u al and mo phological in o ma ion, ha may be a he same o de o scale as he noise [ 35 ]. The accu acy o he es ima ed pa ame e s’ alues is s ongly dependen on he quali y and obus ness o segmen a ion p ocess [36]. A gene alized image p ocessing algo i hm mus be de ined (Table 3) o segmen h ee-dimensional images o complex ma e ials o ex ac he di e en phases (in his s udy, he e a e 2 phases: wood and oids). Buildings 2021, 11, x 7 o 13 2.5. Analysis A e he econs uc ion p ocedu e, he images can be analyzed and pa ame ized o ob ain quan i a i e in o ma ion ( he alues o he pa ame e s o in e es ). This p ocess is highly dependen on he so wa e used. Fo his s udy, CTAn so wa e, p o ided by B uke , was used oge he wi h ImageJ so wa e (h ps://imagej.nih.go /ij/), p o ided by NIH, o image p ocessing and calcula ion. In his case s udy, ImageJ so wa e allows complemen a i y wi h CTAn, enabling easie and mo e lexible de ini ion o egions o in e es (ROI) in image s acks and an au o in e pola ion o a subse o images, which can b ing some ad an ages linked o he elimina ion o local noise in s acks and sub s acks o omog aphic slices, o ins ance. In his case s udy, he segmen a ion s ep is one o he mos impo an imaging ea men s ha is made in his phase and consis s in alidly ans o ming he o iginal images, in a scale o 256 g ay le els, in o simple bina y images (Figu e 5). Wi h hese images, i is now possible o sepa a e he ea u es o in e es (wood and oids). These ea u es mus be sepa a ed as good as possible o he calcula ion p o- cess. No e ha hose images con inue o ha e noise, and i is impossible o emo e i com- ple ely du ing he en i e μ-XCT p ocess. Fo his eason, his s ep mus be ca ied ou wi h special a en ion and by a single use , so ha he s udy is no mis ep esen ed, mis aking, o example, wood (e.g., ea ly g ow h) wi h oids o oids wi h noise. I is e y impo an o emo e he noise while keeping he s uc u al and mo phological in o ma ion, ha may be a he same o de o scale as he noise [35]. The accu acy o he es ima ed pa ame e s’ alues is s ongly dependen on he quali y and obus ness o segmen a ion p ocess [36]. A gene alized image p ocessing algo i hm mus be de ined (Table 3) o segmen h ee- dimensional images o complex ma e ials o ex ac he di e en phases (in his s udy, he e a e 2 phases: wood and oids). (a) (b) (c) (d) Figu e 5. Images o he a ious s eps ela ed wi h μ-XCT me hodology: (a) scanning p ocedu e; (b) a e econs uc ion p ocess; (c) analysis—bina y image; (d) bina y ea ed image eady o calcula ion. Table 3. Image p ocessing algo i hm used o segmen 3D images. Pa ame e Desc ip ion Sugges ed Se ing Th esholding Segmen s he o eg ound om backg ound o bina y images Global; low le el 17, high le el 255 Despeckle Remo es speckles om bina y images Remo e whi e speckles <500 oxels; e- mo e black speckles <150 oxels Mo phological ope a ions Fills he holes and closes he po es 2D space: Closing; Ke nel ound, adius 5 The a ea o be analyzed om he econs uc ed images, i.e., egion o in e es (ROI), mus be selec ed using a ee-hand ool. Then, an au o in e pola ion among di e en ROI le els will p oduce he o al olume o in e es (VOI) o all selec ed ames. Figu e 5. Images o he a ious s eps ela ed wi h µ -XCT me hodology: ( a ) scanning p ocedu e; ( b ) a e econs uc ion p ocess; ( c ) analysis—bina y image; ( d ) bina y ea ed image eady o calcula- ion. Table 3. Image p ocessing algo i hm used o segmen 3D images. Pa ame e Desc ip ion Sugges ed Se ing Th esholding Segmen s he o eg ound om backg ound o bina y images Global; low le el 17, high le el 255 Despeckle Remo es speckles om bina y images Remo e whi e speckles <500 oxels; emo e black speckles <150 oxels Mo phological ope a ions Fills he holes and closes he po es 2D space: Closing; Ke nel ound, adius 5 The a ea o be analyzed om he econs uc ed images, i.e., egion o in e es (ROI), mus be selec ed using a ee-hand ool. Then, an au o in e pola ion among di e en ROI le els will p oduce he o al olume o in e es (VOI) o all selec ed ames. Th esholding is one o he i s and mos impo an pa ame e s o be selec ed. Th esh- old con e s he se o images, o iginally in a g ayscale, in a bina y se . F om he applica ion o his pa ame e , se s o bina y disc e e images a e es ablished, wi h he wo s uc u al pa ame e s o in e es de ined and sepa a ed. Howe e , he choice o h eshold can a y om use o use , and he e o e esul in di e ences ha a e la ge han he expe imen al di e ences hemsel es [ 36 ]. Due o ha ac , global h esholds mus be chosen by one use and kep du ing he en i e s udy. This ein o ces he impo ance o ha ing a de ined basis algo i hm (Table 3). Be o e h esholds, images can and should be il e ed o emo e noise. B uke ’s CTAn so wa e allows he adjus men o analyzing pa ame e s such as il e , h eshold, despeckle, and addi ional mo phological ope a ions. Each one o hese pa ame e s has as a unc ion o ea ing image se s o ge a inal se o images, om which in e es pa ame e s can be easily calcula ed. Finally, wi h he images ea ed acco ding o he in e es s o he s udy and wi h he de ined olume o in e es (VOI), i is possible o calcula e he pa ame e s o in e es (wood and oids) alues. These pa ame e s’ alues a e ob ained h ough a pa ame ic 3D analysis o he objec . Buildings 2021,11, 173 8 o 13 The esul s (ou pu pa ame e s) a e ob ained as a x ile expo ed om CTAn. F om he es ima ed ou pu pa ame e s alues, wo o hem can be highligh ed: issue olume, e e ing o he olume o in e es (VOI), and bone olume, e e ing o he wood i sel (skele on). As his echnique has been de eloped o s udy human bones, se e al less ob ious e ms we e kep e en o di e en uses. The issue olume and he bone olume will co espond o ha o in e es de ined p e iously, as oids olume will co espond o he di e ence be ween VOI and bone olume. Besides hese pa ame e s, i is also possible o ex ac a se o o he pa ame e s, such as he leng h o he unnels inside wood, o example, ha p o ide a mo e ho ough objec analysis. Howe e , by using hese alues one mus be cau ious, in he sense ha he used algo i hm was de ined o he s udy o wo pa icula in e es pa ame e s (wood and oids olumes). A e he scanning p ocedu e, all 17 samples we e condi ioned o 2 weeks in a clima ic chambe (T = 20 ± 2 ◦ C and RH = 65 ± 5%) o s abilize and each a mois u e con en iden ical o ha o e e ence (12%). A e ha , all he samples we e weighed using a scale wi h a p ecision o 0.001 g. O iginal appa en densi y ( e e ence densi y) is calcula ed by Equa ion (1): ρo iginal = m o. (1) in which m is he sample weigh in kg and o. is he objec olume (wood) in m3. Residual appa en densi y (a e deg ada ion) is calcula ed by Equa ion (2): ρ esidual = m . (2) in which m is he sample weigh in kg and . is he o al olume (wood and oids, co esponding o he VOI) in m3. 3. Resul s and Discussion The esul s o he ou pu pa ame e s ( o al olume and wood olume), los ma e ial pe cen ages, and sample weigh s a e p esen ed in Table 4. Table 4. Ou pu pa ame e s, los ma e ial pe cen ages, and sample weigh s. Deg ada ion Le el Sample To al Volume Wood Volume Los Ma e ial Weigh cm3cm3% g Le el 1 1.1 8.798 7.868 10.57 4.541 1.2 9.250 8.339 9.85 4.776 1.3 8.913 8.074 9.41 4.520 1.4 9.168 8.299 9.48 4.308 1.5 7.352 6.731 8.45 4.360 1.6 7.576 6.293 16.94 3.455 Le el 2 2.1 8.529 7.811 8.42 4.763 2.2 7.020 5.413 22.89 2.912 2.3 7.291 5.865 19.56 2.807 2.4 6.319 4.854 23.18 2.152 2.5 8.179 7.158 12.48 3.862 2.6 7.021 6.022 14.23 2.988 Buildings 2021,11, 173 9 o 13 Table 4. Con . Deg ada ion Le el Sample To al Volume Wood Volume Los Ma e ial Weigh cm3cm3% g Le el 3 3.1 8.774 7.071 19.41 3.412 3.2 8.631 6.309 26.90 2.544 3.3 7.216 6.260 13.25 3.084 3.4 8.011 6.224 22.31 3.212 3.5 7.154 5.595 21.79 2.782 Table 5shows he a e age esul s o o al olume, wood olume, los ma e ial pe cen age, mass o he samples and densi ies o he ini ial sample dis ibu ion, based on isual su ey. Table 5. Ini ial sample dis ibu ion. A e age alues and ela i e s anda d de ia ions o he ou pu pa ame e s, mass, densi y, and loss o densi y. Deg ada ion Le el 1V 2V 3V Numbe o samples 6 6 5 Volume (cm3)To al 8.452 7.393 7.957 Wood 7.601 6.187 6.292 Los ma e ial (%) 10.78 ±3.52 16.79 ±5.49 20.73 ±4.87 Weigh (g) 4.327 3.247 3.007 Densi ies (kg/m3) O iginal 571 ±65 518 ±83 481 ±44 Residual 510 ±45 433 ±64 380 ±36 Loss 10.7 ±2.89 16.4 ±4.68 20.9 ±5.65 Th ough analysis and in e p e a ion o Table 5, i can be conside ed ha on a e age he le els o deg ada ion p oposed in Sec ion 2.2 ep esen he eal deg ada ion. The le el o loss o densi y inc eases wi h he deg ada ion le el (Table 4). Ne e heless, his endency is no e i ied o all samples (Figu e 2). One o he samples wi h he highes pe cen age o los ma e ial (sample 2.2–22.89 LM%) belongs o he second “le el o deg ada ion”, which would be whe e he exis ing deg ada ion, as well as he pe cen age o los ma e ial was supposed o be lowe . This sample should ha e been placed a he highes le el o deg ada ion (le el 3V). This con i ms wha was expec ed: isual assessmen o imbe beam samples was no accu a e, and a edis ibu ion o he samples pe each le el needs o be done (in Figu e 2, all samples ha we e edis ibu ed o a di e en le el a e ma ked in ed). I should be no ed ha samples wi h he highes edis ibu ion by le els a e a le els 2V and 3V (5 ou o 7 samples edis ibu ed), which p o es ha a isual g ading app oach is mo e e icien o low le els o deg ada ion. This ime, he samples will be eo de ed pe le el acco ding o he “ eal” los ma e ial pe cen ages (calcula ed h ough µ -XCT s udy). This ac also con i ms ha he e is no a clea linea co ela ion be ween he a ea o eme gence holes a exposed su aces and he wood specimen’s s eng h so ha he s uc u al soundness o anobiids colonized imbe canno be accep ably assessed h ough examina ion o he exposed su aces [6–8]. A eo ganiza ion o he samples pe le el o deg ada ion was p oposed (Table 6). This ime, le el 1 co esponds o <10% o los ma e ial, le el 2 o a los ma e ial be ween 10% and 20%, and le el 3 o >20% o los ma e ial.